CN108763550A - Blast furnace big data application system - Google Patents
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- CN108763550A CN108763550A CN201810554149.2A CN201810554149A CN108763550A CN 108763550 A CN108763550 A CN 108763550A CN 201810554149 A CN201810554149 A CN 201810554149A CN 108763550 A CN108763550 A CN 108763550A
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- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B5/00—Making pig-iron in the blast furnace
- C21B5/006—Automatically controlling the process
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Abstract
The invention discloses a kind of blast furnace big data application system, system includes：Detection and Monitor And Control Subsystem, for acquiring blast furnace ironmaking process data；Data conversion platform obtains the blast furnace ironmaking data of unified representation for integrating blast furnace ironmaking data；Data relation analysis and excavation subsystem are traced to the source for being interacted with the data conversion platform with extraction operation rule, modeling parameters and historical data；Blast furnace modelling by mechanism and digitalized artificial subsystem, for establishing the mathematical model emulated for realizing blast furnace；Blast furnace process is analyzed and predicting subsystem, for analyzing blast furnace production process；Section chief supports subsystem, the result for that will push/show above-mentioned subsystems.Above system can make full use of the data of multiple fields, provide the operation rules reference with universality, enrich blast furnace ironmaking analysis method, and recognizing blast furnace for depth provides tool, and control suggestion or guidance are provided for prolonging campaign, high-quality, low consumption and stable smooth operation.
Description
Technical field
The invention belongs to the Intelligent treatment technology of blast furnace ironmaking data more particularly to a kind of blast furnace big data application systems.
Background technology
China is in from transition process of the manufacture big country to manufacturing power at present, and it is me to promote steel industry intelligence manufacture
State develops the top priority of manufacturing power, wherein industrial big data is as a kind of new assets, resource and production factors, in steel
It plays an important role in Industry Innovation development.
The existing information system of blast furnace ironmaking biases toward basic automatization and production planning management, blast fumance more at present
Condition diagnosing and operation are mostly based on artificial experience and subjective judgement, and the ironmaking data accumulated are not fully utilized also, intelligence
It can manufacture in intelligently ironmaking field with respect to blank.
Therefore, actively dissolve superfluous production capacity towards steel industry and accelerate to promote the demand of industry transition and upgrade, be lasting
It reduces blast furnace ironmaking cost and further solves the problems, such as that each blast-furnace technique index is irregular, carry out the blast furnace based on big data
Intelligence ironmaking research is imperative.
Invention content
For the problems of the prior art, the present invention provides a kind of blast furnace big data application system, which can be abundant
Using the data of multiple fields, the operation rules reference with universality is provided, blast furnace ironmaking analysis method is enriched, recognizes for depth
Know blast furnace and tool is provided, control suggestion or guidance are provided for prolonging campaign, high-quality, low consumption and stable smooth operation.
In a first aspect, the present invention provides a kind of blast furnace big data application system, including：
Detection and Monitor And Control Subsystem 01, for acquiring blast furnace ironmaking process data；
Data conversion platform 02 obtains the blast furnace ironmaking data of unified representation, the height for integrating blast furnace ironmaking data
Stove ironmaking data include detection data, inspection analysis data, production schedule data, process control data, capital equipment data and institute
State the blast furnace ironmaking process data that detection is acquired with Monitor And Control Subsystem 01；
Data relation analysis is advised for being interacted with the data conversion platform 02 with extraction operation with excavation subsystem 03
Then, modeling parameters and historical data are traced to the source；
Blast furnace modelling by mechanism and digitalized artificial subsystem 04, for being closed with the data conversion platform 02, the data
Connection analysis interacts respectively with subsystem 03 is excavated, to establish the mathematical model emulated for realizing blast furnace；
Blast furnace process analyze with predicting subsystem 05, be used for and the data conversion platform 02, the data relation analysis
It is interacted respectively with digitalized artificial subsystem 04 with excavation subsystem 03, blast furnace modelling by mechanism, to be carried out to blast furnace production process
Analysis and prediction；
Section chief supports subsystem 06, for that will push/show the data relation analysis and excavate subsystem 03, blast furnace machine
Reason modeling and digitalized artificial subsystem 04 and/or the result of blast furnace process analysis and predicting subsystem 05.
