CN115879826B

CN115879826B - Fine chemical process quality inspection method, system and medium based on big data

Info

Publication number: CN115879826B
Application number: CN202310134327.7A
Authority: CN
Inventors: 王毅; 袁石安; 李大利
Original assignee: Shenzhen Pfiter Information Technology Co ltd
Current assignee: Shenzhen Pfiter Information Technology Co ltd
Priority date: 2023-02-20
Filing date: 2023-02-20
Publication date: 2023-05-30
Anticipated expiration: 2043-02-20
Also published as: CN115879826A

Abstract

The embodiment of the application provides a quality inspection method, a quality inspection system and a quality inspection medium for a fine chemical process based on big data. Belonging to the technical field of fine intelligence making and big data processing. The method comprises the following steps: extracting product batch information and production process information according to a product process database, extracting node quality inspection standard data and process node quality inspection data according to the generated production process information image, polymerizing to obtain a full quality inspection fluctuation index, obtaining a process complexity index and a process effective utilization coefficient through a model, processing to obtain a process assessment index by combining batch income data and the full quality inspection fluctuation index, and taking production batch corresponding to the optimal index as optimal process scheme information; and then, the process and quality inspection information are processed to obtain the quality inspection fluctuation condition of the process flow, and the process evaluation index is obtained by combining the process related index to evaluate the processes of each batch, so that the process quality inspection technology for processing and evaluating the processes and quality inspection of the batch products by using the big data technology is realized.

Description

Fine chemical process quality inspection method, system and medium based on big data

Technical Field

The application relates to the technical field of fine intelligence manufacturing and big data processing, in particular to a fine chemical process quality inspection method, a fine chemical process quality inspection system and a fine chemical process quality inspection medium based on big data.

Background

In the fine chemical industry, fine chemical production has high requirements on production processes, in market competition, modern fine chemical products have high competition degree and high product updating speed, so that the production process flow is optimized to organically integrate with information in various aspects such as market demands, customer demands and the like, optimization of the process flow content is required to be ensured, and identifiable and guidable choices are made in aspects such as material, equipment application, process routes, quality control and the like. However, the process route of fine chemical industry has various versions which are difficult to balance due to uncontrollable micro-denaturation of production elements, environments and parameters, so that how to find out the optimal process route can effectively and intelligently obtain the optimal process scheme aiming at different fine chemical products in real time through a big data intelligent means, and the method is a common difficulty facing the fine chemical industry processing and manufacturing industry.

In view of the above problems, an effective technical solution is currently needed.

Disclosure of Invention

The embodiment of the application aims to provide a quality inspection method, a system and a medium for a fine chemical process based on big data, which can process and quality inspection information to obtain quality inspection fluctuation conditions of a process flow, and then evaluate each batch of process by combining process related index to obtain process evaluation indexes, so as to realize a process quality inspection technology for processing and evaluating the process and quality inspection of batch products by using big data technology.

The embodiment of the application also provides a quality inspection method of the fine chemical process based on big data, which comprises the following steps:

establishing a product process database of each type of fine chemical product, and extracting product batch information of each production batch of the fine chemical product and production process information corresponding to each production batch;

acquiring product quality inspection information and batch profit information of the production batch according to the product batch information, extracting batch profit data according to the batch profit information, and extracting process flow information, process node detection information and process element information according to the production process information;

generating a production process information portrait according to the process flow information, the process node detection information and the process element information, extracting node quality inspection standard data of each process flow node according to the production process information portrait, and extracting process node quality inspection data corresponding to each process flow node according to the product quality inspection information;

Processing according to the process node quality inspection data and the node quality inspection standard data to obtain quality inspection fluctuation data of each process flow node, and performing aggregation processing on the quality inspection fluctuation data of all process flow nodes to obtain all quality inspection fluctuation indexes of the production batch;

extracting process error data and process index data according to the production process information portrait, and inputting the process error data and the process index data into a process processing identification model to calculate and obtain a process complexity index and a process effective utilization coefficient of the production batch;

processing according to the batch income data, the process complexity index and the process effective utilization coefficient and the full quality inspection fluctuation index to obtain a process evaluation index of the production batch;

and comparing the process evaluation indexes of the production batches according to preset process evaluation requirements to obtain an optimal production batch, and taking the corresponding production process information of the optimal production batch as optimal process scheme information.

