CN115879826A

CN115879826A - Fine chemical process quality inspection method, system and medium based on big data

Info

Publication number: CN115879826A
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: 2023-03-31
Anticipated expiration: 2043-02-20
Also published as: CN115879826B

Abstract

The embodiment of the application provides 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. Belongs to the technical field of fine intelligent construction 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 a generated production process information portrait, aggregating to obtain a full quality detection dynamic index, obtaining a process complex index and a process effective utilization coefficient through a model, processing by combining batch income data and the full quality detection dynamic index to obtain a process evaluation index, and taking the production process information corresponding to the production batch with the optimal index as optimal process scheme information; therefore, the process and quality inspection information is processed to obtain the quality inspection fluctuation condition of the process flow, and then the process evaluation indexes are obtained by combining the process correlation index coefficients to evaluate the processes of all batches, so that the process quality inspection technology for processing and evaluating the processes and quality inspection of the products of the batches by using a 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 intelligent manufacturing and big data processing, in particular to a fine chemical process quality inspection method, a system and a medium based on big data.

Background

In the fine chemical industry, fine chemical production puts high demands on production flow, and in market competition, modern fine chemical products have high competition degree and high product updating speed, so that the production process flow needs to be optimized, and organically integrated with various information such as market demands, customer demands and the like, the optimization of the process flow content needs to be ensured, and identifiable and guidable selection is carried out on the aspects of materials, equipment application, process routes, quality control and the like. However, the production elements, environments and parameters of the fine chemical engineering process route have uncontrollable micro-variability, so that a plurality of versions which are difficult to balance exist, so that how to find out the optimal process route can effectively and intelligently obtain the optimal process schemes for different fine chemical engineering products in real time by a big data intelligent means is a common problem in the fine chemical engineering processing and manufacturing industry.

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

Disclosure of Invention

The embodiment of the application aims to provide 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, which can process and quality inspection information to obtain the quality inspection fluctuation condition of a process flow, and then obtain a process evaluation index by combining with a process correlation index to evaluate each batch of processes, so as to realize a process quality inspection technology for processing and evaluating the processes and quality inspection of batch products by using a big data technology.

The embodiment of the application also provides a fine chemical process quality inspection method 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;

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;

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 dynamic 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 processing identification model, and calculating to 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, the process effective utilization coefficient and the full quality detection kinetic 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 production process information corresponding to the optimal production batch as optimal process scheme information.

Optionally, in the fine chemical process quality inspection method 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 the 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 products according to the product process database;

and acquiring production process information corresponding to the product batch information of the plurality of production batches.

Optionally, in the fine chemical process quality inspection method 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:

obtaining 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 income 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 fine chemical process quality inspection method 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 fine chemical process quality inspection method 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 the quality inspection fluctuation data of each process flow node, and the aggregating processing is performed on the quality inspection fluctuation data of all process flow nodes to obtain the all-quality inspection dynamic index of the production batch includes:

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

and performing aggregation processing according to the quality inspection fluctuation data of all the process flow nodes of the whole process flow nodes to obtain the whole quality inspection fluctuation index of the production batch.

Optionally, in the fine chemical process quality inspection method based on big data according to the embodiment of the present application, the extracting process error data and process indicator data according to the production process information representation, and inputting the process error data and the process indicator data into a process processing identification model to calculate and obtain the process complexity index and the process utilization factor of the production lot includes:

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

the process error data comprises process error data, control error data and preparation 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 processing identification model for calculation processing to respectively obtain the process complexity index and the process effective utilization coefficient of the production batch.

Optionally, in the fine chemical process quality inspection method based on big data according to the embodiment of the present application, the processing according to the lot income data, the process complexity index, the process effective utilization coefficient, and the full quality detection kinetic index to obtain the process evaluation index of the production lot includes:

calculating and processing the full-quality inspection fluctuation index through a fusion program according to the batch income data of the production batch in combination with the process complexity index and the process effective utilization coefficient 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 ,

evaluating an index for a process>

For a process-complicated index, is selected>

Effectively utilizes the coefficient for the process>

Is a full detection actuation index>

For lot gain data, based on the number of lots selected in the lot pool>

、/>

、/>

、/>

Is a preset characteristic coefficient.