Optionally, further include：
Autonomous learning subsystem 07 is used for and the data conversion platform 02, the data relation analysis and excavation subsystem
System 03, blast furnace modelling by mechanism are interacted with digitalized artificial subsystem 04 and/or blast furnace process analysis with predicting subsystem 05, with reality
The operation study of existing historical data.
Optionally, the data relation analysis and excavation subsystem 03, blast furnace modelling by mechanism and digitalized artificial subsystem
04, blast furnace process analysis supports subsystem 06 to be located at blast furnace private clound/server side with predicting subsystem 05 and section chief.
Optionally, the blast furnace ironmaking process data includes：Blast furnace ironmaking PLC production operations data, industrial sensor inspection
The process control data of measured data and DCS system.
Optionally, the data relation analysis is additionally operable to complete being associated with point for blast furnace process parameter with subsystem 03 is excavated
Analysis, the association analysis of blast furnace operating.
Optionally, the blast furnace modelling by mechanism from the data relation analysis and excavates subsystem with digitalized artificial system
Modeling parameters are obtained in 03, establish multi-fluid Blast Furnace Mathematical Model, realize blast furnace emulation, while exporting mechanism and data fusion is repaiied
Positive model.
Optionally, the section chief supports subsystem 06 to be used to push conditions of blast furnace prediction result and matching operation to user,
Blast furnace operating change influence factor is pushed to user, pushes operation of blast furnace trend analysis to user as a result, and/or is pushed to user
Historical data is traced to the source result.
Optionally, the detection is located at side with Monitor And Control Subsystem 01, data conversion platform 02 and autonomous learning subsystem 07
In edge computer, the edge calculations machine is interacted with the blast furnace private clound/server；And the detection and Monitor And Control Subsystem
01, data conversion platform 02 is also connect with blast furnace side data output equipment.
The device have the advantages that as follows：
(1) optimize blast furnace cooperating mechanism, reduce fluctuation number, realize blast furnace stable smooth operation.
(2) improve blast furnace technology economic indicator.
(3) deposit ironmaking big data assets, basic data source is provided for whole process quality analysis and quality control.
(4) digitlization, visualization, the intelligence for realizing personnel training, improve the validity and practicability of personnel training, carry
High post personnel improve the scientific operation of blast furnace and management level to the control ability of blast furnace operating.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention without having to pay creative labor, may be used also for those of ordinary skill in the art
To obtain other attached drawings according to these attached drawings.
Figure 1A is the Organization Chart for the blast furnace big data application system that one embodiment of the invention provides；
Figure 1B is the schematic diagram of the blast furnace big data framework shown in left side in Figure 1A；
Fig. 2 is the structural schematic diagram for the blast furnace big data application system that one embodiment of the invention provides.
Specific implementation mode
In order to preferably explain the present invention, in order to understand, below in conjunction with the accompanying drawings, by specific implementation mode, to this hair
It is bright to be described in detail.
In the following description, by multiple and different aspects of the description present invention, however, for common skill in the art
For art personnel, the present invention can be implemented just with some or all structures or flow of the present invention.In order to explain
Definition for, specific number, configuration and sequence are elaborated, however, it will be apparent that these specific details the case where
Under can also implement the present invention.It in other cases, will no longer for some well-known features in order not to obscure the present invention
It is described in detail.
Embodiment one
As shown in FIG. 1A and 1B, Figure 1B is the enlarged diagram in left side in Figure 1A, the blast furnace big data in the present embodiment
The network architecture of application system is based on edge calculations machine-blast furnace private clound structure.It is real-time that blast fumance is completed by edge calculations machine
Data acquire and data prediction, and the excavation of blast furnace big data analysis, blast furnace modelling by mechanism and digitlization are carried out in blast furnace private clound
Emulation, blast furnace process analysis and prediction and autonomous learning etc..
For layer data analysis, the business structure of the blast furnace big data application system in the present embodiment can be by data perception
Layer, data analysis and accumulation layer, data application layer, data exhibiting layer are constituted.Wherein, data perception layer is in a manner of being bi-directionally connected
It obtains and data is obtained by edge calculations machine, while utilizing edge calculations machine to complete the pretreatment of data in a distributed fashion, carry
The ability of traditional cloud computing centralization data prediction under high mass data environment；Data application layer can be by network to data
The edge calculations machine of acquisition layer sends operational order, completes to handle with the data collaborative of data application layer.Data application layer logarithm
According to analysis and accumulation layer obtain the obtained data of sensing layer carry out analysis mining, modelling by mechanism emulation and blast furnace process analysis with it is pre-
Survey etc..The data that data exhibiting layer completes blast furnace big data application system show function.
Blast furnace big data application system includes：Detection and Monitor And Control Subsystem 01, data conversion platform 02, data correlation point
Analysis and excavation subsystem 03, blast furnace modelling by mechanism and digitalized artificial subsystem 04, blast furnace process analysis and predicting subsystem
05, section chief supports subsystem 06, the autonomous learning subsystem 07 based on data.
Wherein, the integration and optimization of detection parameters are realized in detection with Monitor And Control Subsystem 01.In the present embodiment, detection and prison
Plc data, unstructured data, the calculating parameter etc. that control subsystem 01 is acquired according to blast furnace, analysis data acquiring frequency are thrown
Frequency and business demand, and then determine edge calculations machine in quick connection, real time business, data prediction and optimization etc.
Crucial requirement and the function that need to be realized solve blast furnace big data integrality and consistency problem from data acquisition link.
That is, detection acquires blast furnace ironmaking process data with Monitor And Control Subsystem 01, such as may include：Blast furnace ironmaking
The process control data etc. of PLC production operations data, industrial sensor detection data and DCS system.
The consistency that data conversion platform 02 completes multi-source heterogeneous data is integrated and data unified representation, realization and blast furnace life
Produce the interface of process database.Data conversion platform 02 is for integrating blast furnace ironmaking PLC production operations data, industrial sensor
Detection data, LIMES systems inspection analysis data, the production schedule data of MES system, DCS system process control data and
Capital equipment data of ERP system etc.；Analyze data source, data type, and then layout data link；Pass through industrial wireless network
Network and blast furnace internal lan are by the acquisition of edge calculations machine and treated data access data conversion platform；Looking to the future can expand
Distributed vertical Data Exchange Model can be used in malleability.
Data conversion platform 02 completes acquisition, integration and the unified representation of blast furnace whole process data.Specifically, from detection with
Number is chemically examined in the data of the acquisition of Monitor And Control Subsystem 01, the inspection that LIMES systems are obtained from the two level and three-level system of blast fumance management
According to, the production schedule data of MES system, the capital equipment data of ERP systems etc., BF Design parameter, weather data etc., and
The different multi-source heterogeneous data in these sources are subjected to integration and unified representation.
In the present embodiment, blast furnace private clound can be built：In blast furnace private clound, according to blast furnace technology and process, consider
Blast furnace big data is using demand to data, to edge computer into Mobile state management and combination.
Data relation analysis is disposed in blast furnace private clound and excavates subsystem 03, blast furnace modelling by mechanism and digitalized artificial
Subsystem 04, blast furnace process analysis and predicting subsystem 05, section chief support subsystem 06 and autonomous learning subsystem 07.
Above-mentioned subsystem obtains data from data conversion platform 02, wherein data relation analysis and excavation subsystem 03
It completes the association analysis of blast furnace process parameter, the association analysis of blast furnace operating and historical data to trace to the source, transmits the result to height
Stove process analysis procedure analysis supports system 06 and autonomous learning systems 07 with predicting subsystem 05, blast furnace modelling by mechanism subsystem 04, section chief；
Blast furnace process is analyzed completes working of a furnace analysis and prediction with predicting subsystem 05, and transmits the result to data correlation point
Analysis is used for correction model with subsystem 03 and blast furnace modelling by mechanism is excavated with digitalized artificial subsystem 04, while result also will
Being transmitted to section chief supports system to be pushed into 05 row；Blast furnace modelling by mechanism completes the emulation of overall height stove with digitalized artificial subsystem 04,
And it transmits the result to data relation analysis and is used for completing losing Supplementing Data, simultaneous transmission to blast furnace mistake with subsystem 03 is excavated
Journey is analyzed and 05 correction model of predicting subsystem.