Optionally, in the method for quality inspection of fine chemical process based on big data in the embodiment of the present application, the establishing a product process database of each type of fine chemical product, and extracting product batch information of each production batch of the fine chemical product, and the production process information corresponding to each production batch, includes:

Establishing a product process database of various types of fine chemical products based on preset Hadoop/Hive software;

the product process database comprises production information, product data, process information and processing data information of various types of fine chemical products;

extracting product batch information of a plurality of production batches of the fine chemical product according to the product process database;

and acquiring production process information corresponding to the product lot information of the plurality of production lots.

Optionally, in the method for quality inspection of fine chemical processes based on big data according to the embodiment of the present application, the obtaining product quality inspection information and batch profit information of the production batch according to the product batch information, extracting batch profit data according to the batch profit information, and extracting process flow information, process node detection information and process element information according to the production process information includes:

acquiring product quality inspection information and batch profit information of the production batch according to the product batch information;

the product quality inspection information comprises production node quality inspection information, production sampling inspection information and finished product final inspection information;

extracting batch profit data of the production batch according to the batch profit information;

And extracting process flow information, process node detection information and process element information according to the production process information.

Optionally, in the method for quality inspection of fine chemical process based on big data according to the embodiment of the present application, the generating a production process information portrait according to the process flow information, the process node detection information and the process element information, extracting node quality inspection standard data of each process flow node according to the production process information portrait, and extracting process node quality inspection data corresponding to each process flow node according to the product quality inspection information includes:

generating a production process information portrait of the production batch according to the process flow information, the process node detection information and the process element information;

the production process information portrait maps the process elements, the process standards and the process rules of the whole process flow of the production batch;

extracting node quality inspection standard data of each process flow node in the whole process flow according to the production process information portrait;

and extracting process node quality inspection data corresponding to each process flow node according to the production node quality inspection information of the product quality inspection information.

Optionally, in the quality inspection method for a fine chemical process based on big data according to the embodiment of the present application, the processing according to the process node quality inspection data and the node quality inspection standard data to obtain quality inspection fluctuation data of each process flow node, and performing aggregation processing on the quality inspection fluctuation data of all process flow nodes to obtain a total quality inspection fluctuation index of the production batch includes:

the process node quality inspection data comprise node quality control data, node working hour data, node energy consumption data and node operation error rate data;

processing node quality standard data, node working hour identification data, node energy consumption index data and node operation fault tolerance index data of the node quality inspection standard data according to the node quality control data, the node working hour data, the node energy consumption data and the node operation error rate data to obtain quality inspection fluctuation data of each process flow node;

and carrying out aggregation treatment according to quality inspection fluctuation data of all process flow nodes of the all process flow nodes to obtain the all quality inspection fluctuation index of the production batch.

Optionally, in the method for quality inspection of fine chemical processes based on big data according to the embodiment of the present application, extracting process error data and process index data according to the production process information portrait, inputting the process error data and the process index data into a process processing identification model, and calculating to obtain a process complexity index and a process effective utilization coefficient of the production lot, where the process complexity index and the process effective utilization coefficient include:

Extracting process error data and process index data of the production batch according to the production process information portrait;

the process error data comprises flow error data, control error data and configuration error data;

the process index data comprises equipment effective utilization data, inventory turnover rate data and material loss rate data;

and inputting the process error data and the process index data into a process treatment identification model for calculation treatment to respectively obtain the process complexity index and the process effective utilization coefficient of the production batch.

