CN117390070A - Data interaction processing method and system - Google Patents
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Abstract
The invention provides a data interaction processing method and a data interaction processing system, wherein the method comprises the steps of collecting product data in a production process in real time, classifying and sorting the product data, and obtaining comprehensive weights according to sorting results; pre-storing the collected product data, and storing the data according to the classification result and the sorting result; the method and the system can provide convenient and quick data access and data downloading through real-time acquisition, classification and sequencing, data storage and user access interface setting, ensure that experimental data can be timely and accurately acquired, improve the utilization efficiency and value of the data and improve the monitoring and management of the production process.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a data interaction processing method and system.
Background
The data interaction processing method refers to the process of transmitting, processing and exchanging data through specific technical means and algorithms. With the continuous development of information technology, the application of data interaction processing methods is becoming wider and wider. With the advent of the information age, data interaction is widely applied in various fields, and in the industrial manufacturing field, a large amount of data including mass production data and experimental data can be generated in the production process, in the actual production, the efficiency of data acquisition is relatively low because of huge data volume, and especially for experimental data before mass production, the next decision is often required to be made according to the experimental data, and because of huge data volume, the experimental data can not be acquired at the first time, and is easy to make mistakes, so that the experimental process and the next decision are affected.
Disclosure of Invention
The invention provides a data interaction processing method and a data interaction processing system, which are used for solving the problems:
the invention provides a data interaction processing method, which comprises the following steps:
s1, collecting product data in a production process in real time, classifying and sorting the product data, and obtaining comprehensive weights according to sorting results; the classification includes a first classification set by product category;
s2, setting a pre-storage space and the number of pre-storage nodes, and carrying out distributed pre-storage on the acquired data according to a first classification; acquiring experimental data of different products of the distributed pre-storage node in real time through an API interface for distributed storage; data transmission is carried out according to the comprehensive weight setting transmission priority sequence, and the bandwidth of the transmission data is set; periodically acquiring mass production data in the distributed pre-storage through an API interface, and setting a data transmission sequence according to a data acquisition time sequence and a comprehensive weight result for distributed storage;
s3, setting a user access interface, and performing data access and/or data downloading through the user access interface.
Further, a data interaction processing method, the S1 includes:
collecting product data in the production process in real time;
Classifying the product data according to product categories; obtaining a first classification, and sorting the first classification data according to the importance degree of the product to obtain a first sorting result; setting a first weight according to the first sorting result; the first weight is w i ；
Will firstClassifying the classified data according to mass production data and experimental data; obtaining a second classification; obtaining a second weight a according to the second classification k ；
Sequencing the experimental data in the second classification according to the experimental priority; obtaining a third sorting result; obtaining a third weight q according to the third sorting result j ；
Obtaining a comprehensive weight Z according to the first weight, the second weight and the third weight:
Z＝a k ×(0.6×w i +0.4×q j )
if it is mass production data, q j ＝0；
Grouping the data of the products in the first category according to different workstations to obtain a plurality of worksheets; the data for each workstation contains the product's category, the unique product identification code, and the workstation information.
Further, a data interaction processing method, the S2 includes:
setting a pre-storage space and the number of pre-storage nodes, and carrying out distributed pre-storage on the acquired data according to a first classification; the pre-storage space is:
l is the average daily mass production data volume collected in the last week; t is an average period used by the mass production data acquired every day on average in the last week to be transmitted to the distributed storage space; the unit is hours; s is S L The experimental data volume is acquired in the time T;
acquiring data of a distributed pre-storage node through an API interface, and performing distributed storage;
after data storage, consistency verification is carried out, and the content, the format and the quantity of corresponding data in storage are compared, so that no error or loss of the data in the transmission process is ensured;
the data is arranged into search engines by category and grouping.
Further, a data interaction processing method, the data of the distributed pre-storage node is obtained through an API interface,
performing distributed storage includes:
acquiring experimental data of different products in real time through an API interface; data transmission is carried out according to the comprehensive weight setting transmission priority sequence, and the bandwidth of the transmission data is set;
wherein B is the transmission allocation bandwidth; b (B) z Is the total bandwidth of the system; u is the user accessed at present; u (U) m The maximum user access number in the history record is obtained; the weight of the data to be transmitted in the current queue; s1 is the total data quantity to be transmitted in a current queue; z is Z y The weight of the user download data in the current queue is given, and S2 the total amount of the user download data in the current queue is given;
periodically acquiring mass production data in the distributed pre-storage through an API interface; and setting a data transmission sequence according to the data acquisition time sequence and the comprehensive weight result.
Further, a data interaction processing method, the S3 includes:
the user access interface comprises self-service data analysis and visual display;
the self-service data analysis comprises a product option, a time option, a production or test stage option, a data sheet analysis option and a chart type option;
downloading data and/or automatically generating an analysis chart according to the options; if the user options comprise a plurality of worksheets, the system provides an integration form, and integrates the worksheets together according to the unique product identification codes and the key information;
and setting a cache according to the frequency of accessing the data by the user, the data quantity and the data.
The invention provides a data interaction processing system, which comprises:
and a data acquisition module: collecting product data in the production process in real time, classifying and sorting the product data, and obtaining comprehensive weights according to sorting results; the classification includes a first classification set by product category;
and a data storage module: setting a pre-storage space and the number of pre-storage nodes, and carrying out distributed pre-storage on the acquired data according to a first classification; acquiring experimental data of different products of the distributed pre-storage node in real time through an API interface for distributed storage; data transmission is carried out according to the comprehensive weight setting transmission priority sequence, and the bandwidth of the transmission data is set; periodically acquiring mass production data in the distributed pre-storage through an API interface, and setting a data transmission sequence according to a data acquisition time sequence and a comprehensive weight result for distributed storage;
And a data access module: setting a user access interface, and performing data access and/or data downloading through the user access interface.
Further, a data interaction processing system, the data acquisition module includes:
and the real-time acquisition module is used for: collecting product data in the production process in real time;
a first classification module: classifying the product data according to product categories; obtaining a first classification, and sorting the first classification data according to the importance degree of the product to obtain a first sorting result; setting a first weight according to the first sorting result; the first weight is w i ；
A second classification module: classifying the first classified data according to mass production data and experimental data; obtaining a second classification; obtaining a second weight a according to the second classification k ；
A third sorting module: sequencing the experimental data in the second classification according to the experimental priority; obtaining a third sorting result; obtaining a third weight q according to the third sorting result j ；
The comprehensive weight acquisition module is used for: obtaining a comprehensive weight Z according to the first weight, the second weight and the third weight:
Z＝a k ×(0.6×w i +0.4×q j )
if it is mass production data, q j ＝0；
And a grouping module: grouping the data of the products in the first category according to different workstations to obtain a plurality of worksheets; the data for each workstation contains the product's category, the unique product identification code, and the workstation information.
Further, a data interaction processing system, the data storage module includes:
a data pre-storage module: setting a pre-storage space and the number of pre-storage nodes, and carrying out distributed pre-storage on the acquired data according to a first classification;
the pre-storage space is:
l is the average daily mass production data volume collected in the last week; t is an average period used by the mass production data acquired every day on average in the last week to be transmitted to the distributed storage space; the unit is hours; s is S L The experimental data volume is acquired in the time T;
and a storage module: acquiring data of a distributed pre-storage node through an API interface, and performing distributed storage;
and (3) a verification module: after data storage, consistency verification is carried out, and the content, the format and the quantity of corresponding data in storage are compared, so that no error or loss of the data in the transmission process is ensured;
the search engine setting module: the data is arranged into search engines by category and grouping.
