CN113849732A - Enterprise portrait establishing method and system - Google Patents
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- CN113849732A CN113849732A CN202111115257.8A CN202111115257A CN113849732A CN 113849732 A CN113849732 A CN 113849732A CN 202111115257 A CN202111115257 A CN 202111115257A CN 113849732 A CN113849732 A CN 113849732A
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Abstract
The invention provides a method and a system for establishing an enterprise portrait, wherein the method comprises the following steps: acquiring basic data, and generating a plurality of label types according to the characteristic types of the basic data; acquiring enterprise data, and processing the enterprise data according to different label types to obtain a label under each label type; and constructing an enterprise portrait according to the label. The enterprise portrait establishing method can comprehensively describe the enterprise.
Description
Technical Field
The invention belongs to the technical field of big data, and particularly relates to an enterprise portrait establishing method and system.
Background
The enterprise portrait describes multi-dimensional enterprise business information data such as enterprise basic conditions, business conditions, consumption decisions and product appeal, and is used for helping users to comprehensively know enterprise conditions and find an entry point for later cooperation. However, the prior art does not provide an enterprise representation that can fully describe an enterprise.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an enterprise portrait establishing method and system, which can comprehensively describe an enterprise.
In a first aspect, an enterprise portrait creation method includes:
acquiring basic data, and generating a plurality of label types according to the characteristic types of the basic data;
acquiring enterprise data, and processing the enterprise data according to different label types to obtain a label under each label type;
and constructing an enterprise portrait according to the label.
Preferably, the basic data includes:
macroscopic layer data: an industry attribute for identifying an enterprise;
mesoscopic layer data: a business attribute for identifying a business;
data of the microscopic layer: for identifying enterprise internal role attributes.
Preferably, the basic data is obtained by:
investigating decision makers and users of enterprises;
the analysis method comprises the following steps of (1) carrying out investigation by utilizing a qualitative analysis method and a quantitative analysis method to obtain the product; or
And analyzing the background behavior data, the background transaction data and the third-party monitoring platform data of the enterprise to obtain the data.
Preferably, the tag categories include at least one of:
an industry tag, an enterprise base information tag, an asset tag, an intellectual property tag, a technology development tag, a production and management tag, a marketing tag, an application scenario tag, a vendor tag, a demand customer tag, an action tag, a transaction tag, a talent tag, a policy tag, a competitor tag, and a cooperative object tag.
Preferably, the tags include a fact tag, a model tag, and a prediction tag.
Preferably, the fact label is obtained by cleaning, duplicate removal, invalidation removal, exception removal and feature integration and extraction of the enterprise data;
the model label is formed by combining one or more fact labels;
and the action prediction label is used for predicting the behavior preference of the enterprise through the fact label and the model label.
Preferably, after said constructing an enterprise representation from said tags, further comprising:
and maintaining the labels in the enterprise portrait according to the enterprise data received in real time.
In a second aspect, an enterprise representation creation system includes:
a collecting unit: the label generation device is used for acquiring basic data and generating a plurality of label types according to the characteristic categories of the basic data;
an analysis unit: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for acquiring enterprise data and processing the enterprise data according to different label types to obtain labels under each label type;
a construction unit: for constructing an enterprise representation from the tags.
Preferably, the basic data includes:
macroscopic layer data: an industry attribute for identifying an enterprise;
mesoscopic layer data: a business attribute for identifying a business;
data of the microscopic layer: for identifying enterprise internal role attributes.
Preferably, the basic data is obtained by:
investigating decision makers and users of enterprises;
the analysis method comprises the following steps of (1) carrying out investigation by utilizing a qualitative analysis method and a quantitative analysis method to obtain the product; or
And analyzing the background behavior data, the background transaction data and the third-party monitoring platform data of the enterprise to obtain the data.
According to the technical scheme, the enterprise portrait establishing method and the enterprise portrait establishing system can comprehensively describe the enterprise.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a flowchart illustrating an enterprise representation creation method according to an embodiment.
FIG. 2 is a schematic diagram of a constructed representation of an enterprise.
FIG. 3 is a block diagram of an enterprise representation creation system according to a second embodiment.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby. It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
The first embodiment is as follows:
an enterprise sketch creating method, referring to fig. 1, includes:
s1: acquiring basic data, and generating a plurality of label types according to the characteristic types of the basic data;
s2: acquiring enterprise data, and processing the enterprise data according to different label types to obtain a label under each label type;
s3: and constructing an enterprise portrait according to the label.
Specifically, the method firstly collects basic data, and forms different label types according to different characteristic types of the basic data. The collection mode comprises investigation objects, investigation methods, data mining and the like. And then constructing a label system containing various labels, finally acquiring enterprise data, processing the enterprise data according to various label types to obtain corresponding labels, and finally collecting all the labels to form an enterprise image. Referring to FIG. 2, FIG. 2 shows 10 tags contained in an enterprise representation.
The method forms an enterprise portrait by the obtained label set of the enterprise, and each label is not isolated among all dimensions and has a strong association relation. The method can comprehensively describe the enterprises, meets the requirements of enterprise innovation and creation, cost reduction, quality improvement, efficiency improvement and high-quality development, and realizes efficient matching of resources through a block chain and a big data artificial intelligence algorithm.
Preferably, the basic data includes:
macroscopic layer data: an industry attribute for identifying an enterprise;
mesoscopic layer data: a business attribute for identifying a business;
data of the microscopic layer: for identifying enterprise internal role attributes.
Specifically, the macro layer data refers to the industry attributes of enterprises, and the current situation and the development trend of the target enterprise industry can be known through the industry attributes because different industries have different market structures, operation modes and operation rules. The mesoscopic data refers to enterprise attributes of the enterprise, such as enterprise establishment time, enterprise scale, personnel scale, income scale, active users, usage evaluation and the like. The current situation of the target enterprise can be known through the enterprise attributes. The micro-level data refers to the role attributes inside the enterprise, namely the role characteristics of the decision chain, such as comprising decision makers (boss and high-level management) and users (staff), and the attention points and requirements of the decision makers and the users are greatly different.
Preferably, the basic data is obtained by:
investigating decision makers and users of enterprises;
the analysis method comprises the following steps of (1) carrying out investigation by utilizing a qualitative analysis method and a quantitative analysis method to obtain the product; or
And analyzing the background behavior data, the background transaction data and the third-party monitoring platform data of the enterprise to obtain the data.
Specifically, the research objects include: 1) the decision maker: the method can know the information of strategic targets, development conditions, business modes, business conditions, management requirements, business requirements and the like of the enterprises through decision makers, so that the problems of the enterprises in aspects of increasing revenue, improving efficiency, reducing cost and the like can be concerned. 2) The user: according to the method, a user can know information such as experience, operation details, working details, process details and the like with emphasis, so that the problems of convenience, flexibility and the like of products in the aspect of user experience can be concerned.
The investigation method comprises the following steps: 1) qualitative analysis, for example, data such as behavior motivation, demand, change rule of an enterprise can be mined in interview, insight and other ways according to industry research reports, past experience and the like; 2) quantitative analysis refers to research methods that use data to test certain hypotheses based on data and likelihood studies, such as comparisons and analyses of various indicators, characteristics, and relationships to businesses.
The data mining comprises the steps of mining background behavior data, background transaction data and third-party monitoring platform data, and directly investigating the data.
Preferably, the tag categories include at least one of:
an industry tag, an enterprise base information tag, an asset tag, an intellectual property tag, a technology development tag, a production and management tag, a marketing tag, an application scenario tag, a vendor tag, a demand customer tag, an action tag, a transaction tag, a talent tag, a policy tag, a competitor tag, and a cooperative object tag.
Preferably, the tags include a fact tag, a model tag, and a prediction tag.
The fact label is obtained by cleaning, duplicate removal, invalidation removal, abnormality removal and feature integration and extraction of the enterprise data;
the model label is formed by combining one or more fact labels;
and the action prediction label is used for predicting the behavior preference of the enterprise through the fact label and the model label.
Specifically, the fact label refers to a process of cleaning, duplicate removal, invalidation removal, exception removal and feature integration extraction on basic data, belongs to a process of deepening understanding of data, and can be used for preparing for building a model label. The model tag is formed from a combination of one or more fact tags. The forecasting label is used for forecasting the behavior preference of the enterprise according to the existing fact data and model labels, and reflects the regularity of the enterprise to a certain extent.
For example, the resulting tag may be: label 5 → intellectual property protection → patent information → direction of research and development → technical field → product → parts/components → upstream supplier → raw material. The enterprise images obtained according to the labels can be as follows: enterprise a → intellectual property protection → invention patent: a compact valve driving mechanism → optimization of valve structure → valve driving technology → novel valve driving device → piston part → enterprise B → rubber material.
Preferably, after said constructing an enterprise representation from said tags, further comprising:
and maintaining the labels in the enterprise portrait according to the enterprise data received in real time.
In particular, there is a life cycle (propose-generate-execute) due to the tag. Therefore, the method needs to maintain and update the label in real time according to the real-time data of the enterprise, and the instantaneity of the enterprise image is ensured.
Example two:
an enterprise representation creation system, see FIG. 3, comprising:
a collecting unit: the label generation device is used for acquiring basic data and generating a plurality of label types according to the characteristic categories of the basic data;
an analysis unit: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for acquiring enterprise data and processing the enterprise data according to different label types to obtain labels under each label type;
a construction unit: for constructing an enterprise representation from the tags.
Preferably, the basic data includes:
macroscopic layer data: an industry attribute for identifying an enterprise;
mesoscopic layer data: a business attribute for identifying a business;
data of the microscopic layer: for identifying enterprise internal role attributes.
Preferably, the basic data is obtained by:
investigating decision makers and users of enterprises;
the analysis method comprises the following steps of (1) carrying out investigation by utilizing a qualitative analysis method and a quantitative analysis method to obtain the product; or
And analyzing the background behavior data, the background transaction data and the third-party monitoring platform data of the enterprise to obtain the data.
For the sake of brief description, the system provided by the embodiment of the present invention may refer to the corresponding content in the foregoing embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. An enterprise portrait creation method, comprising:
acquiring basic data, and generating a plurality of label types according to the characteristic types of the basic data;
acquiring enterprise data, and processing the enterprise data according to different label types to obtain a label under each label type;
and constructing an enterprise portrait according to the label.
2. The enterprise image creation method according to claim 1,
the basic data includes:
macroscopic layer data: an industry attribute for identifying an enterprise;
mesoscopic layer data: a business attribute for identifying a business;
data of the microscopic layer: for identifying enterprise internal role attributes.
3. The enterprise image creation method according to claim 1,
the basic data is obtained by the following method:
investigating decision makers and users of enterprises;
the analysis method comprises the following steps of (1) carrying out investigation by utilizing a qualitative analysis method and a quantitative analysis method to obtain the product; or
And analyzing the background behavior data, the background transaction data and the third-party monitoring platform data of the enterprise to obtain the data.
4. The enterprise image creation method according to claim 1,
the tag categories include at least one of:
an industry tag, an enterprise base information tag, an asset tag, an intellectual property tag, a technology development tag, a production and management tag, a marketing tag, an application scenario tag, a vendor tag, a demand customer tag, an action tag, a transaction tag, a talent tag, a policy tag, a competitor tag, and a cooperative object tag.
5. The enterprise image creation method according to claim 1,
the tags include fact tags, model tags, and prediction tags.
6. An enterprise representation creation method as claimed in claim 5,
the fact label is obtained by cleaning, duplicate removal, invalidation removal, abnormality removal and feature integration and extraction of the enterprise data;
the model label is formed by combining one or more fact labels;
and the action prediction label is used for predicting the behavior preference of the enterprise through the fact label and the model label.
7. The method of enterprise portrait creation of claim 1, further comprising, after said building an enterprise portrait from said tags:
and maintaining the labels in the enterprise portrait according to the enterprise data received in real time.
8. An enterprise representation creation system, comprising:
a collecting unit: the label generation device is used for acquiring basic data and generating a plurality of label types according to the characteristic categories of the basic data;
an analysis unit: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for acquiring enterprise data and processing the enterprise data according to different label types to obtain labels under each label type;
a construction unit: for constructing an enterprise representation from the tags.
9. The enterprise image creation method according to claim 8,
the basic data includes:
macroscopic layer data: an industry attribute for identifying an enterprise;
mesoscopic layer data: a business attribute for identifying a business;
data of the microscopic layer: for identifying enterprise internal role attributes.
10. The enterprise image creation method according to claim 8,
the basic data is obtained by the following method:
investigating decision makers and users of enterprises;
the analysis method comprises the following steps of (1) carrying out investigation by utilizing a qualitative analysis method and a quantitative analysis method to obtain the product; or
And analyzing the background behavior data, the background transaction data and the third-party monitoring platform data of the enterprise to obtain the data.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114971532A (en) * | 2022-04-29 | 2022-08-30 | 北京国联视讯信息技术股份有限公司 | Enterprise full-channel member management method and system based on big data |
| CN116776392A (en) * | 2023-07-26 | 2023-09-19 | 园创品牌管理(北京)有限公司 | Double nine-dimensional management method and system for improving intelligent market number |
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| CN109658478A (en) * | 2017-10-10 | 2019-04-19 | 爱信诺征信有限公司 | It is a kind of that the method and system of enterprise's portrait are provided |
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| CN114971532A (en) * | 2022-04-29 | 2022-08-30 | 北京国联视讯信息技术股份有限公司 | Enterprise full-channel member management method and system based on big data |
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