Optionally, in the method for quality inspection of a fine chemical process based on big data according to the embodiment of the present application, the processing according to the batch profit data, the process complexity index, the process effective utilization coefficient, and the full quality inspection fluctuation index to obtain the process evaluation index of the production batch includes:

according to the batch income data of the production batch, combining the process complexity index and the process effective utilization coefficient, and carrying out calculation processing on the process complexity index and the full quality inspection fluctuation index through a fusion program to obtain a process evaluation index of the production batch;

the calculation formula of the fusion program of the process evaluation index is as follows:

；

wherein ,

for the process rating index>

For the index of process complexity>

For the effective utilization of the process, +.>

For quality control fluctuation index->

For the batch profit data, < >>

、/>

、/>

、/>

Is a preset characteristic coefficient.

In a second aspect, embodiments of the present application provide a fine chemical process quality inspection system based on big data, the system comprising: the system comprises a memory and a processor, wherein the memory comprises a program of a fine chemical process quality inspection method based on big data, and the program of the fine chemical process quality inspection method based on big data realizes the following steps when being executed by the processor:

Optionally, in the fine chemical process quality inspection system based on big data according to the embodiment of the present application, the establishing a product process database of each type of fine chemical product, and extracting product batch information of each production batch of the fine chemical product, and production process information corresponding to each production batch, includes:

In a third aspect, an embodiment of the present application further provides a readable storage medium, where the readable storage medium includes a fine chemical process quality inspection method program based on big data, where the fine chemical process quality inspection method program based on big data is executed by a processor, to implement a step of the fine chemical process quality inspection method based on big data as described in any one of the foregoing embodiments.

From the above, the fine chemical process quality inspection method, system and medium based on big data provided by the embodiments of the present application. The method comprises the following steps: extracting product batch information and production process information according to a product process database, acquiring product quality inspection information and batch income data, process flow information, process node detection information and process element information, generating a production process information image, extracting node quality inspection standard data, extracting process node quality inspection data and node quality inspection standard data according to the product quality inspection information, processing to obtain quality inspection fluctuation data, performing polymerization treatment to obtain a full quality inspection fluctuation index, inputting the extracted process error data and process index data into a process treatment identification model, calculating to obtain a process complexity index and a process effective utilization coefficient, combining the batch income data and the full quality inspection fluctuation index to obtain a process evaluation index, performing process evaluation and comparison on the process evaluation index to obtain an optimal production batch, and taking the corresponding production process information as optimal process scheme information; and then, the process information and quality inspection information of the produced batch products are processed to obtain quality inspection fluctuation conditions of the whole process node, and the process related index coefficient is combined to evaluate to obtain a process evaluation index, and each batch of process is evaluated to be excellent according to the process evaluation index, so that a process quality inspection technology for processing and evaluating the process and quality inspection of the batch products by using a big data technology is realized.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a fine chemical process quality inspection method based on big data provided in an embodiment of the present application;

fig. 2 is a flowchart of obtaining product batch information and production process information of a fine chemical process quality inspection method based on big data according to an embodiment of the present application;

FIG. 3 is a flowchart of obtaining batch income data, process flow information, process node detection information and process element information according to the quality inspection method of fine chemical process based on big data provided in the embodiment of the present application;

Fig. 4 is a schematic structural diagram of a fine chemical process quality inspection system based on big data according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.

It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.

Referring to fig. 1, fig. 1 is a flowchart of a quality inspection method for fine chemical process based on big data in some embodiments of the present application. The quality inspection method for the fine chemical process based on big data is used in terminal equipment, such as mobile phones, computers and the like. The quality inspection method for the fine chemical process based on big data comprises the following steps:

s101, establishing a product process database of various types of fine chemical products, and extracting product batch information of each production batch of the fine chemical products and production process information corresponding to each production batch;

s102, acquiring product quality inspection information and batch profit information of the production batch according to the product batch information, extracting batch profit data according to the batch profit information, and extracting process flow information, process node detection information and process element information according to the production process information;

s103, generating a production process information portrait according to the process flow information, the process node detection information and the process element information, extracting node quality inspection standard data of each process flow node according to the production process information portrait, and extracting process node quality inspection data corresponding to each process flow node according to the product quality inspection information;

S104, processing according to the quality inspection data of the process nodes and the quality inspection standard data of the nodes to obtain quality inspection fluctuation data of each process flow node, and carrying out aggregation processing on the quality inspection fluctuation data of all process flow nodes to obtain all quality inspection fluctuation indexes of the production batch;

s105, extracting process error data and process index data according to the production process information portrait, and inputting the process error data and the process index data into a process treatment identification model to calculate and obtain a process complexity index and a process effective utilization coefficient of the production batch;

s106, processing according to the batch income data, the process complexity index and the process effective utilization coefficient and the quality control fluctuation index to obtain a process evaluation index of the production batch;

s107, comparing the process evaluation indexes of the production batches according to preset process evaluation requirements to obtain an optimal production batch, and taking the corresponding production process information of the optimal production batch as optimal process scheme information.

It should be noted that, in order to find an optimal process scheme of a certain fine chemical product, process evaluation indexes of production lot products are obtained by evaluating information such as process and quality inspection of historically produced product lots, products of optimal production lots are selected by evaluating the process evaluation indexes, processes corresponding to the products are used as the optimal process scheme, the quality of the processes corresponding to the products of a certain lot can be reflected by evaluating information data of the products of the certain lot, thus obtaining an optimal process scheme of the certain fine chemical product in the certain production lot, and product lot information of each production lot of a certain product is extracted by product process databases established by product information such as lot numbers, quantity, qualification rate, production man-hour, process information, processing information, energy consumption, raw material information, profit and the like of each type of fine chemical product, the production process information of the batch comprises quality inspection report information, profit information and the like of the batch products, the production process information comprises process information, flow information, process node quality inspection information, process production element information and the like of the batch products, the product quality inspection information and the batch profit information of the production batch are obtained according to the product batch information, the batch profit information is extracted according to the batch profit information, the process flow information, the process node inspection information and the process element information are extracted according to the production process information, a production process information image is generated, the node quality inspection standard data of each process flow node, namely the node quality inspection standard data in a preset node period divided according to the product processing process requirements in the production process, can be extracted according to the process stage, the flow step, dividing the intermediate-stage products, extracting process node quality inspection data corresponding to each process flow node according to product quality inspection information, processing according to the process node quality inspection data and node quality inspection standard data to obtain quality inspection fluctuation data of each process flow node, performing polymerization treatment on the quality inspection fluctuation data of all process flow nodes to obtain all quality inspection fluctuation indexes, extracting process error data and process index data of the production batch through production process information images, inputting the process error data and the process index data into a process treatment identification model to calculate and obtain a process complexity index and a process effective utilization coefficient, finally processing according to batch yield data, the process complexity index and the process effective utilization coefficient and the all quality inspection fluctuation index to obtain a process assessment index, comparing the process assessment index of each production batch according to preset process assessment requirements to obtain an optimal production batch, and processing the corresponding production process information of the optimal production batch as optimal process scheme information, wherein the preset process assessment requirements can be a process assessment threshold according to a process assessment threshold set by the product process, selecting the corresponding process assessment threshold and the optimal process assessment index of the product batch as a technical assessment scheme for realizing the large-scale process assessment of the product batch.

Referring to fig. 2, fig. 2 is a flowchart of a method for acquiring product batch information and production process information according to a fine chemical process quality inspection method based on big data in some embodiments of the present application. According to the embodiment of the invention, the product process database of each type of fine chemical product is established, and the product batch information of each production batch of the fine chemical product and the production process information corresponding to each production batch are extracted, specifically:

s201, establishing a product process database of various types of fine chemical products based on preset Hadoop/Hive software;

s202, the product process database comprises production information, product data, process information and processing data information of various types of fine chemical products;

s203, extracting product batch information of a plurality of production batches of the fine chemical product according to the product process database;

s204, obtaining production process information corresponding to the product batch information of the plurality of production batches.

