CN104090960A - Dynamic multi-theme data warehouse building method based on hot continuous rolling production process - Google Patents
Dynamic multi-theme data warehouse building method based on hot continuous rolling production process Download PDFInfo
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Abstract
The invention provides a dynamic multi-theme data warehouse building method based on a hot continuous rolling production process. The dynamic multi-theme data warehouse building method based on the hot continuous rolling production process comprises the following steps of extraction of service data, visualized analysis of the service data, spiral and dynamical building of a data warehouse and on-line analysis processing. According to the service requirement, based on visualized analysis of the data, a user autonomously and dynamically builds an analysis dimension and an analysis theme, and carries out analysis excavation in the multi-theme data warehouse. According to the characteristic of a large data quantity of the hot continuous rolling production process, the visualized analysis method is provided, the user can observe the data characteristics and select valuable data, the problem of the blind requirement when a user faces mass data is solved, multi-dimensional and multi-theme excavation is dynamically built based on the production process, the requirements for on-site data completeness and efficiency of data warehouse management are met, and the continuously developing service requirement is met. The dynamic multi-theme data warehouse building method based on the hot continuous rolling production process is also applicable to building of a warehouse of process production data in the industrial field.
Description
Technical field
The present invention relates to hot continuous rolling production, data warehouse field, particularly relate to a kind of dynamic multi-threaded data warehouse method for building up based on hot continuous rolling production procedure.
Background technology
Hot continuous rolling production procedure is attended by the project data such as the design, experiment, debugging, produced on-site of magnanimity, and a large amount of system installment and debugging experimental knowledgees etc., these data are important references data of Design of Production Line, construction and rolling model development research, these data scattered depositing always for a long time, systematically do not arranged, more do not carry out analysis and utilization effectively, so, set up rolling model basic database, relevant information resource is arranged, managed and effectively utilizes is an extremely urgent job.
Towards integrated moulding and the Optimized-control Technique demand of the control of iron and steel metallurgy flow process, for the data characteristics (English full name Volume, Velocity, Variety, Value, the abbreviation of Complexity) of " 4V+1C ".Utilize data warehouse technology to provide the integrated modelling approach based on mechanism, data and knowledge for complicated production run control, and process control and optimization technology; Realize towards the control of iron and steel flow process, realize integrated, fusion and the dynamic process of data in production run, Information and knowledge, set up control integration control system; The Steel Production Flow Chart critical process optimisation technique of research based on model, realizes equipment automation control, equipment optimization of operating parameters and online adjustment; Realize the management of iron and steel production efficiency overall target and optimal control and the organic combination with iron and steel production management and data warehouse.
Data warehouse is the basis of information processing, is object-oriented, integration, the permanent also set of the data of a time dependent support management decision.Data warehouse has comprised the granular historical data after integrating, and the data of integration make user can observe the overall data of enterprise-level; And according to the granular characteristic of data, user still can observe the feature of local data, reaction truth.
Gerentocratic decision-making is based upon on structurized record basis conventionally, but, because enterprises exists a large amount of isomeric datas, these data exist and bulk redundancy with the destructuring form of text conventionally, so integrate various isomeric datas, data cleansing, and carry out analysis mining and support that decision-making is the great difficulty that traditional data warehouse faces.
And the mass data that deal with data warehouse produces is also the problem that traditional data warehouse faces.The multi-threaded data warehouse of multidimensional is based upon on mass data basis, and mass data meeting blocking data passage, hindering user carries out business diagnosis, but in these data, has huge break-up value, therefore needs rationally effectively to deposit these significant datas.
Traditional data warehouse system, requires user's request clear and definite, analyzes theme and determines in advance.But along with the continuous increase of data volume, user is to unclear, the even blind demand of the analysis demand of data, so need data warehouse to provide exploratory development mode to allow user operate, the present invention utilizes screw type construction method to improve data warehouse, increase and analyze theme according to business, changes in demand, meet that user constantly increases, clear and definite demand.
Summary of the invention
In order to address the above problem, the object of the invention is for the huge feature of hot continuous rolling production procedure data volume, data visualization disposal route is provided, make the user can observed data feature, select valuable data, solve the feature that occurs blind demand when user faces mass data, based on production procedure Dynamic Establishing various dimensions, multi-threaded excavation, meet the high efficiency of user to field data integrality, data warehouse management, met the dynamic multi-threaded data warehouse method for building up based on hot continuous rolling production procedure of the business demand of development.