Further, a data interaction processing system, the storage module includes:
and the real-time transmission module is used for: acquiring experimental data of different products in real time through an API interface; data transmission is carried out according to the comprehensive weight setting transmission priority sequence, and the bandwidth of the transmission data is set;
Wherein the method comprises the steps ofB is the transmission distribution bandwidth; b (B) z Is the total bandwidth of the system; u is the user accessed at present; u (U) m The maximum user access number in the history record is obtained; the weight of the data to be transmitted in the current queue; s1 is the total data quantity to be transmitted in a current queue; z is Z y The weight of the user download data in the current queue is given, and S2 the total amount of the user download data in the current queue is given;
and a periodic transmission module: periodically acquiring mass production data in the distributed pre-storage through an API interface; setting a data transmission sequence according to the data acquisition time sequence and the comprehensive weight result;
further, a data interaction processing system, the data access module includes:
and (3) an access setting module: the user access interface comprises self-service data analysis and visual display;
self-service analysis module: the self-service data analysis comprises a product option, a time option, a production or test stage option, a data sheet analysis option and a chart type option;
and (3) a downloading module: downloading data and/or automatically generating an analysis chart according to the options; if the user options comprise a plurality of worksheets, the system provides an integration form, and integrates the worksheets together according to the unique product identification codes and the key information;
And the cache setting module is used for: and setting a cache according to the frequency of accessing the data by the user, the data quantity and the data.
The invention has the beneficial effects that: the latest state of the product data can be obtained in real time by collecting the product data in the production process in real time and classifying and sequencing the product data. Meanwhile, the comprehensive weight obtained according to the sorting result can reflect the importance and the priority of the product data, and help the user to better understand and analyze the data. By pre-storing the collected product data and storing the data according to the classification and sequencing results, the data access efficiency and response speed can be improved. The pre-storage can reduce the time delay in the subsequent data access and improve the quick acquisition capability of the user on the data. By setting the user access interface, the user can conveniently perform data access and downloading operations. The user can browse, inquire, analyze and download the data on the access interface according to the self requirements, the authority and the roles, so that the requirements of different users are met. By the method, a user can acquire accurate production data in time, and data analysis and decision making are performed according to comprehensive weights. The method is beneficial to optimizing the production process, improving the product quality, reducing the production cost and the like, and maximally playing the value and the utilization rate of the production data. The method can realize real-time monitoring and management of the product data in the production process. The user can check the change condition of the product data at any time through the access interface, find out the abnormality or problem, and timely take corresponding measures to ensure the stability and reliability of the production process. Through real-time collection, classification and sequencing, data storage and user access interface setting, the system can provide convenient and quick data access and downloading functions, improves the utilization efficiency and value of data, and improves the monitoring and management of the production process, thereby bringing a series of benefits and effects.
Drawings
Fig. 1 is a schematic diagram of a data interaction processing method according to the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, and the described embodiments are merely some, rather than all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The embodiment provides a data interaction processing method, which comprises the following steps:
S1, collecting product data in a production process in real time, classifying and sorting the product data, and obtaining comprehensive weights according to sorting results; the classification includes a first classification set by product category;
s2, setting a pre-storage space and the number of pre-storage nodes, and carrying out distributed pre-storage on the acquired data according to a first classification; acquiring experimental data of different products of the distributed pre-storage node in real time through an API interface for distributed storage; data transmission is carried out according to the comprehensive weight setting transmission priority sequence, and the bandwidth of the transmission data is set; periodically acquiring mass production data in the distributed pre-storage through an API interface, and setting a data transmission sequence according to a data acquisition time sequence and a comprehensive weight result for distributed storage;
s3, setting a user access interface, and performing data access and/or data downloading through the user access interface.
The working principle of the technical scheme is as follows: product data generated during the production process is collected in real time by sensors or other devices. Such data may include product quality, process parameters, production time, etc.; the collected product data is classified, for example, by product type, production line, time, etc. Each category is then ranked according to some index or rule to determine its importance or priority. These ordering results will be used for subsequent data storage and access. The collected product data is pre-stored, and a cache or temporary storage mode can be adopted to ensure the validity and the integrity of the data; the data is then stored in a corresponding database or data warehouse based on the classification and ranking results. Thus, the efficiency and the response speed of data access can be improved; a user access interface is provided, which may be a web page, mobile application, or other form of interface. The user can access and download the data through the access interface, and the user can acquire the required data by using the access interface as required. The system retrieves corresponding data from the data store according to the user's request and returns the data to the user via the access interface. The user can browse, inquire, analyze or download the data.
The technical scheme has the effects that: the latest state of the product data can be obtained in real time by collecting the product data in the production process in real time and classifying and sequencing the product data. Meanwhile, the comprehensive weight obtained according to the sorting result can reflect the importance and the priority of the product data, and help the user to better understand and analyze the data. By pre-storing the collected product data and storing the data according to the classification and sequencing results, the data access efficiency and response speed can be improved. The pre-storage can reduce the time delay in the subsequent data access and improve the quick acquisition capability of the user on the data. By setting the user access interface, the user can conveniently perform data access and downloading operations. The user can browse, inquire, analyze and download the data on the access interface according to the self requirements, the authority and the roles, so that the requirements of different users are met. By the method, a user can acquire accurate production data in time, and data analysis and decision making are performed according to comprehensive weights. The method is beneficial to optimizing the production process, improving the product quality, reducing the production cost and the like, and maximally playing the value and the utilization rate of the production data. The method can realize real-time monitoring and management of the product data in the production process. The user can check the change condition of the product data at any time through the access interface, find out the abnormality or problem, and timely take corresponding measures to ensure the stability and reliability of the production process.
In summary, the data interaction processing method can provide convenient and quick data access and downloading functions through real-time collection, classification and sequencing, data storage and user access interface setting, improve the utilization efficiency and value of data, and improve the monitoring and management of the production process, thereby bringing a series of benefits and effects.
In this embodiment, the data interaction processing method, the S1 includes:
collecting product data in the production process in real time; product data refers to various data related to the product including, but not limited to, quality parameters, manufacturing process parameters, product performance indicators, equipment numbers used for product manufacturing and/or measurement, etc.; these data are typically used to evaluate the quality and performance of the product and to perform quality control and improvement;
classifying the product data according to product categories; obtaining a first classification, and sorting the first classification data according to the importance degree of the product to obtain a first sorting result; setting a first weight according to the first sorting result; the first weight is w i ；
w 1 +w 2 +…+w n ＝1
w 1 >w 2 >…>w n
(n―1)×(w i ―w i+1 )＝1/4
Wherein i is the ith sequencing result; i=1, 2,3 … n; n is the number of first sorting results and the number of non-products, because different products may have the same importance degree; 1 is the most important product; n is the least important product;
Classifying the first classified data according to mass production data and experimental data; obtaining a second classification; obtaining a second weight a according to the second classification k The method comprises the steps of carrying out a first treatment on the surface of the The mass production data weight is smaller than the experimental data weight, and the sum of the two weights is 1;
sequencing the experimental data in the second classification according to the experimental priority; obtaining a third sorting result; obtaining a third weight q according to the third sorting result j ；
q 1 +q 2 +…+q m ＝1
q 1 >q 2 >…>q m
(m―1)×(w j ―w j+1 )＝1/4
Wherein j is the j-th sorting result; j=1, 2,3 … m; m is the third sorting number, 1 is the data with highest priority; m is the data with the lowest priority;
obtaining a comprehensive weight Z according to the first weight, the second weight and the third weight:
Z＝a k ×(0.6×w i +0.4×q j )
if it is mass production data, q j ＝0；
Grouping the data of the products in the first category according to different workstations to obtain a plurality of worksheets; the data of each workstation contains the category of the product, the unique identification code of the product and the workstation information; the workstation comprises different production procedures and test procedures, each test procedure possibly comprises a plurality of tests, and a plurality of data tables are generated; for example, in the hard disk production process, the hard disk production process comprises a plurality of test stations, a high-temperature test station, a normal-temperature test station, a functional test station, a reliability test station and the like, wherein each test station comprises a plurality of test items, and each item generates one or more tables; bit error rate test, signal to noise ratio test, etc., each table contains unique identification information of the test hard disk, product internal name, test station, test temperature, information of each magnetic head, etc.
