CN112989156A

CN112989156A - Big data based policy and enterprise matching method and system

Info

Publication number: CN112989156A
Application number: CN201911214816.3A
Authority: CN
Inventors: 谢晶晶; 刘朝; 聂朝波; 欧燕林; 杨莉美
Original assignee: Chongqing Academy of Science and Technology
Current assignee: Chongqing Academy of Science and Technology
Priority date: 2019-12-02
Filing date: 2019-12-02
Publication date: 2021-06-18

Abstract

The present specification provides a big data based policy and enterprise matching method and system, the method comprising: and collecting information issued by each government department by utilizing big data, screening and classifying the collected information, and determining the information category of the policy information. Meanwhile, the enterprises can be classified, the enterprise categories of the enterprises are determined, the information categories are matched with the enterprise categories, the enterprises matched with the policy information are determined, and the policy information is further pushed to the matched enterprises. The enterprise does not need to inquire and search the policies required by the enterprise from massive policy information, the accurate pushing of the policy information is realized, the workload of the enterprise for manually acquiring the enterprise policies is reduced, meanwhile, the large data is used for acquiring the policy information, the coverage is wide, and the omission of the policy information is avoided.

Description

Big data based policy and enterprise matching method and system

Technical Field

The specification belongs to the technical field of computers, and particularly relates to a big data-based policy and enterprise matching method and system.

Background

With the continuous development of computer and internet technologies, paper office is gradually replaced by electronic office, and most government departments also issue some policy information, work notice, government affair information and the like through the internet. However, the government affair publishing platform has various construction levels, large geographical differences, complicated information and no analysis function, and only provides a simple retrieval function. In addition, various policies are different in types, distribution, release time, management departments to which the policies belong, and objects targeted by the policies are different. Enterprises are usually busy in their core services, and are basically not occupied to actively consider and refer the policy information, and cannot acquire the latest policy in time.

If the enterprise wants to obtain the policies related to the enterprise, a large amount of resources such as manpower and time are needed to be paid, the policies which accord with the self declaration are screened out from the massive policies, the enterprise is further influenced to respond to the government policies, the work center of gravity of the enterprise is adjusted, or the benefits brought to the enterprise by the government policies cannot be enjoyed. Therefore, how to push the policy information issued by the government department to the enterprise matching with the policy is a technical problem which needs to be solved urgently in the field.

Disclosure of Invention

The embodiment of the specification aims to provide a method and a system for matching a policy and an enterprise based on big data, and the policy information is accurately pushed.

In one aspect, an embodiment of the present specification provides a method for matching a big data-based policy with an enterprise, including:

acquiring policy information issued by each government department;

screening and classifying the policy information to determine the information category of the policy information;

matching the information category of the policy information with the enterprise category of each enterprise in a predetermined enterprise category library to obtain a target enterprise matched with the policy information;

and pushing the policy information to a client corresponding to the target enterprise.

Optionally, the screening and classifying the policy information to determine the information category of the policy information includes:

acquiring the title, the release unit and the release time of the policy information;

screening the policy information according to the title, the issuing unit and the issuing time of the policy information, and deleting invalid policy information;

classifying the screened policy information according to the title, the release unit and the release time of the policy information and based on at least one of the technical field of the policy information, the attribute of the policy information, the target to which the policy information is directed, the administrative region where the release unit is located, the administrative level of the release unit and the effective time, and obtaining the information category of the policy information.

Optionally, the matching the information category of the policy information with the enterprise category of each enterprise in a predetermined enterprise category library to obtain the target enterprise matched with the policy information includes:

presetting a matching weight value of each information category;

matching the information types of the policy information with the enterprise types of the enterprises in the enterprise type library in sequence;

and adding the weighted values of the information categories successfully matched with the enterprise categories of the same enterprise, and if the added weighted values are larger than a preset threshold value, determining that the enterprise is a target enterprise matched with the policy information.

Optionally, the method further includes:

the method comprises the steps of obtaining enterprise information of each enterprise in advance, wherein the enterprise information comprises: at least one of the management system of the enterprise, the administrative region of the enterprise, the main operation products of the enterprise and the scientific research achievements of the enterprise;

determining business categories for respective businesses based on the business information, wherein a business comprises one or more business categories;

and constructing the enterprise category library according to the enterprise categories of the enterprises.

Optionally, the method further includes:

acquiring historical policy query records of each enterprise;

determining the historical query policy category of each enterprise according to the historical policy query record;

and taking the historical inquiry policy category as an enterprise category of a corresponding enterprise, and storing the enterprise category in the enterprise category library.

presetting a matching weight value of each enterprise category;

adding weighted values of enterprise categories successfully matched with the information categories of the policy information in the same enterprise to serve as matching weights of the enterprise, wherein if the matching weights are larger than a set threshold value, the enterprise is a target enterprise matched with the policy information.

Optionally, the method further includes:

and acquiring the related enterprises of the target enterprise, and pushing the policy information to the related enterprises.

Optionally, the method further includes:

and updating the enterprise category of each enterprise according to the feedback information of the user and the policy query request of the user.

In another aspect, the present specification provides a system for big data based policy and enterprise matching, comprising:

the policy information acquisition module is used for acquiring policy information issued by each government department;

the policy information classification module is used for screening and classifying the policy information and determining the information category of the policy information;

the policy information matching module is used for matching the information type of the policy information with the enterprise type of each enterprise in a predetermined enterprise type library to obtain a target enterprise matched with the policy information;

and the policy information pushing module is used for pushing the policy information to the client corresponding to the target enterprise.

In yet another aspect, the present specification provides a big data based policy information and enterprise matching push platform, including: the method comprises the following steps: the system comprises a big data acquisition device, a policy information classification device, an enterprise classification device, an information matching device and an information pushing device, wherein the big data acquisition device is connected with a plurality of government departments and an enterprise network publishing platform, and the information pushing device is connected with a plurality of enterprise clients;

the big data acquisition device is used for acquiring release information from a network platform of the government department by adopting a web crawler technology and sending the acquired release information to the policy information classification device;

the big data acquisition device is also used for acquiring enterprise information of each enterprise and policy query records of each enterprise and sending the acquired enterprise information and policy query records to the enterprise classification device;

the policy information classification device is used for screening the received release information, screening out the policy information, classifying the screened policy information according to a preset classification rule, determining the information category of each policy information, and sending each policy information and the information category of the policy information to the information matching device;

the enterprise classification device is used for classifying the enterprises based on the received enterprise information and policy query records of the enterprises, determining the enterprise categories of the enterprises and constructing an enterprise category library;

the information matching device is used for matching the information type of each policy information with the enterprise type in the enterprise type library to obtain a target enterprise matched with the information type of the policy information;

and the information pushing device pushes the received policy information to an enterprise client of the target enterprise.

The big data-based policy and enterprise matching method, system and platform provided by the specification collect information issued by each government department by using big data, and screen and classify the collected information to determine the information category of policy information. Meanwhile, the enterprises can be classified, the enterprise categories of the enterprises are determined, the information categories are matched with the enterprise categories, the enterprises matched with the policy information are determined, and the policy information is further pushed to the matched enterprises. The enterprise does not need to inquire and search the policies required by the enterprise from massive policy information, the accurate pushing of the policy information is realized, the workload of the enterprise for manually acquiring the enterprise policies is reduced, meanwhile, the large data is used for acquiring the policy information, the coverage is wide, and the omission of the policy information is avoided.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.

FIG. 1 is a flow diagram illustrating a method for big-data based policy matching with businesses in one embodiment of the present description;

FIG. 2 is a flow diagram illustrating a method for big data based policy matching with businesses in yet another embodiment of the present description;

FIG. 3 is a block diagram illustrating an exemplary embodiment of a big data based policy and business matching system provided herein;

FIG. 4 is a block diagram of a big data based policy information and enterprise matching push platform provided in one embodiment of the present description;

FIG. 5 is a block diagram of the hardware architecture of a big-data based policy and enterprise matching processing server in one embodiment of the present description.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step should fall within the scope of protection of the present specification.

The policy information issued by the government department can guide the development direction of each enterprise, the government department usually issues the policy information through some network issuing platforms, and the enterprise can only acquire the policy information by checking the network issuing platforms of the government department in real time or regularly. The policy information issued by government departments needs each enterprise to inquire and screen, and whether the policy information is matched with the enterprise is determined, so that the policy information needed by the enterprise is obtained.

The embodiment of the description provides a method for matching policy information with enterprises and accurately sending the policy information, which is characterized in that big data is used for collecting information issued by each government department, and the collected information is screened and classified to determine the information category of the policy information. Meanwhile, the enterprises can be classified, the enterprise categories of the enterprises are determined, the information categories are matched with the enterprise categories, the enterprises matched with the policy information are determined, and the policy information is further pushed to the matched enterprises. The enterprise does not need to inquire and search the required policy from massive policy information, the policy information is accurately pushed, and the workload of manually acquiring the enterprise policy by the enterprise is reduced.

The big data-based policy and enterprise matching method can be applied to a client or a server, and the client can be an electronic device such as a smart phone, a tablet computer, a smart wearable device (such as a smart watch) and a smart vehicle-mounted device.

Fig. 1 is a flowchart illustrating a method for matching a big-data-based policy with a business in an embodiment of the present disclosure, and as shown in fig. 1, the method for matching a big-data-based policy with a business provided in an embodiment of the present disclosure may include:

and 102, acquiring policy information issued by each government department.

In a specific implementation process, a data interface can be set in a system matched with an enterprise based on a big data policy, and the system is connected with network publishing platforms of all government departments, so that the function of monitoring the network publishing platforms of all government departments in real time is realized. The network publishing platform of the government department may include at least one of: government information platforms, government websites, web portals, recruitment websites, bidding websites, and common information publishing platforms such as: official micro-blogging by government departments, and the like. If it is monitored that new information is released by a network release platform of one or some government departments, the information released by the network release platform can be acquired in a data mode such as a web crawler and the like. For example: if a certain government issues a piece of scientific and technological subsidy policy information on a website of the government, the piece of scientific and technological subsidy policy information can be acquired from the website by using a web crawler technology.

When the policy information is obtained, the link information corresponding to the policy information, that is, the website link and the text information, may be obtained, and the link information and the text information may be used as the information content of the policy information. The tags in the web page content corresponding to the hyperlinks of the published information and the corresponding sub-tags under each tag can form a DOM tree form, and according to the set website rules, the title, the issuing authority, the issuing number, the publishing time and the text are extracted from the DOM tree form web page to serve as the text information of the published information.

And 104, screening and classifying the policy information to determine the information category of the policy information.

In a specific implementation process, the information issued by the government department may also be some work notifications or activity information unrelated to policies, and the acquired issued information may be screened to determine whether the issued information is policy information, whether the policy information is correct and valid, and the like. Policy information in some embodiments of the present description may be understood as enterprise-related policy and regulations, such as: technical-related policy information such as: the scientific and technological subsidy policy, the scientific and technological project declaration policy, and the like, and may further include some information such as bidding, and the embodiments of the present description are not specifically limited. After the policy information is screened out, the policy information can be classified, a classification rule or a classification dimension can be preset, and the policy information is classified according to the preset classification rule. For example: the classification may be based on the technical field to which the policy information relates, or may be based on attributes of the policy information such as: subsidy policies, project declaration policies, bid inviting policies, and the like, which can be specifically set according to actual needs. A machine learning model may also be constructed in advance, and the policy information is classified by using the machine learning model, which is not specifically limited in this specification. The information category of one policy information may include one or more, and may be determined according to a classification rule.

And 106, matching the information type of the policy information with the enterprise type of each enterprise in a predetermined enterprise type library to obtain a target enterprise matched with the policy information.

In the specific implementation process, a data interface can be set in a system matched with enterprises based on big data policies, the system is connected with network publishing platforms (such as official networks and recruitment websites) or big search engines of the enterprises, the big data is used for collecting enterprise information of the enterprises, the enterprises are classified, and an enterprise category library is constructed. The enterprise category library may include mapping relationships between each enterprise and enterprise categories, where an enterprise category may serve as a label of an enterprise and may represent information of technical fields related to the enterprise, such as: the division may be based on the business product of the enterprise, etc. After the information type of the policy information is determined, the information type of the policy information can be matched with the enterprise type of each enterprise in the enterprise type library, and a target enterprise matched with the policy information is obtained. When the information category of the policy information is the same as, similar to or related to the enterprise category of a certain enterprise in the enterprise category library, the enterprise can be considered as a target enterprise matched with the policy information. For example: if the information category of certain policy information is determined to be "new energy", the enterprise category in the enterprise category library may be "new energy" or related to "new energy" such as: enterprises such as "wind power generation", "solar energy", and "nuclear energy" are targeted enterprises to which the policy information corresponds.

Fig. 2 is a flow chart illustrating a method for matching big-data-based policies with businesses in another embodiment of the present specification, and as shown in fig. 2, in some embodiments of the present specification, the following method may be used to construct the business category library:

step 202, obtaining enterprise information of each enterprise in advance, wherein the enterprise information comprises: at least one of the management system of the enterprise, the administrative region of the enterprise, the main operation products of the enterprise and the scientific research achievements of the enterprise;

step 204, determining enterprise categories of enterprises based on the enterprise information, wherein one enterprise comprises one or more enterprise categories;

and step 206, constructing the enterprise category library according to the enterprise categories of the enterprises.

In a specific implementation process, the big data may be used to collect enterprise information of each enterprise, for example: the enterprise information of each enterprise can be collected from enterprise official websites or recruitment websites, search engines, large periodicals and patent websites of each enterprise. The collected enterprise information may include at least one of management systems of the enterprise (such as colleges and universities, scientific research units, civil and private enterprises, national and private enterprises, and the like), administrative areas of the enterprise, main and private products of the enterprise, and scientific research results of the enterprise (such as intellectual property rights of published articles, declared patents, copyright, and the like). And determining the enterprise category of each enterprise according to the acquired enterprise information, wherein the enterprise category can be used as a category label of the enterprise, and the enterprise can comprise one or more enterprise categories. For example: an enterprise belongs to a scientific research unit and mainly researches products in the aviation field, scientific research achievements of the enterprise mainly comprise journal articles, patents and the like in the aviation field, and enterprise categories of the enterprise can comprise: scientific research units and aviation. And establishing an enterprise category library according to the corresponding relation between the enterprise categories and the enterprise names of the enterprises.

According to the embodiment of the specification, the enterprise category of each enterprise is determined according to the enterprise information of the enterprise, a data base is laid for matching the related policy information for each enterprise subsequently, and the policy information is pushed accurately.

In some embodiments of the present description, the method may further include:

acquiring historical policy query records of each enterprise;

In a specific implementation process, historical policy query records of each enterprise can be collected through big data, and the historical query policy category of each enterprise is determined based on the historical policy query records. Such as: if enterprise B inquires about a subsidy policy about a patent, the "subsidy policy" can be used as a historical inquiry policy category of enterprise B, and if enterprise B inquires about a project declaration policy about green energy, the "green energy" and the "project declaration" can be used as a historical inquiry policy category of enterprise B. The historical policy query records can be subjected to natural language processing such as word segmentation, normalization, keyword extraction and the like, and then the historical query policy category of the enterprise can be determined. After the historical query policy category of the enterprise is determined, the historical query policy category of the enterprise can be used as the enterprise category of the enterprise and stored in an enterprise category library. Such as: the enterprise B inquires the subsidy policy about the patent, and determines that the subsidy policy is the historical inquiry policy category of the enterprise B, so that the subsidy policy can be the enterprise category of the enterprise B and is stored in a column of the enterprise category corresponding to the enterprise B in the enterprise category library.

The embodiment of the specification comprehensively considers the historical query records of the enterprise, supplements the enterprise categories, fully considers the requirements of users, improves the coverage of the enterprise categories, further improves the accuracy of policy matching, and realizes accurate pushing of policy information.

And step 108, pushing the policy information to a client corresponding to the target enterprise.

In a specific implementation process, after a target enterprise corresponding to policy information is obtained, the policy information may be sent to an enterprise client of the target enterprise, where the policy information may include a link and a text of the information, and may also include a picture, a video, and the like. Such as: the policy information can be sent to a mobile phone client of an enterprise contact person of a target enterprise or an enterprise mailbox in a short message, mail and other modes, and the policy information can also be directly sent to the client of the target enterprise connected with a system matched with the enterprise through a data interface of the system matched with the enterprise based on the big data policy.

On the basis of the above embodiments, in some embodiments of the present specification, when pushing policy information, a related enterprise of the target enterprise may also be obtained, and the policy information is pushed to the related enterprise.

In a specific implementation process, the associated enterprise of the target enterprise may be understood as an enterprise having direct or indirect business communications with the target enterprise, and a knowledge graph of the enterprise may be constructed based on business communications among the enterprises to further determine the associated enterprise of each enterprise. After the target enterprise is determined, the related enterprise of the target enterprise can be obtained, and when the policy information is pushed to the target enterprise, the policy information can also be pushed to the related enterprise having business traffic with the target enterprise. The data processing amount is reduced, and meanwhile, the enterprise associated with the target enterprise has a high possibility that the recommendation information is needed, so that the information sharing among the associated enterprises is realized. In addition, when the policy information is pushed, the policy information can be pushed according to the pushing time set by the user. For example: when the enterprise B registers, the information pushing time is from 9:00 am to 5:00 pm of a working day, and the policy information can be pushed to the enterprise B according to the time period.

It should be noted that, in some embodiments of the present specification, after the information category of each policy information is determined, the information category may be marked in each policy information as a tag, and when information is pushed, the policy information of one category may be sent to the user together according to the category tag, so that the user may compare the policy information with the policy information. The obtained policy information and the information category label of the policy information may also be stored in a policy information base, and when a new enterprise accesses the big data based policy and enterprise matching system in the embodiment of the present specification, the matched policy information is obtained from the policy information base and sent to the newly accessed enterprise.

In addition, in the embodiments of the present description, after the information type of the policy information is determined, the obtained policy information may be subjected to information processing, such as: the policy information is normalized and converted into a format convenient for a user to look up, the policy information can be read, key words can be extracted and the like, the read information, the key words and the information category of the policy information are used as abstract information of the policy information and are sent to the user together, and the user can visually know the content of the pushed information conveniently.

The method for matching the policy and the enterprise based on the big data, provided by the embodiment of the specification, is used for collecting information issued by each government department by using the big data, screening and classifying the collected information and determining the information category of the policy information. Meanwhile, the enterprises can be classified, the enterprise categories of the enterprises are determined, the information categories are matched with the enterprise categories, the enterprises matched with the policy information are determined, and the policy information is further pushed to the matched enterprises. The enterprise does not need to inquire and search the required policy from massive policy information, the policy information is accurately pushed, and the workload of manually acquiring the enterprise policy by the enterprise is reduced. Meanwhile, the big data is used for acquiring the policy information, the coverage is wide, and omission of the policy information is avoided.

On the basis of the foregoing embodiments, in some embodiments of the present specification, the screening and classifying the policy information to determine the information category of the policy information includes:

In a specific implementation process, when policy information issued by each government department is acquired, a title, a issuing unit, issuing time and the like of the policy information can be acquired at the same time, the acquired policy information can be screened firstly based on the title, the issuing unit and the issuing time of the policy information, and expired invalid policy information, some wrong policy information and non-policy information which does not belong to the policy information are deleted. For example: if a piece of policy information issued by a certain government department is crawled from a microblog of a certain person by using a web crawler technology, but the information is not published in an official website of the government department, the information can be regarded as error data and is directly deleted. And the official seal information in the release information can be acquired through an image processing algorithm, and whether the data is accurate and effective is screened according to whether the official seal in the acquired data is accurate or not. And judging whether the current policy information is valid or not according to the release time and the expiration date in the policy information, and deleting the expired policy information. Effective policy information is screened out, invalid policy information is deleted, workload is reduced for classification of subsequent policy information, and meanwhile, trouble caused by pushing some wrong policy information to a user is avoided.

After the policy information is screened, the screened policy information can be classified, and classification rules or classification dimensions can be preset, such as: classifying the screened policy information according to at least one of the technical field of the policy information, the attribute of the policy information (such as subsidy type policies, project application type policies, bid and bid type policies and the like, which represent the main function of the policy information), the target of the policy information (such as colleges and universities, scientific research units, civil enterprises and the like), the administrative region of the issuing unit, the administrative level of the issuing unit and the effective time (from the issuing time to the deadline time).

For example: the method comprises the steps of firstly carrying out natural language processing modes such as keyword extraction or feature extraction on the title or text information of the policy information, obtaining the technical field, attribute, targeted object, release time and deadline time related to the policy information, and further determining the release unit of the policy information according to official seal information or information sources in the policy information, and further determining the administrative region where the release unit of the policy information is located and the administrative level of the release unit. And classifying the policy information based on the acquired information to determine the information category of the policy information. It should be noted that a policy information may include one or more information categories, such as: the technical field of the policy information is financial field, belongs to bidding type policies, and aims at all big banks, and the information category of the policy information may include: finance field, bidding, banking.

It should be noted that, when there are multiple information categories of the policy information and multiple enterprise categories of the enterprise, matching rules may be set, such as: the policy information may be considered to match a business as long as there is one category of information that is the same as, similar to, or related to a business category, or a specified number of categories of information that are the same as, similar to, or related to a business category may be considered to match the policy information to the business. The matching weights of different information categories can also be set, the weight values of all matched information categories are added, when a certain threshold value is reached, the policy information is considered to be matched with the enterprise, the policy information can be specifically set according to actual needs, and the embodiments of the present specification are not specifically limited.

In the embodiment of the specification, the acquired policy information is primarily screened, and then multi-dimensional classification is performed on the policy information based on the technical field, the attribute of the policy information, the object targeted by the policy information, the administrative region where the issuing unit is located, the administrative level of the issuing unit, the effective time and the like, so that the category of each piece of policy information is accurately determined. Based on the category of the policy information, the policy information can be accurately pushed to enterprises conforming to the policy, and troubles of the enterprises which are not needed are avoided.

In some embodiments of the present specification, the matching the information category of the policy information with the enterprise category of each enterprise in a predetermined enterprise category library to obtain the target enterprise matched with the policy information may include:

presetting a matching weight value of each information category;

sequentially matching the information category of the policy information with the enterprise category of each enterprise in the enterprise category library

In a specific implementation process, when there are a plurality of information categories of the policy information, a matching weight value of each information category may be set in advance based on importance levels of different information categories, the weight values of the information categories that are successfully matched are added, and when the added weight values are greater than a preset threshold, the matching is considered to be successful. For example: the policy information a has 3 information categories a, b and c, wherein the matching weight value of the information category a is 0.2, the matching weight value of the information category b is 0.5, and the matching weight value of the information category c is 0.1. Sequentially matching the information type of the policy information with the enterprise type of each enterprise in an enterprise type library, wherein when the policy information A is matched with the enterprise B, the information types B and c are matched with the enterprise type B of the enterprise B₁、c₁If the matching is successful, the matching weight of the policy information a is 0.5+0.1 to 0.6. If the preset threshold value is 0.5, it may be considered that the policy information a and the enterprise B are successfully matched, and the enterprise B is a target enterprise of the policy information a. Of course, if the sum of the matching weight values is less than or equal to the preset threshold, the matching is unsuccessful. The size of the preset threshold value can be adjusted and set according to actual needs, and the description is providedThe examples are not particularly limited.

Through setting the weight value of each information category, the matching condition of the information categories can be controlled, and the information categories which influence the information categories are set to have higher weight, so that the policy information matched with enterprises can be found more accurately, and the policy information can be pushed accurately.

In some embodiments of the present specification, when there are multiple enterprise categories of enterprises, the matching the information category of the policy information with the enterprise category of each enterprise in a predetermined enterprise category library to obtain the target enterprise matched with the policy information may include:

presetting a matching weight value of each enterprise category;

In a specific implementation process, when there are a plurality of enterprise categories of an enterprise, the matching weight values of the enterprise categories may be set in advance based on importance levels of different enterprise categories, the weight values of the enterprise categories that are successfully matched are added, and when the added weight values are greater than a preset threshold, the matching is considered to be successful. For example: the policy information A has 3 information categories a, B and c, and the enterprise information B has 3 information categories a₁、b₁、c₁Wherein the business category a₁Has a matching weight value of 0.3, enterprise class b₁The matching weight value of (2) is 0.2, and the matching weight value of the enterprise category c is 0.2. Sequentially matching the information category of the policy information A with the enterprise category of each enterprise in the enterprise category library, wherein when the policy information A is matched with the enterprise B, the enterprise category a of the enterprise B is₁、b₁If the matching with the information categories a and B of the policy information a is successful, the matching weight of the enterprise B is 0.3+ 0.2-0.5. If the preset threshold is 0.6, the policy message can be considered asAnd if the information A is unsuccessfully matched with the enterprise B, continuously matching the policy information A with the enterprise category of the enterprise B.

Through setting up the weighted value of each enterprise classification, can control the matching condition of information classification, will influence the great enterprise classification and set up than higher weight to more accurate finding and enterprise assorted policy information realize policy information's accurate propelling movement.

In some embodiments of the present specification, the enterprise category library may be updated by the following method: and updating the enterprise category of each enterprise according to the feedback information of the user and the policy query request of the user.

In a specific implementation process, after the policy information is pushed to the target enterprise, the user may return required or unnecessary feedback information according to the needs of the user, for example: after the policy information is matched based on the above embodiment, the information category a in the policy information a and the enterprise category a of the enterprise B₁And if the matching is successful, the policy information A is sent to the client of the enterprise B. After the staff related to the enterprise B reads the policy information, the staff thinks that the enterprise of the policy information does not need the policy information, and then the staff can feed back the feedback information which is not needed. After the feedback information of the enterprise B is received, the enterprise category matched with the policy information A by the enterprise B is considered to be inaccurate, the information of the enterprise category is not needed by the enterprise B, and the matched enterprise category a can be considered₁Deleted from the business category of business b. In addition, the user can be given a policy query request to update the enterprise category of the enterprise, such as: if the staff associated with enterprise B sends a policy query request for a new energy project declaration to the big data based policy and enterprise matching system in this embodiment, new energy may be added to the enterprise label of enterprise B.

The embodiment of the specification fully considers the feedback information and the request information of the user, updates the enterprise labels of all enterprises, ensures that the matched policy information can meet the requirements of the user, and improves the accuracy of normal information matching and pushing.

In the present specification, each embodiment of the method is described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. The relevant points can be obtained by referring to the partial description of the method embodiment.

Based on the method for matching the big data-based policy with the enterprise, one or more embodiments of the present disclosure further provide a system for matching the big data-based policy with the enterprise. The system may include systems (including distributed systems), software (applications), modules, components, servers, clients, etc. that employ the methods described in the embodiments of the present specification in conjunction with any necessary hardware for implementation. Based on the same innovative concept, the embodiments of the present specification provide systems in one or more embodiments as described in the following embodiments. Because the implementation scheme for solving the problem of the system is similar to the method, the implementation of the system in the embodiment of the present specification may refer to the implementation of the foregoing method, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.

Specifically, fig. 3 is a schematic block diagram of an embodiment of a system for matching a big-data-based policy with an enterprise provided in this specification, and as shown in fig. 3, the system for matching a big-data-based policy with an enterprise provided in this specification may include: policy information acquisition module 31, policy information classification module 32, policy information matching module 33, policy information push module 34, wherein:

a policy information acquisition module 31, configured to acquire policy information issued by each government department;

a policy information classification module 32, configured to filter and classify the policy information, and determine an information category of the policy information;

a policy information matching module 33, configured to match an information category of the policy information with an enterprise category of each enterprise in a predetermined enterprise category library, so as to obtain a target enterprise matched with the policy information;

and the policy information pushing module 34 is configured to push the policy information to a client corresponding to the target enterprise.

The system for matching the policy and the enterprise based on the big data, provided by the embodiment of the specification, collects information issued by each government department by using the big data, and screens and classifies the collected information to determine the information category of the policy information. Meanwhile, the enterprises can be classified, the enterprise categories of the enterprises are determined, the information categories are matched with the enterprise categories, the enterprises matched with the policy information are determined, and the policy information is further pushed to the matched enterprises. The enterprise does not need to inquire and search the required policy from massive policy information, the policy information is accurately pushed, and the workload of manually acquiring the enterprise policy by the enterprise is reduced. Meanwhile, the big data is used for acquiring the policy information, the coverage is wide, and omission of the policy information is avoided.

It should be noted that the above-described system may also include other embodiments according to the description of the method embodiment. The specific implementation manner may refer to the description of the above corresponding method embodiment, and is not described in detail herein.

An embodiment of the present specification further provides a data processing device for matching policy information and an enterprise based on big data, including: at least one processor and a memory for storing processor-executable instructions, the processor when executing the instructions implementing the big-data based policy and enterprise matching method of the above embodiments, such as:

acquiring policy information issued by each government department;

Fig. 4 is a schematic structural diagram of a big data-based policy information and enterprise matching pushing platform provided in an embodiment of this specification, and as shown in fig. 4, the big data-based policy information and enterprise matching pushing platform in the embodiment of this specification may include a big data collecting device, a policy information classifying device, an enterprise classifying device, an information matching device, and an information pushing device, where the big data collecting device is connected to a plurality of government departments and an enterprise network publishing platform, and the information pushing device is connected to a plurality of enterprise clients;

It should be noted that the above-described processing device and system may also include other embodiments according to the description of the method embodiment. The specific implementation manner may refer to the description of the above corresponding method embodiment, and is not described in detail herein.

The big data based policy and enterprise matching system or processing device or system provided by the specification can also be applied to various data analysis and processing systems. The system or processing device may comprise any of the above embodiments of a big data based policy matching system for an enterprise. The system or processing device may be a single server or may comprise a cluster of servers, a system (including a distributed system), software (applications), an actual operating system, logical gate systems, quantum computers, etc. that use one or more of the methods or systems of one or more embodiments of the present description in conjunction with a terminal system that implements the hardware as necessary. The system for checking for discrepancies may comprise at least one processor and a memory storing computer-executable instructions that, when executed by the processor, implement the steps of the method of any one or more of the embodiments described above.

The method embodiments provided by the embodiments of the present specification can be executed in a mobile terminal, a computer terminal, a server or a similar computing system. Taking an example of the policy and enterprise matching processing server running on a server, fig. 5 is a block diagram of a hardware structure of the policy and enterprise matching processing server based on big data in an embodiment of the present specification, where the server may be a system for matching a policy and enterprise based on big data, a business authority recommendation data processing device, or a system in the above embodiment. As shown in fig. 5, the server 10 may include one or more (only one shown) processors 100 (the processors 100 may include, but are not limited to, a processing system such as a microprocessor MCU or a programmable logic device FPGA), a memory 200 for storing data, and a transmission module 300 for communication functions. It will be understood by those of ordinary skill in the art that the structure shown in fig. 5 is merely illustrative and is not intended to limit the structure of the electronic system described above. For example, the server 10 may also include more or fewer components than shown in FIG. 5, and may also include other processing hardware, such as a database or multi-level cache, a GPU, or have a different configuration than shown in FIG. 5, for example.

The memory 200 may be used to store software programs and modules of application software, such as program instructions/modules corresponding to the big data based policy and enterprise matching method in the embodiment of the present specification, and the processor 100 executes various functional applications and resource data updates by executing the software programs and modules stored in the memory 200. Memory 200 may include high speed random access memory and may also include non-volatile memory, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state memory. In some examples, memory 200 may further include memory located remotely from processor 100, which may be connected to a computer terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission module 300 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal. In one example, the transmission module 300 includes a Network adapter (NIC) that can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission module 300 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The method or system provided by the present specification and described in the foregoing embodiments may implement the service logic through a computer program and record the service logic on a storage medium, where the storage medium may be read and executed by a computer, so as to implement the effect of the solution described in the embodiments of the present specification.

The storage medium may include a physical system for storing information, typically by digitizing the information and storing it in a medium using electrical, magnetic or optical means. The storage medium may include: systems that store information using electrical energy, such as various types of memory, e.g., RAM, ROM, etc.; systems that store information using magnetic energy such as hard disks, floppy disks, tapes, magnetic core memories, bubble memories, and usb disks; systems that store information optically, such as CDs or DVDs. Of course, there are other ways of storing media that can be read, such as quantum memory, graphene memory, and so forth.

The method or system for matching the policy based on big data with the enterprise provided in the embodiments of the present specification may be implemented in a computer by executing corresponding program instructions by a processor, for example, implemented in a PC using a c + + language of a windows operating system, implemented in a linux system, or implemented in an intelligent terminal using, for example, android, an iOS system programming language, implemented in processing logic based on a quantum computer, and the like.

It should be noted that the descriptions of the system, the computer storage medium, and the system described above according to the related method embodiments may also include other embodiments, and specific implementations may refer to the descriptions of the corresponding method embodiments, which are not described in detail herein.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to only the partial description of the method embodiment.

The embodiments of the present description are not limited to what must be consistent with industry communications standards, standard computer resource data updating and data storage rules, or what is described in one or more embodiments of the present description. Certain industry standards, or implementations modified slightly from those described using custom modes or examples, may also achieve the same, equivalent, or similar, or other, contemplated implementations of the above-described examples. The embodiments using the modified or transformed data acquisition, storage, judgment, processing and the like can still fall within the scope of the alternative embodiments of the embodiments in this specification.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the systems included therein for performing the various functions may also be considered as structures within the hardware component. Or even a system for performing various functions can be considered to be a software module implementing the method or a structure within a hardware component.

The systems, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

Although one or more embodiments of the present description provide method operational steps as described in the embodiments or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive approaches. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When a practical system or end product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing, or even in the context of distributed resource data update) according to the embodiments or methods shown in the drawings. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded. The terms first, second, etc. are used to denote names, but not any particular order.

For convenience of description, the above system is described with the functions divided into various modules, which are described separately. Of course, when implementing one or more of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, etc. The above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, systems or units, and may be in an electrical, mechanical or other form.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, systems (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable resource data updating apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable resource data updating apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable resource data update apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable resource data update apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage, graphene storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

As will be appreciated by one skilled in the art, one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the present specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and the relevant points can be referred to only part of the description of the method embodiments. In the description of the specification, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

The above description is merely exemplary of one or more embodiments of the present disclosure and is not intended to limit the scope of one or more embodiments of the present disclosure. Various modifications and alterations to one or more embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present specification should be included in the scope of the claims.

Claims

1. A big data based policy and enterprise matching method is characterized by comprising the following steps:

acquiring policy information issued by each government department;

2. The method of claim 1, wherein the screening and classifying the policy information to determine the information category of the policy information comprises:

3. The method of claim 1, wherein matching the information category of the policy information with a business category of each business in a predefined business category library to obtain a target business for which the policy information matches comprises:

presetting a matching weight value of each information category;

4. The method of claim 1, wherein the method further comprises:

5. The method of claim 4, wherein the method further comprises:

acquiring historical policy query records of each enterprise;

6. The method of claim 1, wherein matching the information category of the policy information with a business category of each business in a predefined business category library to obtain a target business for which the policy information matches comprises:

presetting a matching weight value of each enterprise category;

7. The method of claim 1, wherein the method further comprises:

8. The method of claim 1, wherein the method further comprises:

9. A big-data based policy and enterprise matching system, comprising:

10. A big data based policy information and enterprise matching push platform is characterized by comprising: the system comprises a big data acquisition device, a policy information classification device, an enterprise classification device, an information matching device and an information pushing device, wherein the big data acquisition device is connected with a plurality of government departments and an enterprise network publishing platform, and the information pushing device is connected with a plurality of enterprise clients;