Technical scheme of the present invention is: a kind of dynamic multi-threaded data warehouse method for building up based on hot continuous rolling production procedure, and the method comprises the following steps:
Step 1, business datum is extracted: traversal Service Database, the metadata structure that simultaneously extracts business datum upgrades field unit table and the list cell table of business datum;
Step 2, business datum visual analyzing: the business datum in Service Database is carried out to visualization processing, show the mathematical feature of business datum, provide basis for building theme, theme leaves data warehouse in the form of subject heading list; Wherein, business datum is the business datum of user-selected number according to item correspondence;
Step 3, spiral dynamic construction data warehouse: according to the business datum visual analyzing result obtaining in step 2, select data, build individual character subject heading list; Select field to build individual character dimension table from Service Database, and upgrade subject information table, dimensional information table;
Step 4, on-line analysis processing, with dimension table and subject heading list structure business diagnosis model, carries out online data analysis.
Further, the metadatabase of described data warehouse comprises field unit table, list cell table, subject information table and the dimensional information table of business datum, field unit table, list cell table, subject information table and the dimensional information table of described business datum is used for realizing synchronizeing of data warehouse data and business datum, list cell table by business datum of the subject heading list field of data warehouse, dimension table field and the table access of field unit be to the data in business datum table, thereby data are carried out to associative operation;
Further, Service Database is made up of multiple business datum tables, and data warehouse shines upon one by one by the field of field unit table, list cell table and the business datum table of Service Database.
Further, value, meta numerical value or the correlativity of the data corresponding with other data item that are characterized as business datum of business datum described in step 2.
Further, described step 3 specifically comprises the following steps:
Step 3.1, builds dimension table, and the dimensional information table obtaining according to step 1, business datum list cell table, traffic data field unit table, determine the every position in business datum database of dimension table, extracts corresponding data build dimension table from Service Database; Step 3.2, the structure of fixing subject heading list, according to existing fixing subject heading list field information in subject information table, build fixing subject heading list by user's specific field item from Service Database, extract business datum according to subject information literary name section and create fixing theme, wherein fix theme and exist with the form of fixing subject heading list in data warehouse;
Step 3.3, user is according to the business datum visual analyzing result of step 2, distribute and select according to business datum, confirm to select after data, upgrade subject information table information, according to traffic data field unit table, business datum list cell table positioning service data position, extract data, build individual character subject heading list.
Further, the item of specific field described in step 3.2 is one or more in target exit thickness, actual measurement exit thickness, target throat width, actual measurement exit width, steel reel number.
Further, described in step 3.3 according to business datum distribute that the data of selecting comprise normal distribution, are uniformly distributed, Data duplication rate is low, non-NULL or the data strong with critical field correlativity.
Further, after step 4, also comprise that repeating step 2 and step 3 add individual character dimension table and individual character subject heading list, the multi-threaded data warehouse of spiral expansion.
Principle of work of the present invention is: from Service Database, extract the conventional business datum of hot continuous rolling production procedure, analyze dimension, and in multi-threaded data warehouse, build fixing subject heading list and fixed dimension table; User can be according to business development and self-demand, by the visualization processing of enterprise's depot data, selection has the data construct individual character subject heading list of break-up value, on establishment individual character dimension table carries out data in new dimension, under volume, bore, realize the dynamic creation of multi-threaded data warehouse by user's search operation.
The invention has the beneficial effects as follows:
1. use the present invention, can get rid of the data item of redundancy in Service Database, from mass data item, extract the data item with break-up value, for the clear and definite demand of user is offered help;
2. use the present invention can realize the dynamic foundation to experience theme, when business datum is extracted, rule of thumb, data, the dimension of extracting common concern in hot continuous rolling production procedure build fixing subject heading list and fixed dimension table;
3. use the present invention can realize the dynamic foundation of data warehouse, visual analyzing according to user to business datum database data, autonomous extraction has data construct individual character subject heading list and the individual character dimension table of break-up value, and analyzes displaying by multi-threaded data warehouse.
4. use the present invention can spirally improve data warehouse, user, according to business development and changes in demand, independently selects data to show, constantly improves data warehouse content, makes warehouse constantly meet the requirement of new stage.
5. the method is equally applicable to the data warehouse structure of industrial circle procedure production class data.
Brief description of the drawings
The dynamic multi-threaded data warehouse method for building up process flow diagram of Fig. 1 iron and steel rolling of the present invention flow process.