In a second aspect, an embodiment of the present application provides a fine chemical process quality inspection system based on big data, where the system includes: the storage comprises a program of the 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:

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 aggregating the quality inspection fluctuation data of all process flow nodes to obtain all-quality inspection dynamic indexes of the production batch;

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

Optionally, in the fine chemical process quality inspection system based on big data according to the embodiment of the present application, the creating a product process database of each type of fine chemical product, and extracting the product batch information of each production batch of the fine chemical product and the 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, and when the fine chemical process quality inspection method program based on big data is executed by a processor, the steps of the fine chemical process quality inspection method based on big data as described in any one of the above are implemented.

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 production process information portrait, 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, aggregating to obtain a full quality detection dynamic index, inputting the extracted process error data and process index data into a process processing identification model to calculate and obtain a process complexity index and a process effective utilization coefficient, processing by combining batch income data and the full quality detection dynamic index to obtain a process evaluation index, performing process evaluation comparison on the process evaluation index to obtain an optimal production batch, and taking corresponding production process information as optimal process scheme information; the process quality inspection technology comprises the steps of processing process information and quality inspection information of batch products to obtain quality inspection fluctuation conditions of all process nodes, evaluating the process related index to obtain a process evaluation index, and evaluating each batch of processes according to the process evaluation index to realize processing and evaluation of the processes and quality inspection of the batch products by using a big data technology.

Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof 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 required to be used 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 therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.

Fig. 1 is a flowchart of a fine chemical process quality inspection method based on big data according to an embodiment of the present disclosure;

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

fig. 3 is a flowchart of the fine chemical process quality inspection method based on big data according to the embodiment of the present application, illustrating batch revenue data, process flow information, process node detection information, and process element information;

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 disclosure.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as presented in the figures, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing 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 fine chemical process quality inspection method based on big data according to some embodiments of the present disclosure. The fine chemical process quality inspection method based on the big data is used in terminal equipment such as mobile phones and computers. The fine chemical process quality inspection method based on the big data comprises the following steps:

s101, 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;

s102, 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;

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 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 dynamic 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 processing 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, the process effective utilization coefficient and the full quality detection kinetic 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 out the optimal process scheme of a certain fine chemical product, the process evaluation index of the product in the production batch is obtained by evaluating the process and quality inspection information of the product batch in the historical production, the product in the optimal production batch is selected by evaluating the process evaluation index, the process corresponding to the product is taken as the optimal process scheme, the process corresponding to the batch of the product can be reflected by evaluating the information data of the product in a certain batch, so as to obtain the optimal process scheme of the certain fine chemical product in a certain production batch, the product process database established by the product information of various types of fine chemical products, such as batch number, quantity, qualification rate, production working hours, process information, processing information, energy consumption, raw material information, profit and the like, is used for extracting the product batch information of the certain product, and the production process information of the batch, the product batch information comprises quality inspection report information, profit information and the like of the batch, the production process information comprises process information, flow information, process node quality inspection information, process production element information and the like of the batch, 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 data are extracted according to the batch profit information, the process flow information, the process node detection information and the process element information are extracted according to the production process information, a production process information portrait is generated, the node quality inspection standard data of each process flow node are extracted according to the production process information portrait, namely the node quality inspection standard data in a preset node time interval divided according to the product processing process requirement in the production process can be obtained according to the process stages, the process steps, the node quality inspection standard data can be obtained according to the process steps, the node quality inspection standard data can be obtained according to the production process steps, dividing the intermediate stage product, extracting process node quality inspection data corresponding to each process flow node according to the product quality inspection information, 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, performing aggregation processing on the quality inspection fluctuation data of all the process flow nodes to obtain an all-quality inspection fluctuation index, extracting process error data and process index data of the production batch through a production process information image, inputting the process error data and the process index data into a process processing identification model to calculate a process complexity index and a process effective utilization coefficient, processing the process error data and the process index effective utilization coefficient and the all-quality inspection fluctuation index according to batch income data, the process complexity index and the process effective utilization coefficient to obtain a process evaluation index, comparing the process evaluation index of each production batch according to a preset process evaluation requirement to obtain an optimal production batch, processing the fine chemical product by using the corresponding production process information of the optimal production batch as optimal process scheme information, and selecting the optimal process evaluation requirement according with the process evaluation threshold to realize the process quality inspection data of the production batch.