The working principle of the technical scheme is as follows: various data related to the product in the production process are collected in real time through various sensors or other devices and converted into a digital form for recording; the collected product data are classified according to the category of the product to which the collected product data belong, so that subsequent data processing and analysis are facilitated; the data in each product category is ordered according to the importance degree, so that preparation is made for subsequent comprehensive weight calculation; according to the first sorting result, calculating the weight of each product in the comprehensive weight through a formula so as to reflect the importance degree of the product; classifying the data of the first classification according to mass production data and experimental data, and reflecting the difference of the importance degrees by setting different weights; for experimental data, the experimental data are ranked according to the priority thereof, and the weight of the comprehensive weight is set to reflect the importance of the experimental data in the overall quality control. Calculating the comprehensive weight of each data by integrating various weights for subsequent data analysis and decision; product data in each workstation are grouped according to the product category to which the product data belong, and a plurality of different worksheets are generated, so that subsequent data processing and analysis are facilitated.
In summary, the data interaction processing method realizes rapid and accurate recording and analysis of various data in the production process through a series of processes of classification, sequencing, weight calculation and the like, and helps users to better control and improve quality in the production process.
The technical scheme has the effects that: by collecting the product data in the production process in real time, important information such as quality parameters, production process parameters, performance indexes and the like of the product can be recorded in time. This facilitates real-time assessment of product quality and performance and provides data support for quality control and improvement. And classifying the product data according to the product categories, and sorting according to the importance degrees. Therefore, the importance of each product in the overall quality and performance can be clarified, and a foundation is provided for subsequent weight setting and comprehensive calculation. By setting weights of different levels, such as product importance degree weights and experiment priority weights, the importance of different data can be reflected more accurately. The comprehensive weight is obtained through comprehensive calculation, and the value of the product data is more comprehensively measured. Product data of each workstation is grouped according to categories to generate a plurality of different worksheets. Thus, the data can be better organized and managed, and the subsequent data processing and analysis are convenient. By the method, the product data can be recorded and analyzed rapidly and accurately. The method is favorable for timely finding quality problems and abnormal performance, and adopts corresponding measures to control and improve the quality, so that the production efficiency and the product quality are improved. And setting corresponding weights according to the importance degree and the experiment priority of the product. Therefore, important product data and experimental data can be transmitted more quickly and stably in the data transmission process, and the accuracy and reliability of key data are ensured. By grouping the product data according to categories and workstations and sorting the product data according to importance and priority, the efficiency of data transmission can be effectively improved. The most important data with high priority is preferentially transmitted and processed, so that time and resources are saved; by setting different levels of weights, different types of product data can be distinguished and managed. Product data and experimental data of high importance can be transmitted and processed preferentially, while secondary data can be processed accordingly. Thus, the fineness of data management can be improved, and the timeliness and accuracy of key data are ensured; and comprehensively evaluating the product data by comprehensively calculating comprehensive weights and taking importance and priority into consideration. Therefore, the product data can be measured and processed more accurately, the quality and the effect of data processing are improved, the data transmission process can be optimized, the efficiency and the accuracy of data transmission are improved, the fine data management is realized, and the effect and the quality of data processing are improved. This will effectively support product quality control and improved operation.
In this embodiment, the data interaction processing method, S2 includes:
setting a pre-storage space and the number of pre-storage nodes, and carrying out distributed pre-storage on the acquired data according to a first classification; the pre-storage space is:
l is the average daily mass production data volume collected in the last week; t is an average period used by the mass production data acquired every day on average in the last week to be transmitted to the distributed storage space; the unit is hours; s is S L The experimental data volume is acquired in the time T;
acquiring data of a distributed pre-storage node through an API interface, and performing distributed storage;
after data storage, consistency verification is carried out, and the content, the format and the quantity of corresponding data in storage are compared, so that no error or loss of the data in the transmission process is ensured;
the data is arranged into search engines by category and grouping.
The working principle of the technical scheme is as follows: firstly, setting the size of the pre-storage space and the number of the pre-storage nodes according to the requirement. These pre-storage nodes will be used for distributed pre-storage to enable parallel storage and processing of data. The acquired data are stored in a distributed mode according to the first classification. Specifically, data is stored in different pre-storage nodes in a scattered manner according to classifications. Therefore, the scattered storage of the data can be realized, and the efficiency of data storage and access is improved. And calculating the size of the pre-storage space through a preset formula. The formula considers the average daily mass production data volume, transmission period and collected experimental data volume in the last week. The size of the pre-storage space can be reasonably set according to the actual data volume. An API interface is used to obtain the data stored in the distributed pre-storage nodes. Through the API interface, required data can be conveniently obtained from each pre-storage node, and centralized management and access of the data are realized. And carrying out distributed storage on the data acquired from the pre-storage node. The distributed storage system may store data in a decentralized manner among a plurality of nodes to improve reliability and fault tolerance of the data. Therefore, redundant storage of data can be realized, and the integrity of the data in the transmission process is ensured. And carrying out consistency verification on the stored data, namely comparing the stored data with the original data in the transmission process. By verifying the content, format and quantity of the data, it is ensured that no errors or losses of the data occur during transmission. This can improve the reliability and data quality of the data transmission. The data is grouped according to the classification settings and search engines are used to retrieve and query the data. Therefore, the required data can be conveniently searched and obtained according to the classification, and the availability and the retrieval efficiency of the data are improved. In summary, the data interaction processing method realizes efficient storage, reliable transmission and convenient management of data through the steps of pre-storage, distributed storage, consistency verification, grouping search and the like.
The technical scheme has the effects that: the collected data are pre-stored in a distributed mode according to the classification, and therefore the efficiency of data storage and access can be improved. Different data classifications can be stored in different pre-storage nodes in a scattered mode, the load of a single node is reduced, and the read-write performance of data is improved. The size of the pre-storage space can be reasonably set according to the actual data volume by considering the factors such as the average daily mass production data volume, the transmission period, the collected experimental data volume and the like in the last week. Therefore, the situation that storage resources are excessively occupied or storage space is insufficient can be avoided, and the efficiency and usability of the pre-storage system are improved. Through distributed storage and consistency verification, the integrity and accuracy of the data in the storage and transmission process can be ensured. The redundancy mechanism of the distributed storage system can prevent data loss, and the consistency verification can detect errors in the data transmission process, so that the consistency and reliability of the data are ensured. The data are grouped according to the classification and the search engine is used for data retrieval, so that the required data can be conveniently managed and retrieved. Therefore, the time and the workload of data searching can be reduced, and the availability and the searching efficiency of the data are improved. In summary, the data interaction processing method can improve the efficiency, reliability and convenience of data processing and meet the requirement of large-scale data processing in practical application through distributed pre-storage, efficient pre-storage space setting, reliable data storage and transmission and convenient data management and retrieval.
In this embodiment, the obtaining, by an API interface, data of a distributed pre-storage node, and performing distributed storage includes:
acquiring experimental data of different products in real time through an API interface; data transmission is carried out according to the comprehensive weight setting transmission priority sequence, and the bandwidth of the transmission data is set;
wherein B is the transmission allocation bandwidth; b (B) z Is the total bandwidth of the system; u is the user accessed at present; u (U) m The maximum user access number in the history record is obtained; the weight of the data to be transmitted in the current queue; s1 is the total data quantity to be transmitted in a current queue; z is Z y The weight of the user download data in the current queue is given, and S2 the total amount of the user download data in the current queue is given;
periodically acquiring mass production data in the distributed pre-storage through an API interface; setting a data transmission sequence according to the data acquisition time sequence and the comprehensive weight result; transmitting according to the acquisition time sequence, and setting the transmission sequence according to the comprehensive weight in the same time sequence;
counting the access frequency and the downloading quantity of different time periods every day in the last week; obtaining average access frequency and download quantity in different time periods; wherein each half hour is a period of time;
Normalizing the average access frequency and the download quantity of different time periods to obtain the normalized average access frequency and download quantity of different time periods;
obtaining load parameters according to the average access frequency and the average download quantity of different time periods after normalization processing;
F＝α×P+β×D
wherein alpha and beta are coefficients, and the range is 0, 1; p is the average access frequency of different time periods after normalization processing, and D is the average download quantity of different time periods after normalization processing;
taking the time period of the minimum load parameter as one of the time periods of mass production data transmission from the distributed pre-storage;
acquiring current access frequency and downloading amount, carrying out normalization processing, acquiring current load parameters according to the access frequency and the downloading amount after the current normalization processing, and comparing the current load parameters with average load parameters of each time period of the last week; if the current load parameter is less than 25% of the average load parameter distribution for each time period of the last week, data transfer may be performed from the mass production data in the distributed pre-store.