That is, the data relation analysis of the present embodiment analyzes blast furnace ironmaking PLC production operations with subsystem 03 is excavated
Production schedule data, the DCS system of data, industrial sensor detection data, the inspection analysis data of LIMES systems, MES system
Process control data and ERP system capital equipment data etc..
Using above-mentioned Data induction, analysis, forecast and monitoring blast furnace section chief problem of interest in operation, (blast furnace is suitable
Row, Control for Kiln Temperature, basicity of slag, molten steel quality, management, basicity of slag, molten steel quality, type of furnace management etc. of slagging tap of tapping a blast furnace), retrospect is high
Occur fluctuating in stove production link chain or abnormal main cause and hidden because according to working of a furnace stable smooth operation, basic direct motion, fluctuation, stove
The not normal equal correlation rule excavated between each smelting link and process of condition, provides foundation for the formulation of blast furnace process operation rules, is
Blast furnace process is analyzed provides modeling parameters with predicting subsystem and blast furnace modelling by mechanism with digitalized artificial subsystem.
Blast furnace modelling by mechanism establishes multi-fluid Blast Furnace Mathematical Model with digitalized artificial subsystem 04, realizes that overall height stove is imitative
Very, meanwhile, mechanism and data fusion correction model are provided, and then obtains more accurate intermediate quantity result of calculation, be reasonable prediction
Performance variable and optimization, which operate to formulate, provides reference, and ginseng is provided with the amendment correlation rule of subsystem 03 is excavated for data relation analysis
It examines.
Blast furnace process is analyzed obtains historical data with predicting subsystem 05 from data conversion platform 02, from data relation analysis
With excavate subsystem 03 obtain with predict relevant input variable, it is extremely relevant regular, relevant with Operating Guideline with the working of a furnace
Synthetic operation guidance rule obtains the required intermediate meter of forecasting model from blast furnace modelling by mechanism and digitalized artificial subsystem 04
Parameter is calculated, finally, these data and rule are based on, using the big data analysis method such as machine learning, in blast furnace production process
The working of a furnace, quality, exception etc. analyzed, judged and forecast, preferably to instruct practical blast furnace operating.
Section chief supports subsystem 06 to complete the push of conditions of blast furnace prediction result and matching operation, complete blast furnace operating change
The push of influence factor, the push for completing operation of blast furnace trend analysis result and historical data are traced to the source the push of result.
Autonomous learning subsystem 07 based on data accumulation builds autonomous learning systems, realizes operative employee's experience accumulation, energy
Technology and data supporting enough are provided for each relevant station, improves data analysis capabilities, the flow cognitive ability of post personnel
With know-how etc..
Blast furnace modelling by mechanism and digitalized artificial system are from data conversion platform, data relation analysis and digging system subsystem
System obtains modeling parameters, establishes multi-fluid Blast Furnace Mathematical Model, realizes the emulation of overall height stove.Meanwhile exporting as a result, provide machine
Reason and data fusion correction model, and then more accurate intermediate quantity result of calculation is obtained, it is reasonable prediction performance variable and optimization
Operation, which is formulated, provides reference, and correcting correlation rule with excavation subsystem for data relation analysis provides reference.
Blast furnace process is analyzed obtains historical data with predicting subsystem data conversion platform, from association analysis and data mining
System obtains and predicts relevant input variable, refers to the extremely relevant rule of the working of a furnace, with the relevant synthetic operation of Operating Guideline
Rule is led, obtaining the required intermediate computations parameter of forecasting model from modelling by mechanism and digitalized artificial subsystem is finally based on
These data and rule, using the big data analysis method such as machine learning, to the working of a furnace, quality, the exception in blast furnace production process
Etc. being analyzed, judged and forecast, preferably to instruct practical blast furnace operating.