It should be noted that, in order to evaluate the process of a certain type of fine chemical product, firstly, product information such as lot number, quantity, qualification rate, production man-hour, process information, processing information, energy consumption, raw material information, profit and the like of each type of fine chemical product needs to be established to form a product process database, the product process database is established by using preset Hadoop/Hive software, the Hadoop/Hive software is common data processing software and comprises Hadoop and Hive, the Hadoop is a software framework capable of performing distributed processing on large data, the Hadoop is the most core of hdfs and mapreduce, the hdfs provides data storage, the mapreduce is used for data calculation, the Hive is the extension of the Hadoop, the Hive is a data warehouse core component providing a query function, the hdfs at the bottom layer provides data storage for Hive, the mapreduce provides distributed operation for Hive, the Hadoop/Hive software can process the product information of a large number of each type of fine chemical product, thus the product process database is established, the product process database comprises the hdfs and the corresponding batch information of each type of fine chemical product can be obtained, and the batch information of each type of fine chemical product can be obtained at the same time, and the batch information of the fine product can be obtained by the production database is produced according to the production information of a lot of the batch of the product.

Referring to fig. 3, fig. 3 is a flowchart of a method for obtaining batch profit data, process flow information, process node detection information, and process element information according to a fine chemical process quality inspection method based on big data in some embodiments of the present application. According to an embodiment of the present invention, the method obtains product quality inspection information and batch profit information of the production batch according to the product batch information, extracts batch profit data according to the batch profit information, and extracts process flow information, process node detection information and process element information according to the production process information, specifically including:

s301, acquiring product quality inspection information and batch profit information of the production batch according to the product batch information;

s302, the product quality inspection information comprises production node quality inspection information, production sampling inspection information and finished product final inspection information;

s303, extracting batch profit data of the production batch according to the batch profit information;

s304, extracting process flow information, process node detection information and process element information according to the production process information.

It should be noted that, product quality inspection information and batch profit information of the production batch are obtained according to product batch information, the product quality inspection information is quality inspection information of preset production nodes in the production and processing processes of the product, quality inspection information of random sampling inspection in the processing process, and finished product final inspection information after the product is processed into a finished product, meanwhile, batch profit data of the production batch product, namely data of production profit condition of the product, are extracted according to the batch profit information, then process flow information, process node detection information and process element information are extracted according to corresponding production process information of the batch product, namely the process flow information reflects information of the processing process flow of the product, the process node detection information reflects related information of detection requirements, detection methods and detection standards of each process node, and the process element information is process related information such as facility equipment application, key process requirements, processing formula, temperature and humidity pressure environment setting, technical direction and technical requirements in the processing process.

According to the embodiment of the invention, the production process information portrait is generated according to the process flow information, the process node detection information and the process element information, the node quality inspection standard data of each process flow node is extracted according to the production process information portrait, and the process node quality inspection data corresponding to each process flow node is extracted according to the product quality inspection information, specifically:

It should be noted that, to better obtain the information description of the production process of the batch of fine chemical products, a production process information image of the batch of products is generated according to the extracted process flow information, process node detection information and process element information, the image is a mapping description of process elements, process standards and process rule related information in the whole flow of the product production process, the image can describe the element information of the processing process such as a formula, a core process, temperature and humidity pressure parameter setting and technical guidelines in the production process, process standard information of each processing link and processing flow and process rule information describing the process procedure, process equipment and a process checking method, node quality checking standard data of each process flow node in the whole process flow is extracted according to the generated production process information image, namely, the quality checking standard data in a preset node period divided according to the processing process requirement of the product is a checking standard of each node stage, and the quality checking data of the process nodes corresponding to each process flow node is extracted according to the production node quality checking information of the product quality checking information, namely, the actual processing data of each process node quality checking data in each process flow is acquired.

