CN111506610A - Real-time streaming data preprocessing method for tobacco industry production field - Google Patents
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Abstract
The invention discloses a real-time streaming data preprocessing method for a tobacco industry production field, which comprises the following steps: when the process data of the production field is collected in real time, the process data collected in real time is automatically matched with an imaging model associated with a data source; during matching, according to defined attributes in the visualized model, splitting the diversified process data flow acquired in real time into real-time data flows corresponding to each attribute of the visualized model; in the data stream splitting process, the real-time process data stream is preprocessed by using a well-defined data preprocessing rule, and the preprocessed process data is stored in the visualization model. The real-time streaming data preprocessing method improves the real-time data quality in the production process, and provides a stable data base for improving the application aspects of big data modeling and artificial intelligence technology in the tobacco industry.
Description
Technical Field
The invention relates to the field of computer artificial intelligence, in particular to a real-time streaming data preprocessing method for a tobacco industry production field.
Background
Nowadays, a new round of industrial revolution with the transformation and upgrade of the manufacturing industry as the primary task is raised globally, and the industrial big data has become one of the hot spots in the industrial field today as the main driving force for leading the revolution. The technologies such as cloud computing, big data, internet of things and artificial intelligence are developed vigorously, new-generation communication technologies such as 5G, NB-IoT are developed, and deep fusion of information technology and manufacturing industry can promote service transformation and product upgrading in the industrial field and reshape the industrial pattern of the global manufacturing industry.
Meanwhile, in order to comply with the requirements of market development, tobacco industry enterprises also accelerate the transformation speed of intelligent equipment, and large-scale deployment of digital tools and equipment such as sensors, radio frequency identification, numerical control machines, robots and gateways is performed, so that mass data from each link of the life cycle of a product is generated along with network access of the equipment, and a premise and a basis are provided for industrial big data analysis, but the mass industrial data are often directly from real-time monitoring data and service management data of an industrial field, and collected contexts such as measurement means and measurement equipment precision are often ignored, so that the data quality is not high, and the data cannot be directly applied to an actual service scene without being subjected to cleaning, processing, modeling and other processing; therefore, a series of data preprocessing methods and systems are urgently needed to be provided, so that the means for preprocessing mass data is perfected, the data quality is improved, and the data service is better provided for the needs of business scenes.
The patent application with application publication number CN108153591A discloses a real-time data stream processing method, which uses the historical characteristic values calculated from the first n-1 data and the historical characteristic values calculated from the nth data to replace the nth data. The patent application with application publication number CN110198266A discloses a real-time data stream processing method, which mainly processes communication signal data. The two data stream real-time processing methods are not suitable for processing real-time detection data of a tobacco industry production field.
Disclosure of Invention
The invention aims to provide a real-time streaming data preprocessing method for a tobacco industry production site, which improves the real-time data quality in the production process and provides a stable data base for improving the application of the tobacco industry in big data modeling and artificial intelligence technology.
The technical scheme of the invention is as follows:
a real-time flow data preprocessing method for a tobacco industry production field comprises the following steps:
when the process data of the production field is collected in real time, the process data collected in real time is automatically matched with an imaging model associated with a data source;
during matching, according to defined attributes in the visualized model, splitting the diversified process data flow acquired in real time into real-time data flows corresponding to each attribute of the visualized model;
in the data stream splitting process, the real-time process data stream is preprocessed by using a well-defined data preprocessing rule, and the preprocessed process data is stored in the visualization model.
Wherein the defined attributes in the imaged model include:
attribute values: the current value of the attribute T moment;
historical data flow: pre-processed historical data streams with time stamps;
shadow data stream: a time-stamped raw historical data stream that has not been pre-processed.
Preferably, the data preprocessing rule includes a single-point data processing rule, when the data point characteristics in the real-time data stream are in accordance with the single-point data processing conditions, the real-time data points are subjected to data elimination and data conversion, and the processed data are written into the historical data stream.
The single-point data processing condition refers to that data does not have continuity, and has instantaneous data at a certain time, for example, the time stamp is 2020-3-1618: 00:00.000, and the single-point data is 51.
