CN115526500A

CN115526500A - Benefit-administration information pushing method, benefit-administration information pushing device, benefit-administration information pushing equipment, benefit-administration information pushing medium and program product

Info

Publication number: CN115526500A
Application number: CN202211209828.9A
Authority: CN
Inventors: 徐禄春; 吴林娟; 陈东时
Original assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Current assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Priority date: 2022-09-30
Filing date: 2022-09-30
Publication date: 2022-12-27

Abstract

The invention discloses a method, a device, equipment, a medium and a program product for promoting political affairs information push, and relates to the technical field of artificial intelligence. The method comprises the following steps: making the issued preferential policy information into a Huifeng type label; constructing a data model according to the heuchement type label and the specific dimension model, determining the matching degree of the heuchement label, the matching degree of the heuchement participation and the matching degree of the issuing activity according to the data model, the configuration of the determinant factor and the timeliness configuration, and calculating a matching value; and determining effective benefit administration information in the benefit policy information and enterprises which accord with the matching degree of each benefit policy information according to the standard data of the matching value to form a pushing pool device, and actively pushing the effective benefit administration information to the enterprises. The technical scheme of the invention effectively avoids the problems of missing sending, mistaken sending and message lag of the heuman messages in the related technology, and improves the accuracy, efficiency and instantaneity of obtaining the heuman messages.

Description

Benefit-administration information pushing method, benefit-administration information pushing device, benefit-administration information pushing equipment, benefit-administration information pushing medium and program product

Technical Field

The embodiment of the invention relates to the technical field of artificial intelligence, in particular to a method, a device, equipment, a medium and a program product for promoting political information push.

Background

In order to help enterprises to smoothly solve development problems, governments release a series of preferential policies on government affair websites according to current market situations.

The current heudy interaction process usually needs to manually input heudy information, such as heudy titles, heudy introduction, heudy types, heudy covers, recommendation or not, declaration time periods, heudy details and the like. Enterprise related personnel or tourists manually browse the benefit administration information resources of the park and judge whether the enterprise meets the conditions of the preferential policy. However, in the aspect of information acquisition, many enterprises are limited by information acquisition tools, information channels, external information blocking, too large information amount and the like, accuracy and timeliness of the acquired administrative information are limited, and the enterprises cannot timely enjoy the benefit of the preferential policy or even miss the benefit of the preferential policy.

Disclosure of Invention

The embodiment of the invention provides an administrative information pushing method, an administrative information pushing device, administrative information pushing equipment, an administrative information pushing medium and a program product, which can solve the problem that accuracy and invalidity of administrative information in the related technology are limited, and ensure accuracy and timeliness of acquiring effective administrative information by enterprises.

In a first aspect, an embodiment of the present invention provides a method for pushing heuman information, including:

making the issued preferential policy information into a Huifeng type label;

constructing a data model according to the heuchement type label and the specific dimension model, determining a heuchement label matching degree, a heuchement participation matching degree and an issuing activity matching degree according to the data model, the determinant factor configuration and the timeliness configuration, and calculating a matching value according to the heuchement label matching degree, the heuchement participation matching degree and the issuing activity matching degree;

and determining effective benefit administration information in the benefit policy information and enterprises which accord with the matching degree of each benefit policy information according to the standard reaching data of the matching value to form a pushing pool device, and actively pushing the effective benefit administration information to the enterprises.

In a second aspect, an embodiment of the present invention further provides an information pushing apparatus, where the apparatus includes:

the label forming module is used for making the issued preferential policy information into a Hui-administrative type label;

the matching degree calculation module is used for constructing a data model according to the heuchement type label and the specific dimension model, determining the matching degree of the heuchement label, the matching degree of the heuchement participation and the matching degree of the issued activity according to the data model, the configuration of the decisive factor and the timeliness configuration, and calculating a matching value according to the matching degree of the heuchement label, the matching degree of the heuchement participation and the matching degree of the issued activity;

and the benefit administration pushing module is used for determining effective benefit administration information in the preferential policy information and enterprises which accord with the matching degree of each preferential policy information according to the standard data of the matching value to form a pushing pool device and actively pushing the effective benefit administration information to the enterprises.

In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the method for pushing newfashioned information according to any one of the embodiments of the present invention.

In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for pushing administrative information according to any one of the embodiments of the present invention.

In a fifth aspect, embodiments of the present invention further provide a computer program product, including a computer program, where the computer program, when executed by a processor, implements the method for pushing newsletter information according to any of the embodiments of the present invention.

In the embodiment of the invention, issued preferential policy information is made into an administrative benefit type label, a data model is constructed according to the administrative benefit type label and a specific dimension model, the matching degree of the administrative benefit label, the matching degree of administrative participation and the matching degree of issuing activities are determined according to the data model, the configuration of a decisive factor and the configuration of timeliness, further, a matching value is calculated, enterprises with the effective administrative benefit information in the preferential policy information and the matching degree of each preferential policy information in accordance are determined according to standard data of the matching value, a pushing pool device is formed, and the effective administrative benefit information is actively pushed to the enterprises according to a triggering mechanism of the pushing pool device. According to the method and the device, the data model is constructed based on the heuman type label and the specific dimension model, the matching value is determined according to the data model, the determinant factor and the timeliness configuration, the effective heuman information in the preferential policy information and the enterprises which are consistent with the matching degree of each preferential policy information are determined according to the standard data of the matching value, the effective heuman information is pushed to the target enterprises which live in each time period, the problems of missing sending, mistaken sending and message lagging of the heuman information in the related technology are effectively avoided, and the accuracy, the efficiency and the instantaneity of obtaining the heuman information are improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

FIG. 1a is a flow chart of a platform and enterprise newsletter interaction business provided by the related art;

FIG. 1b is a technical framework diagram for implementing an hut interaction provided by the related art;

fig. 2 is a flowchart of an administrative information pushing method according to an embodiment of the present invention;

FIG. 3 is an analysis layout of a data model according to an embodiment of the present invention;

fig. 4 is a flowchart of another benefit information pushing method according to an embodiment of the present invention;

fig. 5 is a flowchart of another benefit information pushing method according to an embodiment of the present invention;

fig. 6a is a flowchart of another benefit information pushing method according to an embodiment of the present invention;

fig. 6b is a schematic diagram of pushing interaction in an newsletter information pushing method according to an embodiment of the present invention;

fig. 7 is a block diagram illustrating a structure of an information push apparatus for benefit administration according to an embodiment of the present invention;

fig. 8 is a block diagram of an electronic device according to an 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 noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined or explained in subsequent figures. According to the technical scheme, the data acquisition, storage, use, processing and the like meet relevant regulations of national laws and regulations.

Fig. 1a is a flow chart of a platform and enterprise newsletter interaction service provided by the related art. As shown in fig. 1a, the business operation logic of the benefit interaction includes:

1. and logging in the smart park background system by a system user to perform park management. And creating a new park area, and adding new living enterprise information under the new park area.

2. Newly-increased garden benefit administration information through backstage management system, at newly-increased garden benefit administration information's in-process, the following information content is typeeed in the manual work: a heuchian title, a heuchian introduction, a heuchian type, a heuchian cover, whether recommended, a time period declared, heuchian details, and the like. And issuing the benefit administration information through a background management system so as to synchronously display the preferential policy information on the portal website.

3. And enterprise related personnel or tourists access the intelligent park portal website to check the specific content of the preferential policy information. Enterprise personnel manually browse the benefit administration information resources of the park and automatically judge whether the enterprise self meets the conditions of the preferential policy.

4. For enterprises meeting the preferential policy, corresponding processes of policy business are processed online through other systems or processed on site by going to entity departments.

Fig. 1b is a technical framework diagram for implementing an huygen interaction provided by the related art. As shown in fig. 1b, the technical implementation of the existing wheaten interaction includes: the front-end technician develops two visual interface web system application interfaces, namely a backend management system (hereinafter denoted as ic _ vue) and a web portal (hereinafter denoted as ic-closed-web). Wherein, ic _ vue includes an enterprise management module and a platform benefit administration module. Preferential policies are displayed through the ic-close-web. And triggering a detailed menu control by a service person, and making the page parameters enter a background service layer for specific service logic program processing by json interactive judgment. For example, the interaction between the request message and the response message between the front end and the background service layer is realized through json or http. The background service layer comprises an interface and service module and a database module. The interface and service module comprises an enterprise management module logical interface and service, a platform benefit administration module logical interface and service and other module data interfaces and services.

Although the aforementioned benefits interactive mode can satisfy the requirement of the campus to provide preferential policy service for the resident enterprises, the following drawbacks are also present:

firstly, because each Huiming information needs to be picked and copied manually, incomplete content, missing copying and error copying are easy to happen, labor cost is increased, working efficiency of personnel is reduced, and Huiming interaction effect is more and more successful.

And secondly, the preferential policy information needs manual real-time attention, and the administrative benefit content is newly added in time, so that the administrative benefit information resource is synchronized to the portal website for display. The administrative information is easy to cause lag in manual processing, so that the real-time performance and accuracy of the administrative information are reduced, the progress of handling the administrative services of the resident enterprises is influenced, and even administrative resources are missed.

And thirdly, the page operation is a customized process, service data and process related data need to be maintained in each operation, and the operation is complicated and complex.

Fourth, enterprises entering the park at different times need to manually recheck whether to push the benefit administration message and manually push the benefit administration message, so that the benefit administration message acquisition efficiency is reduced.

In order to solve the above problems, embodiments of the present invention provide a new method for pushing administrative benefit information, which can effectively avoid the problems of missing transmission, false transmission, and message lag of the administrative benefit information in the related art, and improve accuracy, efficiency, and real-time performance of acquiring the administrative benefit information.

Fig. 2 is a flowchart of an benefit information pushing method according to an embodiment of the present invention. The method is suitable for the situation of automatically pushing benefit policy information matched with the benefit type to the enterprise. The method can be executed by a benefit information pushing device, the device can be realized by hardware and/or software, and the device is configured in the electronic equipment.

As shown in fig. 2, the method includes:

s210, making the issued preferential policy information into a Huideng type label.

The preferential policy information may be a preferential policy issued by a government, a website, and other channels. For example, the preferential policy information is content information of a preferential policy issued by a government affair website, and the preferential policy is used for helping enterprises matching the benefit type to smoothly solve the development problem.

The benefit type label is identification information of the preferential policy information. For example, the benefit type label may be determined by a keyword in the coupon policy information.

Illustratively, website content resources are obtained according to the website address of the government affair website, and the website content resources are analyzed to obtain preferential policy information; performing Chinese word segmentation analysis on the preferential policy information to obtain a keyword list, and determining a relation vocabulary set according to the keyword list; and determining the importance degree value of each keyword in the relation vocabulary set, and determining the hewlett-packard type label according to the importance degree value.

S220, constructing a data model according to the heuche style label and the specific dimension model, determining the matching degree of the heuche label, the matching degree of the heuche participation and the matching degree of the issued activity according to the data model, the configuration of the decisive factor and the timeliness configuration, and calculating a matching value according to the matching degree of the heuche label, the matching degree of the heuche participation and the matching degree of the issued activity.

The specific dimension model comprises dimension models such as enterprise information, enterprise behavior data, time, regions and the like.

The business information includes business identification information. The enterprise identification information is used to identify the enterprise, and includes, for example, an enterprise number, an enterprise name, an enterprise type, and the like. The enterprise behavior data represents data of historical behavior of the enterprise. For example, enterprise behavioral data includes enterprise liveness (year, quarter, month year parity), and enterprise historical engagement, among others.

A data model is a model that represents an enterprise from a variety of dimensions, and may also be referred to as a dimensional model. The dimension model is a model for realizing rapid analysis under a large data volume scene. The data model may be constructed by dimensional modeling techniques. The dimensional modeling includes four steps, which are respectively for selecting business processes, declaring granularity, determining dimensions, and constructing facts. Wherein, the business process is an irreparable event in the enterprise activity and is selected by the analysis target. The granularity is the granularity of data records, for example, a transaction of a business is a record, and each commodity in the transaction is the finest granularity. The dimension is the angle of data analysis. A fact is the set of all data describing all the core information of an objective thing.

The degree of matching of the heuchement label represents the degree of matching of the heuchement type label in the heuchement label dimension table and the type-related field value in the enterprise type dimension table.

The newsletter engagement match degree indicates how much of the statistics of the newsletter engagement data of the enterprise are. The degree of matching of the Wheatstone participation can be determined by the number of pieces of Wheatstone participation data of the enterprises.

The publishing activity match degree indicates how much data volume the enterprise publishes the various types of the editorial-related activity data. The matching degree of the issued activities can be determined by the data volume of the heusurvey related activity data issued by the enterprise.

Illustratively, building a data model from the Wheatstone type labels and the dimension-specific model includes: creating an hewlett-packard label dimension table according to the hewlett-packard type label; creating an enterprise information fact table according to the enterprise information; according to the enterprise information, constructing an enterprise type dimension table, an enterprise behavior dimension table, a time dimension table and a region dimension table according to enterprise type dimensions, behavior dimensions, time dimensions and region dimensions; and constructing a data model according to the structure of the snowflake model according to the Huimen label dimension table, the enterprise information fact table, the enterprise type dimension table, the enterprise behavior dimension table, the time dimension table and the region dimension table.

In which a snowflake model is illustrated as multiple snowflakes connected together when one or more dimension tables are not directly connected to a fact table, but are connected to the fact table through other dimension tables. The snowflake model represents direct connection or indirect connection relation between the fact table and the dimension table.

Fig. 3 is an analysis design diagram of a data model according to an embodiment of the present invention. As shown in fig. 3, the data model includes a benefit label dimension table, an enterprise information fact table, an enterprise type dimension table, an enterprise behavior dimension table, a time dimension table and a region dimension table, wherein the enterprise behavior dimension table includes an enterprise historical participation dimension table and an enterprise activity dimension table.

Specifically, an heuchement type label is used as a field value to construct an heuchement label dimension table. For example, the hewlett-packard label dimension table includes a web address URL and a hewlett-packard type label.

And the data entry of the enterprise information is used as fact table data to create an enterprise information fact table. The fields of the enterprise information fact table comprise an enterprise number, an enterprise name, an enterprise type key, an enterprise behavior key, a region key, a time key and the like. And designing a dimension model, wherein the dimension model comprises multi-dimensional analysis such as static dimension, dynamic dimension, time dimension, space dimension and the like. The embodiment of the invention sets up the dimension table from the enterprise type dimension, the enterprise behavior dimension, the time dimension and the region dimension respectively.

For example, for the Business type dimension Table, the International Business type code and custom incremental Business types are stored. The international industry type is put into a database as basic data, and the type identifier is set to be 'B-national standard'. The front-end visual page realizes the new application function of enterprise data, wherein, fields of 'custom type code' and 'custom type name' are added into the database as incremental data, and the type identifier is set as 'Z-custom'.

And for the enterprise behavior dimension table, storing attributes such as enterprise participation activity data, enterprise activity indexes and the like. The historical activity data of the enterprise is processed by the data statistics of the activities of the enterprise participating in the activities published by a park or other enterprises or participating in the activities in the system operation process. The enterprise liveness index is credit, registered capital, operation condition, profit amount, employee condition and release activity statistical data of the enterprise, and model processing is carried out. For example, the enterprise activity information condition (i.e. the liveness index) is processed by combining the time dimension, namely, the percentage of the rise of the year/quarter/month is respectively compared and calculated according to the profit amount corresponding to the year/quarter/month, such as: year-year, season-year, month-year and other field data are used as the enterprise activity index data.

For the time dimension table, attributes of year, quarter, month, week, day, etc. corresponding to the date are stored. And the other dimensions are combined with a time key for data processing according to requirements.

For the region dimension table, the attributes of countries, provinces, urban areas and the like corresponding to the regions are stored. And specifically, filtering keywords for the park amount according to the current enterprise operation location. And performing data processing on other dimensions according to the needs by combining with region keys.

Illustratively, according to the data model, the determinant factor configuration and the timeliness configuration, determining the Huifeng label matching degree, the Huifeng participation matching degree and the issuing activity matching degree comprises the following steps:

and determining an enterprise scope according to the decisive factor configuration, and determining a Huifeng information scope according to the timeliness configuration. The enterprise scope may be a set of enterprises which are determined to be capable of pushing the editorial information according to the configuration of the determinant factor. The heygen information range may be valid benefit policy information determined according to the heygen valid start date and the heygen valid end date configured with timeliness. In calculating the match value, it may be for an enterprise that is enterprise wide, and the information involved is of an information wide.

For each heuchement type label corresponding to the heuchement information range in the heuchement label dimension table in the data model, traversing the enterprise type dimension table of each enterprise corresponding to the enterprise range according to the current heuchement type label, and determining the matching degree of the heuchement labels of each enterprise and the current heuchement type label according to the traversal result;

determining the administrative participation data of the enterprises according to the enterprise historical participation activity data in the enterprise behavior dimension table corresponding to the enterprise scope in the data model, and determining the administrative participation matching degree of each enterprise according to the administrative participation data;

determining the issuing activity data statistic of the enterprises according to the enterprise issuing activity data in the enterprise behavior dimension table corresponding to the enterprise range in the data model, and determining the issuing activity matching degree of each enterprise according to the issuing activity data statistic.

For example, the enterprise type dimension table for each enterprise may be traversed based on each of the hewlett-packard type labels in the hewlett-packard label dimension table. And if the type related field value matched with the Huygen type label is traversed, giving the matching degree of the enterprise setting corresponding to the matched type related field value. The set matching degree can be pre-configured according to the actual service scene, and the specific value is not limited in the embodiment of the invention. For example, if a type-related field value matching the current hewlett packard type tag is traversed, the matching degree of the hewlett packard tag to the enterprise corresponding to the matching type-related field value is 60%. If the Huygen type label matched with the field value related to a certain enterprise type is not traversed, the matching degree of the Huygen label of the current enterprise is 0.

For example, assuming that enterprise a initially has 1 amount of the benefit-administration participation data, the benefit-administration participation matching degree is set to 10%, and then 1% is added for every 10 additional benefit-administration participation data, otherwise, the benefit-administration participation matching degree is 0 in the case of no benefit-administration participation data.

For example, the published activity matching degree increases by 1% for every 10 pieces of data amount of the administrative related activity data published by the enterprise, and if the enterprise does not publish any administrative related activity data, the published activity matching degree is 0.

Optionally, after constructing a data model according to the structure of the snowflake model according to the benefit label dimension table, the enterprise information fact table, the enterprise type dimension table, the enterprise behavior dimension table, the time dimension table, and the region dimension table, the method further includes:

acquiring enterprise information input data and/or enterprise information change data transmitted by a front-end visual page;

and updating the data model according to the enterprise information entry data and/or enterprise information change data.

Specifically, enterprise information input data transmitted from the front-end visual page is acquired, and the data model is updated according to the enterprise information input data. Or acquiring enterprise information change data transmitted by the front-end visual page, and updating the data model according to the enterprise information change data. Or acquiring enterprise information input data and enterprise information change data transmitted by the front-end visual page, and updating the data model according to the enterprise information input data and the enterprise information change data.

For example, a front-end visual page is realized through vue plug-in technology, an enterprise management module in a campus module is called, and a new enterprise type is configured on the page. The user-defined enterprise type can be text input, a pull-down selection mode and the like. If the selection mode is a pull-down selection mode, the content selected by the pull-down selection mode is a national standard enterprise type. And supporting modification of a configuration interface, and processing a request message sent by a front-end visual page by a back-end program, wherein the request message contains related data of data model dimensions. And storing the processed data related to the data model dimension into a database, and adding data of the data model dimension.

Optionally, time-dependent configuration information transmitted by the front-end visualization page is acquired, and a heuchen effective start date and a heuchen effective end date are determined according to the time-dependent configuration information.

Optionally, determining factor configuration information transmitted by the front-end visualization page is acquired, and determining a determining factor according to the determining factor configuration information, wherein the determining factor is used for determining an enterprise which does not meet the push condition. For example, non-compliance with a decision factor may manifest as an enterprise credit rating higher than that specified by the decision factor, an activity index within an enterprise-specified time higher than that specified by the decision factor, and the absence of other push-free conditions, etc.

For example, the determinant configuration information is set through a front-end visualization page, and comprises a business name, business credit degree determinant configuration information, business activity degree determinant configuration information and other determinant configuration information without push condition.

Illustratively, calculating a matching value according to the Huifeng label matching degree, the Huifeng participation matching degree and the issuing activity matching degree comprises: and determining the addition operation result of the heuchement label matching degree, the heuchement participation matching degree and the issuing activity matching degree of each enterprise, and determining the addition operation result as a matching value.

And S230, determining effective benefit administration information in the benefit policy information and enterprises which accord with the matching degree of each benefit policy information according to the standard data of the matching value, forming a pushing pool device, and actively pushing the effective benefit administration information to the enterprises.

The enterprise which is matched with the matching degree of each preferential policy information is an enterprise which is not matched with a decision factor in the campus enterprise and is matched with the preferential policy information according with the matching degree condition.

After the data model is built according to the benefit type labels and the enterprise information, the enterprise credit and the activity index in the enterprise activity dimension table contained in the data model are compared with the decision factor. And if the credit rating of the enterprise is equal to or lower than the rating specified by the decision factor, or the activity index within the specified time of the enterprise is equal to or lower than the activity index specified by the decision factor, or other situations without pushing exist, determining that the enterprise meets the decision factor, otherwise, determining that the enterprise does not meet the decision factor.

And for enterprises meeting decision factors, preferential policy information is not pushed. And for enterprises which do not accord with the decision factors, calculating the sum of the Huygen label matching degree, the Huygen participation matching degree and the issuing activity matching degree. And judging whether the sum of the Hui-political label matching degree, the Hui-political participation matching degree and the issuing activity matching degree exceeds a preset matching degree threshold value or not. And if so, storing the sum of the Huygen label matching degree, the Huygen participation matching degree and the issuing activity matching degree as a field in a record corresponding to the enterprise in the database, and determining the enterprise as a target enterprise. For example, an enterprise with a sum of the Huygen label matching degree, the Huygen participation matching degree and the issuing activity matching degree larger than m is taken as a target enterprise, and m is taken as a field and saved in a record corresponding to the enterprise in the database. And m is a value set artificially and can be dynamically adjusted according to a service scene. And generating a push pool according to the target enterprise and the preferential policy information, pushing effective benefit administration information to the target enterprise according to the sequence of the preferential policy information in the preferential policy information queue by utilizing a Remote Dictionary service (Redis) technology according to the timeliness configuration information, and performing data pop-up processing on the invalid preferential policy information.

The embodiment makes issued preferential policy information into an administrative benefit type label, constructs a data model according to the administrative benefit type label and a specific dimension model, determines the matching degree of the administrative benefit label, the matching degree of the administrative benefit participation and the matching degree of the issuing activity according to the data model, the configuration of a decisive factor and the timeliness configuration, further calculates the matching value, determines enterprises with the matching degrees consistent with the effective administrative benefit information in the preferential policy information and the preferential policy information according to the standard data of the matching value, forms a push pool device, and actively pushes the effective administrative benefit information to the enterprises according to a push pool device trigger mechanism. According to the method and the device, the data model is constructed based on the heuman type label and the specific dimension model, the matching value is determined according to the data model, the determinant factor and the timeliness configuration, the effective heuman information in the preferential policy information and the enterprises which are consistent with the matching degree of each preferential policy information are determined according to the standard data of the matching value, the effective heuman information is pushed to the target enterprises which live in each time period, the problems of missing sending, mistaken sending and message lagging of the heuman information in the related technology are effectively avoided, and the accuracy, the efficiency and the instantaneity of obtaining the heuman information are improved.

Fig. 4 is a flowchart of another benefit information pushing method according to an embodiment of the present invention. The embodiment further defines the steps of obtaining the benefit policy information and determining the benefit type label according to the benefit policy information on the basis of the above embodiment. As shown in fig. 4, the method includes:

s410, analyzing the website of the government affair website, and acquiring the byte stream corresponding to the website content resources.

Illustratively, an operator who inputs the preferential policy information inputs the website of the government affair website to which the preferential policy information belongs on a front-end visual page, and the back-end service layer acquires and analyzes the website of the government affair website to acquire website content resources. For example, a URL (Uniform Resource Locator) address resolution is performed for a website. Firstly, an openStream () method is used to obtain an input stream (a byte stream is obtained) of a website content resource corresponding to a URL address.

And S420, determining a character stream corresponding to the website content resource according to the byte stream, reading the character stream line by line, and filtering the webpage labels and the blank space lines in the read character stream to obtain preferential policy information.

Illustratively, the operation of converting the byte stream into the character stream is performed using an InputStreamReader. And reading the character stream obtained in the step by line through a readLine method in a buffer reader class. And filtering the script, style, html and other webpage tags and space lines in the read character stream through the regular expression, and returning the character string of the preferential policy information. The character string is the text content after URL analysis.

S430, segmenting words of the preferential policy information, filtering stop words and punctuation marks in a segmentation result, and constructing a keyword list according to the rest words.

Exemplarily, chinese word segmentation analysis is performed on the text content after URL analysis by adopting an Analyzer class, and the text content after URL analysis is a String type text. Words in the text content are intelligently intercepted, word segments such as stop words and punctuation marks are filtered out, and a keyword list is generated according to the rest words.

Specifically, the predefined keyword list receives words to be intercepted. IKAnalyzer is initialized by utilizing Analyzer Chinese word segmentation analysis, and is set to intelligent interception (setUsSimalt = true). Calling a token stream method to read a character stream of a text, circularly obtaining the intercepted words through an incementtoken method, converting the token stream into character types, converting the character types into character string types, filtering out all stop words (such as punctuation marks, common words and words except nouns, verbs, adjectives and adverbs), and putting the finally obtained words into a keyword list.

Suppose that the text content is' S city actively applies refinancing, refinish, ordinary Hui Xiao micro loan supporting tools and the like to promote more credit funds to flow to small and medium-sized micro enterprises. "the text content is processed in the above manner to obtain a keyword list of [ S City, loan, reappearance, general, boon, weak, loan, tools, credit funds, more, flow, weak, enterprise ].

S440, traversing the keyword list, constructing an edge between any two keywords by adopting a co-occurrence relationship, and determining a relationship vocabulary set according to the edge between any two keywords.

Illustratively, traversing the keyword list, an edge between any two nodes is constructed using co-occurrence (co-occurrence), if when their corresponding words co-occur in a window of length K, there is an edge between the two nodes, K representing the window size, i.e., at most K words co-occur. And intelligently acquiring K (when the current definition window K =2, the number of the keywords is acquired, and the number of the keywords is intercepted above 2 words) before and after each keyword, and then, removing the coincidence of all the obtained adjacent word nodes until a relation vocabulary set formed by all relation vocabularies is obtained.

Specifically, the co-occurrence relationship is exemplified by: keyword list = [ S city, loan, reappear, general, offer, micro, loan, instrument, credit fund, more, flow, micro, business ], traverse the keyword list, because define window K =2, the traversal result is output as follows:

1) Starting with a first keyword 'S city' as a given node, finding the first 2 words and the second 2 words, and the result is: { S city = [ loan, reappearance ] };

2) And taking the loan as a node, finding the front 2 words and the rear 2 words, and combining the steps to obtain the result: { loan = [ S city, reapplication, general ], S city = [ loan, reapplication ] };

3) And taking 'reappearing' as a node, finding the front 2 words and the rear 2 words, combining the steps, and obtaining a result: { reapplication = [ S, loan, general, offer ], loan = [ S city, reapplication, general ], S city = [ loan, reapplication ] };

4) And taking the 'common' as a node, finding the front 2 words and the rear 2 words, combining the steps, and obtaining a result: { p = [ loan, reappearance, benefit, micro ], reappearance = [ S, loan, p, benefit ], loan = [ S city, reappearance, p ], S city = [ loan, reappearance ] };

5) And by analogy, traversing all the keywords in the keyword list, and removing the duplication to obtain a relational vocabulary set.

S450, for each keyword i in the relational vocabulary set, acquiring the weight of an edge between each keyword j before the current keyword i and the current keyword i, acquiring the sum of the weights of the edges between each keyword j and keywords except the current keyword i in the relational vocabulary set, and acquiring the importance degree value of each keyword j.

And S460, determining the importance degree value of the current keyword i according to the weight of the edge, the sum of the weights of the edges and the importance degree value of each keyword j.

Illustratively, for all the keywords in the relational vocabulary set, the other members of each keyword successively vote and iterate for each keyword in turn according to a TextRank formula, and the keyword corresponding to each vote can be calculated to obtain an importance degree value. It should be noted that the TextRank formula is a formula defined by the TextRank algorithm. The TextRank algorithm is a graph-based ranking algorithm for text. The basic idea is that a text is divided into a plurality of composition units (words and sentences), a graph model is established, important cost in the text is sequenced by using a voting mechanism, and keyword extraction can be realized only by using the information of a single document.

And after each round of voting is finished, judging whether the maximum iteration times is reached or whether the difference value of the importance degree values between two rounds of voting is smaller than x, if so, meeting the convergence condition, stopping the iteration, and otherwise, continuing the iteration. Wherein, x may be set according to a service requirement, for example, x =0.001.

For example, the iteratively used TextRank formula is as follows:

wherein WS (V) _i ) Represents the importance value of keyword i, and WS (Vj) represents the importance value of keyword j. The weight of the keyword i depends on the weight of the (j, i) edge that is composed of the points j preceding i, and the sum of the weights of j this point to other edges. V _i Represents a given point, V _j Represents another node, any two points V _i ，V _j The weight of the edge between is W _ji ,W _ji And W _jk To indicate the different degrees of importance of the edge connections between two nodes. In (V) _i ) Is directed to V _i Set of points of (c), out (V) _j ) Is a point V _j The set of points pointed to. Because the importance degrees of the edges between two points cannot be the same, d is introduced as a damping coefficient, the value range is 0 to 1, the probability that a certain point in the graph points to any other point is represented, and the value of the scheme is set to be 0.85.

S470, sorting the keywords in the relation vocabulary set according to the importance degree value, and determining the target keywords meeting the preset conditions as the heuman type labels according to the sorting result.

Illustratively, keywords in the relational vocabulary set are sorted in a reverse order according to the importance degree value by adopting a Collections method to obtain keywords of text content. The higher the ranking, the higher the importance of the keyword. Data in the top of the ranking can be screened in a configurable mode to serve as keywords, and all keywords serve as benefit type labels from the perspective that qualified enterprises are not omitted as far as possible.

S480, constructing a data model according to the heuchian type label and the specific dimension model, determining the matching degree of the heuchian label, the matching degree of the heuchian participation and the matching degree of the issued activity according to the data model, the configuration of the decisive factor and the timeliness configuration, and calculating a matching value according to the matching degree of the heuchian label, the matching degree of the heuchian participation and the matching degree of the issued activity.

And S490, determining effective benefit administration information in the benefit policy information and enterprises which accord with the matching degrees of the benefit policy information according to the standard reaching data of the matching values, forming a pushing pool device, and actively pushing the effective benefit administration information to the enterprises.

According to the embodiment of the invention, the Uyghur type tag in the preferential policy information content issued by the government affair website is extracted by adopting the TextRank algorithm, so that the webpage content is automatically analyzed, the information management and the data maintenance are facilitated, a data basis is provided for accurately pushing the preferential policy information, the preferential policy content in the government affair information website does not need to be manually copied by an operator, and the risks of incomplete Uyghur message content, missing copy and error copy are reduced.

Fig. 5 is a flowchart of another benefit information pushing method according to an embodiment of the present invention. In this embodiment, on the basis of the above embodiments, the step of determining, according to the degree of matching of the benefit label, the degree of matching of benefit participation, and the degree of matching of the issued activity, a target enterprise whose degree of matching with each of the benefit policy information meets the condition of the degree of matching, and pushing effective benefit policy information in the benefit policy information to the target enterprise is further defined. As shown in fig. 5, the method includes:

s501, analyzing the website of the government affair website, and acquiring the byte stream corresponding to the website content resources.

S502, determining a character stream corresponding to the website content resource according to the byte stream, reading the character stream line by line, and filtering the webpage labels and the blank space lines in the read character stream to obtain preferential policy information.

S503, segmenting words of the preferential policy information, filtering stop words and punctuation marks in the segmentation result, and constructing a keyword list according to the rest words.

S504, traversing the keyword list, constructing an edge between any two keywords by adopting a co-occurrence relation, and determining a relation vocabulary set according to the edge between any two keywords.

S505, for each keyword i in the relational vocabulary set, acquiring the weight of an edge between each keyword j before the current keyword i and the current keyword i, acquiring the sum of the weights of the edges between each keyword j and keywords except the current keyword i in the relational vocabulary set, and acquiring the importance degree value of each keyword j.

S506, determining the importance degree value of the current keyword i according to the weight of the edge, the sum of the weights of the edges and the importance degree value of each keyword j.

And S507, sequencing the keywords in the relational vocabulary set according to the importance degree value, and determining target keywords meeting preset conditions as the heuchy type labels according to the sequencing result.

S508, a data model is built according to the heuche style label and the specific dimension model, the matching degree of the heuche label, the matching degree of the heuche participation and the matching degree of the issued activity are determined according to the data model, the configuration of the decisive factor and the timeliness configuration, and the matching value is calculated according to the matching degree of the heuche label, the matching degree of the heuche participation and the matching degree of the issued activity.

In the embodiment of the invention, the Hui-administrative label matching degree represents the matching degree of the enterprise and the preferential policy information from the perspective of national standard enterprise types or enterprise custom types. Or the Hui-administrative label matching degree represents the matching degree of the enterprise with the preferential policy information from the aspects of national standard enterprise types and enterprise self-defined types. The benefit participation matching degree represents the matching degree of the enterprise with the preferential policy information from the historical participation degree of the enterprise. In addition, the distribution activity matching degree represents the matching degree of the enterprise with the preferential policy information from the perspective of the activity degree of the enterprise.

Illustratively, the addition operation result of the Huygen label matching degree, the Huygen participation matching degree and the release activity matching degree of each enterprise is calculated, and the addition operation result is determined as a matching value, so that the Huygen type matching degree is calculated in a multi-dimensional manner by combining information data such as national standard enterprise types, enterprise self-defined types, enterprise liveness degrees and enterprise historical participation degrees.

S509, for each piece of preferential policy information, comparing the matching value with a preset matching degree threshold, determining standard data of the matching value according to a comparison result, and determining effective benefit administration information according to the timeliness configuration.

Optionally, before the matching degree evaluation, the enterprise can be pre-evaluated through a determinant factor based on the data model. And if the enterprise is determined to meet the pushing conditions through pre-evaluation, continuing to evaluate the matching degree of the enterprise. And if the enterprise is determined not to accord with the pushing conditions through pre-evaluation, no preferential policy information is pushed to the enterprise.

And under the condition that the enterprise accords with the pushing condition, respectively judging whether the degree of matching between the enterprise and the administrative type corresponding to the administrative benefit policy information exceeds a preset matching degree threshold value according to the benefit policy, if so, determining the enterprise as a target enterprise of which the degree of matching with the current benefit policy information accords with the matching condition, and otherwise, determining that the enterprise is not the target enterprise of which the degree of matching with the current benefit policy information accords with the matching condition.

Table 1 is a heuman style matching degree table.

And S510, determining enterprises which accord with the matching degree of each preferential policy information according to the standard data, and forming a pushing pool device according to the effective benefit information and the enterprises which accord with the matching degree.

Illustratively, the preferential policy information, the Huifeng type matching degree value of the target enterprise meeting the matching condition and the corresponding target enterprise are stored in an associated mode to form a pushing pool. Specifically, the URL address of the benefit policy, the heuman type matching degree value of the target enterprise meeting the matching condition, and the name of the target enterprise may be stored in association.

And S511, sequentially and correspondingly pushing the effective benefit administration information to the enterprise according to a push pool device triggering mechanism, and popping the information of the ineffective ground preferential policy.

Illustratively, for the preferential policy information queue in the push pool, according to the huygen validation start date and the huygen validation end date, effective preferential policy information is sequentially and correspondingly pushed to the target enterprise, and pop-off processing is performed on ineffective preferential policy information.

Specifically, URLs corresponding to the preferential policy information are sequentially stored through a list type of Redis, effective preferential policy information relative to a pushing moment is sequentially pushed to a target enterprise with a Huideng type matching degree exceeding a preset matching degree threshold, and invalid preferential policy information is popped.

The embodiment of the invention evaluates the decisive factor and the matching degree of the enterprise based on the data model to form a pushing pool device, actively pushes effective preferential policy information to the enterprise staying in each time period based on a triggering mechanism of the pushing pool device, effectively avoids the missing transmission of the benefit information, improves the working efficiency and improves the accuracy of the pushing information.

Fig. 6a is a flowchart of another benefit information pushing method according to an embodiment of the present invention. As shown in fig. 6a, the method is implemented by a push pool device. The push pool device includes a collection unit 610, a data processing unit 620, and an information push unit 630. The collection unit 610 collects the website, the stop word set, the international industry type information, and the like corresponding to the benefit policy information, and may also collect the enterprise basic data as the initialization data resource. For example, the initialization data may include the country and city in the address of the business, the time of establishment of the business, and the like, as basic data. The data processing unit 620 parses the web page content, obtains a text of the web page content, and performs preprocessing such as deleting a web page tag and a space line on the text content. And performing Chinese word segmentation analysis on the preprocessed character strings through an Analyzer Analyzer class. And circularly obtaining words intercepted from the filtered stopword. And intelligently acquiring vocabulary sets of K =2 before and after each keyword in the keyword list by adopting a window with the window size of K =2 to form a relation vocabulary set. Setting a minimum iteration number min _ diff =0.0001, a maximum iteration number max _ iter =200, and a damping coefficient d =0.85, and calculating the importance degree value of each keyword in the relational vocabulary set by using a TextRank formula in an iteration manner. And comparing the importance values by a Collections method, realizing the reverse order sequencing of the keywords in the relation vocabulary set, and determining part or all of the keywords in the relation vocabulary set as the heuchy type labels according to the sequencing result. The data processing unit 620 is used for creating a data model based on the national standard enterprise type, the enterprise self-defined type, the enterprise activity, the enterprise historical participation degree, the time, the region and other dimensional data, analyzing the matching degree of the enterprise and the heuman type label through the data model, evaluating the matching degree according to the matching degree calculation result, and forming a pushing pool according to the real-time incremental data obtained by the determinant factor and the matching degree evaluation. The information pushing unit 630 processes the pushing pool data by using Redis, and actively pushes effective preferential policy information to each enterprise meeting the matching degree threshold range.

Fig. 6b is a schematic diagram of push interaction in an administrative information push method according to an embodiment of the present invention. After creating the data model, visualization configuration and matching degree evaluation of the determinant factor are performed based on the model data, forming a push pool, as shown in fig. 6 b. For example, model data includes a Huideological type label, a business type, business information, business liveness, business historical engagement, territory, and time. The configuration of the decisive factors comprises the grade C and below of the credit of the enterprise, and the grade 0 and below of the activity of the enterprise in x time. And if the model data of the enterprise is consistent with the decisive factor, no preferential policy information is pushed to the enterprise. And time-efficiency configuration can also be carried out, namely the effective starting time of the preferential policy and the effective ending time of the preferential policy are configured. And through the timeliness configuration and the time requirement of the preferential policy information, the preferential policy information of the expiration date is not pushed, and the preferential policy information of the valid date is used for forming a pushing pool. For the matching degree configuration, for example, the heuman type label includes an enterprise type + matching degree a%, a heuman participation + matching degree b%, a publishing activity + matching degree c%, and if the heuman type matching degree obtained by a% + b% + c% is greater than or equal to 60%, the heuman type matching degree is stored in the database to form a push pool. Redis is adopted to process the data of the push pool, effective preferential policy information is actively pushed to each enterprise meeting the threshold range of the matching degree, and ineffective preferential policy information is directly popped. The mode of the enterprise receiving the preferential policy information of the push message may include a message reminding mode or a mobile phone message reminding mode.

Through the active push mechanism of the push pool, the preferential policy information is effectively pushed to enterprises staying in each time period, the missing sending of the benefit information is effectively avoided, the working efficiency of personnel is improved, the accuracy of the push information is also improved, and the preferential policy information is pushed to the enterprises meeting the requirement.

Fig. 7 is a block diagram of an information push apparatus for benefit administration according to an embodiment of the present invention. The information pushing device can be implemented in hardware and/or software, and can be configured in the electronic device. The hewlett-packard information pushing device can execute the hewlett-packard information pushing method provided by any embodiment of the invention. As shown in fig. 7, the apparatus includes a label forming module 710, a matching degree calculating module 720, and a huygen pushing module 730.

A tag forming module 710, configured to make the issued preferential policy information into a benefit type tag;

the matching degree calculation module 720 is used for constructing a data model according to the hewless style tag and the specific dimension model, determining the hewless style tag matching degree, the hewless participation matching degree and the issuing activity matching degree according to the data model, the determinant factor configuration and the timeliness configuration, and calculating a matching value according to the hewless style tag matching degree, the hewless participation matching degree and the issuing activity matching degree;

and an benefit administration pushing module 730, configured to determine, according to the standard data of the matching value, effective benefit administration information in the benefit policy information and an enterprise that matches the matching degree of each benefit policy information, to form a pushing pool device, and actively push the effective benefit administration information to the enterprise.

Optionally, the label forming module 710 is specifically configured to:

acquiring website content resources according to the website address of a government affair website, and analyzing the website content resources to acquire preferential policy information;

performing Chinese word segmentation analysis on the preferential policy information to obtain a keyword list, and determining a relation vocabulary set according to the keyword list;

and determining the importance degree value of each keyword in the relation vocabulary set, and determining an heuman type label according to the importance degree value.

Optionally, the label forming module 710 is further specifically configured to:

analyzing the website of the government affair website, and acquiring a byte stream corresponding to website content resources;

determining a character stream corresponding to the website content resource according to the byte stream, reading the character stream line by line, and filtering a webpage label and a blank space line in the read character stream to obtain preferential policy information.

Optionally, the label forming module 710 is specifically further configured to:

performing word segmentation on the preferential policy information, filtering stop words and punctuation marks in word segmentation results, and constructing a keyword list according to the rest words;

and traversing the keyword list, constructing an edge between any two keywords by adopting a co-occurrence relation, and determining a relation vocabulary set according to the edge between any two keywords.

Optionally, the label forming module 710 is further specifically configured to:

for each keyword i in the relational vocabulary set, acquiring the weight of an edge between each keyword j before the current keyword i and the current keyword i, acquiring the sum of the weights of the edges between each keyword j and keywords except the current keyword i in the relational vocabulary set, and acquiring the importance degree value of each keyword j;

determining the importance degree value of the current keyword i according to the weight of the edge, the sum of the weights of the edges and the importance degree value of each keyword j;

and sequencing the keywords in the relational vocabulary set according to the importance degree value, and determining the target keywords meeting the preset conditions as the heuchement type labels according to the sequencing result.

Optionally, the matching degree calculating module 720 is specifically configured to:

creating an hewlett-packard label dimension table according to the hewlett-packard type label;

creating an enterprise information fact table according to the enterprise information;

according to the enterprise information, constructing an enterprise type dimension table, an enterprise behavior dimension table, a time dimension table and a region dimension table according to enterprise type dimensions, behavior dimensions, time dimensions and region dimensions;

and constructing a data model according to the structures of the snowflake models according to the Huygen label dimension table, the enterprise information fact table, the enterprise type dimension table, the enterprise behavior dimension table, the time dimension table and the region dimension table.

Optionally, the method further comprises:

the model updating module is used for acquiring enterprise information input data and/or enterprise information change data transmitted by a front-end visual page after a data model is built according to the Huideng label dimension table, the enterprise information fact table, the enterprise type dimension table, the enterprise behavior dimension table, the time dimension table and the region dimension table and according to the structure of the snowflake model;

Optionally, the matching degree calculating module 720 is further specifically configured to:

determining an enterprise range according to the decisive factor configuration, and determining a benefit-administration information range according to the timeliness configuration;

for each heuchy type label corresponding to the heuchy information range in the heuchy label dimension table in the data model, traversing the enterprise type dimension table of each enterprise corresponding to the enterprise range according to the current heuchy type label, and determining the matching degree of each enterprise and the heuchy type label according to the traversal result;

according to enterprise historical participation activity data in an enterprise behavior dimension table corresponding to the enterprise scope in the data model, determining the heuchen participation data of the enterprises, and according to the heuchen participation data, determining the heuchen participation matching degree of each enterprise;

determining the data statistics of the release activity of the enterprises according to the enterprise release activity data in the enterprise behavior dimension table corresponding to the enterprise range in the data model, and determining the matching degree of the release activity of each enterprise according to the data statistics of the release activity.

Optionally, the method further comprises:

and the timeliness configuration module is used for acquiring timeliness configuration information transmitted by the front-end visual page and determining the hecheng administration effective starting date and the hecheng administration effective ending date according to the timeliness configuration information.

Optionally, the method further comprises:

the determinant factor configuration module is used for acquiring determinant factor configuration information transmitted by a front-end visual page and determining a determinant factor according to the determinant factor configuration information, wherein the determinant factor is used for determining enterprises which do not conform to the pushing condition.

Optionally, the benefit push module 730 is specifically configured to:

and determining the addition operation result of the Huidezhen label matching degree, the Huidezhen participation matching degree and the issuing activity matching degree of each enterprise, and determining the addition operation result as a matching value.

Optionally, the benefit push module 730 is specifically further configured to:

for each preferential policy information, comparing the matching value with a preset matching degree threshold value, determining standard data of the matching value according to a comparison result, and determining effective benefit administration information according to the timeliness configuration;

and determining enterprises which accord with the matching degree of each preferential policy information according to the standard reaching data, and forming a pushing pool device according to the effective benefit administration information and the enterprises which accord with the matching degree.

Optionally, the benefit push module 730 is further configured to:

and according to a push pool device trigger mechanism, sequentially and correspondingly pushing effective benefit administration information to the enterprise, and popping the preferential policy information of the failure place.

The hewlett-packard information pushing device provided by the embodiment of the invention can execute the hewlett-packard information pushing method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.

Fig. 8 is a block diagram of an electronic device according to an embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 8, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as a benefit information push method.

In some embodiments, the benefit information pushing method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When loaded into RAM 13 and executed by processor 11, the computer program may perform one or more of the steps of the benefit information pushing method described above. Alternatively, in other embodiments, the processor 11 may be configured to perform the benefit information push method by any other suitable means (e.g., by means of firmware).

A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.

Embodiments of the present invention further provide a computer program product, including a computer program, where the computer program, when executed by a processor, implements the method for pushing administrative information provided in any embodiment of the present application.

Computer program product in implementing the computer program code for carrying out operations 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 + +, 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. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments illustrated herein, but is capable of various obvious changes, rearrangements and substitutions as will now become 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

1. A method for promoting information, comprising:

making the issued preferential policy information into a Huifeng type label;

2. The method of claim 1, wherein the making of issued preferential policy information into a benefit type label comprises:

and determining the importance degree value of each keyword in the relation vocabulary set, and determining the hewlett-packard type label according to the importance degree value.

3. The method according to claim 2, wherein the acquiring website content resources according to the website address of the government affairs website, and parsing the website content resources to acquire preferential policy information comprises:

and determining a character stream corresponding to the website content resource according to the byte stream, reading the character stream line by line, and filtering the webpage label and the blank space line in the read character stream to obtain preferential policy information.

4. The method of claim 2, wherein performing chinese segmentation on the preferential policy information to obtain a keyword list, and determining a relational vocabulary set according to the keyword list comprises:

5. The method of claim 2, wherein determining a degree of importance value for each keyword in the set of relational words and determining a hewlett-packard type tag according to the degree of importance value comprises:

6. The method of claim 1, wherein building a data model from the hewlett-packard type tags and a particular dimension model comprises:

creating an exercise label dimension table according to the exercise type label;

and constructing a data model according to the structure of the snowflake model according to the Huimen label dimension table, the enterprise information fact table, the enterprise type dimension table, the enterprise behavior dimension table, the time dimension table and the region dimension table.

7. The method of claim 6, after constructing the data model according to the structure of the snowflake model according to the heuman label dimension table, the enterprise information fact table, the enterprise type dimension table, the enterprise behavior dimension table, the time dimension table and the region dimension table, further comprising:

8. The method of claim 6, wherein determining a Huifeng label matching degree, a Huifeng participation matching degree and a publishing activity matching degree according to the data model, the determinant configuration and the timeliness configuration comprises:

determining an enterprise range according to the decisive factor configuration, and determining an administrative information range according to the timeliness configuration;

and determining the issuing activity data statistics of the enterprises according to the enterprise issuing activity data in the enterprise behavior dimension table corresponding to the enterprise range in the data model, and determining the issuing activity matching degree of each enterprise according to the issuing activity data statistics.

9. The method of claim 1, further comprising:

and acquiring timeliness configuration information transmitted by a front-end visual page, and determining a Hui-administrative effective starting date and a Hui-administrative effective ending date according to the timeliness configuration information.

10. The method of claim 9, further comprising:

acquiring decisive factor configuration information transmitted by a front-end visual page, and determining a decisive factor according to the decisive factor configuration information, wherein the decisive factor is used for determining enterprises which do not accord with push conditions.

11. The method of claim 1, wherein calculating a match value based on the Huifeng label match, the Huifeng participation match, and the release activity match comprises:

and determining the addition operation result of the heuchement label matching degree, the heuchement participation matching degree and the issuing activity matching degree of each enterprise, and determining the addition operation result as a matching value.

12. The method of claim 1, wherein the determining valid benefit-administration information in the benefit policy information and the enterprises corresponding to the matching degree of each benefit policy information according to the compliance data of the matching value form a push pool device, comprising:

for each piece of preferential policy information, comparing the matching value with a preset matching degree threshold, determining standard data of the matching value according to a comparison result, and determining effective benefit administration information according to the timeliness configuration;

13. The method of claim 12, wherein proactively pushing the effective benefits information to the enterprise comprises:

14. An information push apparatus for benefit, comprising:

the label forming module is used for making the issued preferential policy information into an administrative type label;

the matching degree calculation module is used for constructing a data model according to the heuman type label and the specific dimension model, determining an heuman label matching degree, an heuman participation matching degree and an issuing activity matching degree according to the data model, the determinant factor configuration and the timeliness configuration, and calculating a matching value according to the heuman label matching degree, the heuman participation matching degree and the issuing activity matching degree;

and the benefit administration pushing module is used for determining effective benefit administration information in the benefit policy information and enterprises which accord with the matching degree of each benefit policy information according to the standard data of the matching value to form a pushing pool device, and actively pushing the effective benefit administration information to the enterprises.

15. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the newsletter information pushing method of any one of claims 1-13 when executing the computer program.

16. A computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the hewlett-packard information pushing method according to any one of claims 1-13.

17. A computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the newsletter information pushing method of any of claims 1-13.