CN112700170A - Accurate business inviting platform based on big data of garden - Google Patents
Accurate business inviting platform based on big data of garden Download PDFInfo
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- CN112700170A CN112700170A CN202110056883.8A CN202110056883A CN112700170A CN 112700170 A CN112700170 A CN 112700170A CN 202110056883 A CN202110056883 A CN 202110056883A CN 112700170 A CN112700170 A CN 112700170A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/18—File system types
- G06F16/182—Distributed file systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/904—Browsing; Visualisation therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/16—Real estate
Abstract
The invention discloses an accurate business inviting platform based on garden big data, which comprises: the data layer comprises industrial and commercial data, credit data, intellectual property data, annual report data, judicial data and annual report data which are required by accurate recruitment; the platform layer analyzes data information in the data layer by adopting a big data platform and outputs an enterprise comprehensive evaluation model, an enterprise investment probability model, an enterprise investment preference model and an enterprise transaction model which are suitable for accurate solicitation; the portrait layer is generated by analyzing the output result of the platform layer by utilizing a portrait system, wherein the portrait comprises basic information, technological innovation, international competitiveness, talent, sustainable development and operation capacity; the special topic layer integrates the platform layer and the portrait layer to construct and generate a topic center library; and the application layer is used for realizing functional module application of data based on the theme information data of the theme center library, and the functional module comprises a data visualization module, an enterprise analysis module, an accurate business recruitment module and a policy matching module.
Description
Technical Field
The invention relates to the technical field of big data, in particular to an accurate business inviting platform based on garden big data.
Background
With the emergence and popularization of emerging technologies such as cloud computing, big data, internet plus and the like, the construction of cloud services and big data has become an intelligent supervision government project of a park, and the comprehensive competitiveness of the park is directly influenced by the construction success rate.
The park big data business inviting platform construction supports the realization of the development strategy target of accurate business inviting of the park, is built into a domestic advanced park business inviting platform with demonstration guidance, really realizes the ecological circle of the China big data industry with international influence, and forms an interconnected, efficient, low-carbon, operational, upgradable, experienceable and reproducible park information construction mode. The existing business inviting platform has the defects of difficult accurate business inviting, few information acquisition channels, high cost, low efficiency, difficult judgment of investment willingness of target enterprises and difficult understanding of target business enterprise conditions; the client is difficult to find, a large amount of time is consumed for finding the clues of the client, the efficiency of soliciting the businessmen is low, and the like.
Disclosure of Invention
The invention provides an accurate business inviting platform based on park big data, which promotes and innovates the industry, perfects the industrial chain, promotes the park business inviting service level, improves the core competitiveness and creates more employment opportunities; the method has the advantages of improving market competitiveness of resident enterprises, improving park informatization level, improving office efficiency of the enterprises and reducing operation cost of the enterprises.
In order to achieve the purpose, the invention adopts the following technical scheme:
an accurate tenderer platform based on campus big data, comprising:
the data layer comprises industrial and commercial data, credit data, intellectual property data, annual report data, judicial data and annual report data which are required by accurate recruitment;
the platform layer analyzes data information in the data layer by adopting a big data platform and outputs an enterprise comprehensive evaluation model, an enterprise investment probability model, an enterprise investment preference model and an enterprise transaction model which are suitable for accurate solicitation;
the portrait layer is generated by analyzing the output result of the platform layer by utilizing a portrait system, wherein the portrait comprises basic information, technological innovation, international competitiveness, talent, sustainable development and operation capacity;
the special topic layer integrates the platform layer and the portrait layer to construct and generate a topic center library;
and the application layer is used for realizing functional module application of data based on the theme information data of the theme center library, and the functional module comprises a data visualization module, an enterprise analysis module, an accurate business recruitment module and a policy matching module.
Preferably, the big data platform comprises a data integration module, a data storage module, a data calculation module, a data engine module and a data analysis module.
Preferably, the data integration module comprises an Sqoop sub-module, a Flume sub-module, a Storm sub-module and a Streaming sub-module; the data storage module comprises a Redis sub-module, a MySQL sub-module, an HDFS sub-module and an HBase sub-module; the data calculation module comprises a Hive sub-module, a Spark sub-module, a YARN or TEZ sub-module and a Kylin sub-module; the data Engine module comprises an ElasticSearch submodule, a Titan submodule, an NLP submodule and a Spark-Engine submodule; the data analysis module comprises a sparkml submodule, a TensorFlow submodule, a Mahout submodule and an R or python submodule.
Preferably, the data source of the data layer comprises data inside the fusion park, data of a third-party enterprise information interface, network crawling data and government affair system data.
Preferably, the step of crawling data by the network comprises:
a1, defining a crawler task through a detection management module;
a2, a scheduling module reads a crawler task and acquires crawler resources from a ZooKeeper module and a Redis module;
a3, processing a crawler task and crawler resources by a scheduling module, decomposing the task, and sending the decomposed task to a crawler engine to directionally crawl information data of each large website;
a4, entering data into HBase database by a persistence means;
a5, the data layer crawls specific data from an HBase database, and visual analysis is carried out on the data with different dimensions based on a web analysis module.
Preferably, the portrait system comprises a tag data processing module, a tag data mining and tagging module, a tag construction module, a portrait analysis module and an open platform module.
Preferably, the theme center library comprises a campus theme, a business theme, a solicitation theme and a policy theme.
Preferably, the data visualization module comprises a campus overview and an industry view; the enterprise analysis module comprises enterprise portrait, comprehensive evaluation, investment analysis and interpersonal analysis; the accurate business inviting module comprises progress monitoring, intelligent recommendation, accurate business inviting and internal stable business; the policy matching module includes a coverage analysis and an effect analysis.
After adopting the technical scheme, compared with the background technology, the invention has the following advantages:
1. the invention provides an accurate business inviting platform based on park big data, which promotes and innovates the industry, perfects the industrial chain, promotes the park business inviting service level, improves the core competitiveness and creates more employment opportunities; the method has the advantages of improving market competitiveness of resident enterprises, improving park informatization level, improving office efficiency of the enterprises and reducing operation cost of the enterprises.
2. The invention provides an accurate business inviting platform based on park big data, which is used for processing data of a data layer through a big data platform, improving the working efficiency and reducing the time cost and the labor cost.
Drawings
FIG. 1 is a block diagram of the present invention;
FIG. 2 is a block diagram of an image rendering system according to the present invention;
fig. 3 is a schematic diagram illustrating a data crawling process in the network according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the present invention, it should be noted that the terms "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are all based on the orientation or positional relationship shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the apparatus or element of the present invention must have a specific orientation, and thus, should not be construed as limiting the present invention.
Examples
As shown in fig. 1 to 3, the present invention discloses an accurate business platform based on campus big data, which includes:
the data layer comprises industrial and commercial data, credit data, intellectual property data, annual report data, judicial data and annual report data which are required by accurate recruitment;
the platform layer analyzes data information in the data layer by adopting a big data platform and outputs an enterprise comprehensive evaluation model, an enterprise investment probability model, an enterprise investment preference model and an enterprise transaction model which are suitable for accurate solicitation;
an image layer for generating an image by analyzing the output result of the platform layer by an image system, wherein the image includes basic information, scientific and technological innovation, international competitiveness, talent, sustainable development and operation ability;
the special topic layer integrates the platform layer and the portrait layer to construct and generate a topic center library;
and the application layer is used for realizing functional module application of the data based on the theme information data of the theme center library, and the functional modules comprise a data visualization module, an enterprise analysis module, an accurate business recruitment module and a policy matching module.
The big data platform comprises a data integration module, a data storage module, a data calculation module, a data engine module and a data analysis module.
The data integration module comprises an Sqoop sub-module, a flux sub-module, a Storm sub-module and a Streaming sub-module; the data storage module comprises a Redis sub-module, a MySQL sub-module, an HDFS sub-module and an HBase sub-module; the data calculation module comprises a Hive sub-module, a Spark sub-module, a YARN or TEZ sub-module and a Kylin sub-module; the data Engine module comprises an ElasticSearch submodule, a Titan submodule, an NLP submodule and a Spark-Engine submodule; the data analysis module comprises a sparkml submodule, a TensorFlow submodule, a Mahout submodule and an R or python submodule.
The big data processing platform provides three operation capabilities of off-line calculation capability, quasi-real-time calculation capability and real-time calculation capability for data operation, and three data engines of a full-text retrieval engine, a graph database engine and an algorithm execution engine.
The data source of the data layer comprises data inside the fusion garden, data of a third-party enterprise information interface, network crawling data and government affair system data.
The step of crawling data by the network comprises:
a1, defining a crawler task through a detection management module;
a2, a scheduling module reads a crawler task and acquires crawler resources from a ZooKeeper module and a Redis module;
a3, processing a crawler task and crawler resources by a scheduling module, decomposing the task, and sending the decomposed task to a crawler engine to directionally crawl information data of each large website;
a4, entering data into HBase database by a persistence means;
a5, the data layer crawls specific data from an HBase database, and visual analysis is carried out on the data with different dimensions based on a web analysis module.
The crawler engine comprises a crawler downloading module, a crawler data analyzing module, a crawler data storage module and a crawler distributed queue resource module.
The crawler downloading module: when the crawler is started, a module which is acquired from the queue resources and is called after being converted into the url is obtained, a page corresponding to the url is crawled according to the url simulation browser browsing behavior, the page is downloaded and analyzed, a bottom layer uses a phantomjs headless browser and httpparent, and the module has an agent anti-shielding function.
The crawler data analysis module: and after the page data is downloaded, data analysis is required to be carried out on the html page, and a format drawing data set required by the service is generated. Different data analysis processors need to be written for different services, so that the number of the modules is increased when the services are expanded.
Crawler data storage module: after the data is parsed and formatted, the formatted data needs to be persisted for service analysis. Because the existing data persistence modes are various, the module can persist corresponding codes according to different persistence requirements, and formatted data can be stored in different data warehouses.
A crawler distributed queue resource module: when the crawler is started, the module is used, and queue resources are generated to redis for consumption by the cluster crawler; the queue resources are originally stored in a database, and when the queue resources are started, the database is searched in a paging sorting mode and is placed into the redis in batches (after consumption is finished, the queue resources are placed again). To prevent data from being written repeatedly, the resource is locked each time it is put in, and only a single thread can operate the resource.
The portrait system comprises a tag data processing module, a tag data mining and tagging module, a tag construction module, a portrait analysis module and an open platform module.
The portrait construction module is used for construction management of the whole label system and comprises a resource management submodule, a scheduling management submodule, a message pushing submodule, a log tracking submodule and a portrait management submodule. The method supports flexible label hierarchy management configuration, data storage and display visualization configuration, data writing operation management and control and the like, and fully prepares for subsequent query, analysis and data output.
The portrait analysis module comprises a multidimensional query sub-module, a portrait detail sub-module, a group analysis sub-module, a report analysis sub-module and an API service sub-module. Each function page is closely associated with the metadata configuration in the label management, and various interface effects are rendered through different configuration combinations. And the display content of part of the pages can be personalized and customized by combining the actual service data condition.
The theme center library comprises park themes, enterprise themes, recruiting themes and policy themes.
Park theme: for holographic representation of all label data for a campus. Operation analysis can be carried out on the visual views with different dimensions;
the enterprise theme: the method comprises the following steps that labels of a portrait system are combined to inquire the information of a certain type of enterprise group, and the inquired enterprise group is analyzed in detail;
the theme of soliciting business: displaying the completion progress of each recruiter project, and monitoring each recruiter project; and (4) customizing the crawled external enterprise data, and combining and searching the enterprise attracting the business target based on the crawled external enterprise data tags.
Policy subject: analyzing the matching degree of the policy and the enterprise, matching the enterprise in the park with the policy, and screening out a supporting target enterprise; and matching the enterprises outside the park with the policies, and screening out the business recruitment target enterprises.
The data visualization module comprises a park overview and an industry view; the enterprise analysis module comprises enterprise portrait, comprehensive evaluation, investment analysis and interpersonal analysis; the accurate business inviting module comprises progress monitoring, intelligent recommendation, accurate business inviting and internal business stabilizing; the policy matching module includes a coverage analysis and an effect analysis.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. An accurate business platform based on garden big data, comprising:
the data layer comprises industrial and commercial data, credit data, intellectual property data, annual report data, judicial data and annual report data which are required by accurate recruitment;
the platform layer analyzes data information in the data layer by adopting a big data platform and outputs an enterprise comprehensive evaluation model, an enterprise investment probability model, an enterprise investment preference model and an enterprise transaction model which are suitable for accurate solicitation;
the portrait layer is generated by analyzing the output result of the platform layer by utilizing a portrait system, wherein the portrait comprises basic information, technological innovation, international competitiveness, talent, sustainable development and operation capacity;
the special topic layer integrates the platform layer and the portrait layer to construct and generate a topic center library;
and the application layer is used for realizing functional module application of data based on the theme information data of the theme center library, and the functional module comprises a data visualization module, an enterprise analysis module, an accurate business recruitment module and a policy matching module.
2. The accurate tenderer platform based on campus big data as claimed in claim 1, characterized in that: the big data platform comprises a data integration module, a data storage module, a data calculation module, a data engine module and a data analysis module.
3. The accurate tenderer platform based on campus big data as claimed in claim 2, characterized in that: the data integration module comprises an Sqoop sub-module, a flux sub-module, a Storm sub-module and a Streaming sub-module; the data storage module comprises a Redis sub-module, a MySQL sub-module, an HDFS sub-module and an HBase sub-module; the data calculation module comprises a Hive sub-module, a Spark sub-module, a YARN or TEZ sub-module and a Kylin sub-module; the data Engine module comprises an ElasticSearch submodule, a Titan submodule, an NLP submodule and a Spark-Engine submodule; the data analysis module comprises a sparkml submodule, a TensorFlow submodule, a Mahout submodule and an R or python submodule.
4. The accurate tenderer platform based on campus big data as claimed in claim 1, characterized in that: the data source of the data layer comprises data inside the fusion garden, data of a third-party enterprise information interface, network crawling data and government affair system data.
5. The accurate tenderer platform based on campus big data as claimed in claim 4, characterized in that: the step of crawling data by the network comprises:
a1, defining a crawler task through a detection management module;
a2, a scheduling module reads a crawler task and acquires crawler resources from a ZooKeeper module and a Redis module;
a3, processing a crawler task and crawler resources by a scheduling module, decomposing the task, and sending the decomposed task to a crawler engine to directionally crawl information data of each large website;
a4, entering data into HBase database by a persistence means;
a5, the data layer crawls specific data from an HBase database, and visual analysis is carried out on the data with different dimensions based on a web analysis module.
6. The accurate tenderer platform based on campus big data as claimed in claim 1, characterized in that: the portrait system comprises a tag data processing module, a tag data mining and tagging module, a tag construction module, a portrait analysis module and an open platform module.
7. The accurate tenderer platform based on campus big data as claimed in claim 1, characterized in that: the theme center library comprises park themes, enterprise themes, recruiting themes and policy themes.
8. The accurate tenderer platform based on campus big data as claimed in claim 1, characterized in that: the data visualization module comprises a park overview and an industry view; the enterprise analysis module comprises enterprise portrait, comprehensive evaluation, investment analysis and interpersonal analysis; the accurate business inviting module comprises progress monitoring, intelligent recommendation, accurate business inviting and internal stable business; the policy matching module includes a coverage analysis and an effect analysis.
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| CN116739535A (en) * | 2023-08-15 | 2023-09-12 | 三体智慧网络科技(苏州)有限公司 | Accurate digital twinning planning display platform system for quotients and construction method thereof |
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