The data conversion refers to performing formula operation, data type conversion or rounding estimation according to a service specific rule, wherein the formula operation includes a quadratic operation and a trigonometric function operation, and the following operations are performed: sine function, cosine function, tangent function, etc., calculus equations, such as: derivation, curvature, definite integral, indefinite integral, etc., exponential function, power function, evolution, etc., other common mathematical formulas such as absolute value, remainder, integer division, binary transformation, etc., fourier transformation, and user-defined mathematical formula transformation.
Preferably, the data preprocessing rule includes a continuous point data processing rule, when the data point feature in the real-time data stream conforms to the continuous point data processing condition, the data point is marked as a data processing starting point, when the data point feature and the continuous point data processing condition are ended, the data point is marked as a data processing end point, then, data between the data processing starting point and the data processing end point is preprocessed, and the processed data is written into the historical data stream.
The continuous point data processing condition means that data has continuity in time T + n, that is, continuous data values in a certain time segment can be regarded as an array.
The ending of the continuous point data processing condition refers to grouping continuous data according to a data processing rule, for example, dividing the continuous data by time, taking each time from T to T + n as a time scale, or dividing the data by the number of the data, starting counting at the time T, ending when the data in the array reaches a threshold value, or dividing by the condition, starting from the time T, and ending when the data triggers a certain condition rule.
The data preprocessing method comprises the steps of preprocessing data between a data processing starting point and a data processing end point, and performing data aggregation, equalization and the like. The method specifically comprises the steps of carrying out mean value calculation, standard deviation calculation, CPK calculation, naive Bayes method calculation, confidence coefficient calculation, clustering function, linear regression, variance analysis processing, central value calculation, fuzzy processing, square difference, cubic sum, difference, total expectation formula, total probability formula, sum-difference square, sum-difference cube, sum-square, sum-cube, foreign feature standard formula, algebraic mathematical formula, permutation and combination formula and user-defined mathematical formula processing on data.
Preferably, the data preprocessing rule includes a cache data backtracking processing rule, after a period of time matching, if a previous segment of data stream meets a cache data backtracking processing condition, backtracking to a previous state of the shadow data stream, taking out a group of segment data from the previous state of the shadow data stream for preprocessing, writing the processed data into the historical data stream, and replacing the original historical data.
The preprocessing of the extracted group of fragment data comprises the processing of interpolation calculation, data sparse calculation and the like of the group of fragment data.
In order to avoid the situation that the acquired real-time data is generated at high speed and continuously, the processing of the data is complex sometimes, and the acquisition frequency of the data is influenced in the processing process, the invention establishes the cache data of a real data stream, intercepts a part of the cache data according to the processing rule of the continuous data, processes the data, inserts the processed data into the original data stream according to the time sequence after the processing is finished, thus not destroying the original timestamp, but also performing complex data processing with long time consumption, the processing process is possibly repeated, the processing rule is possibly different when each processing is performed, in order to ensure that the original data is not destroyed, a state is recorded when each processing is performed, the shadow data stream is rolled back according to the state after the processing is finished, so as to facilitate the next data processing.
Compared with the prior art, the invention has the beneficial effects that:
the invention realizes the cleaning, calibration, processing and other processing of the real-time data in the production process of the tobacco industry enterprise through the established visualization model, the shunt processing method and the data preprocessing rule, reduces the data noise, realizes the overall planning and aggregation of the real-time data flow and improves the quality of the real-time data in the production process.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a real-time flow data preprocessing method for a tobacco industry production site provided by an embodiment;
FIG. 2 is a schematic diagram of a real-time data stream splitting provided by an embodiment;
FIG. 3 is a schematic diagram of a single point data processing provided by an embodiment;
FIG. 4 is a schematic diagram of continuous point data processing provided by an embodiment;
fig. 5 is a schematic diagram of cache data backtracking processing according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the detailed description and specific examples, while indicating the scope of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
In order to improve the capacity of the tobacco industry in the aspects of big data modeling and artificial intelligence technology application, the invention provides a real-time streaming data preprocessing technology from the perspective of improving the real-time data quality in the production process, so that the processing of cleaning, calibration, processing and the like of the real-time data in the production process of tobacco industry enterprises is realized, the data noise is reduced, and the overall planning and aggregation of the real-time data streams are realized.