The working principle of the technical scheme is as follows: and acquiring experimental data of different products in real time through an API interface. These data are transmitted to the distributed pre-storage nodes for storage and processing. Setting the priority order of data transmission according to the comprehensive weight, and setting the bandwidth of the transmission data; the allocation formula of the transmission bandwidth is as follows:
Wherein B is the transmission allocation bandwidth; b (B) z Is the total bandwidth of the system; u is the user accessed at present; u (U) m The maximum user access number in the history record is obtained; the weight of the data to be transmitted in the current queue; s1 is the total data quantity to be transmitted in a current queue; z is Z y The weight of the user download data in the current queue is given, and S2 the total amount of the user download data in the current queue is given;
according to the different weight and data quantity, the priority and bandwidth allocation of data transmission can be adjusted;
and periodically acquiring mass production data in the distributed pre-storage through an API interface. And setting a data transmission sequence according to the data acquisition time sequence and the comprehensive weight result. Transmitting according to the acquisition time sequence, and setting the transmission sequence of the data of the same time sequence according to the comprehensive weight;
and counting the access frequency and the downloading quantity of different time periods every day in the last week, and obtaining the average access frequency and the downloading quantity of different time periods. Counting every half hour as a time period;
and carrying out normalization processing on the average access frequency and the download quantity in different time periods to obtain normalized load parameters. Calculating load parameters according to the average access frequency and the download quantity after normalization processing;
The time period in which the minimum load parameter is located is taken as one of the time periods of mass production data transmission from the distributed pre-storage. Therefore, data transmission can be performed in a lower load period, and the transmission efficiency is improved;
judging the current load: and acquiring the current access frequency and downloading amount, and carrying out normalization processing. And calculating the current load parameter according to the current normalized access frequency and downloading amount, and comparing the current load parameter with the average load parameter of each time period of the last week. If the current load parameter is less than 25% of the average load parameter distribution per time period of the last week, data transmission can be performed from the mass production data in the distributed pre-storage to reduce the system load.
The technical scheme has the effects that: by setting the transmission priority order and bandwidth allocation according to the comprehensive weight, it is possible to ensure that data of high priority is preferentially transmitted under a limited bandwidth, and to improve the efficiency of data transmission. And obtaining a load parameter by counting the access frequency and the download quantity and normalizing, and selecting a transmission time period according to the load parameter. Therefore, data transmission can be performed in a time period with lower load, the overload condition of the system is avoided, and load balancing is optimized. By setting the data transmission sequence according to the acquisition time sequence and the comprehensive weight, the continuity of the data in the same time sequence can be ensured, and the reading performance of the data is improved. The experimental data of different products are obtained in real time through the API interface, the mass production data in the distributed pre-storage are obtained regularly, and timeliness and instant updating of the data are guaranteed. And comparing the current load parameter with the average load parameter of each time period of the last week, automatically judging the current load state of the system, and if the load is lower, carrying out data transmission from mass production data in the distributed pre-storage. Thus, the transmission strategy can be flexibly adjusted according to the actual load condition. By associating the transmission priority of the data with its comprehensive weight, it is possible to ensure the priority transmission of the data of high priority. Therefore, timeliness and accuracy of processing of important data can be improved, and priority processing of key data is guaranteed. By comparing the number of current access users with the maximum number of user accesses in the history, the transmission bandwidth can be dynamically adjusted according to the change in the number of user accesses. Therefore, bandwidth resources can be distributed more reasonably, and the situation that data transmission is slow or system load is too high due to too large access quantity of users is avoided. The importance degree of different data can be better considered by carrying out product calculation on the weight of the data to be transmitted in the current queue and the total data quantity. Therefore, important data can occupy more bandwidth resources in the transmission process, and the processing efficiency of the important data is improved. The product calculation is carried out on the weight of the downloaded data of the user in the current queue and the total amount of the downloaded data, so that the degree of the user's requirement on different data can be better understood. Therefore, the bandwidth allocation of data transmission can be adjusted according to the requirements of users, and the satisfaction degree and experience of the users are improved. The transmission bandwidth allocation formula can reasonably allocate the transmission bandwidth according to the comprehensive weight of the data, the access quantity of the user, the data quantity, the user demand and other factors, thereby realizing the priority management of the data transmission and the optimization of the resource utilization. Therefore, the efficiency and the quality of data transmission can be improved, the processing requirements of different data are met, and the satisfaction degree of users on the data is improved.
In summary, the data interaction processing method can improve data transmission efficiency, optimize load balance, improve data reading performance, automatically adjust transmission strategies, adapt to different load conditions, and provide better data interaction processing effects.
The data interaction processing method is characterized in that the step S3 includes:
the user access interface comprises self-service data analysis and visual display;
the self-service data analysis comprises a product option, a time option, a production or test stage option, a data sheet analysis option and a chart type option;
downloading data and/or automatically generating an analysis chart according to the options; if the user options comprise a plurality of worksheets, the system provides an integration form, and integrates the worksheets together according to the unique product identification codes and the key information;
setting a cache according to the frequency of accessing data by a user, the data volume, and the method comprises the following steps:
counting the frequency of different data access in three days to obtain distribution diagram of different data access frequency,
setting a frequency threshold according to the frequency distribution diagram, accessing data corresponding to the frequency distribution diagram which is larger than the frequency threshold as a cache range, and setting the size of a cache space;
Maintaining data access frequency and data heat information: recording the accessed frequency and the access mode in the last period of time of each cached data object for evaluating the heat of the data;
defining a replacement strategy: and carrying out replacement decision according to the cache capacity. The following are two common alternative strategies:
least recently used (Least Recently Used, LRU): selecting the least recently accessed data for replacement based on the access time stamp of the data;
least frequently used (Least Frequently Used, LFU): selecting the least frequently accessed data to replace based on the access frequency of the data;
evaluating cache hit rate: calculating a cache hit rate by tracking whether each request hits the cache;
dynamically adjusting the cache size: and dynamically adjusting the cache size according to the cache hit rate and the resource limit. If the hit rate is low, it may be necessary to increase the cache capacity; if the hit rate is high, the cache capacity can be properly reduced;
implementing a Cache Pre-fetch (Cache) policy: according to the data heat and the access mode, the data which can be requested are preloaded into the cache in advance, so that the hit rate is improved.
The working principle of the technical scheme is as follows: and the user performs data analysis and visual display through the access interface. The interface provides self-service data analysis and visualization options. The user may select a product option, a time option, a production or test phase option, a data sheet analysis option, a chart type option, etc. to meet their particular needs. Depending on the user's options, the system may download data and/or automatically generate an analysis chart as desired. If the user option contains a plurality of worksheets, the system integrates the worksheets together, thereby facilitating comprehensive analysis. According to factors such as the access frequency of a user to data, the data quantity and the like, the system can set the cache range and the cache space according to a distribution diagram obtained by counting the accessed frequency of different data in three days. Data corresponding to the frequency distribution larger than the frequency threshold value is cached, so that the access efficiency is improved. The system will record the access frequency and access pattern of each cached data object for use in evaluating the heat of the data. This information facilitates subsequent replacement policy and cache hit rate evaluation. And the system makes a replacement decision according to the cache capacity. Common replacement strategies are Least Recently Used (LRU) and Least Frequently Used (LFU). The least recently accessed or least frequently accessed data is selected for replacement based on the access time stamp or access frequency of the data. By tracking whether each request hits in the cache, the system can calculate the cache hit rate. The cache hit rate is an important indicator for measuring the effectiveness of cache. According to the cache hit rate and the resource limitation, the system can dynamically adjust the cache size. If the hit rate is low, the system may need to increase the cache capacity to increase efficiency; if the hit rate is high, the cache capacity can be properly reduced to save resources. Depending on the warmth and access pattern of the data, the system may implement a cache prefetch policy. The data which may be requested is loaded into the cache in advance to improve the cache hit rate and response speed.