Referring to fig. 2, fig. 2 is a flowchart of a method for quality inspection of a fine chemical process based on big data to obtain product lot information and production process information according to some embodiments of the present disclosure. According to the embodiment of the invention, the establishing of the product process database of each type of fine chemical product, the extracting of the product batch information of each production batch of the fine chemical product and the production process information corresponding to each production batch specifically comprise:

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

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

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

and S204, acquiring production process information corresponding to the product batch information of the plurality of production batches.

The method comprises the steps that a product process database is established for evaluating the process of a certain type of fine chemical products, product information of the various types of fine chemical products such as batch numbers, quantities, yield, production working hours, process information, processing information, energy consumption, raw material information, profit and the like, the product process database is established by utilizing 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 mainly composed of hdfs and maproduce, hdfs provides data storage, the maproduce is used for data calculation, hive is an extension of the Hadoop, hive is a data warehouse core component providing an inquiry function, hdfs provides data storage for Hive, maprodrodrodudu provides distributed operation for Hive, a large amount of collected fine chemical product information of the type can be processed by the Hadoop/Hive software, data of the batch information of the Hadoop warehouse provides data storage for Hive, and the batch of the product process information of the product production process database can be obtained according to batch information of the batch of the product production process, and batch of the product information, and batch of the product production process database, and batch information of the product production process database can be obtained by the batch information of the batch of the product.

Referring to fig. 3, fig. 3 is a flowchart of a method for fine chemical process quality inspection based on big data according to some embodiments of the present disclosure for obtaining lot revenue data, process flow information, process node detection information, and process element information. According to the embodiment of the present invention, the obtaining of the product quality inspection information and the batch profit information of the production batch according to the product batch information, the extracting of the batch profit data according to the batch profit information, and the extracting of the process flow information, the process node detection information, and the process element information according to the production process information specifically include:

s301, obtaining 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 income 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, the product quality inspection information and the batch profit information of the production batch are obtained according to the product batch information, the product quality inspection information is the quality inspection information of a preset production node in the production and processing process of the product, the quality inspection information of random spot inspection in the processing process, and the finished product final inspection information after being processed into a finished product, meanwhile, the batch profit data of the production batch of the product, i.e., the data of the product production profit condition, are extracted according to the production process information corresponding to the batch of the product, i.e., the process flow information reflects the information in the product processing process flow, the process node detection information reflects the detection requirements, the detection methods, and the relevant information of the detection standards of each process node, and the process element information is the relevant information of the processes such as facility equipment application, key process requirements, processing formula, temperature and humidity environment setting, technical guidance, technical requirements, and the like in the processing process.

According to the embodiment of the invention, the generating a production process information portrait according to the process flow information, the process node detection information and the process element information, extracting the node quality inspection standard data of each process flow node according to the production process information portrait, and extracting the process node quality inspection data corresponding to each process flow node according to the product quality inspection information specifically comprises:

It should be noted that, in order to better obtain the information description of the production process of the batch of fine chemical products, a production process information portrait of the batch of products is generated according to the extracted process flow information, process node detection information and process element information, the portrait is a mapping description of process elements, process standards and process procedure related information in all the flows of the production process of the products, the portrait can describe element information of the processing processes of the process elements in the production process, such as formula, core process, temperature, humidity, pressure parameter setting, technical guidance and the like, the process standard information of each processing link and the processing flow is also described, and the process procedure information of the process, process equipment and process inspection method is described, the node quality inspection standard data of each process flow node in the whole process flow is extracted according to the generated production process information, namely, the quality inspection standard data in a preset node time period divided according to the product processing process requirements is extracted, the data of the processing quality of each process flow node in each node stage is the inspection standard set, and the process node quality inspection data corresponding to each process node quality inspection information of the product quality inspection information is extracted, namely, the process node quality inspection data of the processing data of each process node quality inspection data is acquired in each process flow.

According to the embodiment of the invention, the step of processing the quality inspection data of the process nodes and the node quality inspection standard data to obtain the quality inspection fluctuation data of each process flow node and the step of aggregating the quality inspection fluctuation data of all process flow nodes to obtain the all-quality inspection dynamic index of the production batch comprises the following steps:

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 qualities of different nodes are also different, there is fluctuation in the processing in the production process, which causes fluctuation in the process processing quality and process implementation quality of different nodes, and the batch of products with smaller quality inspection fluctuation indicates that the stability of the processing process is good and the quality of the corresponding processed products 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, so that quality inspection fluctuation data is introduced, and the quality inspection fluctuation condition of the whole production process needs to be evaluated, calculating and processing according to node quality control data, node man-hour data, node energy consumption data, node operation error rate data of process node quality inspection data and node quality standard data, node man-hour identification data, node energy consumption index data and node operation fault tolerance index data of the node quality inspection standard data to obtain quality inspection fluctuation data of each process flow node, wherein the node quality control data is quality inspection data after the completion of the node, the node man-hour data is man-hour data for node processing, the node energy consumption data is energy consumption condition data of the node, the node operation error rate data is statistical data of production error rate in the node, the node quality inspection standard data comprises standard data corresponding to the quality inspection data of each process flow node, the quality inspection fluctuation data of each process flow node is obtained through calculation, and then, the quality inspection fluctuation data is aggregated to obtain a full-quality inspection dynamic index;

wherein, the calculation formula of the quality inspection fluctuation data of each process flow node is as follows:

；

wherein ,

for the quality inspection fluctuation data of the ith process flow node>

In order to preset the index of the technical characteristics of the product,

、/>

、/>

、/>

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

、/>

、/>

、/>

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

、/>

、/>

、/>

For the corresponding feature coefficients (the feature coefficients are obtained from the product process database);

the calculation formula of the full quality detection kinetic index of the production batch is as follows:

； wherein ,/>

Is a full detection actuation index>

Is a preset coefficient, and n is the number of process flow nodes.

According to the embodiment of the invention, the extracting of the process error data and the process index data according to the production process information portrait, and the inputting of the process error data and the process index data into the process processing identification model to calculate and obtain the process complexity index and the process effective utilization coefficient of the production batch specifically comprise:

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 products, the process complexity is measured according to the error condition in the process implementation, the effective utilization condition of the process is evaluated according to the conditions of equipment utilization, inventory turnover and material loss 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 types of error conditions existing in the process implementation process, including process error data of the process implementation error, operation error data of production operation errors and preparation error data occurring in the material preparation process, the process index data reflect several key indexes in the process implementation, including equipment effective utilization data, inventory turnover rate data and material loss rate data, the effective utilization condition of the production data in the process implementation process can be measured through the index data, then the process error data and the process index data are input into the process processing identification model for calculation processing, the process complexity and the process effective utilization condition of the production batch can be evaluated, wherein the process processing identification model comprises the effective utilization coefficient and the process index of the process utilization coefficient, and the process utilization coefficient of the process can be calculated according to the process processing identification model, and the effective utilization coefficient of the process index and the process utilization coefficient of the process of the production batch product can be evaluated;

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

；

wherein ,

for a process-complicated index, is selected>

、/>

、/>

Based on the flow error data, the manipulated error data, the formulated error data, respectively>