As shown in fig. 1, the method for preprocessing real-time streaming data for a tobacco industry production site provided by the invention comprises the following steps:
in the process that a digital acquisition system acquires industrial data of a production field in real time, redirecting real-time data stream (the real-time data refers to data stream generated when the data acquisition frequency is within 200 ms) to an imaging model library, automatically matching an imaging model associated with the data stream according to a data source, and splitting diversified complex real-time data stream into a group of real-time data stream with single attribute in the imaging model according to defined attribute in the imaging model; and in the data stream splitting process, loading the well-defined data preprocessing rules in the data preprocessing rule base at the same time, preprocessing each data in the data stream, outputting the processed high-quality data stream, and supplying the high-quality data stream to the algorithm model for use.
When the data acquisition system performs data acquisition, the data acquisition system usually performs periodic polling on IO points therein according to equipment, sensors or the like as an acquisition unit or according to a fixed time, namely, a data pulling mode; or when the IO point data in the data is changed, data pushing is carried out, namely data acquisition is carried out in a data pushing mode and other modes; no matter which mode is adopted to realize data acquisition, different IO point data of various different devices exist in a real-time data stream formed at the same moment, and the data streams are interwoven and mixed together; as shown in fig. 2, the present invention synchronously captures data from a real-time data stream through a predefined device visualization model, writes the data into a corresponding attribute according to a data source, and stores the data, wherein the device visualization attribute is composed of three parts:
the attribute value is used for storing the current value of the attribute T moment;
the historical data stream is stored with the timestamp and is subjected to preprocessing;
shadow data stream, holding the original history data stream with time stamp without preprocessing.
When data enters a shadow data stream, identifying and calibrating each data point according to data characteristics, and processing the data according to a data preprocessing rule defined in a data preprocessing rule base; real-time data preprocessing is divided into three types: single-point data processing, continuous point data processing and cache data backtracking processing.
As shown in fig. 3, for single-point data processing, when the data point characteristics are consistent with the single-point data processing conditions, the data is immediately subjected to processing such as removing and converting, and written into a historical data stream.
As shown in fig. 4, for the continuous point data processing: and when the data point characteristics are in accordance with the continuous data processing conditions, marking the data point as a data processing starting point, when the data point characteristics and the data processing conditions are ended, marking the data point as a data processing end point, and simultaneously performing data aggregation, equalization and other processing on data between the starting point and the end point, and writing the data into a historical data stream.
As shown in fig. 5, for the cache data backtracking process: after a period of time identification, if the previous section of data is found to accord with the cache data backtracking processing condition, backtracking to a state before the shadow data stream, taking out a group of fragment data from the state, performing interpolation calculation, data sparse calculation and other processing, writing the fragment data into the historical data stream, and replacing the historical data with the processed data stream.
The real-time streaming data preprocessing method facing the tobacco industry production field is realized in a software program mode; defining a data preprocessing rule in a data preprocessing rule base according to the real-time data characteristics of a tobacco industry production field, accessing a real-time data stream generated during data acquisition into a program, automatically starting data preprocessing according to the rule by the program, storing the processed data into the database, and checking the data through an equipment imaging model provided by the program.
The real-time stream data preprocessing method for the tobacco industry production site has the advantages that:
firstly, a plurality of data preprocessing rules established aiming at the data characteristics of the tobacco industry are defined in a data preprocessing rule base designed in the invention, the data characteristics can be identified in real time through the rules, the data preprocessing is automatically carried out according to the rules, the manual intervention processing is not needed, and the automation of the real-time data preprocessing is realized.
Secondly, the real-time data splitting method designed by the invention can synchronously capture data from the real-time data stream through a predefined equipment imaging model, and write the data into corresponding attributes according to data sources for storage, so that the data dimensionality in the real-time data stream can be greatly reduced, the complexity of data preprocessing is reduced, and the processing efficiency is improved; meanwhile, attribute values, historical data streams and shadow data streams exist in the attributes of the equipment imaging model, so that the advantages that once data are damaged due to errors in the data preprocessing process, data can be quickly recovered from the shadow data streams in a manual intervention mode, and stable operation of data services is guaranteed.
Thirdly, the shadow data stream designed in the invention is used for storing the original historical data stream with the timestamp without preprocessing, and the historical data stream is used for storing the historical data stream with the timestamp after preprocessing, so that the method can simultaneously support the single-point data processing, continuous point data processing and cache data backtracking data preprocessing method, and the data preprocessing process is more flexible and has higher universality;
fourthly, most of the existing data preprocessing methods are historical data preprocessing methods, a certain accumulation amount exists after data are stored, generated data are analyzed manually, and post processing is carried out on the data according to data characteristics and business requirements; when the data is generated, the data is preprocessed in real time according to the preset data processing rule and then stored, the processing mode has the advantages of real-time processing, high-quality data can be obtained without waiting, data service is provided for the algorithm model needing real-time operation in the tobacco production field, and the data service cannot be realized by the traditional data preprocessing method.
In a word, the real-time streaming data preprocessing method for the tobacco industry production site improves the data quality of the real-time streaming data, can reduce 50% of data preprocessing cost (manpower and time), and paves a way for the wide application of artificial intelligence in the tobacco industry.
The above-mentioned embodiments are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only the most preferred embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions, equivalents, etc. made within the scope of the principles of the present invention should be included in the scope of the present invention.
Claims (8)
1. A real-time flow data preprocessing method for a tobacco industry production field is characterized by comprising the following steps:
when the process data of the production field is collected in real time, the process data collected in real time is automatically matched with an imaging model associated with a data source;
during matching, according to defined attributes in the visualized model, splitting the diversified process data flow acquired in real time into real-time data flows corresponding to each attribute of the visualized model;
in the data stream splitting process, the real-time process data stream is preprocessed by using a well-defined data preprocessing rule, and the preprocessed process data is stored in the visualization model.
2. The tobacco industry production site oriented real-time flow data preprocessing method of claim 1, wherein the defined attributes in the imaged model include:
attribute values: the current value of the attribute T moment;
historical data flow: pre-processed historical data streams with time stamps;
shadow data stream: a time-stamped raw historical data stream that has not been pre-processed.
3. The tobacco industry production site-oriented real-time stream data preprocessing method as claimed in claim 1, wherein the data preprocessing rules include single-point data processing rules, when the data point characteristics in the real-time data stream are in accordance with the single-point data processing conditions, the real-time data points are subjected to data elimination and data conversion processing, and the processed data are written into the historical data stream.
4. The tobacco industry production site-oriented real-time stream data preprocessing method as claimed in claim 1, wherein the data preprocessing rule includes a continuous point data processing rule, when the data point feature in the real-time data stream meets the continuous point data processing condition, the data point is marked as a data processing starting point, when the data point feature and the continuous point data processing condition are ended, the data point is marked as a data processing end point, then, the data between the data processing starting point and the data processing end point is preprocessed, and the processed data is written into the historical data stream.
5. The tobacco industry production site-oriented real-time streaming data preprocessing method as claimed in claim 1, wherein the data preprocessing rules include cache data backtracking processing rules, after a period of time matching, if a previous segment of data stream meets the cache data backtracking processing conditions, backtracking to a state before a shadow data stream, taking a group of segment data therefrom for preprocessing, writing the processed data into a historical data stream, and replacing the original historical data.
6. The tobacco industry production site-oriented real-time stream data preprocessing method as claimed in claim 3, wherein the single-point data processing condition is that there is no continuity between data and there is a transient data at a certain time.
7. The tobacco industry production site oriented real-time flow data preprocessing method according to claim 3, wherein the data conversion is formula operation, data type conversion or rounding estimation according to business specific rules.
8. The method of claim 4, wherein the ending of the continuous point data processing condition is grouping the continuous data according to a data processing rule, such as dividing the continuous data by time, dividing the continuous data by a time scale from T to T + n, or dividing the continuous data by the number of data, counting the continuous data by time T, ending when the data in the array reaches a threshold value, or dividing the continuous data by conditions, starting from time T, and ending when the data triggers a condition rule.
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