Dynamic multi-threaded data warehouse Service Database and the visual analyzing figure of Fig. 2 iron and steel rolling of the present invention flow process.
The individual Sexual Themes custom stream journey figure of the dynamic multi-threaded data warehouse theme customization of Fig. 3 iron and steel rolling of the present invention flow process.
The fixing theme fact table of dynamic multi-threaded data warehouse of Fig. 4 iron and steel rolling of the present invention flow process builds.
The dynamic multi-threaded data warehouse of a Fig. 5 iron and steel rolling of the present invention flow process Sexual Themes fact table builds.
Embodiment
Describe according to the method for building up of the dynamic multi-threaded data warehouse based on iron and steel rolling flow process of the embodiment of the present invention below with reference to Figure of description.
The invention provides a kind of method for building up of the dynamic multi-threaded data warehouse based on iron and steel rolling flow process, build data warehouse flow process as shown in Figure 1, comprise the steps such as data extraction, data visualization fractional analysis, dynamic construction multiagent data warehouse, complete from data acquisition, data loading, metadatabase establishment, data warehouse structure etc.This routine Service Database is stored in oracle database, (data item is the calculated value producing in the operation of rolling to comprise 57456 data item, setting value and detected value), from Service Database, extract business datum and (comprise slab essential information, rolling line data, roughing setting up result, roughing correction result, finishing stand setup result, finish rolling correction result, board form data, curling data, the cooling data of laminar flow etc.), user is that the data set (Service Database) that had is while customizing intrinsic subject heading list according to iron and steel rolling field business demand and experience, select corresponding data to upgrade subject information table, while creating subject heading list, according to subject information table, traffic data field unit table, list cell table positioning service data, extract data construct subject heading list, when business demand changes, user can be according to business datum visual analyzing, the data characteristics of observed traffic data (maximal value, minimum value, median, and other data between correlation analysis) independently select data, and deposit subject information table in, after confirming to create, from Service Database, extract data according to subject information literary name segment information and carry out a Sexual Themes establishment, from data item such as rolling line extracting data time, teams and groups, steel grades, build the fixed dimension table that iron and steel rolling field is generally used, select to deposit data message in dimensional information table after data, specified dimension father-subrelation, extracts corresponding data according to dimensional information table from Service Database after confirming to create and builds dimension table, in the time that fixed dimension can not meet analysis demand, user can from Service Database, extract data item and specified dimension father-subrelation builds individual character dimension table, and upgrades dimension information table, data warehouse will be fixed theme, individual Sexual Themes carries out analysis mining in fixed dimension, individual character dimension, comprise data and show analysis by dimension, such as thickness distribution situation, certain factory's annual production etc. of each teams and groups rolling coil of strip in a week by fixing theme, individual Sexual Themes.Above-mentioned subject heading list, field unit table, list cell table, the unification of subject information table are stored in (as Oracle) database.
The customization of theme is exactly the process of setting up subject heading list according to subject information table.Subject information table is the filing theme collection with multiple fixing themes (as: thickness theme, width theme, coil of strip specification theme etc.) having customized, and each theme comprises some theme items, comprises the related datas such as table name, row name and subject.Fixing theme is the data digging system by more existing association areas, inherit the good fairly perfect theme storehouse (user is corresponding with subject information literary name section according to Service Database different structure given data item) of filing that they have customized, all themes are deposited in data warehouse with the form of tables of data.Individual Sexual Themes custom stream journey is as follows: user selects the data item in Service Database, in the time that user selectes the data item of a tables of data, the visual analyzing (showing the value, meta numerical value of data, correlation analysis with other data item) that carries out data, whether selection uses this data item according to the actual requirements; When user-selected number is according to also customizing after a Sexual Themes, new theme item is registered into subject information table, according to the field unit table of business datum table, the list cell table locator data of business datum table, from Service Database, extract the corresponding theme fact table of data construct, i.e. subject heading list; The process of establishing of dimension table is identical with subject heading list, dimension related data deposits in dimensional information table, position according to the field unit table of corresponding service tables of data, the list cell table of business datum table, from Service Database, extract the fact table of corresponding data structure dimension, i.e. dimension table.
Based on said method, data visualization fractional analysis to iron and steel rolling field complex data collection, build theme, dimension, and then reach the object of dynamic creation data warehouse, wherein concrete grammar flow process is as follows:
Step 1, business datum is extracted: traversal Service Database, the field unit that simultaneously generates the metadata updates business datum of business datum shows, business datum list cell table.
