CN108509569B - Method and device for generating enterprise portrait, electronic equipment and storage medium - Google Patents
Method and device for generating enterprise portrait, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a method and a device for generating an enterprise portrait, electronic equipment and a storage medium, wherein the method comprises the following steps: capturing enterprise data of a target enterprise under at least one statistical dimension in the Internet, and storing the captured enterprise data in corresponding statistical dimension groups; determining a category attribute value of the target enterprise under at least one category attribute according to enterprise data matched with the category attribute in the statistical dimension group; obtaining a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute; and presenting the enterprise portrait of the target enterprise according to the target enterprise keyword. The technical scheme of the embodiment of the invention realizes the technical effects of obtaining enterprise keywords for describing enterprises in a targeted manner under the required statistical dimension according to the information disclosed in the Internet and finally generating the enterprise portrait, provides a new mode for generating the enterprise portrait and improves the accuracy of the enterprise portrait in the depiction of the enterprises.
Description
Technical Field
The embodiment of the invention relates to the technical field of data processing, in particular to a method and a device for generating an enterprise portrait, electronic equipment and a storage medium.
Background
With the continuous development of computer technology and big data technology, user portrait has been widely applied in various fields as an effective tool for delineating target users and connecting user appeal and design direction. A user representation is a tagged user model that is abstracted from information such as user social attributes, lifestyle habits, and consumption behaviors. The core task in constructing a user representation is to label the user with a "tag", which is a highly refined feature identifier obtained by analyzing the user information.
The enterprise, as the subject of the socio-economic activity, is involved in various aspects of the socio-economic activity. Accordingly, there is an increasing demand for enterprise representations (i.e., enterprise-level user representations), which are, however, very different from personal user representations. A personal user representation is a tagged user model that is abstracted based on information such as user social attributes, lifestyle, and consumption behaviors. And enterprises do not have these features.
Therefore, how to accurately and purposefully generate an enterprise sketch for drawing an enterprise is an important issue to be solved.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for generating an enterprise sketch, an electronic device, and a computer storage medium, so as to optimize a conventional generation manner of an enterprise sketch and improve accuracy of the enterprise sketch in describing an enterprise.
In a first aspect, an embodiment of the present invention provides a method for generating an enterprise portrait, including:
the method comprises the steps that enterprise data of a target enterprise under at least one statistical dimension are captured in the Internet, and the captured enterprise data are stored in corresponding statistical dimension groups, wherein the same statistical dimension corresponds to at least one category attribute, and different category attributes correspond to at least two category attribute values;
determining a category attribute value of the target enterprise under at least one category attribute according to enterprise data matched with the category attribute in the statistical dimension group;
obtaining a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute;
and presenting the enterprise portrait of the target enterprise according to the target enterprise keyword.
In a second aspect, an embodiment of the present invention further provides an apparatus for generating an enterprise portrait, including:
the enterprise data capturing module is used for capturing enterprise data of a target enterprise under at least one statistical dimension in the Internet and storing the captured enterprise data in corresponding statistical dimension groups, wherein the same statistical dimension corresponds to at least one category attribute, and different category attributes correspond to at least two category attribute values;
the category attribute value determining module is used for determining a category attribute value of the target enterprise under at least one category attribute according to enterprise data matched with the category attribute in the statistical dimension group;
the target enterprise keyword acquisition module is used for acquiring a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute;
and the enterprise portrait generating module is used for presenting the enterprise portrait of the target enterprise according to the target enterprise keyword.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
storage means for storing one or more programs;
When executed by the one or more processors, the one or more programs cause the one or more processors to implement any of the above-described methods of generating an enterprise representation.
In a fourth aspect, an embodiment of the present invention further provides a computer storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements any of the above-mentioned enterprise representation generation methods.
The enterprise data of the target enterprise under at least one statistical dimension is captured in the internet, the category attribute value of the target enterprise under at least one category attribute is obtained according to the enterprise data matched with different category attributes, the target enterprise keyword corresponding to the target enterprise is obtained according to the category attribute value to generate the enterprise portrait of the target enterprise, the enterprise keyword used for describing the enterprise is obtained in a targeted mode under the required statistical dimension according to information disclosed in the internet, and the technical effect of generating the enterprise portrait is achieved.
Drawings
FIG. 1a is a flowchart of a method for generating an enterprise representation according to an embodiment of the present invention;
FIG. 1b is a block diagram of an enterprise keyword generation according to an embodiment of the present invention;
FIG. 1c is a schematic diagram of an enterprise representation according to an embodiment of the present invention;
FIG. 1d is a schematic illustration showing another enterprise representation provided in accordance with an embodiment of the present invention;
FIG. 2 is a flowchart of a method for generating an enterprise representation according to a second embodiment of the present invention;
FIG. 3a is a flowchart of a method for generating an enterprise representation according to a third embodiment of the present invention;
FIG. 3b is a block diagram of an enterprise sketch generation based on a classification model according to a third embodiment of the present invention;
FIG. 4a is a flowchart of a method for generating an enterprise representation according to a fourth embodiment of the present invention;
FIG. 4b is a schematic diagram of an enterprise representation display according to a fourth embodiment of the present invention;
FIG. 5 is a schematic diagram of an apparatus for generating an enterprise representation according to a fifth embodiment of the present invention;
fig. 6 is a schematic structural diagram of a computer device according to a sixth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
It should be further noted that, for the convenience of description, only some but not all of the relevant elements of the present invention are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example one
Fig. 1a is a flowchart of a method for generating an enterprise portrait according to an embodiment of the present invention, which is applicable to a situation where an enterprise portrait corresponding to an enterprise is generated through information disclosed by the enterprise in the internet. The method may be performed by an enterprise representation generating apparatus, which may be implemented by software and/or hardware, and may be generally integrated in a computer device (e.g., a terminal or a server, etc.) capable of implementing an enterprise representation generating function, the method comprising the following operations:
S110, enterprise data of the target enterprise under at least one statistical dimension are captured in the Internet, and the captured enterprise data are stored in corresponding statistical dimension groups.
The same statistical dimension corresponds to at least one category attribute, and different category attributes correspond to at least two category attribute values.
In this embodiment, the target enterprise may refer to an enterprise selected by a requesting party and needing to generate an enterprise representation. The statistical dimension may be a statistical parameter which is provided by a user of a demand party or fixedly set by a system, and the statistical parameter may be used to describe attributes or features of an enterprise in one or more directions.
Typically, the statistical dimensions may include: statistical parameters for measuring social attributes of businesses, such as: the area, the industry, the scale, the nature of the enterprise, etc.; statistical parameters for measuring the business conditions of the enterprise, such as business income, tax intake, personnel recruitment, bidding, intellectual property rights and the like; statistical parameters for measuring enterprise marketing characteristics, such as communication channels, contact levels, communication depth and the like; and statistical parameters for risk prediction for the enterprise, such as, for example, deceased individuals, court litigation, administrative penalties, blacklists, and business anomalies. Of course, it is understood that those skilled in the art can set other types of statistical dimensions according to actual requirements, and the present embodiment does not limit this.
The category attribute specifically refers to a statistical parameter at a next level of the statistical dimension, where one statistical dimension may have one or more category attributes. The category attribute value specifically refers to a plurality of possible values of different enterprises under the same category attribute.
In one specific example, if the statistical dimension is business basic information, the category attributes under the statistical dimension may include: geographic, business age, and business funding. The category attribute value corresponding to the zone may include: "Beijing", "Tianjin" and "Shanghai"; the category attribute value corresponding to the commercial age may include: "less than 1 year", "2-5 years" and "more than 5 years"; the category attribute values corresponding to the business funds may include: "300 ten thousand or less", "300-800 ten thousand" and "800 ten thousand or more", etc.
Typically, if a statistical dimension corresponds to only one category attribute, the category attribute may be consistent with the statistical dimension, for example, if one statistical dimension is the size of an enterprise in a set industry, the category attribute in the statistical dimension is also the size of the enterprise in the set industry. Generally, the demarcation of the scale of the enterprise is different under different industries (such as industry, building industry or transportation industry, etc.), and accordingly, when determining the scale of the enterprise, the industry where the enterprise is located needs to be referred to at the same time. Correspondingly, the category attribute value corresponding to the enterprise size of the set industry may be: "micro", "small", "medium", and "large", etc.
In this embodiment, after a target enterprise, such as "XX company", that needs to obtain a representation of the enterprise is determined, enterprise data matching the target enterprise may be searched in the internet based on the enterprise name of the target enterprise or a pre-constructed seed lexicon (e.g., enterprise abbreviation, enterprise address, or enterprise phone, etc.) associated with the enterprise.
The enterprise data of the target enterprise may be searched for over the internet, or may be searched for only at a set website (e.g., a business website, a recruitment website, or a credit investigation website), which is not limited in this embodiment.
Furthermore, the captured enterprise data can be stored in corresponding statistical dimension groups according to identification keywords matched with different statistical dimensions. For example, the identification keyword corresponding to the statistical dimension of the enterprise scale in the industry a is "number of people" or "total number of employees"; the identification key corresponding to the statistical dimension of the basic information of the enterprise is "place, address, creation time, registered fund, and the like". The captured enterprise data may be grouped into different statistical dimensions based on the identification keywords that match the different statistical dimensions.
