CN105260835A - Method for modeling, analyzing, and self-optimizing multi-source business big data - Google Patents
Method for modeling, analyzing, and self-optimizing multi-source business big data Download PDFInfo
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Abstract
The invention provides a method for modeling, analyzing, and self-optimizing multi-source business big data. The method comprises steps of: receiving internal data from an enterprise system and external data from the Internet; analyzing the internal data and the external data in order to classify the internal data and the external data into structured data and unstructured data; converting the unstructured data into corresponding text content and extracting and cleaning the converted unstructured data and the structured data; establishing multiple business models according an internal demand of an enterprise, and enabling the business models to perform self-learning and self-optimization according to data input in order to automatically generate a master data center; processing and analyzing data in the master data center in order to obtain a business analysis result; and displaying the business analysis result by using a digital and graphical interface. The method accurately analyzes business data on the premise of data authenticity in order that the business data models are automatically established, optimized and expanded and correlation analysis and display for individualized demands are solved.
Description
Technical field
The present invention relates to technical field of data processing, the method for the particularly modeling of the large data of a kind of multi-source business, analysis, self-optimization.
Background technology
Along with the accumulation of the daily operation structural data of mechanism, the industry related data of interconnected Web realease constantly increases, and the data that business is relevant are increased sharply.Apply large data technique and promote achievement for mechanism being urgent need instantly.
But, the surge data existence knot quality of data is low, structure otherness is large, capture range extensive, it is high to deal with complexity, data model extendability is poor, the inaccurate problem of data analysis algorithm result, the data integrity collected cannot be accomplished to be pooled to primary data center, thus cannot to realize comprehensively analyzing data and showing.
Summary of the invention
Object of the present invention is intended at least solve one of described technological deficiency.
For this reason, the object of the invention is to propose the method for the modeling of the large data of a kind of multi-source business, analysis, self-optimization, carry out accurate analysis business datum, make business data model carry out robotization foundation, optimization, expansion under guarantee data validity, carry out correlation analysis for individual demand and displaying is resolved.
To achieve these goals, embodiments of the invention provide a kind of management method of multi-source business datum, comprise the steps:
Step S1, receives the internal data from business system and the external data from internet;
Step S2, is categorized as structured data and non-structural data to described internal data and external data analysis;
Step S3, changes to be converted to corresponding content of text to described non-structural data, extracts the non-structural data after conversion and described structured data and clean;
Step S4, multiple business model is set up according to the internal demands of enterprise, wherein, described multiple business model is combined by the non-structural data after extracting in machine and step S3 and cleaning and structured data, described business model flows into self-teaching and optimization according to data, automatically generates primary data center;
Step S5, carries out Treatment Analysis to obtain business diagnosis result to the data in described primary data center, and described business diagnosis result comprises contact details and the contact details of business and time of contact details, business and external factor between different business;
Step S6, is shown described business diagnosis result by digitizing and graphic interface.
Further, described internal data is the business datum of business system, and described business datum comprises: management data, marketing data, procurement data, inventory data, qualitative data, cost data, financial data, Row control data and industrial chain data.
Further, described structured data is Document type data, and described non-structural data comprise: pdf document, WORD document, XML storehouse and voice document.
Further, in described step S3, change to be converted to corresponding content of text to described non-structural data, comprise the steps:
Set up speech model, according to described speech model, institute's voice file is converted to content of text;
Set up content extraction model, according to described content extraction model, described pdf document, WORD document and XML storehouse are converted to content of text.
Further, in described step S3, also comprise the steps: to set up quality of data index to described structural data.
Further, in described step S5, Treatment Analysis is carried out to the data in described primary data center, comprises the steps: to classify to the data in described primary data center, cluster, recurrence, association, neural network, data prediction and business model enlarging.
Further, described business diagnosis result also comprises: the relation information of the relation information of operation flow, the relation information between data and data, external factor and internal factor.
Further, in described step S6, described business diagnosis result represents with the form of form, static map and Dynamic Graph.
According to the method for the modeling of the large data of multi-source business of the embodiment of the present invention, analysis, self-optimization, while importing inner business datum and outside internet data, structured data and non-structural data are extracted, cleaned, artificial escape, merging, classification are carried out to non-structural data, and after quality of data index is set up to business datum and internet data, adopt multiple stage machine Distributed Calculation and storage, so very large improves data collection capability.The present invention is based on paid close attention to business model, the analysis of robotization and displaying, the problem with business correlativity can be found fast.Under guarantee data validity, carry out accurate analysis business datum, make business data model carry out robotization foundation, optimization, expansion, correlation analysis is carried out and displaying is resolved for individual demand, and solve the collection of existing business data, process, collect, analyze, the deficiency of exhibiting method, the mistaken ideas being hidden in mechanism's operation can be found out, monitor and managment is deepened to operation process.
The aspect that the present invention adds and advantage will part provide in the following description, and part will become obvious from the following description, or be recognized by practice of the present invention.
Accompanying drawing explanation
Above-mentioned and/or additional aspect of the present invention and advantage will become obvious and easy understand from accompanying drawing below combining to the description of embodiment, wherein:
Fig. 1 is the FB(flow block) of method of the modeling of large data of multi-source business according to the embodiment of the present invention, analysis, self-optimization;
Fig. 2 is the frame diagram of method of the modeling of large data of multi-source business according to the embodiment of the present invention, analysis, self-optimization;
Fig. 3 is the schematic diagram of method of the modeling of large data of multi-source business according to the embodiment of the present invention, analysis, self-optimization.
Embodiment
Be described below in detail embodiments of the invention, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or has element that is identical or similar functions from start to finish.Be exemplary below by the embodiment be described with reference to the drawings, be intended to for explaining the present invention, and can not limitation of the present invention be interpreted as.
The invention provides the method for the modeling of the large data of a kind of multi-source business, analysis, self-optimization, the function that the method provides the collection to business datum, processes, collects, analyzes, represents, Establishing process standardization in source procedure can be carried out in collection data and process diversity business datum, and be combined with each other with business demand, automatically build business model by machine learning, valid data are pooled to primary data center.The data of primary data center carry out data display by the result after analysis.
Below with reference to Fig. 1 to Fig. 3, the method for the modeling of the large data of multi-source business of the embodiment of the present invention, analysis, self-optimization is described.
As shown in Figure 1, the method for the modeling of the large data of multi-source business of the embodiment of the present invention, analysis, self-optimization, comprises the steps:
Step S1, receives the internal data from business system and the external data from internet.
In one embodiment of the invention, internal data is the business datum of business system, comprise from ERP (EnterpriseResourcePlanning, Enterprise Resources Plan) system, CRM (CustomerRelationshipManagement, customer relation management) system, OA (OfficeAutomation, office automation) system.
Wherein, business datum comprises: management data, marketing data, procurement data, inventory data, qualitative data, cost data, financial data, Row control data and supply chain data.
It should be noted that, because of the difference of each institution business type, the data type of internal data sources also can be various.
Step S2, is categorized as structured data and non-structural data to internal data and external data analysis.
Particularly, the file layout according to internal data and external data is analyzed, and is structured data and non-structural data by Data Placement.Wherein, structured data is Document type data.Non-structural data comprise: pdf document, WORD document, XML storehouse and voice document.
Step S3, changes to be converted to corresponding content of text to non-structural data, extracts the non-structural data after conversion and structured data and clean.
In this step, change to be converted to corresponding content of text to non-structural data, comprise the steps:
(1) for voice document:
Set up speech model, according to speech model, voice document is converted to content of text and exports.
(2) for pdf document, WORD document and XML library file:
Set up content extraction model, according to content extraction model, pdf document, WORD document and XML storehouse are converted to content of text and export.
It should be noted that, in this step, also need to set up quality of data index to structural data.
Particularly, structural data needs to set up quality of data index, and wherein quality of data index comprises: the accuracy, integrality, consistance, completeness, uniqueness, accessibility, accuracy, promptness, correlativity, workability, definition, objectivity etc. of data.Wherein, the content of quality of data index can be arranged according to the needs of user, adds or deletes, not repeating them here.
Then, with reference to figure 2, by core processor, said structure data and non-structural data are extracted and cleaned.
Step S4, set up multiple business model according to the internal demands of enterprise, wherein, multiple business model is combined by the non-structural data after extracting in machine and step S3 and cleaning and structured data, business model flows into self-teaching and optimization according to data, automatically generates primary data center.
In one embodiment of the invention, set up multiple business model to comprise the steps: first to build model, then deployment model, assessment models and model measurement.
For the business demand that each mechanism is different, each mechanism, in conjunction with own service feature, by a series of flow process such as structure, assessment, deployment, test of primary data center to data, forms business model accurately.Business model passes through the continuous self-perfection of algorithm in machine learning, thus reaches the optimum of business model, reaches lifecycle management business model effect.
Assessed by a series of correctness School Affairs making business model and extraction and cleaned data integrate by core processor, business model just can be disposed after a test, by the continuous inflow of data, business model also can be changed.
Business model and the data after processing be combined with each other, and by machine self-teaching and optimization, automatically form primary data center.Particularly, structural data and unstructured data process in conjunction with business model and core processor after data cleansing, after reaching the requirement that readability is strong, ease for use is high, data are unique, finally forward primary data center to.
Step S5, carries out Treatment Analysis to obtain business diagnosis result to the data in primary data center, and business diagnosis result comprises contact details and the contact details of business and time of contact details, business and external factor between different business.
In this step, with reference to figure 3, Treatment Analysis is carried out to the data in primary data center, comprises the steps: to classify to the data in primary data center, cluster, recurrence, association, neural network, data prediction and business model enlarging, thus, business diagnosis result can be obtained.
Wherein, business diagnosis result be in business scope between relation, comprising: the relation (such as: the relation between production business and marketing) of (between business and business), the contact details of the relation (such as: the adjustment of policy is on the impact of internal) of the relation (such as: what affect production business has which) of operation flow, relation (such as: the reason of the difference of the sales volume of last month), external factor and the internal factor between data and data, business and external factor and the contact details of business and time between different business.
The data of primary data center carry out being polymerized, classification etc. is comprehensive analyze after the result that calculates can pull calculating, allow data oneself deduces, expression business oneself viewpoint.The data that primary data center provides can better embody operation level and structure, and data are more concentrated, and quality is more accurate.The data analysis of primary data center can be expanded according to the demand of business, carries out the integrated of data algorithm, thus can analyze plurality of application scenes, promotes existing business to the analysis result of tera incognita.
Step S6, is shown business diagnosis result by digitizing and graphic interface.
Particularly, the business diagnosis result that step S5 obtains can be shown by digitizing with graphically, and such as the form of form, static map and Dynamic Graph represents.Business diagnosis result is carried out data display, by Stroage (M): shap number CURD manages, Painter (V): the life cycle management of canvase element, view rendering, drawing, renewal control, Handler (C): event interaction process, create the infrastructure components such as service parameter coordinate system, legend, prompting, tool box, and on this, construct the multifarious form of expression.Such as: broken line graph, histogram, scatter diagram, K line chart, pie chart, radar map, map, chord figure, power guiding layout, time diagram, panel board and crater blasting, support that the many chart mixing of the heap sum of any dimension represent simultaneously.
User by the demand of business, can carry out the user interactions behaviors such as graticule is auxiliary, event is mutual, dynamic interpolation data to these charts.By the visual presentation of analysis result, show the cascade of data and springing up property fast, reacted state and the trend of business more directly, more efficiently.
According to the method for the modeling of the large data of multi-source business of the embodiment of the present invention, analysis, self-optimization, while importing inner business datum and outside internet data, structured data and non-structural data are extracted, cleaned, artificial escape, merging, classification are carried out to non-structural data, and after quality of data index is set up to business datum and internet data, adopt multiple stage machine Distributed Calculation and storage, so very large improves data collection capability.The present invention is based on paid close attention to business model, the analysis of robotization and displaying, the problem with business correlativity can be found fast.Under guarantee data validity, carry out accurate analysis business datum, make business data model carry out robotization foundation, optimization, expansion, correlation analysis is carried out and displaying is resolved for individual demand, and solve the collection of existing business data, process, collect, analyze, the deficiency of exhibiting method, the mistaken ideas being hidden in mechanism's operation can be found out, monitor and managment is deepened to operation process.
In the description of this instructions, specific features, structure, material or feature that the description of reference term " embodiment ", " some embodiments ", " example ", " concrete example " or " some examples " etc. means to describe in conjunction with this embodiment or example are contained at least one embodiment of the present invention or example.In this manual, identical embodiment or example are not necessarily referred to the schematic representation of above-mentioned term.And the specific features of description, structure, material or feature can combine in an appropriate manner in any one or more embodiment or example.
Although illustrate and describe embodiments of the invention above, be understandable that, above-described embodiment is exemplary, can not be interpreted as limitation of the present invention, those of ordinary skill in the art can change above-described embodiment within the scope of the invention when not departing from principle of the present invention and aim, revising, replacing and modification.Scope of the present invention is by claims extremely equivalency.
Claims (8)
1. a method for the modeling of the large data of multi-source business, analysis, self-optimization, is characterized in that, comprise the steps:
Step S1, receives the internal data from business system and the external data from internet;
Step S2, is categorized as structured data and non-structural data to described internal data and external data analysis;
Step S3, changes to be converted to corresponding content of text to described non-structural data, extracts the non-structural data after conversion and described structured data and clean;
Step S4, multiple business model is set up according to the internal demands of enterprise, wherein, described multiple business model is combined by the non-structural data after extracting in machine and step S3 and cleaning and structured data, described business model flows into self-teaching and optimization according to data, automatically generates primary data center;
Step S5, carries out Treatment Analysis to obtain business diagnosis result to the data in described primary data center, and described business diagnosis result comprises contact details and the contact details of business and time of contact details, business and external factor between different business;
Step S6, is shown described business diagnosis result by digitizing and graphic interface.
2. the method for the modeling of the large data of multi-source business as claimed in claim 1, analysis, self-optimization, it is characterized in that, described internal data is the business datum of business system, and described business datum comprises: management data, marketing data, procurement data, inventory data, qualitative data, cost data, financial data, Row control data and industrial chain data.
3. the method for the modeling of the large data of multi-source business as claimed in claim 1, analysis, self-optimization, it is characterized in that, described structured data is Document type data, and described non-structural data comprise: pdf document, WORD document, XML storehouse and voice document.
4. the method for the modeling of the large data of multi-source business as claimed in claim 3, analysis, self-optimization, is characterized in that, in described step S3, change to be converted to corresponding content of text, comprise the steps: described non-structural data
Set up speech model, according to described speech model, institute's voice file is converted to content of text;
Set up content extraction model, according to described content extraction model, described pdf document, WORD document and XML storehouse are converted to content of text.
5. the method for the modeling of the large data of multi-source business as claimed in claim 1, analysis, self-optimization, is characterized in that, in described step S3, also comprise the steps: to set up quality of data index to described structural data.
6. the method for the modeling of the large data of multi-source business as claimed in claim 1, analysis, self-optimization, it is characterized in that, in described step S5, Treatment Analysis is carried out to the data in described primary data center, comprises the steps: to classify to the data in described primary data center, cluster, recurrence, association, neural network, data prediction and business model enlarging.
7. the method for the modeling of the large data of multi-source business as claimed in claim 1, analysis, self-optimization, it is characterized in that, described business diagnosis result also comprises: the relation information of the relation information of operation flow, the relation information between data and data, external factor and internal factor.
8. the method for the modeling of the large data of multi-source business as claimed in claim 1, analysis, self-optimization, it is characterized in that, in described step S6, described business diagnosis result represents with the form of form, static map and Dynamic Graph.
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| C06 | Publication | ||
| PB01 | Publication | ||
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Application publication date: 20160120 |