According to the embodiment of the invention, the quality inspection fluctuation data of each process flow node is obtained by processing the quality inspection data of the process nodes and the quality inspection standard data of the nodes, and the quality inspection fluctuation data of all process flow nodes is aggregated to obtain the all quality inspection fluctuation index of the production batch, specifically:

It should be noted that, because the process requirements and quality inspection requirements of different nodes in the production process of the product have differences, and the processing quality of different nodes is also different, the fluctuation of processing exists in the production process, so that the processing quality and the process implementation quality of different nodes have fluctuation, the batch product with smaller quality inspection fluctuation shows that the processing process stability is good, and the corresponding processed product quality is good, so that in order to obtain good products and good processes, the quality inspection stability condition of each batch of products in the production process needs to be evaluated, quality inspection fluctuation data is introduced, the quality inspection fluctuation condition of the whole production process needs to be measured, and the quality inspection fluctuation condition of each process flow node needs to be evaluated, according to node quality control data, node working hour data, node energy consumption data and node operation error rate data of process node quality inspection standard data and node quality inspection standard data of node working hour identification data, node energy consumption index data and node operation fault tolerance index data of the process node quality inspection data, calculating to obtain quality inspection fluctuation data of each process flow node, wherein the node quality control data are quality inspection data after the completion of the node, the node working hour data are working hour data of node processing, the node energy consumption data are energy consumption condition data of the node, the node operation error rate data are statistical data of production error rate of the node, the node quality inspection standard data comprise standard data corresponding to the quality inspection data of each process node, calculating to obtain quality inspection fluctuation data of each process flow node, and then aggregating to obtain a full quality inspection fluctuation index;

The calculation formula of quality inspection fluctuation data of each process flow node is as follows:

；

wherein ,

quality control fluctuation data for the ith process flow node,/-for the process flow node>

In order to preset the technological characteristic index of the product,

、/>

、/>

、/>

node quality control data, node man-hour data, node energy consumption data and node operation error rate data of the ith process flow node respectively>

、/>

、/>

、/>

Respectively node quality standard data, node man-hour identification data, node energy consumption index data and node operation fault tolerance index data of the ith process flow node>

、/>

、/>

、/>

For the corresponding characteristic coefficient (the characteristic coefficient is obtained through a product process database);

the calculation formula of the full quality inspection fluctuation index of the production batch is as follows:

； wherein ,/>

For quality control fluctuation index->

N is the number of nodes in the process flow and is a preset coefficient。

According to the embodiment of the invention, the process error data and the process index data are extracted according to the production process information portrait, and the process complexity index and the process effective utilization coefficient of the production batch are obtained by inputting the process error data and the process index data into a process treatment identification model for calculation, specifically:

It should be noted that, the process complexity and the effective utilization condition of the production process data are described for the process of the production batch product, the process complexity is measured according to the error condition in the process implementation, the process effective utilization condition is evaluated according to the equipment utilization, inventory turnover and material loss condition in the process implementation, the error data and the index data describing the process implementation process are extracted according to the production process information image of the production batch, the process error data reflect various error conditions in the process implementation process, including the flow error data of the process implementation error, the operation error data of the production operation error and the preparation error data in the preparation process, the process index data reflect several key indexes including the equipment effective utilization data, inventory turnover rate data and material loss rate data in the process implementation, the effective utilization condition of the production data in the process implementation process can be measured through the index data, and the process index data are input into a process treatment identification model for calculation treatment, the process complexity and the process effective utilization condition of the production batch can be evaluated, wherein the process treatment identification model comprises the calculation of the complex index and the effective utilization coefficient of the process;

The calculation program formula of the process complexity index is as follows:

；

wherein ,

for the index of process complexity>

、/>

、/>

Flow error data, handling error data, formulation error data, +.>

、/>

、/>

Is a characteristic coefficient; />

The calculation program formula of the effective utilization coefficient of the process is as follows:

；

wherein ,

for the effective utilization of the process, +.>

To set upEffective utilization data->

For the inventory turnover rate data,

for the material loss rate data, +.>

、/>

Is a characteristic coefficient (the characteristic coefficient is obtained through a product process database).

According to the embodiment of the invention, the process evaluation index of the production batch is obtained by processing the batch income data, the process complexity index, the process effective utilization coefficient and the quality control fluctuation index, specifically:

；

wherein ,

for the process rating index>

For the index of process complexity>

For the effective utilization of the process, +. >

For quality control fluctuation index->

For the batch profit data, < >>

、/>

、/>

、/>

Is a preset characteristic coefficient (the characteristic coefficient is obtained through a product process database).

It should be noted that, by calculating the obtained batch income data, the process complexity index, the process effective utilization coefficient and the full quality inspection fluctuation index of the production batch, the process evaluation condition of the production process flow of the product of the production batch can be obtained, the process evaluation condition of the batch of product can be measured by the process evaluation index calculated by the fusion of the data, and then the process is evaluated and optimized according to the process evaluation index of each production batch, so as to select a proper process route scheme.

As shown in fig. 4, the invention also discloses a fine chemical process quality inspection system 4 based on big data, which comprises a memory 41 and a processor 42, wherein the memory comprises a fine chemical process quality inspection method program based on big data, and the fine chemical process quality inspection method program based on big data realizes the following steps when being executed by the processor:

According to the embodiment of the invention, the product process database of each type of fine chemical product is established, and the product batch information of each production batch of the fine chemical product and the production process information corresponding to each production batch are extracted, specifically:

According to an embodiment of the present invention, the method obtains product quality inspection information and batch profit information of the production batch according to the product batch information, extracts batch profit data according to the batch profit information, and extracts process flow information, process node detection information and process element information according to the production process information, specifically including:

；

wherein ,

In order to preset the technological characteristic index of the product,

、/>

、/>

、/>

、/>

、/>

、/>

、/>

、/>

、/>

； wherein ,/>

For quality control fluctuation index->

And n is the number of nodes in the process flow and is a preset coefficient.

The calculation program formula of the process complexity index is as follows:

；

wherein ,

for the index of process complexity>

、/>

、/>

Flow error data, handling error data, formulation error data, +.>

、/>

、/>

Is a characteristic coefficient;

；

wherein ,

for the effective utilization of the process, +.>

Efficient use of data for devices, < >>

For the inventory turnover rate data,

as the material loss rate data, the device can be used for detecting the material loss rate,/>

、/>

；

wherein ,

for the process rating index>

For the index of process complexity>

For the effective utilization of the process, +. >

For quality control fluctuation index->

For the batch profit data, < >>

、/>

、/>

、/>

A third aspect of the present invention provides a readable storage medium having embodied therein a big data based fine chemical process quality inspection method program which, when executed by a processor, implements the steps of a big data based fine chemical process quality inspection method as described in any of the above.

The invention discloses a quality inspection method, a system and a medium for a fine chemical process based on big data, which are characterized in that product batch information and production process information are extracted through a product process database, product quality inspection information and batch income data are obtained, process flow information, process node detection information and process element information are obtained, production process information portraits are generated to extract node quality inspection standard data, the process node quality inspection data and the node quality inspection standard data are extracted according to the product quality inspection information to be processed to obtain quality inspection fluctuation data, then the quality inspection fluctuation index is polymerized, the process complexity index and the process effective utilization coefficient are obtained by calculation in a process processing identification model according to the extracted process error data and the process index data, the process evaluation index is obtained by combining the batch income data and the full quality inspection fluctuation index, the optimal production batch is obtained by performing process evaluation comparison on the process evaluation index, and the corresponding production process information is used as optimal process scheme information; and then, the process information and quality inspection information of the produced batch products are processed to obtain quality inspection fluctuation conditions of the whole process node, and the process related index coefficient is combined to evaluate to obtain a process evaluation index, and each batch of process is evaluated to be excellent according to the process evaluation index, so that a process quality inspection technology for processing and evaluating the process and quality inspection of the batch products by using a big data technology is realized.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.

The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.

Alternatively, the above-described integrated units of the present invention may be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.

Claims

1. A quality inspection method for fine chemical technology based on big data is characterized by comprising the following steps:

processing the process node quality inspection data and the node quality inspection standard data to obtain quality inspection fluctuation data of each process flow node, and carrying out aggregation processing on the quality inspection fluctuation data of all process flow nodes to obtain all quality inspection fluctuation indexes of the production batch;

Extracting process error data and process index data according to the production process information portrait, inputting the process error data and the process index data into a process treatment identification model for calculation, and obtaining a process complexity index and a process effective utilization coefficient of the production batch;

processing the batch income data, the process complexity index and the process effective utilization coefficient and the full quality inspection fluctuation index to obtain a process evaluation index of the production batch;

2. The method for quality inspection of fine chemical process based on big data according to claim 1, wherein the steps of creating a product process database of each type of fine chemical product, extracting product lot information of each production lot of the fine chemical product, and the production process information corresponding to each production lot, comprise:

3. The big data based fine chemical process quality inspection method according to claim 2, wherein the obtaining product quality inspection information and batch profit information of the production batch according to the product batch information, extracting batch profit data according to the batch profit information, and extracting process flow information, process node detection information, and process element information according to the production process information, comprises:

4. A fine chemical process quality inspection method based on big data according to claim 3, wherein the generating a production process information portrait according to the process flow information, process node detection information and process element information, extracting node quality inspection standard data of each process flow node according to the production process information portrait, and extracting process node quality inspection data corresponding to each process flow node according to the product quality inspection information comprises:

5. The method for quality inspection of fine chemical processes based on big data according to claim 4, wherein the processing the process node quality inspection data and the node quality inspection standard data to obtain quality inspection fluctuation data of each process flow node, and performing aggregation processing on the quality inspection fluctuation data of all process flow nodes to obtain all quality inspection fluctuation indexes of the production batch comprises:

processing the node quality control data, the node working hour data, the node energy consumption data and the node operation error rate data with the node quality standard data, the node working hour identification data, the node energy consumption index data and the node operation fault tolerance index data of the node quality inspection standard data to obtain quality inspection fluctuation data of each process flow node;

6. The method for quality inspection of fine chemical processes based on big data according to claim 5, wherein the extracting process error data and process index data from the production process information representation, inputting the process error data and the process index data into a process treatment identification model for calculation, obtaining a process complexity index and a process effective utilization coefficient of the production lot, comprises:

7. The method of claim 6, wherein the processing the batch profit data, the process complexity index, and the process availability factor with the full quality inspection surge index to obtain the process assessment index for the production batch comprises:

According to the batch income data of the production batch, combining the process complexity index, the process effective utilization coefficient and the full quality inspection fluctuation index, and carrying out calculation processing through a fusion program to obtain a process evaluation index of the production batch;

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wherein ,

for the process rating index>

For the index of process complexity>

For the effective utilization of the process, +.>

For quality control fluctuation index->

For the batch profit data, < >>

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Is a preset characteristic coefficient. />

8. A fine chemical process quality inspection system based on big data, which is characterized in that the system comprises: the system comprises a memory and a processor, wherein the memory comprises a program of a fine chemical process quality inspection method based on big data, and the program of the fine chemical process quality inspection method based on big data realizes the following steps when being executed by the processor:

9. The big data based fine chemical process quality inspection system according to claim 8, wherein the creating a product process database of each type of fine chemical product and extracting product lot information of each production lot of the fine chemical product and the production process information corresponding to each production lot comprises:

10. A computer-readable storage medium, characterized in that the computer-readable storage medium includes therein a big data based fine chemical process quality inspection method program, which when executed by a processor, implements the steps of a big data based fine chemical process quality inspection method according to any one of claims 1 to 7.