S120, determining a category attribute value of the target enterprise under at least one category attribute according to the enterprise data matched with the category attribute in the statistical dimension group.
In an alternative implementation of this embodiment, especially for some simpler, specific category attributes, for example: the scale of the enterprise under the industry A can be obtained by extracting and comparing the field values of the key fields. For example: by searching key fields such as 'enterprise size' or 'enterprise number' in enterprise data corresponding to the statistical dimension group of the enterprise size of a target enterprise in the industry A, and acquiring field values of the key fields, for example: "1000 persons" according to the pre-established correspondence: 100-200 persons: micro "," 200-: small "," 500-: and "and: "1500 or more: large-scale ", the category attribute value of the target enterprise under the category attribute of enterprise scale under industry a can be determined to be" medium-scale ".
In another optional implementation of this embodiment, for some relatively abstract or highly generalized category attributes, for example: the category attribute corresponding to the category attribute value cannot be directly determined from enterprise information, such as "enterprise maturity", "enterprise innovation", or "enterprise investment risk", and a classification model may be trained in advance for the category attributes of the types described above, and the classification model may have an input of enterprise data matching the category attribute and an output of the classification model as the category attribute value. The classification model can be used for obtaining the category attribute value of the target enterprise under at least one category attribute.
S130, obtaining a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute.
The enterprise keywords are specifically descriptive words that can be used for measuring an enterprise portrait of a target enterprise. In general, the category attribute values are more specific and more quantitative, such as: 500 ten thousand registered funds, 5 years of commercial age, and the like; while the enterprise keywords are more popular or convenient for the user to quickly understand the enterprise, such as: "potential enterprises", "financial atmosphere is coarse", or "investment optimization", etc. Of course, in an extreme case, the category attribute value corresponding to the category attribute may be directly used as the target enterprise keyword.
In an optional implementation manner of this embodiment, a relationship comparison table between the category attribute value and the enterprise keyword may be pre-established, and the target enterprise keyword corresponding to the target enterprise may be obtained by searching the relationship comparison table based on the determined category attribute value corresponding to the category attribute.
In another optional implementation manner of this embodiment, one or more keyword generation models may also be established in advance, where an input of the keyword generation model is a category attribute value, and an output of the keyword generation model is an enterprise keyword. And inputting the determined category attribute value corresponding to the category attribute into the keyword generation model to obtain a target enterprise keyword corresponding to the target enterprise.
For convenience of understanding, fig. 1b shows a block diagram for generating an enterprise keyword according to an embodiment of the present invention, in this example, one statistical dimension corresponds to one category attribute, one category attribute corresponds to one classification model, and one category attribute value corresponds to one keyword.
As shown in fig. 1b, in order to obtain a target enterprise keyword corresponding to a target enterprise, enterprise data of the target enterprise may be firstly grouped into n statistical dimensions according to n statistical dimensions (statistical dimensions 1, …, statistical dimension n), for example, statistical dimension group corresponding to statistical dimension 1 stores data 1 … mlThe statistical dimension group corresponding to the statistical dimension n stores data 1 … mn。
According to a set algorithm (in fig. 1b, a clustering algorithm is taken as an example), n classification models respectively corresponding to n statistical dimensions are obtained in advance, and different classification models correspond to different class attributes and are used for outputting class attribute values of different enterprise data under different class attributes. For example, classification model 1 corresponds to K1Individual category attribute value (Classification 1 … K)1) The classification model n corresponds to KnIndividual category attribute value (Category 1 … K)n) After the data stored in the statistical dimension groups are input into the corresponding classification models, n category attribute values respectively corresponding to the statistical dimension groups can be obtained. And finally obtaining n target enterprise keywords corresponding to the target enterprise according to the one-to-one correspondence between the category attribute values and the enterprise keywords.
And S140, presenting the enterprise portrait of the target enterprise according to the target enterprise keyword.
In this embodiment, the enterprise image is obtained by the target enterprise keyword obtained in the foregoing steps. Correspondingly, a mode customized by a demand side or a preset mode can be selected, and the enterprise portrait of the target enterprise is presented based on the target enterprise keyword.
By way of example and not limitation, two ways of presenting an enterprise representation of a target enterprise are shown in FIG. 1c and FIG. 1d, respectively.
As shown in fig. 1c, an enterprise Logo (e.g., enterprise Logo, enterprise abbreviation, or enterprise abbreviation) of a target enterprise may be presented at a central location, and a target enterprise keyword corresponding to the target enterprise is displayed around the enterprise Logo in a surrounding manner to obtain an enterprise representation; as shown in fig. 1d, a target business keyword corresponding to a target business may be filled in a graph with a set shape (for example, a shape of a business Logo or a typical business marker) to obtain a business representation.
Of course, those skilled in the art may present the enterprise representation in other ways according to actual needs, and the embodiment is not limited thereto.
The enterprise data of the target enterprise under at least one statistical dimension is captured in the internet, the category attribute value of the target enterprise under at least one category attribute is obtained according to the enterprise data matched with different category attributes, the target enterprise keyword corresponding to the target enterprise is obtained according to the category attribute value to generate the enterprise portrait of the target enterprise, the enterprise keyword used for describing the enterprise is obtained in a targeted mode under the required statistical dimension according to information disclosed in the internet, and the technical effect of generating the enterprise portrait is achieved.
Example two
Fig. 2 is a flowchart of a method for generating an enterprise representation according to a second embodiment of the present invention, which is embodied on the basis of the second embodiment, in this embodiment, a category attribute value of the target enterprise under at least one category attribute is determined according to enterprise data matching the category attribute in the statistical dimension group, specifically: acquiring enterprise data matched with the target category attribute in the statistical dimension group as current processing data; searching a field value corresponding to at least one key field in the current processing data; determining a category attribute value of the target enterprise under the target category attribute according to the at least one found field value;
And obtaining a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute, specifically: and obtaining a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute and the mapping relation between the preset category attribute value and the enterprise keyword. Correspondingly, the method of the embodiment may include:
s210, enterprise data of the target enterprise under at least one statistical dimension are captured in the Internet, and the captured enterprise data are stored in corresponding statistical dimension groups.
The same statistical dimension corresponds to at least one category attribute, and different category attributes correspond to at least two category attribute values.
And S220, acquiring enterprise data matched with the target category attribute in the statistical dimension group as current processing data.
In this embodiment, the category attribute values of the target enterprise under the category attributes of the statistical dimension group are obtained according to the enterprise data of the category attributes corresponding to the same statistical dimension group, and thus the category attribute value of each category attribute in each statistical dimension group can be obtained.
And S230, searching a field value corresponding to at least one key field in the current processing data.
The key field specifically refers to a predetermined key word that is strongly associated with the category attribute value of the category attribute. For example, if a category attribute corresponding to a target business is "region of residence," the key fields corresponding to the category attribute may be: "office location", "headquarters", or "business address", etc.
After determining the key field in the currently processed data, the field value corresponding to the key field can be finally obtained by searching the set equivalence relation characters (for example, "at", "set" or "yes" etc.) around the key field.
For example, for a category attribute of "region of interest" in the statistical dimension group, the current processing data is: the "headquarter of the XX company is set in beijing city", the preset key field is "headquarter", the preset equivalence relation character is "set", and the value of the "beijing city" field can be finally obtained by searching the data.
S240, determining a category attribute value of the target enterprise under the target category attribute according to the at least one found field value.
In this embodiment, the category attribute value of the target enterprise under the set category attribute may be finally determined according to the correspondence between the pre-established field value and the category attribute value and by the found field value.
For example: the correspondence between the field value and the category attribute value pre-established for the category attribute of the 'region of the user' is as follows: "Beijing, Shanghai, Shenzhen" < - > "first-line city"; "Tianjin, Nanjing, Guangzhou" < - > "second-line city", according to the field value "Beijing" determined in the previous example, the category attribute value corresponding to the category attribute of "the area" can be finally determined as "first-line city".
And S250, obtaining a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute and the mapping relation between the preset category attribute value and the enterprise keyword.
In this embodiment, a one-to-one correspondence between category attribute values and enterprise keywords may be established, and a correspondence between two or more category attribute values (belonging to the same statistical dimension or belonging to different statistical dimensions) and one or more enterprise keywords may be established, which is not limited in this embodiment.
And S260, presenting the enterprise portrait of the target enterprise according to the target enterprise keyword.
The technical scheme of the embodiment of the invention determines the category attribute value of the target enterprise under the set category attribute by using the characteristic attribute (field value of the key field) directly contained in the enterprise data, provides a method for determining the corresponding category attribute value under the more intuitive or specific category attribute, and can quickly and accurately determine the enterprise keyword corresponding to the category attribute.
EXAMPLE III
Fig. 3a is a flowchart of a method for generating an enterprise representation according to a third embodiment of the present invention, which is embodied on the basis of the foregoing embodiment, and in this embodiment, a category attribute value of the target enterprise under at least one category attribute is determined according to enterprise data in the statistical dimension group that matches the category attribute, specifically: acquiring enterprise data matched with the target category attribute in the statistical dimension group as current processing data; inputting the current processing data into a target classification model matched with the target class attribute, and taking an output result of the target classification model as a class attribute value of the target enterprise under the target class attribute;
And obtaining a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute, specifically: and inputting the category attribute value corresponding to the category attribute into at least one keyword generation model, and taking an output result of the at least one keyword generation model as a target enterprise keyword corresponding to the target enterprise. Correspondingly, the method of the embodiment may include:
s310, capturing enterprise data of the target enterprise under at least one statistical dimension in the Internet, and storing the captured enterprise data in corresponding statistical dimension groups.
The same statistical dimension corresponds to at least one category attribute, and different category attributes correspond to at least two category attribute values.
S320, acquiring the enterprise data matched with the target category attribute in the statistical dimension group as current processing data.
S330, inputting the current processing data into a target classification model matched with the target class attribute, and taking an output result of the target classification model as a class attribute value of the target enterprise under the target class attribute.
Correspondingly, before inputting the current processing data into the target classification model matched with the target class attribute, the method further comprises the following steps:
acquiring sample data corresponding to at least one category attribute of at least one statistical dimension, wherein a category attribute value under the category attribute is marked in the sample data in advance; and training the standard classification model matched with the corresponding class attribute by using the sample data to obtain a classification model matched with the class attribute.
In a specific example, an enterprise volume data set can be collected on the internet aiming at one or more selected enterprises in advance, classification information respectively corresponding to m category attributes is obtained according to m preset statistical dimensions (one statistical dimension is set to only correspond to one category attribute), Xk category sets respectively corresponding to different classification information are obtained through a clustering algorithm, and k belongs to [1, m ]; and according to the correspondence between the category sets and the keywords (i.e., the category attribute values described above), Xk category attribute values corresponding to the Xk category sets, respectively, are determined, and finally m classification models corresponding to the m category attributes, respectively, are established.
In one specific example: clustering the category attributes of the enterprise scale of a set industry to obtain four category sets, namely, different category sets correspond to different category attribute values, namely: the category attribute a corresponds to "small", the category attribute B corresponds to "medium", the category attribute C corresponds to "large", and the category attribute D corresponds to "extra large". By training a standard classification model (e.g., a support vector machine model, a classification tree model, or a regression model, etc.) using a class set labeled with corresponding class attribute values, a classification model matching the class attributes can be obtained. Correspondingly, after the enterprise data matched with the enterprise scale of the set industry of the target enterprise is input into the pre-trained classification model matched with the enterprise scale of the set industry, the category attribute value corresponding to the enterprise scale of the set industry of the target enterprise can be obtained.
It should be noted that the class attribute values corresponding to different class attributes are obtained by training the classification model, so that the effect of dynamically updating the classification model along with the input data can be realized, the classification model can continuously adapt to new data appearing in the internet, and the universality and adaptability of the scheme are stronger.
S340, inputting the category attribute value corresponding to the category attribute into at least one keyword generation model, and taking the output result of the at least one keyword generation model as a target enterprise keyword corresponding to the target enterprise.
Wherein the same keyword generation model corresponds to at least one category attribute.
Similarly, by training a keyword generation model establishing a linear or nonlinear relationship between the category attribute value and the enterprise keyword in advance, the corresponding target enterprise keyword can be obtained according to the input category attribute value.
Typically, different keyword generation models may be trained for different category attributes, or a unified keyword generation model may be trained for a plurality of category attributes, which is not limited in this embodiment.
And S350, presenting the enterprise portrait of the target enterprise according to the target enterprise keyword.
Fig. 3b shows a block diagram of generating an enterprise portrait based on a classification model according to a third embodiment of the present invention. As shown in fig. 3b, after m classification models respectively corresponding to m class attributes are finally built by means of "acquiring massive enterprise information- > extracting classification information m- > clustering formation class information mXk- > class corresponding keywords (class attribute values) mXk- > model building m", acquiring enterprise information, and obtaining enterprise data respectively corresponding to different class attributes of the enterprise, the enterprise data are respectively input to the corresponding classification models, and then an enterprise portrait used for describing the enterprise can be finally obtained based on the m class attribute values output by the classification models.
According to the technical scheme of the embodiment of the invention, the classification model matched with the class attribute is trained, and the class attribute value corresponding to the class attribute is obtained based on the classification model, so that the technical effect of effectively determining the corresponding class attribute value aiming at the relatively abstract or highly generalized class attribute can be realized, and the universality and the applicability are stronger.
It should be emphasized again that, when actually determining the category attribute value corresponding to the category attribute, the manner of determining the category attribute value by extracting the field value of the key field and the manner of determining the category attribute value by the classification model may be used in combination according to the specific type of the category attribute, so as to further improve the effectiveness of the scheme.
Example four
Fig. 4a is a flowchart of a method for generating an enterprise representation according to a third embodiment of the present invention, which is embodied on the basis of the third embodiment, in this embodiment, the presenting an enterprise representation of the target enterprise according to the target enterprise keyword includes: constructing a circle with a set radius, and presenting an enterprise identifier of the target enterprise in the center of the circle; according to the number of the statistical dimensions and the number of the category attributes corresponding to different statistical dimensions, segmenting and expanding the circumference of the circle to obtain arcs corresponding to different statistical dimensions; and presenting corresponding statistical dimensions on one side of the circular arc close to the circle center, and presenting target enterprise keywords corresponding to the statistical dimensions on one side of the circular arc far away from the circle center. Correspondingly, the method of the embodiment may include:
S410, capturing enterprise data of the target enterprise under at least one statistical dimension in the Internet, and storing the captured enterprise data in a corresponding statistical dimension group.
The same statistical dimension corresponds to at least one category attribute, and different category attributes correspond to at least two category attribute values.
S420, determining a category attribute value of the target enterprise under at least one category attribute according to the enterprise data matched with the category attribute in the statistical dimension group.
S430, obtaining a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute.
S440, constructing a circle with a set radius, and presenting the enterprise identification of the target enterprise in the center of the circle.
S450, according to the number of the statistical dimensions and the number of the category attributes corresponding to the different statistical dimensions, the circle is segmented and expanded, and arcs corresponding to the different statistical dimensions are obtained.
And S460, presenting the corresponding statistical dimension on one side of the circular arc close to the circle center, and presenting the target enterprise keyword corresponding to the statistical dimension on one side of the circular arc far away from the circle center.
Fig. 4b shows a schematic diagram of displaying an enterprise portrait according to a fourth embodiment of the present invention. As shown in fig. 4b, the enterprise id of the target enterprise is displayed at the center of the circle, and three statistical dimensions are respectively displayed at the inner side of the circle, that is: the "business situation", "risk prediction", and "basic information" are different from the lengths of the arcs corresponding to different statistical dimensions according to the number of category attributes included under different statistics. The outer sides of the different arcs are shown with the class attribute values of the corresponding class attributes, respectively (in fig. 4b, for the sake of example, only generic class attributes are identified and the actual class attribute values are not identified). Further, the values of the attributes of different categories may be displayed differently (for example, the display size of the circle corresponding to the attribute of "IT technology" is the largest) according to the preset importance levels of the attributes of different categories.
The technical scheme of the embodiment of the invention provides a method for effectively presenting the enterprise portrait, and through the presentation mode of the enterprise portrait, a user can quickly and effectively master the category attribute values of various category attributes of a target enterprise under different statistical dimensions, thereby further providing user experience.
EXAMPLE five
Fig. 5 is a schematic diagram of an apparatus for generating an enterprise representation according to a fifth embodiment of the present invention, as shown in fig. 5, the apparatus includes: an enterprise data crawling module 510, a category attribute value determining module 520, a target enterprise keyword obtaining module 530, and an enterprise representation generating module 540. Wherein:
the enterprise data capturing module 510 is configured to capture enterprise data of a target enterprise in at least one statistical dimension in the internet, and store the captured enterprise data in corresponding statistical dimension groups, where a same statistical dimension corresponds to at least one category attribute, and different category attributes correspond to at least two category attribute values.
A category attribute value determining module 520, configured to determine, according to the enterprise data in the statistical dimension group that matches the category attribute, a category attribute value of the target enterprise under at least one category attribute.
The target enterprise keyword obtaining module 530 is configured to obtain a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute.
And an enterprise sketch generating module 540, configured to present an enterprise sketch of the target enterprise according to the target enterprise keyword.
The enterprise data of the target enterprise under at least one statistical dimension is captured in the internet, the category attribute value of the target enterprise under at least one category attribute is obtained according to the enterprise data matched with different category attributes, the target enterprise keyword corresponding to the target enterprise is obtained according to the category attribute value to generate the enterprise portrait of the target enterprise, the enterprise keyword used for describing the enterprise is obtained in a targeted mode under the required statistical dimension according to information disclosed in the internet, and the technical effect of generating the enterprise portrait is achieved.
On the basis of the foregoing embodiments, the category attribute value determining module 520 may be specifically configured to:
acquiring enterprise data matched with the target category attribute in the statistical dimension group as current processing data; searching a field value corresponding to at least one key field in the current processing data; and determining the category attribute value of the target enterprise under the target category attribute according to the at least one searched field value.
On the basis of the foregoing embodiments, the category attribute value determining module 520 may be specifically configured to:
acquiring enterprise data matched with the target category attribute in the statistical dimension group as current processing data; and inputting the current processing data into a target classification model matched with the target class attribute, and taking an output result of the target classification model as a class attribute value of the target enterprise under the target class attribute.
On the basis of the foregoing embodiments, the method may further include a classification model generating module, configured to obtain sample data corresponding to at least one class attribute of at least one statistical dimension before inputting the current processing data into a target classification model matched with the target class attribute, where a class attribute value under the class attribute is pre-marked in the sample data; and training the standard classification model matched with the corresponding class attribute by using the sample data to obtain a classification model matched with the class attribute.
On the basis of the foregoing embodiments, the target enterprise keyword obtaining module 530 may be configured to:
and obtaining a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute and the mapping relation between the preset category attribute value and the enterprise keyword.
On the basis of the foregoing embodiments, the target enterprise keyword obtaining module 530 may be configured to: inputting the category attribute value corresponding to the category attribute into at least one keyword generation model, and taking an output result of the at least one keyword generation model as a target enterprise keyword corresponding to the target enterprise; wherein the same keyword generation model corresponds to at least one category attribute.
On the basis of the foregoing embodiments, the enterprise representation generating module 540 may be configured to: constructing a circle with a set radius, and presenting the enterprise identification of the target enterprise in the center of the circle; according to the number of the statistical dimensions and the number of the category attributes corresponding to different statistical dimensions, segmenting and expanding the circumference of the circle to obtain arcs corresponding to different statistical dimensions; and presenting corresponding statistical dimensions on one side of the circular arc close to the circle center, and presenting target enterprise keywords corresponding to the statistical dimensions on one side of the circular arc far away from the circle center.
The enterprise portrait generation device can execute the enterprise portrait generation method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For details of the technology not described in detail in this embodiment, reference may be made to a method for generating an enterprise representation provided in any embodiment of the present invention.
EXAMPLE six
Fig. 6 is a schematic structural diagram of a computer device according to a sixth embodiment of the present invention. FIG. 6 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in FIG. 6 is only an example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 6, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, and commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of embodiments of the invention as described.
The processing unit 16 executes various functional applications and data processing by running a program stored in the system memory 28, for example, to implement a method for generating an enterprise representation according to an embodiment of the present invention.
That is, the processing unit implements, when executing the program: the method comprises the steps that enterprise data of a target enterprise under at least one statistical dimension are captured in the Internet, and the captured enterprise data are stored in corresponding statistical dimension groups, wherein the same statistical dimension corresponds to at least one category attribute, and different category attributes correspond to at least two category attribute values; determining a category attribute value of the target enterprise under at least one category attribute according to enterprise data matched with the category attribute in the statistical dimension group; obtaining a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute; and presenting the enterprise portrait of the target enterprise according to the target enterprise keyword.
EXAMPLE seven
An embodiment of the present invention further provides a computer storage medium storing a computer program, which is used to execute the method for generating an enterprise representation according to any one of the above embodiments of the present invention when executed by a computer processor.
That is, the processing unit implements, when executing the program: the method comprises the steps that enterprise data of a target enterprise under at least one statistical dimension are captured in the Internet, and the captured enterprise data are stored in corresponding statistical dimension groups, wherein the same statistical dimension corresponds to at least one category attribute, and different category attributes correspond to at least two category attribute values; determining a category attribute value of the target enterprise under at least one category attribute according to enterprise data matched with the category attribute in the statistical dimension group; obtaining a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute; and presenting the enterprise portrait of the target enterprise according to the target enterprise keyword.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM) or flash Memory), an optical fiber, a portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, Radio Frequency (RF), etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing description is only exemplary of the invention and that the principles of the technology may be employed. Those skilled in the art will appreciate that the present invention is not limited to the particular embodiments described herein, and that various obvious changes, rearrangements and substitutions will now be apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (7)
1. A method for generating an enterprise portrait, comprising:
the method comprises the steps that enterprise data of a target enterprise under at least one statistical dimension are captured in the Internet, and the captured enterprise data are stored in corresponding statistical dimension groups, wherein the same statistical dimension corresponds to at least one category attribute, and different category attributes correspond to at least two category attribute values;
determining a category attribute value of the target enterprise under at least one category attribute according to enterprise data matched with the category attribute in the statistical dimension group;
Obtaining a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute;
presenting an enterprise portrait of the target enterprise according to the target enterprise keyword;
determining a category attribute value of the target enterprise under at least one category attribute according to enterprise data matched with the category attribute in the statistical dimension group, wherein the determining comprises the following steps:
acquiring enterprise data matched with the target category attribute in the statistical dimension group as current processing data;
searching a field value corresponding to at least one key field in the current processing data;
determining a category attribute value of the target enterprise under the target category attribute according to the at least one found field value;
further comprising:
inputting the current processing data into a target classification model matched with the target class attribute, and taking an output result of the target classification model as a class attribute value of the target enterprise under the target class attribute;
before inputting the current processing data into the target classification model matched with the target class attribute, the method further comprises the following steps:
Acquiring sample data corresponding to at least one category attribute of at least one statistical dimension, wherein a category attribute value under the category attribute is marked in the sample data in advance;
and training the standard classification model matched with the corresponding class attribute by using the sample data to obtain a classification model matched with the class attribute.
2. The method of claim 1, wherein obtaining a target business keyword corresponding to the target business according to the determined category attribute value corresponding to the category attribute comprises:
and obtaining a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute and the mapping relation between the preset category attribute value and the enterprise keyword.
3. The method according to any one of claim 1, wherein obtaining a target business keyword corresponding to the target business according to the determined category attribute value corresponding to the category attribute comprises:
inputting the category attribute value corresponding to the category attribute into at least one keyword generation model, and taking an output result of the at least one keyword generation model as a target enterprise keyword corresponding to the target enterprise;
Wherein the same keyword generation model corresponds to at least one category attribute.
4. The method of claim 1, wherein presenting a business representation of the target business in accordance with the target business keyword comprises:
constructing a circle with a set radius, and presenting the enterprise identification of the target enterprise in the center of the circle;
according to the number of the statistical dimensions and the number of the category attributes corresponding to different statistical dimensions, segmenting and expanding the circumference of the circle to obtain arcs corresponding to different statistical dimensions;
and presenting corresponding statistical dimensions on one side of the circular arc close to the circle center, and presenting target enterprise keywords corresponding to the statistical dimensions on one side of the circular arc far away from the circle center.
5. An apparatus for generating an enterprise representation, comprising:
the enterprise data capturing module is used for capturing enterprise data of a target enterprise under at least one statistical dimension in the Internet and storing the captured enterprise data in corresponding statistical dimension groups, wherein the same statistical dimension corresponds to at least one category attribute, and different category attributes correspond to at least two category attribute values;
The category attribute value determination module is used for determining a category attribute value of the target enterprise under at least one category attribute according to enterprise data matched with the category attribute in the statistical dimension group;
the target enterprise keyword acquisition module is used for acquiring a target enterprise keyword corresponding to the target enterprise according to the determined category attribute value corresponding to the category attribute;
the enterprise sketch generation module is used for presenting an enterprise sketch of the target enterprise according to the target enterprise keyword;
the category attribute value determination module is specifically configured to:
acquiring enterprise data matched with the target category attribute in the statistical dimension group as current processing data; inputting the current processing data into a target classification model matched with the target class attribute, and taking an output result of the target classification model as a class attribute value of the target enterprise under the target class attribute;
the enterprise portrait generation device also comprises a classification model generation module used for inputting the current processing data Obtaining at least one statistical dimension of the target classification model before entering the target classification model matched with the target class attribute Sample data corresponding to each class attribute, wherein the number of samplesThe category attribute values under the category attributes are pre-marked in the data; using sample data, for matching with corresponding class attributeTraining the standard classification model to obtain a classification model matched with the class attribute;
the target enterprise keyword acquisition module is further configured to: inputting the category attribute value corresponding to the category attribute into at least one keyword generation model, and taking an output result of the at least one keyword generation model as a target enterprise keyword corresponding to the target enterprise; wherein the same keyword generation model corresponds to at least one category attribute.
6. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more programs, cause the one or more processors to implement a method of enterprise representation generation as recited in any of claims 1-4.
7. A computer storage medium having a computer program stored thereon, the program, when executed by a processor, implementing a method of generating an enterprise representation as claimed in any one of claims 1 to 4.
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| CN110795471B (en) * | 2019-10-31 | 2022-06-07 | 北京金堤科技有限公司 | Data matching method and device, computer readable storage medium and electronic equipment |
| CN111178747A (en) * | 2019-12-26 | 2020-05-19 | 金蝶征信有限公司 | Enterprise portrait generation method and device, computer equipment and storage medium |
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