CN111241074A - Steel enterprise data center application system based on time sequence data and relation data - Google Patents
Steel enterprise data center application system based on time sequence data and relation data Download PDFInfo
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- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 abstract description 18
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Abstract
A data center application system of an iron and steel enterprise based on time sequence data and relationship data belongs to the field of data center application of the iron and steel enterprise. The system comprises 3 servers, an application server provided with a Windows Server 2016 operating system and provided with a 1T hard disk; a relational database server provided with a Windows Server 2016 operating system and provided with a 4T hard disk; a time sequence database file system server provided with a Windows Server 2016 operating system is provided with a 1T hard disk. The system also comprises a data acquisition module, a data processing module, a data calculation module, a data configuration module, a data cache module, a data storage module, a data subscription module and an event processing module. The system has the advantages that the system is convenient for users to schedule and control the production progress, the energy is fully utilized, and the production efficiency of enterprises is improved.
Description
Technical Field
The invention relates to the field of data center application of iron and steel enterprises, in particular to an application system of the data center of the iron and steel enterprises based on time sequence data and relational data.
Background
In steel enterprises, a large amount of data is generated every day. Where timing data and relational data are the primary data types. The time-series data is time-tagged data generated over time. These data are characterized by: the acquisition frequency is fast, and acquisition is carried out once in tens of milliseconds; collection time is heavily relied upon; the number of measuring points is large, the information amount is large, and a large amount of storage space is needed every day. The relational data is traditional business data, including user data, process data, quality data, equipment data, and the like.
Due to the different types of data in iron and steel enterprises, different storage strategies are required for storage. At present, the storage and processing methods for time series data mainly include a file system, relational database software and real-time database software. Due to the characteristics of time series data, traditional database software, file systems and the like cannot meet the requirements on compression, reading and writing speeds of the time series data, and therefore a database specially aiming at the time series data is needed. The storage strategy of the relational data is relatively mature relative to the time sequence data, and currently, the mainstream relational data includes Oracle, MySQL, SqlServer, DB2 and the like.
At present, a single storage strategy cannot meet the storage requirements of the iron and steel enterprises, so that an application system of the iron and steel enterprise data center for storing different data types together is needed.
Disclosure of Invention
The invention aims to provide a data center application system of a steel enterprise based on time sequence data and relational data, which solves the problems of processing, calculating, storing, reading, writing and the like of various types of data.
The system comprises 3 servers, a hardware platform of the system is formed, wherein one application server provided with a Windows Server 2016 operating system is provided with a 1T hard disk; a relational database server provided with a Windows Server 2016 operating system and provided with a 4T hard disk; a time sequence database file system server provided with a Windows Server 2016 operating system is provided with a 1T hard disk. The application server and the relational database server are connected through a TCP/IP protocol, and the time sequence database file system server is also connected through the TCP/IP protocol.
The system also comprises a data acquisition module, a data processing module, a data calculation module, a data configuration module, a data cache module, a data storage module, a data subscription module and an event processing module, wherein the data acquisition module, the data processing module, the data calculation module, the data configuration module, the data subscription module and the event processing module are arranged on the application server, and the data storage module is arranged on the two database servers. And all modules are connected through reserved interfaces. Each module is a service.
The data acquisition module in the invention acquires instrument data points, PLC data points and service data in the production process into a server through a data acquisition tool, each signal (time signal and event signal) is used as a label, and then data processing is carried out.
The data processing module in the invention filters the error of the collected data points according to the fluctuation characteristics, thereby improving the quality of the data. And filtering the collected data and then calculating the data. The filter rule filter algorithm and the rule are as follows:
absoult: tags with values in the range [ R-L, R + U ] will be filtered. R is a reference value, the initial value is zero, and if the current value passes through the filter, the current value is used as the reference value for the next tag filtering; ABSBETWEEN, if the change of two adjacent values is less than zero, if the change value/elapsed time > (R-L)/3600, the value is filtered; if the change of the two adjacent values is larger than zero, if the change value/elapsed time > (R + U)/3600, the value is filtered;
CutOff: processing all types of labels, directly filtering null values, and directly passing non-numerical label values; if it is a value, the current value is compared with the upper and lower limits of the filter condition, if it is within the range, the filter is passed directly, if the current value is less than the minimum value, the value is replaced by the minimum value, if the current value is greater than the maximum value, the value is replaced by the maximum value, and then the filter is passed.
Between: tags with values outside the range [ L, U ] will be filtered, with L being the lower limit and U being the upper limit.
ABSBETWEEN: if the change of the two adjacent values is less than zero, if the change value/elapsed time > (R-L)/3600, the value is filtered; if the change of the two adjacent values is larger than zero, if the change value/elapsed time > (R + U)/3600, the value is filtered;
edge: tags whose values are within the range R-L, R + U will be filtered, similar in function to Absolute, with R being a reference value, except that whether or not the current value is filtered, the current value will be the reference value for the next tag filtering.
Equal: if the tag current value is equal to the last value, it will be filtered.
Between Equal: firstly, judging whether the current value is in a defined limit range [ L, U ] or not, and filtering the current value which is not in the range; if the current value and the latest value pvLast are equal, the equal value is filtered.
ObjectEqual: the tag current value is equal to the last value and will be filtered.
Relative: if the reference value R is less than 0, then tags whose values are within the range (R (1+ L), R (1.0-U)) will be filtered; if the reference value R is greater than 0, then tags whose values are within the range (R (1-L), R (1.0+ U)) will be filtered; and if the current value is not filtered, using the current value as a reference value for next tag filtering.
The data calculation module meets the requirements of multi-time scale transformation (second, day, month) of data and optimization of a storage mode (storage is performed when the data is changed), and adopts a uniform data calculation mode. The current system provides the following calculation modes:
event: and storing the original value of the tag when the tag is changed, and is suitable for data storage of no cycle, production time and production signals.
Minus: the difference between two periods of an accumulated amount is stored in terms of periods.
Current: the current value. And storing the first piece of data of the period according to the period storage.
Integral value: the integrated value of one cycle is stored.
Simple difference value: stored on a periodic basis, the difference between two periods of a quantity is stored.
And (3) accumulation: and counting according to the period, and counting the total amount of one physical quantity per period.
Average value: an average of an instantaneous quantity over a period is calculated.
Arithmetic mean: the data of the tag is simply summed and divided by the count of the data of the cycle.
Bottom amount of the table: stored on a periodic basis. A table base quantity is stored for a start value of a cycle.
Maximum value: stored on a periodic basis. A maximum value of one cycle of one tag is stored.
Minimum value: stored on a periodic basis. The minimum value of one tag cycle is stored.
The data configuration module provides one-stop configuration service for the system, and the configurations can immediately act on modules for data acquisition, data processing and the like. The method mainly comprises two categories: one type is configuration for tag calculation, including null setting, filter setting, trigger setting, calculation formula, timer, etc.; one type is a configuration for the tag to store information, including a tag storage configuration and a compression configuration.
In the data storage module, a data storage architecture adopts a storage structure of (ID, Clock and Val), wherein the ID is a unique number assigned to each storage point (label) by a system, the Clock is a timestamp, and the Val is a value of the current time of data. When the system reads data of a time dimension, the reading pressure is relatively large, the efficiency is slow when the system is used for integration processing, and the user experience is poor. Therefore, each tag can be configured with a plurality of storage points to meet the business requirements, and each storage point contains configuration information of the unique ID of the ID tag, the storage period of the Interval tag and the calculation type of the Action tag. And dividing the label time scale into two storage strategies. The tags in the millisecond and second levels of the system are time series data. The time sequence data is written in the module through the data, the data compression module is stored in the time sequence database, and the data is read through the data reading module. The data of the hour level, the date level and the month level in the system are directly stored in a relational database and checked and read through a two-dimensional table structure.
The data cache module in the invention provides high-efficiency reading of adjacent data, and when the data in the database is not changed for a long time, the data cache can reduce the huge pressure of the business system on the increase, deletion, check and change of the database. On the premise that the data is not expired or changed, the next request directly acquires the data from the cache, so that the pressure of the database is greatly relieved.
The data subscription module in the invention enables a user to generate data in a report form mode at regular time, thereby automatically acquiring the required data. The data subscription module is a task of autonomously and pertinently subscribing report forms by system users, each user can subscribe data in the system, different sending time periods can be set for the data during subscription, report form results are sent by the system according to specified time periods after subscription, and the users can inquire information in the specified report forms.
The event processing module in the invention provides different processing modes according to the requirements of different users. When the user has data requirement, the user is judged as an event source, and an event listener in the system responds correspondingly according to the event source. For example, when the production level two needs specific data, the system will issue data according to the level two needs.
The invention has the following advantages:
1. the invention solves a series of data processing problems from data acquisition to data application in the data center of the iron and steel enterprise, provides a function that two kinds of data appear in the business at the same time, improves the data storage efficiency, reduces the data storage space and increases the user experience.
2. The invention provides the establishment of the data center application system of the iron and steel enterprise, and the ERP system, the MES system and the EMS system of the enterprise are combined, so that a user can conveniently schedule and control the production progress; the convenience of customers makes full use of the energy, and the production efficiency of enterprises is improved.
Drawings
FIG. 1 is a block diagram of the present invention.
Detailed Description
The system comprises 3 servers, a hardware platform of the system is formed, wherein one application server provided with a Windows Server 2016 operating system is provided with a 1T hard disk; a relational database server provided with a Windows Server 2016 operating system and provided with a 4T hard disk; a time sequence database file system server provided with a Windows Server 2016 operating system is provided with a 1T hard disk. The application server and the relational database server are connected through a TCP/IP protocol, and the time sequence database file system server is also connected through the TCP/IP protocol.
The system also comprises a data acquisition module, a data processing module, a data calculation module, a data configuration module, a data cache module, a data storage module, a data subscription module and an event processing module, wherein the data acquisition module, the data processing module, the data calculation module, the data configuration module, the data subscription module and the event processing module are arranged on the application server, and the data storage module is arranged on the two database servers. And all modules are connected through reserved interfaces. Each module is a service.
1) Relational database software (Oracle, MySQL, or the like) is installed on the relational database server, and a time series database file system is installed on the time series database server.
2) The method comprises the steps that an acquisition module, a data processing module, a data calculation module, a data configuration module, a data cache module, a data storage module, a data subscription module and an event processing module are deployed on an application server.
3) And starting various module services on the application server, wherein each service automatically runs.
4) And defining a tag name, and naming the tag according to the original data name.
5) And defining a label acquisition mode, and acquiring data through a data acquisition module. And after data acquisition, the data is filtered by the data processing module, so that the data quality is improved.
6) And (4) filtering and configuring the tags, and setting different storage configurations (configuring a calculation formula, an hour difference value, a day difference value and the like) according to the requirements of users.
7) Setting a storage mode of the label, storing the label into different server databases according to different data types, and then subscribing and forwarding the data.
Claims (8)
1. A steel enterprise data center application system based on time sequence data and relational data is characterized by comprising 3 servers, wherein a hardware platform of the system is formed, one application server provided with a Windows Server 2016 operating system is provided with a 1T hard disk; a relational database server provided with a Windows Server 2016 operating system and provided with a 4T hard disk; a file system server for installing a Windows Server 2016 operating system time sequence database, which is provided with a 1T hard disk; the application server and the relational database server are connected through a TCP/IP protocol, and the time sequence database file system server is also connected through the TCP/IP protocol.
The system also comprises a data acquisition module, a data processing module, a data calculation module, a data configuration module, a data cache module, a data storage module, a data subscription module and an event processing module, wherein the data acquisition module, the data processing module, the data calculation module, the data configuration module, the data subscription module and the event processing module are arranged in the application server, and the data storage module is arranged in the two database servers; and all modules are connected through reserved interfaces.
2. The system of claim 1, wherein the data collection module collects meter data points, PLC data points and service data in the production process into the server through the data collection tool, and each signal: the time signal and the event signal are used as a label, and then data processing is carried out;
the data processing module is used for carrying out error filtering on the collected data points according to the fluctuation characteristics and carrying out data calculation after the collected data are filtered, wherein the filtering rule filter algorithm and the filtering rule have the following rules:
absoult: the label with the value in the range [ R-L, R + U ] is filtered, R is a reference value, the initial value is zero, and when the current value passes through the filter, the current value is used as the reference value for the next label filtering; ABSBETWEEN, when the change of two adjacent values is less than zero, and when the change value/elapsed time > (R-L)/3600, the value is filtered; when the change of two adjacent values is larger than zero, and when the change value/elapsed time > (R + U)/3600, the value is filtered;
CutOff: processing all types of labels, directly filtering null values, and directly passing non-numerical label values; if the current value is a numerical value, comparing the current value with the upper limit and the lower limit of the filtering condition, if the current value is in the range, directly passing through a filter, replacing the numerical value by the minimum value when the current value is smaller than the minimum value, and replacing the numerical value by the maximum value when the current value is larger than the maximum value, and then passing through;
between: tags with values outside the range [ L, U ] will be filtered, L being the lower limit and U being the upper limit;
ABSBETWEEN: when the change of the two adjacent values is less than zero, and when the change value/elapsed time > (R-L)/3600, the value is filtered; when the change of two adjacent values is larger than zero, and when the change value/elapsed time > (R + U)/3600, the value is filtered;
edge: tags whose values are within the range [ R-L, R + U ] will be filtered, similar in function to Absolute, with R being a reference value, except that whether or not the current value is filtered, the current value will be the reference value for the next tag filtering;
equal: if the current value of the tag is equal to the last value, then the tag will be filtered;
between Equal: firstly, judging whether the current value is in a defined limit range [ L, U ] or not, and filtering the current value which is not in the range; when the current value is within the limit range, judging whether the current value is equal to the latest value pvLast, and filtering the equal values;
ObjectEqual: the current value of the tag is equal to the last value, then it will be filtered;
relative: when the reference value R is less than 0, then tags whose values are within the range (R (1+ L), R (1.0-U)) will be filtered; when the reference value R is greater than 0, then tags whose values are within the range (R (1-L), R (1.0+ U)) will be filtered; and if the current value is not filtered, using the current value as a reference value for next tag filtering.
3. The system of claim 1, wherein the data computation module satisfies a data multi-time scale transformation: second, day, month; and optimizing the storage mode of data: when the data is changed, the data is stored, and a unified data calculation mode is adopted; the system provides the following calculation modes:
event: storing the original value of the label when the label is changed, and being suitable for data storage of no cycle, production time and production signals;
minus: storing a difference between two periods of a cumulative amount by period;
current: storing the current value according to the period, and storing the first piece of data of the period;
integral value: storing an integrated value for one cycle;
simple difference value: storing the difference value between two periods by one period;
and (3) accumulation: counting according to the period, and counting the total amount of a physical quantity in one period;
average value: calculating an average value of an instantaneous quantity in a period;
arithmetic mean: simply adding the data of the label, and dividing the sum by the count of the data in the period;
bottom amount of the table: storing according to a period, and storing a starting value of a table base quantity for one period;
maximum value: storing according to periods, and storing the maximum value of one period of one label;
minimum value: storing by period, storing a minimum value of one period of one tag.
4. The system of claim 1, wherein the data configuration module provides a one-stop configuration service for the system, and the configurations immediately act on the data acquisition module, the data processing module and the like; the method is divided into two categories: one type is configuration for tag calculation, including null value setting, filtering setting, trigger setting, calculation formula, timer; one type is a configuration for the tag to store information, including a tag storage configuration and a compression configuration.
5. The system according to claim 1, wherein the data storage module and the data storage architecture adopt a storage structure of ID, Clock and Val, the ID is a unique number assigned to each storage point label by the system, the Clock is a timestamp, and the Val is a value of the current time of the data; when the system reads data of a time dimension, the reading pressure is relatively large, the efficiency is slow when integration processing is carried out, and the user experience is poor; (ii) a Each tag can be configured with a plurality of storage points to meet the service requirement, and each storage point contains the following configuration information: the unique ID of the ID tag, the storage period of the Interval tag and the calculation type of the Action tag; dividing the label into two storage strategies according to different time scales of the label; the labels of millisecond level and second level in the system are time sequence data; the time sequence data is written into the module through the data, the data compression module is stored into the time sequence database, and the data is read through the data reading module; the data of the hour level, the date level and the month level in the system are directly stored in a relational database and checked and read through a two-dimensional table structure.
6. The system of claim 1, wherein the data caching module provides efficient reading of nearby data, and when data in the database is not changed for a long time, the data caching can reduce the enormous pressure of the business system on adding, deleting, searching and changing the database; on the premise that the data is not expired or changed, the next request directly acquires the data from the cache, so that the pressure of the database is greatly relieved.
7. The system of claim 1, wherein the data subscription module enables a user to generate data in a report form at regular time by himself, so as to automatically acquire required data; the data subscription module is a task of autonomously and pertinently subscribing report forms by system users, each user can subscribe data in the system, different sending time periods can be set for the data during subscription, report form results are sent by the system according to specified time periods after subscription, and the users can inquire information in the specified report forms.
8. The system of claim 1, wherein the event processing module provides different processing modes according to the requirements of different users; when a user has data requirements, the user is judged as an event source, and an event listener in the system makes a corresponding response according to the event source; when the production level two needs specific data, the system can issue the data according to the second level requirement.
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| CN114547018A (en) * | 2022-04-24 | 2022-05-27 | 西安热工研究院有限公司 | Method and system for automatically cleaning waste points of SIS real-time database |
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| CN110413599A (en) * | 2019-06-18 | 2019-11-05 | 上海展湾信息科技有限公司 | Generating date and storage system and method |
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