Service Database is made up of multiple business datum tables, business datum table is as table 1, between field unit table (also claiming traffic data field unit table), list cell table (also claiming business datum list cell table) and the field of business datum table of business datum table, pass through the mapping mode one by one of position, in the time of service data, carry out service data by the position of traffic data field unit table, list cell table map field and (extract data construct subject heading list and dimension table, when visual analyzing, selected field is positioned, show the field unit table of this field).Field unit table and the list cell table of Service Database are the Main Basiss of customization theme, it is the main contact between subject heading list field, dimension table field and business datum table, subject heading list field, dimension table field by business datum list cell table, the table access of field unit to the data in business datum table, thereby data are carried out to relevant statement operation.
To show F01_06_FMSC_DATA in Service Database as example (business datum table is as shown in table 1), data travel through business datum table F01_06_FMSC_DATA while extraction, extract data in table, upgrade traffic data field unit table and list cell table, as shown in table 2, table 3.
Table 1 business datum table F01_06_FMSC_DATA
| ID (Table I D) | TABLE_NAME (table name) | ATTR_COUNT (field quantity) |
| 1 | F01_01_HEADER | 707 |
| 2 | F01_02_PRI_DATA | 707 |
| 3 | F01_03_MILLLINEDATA | 707 |
| 4 | F01_04_RPSC_DATA | 707 |
| 5 | F01_05_PRCC_DATA | 707 |
| 6 | F01_06_FMSC_DATA | 707 |
Table 2 business datum list cell table
Table 3 traffic table field unit table
In addition,, in the business datum leaching process of step 1, in multi-threaded data warehouse, upgrade subject information table, the dimensional information table of data warehouse; Wherein, subject information table, according to the default fixing subject heading list field of industry experience, is selected corresponding business datum by user, completes fixing subject information table.To fix theme-specification (STANDARD) as example, Type (subject categories): 1-fixes theme, 2-Sexual Themes, Method is that data extraction method is described, the operation that need to carry out while obtaining these data, comprises SQL statement or for to obtain the required program code of data etc., while creating each subject heading list, the information providing according to subject information table, extracts corresponding data item in Service Database and builds subject heading list.Detailed process is as follows:
By the data digging system in more existing steel rolling fields, inherit fairly perfect theme storehouse (if the fixing subject information table of the non-selected data of table 4a is the perfect theme storehouse of inheriting), all themes are deposited in data warehouse with the form of tables of data.User selects business datum from Service Database, upgrade subject information table (as the fixing subject information table after table 4b user-selected number certificate), according to traffic data field unit table, the business datum of business datum list cell table positioning storage in Service Database.
| Name | Field_name | Type | Field | TABLE_ID | Method |
| STANDARD | time_key | 1 | Null | Null | Null |
| STANDARD | coil_thk | 1 | Null | Null | Null |
| STANDARD | exit_thk | 1 | Null | Null | Null |
| STANDARD | coil_wid | 1 | Null | Null | Null |
| STANDARD | exit_wid | 1 | Null | Null | Null |
| STANDARD | id | 1 | Null | Null | Null |
The fixing subject information table of the non-selected data of table 4a
| Name | Field_name | Type | Field | TABLE_ID | Method |
| STANDARD | time_key | 1 | ID | 1 | SQL (concrete statement) |
| STANDARD | coil_thk | 1 | Coilthk7_1247 | 6 | SQL (concrete statement) |
| STANDARD | exit_thk | 1 | Exitthk7_32324 | 47 | SQL (concrete statement) |
| STANDARD | coil_wid | 1 | Coilthk7_1352 | 6 | SQL (concrete statement) |
| STANDARD | exit_wid | 1 | Exitwid7_33224 | 47 | SQL (concrete statement) |
| STANDARD | id | 1 | ID | 1 | SQL (concrete statement) |
Fixing subject information table after table 4b user selects
Dimensional information table renewal process is identical with subject information table, and as table 5, it is identical that table comprises field, and Method is that data are extracted code used, can carry out by interface, creates dimension table.
According to industry characteristic, the data warehouse fixed dimension table (as the fixed dimension information table of table 5a non-selected data) that prestores, user selects business datum from Service Database, upgrade dimensional information table (as table 5b fixed dimension information table), according to traffic data field unit table, the business datum of business datum list cell table positioning storage in Service Database.