、/>

、/>

Is characterized in thatA coefficient; />

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

；

wherein ,

effectively utilizes the coefficient for the process>

Effectively utilizing data for a device>

For inventory turnover rate data, based on the status of the inventory>

Is the data of the material loss rate, is judged>

、/>

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

According to the embodiment of the present invention, the processing according to the lot income data, the process complexity index, the process effective utilization coefficient and the qualitative detection kinetics index to obtain the process assessment index of the production lot specifically comprises:

；

wherein ,

evaluating an index for a process>

For a process-complicated index, is selected>

Effectively utilizes the coefficient for the process>

Is a full detection actuation index>

For lot gain data, based on the number of lots selected in the lot pool>

、/>

、/>

、/>

Is a preset characteristic coefficient (the characteristic coefficient is obtained by a product technology database).

It should be noted that, the process evaluation condition of the production process flow of the product of the production batch can be obtained by calculating and processing the obtained batch income data, the process complexity index, the process effective utilization coefficient and the full quality detection kinetic index of the production batch, the process evaluation condition of the product of the production batch can be measured by the process evaluation index calculated by fusing the data, and then the process is evaluated to be optimal according to the process evaluation index of each production batch, so that a suitable process route scheme is selected.

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

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

According to the embodiment of the present invention, the establishing of the product process database of each type of fine chemical product, the extracting of the product batch information of each production batch of the fine chemical product, and the extracting of the production process information corresponding to each production batch specifically include:

According to the embodiment of the present invention, the obtaining of the product quality inspection information and the batch profit information of the production batch according to the product batch information, the extracting of the batch profit data according to the batch profit information, and the extracting of the process flow information, the process node detection information, and the process element information according to the production process information specifically include:

It should be noted that, in order to better obtain the information description of the production process of the batch of fine chemical products, a production process information sketch of the batch of products is generated according to the extracted process flow information, process node detection information and process element information, the sketch is a mapping description of process elements, process standards and process procedure related information in all the flows of the product production process, the process elements in the production process such as a formula, a core process, temperature, humidity, pressure parameter setting, technical guidance and the like can be described through the sketch, the process standard information of each processing link and processing flow is also described, the process procedure information of a process, process equipment and a process inspection method is described, node quality inspection standard data of each process flow node in the whole process flow is extracted according to the generated production process information sketch, namely, quality inspection standard data in a preset node time period divided according to the product processing process requirements are extracted, the data are the inspection standard set for the processing quality of each node in each node stage, and process node quality inspection data corresponding to each process node quality inspection information are extracted according to the production node quality inspection information of the product quality inspection information, namely, the process node quality inspection data are acquired at each processing node quality inspection data of the processing nodes.

According to the embodiment of the present invention, the processing according to the process node quality inspection data and the node quality inspection standard data to obtain the quality inspection fluctuation data of each process flow node, and the aggregating processing is performed on the quality inspection fluctuation data of all process flow nodes to obtain the all-quality inspection dynamic index of the production batch, specifically:

；

wherein ,

for the quality inspection fluctuation data of the ith process flow node>

For presetting a product process characteristic index>

、/>

、/>

、/>

、/>

、/>

、/>

、/>

、/>

、/>

； wherein ,/>

Is a full detection actuation index>

Is a preset coefficient, and n is the number of process flow nodes.

According to the embodiment of the invention, the extracting of the process error data and the process index data according to the production process information portrait, and the inputting of the process error data and the process index data into the process processing identification model according to the process error data and the process index data are used for calculating and obtaining the process complexity index and the process effective utilization coefficient of the production batch, which are specifically as follows:

；

wherein ,

for a process-complicated index, is selected>

、/>

、/>

、/>

、/>

Is a characteristic coefficient;

；

wherein ,

effectively utilizes the coefficient for the process>

Effectively utilizing data for a device>

For inventory turnover rate data, based on the status of the inventory>

Is the data of the material loss rate, is judged>

、/>

According to the embodiment of the present invention, the processing according to the lot income data, the process complexity index, the process effective utilization coefficient and the full quality detection kinetics index to obtain the process evaluation index of the production lot specifically comprises:

；

wherein ,

evaluating an index for a process>

For a process-complicated index>

Effectively utilizes the coefficient for the process>

Is a full detection actuation index>

For lot gain data, based on the number of lots selected in the lot pool>

、/>

、/>

、/>

A third aspect of the present invention provides a readable storage medium, where the readable storage medium includes a fine chemical process quality inspection method program based on big data, and when the fine chemical process quality inspection method program based on big data is executed by a processor, the steps of the fine chemical process quality inspection method based on big data as described in any of the above are implemented.

The invention discloses a fine chemical process quality inspection method, a system and a medium 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, process flow information, process node detection information and process element information are obtained, production process information portrait is generated, node quality inspection standard data are extracted, process node quality inspection data and node quality inspection standard data are extracted according to the product quality inspection information and processed to obtain quality inspection fluctuation data, the quality inspection fluctuation data are aggregated into full quality detection dynamic indexes, process error data and process index data are input into a process processing identification model according to the extracted process error data and process index data to calculate and obtain a process complexity index and a process effective utilization coefficient, the batch income data and the full quality detection dynamic indexes are combined to process and obtain a process evaluation index, the process evaluation index is compared to obtain an optimal production batch, and corresponding production process information is used as optimal process scheme information; the process quality inspection technology comprises the steps of processing process information and quality inspection information of batch products to obtain quality inspection fluctuation conditions of all process nodes, evaluating the process related index to obtain a process evaluation index, and evaluating each batch of processes according to the process evaluation index to realize processing and evaluation of the processes and quality inspection of the batch products by using a big data technology.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or in other forms.

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

In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

Alternatively, the integrated unit of the present invention may be stored in a readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) 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, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.

Claims

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

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 according to the process error data and the process index data, and calculating to obtain a process complexity index and a process effective utilization coefficient of the production batch;

2. The method as claimed in claim 1, wherein the step of creating the product process database for each type of the fine chemical products and extracting the product lot information of each production lot of the fine chemical products and the production process information corresponding to each production lot comprises:

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

4. The 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, the process node detection information and the process element information, extracting the node quality inspection standard data of each process flow node according to the production process information portrait, and extracting the process node quality inspection data corresponding to each process flow node according to the product quality inspection information comprises:

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

6. The fine chemical process quality inspection method based on big data as claimed in claim 5, wherein the extracting process error data and process index data according to the production process information portrait, and inputting the process error data and process index data into a process identification model to calculate and obtain the process complexity index and the process utilization factor of the production batch comprises:

7. The fine chemical process quality inspection method based on big data as claimed in claim 6, wherein the processing according to the lot income data, the process complexity index, the process effective utilization coefficient and the full quality detection kinetic index to obtain the process assessment index of the production lot comprises:

calculating and processing the overall quality inspection fluctuation index through a fusion program according to the batch income data of the production batch in combination with the process complexity index and the process effective utilization coefficient to obtain a process evaluation index of the production batch;

；

wherein ,

evaluation index for Process>

For a process-complicated index, is selected>

Effectively utilizes the coefficient for the process>

Is a full detection actuation index>

For lot revenue data, <' > based on a lot>

、/>

、/>

、/>

Is a preset characteristic coefficient.

8. A fine chemical industry technology quality inspection system based on big data is characterized by comprising: the storage comprises a program of the 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 establishing of the product process database for each type of fine chemical product and the extracting of the product batch information of each production batch of the fine chemical product and the production process information corresponding to each production batch comprises:

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

10. A computer-readable storage medium, wherein the computer-readable storage medium includes a big data-based fine chemical process quality inspection method program, and when the big data-based fine chemical process quality inspection method program is executed by a processor, the steps of the big data-based fine chemical process quality inspection method according to any one of claims 1 to 7 are implemented.