CN115187122A

CN115187122A - Enterprise policy deduction method, device, equipment and medium

Info

Publication number: CN115187122A
Application number: CN202210884296.2A
Authority: CN
Inventors: 陈谟; 王玉德; 赵国森; 崔乐乐
Original assignee: Tianyuan Big Data Credit Management Co Ltd
Current assignee: Tianyuan Big Data Credit Management Co Ltd
Priority date: 2022-07-25
Filing date: 2022-07-25
Publication date: 2022-10-14

Abstract

The embodiment of the specification discloses a method, a device, equipment and a medium for enterprise policy deduction, wherein the method comprises the following steps: acquiring data of an enterprise, and constructing an enterprise portrait of the enterprise according to the data of the enterprise; obtaining a matching enterprise corresponding to a specified policy according to a preset intelligent policy contract and the enterprise picture, wherein the intelligent policy contract comprises the relevant content of the policy of the specified policy; and according to a preset policy deduction module, deducting the specified policy by the matching enterprise to obtain the implementation effect of implementing the specified policy by the matching enterprise, and formally implementing the specified policy when the implementation effect reaches a preset index.

Description

Enterprise policy deduction method, device, equipment and medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a medium for enterprise policy deduction.

Background

Enterprise policy refers to a series of guidelines, measures and regulations for enterprises in various industries according to the actual conditions of the enterprises and the characteristics of related industry development. Enterprise policies can be divided into restrictive policies and supportive policies. In order to promote the modernization of an enterprise, enlarge the scale of the enterprise, and adjust the industrial structure, so that the enterprise can develop better, the enterprise policy needs to be deduced, and the issued enterprise policy is prevented from playing a role in opposition, but is not beneficial to the development of the enterprise.

Disclosure of Invention

One or more embodiments of the present specification provide a method, an apparatus, a device, and a medium for enterprise policy deduction, which are used to solve the technical problems in the background art.

One or more embodiments of the present disclosure adopt the following technical solutions:

one or more embodiments of the present specification provide a method for enterprise policy deduction, including:

acquiring data of an enterprise, and constructing an enterprise portrait of the enterprise according to the data of the enterprise;

obtaining a matching enterprise corresponding to a specified policy according to a preset intelligent policy contract and the enterprise picture, wherein the intelligent policy contract comprises the relevant content of the policy of the specified policy;

and according to a preset policy deduction module, deducting the specified policy by the matching enterprise to obtain the implementation effect of implementing the specified policy by the matching enterprise, and formally implementing the specified policy when the implementation effect reaches a preset index.

One or more embodiments of the present specification provide an enterprise policy deduction apparatus, comprising:

the enterprise portrait construction unit is used for acquiring data of an enterprise and constructing an enterprise portrait of the enterprise according to the data of the enterprise;

the policy matching unit is used for obtaining a matched enterprise corresponding to a specified policy according to a preset intelligent policy contract and the enterprise picture, wherein the intelligent policy contract comprises the policy-related content of the specified policy;

and the policy implementation unit is used for deducing the specified policy by the matching enterprise according to a preset policy deduction module to obtain the implementation effect of implementing the specified policy by the matching enterprise, and formally implementing the specified policy when the implementation effect reaches a preset index.

One or more embodiments of the present specification provide an enterprise policy deduction apparatus, including:

at least one processor; and (c) a second step of,

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to:

One or more embodiments of the present specification provide a non-transitory computer storage medium storing computer-executable instructions configured to:

obtaining a matching enterprise corresponding to a specified policy according to a preset intelligent policy contract and the enterprise picture, wherein the intelligent policy contract comprises policy related content of the specified policy;

The embodiment of the specification adopts at least one technical scheme which can achieve the following beneficial effects:

the embodiment of the specification can analyze and screen the content after enterprise information is matched, automatically match with policy declaration terms, intelligently deduce the pre-implementation effect of the policy, enable management departments such as governments and the like to visually know the implementation result of the policy, optimize aiming at partial elements, and guarantee that the formulated policy is more accurate and effective.

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. In the drawings:

fig. 1 is a flowchart illustrating a method for enterprise policy deduction according to one or more embodiments of the present disclosure;

fig. 2 is a schematic structural diagram of an enterprise policy deduction device according to one or more embodiments of the present disclosure;

fig. 3 is a schematic structural diagram of an enterprise policy deduction device according to one or more embodiments of the present disclosure.

Detailed Description

The embodiment of the specification provides an enterprise policy deduction method, device, equipment and medium.

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 of ordinary skill in the art based on the embodiments of the present specification without any creative effort shall fall within the protection scope of the present specification.

Fig. 1 is a schematic flow chart of an enterprise policy deduction method according to one or more embodiments of the present disclosure, where the flow chart may be executed by an enterprise policy deduction system, and deduction is performed according to a issued enterprise policy, so as to deduce implementation effects of a corresponding enterprise. Certain input parameters or intermediate results in the procedure allow for manual intervention adjustments to help improve accuracy.

The method of the embodiments of the present specification comprises the following steps:

s102, acquiring data of an enterprise, and constructing an enterprise portrait of the enterprise according to the data of the enterprise.

In this embodiment of the present specification, when an enterprise portrait of an enterprise is constructed according to data of the enterprise, the data of the enterprise may be processed according to business requirements of the enterprise to obtain a data tag of the enterprise; and constructing an enterprise portrait of the enterprise according to the data tags.

Further, the data tag of the embodiments of the present specification may include trait information, asset value information, stockholder information, credit risk information of the enterprise; the speciality information of the enterprise may include: the industry, regional area and enterprise nature of the enterprise.

In this embodiment, before the enterprise representation of the enterprise is constructed according to the data of the enterprise, enterprise feature analysis may be performed on the enterprise, where the enterprise feature analysis may be used to determine data quality and a single variable occurrence ratio of each variable in the data of the enterprise, check whether the data of the enterprise meets a preset business logic, and derive implicit data features according to the variables in the data of the enterprise.

It should be noted that the enterprise characteristic analysis may include one or more of enterprise information, corporate information, enterprise account information, transaction records, asset liability information, loan repayment behavior, and corporate credit investigation reports.

And S104, obtaining a matched enterprise corresponding to the specified policy according to a preset intelligent policy contract and the enterprise picture, wherein the intelligent policy contract comprises the policy-related content of the specified policy.

And S106, according to a preset policy deduction module, deducting the specified policy by the matching enterprise to obtain an implementation effect of implementing the specified policy by the matching enterprise, and formally implementing the specified policy when the implementation effect reaches a preset index.

In the embodiment of the present specification, when the matching enterprise deduces the specified policy to obtain the implementation effect of the matching enterprise for implementing the specified policy, the application quantity, benefit range and fund use of the specified policy may be analyzed to predict the first implementation effect of the specified policy on the matching enterprise; evaluating the coverage industry, the enterprise quantity and the fund demand of the specified policy, and predicting a second execution effect of the specified policy on the matched enterprise; finally, the implementation effect of the matching enterprise for implementing the specified policy can be obtained according to the first implementation effect and the second implementation effect.

Further, before the enterprise portrait of the enterprise is constructed according to the data of the enterprise, data governance can be performed on the data of the enterprise so that the data of the enterprise meets the preset requirements, wherein the data governance comprises data naming, data field types, data field lengths and data formats.

When the technical characteristics are implemented, the technologies can be introduced in detail through a large data quality detection technology, a multi-source heterogeneous data fusion technology and an enterprise label portrait technology as follows:

the big data quality detection technology is characterized in that a data quality evaluation model is established according to a data quality evaluation index system and the requirements of data quality detection in production, data quality evaluation suitable for data per se is carried out on different service data, and data is reversely tracked according to an evaluation result for perfection.

The multi-source heterogeneous data fusion technology is characterized in that massive internet data related to enterprise credit evaluation are crawled based on a big data acquisition technology and comprise enterprise multi-dimensional data such as enterprise management conditions, enterprise development and development, enterprise management risks, judicial risks, public opinion data and the like. By enterprise authorization, government data of various places are fused, platform data such as financial system online transaction and enterprise loan application behavior data are gradually accumulated, data extraction, data conversion and data fusion are carried out on the multi-source data by using large data processing technologies such as ETL and large data technology components such as Hadoop, spark, storm and Kafka on the basis of a multi-source data set, and finally the multi-source data are collected in a standard data warehouse established by technologies such as Hive, hbase and a relational database to establish a standard data directory and provide standard data service.

The enterprise sketch technology comprises enterprise data set construction, knowledge learning, knowledge reasoning and enterprise sketch label construction.

And (3) enterprise data set construction: determining the range of enterprise information, preprocessing the data after acquiring the enterprise information to improve the data quality, forming enterprise indexes, and extracting enterprise attributes and enterprise employee attributes. And finally, carrying out statistical analysis on the enterprise information.

Knowledge learning: a large amount of knowledge is hidden under surface layer data, the value of the query predicate is predicted under the condition of the given evidence predicate by means of a discriminant knowledge learning algorithm DSL, and the L-BFGS algorithm is used for weight learning, so that a network structure with a reasonable structure is generated.

Knowledge reasoning: based on the model learned in the knowledge learning stage and the weight thereof, the Lazy-MC-SAT algorithm is used for reasoning the relation and the entity attribute among the entities and comparing with other knowledge reasoning algorithms.

Constructing an enterprise portrait label: on the basis of the enterprise knowledge graph, the enterprise is represented in a graph mode. And enterprise situation analysis is performed from the dimensions of enterprise credit, development stage, scientific research capability and the like by means of enterprise portrait. Through the enterprise portrait, various aspects of information of the enterprise can be visually checked, and the method has great significance for aspects such as enterprise background investigation, enterprise credit assessment and high-quality enterprise mining.

The embodiment of the specification discloses a policy deduction system based on a big data technology. The system can effectively collect and decompose the existing policy based on the enterprise accurate portrait formed by enterprise full-quantization data aggregation, and can carry out result pre-deduction on the actual policy implementation effect, thereby providing technology and data support for adjusting policy elements and making more accurate and effective policies.

It should be noted that the above system aims to improve the accuracy of government favorable enterprise policy making by using big data technology. The system supports pre-evaluation of the quantity, scale, benefit area, potential benefit enterprise list and other contents of the benefit enterprises before the policy file is released. The manager can initiate a policy matching request according to the policy management needs. And the system automatically matches a potential enterprise list meeting the conditions to generate a pre-evaluation report. When the policy file changes, after structured analysis of the policy file, the manager can perform policy pre-evaluation tests for many times. The manager can choose to save or discard the pre-evaluation results.

It should be noted that, the business process of the embodiment of this specification may be as follows:

1) And establishing an enterprise information base. And the government affair data sharing platform is utilized to collect the basic information, the operation information, the credit information and the like of the enterprises in the jurisdiction, and after the enterprises log in the system, the data parameters can be supplemented and perfected by themselves, and finally, an enterprise database in the jurisdiction is formed and is used for matching conditions of intelligent contracts.

2) And constructing an enterprise portrait. And carrying out accurate portrait on the enterprise by using a big data technology and a knowledge graph technology according to the business requirements. The one-time complete enterprise portrait construction process comprises the following steps: data collection, data cleaning, data modeling and portrait construction. The key of the enterprise portrait is outputting labels, performing statistical analysis on original data of the data warehouse to obtain fact labels, performing business modeling analysis to obtain model labels, and performing model prediction to obtain prediction labels. And confirming that the enterprise portrait modeling is carried out by adopting a cluster analysis method according to the service requirements, finally outputting enterprise portrait labels comprising three categories of enterprise self labels, enterprise portrait modeled model labels and final prediction labels, and completely entering a subsequent data modeling process through fields obtained by screening conditions.

3) The policies are automatically matched. The system automatically analyzes and screens the content and intelligently matches the enterprise condition with the declared clauses through policy intelligent contracts and enterprise library information.

4) And (5) intelligently analyzing policy effects. According to the policy matching result, the system carries out intelligent analysis on the application quantity, the benefit range, the fund use and the like of a certain policy and feeds back the prying and leading effects of the policy. The method has the advantages that pre-evaluation is carried out on the coverage industry, the enterprise quantity and the fund demand of a newly-formulated policy, the implementation effect is predicted, adjustment and accurate enforcement are carried out in advance, the idle fund of the policy is avoided, the fuzzy overlapping of the policy is solved, and the maximum effect of the policy is exerted.

It should be noted that, the specific schemes of the embodiments in this specification are as follows:

1) Establishing enterprise big data center

The system needs to establish a large database related to enterprise development, and data management and quality control are carried out from the perspective of data fusion application by collecting government data, enterprise data, internet data and self data of a management platform to form a multidimensional enterprise data center.

1. Data acquisition

a) Tissue data collection

The data aggregation service provides a high-speed channel for data migration, conversion and filtration among different data source types, and simultaneously provides a one-stop service platform for various complex query calculations such as aggregation, association and the like among heterogeneous data sources. A user can visually configure complex task templates, start an instant task by one key, monitor the execution condition of the task in real time, monitor the health state of the whole cluster and other information through the data aggregation service.

b) Internet data acquisition

And providing an internet data acquisition function. The internet data acquisition needs to automatically capture information related to enterprises from the internet periodically, and process the captured result process to form structured data. Information, public opinion information, market behavior data and the like about enterprises in the Internet are collected, and portrait analysis, risk analysis, operation early warning and the like of the enterprises are achieved.

c) Network information collection

The method supports the functions of deep multi-channel network information acquisition and monitoring source configuration, and can monitor a plurality of channel data sources.

d) Information preprocessing mechanism

The system can provide a perfect information preprocessing mechanism, hyperlink analysis, code recognition, URL duplication removal, anchor text processing, junk information filtering, content duplication removal, keyword extraction, text extraction and the like.

e) Hot public opinion information focusing

Public opinion data collection of news, forums, microblogs and the like is achieved through the internet data collection function.

f) Topical data collection

The method supports the collection of thematic report data, and carries out thematic detection and tracking on network topics, such as enterprise support fund release, enterprise risk assessment and the like.

g) Incident data collection

The data acquisition of the comprehensive content of the emergency including cross-time and cross-space is supported, and the occurrence of the event can be known.

2. Data development

Data development operates on data in units of projects. And model development, script development and ETL development are supported. A data developer can construct a data processing flow based on business through a data model development tool, an SQL online editor and an ETL development tool provided by data development, and simultaneously support multi-user collaborative development.

3. Data governance

a) Data standards

Naming standard: the naming standard for determining the item is mainly the standard of table name and field name, and comprises the following categories: all capital, all lowercase, hump, prefix, suffix and regular mode selection. By setting corresponding naming standards, whether the naming of the logic model and the physical model meets the specification or not can be regularly detected in the project implementation process, and therefore a detection report is generated.

Field standard: the field standard of the set item refers to whether the setting of the field type and the length limit accords with the standard or not, and the field type redundancy is more caused by supporting most common database types, so the set field standard is determined by the bound data source type of the item and is automatically loaded.

Content standard: the content standard mainly refers to detection of data content with a fixed format, and is divided into a built-in type and a manual adding type, wherein the built-in type comprises an IP address, a URL (uniform resource locator), a domain name, a fixed telephone, a mobile phone number, time, date, an identity card and the like.

Standard checking: the standard check provides data standardized physical examination capability, supports the selected standard to carry out standardized examination on a specific data table, fields, data types, processes, tasks and the like and outputs results.

b) Data cleansing

Cleansing transforms on data are required to filter incomplete data, erroneous data, and duplicate data. The method comprises the steps of cleaning rules, data conversion, parameter management, cleaning logs and the like.

And (4) cleaning rules: and providing the configuration of the data cleaning rule and detecting the data item. Incomplete data determines missing value ranges: and respectively making strategies according to the missing proportion and the field importance.

Data conversion: the data conversion supports a plurality of processing methods to convert the data format.

Parameter management: and providing data standardization and data conversion mapping rule parameters for unified configuration management service, including addition, edition, deletion and the like.

Log cleaning: and two cleaning log modes of intelligent segmentation and custom segmentation are supported.

c) Quality of data

Quality rule management: aiming at a specific data table, the following field level and table level class quality rule configuration is supported; the rules present configured rules in a list format, the presentation columns including: rule type, rule name, creator, next execution time. The rules can be added, deleted, modified, and executed under the page.

Starting a quality task: and configuring and executing a quality audit task based on the quality audit rule, wherein the task execution supports timing starting, conditional starting and starting failure mail notification.

Quality task monitoring: the execution condition of the data inspection rule is mainly monitored, and the list display item comprises the following components: task name, execution start time, execution end time, task execution condition (success, failure)

And (5) checking the result details: and detail checking of the audit results after the completion of the operation is supported, wherein the detail checking comprises the operation time of each audit, the audit results, the problem description and the like.

And (4) result history checking: one instance is generated when the task runs once, and the result of each instance is checked in a supporting mode.

2) Building an enterprise representation

1. Enterprise feature analysis

The data analysis part needs to be divided into two blocks in the flow, namely enterprise basic information analysis and enterprise characteristic analysis.