The unselected fixed dimension information table of table 5a
| Name | Field_name | Type | Field | TABLE_ID | Method |
| TIME | time_key | 1 | ID | 1 | SQL (concrete statement) |
| TIME | YEAR | 1 | ID | 1 | SQL (concrete statement) |
| TIME | MONTH | 1 | ID | 1 | SQL (concrete statement) |
| TIME | DAY | 1 | ID | 1 | SQL (concrete statement) |
Table 5b fixed dimension information table
While setting up multi-threaded data warehouse, according to the list cell table in business datum metadatabase, the required business datum in field unit table location, build subject heading list and dimension table; Wherein field unit table, also for visual analyzing, provides data feature description information, for user selects to do guide.
Data warehouse comprises fixing subject heading list, but subject field not corresponding with Service Database (as table 5), in this step 1, user selects corresponding data according to the fixing subject heading list of data warehouse, fixed dimension literary name section from Service Database, and upgrade dimensional information table, subject information table, after step 3 in extract data construct theme fact table, dimension fact table from Service Database relevant position according to dimensional information table, subject information table.
Step 2, business datum visual analyzing: the business datum in Service Database is carried out to visualization processing, show the feature of business datum, provide basis for building theme, theme leaves data warehouse in the form of subject heading list.Wherein, business datum is the business datum of user-selected number according to item correspondence.
Taking table F01_06_FMSC_DATA as example, user selects the fields such as ENTRYTHK0_1249, actual is according to service metadata table, Service Database to be operated and (for example carries out SQL statement, select ENTRYTHK0_1249, ENTRYTHK1_1250from F01_06_FMSC_DATA obtains business datum), from the data of the fields such as querying service database ENTRYTHK0_1249 (the first passage exit thickness), utilize R language to carry out visual analyzing, data visualization fractional analysis as shown in Figure 2, user selects data item from Service Database, and (data item is actual is field, for example ID: steel reel number, target exit thickness: Coilthk7_1247, selecting data item practical operation is that the data of this field are deposited in to data warehouse), the service metadata table location selected data that multi-threaded data warehouse is set up according to step 1 is in the position of Service Database, by selected business datum being carried out to visual analyzing (employing of this example is called R language and analyzed), comprise the data distribution situation of selected field and the correlation analysis with other selected datas, and the field unit that shows selected business datum shows, selected finish rolling the 1st passage inlet thickness (ENTRYTHK0_1249) in Fig. 2, finish rolling the 2nd passage inlet thickness (ENTRYTHK1_1250), finish rolling the 3rd passage inlet thickness (ENTRYTHK2_1251), finish rolling the 4th passage inlet thickness (ENTRYTHK3_1252), 5 business datum repetition values such as finish rolling the 5th passage inlet thickness (ENTRYTHK4_1253) are little, and contained 707 data of each field distribute and have certain rule, user can select this data construct subject heading list to carry out exploratory analysis.Build after subject heading list when user confirms to select business datum, the metadata of selected business datum will write subject information table, upgrade metadatabase; While selecting to build dimension table, selected business datum metadata writes dimensional information table.
Subject information table changes as table 6:
Table 6 subject information table (user-selected number is according to rear)
Step 3, spiral dynamic construction data warehouse: in conjunction with data and the continuous business demand changing of hot continuous rolling production procedure common concern, spiral dynamic construction data warehouse, the flow process of the individual Sexual Themes customization operations of data warehouse theme customization as shown in Figure 3, user is by data visualization result, can select Data duplication rate low, with theme key index (in coil of strip specification theme, one of product quality important indicator is coil of strip thickness) data that correlativity is strong, build individual character subject heading list; Build individual character dimension according to business demand, wherein individual character dimension is outside fixed dimension (as time, factory), it (is that user selects business datum that user selects field to build individual character dimension table according to business demand from Service Database, the Data Update of the metadata of selected data, positional information is entered to dimensional information table, while setting up warehouse, build individual character subject heading list according to main information table), business datum is analyzed in individual character dimension.For example, user selects business datum-thickness stepping to build individual character dimension thickness dimension, can analyze thickness stepping and be the impact of 5 coil of strip roll-force, rolling temperature etc., and upgrade subject information table, dimensional information table, enter dimensional information table, subject information table by the metadata updates of selected data, shine upon one by one according to dimensional information table and business datum, extract business datum and build dimension table and subject heading list.
Step 3 specifically comprises the following steps:
Step 3.1, builds dimension table, and the dimensional information table obtaining according to step 1, business datum list cell table, traffic data field unit table, determine the every position in business datum database of dimension table, extracts corresponding data build dimension table from Service Database.As an example of fixed dimension-time dimension example (because data volume is large, extracting on Dec 12nd, 1 2012 Dec 4 in 2012 707 coil of strips is example): from business database table F01_01_HEADER, extract ID (steel reel number) field, as table 7, describe according to data extraction method in dimensional information table, utilize the every data of respective code dimension table update time in the description of database interface (this example is utilized JAVA program) executing data extracting method, time dimension table is as shown in table 8.Steel reel number from left to right the first seven position has comprised the rolling date, getting the first seven position of steel reel number is time key, in order to connect subject heading list, wherein front two is rolling time-year, utilize SQL statement programmed statements such as (or) JAVA to intercept front two access time dimension table YEAR item, 3-4 position is rolling time-moon, access time dimension table MONTH item after intercepting, 6-7 position is rolling time-, specify father-subrelation, YEAR (parent)-MONTH (1 straton level)-DAY (2 straton level), the structure of deadline dimension;
ID
121200907040
121200602060
121200908010
121200909010
121201001010
121200603030
121201001070
121201004020
…
Table 7 steel reel number
| TIME_KEY | YEAR | MONTH | DAY |
| 1211001 | 12 | 11 | 1 |
| 1211003 | 12 | 11 | 3 |
| 1211004 | 12 | 11 | 4 |
| 1212001 | 12 | 12 | 1 |
| 1212002 | 12 | 12 | 2 |
| 1212003 | 12 | 12 | 3 |
| 1212004 | 12 | 12 | 4 |
| 1212005 | 12 | 12 | 5 |
| 1212006 | 12 | 12 | 6 |
| 1212007 | 12 | 12 | 7 |
| 1212008 | 12 | 12 | 8 |
| 1212009 | 12 | 12 | 9 |
| 1212010 | 12 | 12 | 10 |
| 1212011 | 12 | 12 | 11 |
| 1212012 | 12 | 12 | 12 |
Table 8TIME (time) dimension table
The structure of individual character dimension table, user is according to needs of production, select data construct individual character dimension in Service Database, taking individual character dimension thickness dimension as example, institute of factory rolled steel coils thickness stepping is 0-24, and different steppings represent institute's rolled steel coils thickness range, and user selects the corresponding data of thickness stepping (ThkClassNo) in Service Database, and upgrade dimensional information table, complete the structure of thickness dimension table.
Individual character dimension table is as shown in table 9, and Thk_grade is enterprise's thickness stepping number, and Thk represents thickness range, if thickness stepping number 1 expression institute rolled steel coils actual (real) thickness is 1.25-1.45 (mm).By building thickness dimension table, roll-force changes in distribution when user can study rolling different-thickness coil of strip, optimizes rolling procedure thereby optimize each passage load distribution, improves effect of rolling.
| Thk_grade | Thk(mm) |
| 1 | 1.25 |
| 2 | 1.45 |
| … | … |
| 24 | 25 |
Table 9 individual character dimension (thickness dimension) table
Step 3.2, the structure of fixing subject heading list.Fixing theme is the conventional theme of analyzing of industry, fixing subject heading list (only having field name) is stored in data warehouse, by respective field in user's specified services database, as table 4, according to existing fixing subject heading list field information in subject information table, from Service Database, specified hot continuous rolling industry conventional analysis field item as target exit thickness by user, actual measurement exit thickness, target throat width, actual measurement exit width, the data construct product specifications (Standard) such as steel reel number-fixing subject heading list, extract business datum according to subject information literary name section and create fixing theme (as table 4), fixing theme exists with the form of fixing subject heading list in data warehouse.As shown in Figure 4, the specification subject heading list of generation is as table 10 for process;
| TIME_KEY | COIL_THK | COIL_WID | EXIT_THK | EXIT_WID | ID |
| 1212004 | 2.41384 | 1262.94 | 2.43211 | 1279.36 | 121200504060 |
| 1212005 | 2.41384 | 1262.94 | 2.45682 | 1277.63 | 121200505020 |
| 1212005 | 2.41384 | 1262.94 | 2.44201 | 1278.9 | 121200505030 |
| 1212005 | 2.41384 | 1262.94 | 2.45871 | 1278.46 | 121200505040 |
| 1212005 | 2.41384 | 1262.94 | 2.42288 | 1279.46 | 121200505050 |
| 1212005 | 2.77032 | 1262.94 | 2.72196 | 1277.96 | 121200508080 |
| 1212005 | 2.41384 | 1262.94 | 2.42684 | 1279.25 | 121200505070 |
| 1212005 | 2.77032 | 1262.94 | 2.76453 | 1277.51 | 121200505080 |
| 1212005 | 2.41384 | 1262.94 | 2.46554 | 1277.83 | 121200506010 |
| 1212005 | 2.41384 | 1262.94 | 2.46552 | 1277.93 | 121200506020 |
| 1212005 | 3.02495 | 1262.94 | 3.11608 | 1275.2 | 121200508070 |
| 1212005 | 2.77032 | 1262.94 | 2.75047 | 1278.36 | 121200505010 |
| 1212005 | 2.41384 | 1262.94 | 2.43269 | 1278.48 | 121200505060 |
…
Table 10 specification subject heading list
Step 3.3, user is according to visual analyzing result, as Fig. 2, select data to distribute and have rule (normal distribution, be uniformly distributed etc.), Data duplication rate is low, non-NULL or the data strong with critical field correlativity, confirms to select after data, upgrade subject information table information, system, according to metadatabase positioning service data position, is extracted data, builds individual character subject heading list.Carry out exploratory analysis as example to add precision (Accuracy)-individual Sexual Themes, convexity, flatness is to weigh the important indicator of Product Precision, user selects convexity data (as step 2) as observation index from Service Database, from Service Database, choose arbitrarily data item and observe the correlativity between these data and convexity data, and select data that correlativity is higher as target exit thickness, actual measurement exit thickness, target exit width, actual measurement exit width, target outlet temperature, actual measurement outlet temperature, coil of strip thickness hits, width of steel coil hits, steel roll temperature hits, the data construct precision themes such as steel reel number continue to analyse in depth, constructive process as shown in Figure 5, precision subject heading list is as shown in table 11,
| TIME_KEY | COILTHK | COILWID | COILTEMP | EXITTHK | EXITWID | EXITTEMP | THK_HIT | … |
| 1212005 | 2.37 | 1240 | 950 | 2.43211 | 1257.29 | 1035.17 | 1 | ? |
| 1212005 | 2.72 | 1240 | 950 | 2.75047 | 1258.45 | 1038.56 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.45682 | 1256.03 | 1038.09 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.44201 | 1256.03 | 1036.19 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.45871 | 1256.03 | 1033.25 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.42288 | 1256.04 | 1033.68 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.43269 | 1256.04 | 1030.83 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.42684 | 1256.05 | 1034.09 | 1 | ? |
| 1212005 | 2.72 | 1240 | 950 | 2.76455 | 1258.48 | 1033.81 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.46554 | 1256.04 | 1035.7 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.46552 | 1256.05 | 1033.37 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.44389 | 1257.2 | 1049.51 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.46811 | 1257.22 | 1049.66 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.4692 | 1257.2 | 1042.26 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.46111 | 1257.21 | 1040.68 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.43992 | 1257.31 | 1038.92 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.44059 | 1257.24 | 1038.18 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.34796 | 1257.23 | 1031.75 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.47941 | 1257.22 | 1033.74 | 0 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.45849 | 1257.22 | 1034.48 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.43964 | 1257.23 | 1032.95 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.44958 | 1257.21 | 1033.39 | 1 | ? |
| 1212005 | 2.97 | 1240 | 950 | 3.11608 | 1258.33 | 1026.61 | 0 | ? |
| 1212005 | 2.72 | 1240 | 950 | 2.72196 | 1258.48 | 1042.21 | 1 | ? |
| 1212005 | 2.37 | 1240 | 950 | 2.45551 | 1257.23 | 1032.38 | 1 | ? |
Table 11 precision subject heading list
Step 4, on-line analysis processing, with dimension table and subject heading list structure business diagnosis model, as shown in Figure 6, and carry out data on-line analysis, for example user analyzes according to business demand, select data according to required theme (as product quality, thickness rolling situation), analysis by different dimensions obtains result, as equipment control accuracy, parameter adjustment, rolling schedule optimization, the coil of strip condition of production, each teams and groups production efficiency, thickness hit situation dimension conditions such as (under) time, teams and groups, thickness steppings etc.; User changes with business according to demand, and repeating step 2 and step 3 are added individual character dimension table and individual character subject heading list, and upgrade metadatabase, adds respective field, the multi-threaded data warehouse of spiral expansion.
This embodiment has substantially completed from data pick-up, warehouse and has built, theme customizes and mining analysis, allow user can easily utilize existing data resource to excavate targetedly how valuable rule, guiding user screens the data with break-up value from mass data, and dynamic construction data warehouse carries out analysis mining.
Although illustrated and described embodiments of the invention, for the ordinary skill in the art, be appreciated that without departing from the principles and spirit of the present invention and can carry out multiple variation, amendment, replacement and modification to these embodiment, scope of the present invention is by claims and be equal to and limit.
Claims (8)
1. the dynamic multi-threaded data warehouse method for building up based on hot continuous rolling production procedure, is characterized in that, said method comprising the steps of:
Step 1, business datum is extracted: traversal Service Database, the metadata structure that simultaneously extracts business datum upgrades field unit table and the list cell table of business datum;
Step 2, business datum visual analyzing: the business datum in Service Database is carried out to visualization processing, show the mathematical feature of business datum, provide basis for building theme, theme leaves data warehouse in the form of subject heading list; Wherein, business datum is the business datum of user-selected number according to item correspondence;
Step 3, spiral dynamic construction data warehouse: according to the business datum visual analyzing result obtaining in step 2, select data, build individual character subject heading list; Select field to build individual character dimension table from Service Database, and upgrade subject information table, dimensional information table;
Step 4, on-line analysis processing, with dimension table and subject heading list structure business diagnosis model, carries out online data analysis.
2. a kind of dynamic multi-threaded data warehouse method for building up based on hot continuous rolling production procedure as claimed in claim 1, it is characterized in that, the metadatabase of described data warehouse comprises the field unit table of business datum, list cell table, subject information table and dimensional information table, the field unit table of described business datum, list cell table, subject information table and dimensional information table are for realizing synchronizeing of data warehouse data and business datum, the subject heading list field of data warehouse, the list cell table of dimension table field by business datum and the table access of field unit are to the data in business datum table, thereby data are carried out to associative operation.
3. a kind of dynamic multi-threaded data warehouse method for building up based on hot continuous rolling production procedure as claimed in claim 1, it is characterized in that, Service Database is made up of multiple business datum tables, and data warehouse shines upon one by one by the field of field unit table, list cell table and the business datum table of Service Database.
4. a kind of dynamic multi-threaded data warehouse method for building up based on hot continuous rolling production procedure as claimed in claim 1, it is characterized in that value, meta numerical value or the correlativity of the data corresponding with other data item that are characterized as business datum of business datum described in step 2.
5. a kind of dynamic multi-threaded data warehouse method for building up based on hot continuous rolling production procedure as claimed in claim 1, is characterized in that, step 3 specifically comprises the following steps:
Step 3.1, builds dimension table, and the dimensional information table obtaining according to step 1, business datum list cell table, traffic data field unit table, determine the every position in business datum database of dimension table, extracts corresponding data build dimension table from Service Database;
Step 3.2, the structure of fixing subject heading list, according to existing fixing subject heading list field information in subject information table, build fixing subject heading list by user's specific field item from Service Database, extract business datum according to subject information literary name section and create fixing theme, wherein fix theme and exist with the form of fixing subject heading list in data warehouse;
Step 3.3, user is according to the business datum visual analyzing result of step 2, distribute and select according to business datum, confirm to select after data, upgrade subject information table information, according to traffic data field unit table, business datum list cell table positioning service data position, extract data, build individual character subject heading list.
6. a kind of dynamic multi-threaded data warehouse method for building up based on hot continuous rolling production procedure as claimed in claim 5, it is characterized in that, the item of specific field described in step 3.2 is one or more in target exit thickness, actual measurement exit thickness, target throat width, actual measurement exit width, steel reel number.
7. a kind of dynamic multi-threaded data warehouse method for building up based on hot continuous rolling production procedure as claimed in claim 5, it is characterized in that, described in step 3.3 according to business datum distribute that the data of selecting comprise normal distribution, are uniformly distributed, Data duplication rate is low, non-NULL or the data strong with critical field correlativity.
8. a kind of dynamic multi-threaded data warehouse method for building up based on hot continuous rolling production procedure as claimed in claim 1, it is characterized in that, after step 4, also comprise that repeating step 2 and step 3 add individual character dimension table and individual character subject heading list, the multi-threaded data warehouse of spiral expansion.
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