CN112765338A - Policy data pushing method, policy calculator and computer equipment - Google Patents
Policy data pushing method, policy calculator and computer equipment Download PDFInfo
- Publication number
- CN112765338A CN112765338A CN202011627573.9A CN202011627573A CN112765338A CN 112765338 A CN112765338 A CN 112765338A CN 202011627573 A CN202011627573 A CN 202011627573A CN 112765338 A CN112765338 A CN 112765338A
- Authority
- CN
- China
- Prior art keywords
- policy data
- public policy
- data
- public
- result
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/335—Filtering based on additional data, e.g. user or group profiles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- 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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Databases & Information Systems (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Computational Linguistics (AREA)
- Development Economics (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Game Theory and Decision Science (AREA)
- Evolutionary Computation (AREA)
- Health & Medical Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the application discloses a policy data pushing method, a policy calculator and computer equipment, wherein the method comprises the following steps: automatically collecting public policy data, wherein the public policy data at least comprises policy data aiming at talent preferential; preprocessing public policy data, inputting the preprocessed public policy data into a neural network model to obtain a prediction result, wherein the prediction result comprises a classification result about the public policy data; the talent information sent by the terminal is received in real time, the characteristic data of the talent information is extracted, and the characteristic data of the talent information is matched with the classification result of the public policy data; and judging whether the matching result is consistent with the preset condition, if so, sending the corresponding public policy data to the corresponding terminal when the matching result is consistent with the preset condition. The application can enable the talents of enterprises to enjoy subsidy policies of governments at the first time, improves the convenience and the real-time property of obtaining policy information of the talents, and provides all-around and comprehensive policy service.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a policy data pushing method, a policy calculator and computer equipment.
Background
With the formal implementation of "optimizing operator environment regulations", it is clear that government departments need to continuously perfect policy measures in the aspect of policy service, and the policy benefits enterprises and talent bodies are implemented. Although the preferential policies are various in variety, the policies are relatively dispersed, reporting conditions are different, information is asymmetric and other problems, so that many enterprises and talents miss good policies, real support cannot be obtained, the policies are just set, and the enterprises and talents are quite disappointed. In order to complete the last kilometer of policy service, more enterprises and talents can obtain policy benefits, and how to really release the policy benefits from massive data, so that the enterprises can solve worries behind, obtain more benefits, and make talents innovate and specialized in entrepreneurship, and the method becomes an important research direction in the field of information technology processing.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a policy data pushing method, a policy calculator and computer equipment. The method can enable the talents of enterprises to enjoy subsidy policies of the government at the first time through policy matching calculation, improve the convenience and the real-time property of obtaining policy information by the talents, open a policy service channel for the talents and the government, and provide all-around and comprehensive policy service.
The embodiment of the invention provides the following specific technical scheme:
in a first aspect, a policy data pushing method is provided, where the method includes:
automatically collecting public policy data, the public policy data including at least policy data for talent offers;
preprocessing the public policy data, inputting the preprocessed public policy data into a neural network model to obtain a prediction result, wherein the prediction result comprises a classification result about the public policy data;
the talent information sent by a terminal is received in real time, the characteristic data of the talent information is extracted, and the characteristic data of the talent information is matched with the classification result of the public policy data;
and judging whether the matching result is consistent with the preset condition, if so, sending the corresponding public policy data to the corresponding terminal when the matching result is consistent with the preset condition.
In some embodiments, the method further comprises:
updating a preset sample library at regular time based on the automatically acquired public policy data;
updating the neural network model with the sample base updated each time;
the inputting the preprocessed public policy data into the neural network model to obtain the prediction result specifically includes:
and inputting the preprocessed public policy data into the updated neural network model to obtain a prediction result.
In some embodiments, the public policy data further includes policy data for enterprise offers; the method further comprises the following steps:
acquiring enterprise information, extracting characteristic data of the enterprise information, and matching the characteristic data of the enterprise information with a classification result of the public policy data;
and judging whether the matching result is consistent with the preset condition, and if so, sending the corresponding public policy data to the corresponding enterprise when the matching result is consistent with the preset condition.
In some embodiments, the enterprise information includes public relevant information stored in an enterprise repository, and/or relevant information entered by the enterprise in real-time.