The purpose of enterprise basic information analysis is to arrange and classify existing data collected by the platform data center, and select variables suitable for enterprise portrait from the data to form a data label. Comprises the following steps:

(1) Enterprise speciality analysis (including local industry, regional area, enterprise nature, etc.)

(2) Analysis of asset value (including recent interval change of assets and liabilities, business profit, analysis map, etc.)

(3) Basic information of the enterprise shareholder (including the share right distribution situation of the current shareholder of the enterprise, the enterprise association map situation of each shareholder)

(4) Credit risk analysis

The characteristic analysis aims at analyzing the data quality and the single variable occurrence ratio of each variable of the data, checking whether the variable accords with business logic or not, and deriving a new variable according to the existing variable so that a clustering model can better learn the rule in the data. The analysis content comprises basic enterprise information, basic corporate information, basic enterprise account information, transaction records, information of assets and liabilities, loan repayment behaviors, credit investigation reports of corporate accounts and the like. Comprises the following steps:

(1) And checking the overall quality of the data, and comprehensively considering the data and the service, and processing or discarding the variable with poor quality.

(2) And analyzing the statistical indexes of each variable and the distribution condition of each interval to obtain the distribution trend of the enterprise in each variable and eliminate the variable with abnormal data expression.

(3) And deriving partial variables, further exploring the data characteristics implicit in the original variables, and improving the accuracy of enterprise portrait.

2. Enterprise tag integration

Through data analysis and data preprocessing, the data width table of each dimension of an enterprise client can be obtained, and all original fields and derived fields in the table are defined as secondary indexes. According to the business meaning of the secondary indexes, all the secondary indexes are integrated into a plurality of primary indexes, such as: enterprise specialties, credit risk, asset value, etc. After the integration is finished, the indexes are screened and filtered according to the relevance among the secondary indexes, some characteristics with high relevance are removed, and the secondary indexes with obvious characteristics are reserved after the integration.

3. Enterprise index analysis

Enterprise secondary indexes: such as industry category, environment, assets, liabilities, scale, nature of the business, age of the business, etc.; and corporate information such as gender, age group, marital status, academic history, credit record, etc.

Enterprise level one indexes: and clustering and integrating the secondary indexes into a plurality of primary indexes according to the business meaning. For example, the business characteristic indicators may include: industry category, environment, scale, enterprise nature, enterprise age, etc.; the characteristic index of the legal person can comprise; gender, age group, marital status, academic history, etc.; the enterprise asset value indicators may include: total investment, mobile asset value, fixed asset value, claim, liability, investment of the enterprise, fund, etc.; the credit risk indexes are taken from credit investigation conditions of enterprises and legal persons thereof and comprise five-grade credit levels, overdue credit times, overdue credit amount, overdue credit card number of legal persons, overdue credit card amount of legal persons, deposit account freezing times, historical lowest credit grade and the like.

4. Establishing an enterprise tag library

The platform establishes an enterprise tag library, and the enterprise tag library is established according to dimensions such as the area, the industry category, the main business, the qualification, the registered fund and the like. When the enterprise logs in the platform for the first time, the enterprise is labeled according to enterprise typing standards, and the labeled enterprise label dictionary guarantees the standardized management of enterprise labels. The enterprise tag may be self-maintained and modified by the enterprise user in the personal center. The platform provides a verification function for self-maintenance and modification of enterprise users so as to conveniently ensure the accuracy and the rigor of enterprise labels. The enterprise tag dictionary information is managed independently, and information such as enterprise tag code values, tag names and remarks can be maintained.

3) Intelligent policy deduction

The system supports pre-evaluation of the quantity, scale, benefit area, potential benefit enterprise list and other contents of the benefit enterprises before the policy file is released. The manager can initiate a policy matching request according to the policy management needs. And the system automatically matches a potential enterprise list meeting the conditions to generate a pre-evaluation report.

When the policy file changes, the administrator can perform policy pre-evaluation tests for many times after structured analysis of the policy file.

The manager can choose to save or discard the pre-evaluation results.

And the manager can inform the legal person or the sponsor of the enterprise of the stored pre-evaluation report by short messages.

And (4) policy pre-evaluation result retention: after policy pre-evaluation, pre-evaluation results need to be stored for a third-party interface to call, and the stored data comprises: beneficiary information, beneficiary amount, pre-evaluation time, pre-evaluation policy information, and the like.

4) Policy tuning and statistics

The system provides the system operation conditions such as the policy forecasting enterprise declaration condition, each link of the application process, the policy matching condition and the like, statistical analysis is carried out from multiple dimensions such as industry, region, time and the like, and the result is displayed in a graphical mode. The manager can master the service operation condition generally, and the manager can adjust and optimize the policy in real time conveniently.

And (4) carrying out policy matching condition statistics, wherein the platform provides the statistics of the policy matching conditions, and the statistics comprises statistics according to enterprise type matching conditions, statistics according to regional comparison policy matching conditions, statistics according to enterprise industry policy matching conditions, statistics according to time policy matching conditions and the like, and the statistics provides graphical and tabular displays. And functions of exporting, downloading, printing on line and the like of the general report are supported.

The embodiment of the specification can perform content analysis and screening after enterprise information is matched based on the policy matching auxiliary declaration functions of government affair data sharing, big data analysis and block chain technology, automatically match with policy declaration terms, intelligently deduce the pre-implementation effect of the policy, enable management departments such as governments and the like to visually know the implementation result of the policy, optimize aiming at partial elements, and guarantee that the formulated policy is more accurate and effective.

Fig. 2 is a schematic structural diagram of an enterprise policy deduction device according to one or more embodiments of the present disclosure, where the device includes: an enterprise representation construction unit 202, a policy matching unit 204 and a policy enforcement unit 206.

the policy matching unit is used for obtaining a matching enterprise corresponding to the specified policy according to a preset intelligent policy contract and the enterprise picture, wherein the intelligent policy contract comprises the relevant content of the policy of the specified policy;

Fig. 3 is a schematic structural diagram of an enterprise policy deduction device according to one or more embodiments of the present disclosure, including:

at least one processor; and (c) a second step of,

the memory stores instructions executable by the at least one processor to cause the at least one processor to:

and according to a preset policy deduction module, deducting the specified policy by the matching enterprise to obtain the implementation effect of the matching enterprise for implementing the specified policy, and formally implementing the specified policy when the implementation effect reaches a preset index.

All the embodiments in the present specification are 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 other embodiments. In particular, for the apparatus, device, and non-volatile computer storage medium embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference may be made to the partial description of the method embodiments for relevant points.

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

Claims

1. A method for enterprise policy deduction, the method comprising:

2. The method of claim 1, wherein constructing an enterprise representation of the enterprise from the data of the enterprise comprises:

processing the data of the enterprise according to the business requirements of the enterprise to obtain a data tag of the enterprise;

and constructing an enterprise portrait of the enterprise according to the data tags.

3. The method of claim 2, wherein the data tag includes trait information, asset value information, shareholder information, credit risk information for the business; wherein the content of the first and second substances,

the trait information of the enterprise includes: the business, regional area and business property of the enterprise.

4. The method of claim 1, wherein prior to constructing an enterprise representation of the enterprise from the data of the enterprise, the method further comprises:

and carrying out enterprise characteristic analysis on the enterprise, wherein the enterprise characteristic analysis is used for analyzing the data quality and the single variable occurrence ratio of each variable in the enterprise data, checking whether the enterprise data conforms to preset business logic or not, and deriving implicit data characteristics according to the variables in the enterprise data.

5. The method of claim 4, wherein the business characterization analysis includes one or more of business information, corporate information, business account information, transaction records, liability information, loan repayment behavior, and corporate credit assessment reports.

6. The method according to claim 1, wherein the deducting the specified policy at the matching enterprise to obtain the effect of implementing the specified policy by the matching enterprise comprises:

analyzing the application quantity, benefit range and fund use of the specified policy, and predicting a first execution effect of the specified policy on the matched enterprise;

evaluating the coverage industry, the enterprise quantity and the fund demand of the specified policy, and predicting a second execution effect of the specified policy on the matched enterprise;

and obtaining the implementation effect of the matching enterprise for implementing the specified policy according to the first implementation effect and the second implementation effect.

7. The method of claim 1, wherein prior to constructing an enterprise representation of the enterprise from the data of the enterprise, the method further comprises:

and carrying out data governance on the data of the enterprise so that the data of the enterprise meet the preset requirements, wherein the data governance comprises data naming, data field types, data field lengths and data formats.

8. An enterprise policy deduction apparatus, the apparatus comprising:

9. An enterprise policy deduction device, comprising:

at least one processor; and (c) a second step of,

a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,

10. A non-transitory computer storage medium having stored thereon computer-executable instructions configured to: