CN107193994A - Business decision point method for digging and its system based on mass data - Google Patents
Business decision point method for digging and its system based on mass data Download PDFInfo
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- CN107193994A CN107193994A CN201710423122.5A CN201710423122A CN107193994A CN 107193994 A CN107193994 A CN 107193994A CN 201710423122 A CN201710423122 A CN 201710423122A CN 107193994 A CN107193994 A CN 107193994A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
Abstract
The present invention relates to the point method for digging of the business decision based on mass data and its system, this method includes the enterprise for needing to service is classified and is layered, and obtains enterprise's subitem；With reference to business event feature, enterprise's subitem is associated with all kinds of decision requirements of enterprise, decision point disaggregated model is formed；Based on proprietary data source and mass data, Mobile state adjustment is entered to decision point disaggregated model；The products ＆ services of decision point disaggregated model and enterprise operation after Matching and modification, obtain business decision point.The present invention realizes that mass data is combined with enterprise practical performance analysis, active, dynamic are provided for enterprise management decision-making and timely data are supported, enterprise development demand can be predicted to a certain extent and is managed changes, predictive and normative analysis is more, improve the assurance degree of accuracy to enterprise demand, so that decision-making is accurate, by the big data analysis and machine learning to different industries field enterprise, decision point disaggregated model can effectively serve in various types of enterprises.
Description
Technical field
The present invention relates to business decision point method for digging, more specifically refer to that the business decision point based on mass data is dug
Pick method and its system.
Background technology
Business decision refers to enterprise to the target of following operation and development and realizes that the strategy or means of target are most preferably selected
The process selected, be also business administration all work core contents, in whole management and administration of enterprise, decision-making it is correct
Whether, it is directly connected to enterprise's success or failure and survival and development.
The product of business data service type is all based on the supply type thinking of existing procucts or service in the market, though
So some are also based on the huge and complicated structuring of the scale of construction or unstructured data, but generally or with based on history number
According to analysis form and descriptive analysis based on, not only predictive and normative analysis is relatively fewer, and does not divide data
Analysis technology is embedded in operation flow, causes isolating for data analysis and business event, it is impossible to provide initiative to enterprise operation decision-making
Support and help.
Chinese patent 201310059170.2 discloses a kind of web topic tendentiousness excavation and the method for decision support, bag
Include step：S1. network information extraction and storage, by Web Mining technology, obtain information, and result is stored on the internet
Database and local file system；S2. the viewpoint topic detection of information and tracking, using thematic comment data, detection is identified
Viewpoint theme with integrated semantic interested, and continue to track and pay close attention to the viewpoint theme；S3. viewpoint theme emotion
Orientation identification, the much-talked-about topic to enterprise carries out topic emotion tendency classification, excavates the emotion tendency of viewpoint theme.
Above-mentioned patent is fast and effeciently excavated by obtaining relative commercial information from internet from mass network information
The related theme tendentiousness tendency of enterprise, realizes instant business wisdom, preferably provides decision support service for enterprise, but simultaneously
Big data can not be combined with business, there is the inaccurate phenomenon of the assurance to enterprise demand, decision-making is inaccurate.
Therefore, it is necessary to design a kind of business decision point method for digging based on mass data, mass data and enterprise are realized
The actual performance analysis of industry is combined, and active, dynamic and timely data support are provided for enterprise management decision-making, predictive and normative
Analysis is more, improves the assurance degree of accuracy to enterprise demand so that decision-making is accurate.
The content of the invention
It is an object of the invention to the defect for overcoming prior art, there is provided the point of the business decision based on mass data excavation side
Method and its system.
To achieve the above object, the present invention uses following technical scheme：Business decision point excavation side based on mass data
Method, methods described includes：
The enterprise for needing to service is classified and is layered, enterprise's subitem is obtained；
With reference to business event feature, enterprise's subitem is associated with all kinds of decision requirements of enterprise, decision point point is formed
Class model；
Based on proprietary data source and mass data, Mobile state adjustment is entered to decision point disaggregated model；
The products ＆ services of decision point disaggregated model and enterprise operation after Matching and modification, obtain business decision point.
Its further technical scheme is：The enterprise for needing to service is classified and is layered, the step of obtaining enterprise's subitem,
Including step in detail below：
Obtaining needs what is serviced to include enterprise；
Business activities according to Management countermeasure or scope of operation to the enterprise are classified and are layered, and obtain enterprise's subitem.
Its further technical scheme is：With reference to business event feature, all kinds of decision-makings of enterprise's subitem and enterprise are needed
Association is asked, the step of forming decision point disaggregated model, including step in detail below：
With reference to business event feature, predict what enterprise's subitem was matched with all kinds of decision requirements faced in enterprise operation
Probability；
With reference to the incidence relation between all kinds of decision requirements faced in enterprise operation, enterprise's subitem and institute are integrated
Probability is stated, decision point disaggregated model is formed.
Its further technical scheme is：Based on proprietary data source and mass data, Mobile state is entered to decision point disaggregated model
The step of adjustment, including step in detail below：
Obtain proprietary data source and all kinds of mass datas on enterprise；
All kinds of mass datas according to proprietary data source and on enterprise, the development of dynamically recording enterprise management condition becomes
Gesture；
Decision point disaggregated model is dynamically adjusted with reference to the development trend.
Its further technical scheme is：The products ＆ services of decision point disaggregated model and enterprise operation after Matching and modification,
The step of obtaining business decision point, including step in detail below：
According to the products ＆ services of enterprise operation, analyze and obtain enterprise's subitem residing for the current business activities of enterprise；
Corresponding decision requirements are inquired about in the decision point disaggregated model according to enterprise's subitem, business decision point is obtained.
Present invention also offers the point digging system of the business decision based on mass data, including enterprise's subitem acquiring unit,
Model formation unit, adjustment unit and decision point acquiring unit；
Enterprise's subitem acquiring unit, for the enterprise for needing to service to be classified and is layered, obtains enterprise's subitem；
The model formation unit, for combining business event feature, by all kinds of decision-makings of enterprise's subitem and enterprise
Demand is associated, and forms decision point disaggregated model；
The adjustment unit, is adjusted for based on proprietary data source and mass data, entering Mobile state to decision point disaggregated model
It is whole；
The decision point acquiring unit, for the decision point disaggregated model and the product kimonos of enterprise operation after Matching and modification
Business, obtains business decision point.
Its further technical scheme is：Enterprise's subitem acquiring unit includes enterprise's acquisition module and classification layering mould
Block；
Enterprise's acquisition module, needs what is serviced to include enterprise for obtaining；
The classification hierarchical block, for according to Management countermeasure or scope of operation to the business activities classification of the enterprise and
Layering, obtains enterprise's subitem.
Its further technical scheme is：The model formation unit includes probabilistic forecasting module and integrates module；
The probabilistic forecasting module, for combining business event feature, predicts enterprise's subitem and face in enterprise operation
The probability for all kinds of decision requirements matching faced；
The integration module, for combining the incidence relation between all kinds of decision requirements faced in enterprise operation, is integrated
Enterprise's subitem and the probability, form decision point disaggregated model.
Its further technical scheme is：The adjustment unit includes data acquisition module, logging modle and dynamic adjustment
Module；
The data acquisition module, for obtaining proprietary data source and all kinds of mass datas on enterprise；
The logging modle, for all kinds of mass datas according to proprietary data source and on enterprise, dynamically recording enterprise
The development trend of industry management state；
The dynamic adjusting module, for dynamically adjusting decision point disaggregated model with reference to the development trend.
Its further technical scheme is：The decision point acquiring unit includes analysis acquisition module and enquiry module；
The analysis acquisition module, for the products ＆ services according to enterprise operation, analyzes and obtains the warp of enterprise currently
Enterprise's subitem residing for battalion's activity；
The enquiry module, for inquiring about corresponding decision-making need in the decision point disaggregated model according to enterprise's subitem
Ask, obtain business decision point.
Compared with the prior art, the invention has the advantages that：The point of the business decision based on mass data of the present invention is excavated
Method, by the way that the business activities of enterprise are classified and are layered, forms enterprise's subitem, subitem is matched with decision requirements by enterprise, is obtained
Decision point disaggregated model is taken, Mobile state adjustment is entered to model according to mass data and proprietary data source, then by enterprise operation
Decision point disaggregated model after products ＆ services and adjustment obtains decision point, realizes mass data and enterprise practical performance analysis
With reference to for enterprise management decision-making provides active, dynamically and timely data are supported, enterprise development can be predicted to a certain extent
Demand and managing changes, and predictive and normative analysis is more, improves the assurance degree of accuracy to enterprise demand so that decision-making is accurate,
By the big data analysis and machine learning to different industries field enterprise, decision point disaggregated model can effectively serve in various
The enterprise of type.
The invention will be further described with specific embodiment below in conjunction with the accompanying drawings.
Brief description of the drawings
The flow chart for the point method for digging of the business decision based on mass data that Fig. 1 provides for the specific embodiment of the invention；
The particular flow sheet for obtaining enterprise's subitem that Fig. 2 provides for the specific embodiment of the invention；
The particular flow sheet for the formation decision point disaggregated model that Fig. 3 provides for the specific embodiment of the invention；
The particular flow sheet for entering Mobile state adjustment to decision point disaggregated model that Fig. 4 provides for the specific embodiment of the invention；
The particular flow sheet for the acquisition business decision point that Fig. 5 provides for the specific embodiment of the invention；
The structural frames for the point digging system of the business decision based on mass data that Fig. 6 provides for the specific embodiment of the invention
Figure；
The structured flowchart for enterprise's subitem acquiring unit that Fig. 7 provides for the specific embodiment of the invention；
The structured flowchart for the model formation unit that Fig. 8 provides for the specific embodiment of the invention；
The structured flowchart for the adjustment unit that Fig. 9 provides for the specific embodiment of the invention；
The structured flowchart for the decision point acquiring unit that Figure 10 provides for the specific embodiment of the invention；
Embodiment
In order to more fully understand the technology contents of the present invention, technical scheme is entered with reference to specific embodiment
One step introduction and explanation, but it is not limited to this.
Specific embodiment as shown in Fig. 1~10, the point of the business decision based on mass data that the present embodiment is provided is excavated
Method, during being used in enterprise management decision-making, realizes that mass data is combined with enterprise practical performance analysis, is enterprise
Business decision provides active, dynamic and timely data support that predictive and normative analysis is more, handle of the raising to enterprise demand
Hold the degree of accuracy so that decision-making is accurate.
As shown in figure 1, present embodiments providing the business decision point method for digging based on mass data, it is characterised in that
Methods described includes：
S1, the enterprise to needs service are classified and are layered, and obtain enterprise's subitem；
S2, with reference to business event feature, enterprise's subitem is associated with all kinds of decision requirements of enterprise, formed decision point
Disaggregated model；
S3, based on proprietary data source and mass data, decision point disaggregated model is entered Mobile state adjustment；
The products ＆ services of decision point disaggregated model and enterprise operation after S4, Matching and modification, obtain business decision point.
For above-mentioned S1 steps, the enterprise for needing to service is classified and is layered, the step of obtaining enterprise's subitem, bag
Include step in detail below：
S11, acquisition need what is serviced to include enterprise；
S12, the business activities according to Management countermeasure or scope of operation to the enterprise are classified and are layered, and obtain enterprise's
.
For above-mentioned S11 steps, classified just for the enterprise included, the enterprise having no truck with can be rejected,
Improve the efficiency of classification.
For S12 steps, after first classifying to the business activities of enterprise, then it is layered, each classification can be segmented
Into many subitems.
Above-mentioned Management countermeasure or scope of operation can extract keyword and patent classification number out of patent database, with
Classify and be layered for the business activities to the enterprise.It is, of course, also possible to carry out acquisition operation according to the trademark class of enterprise
Object or scope of operation.
Further, above-mentioned S2 steps, with reference to business event feature, enterprise's subitem and all kinds of of enterprise are determined
Plan demand is associated, the step of forming decision point disaggregated model, including step in detail below：
S21, with reference to business event feature, predict enterprise's subitem and all kinds of decision requirements faced in enterprise operation
The probability matched somebody with somebody；
S22, with reference to the incidence relation between all kinds of decision requirements faced in enterprise operation, integrate enterprise's subitem with
And the probability, form decision point disaggregated model.
For above-mentioned S21 steps, all kinds of decision requirements faced in enterprise operation are exactly business event in practice
The selection for the different scenes that each enterprise's subitem is faced and its comprehensive measurement for the consequence that may be brought, it is main during prediction probability
If the calculating and prediction of the probability that every kind of scene occurs.
For above-mentioned S22 steps, the calculating and prediction of the probability that every kind of scene is occurred, with reference to the pass between each scene
Connection relation, selects that probability of happening is high and the good decision-making of consequence is corresponding with subitem, and the decision-making provided for enterprise most useful for enterprise is built
View.
Further, above-mentioned S3 steps, based on proprietary data source and mass data, are carried out to decision point disaggregated model
The step of dynamic adjustment, including step in detail below：
S31, acquisition proprietary data source and all kinds of mass datas on enterprise；
S32, all kinds of mass datas according to proprietary data source and on enterprise, the hair of dynamically recording enterprise management condition
Exhibition trend；
S33, with reference to the development trend dynamically adjustment decision point disaggregated model.
For above-mentioned S31 steps, technology is crawled particular by data, in setting time from internet collection and
Enterprise's related data is crawled, mass data is used as；After mass data is got, in addition it is also necessary to these mass datas are carried out regular
Update, accumulate business data with this, the support of magnanimity authentic data, enterprise management decision-making is realized actively, dynamic and in time
Data support, and can predict to a certain extent enterprise development demand and manage change.
For above-mentioned S32 steps, the development that enterprise management condition is recorded according to the data crawled in setting time becomes
Gesture, with the development of society and science and technology, enterprise can be potentially encountered new decision requirements, in other words can not in decision point disaggregated model
All decision requirements of enterprise are completely covered, accordingly, it would be desirable to the development trend of record enterprise management condition in real time.
For above-mentioned S33 steps, by proprietary data source and the Massive Sample from internet uses machine learning
Mode, the dynamic existing decision point disaggregated model of adjustment is to adapt to the decision requirements of enterprise's different phase, and realization can be to enterprise
Active, dynamic and timely data support, and can predict enterprise development demand to a certain extent are realized in industry business decision
Change with managing.
Above-mentioned S31 steps, by big data technology and the combination of enterprise practical performance analysis, overcome to S33 steps
Existing big data analyzes static, passive defect, with combining closely for service feature so that the assurance to enterprise demand is more smart
Standard, is more convenient for providing effective service.
The decision point disaggregated model of acquisition possesses scalability and transplantability, passes through the big number to different industries field enterprise
According to analysis and machine learning, various types of enterprises can be effectively served in.
Further, for above-mentioned S4 steps, the production of decision point disaggregated model and enterprise operation after Matching and modification
Product and service, the step of obtaining business decision point, including step in detail below：
S41, the products ＆ services according to enterprise operation, analyze and obtain of the enterprise residing for the current business activities of enterprise
；
S42, inquire about corresponding decision requirements in the decision point disaggregated model according to enterprise's subitem, obtain business decision
Point.
For above-mentioned S41 steps, it is necessary to first obtain the products ＆ services of enterprise operation in practice, then analyze
Go out enterprise's subitem that enterprise is presently in, business decision point is obtained according to enterprise's subitem.
For above-mentioned S42 steps, the product relevant with enterprise operation is matched according to the decision point disaggregated model after tuning
And service, the customization and precision of enterprises service are realized, can according to the probability to decision-making scene and the calculating of consequence and prediction
It is excellent to obtain competition to cause enterprise that for concrete scene the products ＆ services of enterprise are made with the adjustment most beneficial for enterprise
Gesture.
The above-mentioned point method for digging of the business decision based on mass data, by the way that the business activities of enterprise are classified and divided
Layer, forms enterprise subitem, subitem is matched with decision requirements by enterprise, acquisition decision point disaggregated model, according to mass data and
Proprietary data source enters Mobile state adjustment, then the decision point classification mould after products ＆ services by enterprise operation and adjustment to model
Type obtain decision point, realize that mass data is combined with enterprise practical performance analysis, for enterprise management decision-making provide active, dynamically and
Timely data are supported, enterprise development demand can be predicted to a certain extent and change is managed, predictive and normative analysis
It is many, improve the assurance degree of accuracy to enterprise demand so that decision-making is accurate, analyzed by the big data to different industries field enterprise
And machine learning, decision point disaggregated model can effectively serve in various types of enterprises.
As shown in fig. 6, the present embodiment additionally provides the business decision point digging system based on mass data, including enterprise's
Item acquiring unit 1, model formation unit 2, adjustment unit 3 and decision point acquiring unit 4.
Enterprise's subitem acquiring unit 1, for the enterprise for needing to service to be classified and is layered, obtains enterprise's subitem.
Model formation unit 2, for combining business event feature, by enterprise's subitem and all kinds of decision requirements of enterprise
Association, forms decision point disaggregated model.
Adjustment unit 3, for based on proprietary data source and mass data, entering Mobile state adjustment to decision point disaggregated model.
Decision point acquiring unit 4, for the decision point disaggregated model and the products ＆ services of enterprise operation after Matching and modification,
Obtain business decision point.
Further, enterprise's subitem acquiring unit 1 includes enterprise's acquisition module 11 and classification hierarchical block 12.
Enterprise's acquisition module 11, needs what is serviced to include enterprise for obtaining.
Classification hierarchical block 12, classifies and divides for the business activities according to Management countermeasure or scope of operation to the enterprise
Layer, obtains enterprise's subitem.
Above-mentioned enterprise's acquisition module 11 is classified just for the enterprise included, can reject the enterprise having no truck with
Industry, improves the efficiency of classification.
After above-mentioned classification hierarchical block 12 is first classified to the business activities of enterprise, then it is layered, each classification
Many subitems can be subdivided into.
Further, above-mentioned model formation unit 2 includes probabilistic forecasting module 21 and integrates module 22.
Probabilistic forecasting module 21, for combining business event feature, predicts enterprise's subitem with being faced in enterprise operation
All kinds of decision requirements matching probability.
Module 22 is integrated, for combining the incidence relation between all kinds of decision requirements faced in enterprise operation, institute is integrated
Enterprise's subitem and the probability are stated, decision point disaggregated model is formed.
All kinds of decision requirements faced in above-mentioned enterprise operation are exactly each enterprise's subitem of business event in practice
The selection of the different scenes faced and its comprehensive measurement for the consequence that may be brought, during prediction probability, mainly to every kind of field
The calculating and prediction for the probability that scape occurs.
The calculating and prediction for the probability that every kind of scene is occurred, with reference to the incidence relation between each scene, select generation general
Rate is high and the good decision-making of consequence is corresponding with subitem, and the decision recommendation most useful for enterprise is provided for enterprise.
Further, adjustment unit 3 includes data acquisition module 31, logging modle 32 and dynamic adjusting module 33.
Data acquisition module 31, for obtaining proprietary data source and all kinds of mass datas on enterprise.
Logging modle 32, for all kinds of mass datas according to proprietary data source and on enterprise, dynamically recording enterprise
The development trend of management state.
Dynamic adjusting module 33, for dynamically adjusting decision point disaggregated model with reference to the development trend.
Above-mentioned data acquisition module 31 crawls technology particular by data, is gathered in setting time from internet
With crawl enterprise's related data, be used as mass data；After mass data is got, in addition it is also necessary to which these mass datas are determined
Phase updates, and accumulates business data with this, the support of magnanimity authentic data, enterprise management decision-making is realized actively, dynamic and and
When data support, and can predict to a certain extent enterprise development demand and manage change.
Above-mentioned logging modle 32 records the development trend of enterprise management condition according to the data crawled in setting time,
With the development of society and science and technology, enterprise can be potentially encountered new decision requirements, in other words can not be complete in decision point disaggregated model
All decision requirements of all standing enterprise, accordingly, it would be desirable to the development trend of record enterprise management condition in real time.
Above-mentioned dynamic adjusting module 33 is by proprietary data source and the Massive Sample from internet uses engineering
The mode of habit, the existing decision point disaggregated model of dynamic adjustment is to adapt to the decision requirements of enterprise's different phase, and realization can be right
Active, dynamic and timely data support are realized in enterprise management decision-making, and can predict that enterprise development is needed to a certain extent
Change is managed in summation.
By big data technology and the combination of enterprise practical performance analysis, existing big data analysis is overcome static, passive
Defect, with combining closely for service feature so that the assurance to enterprise demand is more accurate, be more convenient for providing effective service.
The decision point disaggregated model of acquisition possesses scalability and transplantability, passes through the big number to different industries field enterprise
According to analysis and machine learning, various types of enterprises can be effectively served in.
Further, above-mentioned decision point acquiring unit 4 includes analysis acquisition module 41 and enquiry module 42.
Acquisition module 41 is analyzed, for the products ＆ services according to enterprise operation, analyzes and obtains the operation of enterprise currently
Enterprise's subitem residing for activity.
Enquiry module 42, for inquiring about corresponding decision requirements in the decision point disaggregated model according to enterprise's subitem,
Obtain business decision point.
Above-mentioned analysis acquisition module 41, it is necessary to first obtain the products ＆ services of enterprise operation, then divides in practice
Enterprise's subitem that enterprise is presently in is separated out, business decision point is obtained according to enterprise's subitem.
Above-mentioned enquiry module 42 matches the product kimonos relevant with enterprise operation according to the decision point disaggregated model after tuning
Business, realizes the customization and precision of enterprises service, according to the probability to decision-making scene and the calculating of consequence and prediction, can make
Enterprise makes adjustment most beneficial for enterprise to the products ＆ services of enterprise for concrete scene, to obtain competitive advantage.
The above-mentioned point digging system of the business decision based on mass data, by the way that the business activities of enterprise are classified and divided
Layer, forms enterprise subitem, subitem is matched with decision requirements by enterprise, acquisition decision point disaggregated model, according to mass data and
Proprietary data source enters Mobile state adjustment, then the decision point classification mould after products ＆ services by enterprise operation and adjustment to model
Type obtain decision point, realize that mass data is combined with enterprise practical performance analysis, for enterprise management decision-making provide active, dynamically and
Timely data are supported, enterprise development demand can be predicted to a certain extent and change is managed, predictive and normative analysis
It is many, improve the assurance degree of accuracy to enterprise demand so that decision-making is accurate, analyzed by the big data to different industries field enterprise
And machine learning, decision point disaggregated model can effectively serve in various types of enterprises.
The above-mentioned technology contents that the present invention is only further illustrated with embodiment, in order to which reader is easier to understand, but not
Represent embodiments of the present invention and be only limitted to this, any technology done according to the present invention extends or recreated, by the present invention's
Protection.Protection scope of the present invention is defined by claims.
Claims (10)
1. the business decision point method for digging based on mass data, it is characterised in that methods described includes：
The enterprise for needing to service is classified and is layered, enterprise's subitem is obtained；
With reference to business event feature, enterprise's subitem is associated with all kinds of decision requirements of enterprise, decision point classification mould is formed
Type；
Based on proprietary data source and mass data, Mobile state adjustment is entered to decision point disaggregated model；
The products ＆ services of decision point disaggregated model and enterprise operation after Matching and modification, obtain business decision point.
2. the business decision point method for digging according to claim 1 based on mass data, it is characterised in that to needing clothes
The enterprise of business is classified and is layered, the step of obtaining enterprise's subitem, including step in detail below：
Obtaining needs what is serviced to include enterprise；
Business activities according to Management countermeasure or scope of operation to the enterprise are classified and are layered, and obtain enterprise's subitem.
3. the business decision point method for digging according to claim 1 or 2 based on mass data, it is characterised in that with reference to
Business event feature, enterprise's subitem is associated with all kinds of decision requirements of enterprise, the step of forming decision point disaggregated model,
Including step in detail below：
With reference to business event feature, predict that enterprise's subitem is matched with all kinds of decision requirements faced in enterprise operation general
Rate；
With reference to the incidence relation between all kinds of decision requirements faced in enterprise operation, enterprise's subitem is integrated and described general
Rate, forms decision point disaggregated model.
4. the business decision point method for digging according to claim 3 based on mass data, it is characterised in that based on proprietary
Data source and mass data, the step of Mobile state is adjusted, including step in detail below are entered to decision point disaggregated model：
Obtain proprietary data source and all kinds of mass datas on enterprise；
All kinds of mass datas according to proprietary data source and on enterprise, the development trend of dynamically recording enterprise management condition；
Decision point disaggregated model is dynamically adjusted with reference to the development trend.
5. the business decision point method for digging according to claim 4 based on mass data, it is characterised in that Matching and modification
Decision point disaggregated model afterwards and the products ＆ services of enterprise operation, the step of obtaining business decision point, including walk in detail below
Suddenly：
According to the products ＆ services of enterprise operation, analyze and obtain enterprise's subitem residing for the current business activities of enterprise；
Corresponding decision requirements are inquired about in the decision point disaggregated model according to enterprise's subitem, business decision point is obtained.
6. the business decision point digging system based on mass data, it is characterised in that including enterprise's subitem acquiring unit, model shape
Into unit, adjustment unit and decision point acquiring unit；
Enterprise's subitem acquiring unit, for the enterprise for needing to service to be classified and is layered, obtains enterprise's subitem；
The model formation unit, for combining business event feature, by enterprise's subitem and all kinds of decision requirements of enterprise
Association, forms decision point disaggregated model；
The adjustment unit, for based on proprietary data source and mass data, entering Mobile state adjustment to decision point disaggregated model；
The decision point acquiring unit, for the decision point disaggregated model and the products ＆ services of enterprise operation after Matching and modification,
Obtain business decision point.
7. the business decision point digging system according to claim 6 based on mass data, it is characterised in that the enterprise
Subitem acquiring unit includes enterprise's acquisition module and classification hierarchical block；
Enterprise's acquisition module, needs what is serviced to include enterprise for obtaining；
The classification hierarchical block, classifies and divides for the business activities according to Management countermeasure or scope of operation to the enterprise
Layer, obtains enterprise's subitem.
8. the business decision point digging system according to claim 6 based on mass data, it is characterised in that the model
Forming unit includes probabilistic forecasting module and integrates module；
The probabilistic forecasting module, for combining business event feature, predicts what is faced in enterprise's subitem and enterprise operation
The probability of all kinds of decision requirements matchings；
The integration module, for combining the incidence relation between all kinds of decision requirements faced in enterprise operation, is integrated described
Enterprise's subitem and the probability, form decision point disaggregated model.
9. the business decision point digging system according to claim 6 based on mass data, it is characterised in that the adjustment
Unit includes data acquisition module, logging modle and dynamic adjusting module；
The data acquisition module, for obtaining proprietary data source and all kinds of mass datas on enterprise；
The logging modle, for all kinds of mass datas according to proprietary data source and on enterprise, dynamically recording enterprise warp
The development trend of battalion's situation；
The dynamic adjusting module, for dynamically adjusting decision point disaggregated model with reference to the development trend.
10. the business decision point digging system according to claim 6 based on mass data, it is characterised in that described to determine
Plan point acquiring unit includes analysis acquisition module and enquiry module；
The analysis acquisition module, for the products ＆ services according to enterprise operation, analyzes and obtains the operation of enterprise currently and live
Move residing enterprise's subitem；
The enquiry module, for inquiring about corresponding decision requirements in the decision point disaggregated model according to enterprise's subitem, is obtained
Take business decision point.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710423122.5A CN107193994A (en) | 2017-06-07 | 2017-06-07 | Business decision point method for digging and its system based on mass data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710423122.5A CN107193994A (en) | 2017-06-07 | 2017-06-07 | Business decision point method for digging and its system based on mass data |
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| CN110097216A (en) * | 2019-04-11 | 2019-08-06 | 企家有道网络技术(北京)有限公司 | For the prediction technique and device of enterprise development, server |
| CN111400414A (en) * | 2020-03-12 | 2020-07-10 | 杭州城市大数据运营有限公司 | Decision-making method and system based on standardized enterprise data and electronic equipment |
| CN112990498A (en) * | 2021-03-25 | 2021-06-18 | 商运(江苏)科创发展有限公司 | Client management system and method based on digital twin technology |
| CN116029571A (en) * | 2023-03-29 | 2023-04-28 | 肯特智能技术（深圳）股份有限公司 | Meta universe-based data processing method and related device |
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Application publication date: 20170922 |