Embodiment two
As shown in Fig. 2, blast furnace big data application system includes：Detection and Monitor And Control Subsystem 01, data conversion platform 02, number
According to association analysis and excavation subsystem 03, blast furnace modelling by mechanism and digitalized artificial subsystem 04, blast furnace process analysis and prediction
Subsystem 05, section chief support subsystem 06, the autonomous learning subsystem 07 based on data.
In above-mentioned subsystem, detection is with Monitor And Control Subsystem 01 with the autonomous learning subsystem 07 based on data by edge calculations
Machine realizes that data relation analysis divides with excavation subsystem 03, blast furnace modelling by mechanism and digitalized artificial subsystem 04, blast furnace process
Analysis is deployed in predicting subsystem 05 in blast furnace private clound.
Detection acquires blast furnace ironmaking process data with Monitor And Control Subsystem 01, including：Blast furnace ironmaking PLC production operations data,
The process control data etc. of industrial sensor detection data and DCS system.
Data conversion platform 02 completes acquisition, integration and the unified representation of blast furnace whole process data, including：From detection and prison
Control subsystem obtain data, from the two level and three-level system of blast fumance management obtain LIMES systems inspection analysis data,
The production schedule data of MES system, capital equipment data of ERP system etc., BF Design parameter, weather data etc., and by this
The different multi-source heterogeneous data in a little sources carry out integration and unified representation.
Data relation analysis obtains related data with subsystem 03 is excavated from data conversion platform 02, completes operation rules
Extraction, the extraction of modeling parameters and historical data are traced to the source.Data include：Blast furnace ironmaking PLC production operations data, industrial sensor
Detection data, LIMES systems inspection analysis data, the production schedule data of MES system, DCS system process control data and
Capital equipment data of ERP system etc..Extraction for blast furnace operating rule：It is asked for blast furnace section chief is of interest in operation
Inscribe (smooth operation of furnace, Control for Kiln Temperature, basicity of slag, molten steel quality, management, type of furnace management etc. of slagging tap of tapping a blast furnace), to above-mentioned data into
Row association analysis and excavation excavate each smelting link and work according to working of a furnace stable smooth operation, basic direct motion, fluctuation, furnace condition disorder etc.
Correlation rule between sequence provides foundation for the formulation of blast furnace process operation rules；For blast furnace modelling by mechanism and blast furnace process point
Analyse the extraction of modeling parameters：By Association Rule Analysis method, main cause is extracted and hidden because being analyzed for blast furnace process and predicting subsystem
System 05, blast furnace modelling by mechanism and digitalized artificial subsystem 04 provide modeling parameters.It traces to the source for historical data：It is given birth to by blast furnace
The time lapse analysis of production factor and operating parameter extracts in blast fumance link chain in conjunction with association analysis and method for digging and wave occurs
Dynamic or abnormal main cause and it is hidden because.
Blast furnace modelling by mechanism and digitalized artificial system 04 are from data conversion platform 02, data relation analysis and digging system
Subsystem 03 obtains modeling parameters, establishes multi-fluid Blast Furnace Mathematical Model, realizes the emulation of overall height stove.Meanwhile exporting as a result,
Mechanism and data fusion correction model are provided, and then obtains more accurate intermediate quantity result of calculation, is reasonable prediction performance variable
It is formulated with optimization operation and reference is provided, reference is provided with the amendment correlation rule of subsystem 03 is excavated for data relation analysis.
Blast furnace process is analyzed obtains historical data with predicting subsystem 05 from data conversion platform 02, from data relation analysis
With excavate subsystem 03 obtain with predict relevant input variable, it is extremely relevant regular, relevant with Operating Guideline with the working of a furnace
Synthetic operation guidance rule obtains the required intermediate meter of forecasting model from blast furnace modelling by mechanism and digitalized artificial subsystem 04
Parameter is calculated, finally, these data and rule are based on, using the big data analysis method such as machine learning, in blast furnace production process
The working of a furnace, quality, exception etc. analyzed, judged and forecast, preferably to instruct practical blast furnace operating.
Section chief support subsystem 06 be blast furnace big data application system exposition, complete conditions of blast furnace prediction result and
Pushing away for blast furnace operating change influence factor is completed in the push (from blast furnace process analysis and predicting subsystem 05) of matching operation
It send (from data relation analysis and excavation subsystem 03), complete the push of operation of blast furnace trend analysis result (from height
Stove process analysis procedure analysis and predicting subsystem 05) and historical data trace to the source result push (from data relation analysis with excavate it is sub
System 03).
Autonomous learning subsystem 07 based on data accumulation is to promote the learning system built of blast furnace personnel training, in system
It can system operatio case study (be derived partly from data relation analysis and trace to the source with the historical data for excavating subsystem) and experience friendship
Stream etc. realizes operative employee's experience accumulation, and technology and data supporting can be provided for each relevant station, improves post personnel's
Data analysis capabilities, flow cognitive ability and know-how etc..
Such as：Forecast for molten iron [Si] content
S1) data relation analysis with excavate subsystem first from data conversion platform obtain after testing with monitoring subsystem
The system pretreated data of edge calculations machine, including：Crude fuel data, material speed, air quantity, wind pressure, permeability index, wind-warm syndrome, coal
The inspection analysis data etc. of gas utilization rate, coal powder injection parameter, top temperature and oxygen enrichment percentage, weather data, LIMES systems.
S2 principal component analysis and data relation analysis) are carried out to above-mentioned data, obtain the main cause for influencing molten iron [Si] content
With hidden because and by obtained main cause and hidden because sorting according to the size of the relationship of influence, the forecast to establish molten iron [Si] content carries
For foundation and reference.
S3 clustering) is carried out to above-mentioned data and history molten iron [Si] content data, obtains data distribution rule, is carried out
After data relation analysis, the influence relationship, the opereating specification of optimization and a certain item operating parameter that obtain operating parameter are adjusted to rear
The impact analysis of continuous blast furnace process.Such as：
The opereating specification of optimization：Under the conditions of current crude fuel, improves cold flow and hot wind wind pressure is conducive to improve
Molten steel quality, cold flow 3200-3500m3And hot-blast pressure controls between 0.03-0.06Mpa etc. respectively.
Operating parameter adjusts the influence to subsequent smelting：Furnace temperature can be moved towards have certain influence by improving injecting coal quantity.
S4) by above-mentioned steps S2) obtained result sends blast furnace modelling by mechanism and digitalized artificial system and blast furnace mistake to
Journey is analyzed and predicting subsystem, the forecasting model for establishing overall height stove simulation model and molten iron [Si] content.
S5) above-mentioned steps S3) obtained parameter adjustment to the opereating specification of impact analysis, the optimization of follow-up blast furnace process and
Influence after parameter adjustment to follow-up blast furnace process is respectively transmitted to section chief and system is supported to push, and finger is provided for section chief's operation
It leads；The autonomous learning systems based on data can also be transmitted to as study case.
S6) blast furnace modelling by mechanism and digitalized artificial system are from data conversion platform, data relation analysis and digging system
Subsystem obtains the result of modeling parameters (step S2)), multi-fluid Blast Furnace Mathematical Model is established, realizes the emulation of overall height stove.It is based on
The multi-fluid Blast Furnace Mathematical Model of foundation, simulation parsing blast furnace ironmaking process, predicts blase furnace cast iron yield and molten iron [Si] content.
S7 step S6) is utilized) obtained molten steel quality prediction result amendment step S2) obtained modeling parameters.
S8) blast furnace process analysis with predicting subsystem by step S6) obtained result and step S7) obtained result makees
For the input of hot metal temperature estimation model, the forecasting model merged based on data and mechanism is established, molten iron [Si] content is obtained
Forecast.
Finally it should be noted that：Above-described embodiments are merely to illustrate the technical scheme, rather than to it
Limitation；Although the present invention is described in detail referring to the foregoing embodiments, it will be understood by those of ordinary skill in the art that：
It can still modify to the technical solution recorded in previous embodiment, or to which part or all technical features into
Row equivalent replacement；And these modifications or substitutions, it does not separate the essence of the corresponding technical solution various embodiments of the present invention technical side
The range of case.
Claims (8)
1. a kind of blast furnace big data application system, which is characterized in that including：
Detection and Monitor And Control Subsystem (01), for acquiring blast furnace ironmaking process data；
Data conversion platform (02) obtains the blast furnace ironmaking data of unified representation, the blast furnace for integrating blast furnace ironmaking data
Ironmaking data include detection data, inspection analysis data, production schedule data, process control data, capital equipment data and described
The blast furnace ironmaking process data of detection and Monitor And Control Subsystem (01) acquisition；
Data relation analysis is advised for being interacted with the data conversion platform (02) with extraction operation with excavation subsystem (03)
Then, modeling parameters and historical data are traced to the source；
Blast furnace modelling by mechanism and digitalized artificial subsystem (04), for being closed with the data conversion platform (02), the data
Connection analysis interacts respectively with subsystem (03) is excavated, to establish the mathematical model emulated for realizing blast furnace；
Blast furnace process analyze with predicting subsystem (05), be used for and the data conversion platform (02), the data relation analysis
It is interacted respectively with digitalized artificial subsystem (04) with excavation subsystem (03), blast furnace modelling by mechanism, with to blast furnace production process
It is analyzed and predicted；
Section chief supports subsystem (06), for that will push/show the data relation analysis and excavate subsystem (03), blast furnace machine
Reason modeling and digitalized artificial subsystem (04) and/or the result of blast furnace process analysis and predicting subsystem (05).
2. blast furnace big data application system according to claim 1, which is characterized in that further include：
Autonomous learning subsystem (07) is used for and the data conversion platform (02), the data relation analysis and excavation subsystem
System (03), blast furnace modelling by mechanism and digitalized artificial subsystem (04) and/or blast furnace process analysis are handed over predicting subsystem (05)
Mutually, to realize that the operation of historical data learns.
3. blast furnace big data application system according to claim 2, which is characterized in that
The data relation analysis and excavation subsystem (03), blast furnace modelling by mechanism and digitalized artificial subsystem (04), blast furnace
Process analysis procedure analysis supports subsystem (06) to be located at blast furnace private clound/server side with predicting subsystem (05) and section chief.
4. blast furnace big data application system according to claim 3, which is characterized in that
The blast furnace ironmaking process data includes：Blast furnace ironmaking PLC production operations data, industrial sensor detection data and DCS
The process control data of system.
5. blast furnace big data application system according to claim 4, which is characterized in that
The data relation analysis is additionally operable to complete association analysis, the blast furnace operating of blast furnace process parameter with subsystem (03) is excavated
Association analysis.
6. blast furnace big data application system according to claim 5, which is characterized in that
The blast furnace modelling by mechanism from the data relation analysis and excavates in subsystem (03) with digitalized artificial system (04)
Modeling parameters are obtained, multi-fluid Blast Furnace Mathematical Model is established, realize blast furnace emulation, while exporting mechanism and data fusion amendment mould
Type.
7. blast furnace big data application system according to claim 6, which is characterized in that
The section chief supports subsystem (06) to be used to push conditions of blast furnace prediction result and matching operation to user, is pushed to user
Blast furnace operating change influence factor pushes operation of blast furnace trend analysis as a result, and/or tracing back to user's push historical data to user
Source result.
8. blast furnace big data application system according to claim 3, which is characterized in that
The detection is located at edge calculations with Monitor And Control Subsystem (01), data conversion platform (02) and autonomous learning subsystem (07)
In machine, the edge calculations machine is interacted with the blast furnace private clound/server；And
The detection is also connect with blast furnace side data output equipment with Monitor And Control Subsystem (01), data conversion platform (02).
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