In summary, the data interaction processing method selects data analysis and visualization options through the user interface, and combines technical means such as cache setting, replacement policy, cache hit rate evaluation and dynamic adjustment, so that efficient access and processing of data are realized, and system performance and user experience are improved.
The technical scheme has the effects that: the user can perform autonomous data analysis and visual display through the interface without relying on professionals or complex tools. The system provides a number of options including product, time, stage, data sheet analysis, chart type, etc., to meet the user's specific needs. For data containing a plurality of worksheets, the system can integrate the worksheets together, and comprehensive analysis is convenient. According to user options, the system can rapidly download data and automatically generate corresponding analysis charts, and the efficiency of data access and processing is improved. The system sets the cache range and the space size according to the access frequency and the heat information of the data, and determines the effectiveness of the cache by evaluating the cache hit rate. This can reduce repeated data loading and speed up data access. And the system makes a replacement decision according to the cache capacity, and selects a proper replacement object through strategies such as LRU or LFU. Meanwhile, the system can dynamically adjust the cache size according to the cache hit rate and the resource limit, and the optimal performance and the optimal resource utilization rate are maintained. The system loads the data which can be requested into the cache in advance according to the heat degree and the access mode of the data so as to improve the hit rate and the response speed. This may reduce user latency and enhance user experience. The data interaction processing method can improve the data access efficiency, accelerate the analysis chart generation and response speed and provide better user experience by providing technical means such as self-service data analysis, visual display, cache management, optimization and the like.
The embodiment provides a data interaction processing system, which includes:
and a data acquisition module: collecting product data in the production process in real time, classifying and sorting the product data, and obtaining comprehensive weights according to sorting results; the classification includes a first classification set by product category;
and a data storage module: setting a pre-storage space and the number of pre-storage nodes, and carrying out distributed pre-storage on the acquired data according to a first classification; acquiring experimental data of different products of the distributed pre-storage node in real time through an API interface for distributed storage; data transmission is carried out according to the comprehensive weight setting transmission priority sequence, and the bandwidth of the transmission data is set; periodically acquiring mass production data in the distributed pre-storage through an API interface, and setting a data transmission sequence according to a data acquisition time sequence and a comprehensive weight result for distributed storage;
and a data access module: setting a user access interface, and performing data access and/or data downloading through the user access interface.
The working principle of the technical scheme is as follows: product data generated during the production process is collected in real time by sensors or other devices. Such data may include product quality, process parameters, production time, etc.; the collected product data is classified, for example, by product type, production line, time, etc. Each category is then ranked according to some index or rule to determine its importance or priority. These ordering results will be used for subsequent data storage and access. The collected product data is pre-stored, and a cache or temporary storage mode can be adopted to ensure the validity and the integrity of the data; the data is then stored in a corresponding database or data warehouse based on the classification and ranking results. Thus, the efficiency and the response speed of data access can be improved; a user access interface is provided, which may be a web page, mobile application, or other form of interface. The user can access and download the data through the access interface, and the user can acquire the required data by using the access interface as required. The system retrieves corresponding data from the data store according to the user's request and returns the data to the user via the access interface. The user can browse, inquire, analyze or download the data.
The technical scheme has the effects that: the latest state of the product data can be obtained in real time by collecting the product data in the production process in real time and classifying and sequencing the product data. Meanwhile, the comprehensive weight obtained according to the sorting result can reflect the importance and the priority of the product data, and help the user to better understand and analyze the data. By pre-storing the collected product data and storing the data according to the classification and sequencing results, the data access efficiency and response speed can be improved. The pre-storage can reduce the time delay in the subsequent data access and improve the quick acquisition capability of the user on the data. By setting the user access interface, the user can conveniently perform data access and downloading operations. The user can browse, inquire, analyze and download the data on the access interface according to the self requirements, the authority and the roles, so that the requirements of different users are met. By the method, a user can acquire accurate production data in time, and data analysis and decision making are performed according to comprehensive weights. The method is beneficial to optimizing the production process, improving the product quality, reducing the production cost and the like, and maximally playing the value and the utilization rate of the production data. The method can realize real-time monitoring and management of the product data in the production process. The user can check the change condition of the product data at any time through the access interface, find out the abnormality or problem, and timely take corresponding measures to ensure the stability and reliability of the production process.
In summary, the data interaction processing method can provide convenient and quick data access and downloading functions through real-time collection, classification and sequencing, data storage and user access interface setting, improve the utilization efficiency and value of data, and improve the monitoring and management of the production process, thereby bringing a series of benefits and effects.
The data interaction processing system of this embodiment, the data acquisition module includes:
and the real-time acquisition module is used for: collecting product data in the production process in real time;
a first classification module: classifying the product data according to product categories; obtaining a first classification, and sorting the first classification data according to the importance degree of the product to obtain a first sorting result; setting a first weight according to the first sorting result; the first weight is w i ；
w 1 +w 2 +…+w n ＝1
w 1 >w 2 >…>w n
(n-1)×(w i -w i+1 )＝1/4
Wherein i is the ith sequencing result; i=1, 2,3 … n; n is the number of first sorting results and the number of non-products, because different products may have the same importance degree; 1 is the most important product; n is the least important product;
classifying the first classified data according to mass production data and experimental data; obtaining a second classification; obtaining a second weight a according to the second classification k The method comprises the steps of carrying out a first treatment on the surface of the The mass production data weight is smaller than the experimental data weight, and the sum of the two weights is 1;
sequencing the experimental data in the second classification according to the experimental priority; obtaining a third sorting result; obtaining a third weight q according to the third sorting result j ；
q 1 +q 2 +…+q m ＝1
q 1 >q 2 >…>q m
(m-1)×(w j -w j+1 )＝1/4
Wherein j is the j-th sorting result; j=1, 2,3 … m; m is the third sorting number, 1 is the data with highest priority; m is the data with the lowest priority;
the comprehensive weight acquisition module is used for: obtaining a comprehensive weight Z according to the first weight, the second weight and the third weight:
Z＝a k ×(0.6×w i +0.4×q j )
if it is mass production data, q j ＝0；
And a grouping module: grouping the data of the products in the first category according to different workstations to obtain a plurality of worksheets; the data for each workstation contains the product's category, the unique product identification code, and the workstation information.
The working principle of the technical scheme is as follows: various data related to the product in the production process are collected in real time through various sensors or other devices and converted into a digital form for recording; the collected product data are classified according to the category of the product to which the collected product data belong, so that subsequent data processing and analysis are facilitated; the data in each product category is ordered according to the importance degree, so that preparation is made for subsequent comprehensive weight calculation; according to the first sorting result, calculating the weight of each product in the comprehensive weight through a formula so as to reflect the importance degree of the product; classifying the data of the first classification according to mass production data and experimental data, and reflecting the difference of the importance degrees by setting different weights; for experimental data, the experimental data are ranked according to the priority thereof, and the weight of the comprehensive weight is set to reflect the importance of the experimental data in the overall quality control. Calculating the comprehensive weight of each data by integrating various weights for subsequent data analysis and decision; product data in each workstation are grouped according to the product category to which the product data belong, and a plurality of different worksheets are generated, so that subsequent data processing and analysis are facilitated.
In summary, the data interaction processing method realizes rapid and accurate recording and analysis of various data in the production process through a series of processes of classification, sequencing, weight calculation and the like, and helps users to better control and improve quality in the production process.
The technical scheme has the effects that: by collecting the product data in the production process in real time, important information such as quality parameters, production process parameters, performance indexes and the like of the product can be recorded in time. This facilitates real-time assessment of product quality and performance and provides data support for quality control and improvement. And classifying the product data according to the product categories, and sorting according to the importance degrees. Therefore, the importance of each product in the overall quality and performance can be clarified, and a foundation is provided for subsequent weight setting and comprehensive calculation. By setting weights of different levels, such as product importance degree weights and experiment priority weights, the importance of different data can be reflected more accurately. The comprehensive weight is obtained through comprehensive calculation, and the value of the product data is more comprehensively measured. Product data of each workstation is grouped according to categories to generate a plurality of different worksheets. Thus, the data can be better organized and managed, and the subsequent data processing and analysis are convenient. By the method, the product data can be recorded and analyzed rapidly and accurately. The method is favorable for timely finding quality problems and abnormal performance, and adopts corresponding measures to control and improve the quality, so that the production efficiency and the product quality are improved. And setting corresponding weights according to the importance degree and the experiment priority of the product. Therefore, important product data and experimental data can be transmitted more quickly and stably in the data transmission process, and the accuracy and reliability of key data are ensured. By grouping the product data according to categories and workstations and sorting the product data according to importance and priority, the efficiency of data transmission can be effectively improved. The most important data with high priority is preferentially transmitted and processed, so that time and resources are saved; by setting different levels of weights, different types of product data can be distinguished and managed. Product data and experimental data of high importance can be transmitted and processed preferentially, while secondary data can be processed accordingly. Thus, the fineness of data management can be improved, and the timeliness and accuracy of key data are ensured; and comprehensively evaluating the product data by comprehensively calculating comprehensive weights and taking importance and priority into consideration. Therefore, the product data can be measured and processed more accurately, the quality and the effect of data processing are improved, the data transmission process can be optimized, the efficiency and the accuracy of data transmission are improved, the fine data management is realized, and the effect and the quality of data processing are improved. This will effectively support product quality control and improved operation.
In this embodiment, the data storage module includes:
a data pre-storage module: setting a pre-storage space and the number of pre-storage nodes, and carrying out distributed pre-storage on the acquired data according to a first classification;
the pre-storage space is:
/>
l is the average daily mass production data volume collected in the last week; t is an average period used by the mass production data acquired every day on average in the last week to be transmitted to the distributed storage space; the unit is hours; s is S L The experimental data volume is acquired in the time T;
and a storage module: acquiring data of a distributed pre-storage node through an API interface, and performing distributed storage;
and (3) a verification module: after data storage, consistency verification is carried out, and the content, the format and the quantity of corresponding data in storage are compared, so that no error or loss of the data in the transmission process is ensured;
the search engine setting module: the data is arranged into search engines by category and grouping.
The working principle of the technical scheme is as follows: firstly, setting the size of the pre-storage space and the number of the pre-storage nodes according to the requirement. These pre-storage nodes will be used for distributed pre-storage to enable parallel storage and processing of data. The acquired data are stored in a distributed mode according to the first classification. Specifically, data is stored in different pre-storage nodes in a scattered manner according to classifications. Therefore, the scattered storage of the data can be realized, and the efficiency of data storage and access is improved. And calculating the size of the pre-storage space through a preset formula. The formula considers the average daily mass production data volume, transmission period and collected experimental data volume in the last week. The size of the pre-storage space can be reasonably set according to the actual data volume. An API interface is used to obtain the data stored in the distributed pre-storage nodes. Through the API interface, required data can be conveniently obtained from each pre-storage node, and centralized management and access of the data are realized. And carrying out distributed storage on the data acquired from the pre-storage node. The distributed storage system may store data in a decentralized manner among a plurality of nodes to improve reliability and fault tolerance of the data. Therefore, redundant storage of data can be realized, and the integrity of the data in the transmission process is ensured. And carrying out consistency verification on the stored data, namely comparing the stored data with the original data in the transmission process. By verifying the content, format and quantity of the data, it is ensured that no errors or losses of the data occur during transmission. This can improve the reliability and data quality of the data transmission. The data is grouped according to the classification settings and search engines are used to retrieve and query the data. Therefore, the required data can be conveniently searched and obtained according to the classification, and the availability and the retrieval efficiency of the data are improved. In summary, the data interaction processing method realizes efficient storage, reliable transmission and convenient management of data through the steps of pre-storage, distributed storage, consistency verification, grouping search and the like.
The technical scheme has the effects that: the collected data are pre-stored in a distributed mode according to the classification, and therefore the efficiency of data storage and access can be improved. Different data classifications can be stored in different pre-storage nodes in a scattered mode, the load of a single node is reduced, and the read-write performance of data is improved. The size of the pre-storage space can be reasonably set according to the actual data volume by considering the factors such as the average daily mass production data volume, the transmission period, the collected experimental data volume and the like in the last week. Therefore, the situation that storage resources are excessively occupied or storage space is insufficient can be avoided, and the efficiency and usability of the pre-storage system are improved. Through distributed storage and consistency verification, the integrity and accuracy of the data in the storage and transmission process can be ensured. The redundancy mechanism of the distributed storage system can prevent data loss, and the consistency verification can detect errors in the data transmission process, so that the consistency and reliability of the data are ensured. The data are grouped according to the classification and the search engine is used for data retrieval, so that the required data can be conveniently managed and retrieved. Therefore, the time and the workload of data searching can be reduced, and the availability and the searching efficiency of the data are improved. In summary, the data interaction processing method can improve the efficiency, reliability and convenience of data processing and meet the requirement of large-scale data processing in practical application through distributed pre-storage, efficient pre-storage space setting, reliable data storage and transmission and convenient data management and retrieval.
In this embodiment, the storage module includes:
and the real-time transmission module is used for: acquiring experimental data of different products in real time through an API interface; data transmission is carried out according to the comprehensive weight setting transmission priority sequence, and the bandwidth of the transmission data is set;
/>
wherein B is the transmission allocation bandwidth; b (B) z Is the total bandwidth of the system; u is the user accessed at present; u (U) m The maximum user access number in the history record is obtained; the weight of the data to be transmitted in the current queue; s1 is the total data quantity to be transmitted in a current queue; z is Z y The weight of the user download data in the current queue is given, and S2 the total amount of the user download data in the current queue is given;
and a periodic transmission module: periodically acquiring mass production data in the distributed pre-storage through an API interface; setting a data transmission sequence according to the data acquisition time sequence and the comprehensive weight result; transmitting according to the acquisition time sequence, and setting the transmission sequence according to the comprehensive weight in the same time sequence;
counting the access frequency and the downloading quantity of different time periods every day in the last week; obtaining average access frequency and download quantity in different time periods; wherein each half hour is a period of time;
normalizing the average access frequency and the download quantity of different time periods to obtain the normalized average access frequency and download quantity of different time periods;
Obtaining load parameters according to the average access frequency and the average download quantity of different time periods after normalization processing;
F＝α×P+β×D
wherein alpha and beta are coefficients, and the range is 0, 1; p is the average access frequency of different time periods after normalization processing, and D is the average download quantity of different time periods after normalization processing;
taking the time period of the minimum load parameter as one of the time periods of mass production data transmission from the distributed pre-storage;
acquiring current access frequency and downloading amount, carrying out normalization processing, acquiring current load parameters according to the access frequency and the downloading amount after the current normalization processing, and comparing the current load parameters with average load parameters of each time period of the last week; if the current load parameter is less than 25% of the average load parameter distribution for each time period of the last week, data transfer may be performed from the mass production data in the distributed pre-store.
The working principle of the technical scheme is as follows: and acquiring experimental data of different products in real time through an API interface. These data are transmitted to the distributed pre-storage nodes for storage and processing. And setting the priority order of data transmission according to the comprehensive weight, and setting the bandwidth of the transmission data. The allocation formula of the transmission bandwidth is as follows:
Wherein B is the transmission allocation bandwidth; b (B) z Is the total bandwidth of the system; u is the user accessed at present; u (U) m The maximum user access number in the history record is obtained; the weight of the data to be transmitted in the current queue; s1 is the total data quantity to be transmitted in a current queue; z is Z y The weight of the user download data in the current queue is given, and S2 the total amount of the user download data in the current queue is given;
according to the different weight and data quantity, the priority and bandwidth allocation of data transmission can be adjusted;
and periodically acquiring mass production data in the distributed pre-storage through an API interface. And setting a data transmission sequence according to the data acquisition time sequence and the comprehensive weight result. Transmitting according to the acquisition time sequence, and setting the transmission sequence of the data of the same time sequence according to the comprehensive weight;
and counting the access frequency and the downloading quantity of different time periods every day in the last week, and obtaining the average access frequency and the downloading quantity of different time periods. Counting every half hour as a time period;
and carrying out normalization processing on the average access frequency and the download quantity in different time periods to obtain normalized load parameters. Calculating load parameters according to the average access frequency and the download quantity after normalization processing;
The time period in which the minimum load parameter is located is taken as one of the time periods of mass production data transmission from the distributed pre-storage. Therefore, data transmission can be performed in a lower load period, and the transmission efficiency is improved;
judging the current load: and acquiring the current access frequency and downloading amount, and carrying out normalization processing. And calculating the current load parameter according to the current normalized access frequency and downloading amount, and comparing the current load parameter with the average load parameter of each time period of the last week. If the current load parameter is less than 25% of the average load parameter distribution per time period of the last week, data transmission can be performed from the mass production data in the distributed pre-storage to reduce the system load.
The technical scheme has the effects that: by setting the transmission priority order and bandwidth allocation according to the comprehensive weight, it is possible to ensure that data of high priority is preferentially transmitted under a limited bandwidth, and to improve the efficiency of data transmission. And obtaining a load parameter by counting the access frequency and the download quantity and normalizing, and selecting a transmission time period according to the load parameter. Therefore, data transmission can be performed in a time period with lower load, the overload condition of the system is avoided, and load balancing is optimized. By setting the data transmission sequence according to the acquisition time sequence and the comprehensive weight, the continuity of the data in the same time sequence can be ensured, and the reading performance of the data is improved. The experimental data of different products are obtained in real time through the API interface, the mass production data in the distributed pre-storage are obtained regularly, and timeliness and instant updating of the data are guaranteed. And comparing the current load parameter with the average load parameter of each time period of the last week, automatically judging the current load state of the system, and if the load is lower, carrying out data transmission from mass production data in the distributed pre-storage. Thus, the transmission strategy can be flexibly adjusted according to the actual load condition. By associating the transmission priority of the data with its comprehensive weight, it is possible to ensure the priority transmission of the data of high priority. Therefore, timeliness and accuracy of processing of important data can be improved, and priority processing of key data is guaranteed. By comparing the number of current access users with the maximum number of user accesses in the history, the transmission bandwidth can be dynamically adjusted according to the change in the number of user accesses. Therefore, bandwidth resources can be distributed more reasonably, and the situation that data transmission is slow or system load is too high due to too large access quantity of users is avoided. The importance degree of different data can be better considered by carrying out product calculation on the weight of the data to be transmitted in the current queue and the total data quantity. Therefore, important data can occupy more bandwidth resources in the transmission process, and the processing efficiency of the important data is improved. The product calculation is carried out on the weight of the downloaded data of the user in the current queue and the total amount of the downloaded data, so that the degree of the user's requirement on different data can be better understood. Therefore, the bandwidth allocation of data transmission can be adjusted according to the requirements of users, and the satisfaction degree and experience of the users are improved. The transmission bandwidth allocation formula can reasonably allocate the transmission bandwidth according to the comprehensive weight of the data, the access quantity of the user, the data quantity, the user demand and other factors, thereby realizing the priority management of the data transmission and the optimization of the resource utilization. Therefore, the efficiency and the quality of data transmission can be improved, the processing requirements of different data are met, and the satisfaction degree of users on the data is improved.
In summary, the data interaction processing method can improve data transmission efficiency, optimize load balance, improve data reading performance, automatically adjust transmission strategies, adapt to different load conditions, and provide better data interaction processing effects.
In this embodiment, the data access module includes:
and (3) an access setting module: the user access interface comprises self-service data analysis and visual display;
self-service analysis module: the self-service data analysis comprises a product option, a time option, a production or test stage option, a data sheet analysis option and a chart type option;
and (3) a downloading module: downloading data and/or automatically generating an analysis chart according to the options; if the user options comprise a plurality of worksheets, the system provides an integration form, and integrates the worksheets together according to the unique product identification codes and the key information;
and the cache setting module is used for: setting a cache according to the frequency of accessing data by a user, the data volume, and the method comprises the following steps:
counting the frequency of different data access in three days to obtain distribution diagram of different data access frequency,
setting a frequency threshold according to the frequency distribution diagram, accessing data corresponding to the frequency distribution diagram which is larger than the frequency threshold as a cache range, and setting the size of a cache space;
Maintaining data access frequency and data heat information: recording the accessed frequency and the access mode in the last period of time of each cached data object for evaluating the heat of the data;
defining a replacement strategy: and carrying out replacement decision according to the cache capacity. The following are two common alternative strategies:
least recently used (Least Recently Used, LRU): selecting the least recently accessed data for replacement based on the access time stamp of the data;
least frequently used (Least Frequently Used, LFU): selecting the least frequently accessed data to replace based on the access frequency of the data;
evaluating cache hit rate: calculating a cache hit rate by tracking whether each request hits the cache;
dynamically adjusting the cache size: and dynamically adjusting the cache size according to the cache hit rate and the resource limit. If the hit rate is low, it may be necessary to increase the cache capacity; if the hit rate is high, the cache capacity can be properly reduced;
implementing a Cache Pre-fetch (Cache) policy: according to the data heat and the access mode, the data which can be requested are preloaded into the cache in advance, so that the hit rate is improved.
The working principle of the technical scheme is as follows: and the user performs data analysis and visual display through the access interface. The interface provides self-service data analysis and visualization options. The user may select a product option, a time option, a production or test phase option, a data sheet analysis option, a chart type option, etc. to meet their particular needs. Depending on the user's options, the system may download data and/or automatically generate an analysis chart as desired. If the user option contains a plurality of worksheets, the system integrates the worksheets together, thereby facilitating comprehensive analysis. According to factors such as the access frequency of a user to data, the data quantity and the like, the system can set the cache range and the cache space according to a distribution diagram obtained by counting the accessed frequency of different data in three days. Data corresponding to the frequency distribution larger than the frequency threshold value is cached, so that the access efficiency is improved. The system will record the access frequency and access pattern of each cached data object for use in evaluating the heat of the data. This information facilitates subsequent replacement policy and cache hit rate evaluation. And the system makes a replacement decision according to the cache capacity. Common replacement strategies are Least Recently Used (LRU) and Least Frequently Used (LFU). The least recently accessed or least frequently accessed data is selected for replacement based on the access time stamp or access frequency of the data. By tracking whether each request hits in the cache, the system can calculate the cache hit rate. The cache hit rate is an important indicator for measuring the effectiveness of cache. According to the cache hit rate and the resource limitation, the system can dynamically adjust the cache size. If the hit rate is low, the system may need to increase the cache capacity to increase efficiency; if the hit rate is high, the cache capacity can be properly reduced to save resources. Depending on the warmth and access pattern of the data, the system may implement a cache prefetch policy. The data which may be requested is loaded into the cache in advance to improve the cache hit rate and response speed.
In summary, the data interaction processing method selects data analysis and visualization options through the user interface, and combines technical means such as cache setting, replacement policy, cache hit rate evaluation and dynamic adjustment, so that efficient access and processing of data are realized, and system performance and user experience are improved.
The technical scheme has the effects that: the user can perform autonomous data analysis and visual display through the interface without relying on professionals or complex tools. The system provides a number of options including product, time, stage, data sheet analysis, chart type, etc., to meet the user's specific needs. For data containing a plurality of worksheets, the system can integrate the worksheets together, and comprehensive analysis is convenient. According to user options, the system can rapidly download data and automatically generate corresponding analysis charts, and the efficiency of data access and processing is improved. The system sets the cache range and the space size according to the access frequency and the heat information of the data, and determines the effectiveness of the cache by evaluating the cache hit rate. This can reduce repeated data loading and speed up data access. And the system makes a replacement decision according to the cache capacity, and selects a proper replacement object through strategies such as LRU or LFU. Meanwhile, the system can dynamically adjust the cache size according to the cache hit rate and the resource limit, and the optimal performance and the optimal resource utilization rate are maintained. The system loads the data which can be requested into the cache in advance according to the heat degree and the access mode of the data so as to improve the hit rate and the response speed. This may reduce user latency and enhance user experience. The data interaction processing method can improve the data access efficiency, accelerate the analysis chart generation and response speed and provide better user experience by providing technical means such as self-service data analysis, visual display, cache management, optimization and the like.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. A method for data interaction processing, the method comprising:
s1, collecting product data in a production process in real time, classifying and sorting the product data, and obtaining comprehensive weights according to sorting results; the classification includes a first classification set by product category;
s2, setting a pre-storage space and the number of pre-storage nodes, and carrying out distributed pre-storage on the acquired data according to a first classification; acquiring experimental data of different products of the distributed pre-storage node in real time through an API interface for distributed storage; data transmission is carried out according to the comprehensive weight setting transmission priority sequence, and the bandwidth of the transmission data is set; periodically acquiring mass production data in the distributed pre-storage through an API interface, and setting a data transmission sequence according to a data acquisition time sequence and a comprehensive weight result for distributed storage;
S3, setting a user access interface, and performing data access and/or data downloading through the user access interface.
2. The method for processing data interaction according to claim 1, wherein the step S1 comprises:
collecting product data in the production process in real time;
classifying the product data according to product categories; obtaining a first classification, and sorting the first classification data according to the importance degree of the product to obtain a first sorting result; setting a first weight according to the first sorting result; the first weight is w i ；
Classifying the first classified data according to mass production data and experimental data; obtaining a second classification; obtaining a second weight a according to the second classification k ；
Sequencing the experimental data in the second classification according to the experimental priority; obtaining a third sorting result; obtaining a third weight q according to the third sorting result j ；
Obtaining a comprehensive weight Z according to the first weight, the second weight and the third weight;
Z＝a k ×(0.6×w i +0.4×q j )
if it is mass production data, q j ＝0；
Grouping the data of the products in the first category according to different workstations to obtain a plurality of worksheets; the data for each workstation contains the product's category, the unique product identification code, and the workstation information.
3. The method for processing data interaction according to claim 1, wherein the step S2 comprises:
setting a pre-storage space and the number of pre-storage nodes, and carrying out distributed pre-storage on the acquired data according to a first classification;
the pre-storage space is:
l is the average daily mass production data volume collected in the last week; t is an average period used by the mass production data acquired every day on average in the last week to be transmitted to the distributed storage space; the unit is hours; s is S L The experimental data volume is acquired in the time T;
acquiring data of a distributed pre-storage node through an API interface, and performing distributed storage;
after data storage, consistency verification is carried out, and the content, the format and the quantity of corresponding data in storage are compared, so that no error or loss of the data in the transmission process is ensured;
the data is arranged into search engines by category and grouping.
4. A method for processing data interaction according to claim 3, wherein the obtaining, through an API interface, data of a distributed pre-storage node, and performing distributed storage includes:
acquiring experimental data of different products in real time through an API interface; data transmission is carried out according to the comprehensive weight setting transmission priority sequence, and the bandwidth of the transmission data is set;
Wherein B is the transmission allocation bandwidth; b (B) z Is the total bandwidth of the system; u is the user accessed at present; u (U) m The maximum user access number in the history record is obtained; the weight of the data to be transmitted in the current queue; s1 is the total data quantity to be transmitted in a current queue; z is Z y The weight of the user download data in the current queue is given, and S2 the total amount of the user download data in the current queue is given;
periodically acquiring mass production data in the distributed pre-storage through an API interface; and setting a data transmission sequence according to the data acquisition time sequence and the comprehensive weight result.
5. The method for processing data interaction according to claim 1, wherein the step S3 comprises:
the user access interface comprises self-service data analysis and visual display;
the self-service data analysis comprises a product option, a time option, a production or test stage option, a data sheet analysis option and a chart type option;
downloading data and/or automatically generating an analysis chart according to the options; if the user options comprise a plurality of worksheets, the system provides an integration form, and integrates the worksheets together according to the unique product identification codes and the key information;
and setting a cache according to the frequency of accessing the data by the user, the data quantity and the data.
6. A data interaction processing system, the system comprising:
and a data acquisition module: collecting product data in the production process in real time, classifying and sorting the product data, and obtaining comprehensive weights according to sorting results; the classification includes a first classification set by product category;
and a data storage module: setting a pre-storage space and the number of pre-storage nodes, and carrying out distributed pre-storage on the acquired data according to a first classification; acquiring experimental data of different products of the distributed pre-storage node in real time through an API interface for distributed storage; data transmission is carried out according to the comprehensive weight setting transmission priority sequence, and the bandwidth of the transmission data is set; periodically acquiring mass production data in the distributed pre-storage through an API interface, and setting a data transmission sequence according to a data acquisition time sequence and a comprehensive weight result for distributed storage;
and a data access module: setting a user access interface, and performing data access and/or data downloading through the user access interface.
7. The data interaction processing system of claim 6, wherein the data acquisition module comprises:
and the real-time acquisition module is used for: collecting product data in the production process in real time;
A first classification module: classifying the product data according to product categories; obtaining a first classification, and sorting the first classification data according to the importance degree of the product to obtain a first sorting result; setting a first weight according to the first sorting result; the first weight is w i ；
A second classification module: classifying the first classified data according to mass production data and experimental data; obtaining a second classification; obtaining a second weight a according to the second classification k ；
A third sorting module: sequencing the experimental data in the second classification according to the experimental priority; obtaining a third sorting result; obtaining a third weight q according to the third sorting result j ；
The comprehensive weight acquisition module is used for: obtaining a comprehensive weight Z according to the first weight, the second weight and the third weight:
Z＝a k ×(0.6×w i +0.4×q j )
if it is mass production data, q j ＝0；
And a grouping module: grouping the data of the products in the first category according to different workstations to obtain a plurality of worksheets; the data for each workstation contains the product's category, the unique product identification code, and the workstation information.
8. The data interaction processing system of claim 6, wherein the data storage module comprises:
a data pre-storage module: setting a pre-storage space and the number of pre-storage nodes, and carrying out distributed pre-storage on the acquired data according to a first classification;
The pre-storage space is:
l is the average daily mass production data volume collected in the last week; t is an average period used by the mass production data acquired every day on average in the last week to be transmitted to the distributed storage space; the unit is hours; s is S L The experimental data volume is acquired in the time T;
and a storage module: acquiring data of a distributed pre-storage node through an API interface, and performing distributed storage;
and (3) a verification module: after data storage, consistency verification is carried out, and the content, the format and the quantity of corresponding data in storage are compared, so that no error or loss of the data in the transmission process is ensured;
the search engine setting module: the data is arranged into search engines by category and grouping.
9. The data interaction processing system of claim 8, wherein the memory module comprises:
and the real-time transmission module is used for: acquiring experimental data of different products in real time through an API interface; data transmission is carried out according to the comprehensive weight setting transmission priority sequence, and the bandwidth of the transmission data is set;
wherein,b is the transmission allocation bandwidth; b (B) z Is the total bandwidth of the system; u is the user accessed at present; u (U) m The maximum user access number in the history record is obtained; the weight of the data to be transmitted in the current queue; s1 is the total data quantity to be transmitted in a current queue; z is Z y The weight of the user download data in the current queue is given, and S2 the total amount of the user download data in the current queue is given;
and a periodic transmission module: periodically acquiring mass production data in the distributed pre-storage through an API interface; and setting a data transmission sequence according to the data acquisition time sequence and the comprehensive weight result.
10. The data interaction processing system of claim 6, wherein the data access module comprises:
and (3) an access setting module: the user access interface comprises self-service data analysis and visual display;
self-service analysis module: the self-service data analysis comprises a product option, a time option, a production or test stage option, a data sheet analysis option and a chart type option;
and (3) a downloading module: downloading data and/or automatically generating an analysis chart according to the options; if the user options comprise a plurality of worksheets, the system provides an integration form, and integrates the worksheets together according to the unique product identification codes and the key information;
and the cache setting module is used for: and setting a cache according to the frequency of accessing the data by the user, the data quantity and the data.
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