In some embodiments, when the matching result is determined to be consistent with the preset condition, the method further includes:
combining the matching result with corresponding public policy data when the matching result is consistent with preset conditions to generate a matching report;
the sending of the corresponding public policy data to the corresponding terminal when the matching result is consistent with the preset condition specifically includes:
and sending the matching report to a corresponding terminal.
In some embodiments, preprocessing the public policy data specifically includes:
associating and storing the public policy data according to a preset rule;
and performing data cleaning on the stored public policy data.
In some embodiments, after data washing the stored public policy data, the method further comprises:
auditing the cleaned data by using a machine learning algorithm to obtain public policy data inconsistent with the preset rule;
automatically adjusting the incidence relation of the public policy data inconsistent with the preset rule;
inputting the preprocessed public policy data into a neural network model, and obtaining a prediction result comprises:
and inputting the public policy data after the incidence relation is adjusted into the neural network model to obtain a prediction result.
In some embodiments, the method further comprises:
receiving a request for modification of the public policy data;
modifying the public policy data according to the modification request;
the preprocessing the public policy data specifically includes:
and preprocessing the modified public policy data.
In a second aspect, there is provided a policy calculator, the policy calculator comprising:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for automatically acquiring public policy data, and the public policy data at least comprises policy data for talent preferential;
the first processing unit is used for preprocessing the public policy data, inputting the preprocessed public policy data into a neural network model and obtaining a prediction result, wherein the prediction result comprises a classification result related to the public policy data;
the second processing unit is used for receiving talent information sent by a terminal in real time, extracting feature data of the talent information and matching the feature data of the talent information with the classification result of the public policy data; judging whether the matching result is consistent with a preset condition or not;
and the first sending unit is used for sending the corresponding public policy data to the corresponding terminal when the matching result is consistent with the preset condition.
In a third aspect, a computer device is provided, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the method according to the first aspect when executing the computer program.
The embodiment of the invention has the following beneficial effects:
1. according to the invention, through policy matching calculation, talents can enjoy subsidy policies of the government at the first time, so that the convenience and the real-time property of obtaining policy information by talents are improved, a policy service channel is opened for talents and the government, and all-round and comprehensive policy service is provided;
2. the invention can also enable the enterprise to obtain the latest valuable policy information applicable to the enterprise at the first time, thereby obtaining the declaration policy or project applicable to the enterprise.
3. The neural network model used for predicting policy classification is updated regularly, so that the neural network model is predicted more accurately, and the matching degree of the later-stage policy with talents and enterprises is improved;
4. the method and the device utilize the machine learning algorithm to check the policy data, further utilize the checked policy data to carry out classified prediction, and can improve the prediction precision.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is an exemplary system architecture diagram to which some embodiments of the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a policy data push method according to the present application;
FIG. 3 is a flow diagram of yet another embodiment of a policy data pushing method according to the present application;
FIG. 4 is a flow diagram of yet another embodiment of a policy data pushing method according to the present application;
FIG. 5 is a schematic block diagram of one embodiment of a policy calculator according to the present application;
FIG. 6 is a schematic block diagram of a computer device suitable for use in implementing some embodiments of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As described in the background, the existing policies of benefiting people and benefiting enterprises are more dispersed, and enterprises and talents often miss some preferential policies and cannot obtain real support. Based on this, the applicant of the present application creatively thinks of constructing a policy database, classifying and processing policy data, and matching talent information with policy data when obtaining talent information, so as to find out policies that accord with talent declaration.
Fig. 1 shows an exemplary system architecture to which an embodiment of a policy data pushing method or policy calculator of the present application may be applied. As shown in fig. 1, the system architecture includes a terminal device 101, a network 102, and a server 103.
The terminal device 101 is a device for providing an interface for a user to input relevant talent information and transmit the talent information to the server 103 through the network 102. The server 103 may be hardware or software. When the server 103 is hardware, it may be a variety of computer devices including, but not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart air conditioner, a smart sound box, a smart speaker, and the like. When the server 103 is software, it can be installed in the above-listed computer devices.
The server 103 is configured to receive the talent information sent by the terminal apparatus 101, and process the talent information to match the public policy data. The server 103 may be implemented by an independent server or a server cluster composed of a plurality of servers.
It should be understood that the number of terminal devices, networks, servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, servers, as desired for implementation.
Referring to fig. 2, a flowchart of an embodiment of a policy data pushing method according to the present application is shown, which specifically includes the following steps:
201. public policy data is automatically collected, and the public policy data at least comprises policy data for talent preference.
In this embodiment, the executing body of the policy data pushing method may be a server (e.g., the server 103 shown in fig. 1).
Specifically, the server collects public policy data of government open websites within a preset range. Wherein, the preset range includes: science and technology, industrial and telecommunications, society, business, intellectual property, culture and development, agricultural policy and other systems.
In some optional implementations of this embodiment, the server may periodically collect the public policy data in the above range every day through python technology.
202. And preprocessing the public policy data, and inputting the preprocessed public policy data into the neural network model to obtain a prediction result, wherein the prediction result comprises a classification result about the public policy data.
The neural network model is a pre-trained model, and preferably, a convolutional neural network model can be selected.
In some optional implementation manners of this embodiment, the preprocessing the public policy data may specifically include the following steps:
associating and storing the public policy data according to a preset rule;
and performing data cleaning on the stored public policy data.
For each piece of public policy data, the public policy data may be associated and stored in a database (e.g., the database 104 shown in fig. 1) according to a policy title, a policy source, a province and city area to which the policy belongs, a text-sending unit to which the policy belongs, a text-sending number, and a text-sending time. In addition, the data cleaning method is widely researched, so that the process of carrying out data clearness on the public policy data is not repeated.
The purpose of the above-mentioned associative storage and data cleaning of the public policy data is to obtain more accurate basic data, so that a more accurate prediction result can be obtained when the basic data is predicted.
In some optional implementations of this embodiment, after performing data washing on the stored public policy data, the following steps may be further included:
auditing the cleaned data by using a machine learning algorithm to obtain public policy data inconsistent with a preset rule;
and automatically adjusting the association relation of the public policy data inconsistent with the preset rule.
Therefore, the server can input the public policy data with the adjusted incidence relation to the neural network model to obtain a prediction result.
The cleaned data is checked by utilizing a machine learning algorithm, incorrect public policy data such as wrong typesetting formats, policy attribution systems, affiliated areas and the like can be adjusted, and the correctness of the public policy data is further ensured.
203. And receiving talent information sent by the terminal in real time, extracting feature data of the talent information, and matching the feature data of the talent information with the classification result of the public policy data.
In the present embodiment, the server receives talent information transmitted by a terminal device (e.g., terminal device 101 shown in fig. 1). The terminal device is used for providing a page convenient for a user to input information. And after receiving the talent information, the server extracts the characteristic data of the talent information and matches the characteristic data of the talent information with the classification result of the public policy data.
Preferably, the server may extract the features through a neural network model. In addition, text matching algorithms such as BF, RK, KMP, BM and the like can be adopted to realize matching of talent information and public feature data.
204. And judging whether the matching result is consistent with the preset condition, if so, sending the corresponding public policy data to the corresponding terminal when the matching result is consistent with the preset condition.
In this embodiment, the preset condition may be a certain range, such as > 0.7. For example, if the matching degree of the talent information sent by a terminal device and certain public policy data is 0.8, the server sends the public policy data to the corresponding terminal device.
In some optional implementation manners of this embodiment, before the server sends the public policy data to the corresponding terminal device, the following processing steps may be further included:
and combining the matching result with the corresponding public policy data when the preset conditions are consistent to generate a matching report.
Thus, the server can send the matching report to the corresponding terminal device.
According to the method provided by the embodiment of the application, the policy database is constructed by collecting and processing the public policy information, and when the inquiry request carrying the talent information of the user is received, the talent information can be matched with the data in the policy database, so that some preferential policies are pushed to the user, and the user can conveniently obtain policy benefits.
With continued reference to fig. 3, a flowchart of yet another embodiment of a policy data pushing method according to the present application is shown, which specifically includes the following steps:
301. acquiring enterprise information, extracting characteristic data of the enterprise information, and matching the characteristic data of the enterprise information with a classification result of public policy data; wherein the public policy data further comprises policy data for enterprise offers.
302. And judging whether the matching result is consistent with the preset condition, and if so, sending the corresponding public policy data to the corresponding enterprise when the matching result is consistent with the preset condition.
In some optional implementations of this embodiment, the enterprise information includes public related information stored in an enterprise repository, and/or related information entered by an enterprise in real time.
In some optional implementation manners of this embodiment, before the server sends the public policy data to the corresponding terminal device, the following processing steps may be further included:
and combining the matching result with the corresponding public policy data when the preset conditions are consistent to generate a matching report.
Thus, the server can send the matching report to the corresponding enterprise.
The method provided by the embodiment of the application can enable an enterprise to obtain latest valuable policy information applicable to the enterprise at the first time, so that a declaration policy or project applicable to the enterprise can be obtained.
With continued reference to fig. 4, a flowchart of yet another embodiment of a policy data pushing method according to the present application is shown, which specifically includes the following steps:
401. receiving a request for modification of public policy data;
402. the public policy data is modified according to the modification request to preprocess the modified public policy data.
In the embodiment, the real-time modification of the public policy data can be realized, and the condition that the matching with talents and enterprises fails due to incorrect data is prevented.
With further reference to fig. 5, as an implementation of the method shown in the above figures, the present application provides an embodiment of a policy calculator, which corresponds to the embodiment of the method shown in fig. 2, and which can be applied to various computer devices.
As shown in fig. 5, the policy calculator of the present embodiment includes:
the collecting unit 501 is used for automatically collecting public policy data, wherein the public policy data at least comprises policy data preferential to talents;
a first processing unit 502, configured to pre-process public policy data, and input the pre-processed public policy data into a neural network model to obtain a prediction result, where the prediction result includes a classification result related to the public policy data;
the second processing unit 503 is configured to receive talent information sent by the terminal in real time, extract feature data of the talent information, and match the feature data of the talent information with a classification result of the public policy data; judging whether the matching result is consistent with a preset condition or not;
a first sending unit 504, configured to send, when the matching result is consistent with the preset condition, the public policy data corresponding to the matching result is consistent with the preset condition to the corresponding terminal.
In some optional implementations of this embodiment, the policy calculator further includes:
an updating unit 505, configured to update a preset sample library at regular time based on automatically acquired public policy data;
updating the neural network model by using the updated sample library each time;
the first processing unit 501 is further configured to input the preprocessed public policy data into the updated neural network model to obtain a prediction result.
In some optional implementations of this embodiment, the public policy data further includes policy data for enterprise privileges; the policy calculator further includes:
the third processing unit 506 is configured to collect the enterprise information, extract feature data of the enterprise information, and match the feature data of the enterprise information with a classification result of the public policy data; judging whether the matching result is consistent with a preset condition or not;
a second sending unit 507, configured to send, when the matching result is consistent with the preset condition, the public policy data corresponding to the matching result when the matching result is consistent with the preset condition to the corresponding enterprise.
In some optional implementations of this embodiment, the enterprise information includes public related information stored in an enterprise repository, and/or related information input by an enterprise in real time.
In some optional implementation manners of this embodiment, the policy calculator further includes a combining unit 508, configured to, when it is determined that the matching result is consistent with the preset condition, combine the public policy data corresponding to the matching result that is consistent with the preset condition, and generate a matching report;
the first sending unit 504 is further configured to send the matching report to the corresponding terminal.
In some optional implementations of this embodiment, the first processing unit 502 is specifically configured to:
associating and storing the public policy data according to a preset rule;
and performing data cleaning on the stored public policy data.
In some optional implementation manners of this embodiment, the first processing unit 502 is further specifically configured to:
after data cleaning is carried out on the stored public policy data, auditing the cleaned data by utilizing a machine learning algorithm to obtain the public policy data inconsistent with a preset rule;
automatically adjusting the incidence relation of the public policy data inconsistent with the preset rule;
and inputting the public policy data after the incidence relation is adjusted into the neural network model to obtain a prediction result.
In some optional implementations of this embodiment, the policy calculator further includes a modification unit 509, configured to receive a modification request for the public policy data;
modifying the public policy data according to the modification request;
the first processing unit 502 is used for preprocessing the modified public policy data.
Reference is now made to FIG. 6, which illustrates a schematic block diagram of a computer device (e.g., server 103 shown in FIG. 1) suitable for use in implementing embodiments of the present application. The computer device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
The computer device shown in fig. 6 includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a policy data pushing method.
Those skilled in the art will appreciate that the configuration shown in fig. 6 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing devices to which aspects of the present invention may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only show some embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for pushing policy data, the method comprising:
automatically collecting public policy data, the public policy data including at least policy data for talent offers;
preprocessing the public policy data, inputting the preprocessed public policy data into a neural network model to obtain a prediction result, wherein the prediction result comprises a classification result about the public policy data;
the talent information sent by a terminal is received in real time, the characteristic data of the talent information is extracted, and the characteristic data of the talent information is matched with the classification result of the public policy data;
and judging whether the matching result is consistent with the preset condition, if so, sending the corresponding public policy data to the corresponding terminal when the matching result is consistent with the preset condition.
2. The method of claim 1, further comprising:
updating a preset sample library at regular time based on the automatically acquired public policy data;
updating the neural network model with the sample base updated each time;
the inputting the preprocessed public policy data into the neural network model to obtain the prediction result specifically includes:
and inputting the preprocessed public policy data into the updated neural network model to obtain a prediction result.
3. The method of claim 1, wherein the public policy data further comprises policy data for enterprise offers; the method further comprises the following steps:
acquiring enterprise information, extracting characteristic data of the enterprise information, and matching the characteristic data of the enterprise information with a classification result of the public policy data;
and judging whether the matching result is consistent with the preset condition, and if so, sending the corresponding public policy data to the corresponding enterprise when the matching result is consistent with the preset condition.
4. The method of claim 3, wherein the enterprise information comprises public related information stored in an enterprise repository and/or related information entered by an enterprise in real-time.
5. The method according to claim 1, wherein when the matching result is determined to be consistent with the preset condition, the method further comprises:
combining the matching result with corresponding public policy data when the matching result is consistent with preset conditions to generate a matching report;
the sending of the corresponding public policy data to the corresponding terminal when the matching result is consistent with the preset condition specifically includes:
and sending the matching report to a corresponding terminal.
6. The method according to any of claims 1-5, wherein preprocessing the public policy data specifically comprises:
associating and storing the public policy data according to a preset rule;
and performing data cleaning on the stored public policy data.
7. The method of claim 6, wherein after data washing the stored public policy data, the method further comprises:
auditing the cleaned data by using a machine learning algorithm to obtain public policy data inconsistent with the preset rule;
automatically adjusting the incidence relation of the public policy data inconsistent with the preset rule;
inputting the preprocessed public policy data into a neural network model to obtain a prediction result;
inputting the preprocessed public policy data into a neural network model, and obtaining a prediction result comprises:
and inputting the public policy data after the incidence relation is adjusted into the neural network model to obtain a prediction result.
8. The method according to any one of claims 1 to 5 and 7, further comprising:
receiving a request for modification of the public policy data;
modifying the public policy data according to the modification request;
the preprocessing the public policy data specifically includes:
and preprocessing the modified public policy data.
9. A policy calculator, the policy calculator comprising:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for automatically acquiring public policy data, and the public policy data at least comprises policy data for talent preferential;
the first processing unit is used for preprocessing the public policy data, inputting the preprocessed public policy data into a neural network model and obtaining a prediction result, wherein the prediction result comprises a classification result related to the public policy data;
the second processing unit is used for receiving talent information sent by a terminal in real time, extracting feature data of the talent information and matching the feature data of the talent information with the classification result of the public policy data; judging whether the matching result is consistent with a preset condition or not;
and the first sending unit is used for sending the corresponding public policy data to the corresponding terminal when the matching result is consistent with the preset condition.
10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that:
the processor, when executing the computer program, implements the method of any of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011627573.9A CN112765338A (en) | 2020-12-30 | 2020-12-30 | Policy data pushing method, policy calculator and computer equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011627573.9A CN112765338A (en) | 2020-12-30 | 2020-12-30 | Policy data pushing method, policy calculator and computer equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112765338A true CN112765338A (en) | 2021-05-07 |
Family
ID=75698995
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011627573.9A Pending CN112765338A (en) | 2020-12-30 | 2020-12-30 | Policy data pushing method, policy calculator and computer equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112765338A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117556151A (en) * | 2024-01-11 | 2024-02-13 | 四川经科企业管理服务有限公司 | Intelligent data acquisition and information pushing method and system based on big data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20120028507A (en) * | 2010-09-15 | 2012-03-23 | 이지성 | Goverment policy information service providing system and method |
| CN110275935A (en) * | 2019-05-10 | 2019-09-24 | 平安科技（深圳）有限公司 | Processing method, device and storage medium, the electronic device of policy information |
| CN110457696A (en) * | 2019-07-31 | 2019-11-15 | 福州数据技术研究院有限公司 | A kind of talent towards file data and policy intelligent Matching system and method |
| CN111931031A (en) * | 2020-08-19 | 2020-11-13 | 太仓中科信息技术研究院 | Method for calculating policy information matching degree |
| CN112102137A (en) * | 2020-09-21 | 2020-12-18 | 江苏风云科技服务有限公司 | Policy data processing method, device and storage medium |
-
2020
- 2020-12-30 CN CN202011627573.9A patent/CN112765338A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20120028507A (en) * | 2010-09-15 | 2012-03-23 | 이지성 | Goverment policy information service providing system and method |
| CN110275935A (en) * | 2019-05-10 | 2019-09-24 | 平安科技（深圳）有限公司 | Processing method, device and storage medium, the electronic device of policy information |
| CN110457696A (en) * | 2019-07-31 | 2019-11-15 | 福州数据技术研究院有限公司 | A kind of talent towards file data and policy intelligent Matching system and method |
| CN111931031A (en) * | 2020-08-19 | 2020-11-13 | 太仓中科信息技术研究院 | Method for calculating policy information matching degree |
| CN112102137A (en) * | 2020-09-21 | 2020-12-18 | 江苏风云科技服务有限公司 | Policy data processing method, device and storage medium |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117556151A (en) * | 2024-01-11 | 2024-02-13 | 四川经科企业管理服务有限公司 | Intelligent data acquisition and information pushing method and system based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110752942B (en) | Alarm information decision method and device, computer equipment and storage medium | |
| CN106992994B (en) | Automatic monitoring method and system for cloud service | |
| WO2022021696A1 (en) | Multi-information source-based whole-process blockchain system | |
| EP3413212A1 (en) | Random forest model training method, electronic apparatus and storage medium | |
| CN106940679A (en) | Data processing method and device | |
| CN110991170B (en) | Chinese disease name intelligent standardization method and system based on electronic medical record information | |
| CN102880501A (en) | Realizing method, device and system for recommending applications | |
| WO2015050567A1 (en) | System and method for performing set operations with defined sketch accuracy distribution | |
| CN113254833B (en) | Information pushing method and service system based on birth teaching fusion | |
| CN109002424B (en) | File format conversion method and device, computer equipment and storage medium | |
| CN110679114B (en) | Method for estimating deletability of data object | |
| CN113535825A (en) | Cloud computing intelligence-based data information wind control processing method and system | |
| WO2019061664A1 (en) | Electronic device, user's internet surfing data-based product recommendation method, and storage medium | |
| CN108681493B (en) | Data anomaly detection method, device, server and storage medium | |
| CN115345390B (en) | Behavior trajectory prediction method and device, electronic equipment and storage medium | |
| CN110689368A (en) | Method for designing advertisement click rate prediction system in mobile application | |
| CN114205690A (en) | Flow prediction method, flow prediction device, model training method, model training device, electronic equipment and storage medium | |
| CN111913936A (en) | Relate to multi-field traceability system | |
| CN112765338A (en) | Policy data pushing method, policy calculator and computer equipment | |
| CN114356712A (en) | Data processing method, device, equipment, readable storage medium and program product | |
| CN117236999A (en) | Activity determination method and device, electronic equipment and storage medium | |
| CN107644042B (en) | Software program click rate pre-estimation sorting method and server | |
| CA3129816A1 (en) | Accelerated reasoning graph evaluation | |
| US20230353648A1 (en) | Data tracking for data owners | |
| US10481836B2 (en) | Machine learning method and system for predicting file controls |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |