CN116244315B - Method and system for dynamically updating timeliness of legal and regulatory database - Google Patents
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Abstract
The application discloses a method for dynamically updating timeliness of a legal and regulatory database, which comprises the following steps: s1, acquiring full-scale legal and regulation data from a public authority legal and regulation database, and constructing the legal and regulation database; s2, constructing a language model for identifying the timeliness change of laws and regulations; s3, according to law and regulation timeliness change trigger rules, constructing an timeliness change trigger processing module; s4, searching data in the legal and legal library according to the rule in the S3 and screening out a current valid list to be processed; s5, identifying legal data in the current effective to-be-processed list by using the model obtained in the S2 to obtain a 'time-dependent change to-be-processed list'; s6, sending the timeliness change pending list to manual checking processing, and executing change by the submitting system after the manual checking is finished. The application provides a rule for triggering and processing the timeliness change of laws and regulations, and combines the processes of model identification and manual auditing, thereby ensuring the final accuracy of timeliness update of a laws and regulations library.
Description
Technical Field
The application relates to a method and a system for dynamically updating timeliness of a legal and regulatory database, belonging to the technical field of natural language data processing.
Background
The law should specify the date of enforcement, as specified. The law is modified and new legal text should be published. The law is revoked and will be promulgated in addition to its revocation by other legal regulations.
An unofficial legal regulation library is established by collecting and arranging published legal regulations, and can be used as a basis for large data analysis in the subsequent legal regulation vertical field. However, since the laws and regulations themselves define the effective time limit or terminate with the release and execution of new laws and regulations, the constructed laws and regulations library needs to continuously maintain the change of the timeliness of the laws and regulations. The time course of law and regulation is the time regulation of the law and regulation itself and the effectiveness of the law and behavior, and is a constituent element of the law and regulation, and the definite regulation of the time effect is the necessary content of law. Since new regulations are released continuously, old regulations are revised or abandoned, and how to efficiently maintain the timeliness of the regulation database is a key for ensuring the timeliness and accuracy of the regulation database.
For the timeliness of the regulations, the following three cases are specified in law:
(1) The law and regulation itself prescribes an effective time limit, and the regulation is in control;
(2) The expiration date of the law and regulation is terminated from the new law and regulation enforcement date;
(3) The validity time limit of the law and regulation is terminated from the date of declaring revocation.
In the related art, for a method for constructing a legal and legal database, chinese patent CN108073673a discloses a method for constructing a legal knowledge graph based on machine learning, which comprises the following steps: s1, recognizing updated laws and regulations, and generating a text corpus with legal characteristics; s2, recognizing legal entities and/or legal relations by using a text corpus and a legal regulation library, performing legal regulation processing and generating a legal knowledge feature library; s3, carding to establish a legal rule model, identifying legal concepts by using the legal rule model, and storing the legal concepts as legal knowledge features in a legal knowledge feature library; s4, carrying out semantic understanding and/or text intention recognition based on a text corpus, analyzing and extracting text characteristics from the text corpus context, and storing the text characteristics in a legal knowledge characteristic library; s5, performing machine learning training by utilizing the legal knowledge feature library, and storing legal features after the machine learning training in the legal knowledge feature library; s6, extracting the obtained text features by using a natural language understanding module, obtaining legal knowledge features by using a feature machine learning module and/or identifying obtained legal concepts by using a knowledge engineering module, identifying legal knowledge points, and establishing association of the legal knowledge points through a legal concept framework; and S7, displaying a legal knowledge graph passing through the legal knowledge points subjected to the association processing and storing the legal knowledge graph. In this way, the legal content may be structured and stored into a knowledge graph to be legal knowledge.
When the prior art builds a legal knowledge graph, the connection applicable relation between the new law and the old law is not considered, and the accuracy of legal knowledge cannot be ensured. Chinese patent CN202011373245 discloses a legal knowledge graph construction system and method, comprising the steps of: s1, acquiring published updated laws and regulations from a legal database, and updating original laws and regulations in a laws and regulations library according to the updated laws and regulations; s2, recognizing updated laws and regulations, and generating a text corpus with legal characteristics; identifying legal entities and/or legal relations by using the text corpus and the legal regulation library to generate a legal knowledge feature library; s3, establishing a legal rule model, and identifying legal concepts by using the legal rule model; semantic understanding is carried out based on a text corpus, context analysis is carried out on text corpus context, and text features are extracted; s4, storing legal concepts and text features as legal knowledge features in a legal knowledge feature library, performing machine learning training by using the legal knowledge feature library, and storing the learned and trained legal features in the legal knowledge feature library; s5, identifying legal knowledge points by using text features, legal knowledge features and/or legal knowledge concepts, establishing association of the legal knowledge points through a legal concept framework, and generating a legal knowledge map. Thus, old laws in the law and regulation database can be updated, and the law knowledge graph is updated, so that the valuable law knowledge graph is constructed.
However, in the above prior art, the updated legal regulations issued are obtained from the legal database of authority, and the original legal regulations are updated according to the latest legal regulations, so that the fact that the newly issued legal regulations may cause the timeliness of other legal regulations which do not belong to the same legal regulations to be changed, such as the issuing of the national code, and the timeliness of other methods such as the marital method and the inheritance method may be changed is not considered; and because the original legal regulation database is only updated according to the latest changed regulation, the condition that the timeliness of the original legal regulation is changed due to the valid time limit specified by the original legal regulation in the legal regulation database is not considered.
Disclosure of Invention
The application aims to provide a method and a system for dynamically updating timeliness of a legal regulation database, which are used for solving the problem that the timeliness of the legal regulation database is not integrally considered when the legal regulation database is constructed in the prior related technology.
In order to achieve the above purpose, the technical scheme adopted by the application is as follows:
in a first aspect, the present application provides a method for time-efficient dynamic updating of a legal and regulatory database, comprising the steps of:
s1, acquiring full-scale legal and regulation data from a public authority legal and regulation database, and constructing the legal and regulation database;
s2, marking a law and regulation timeliness change data set, and constructing a language model for identifying law and regulation timeliness change;
s3, according to law and regulation timeliness change trigger rules, constructing an timeliness change trigger processing module;
s4, searching data in the legal and legal library according to the rule in the S3 and screening out a current valid list to be processed;
s5, identifying legal data in the current effective to-be-processed list by using the model obtained in the S2 to obtain a 'time-dependent change to-be-processed list';
s6, sending the timeliness change pending list to manual checking processing, and executing change by the submitting system after the manual checking is finished.
Further, the step S1 specifically includes:
s1.1, acquiring full-quantity legal regulation text data from a legal regulation database of public authorities;
s1.2, analyzing attribute information of the full-scale legal and legal text data, wherein the attribute information comprises: the legal name, release date, implementation date, and level of efficacy;
s1.3, carrying out segmentation analysis on the full-scale legal and legal text data to obtain legal and legal codes, chapters, sections, strips, money and items.
Further, the step S2 specifically includes:
s2.1, extracting sample data from a law and regulation library, introducing the sample data into a marking tool, marking the modified or abolished law and regulation specified in the law and regulation, and taking the sample data as a law and regulation timeliness change marking data set;
s2.2, randomly dividing the data marked in the step S2.1 into a training data set and a test data set according to a certain proportion;
s2.3, importing the training data set obtained in the step S2.2 into a model training program to perform law and regulation timeliness change identification model training to obtain a model which can identify that law and regulation timeliness change is required in a given text and is used as a model A;
s2.4, testing the model A by using the test data set obtained in the step S2.2, comparing the identification result of the model A with the reference test evaluation result of the basic model, if the identification result of the model A does not reach the reference test effect, adjusting model training program parameters, repeating the steps S2.2 and S2.3 to obtain a model N correspondingly until the identification result of the model N reaches the model reference test result, and ending the step S2 to obtain the final model N.
Further, the rule of triggering the legal regulation timeliness change in the step S3 is as follows:
s3.1, retrieving legal regulations with implementation date larger than the current date and timeliness not being 'not yet effective' from a legal regulation library, modifying the timeliness of the legal regulations to 'not yet effective', and adding a 'not yet effective pending list';
and S3.2, retrieving laws and regulations with implementation date being current date and timeliness being equal to 'not yet effective' from a laws and regulations library, modifying timeliness into 'current effective', and adding a 'current effective pending list'.
Further, the step S4 specifically includes:
s4.1, triggering a processing module through timeliness change of the S3, and periodically acquiring a legal rule that timeliness needs to be changed into current validity, namely a current valid list to be processed;
s4.2, identifying each legal rule in the current valid list to be processed, moving the model content obtained in the S2 to the timeliness change list to be processed, and simultaneously adding the identified legal rule for timeliness change into the timeliness change list.
Further, after the model identifies the 'timeliness change pending list', the terms belonging to the same legal regulation in the list are firstly divided into the same group, and then the grouped list is sent to manual auditing processing.
In a second aspect, the present application also provides a system for time-efficient dynamic updating of a legal regulation database, comprising:
the legal and regulation data acquisition and module is used for acquiring legal and regulation full-quantity data from the public authority legal database;
the legal regulation content analysis module is used for carrying out attribute information analysis and strip analysis on the full-scale legal regulation text data;
the law and rule timeliness change trigger processing module is used for retrieving law and rule that the implementation date is larger than the current date and timeliness is not effective yet from the law and rule library according to the law and rule timeliness change trigger rule, modifying the timeliness of the law and rule into an effective yet to be processed list, retrieving law and rule that the implementation date is the current date and timeliness is equal to the effective yet from the law and rule library, modifying the timeliness of the law and rule into an effective yet to be processed list, and adding the effective yet to be processed list;
the law and rule timeliness change identification model is used for identifying each law and rule in the current effective to-be-processed list and moving the law and rule to the timeliness change to-be-processed list, and meanwhile, the identified law and rule needing timeliness change is added into the timeliness change to-be-processed list;
the manual auditing module is used for manually auditing;
and the timeliness change execution module is used for executing the change.
In a third aspect, the present application also provides an electronic device comprising a processor, a memory and a program or instruction stored on the memory and executable on the processor, which when executed by the processor implements the steps of the method of aged dynamic update of the legal rules database according to the first aspect.
Compared with the prior art, the application has the beneficial effects that:
(1) The application provides a method for constructing a law and regulation timeliness change identification model, which can more comprehensively and accurately discover laws and regulations requiring timeliness change.
(2) The application provides a rule for triggering and processing the timeliness change of laws and regulations, and combines the processes of model identification and manual auditing, thereby ensuring the final accuracy of timeliness update of a laws and regulations library.
Drawings
Fig. 1 is a flow chart of the present application.
Description of the embodiments
The application will now be described in detail with reference to the drawings and specific examples.
Examples
As shown in fig. 1, a method for dynamically updating timeliness of a legal and regulatory database comprises the following steps:
s1, acquiring full-scale legal and regulation data from a public authority legal and regulation database, and constructing the legal and regulation database;
s2, marking a law and regulation timeliness change data set, and constructing a language model for identifying law and regulation timeliness change;
s3, according to law and regulation timeliness change trigger rules, constructing an timeliness change trigger processing module;
s4, searching data in the legal and legal library according to the rule in the S3 and screening out a current valid list to be processed;
s5, identifying legal data in the current effective to-be-processed list by using the model obtained in the S2 to obtain a 'time-dependent change to-be-processed list';
s6, sending the timeliness change pending list to manual checking processing, and executing change by the submitting system after the manual checking is finished.
In this embodiment, step S1 specifically includes:
s1.1, acquiring full-quantity legal regulation text data from a legal regulation database of public authorities;
s1.2, analyzing attribute information of the full-scale legal and legal text data, wherein the attribute information comprises: the legal name, release date, implementation date, and level of efficacy;
s1.3, carrying out segmentation analysis on the full-scale legal and legal text data to obtain legal and legal codes, chapters, sections, strips, money and items.
In this embodiment, step S2 specifically includes:
s2.1, extracting sample data from a law and regulation library, introducing the sample data into a marking tool, marking the modified or abolished law and regulation specified in the law and regulation, and taking the sample data as a law and regulation timeliness change marking data set;
s2.2, randomly dividing the data marked in the step S2.1 into a training data set and a test data set according to a certain proportion;
s2.3, importing the training data set obtained in the step S2.2 into a model training program to perform law and regulation timeliness change identification model training to obtain a model which can identify that law and regulation timeliness change is required in a given text and is used as a model A;
s2.4, testing the model A by using the test data set obtained in the step S2.2, comparing the identification result of the model A with the reference test evaluation result of the basic model, if the identification result of the model A does not reach the reference test effect, adjusting model training program parameters, repeating the steps S2.2 and S2.3 to obtain a model N correspondingly until the identification result of the model N reaches the model reference test result, and ending the step S2 to obtain the final model N.
In this embodiment, the rule for triggering the law and regulation timeliness change in step S3 is as follows:
s3.1, retrieving legal regulations with implementation date larger than the current date and timeliness not being 'not yet effective' from a legal regulation library, modifying the timeliness of the legal regulations to 'not yet effective', and adding a 'not yet effective pending list';
and S3.2, retrieving laws and regulations with implementation date being current date and timeliness being equal to 'not yet effective' from a laws and regulations library, modifying timeliness into 'current effective', and adding a 'current effective pending list'. For example: searching the law database to find the implementation date of law A today, changing the timeliness of law and regulation A from 'not yet effective' to 'current effective', and adding the current effective list to 'current effective pending list'.
In this embodiment, the step S4 specifically includes:
s4.1, triggering a processing module through timeliness change of the S3, and periodically acquiring a legal rule that timeliness needs to be changed into current validity, namely a current valid list to be processed;
s4.2, identifying each legal rule in the current valid list to be processed, moving the model content obtained in the S2 to the timeliness change list to be processed, and simultaneously adding the identified legal rule for timeliness change into the timeliness change list. For example: and identifying the legal regulation A which is validated on the same day through a model, wherein the legal regulation A is validated through the identification, and a plurality of terms in the legal regulation B, the legal regulation C and the legal regulation D are influenced, so that the legal regulation A, the legal regulation B, the legal regulation C, the legal regulation D and corresponding terms thereof are added into a 'timeliness change pending list'.
In this embodiment, after the model identifies that the "timeliness change pending list" is obtained, the terms belonging to the same legal regulation in the list are first divided into the same group, and then the grouped list is sent to the manual auditing process. The data belonging to the same laws and regulations in the list to be processed identified by the model are arranged into a group, so that the verification is convenient for manual work.
Examples
Based on the same inventive concept as in the first embodiment, the present embodiment provides a system for time-efficient dynamic update of a legal and regulatory database, which includes:
the legal and regulation data acquisition and module is used for acquiring legal and regulation full-quantity data from the public authority legal database;
the legal regulation content analysis module is used for carrying out attribute information analysis and strip analysis on the full-scale legal regulation text data;
the law and rule timeliness change trigger processing module is used for retrieving law and rule that the implementation date is larger than the current date and timeliness is not effective yet from the law and rule library according to the law and rule timeliness change trigger rule, modifying the timeliness of the law and rule into an effective yet to be processed list, retrieving law and rule that the implementation date is the current date and timeliness is equal to the effective yet from the law and rule library, modifying the timeliness of the law and rule into an effective yet to be processed list, and adding the effective yet to be processed list;
the law and rule timeliness change identification model is used for identifying each law and rule in the current effective to-be-processed list and moving the law and rule to the timeliness change to-be-processed list, and meanwhile, the identified law and rule needing timeliness change is added into the timeliness change to-be-processed list;
the manual auditing module is used for manually auditing;
and the timeliness change execution module is used for executing the change.
Examples
Based on the same inventive concept as the first embodiment, the present embodiment further provides an electronic device, which includes a processor, a memory, and a program or an instruction stored on the memory and executable on the processor, wherein the program or the instruction implements the steps of the method for time-efficient dynamic update of the legal and regulatory database as described in the first embodiment when executed by the processor.
The processor may comprise a central processing unit, or a specific integrated circuit, or may be configured to implement one or more integrated circuits of embodiments of the present application.
The memory may include mass storage for data or instructions. By way of example, and not limitation, the memory may comprise a hard disk drive, floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or universal serial bus drive, or a combination of two or more of the foregoing. The memory may include removable or non-removable (or fixed) media, where appropriate. The memory may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory is a non-volatile solid state memory.
The foregoing has shown and described the basic principles, principal features and advantages of the application. It should be understood by those skilled in the art that the above embodiments do not limit the scope of the present application in any way, and all technical solutions obtained by equivalent substitution and the like fall within the scope of the present application.
The application is not related in part to the same as or can be practiced with the prior art.
Claims (6)
1. The method for dynamically updating the timeliness of the legal and regulatory database is characterized by comprising the following steps of:
s1, acquiring full-scale legal and regulation data from a public authority legal and regulation database, and constructing the legal and regulation database;
s2, marking a law and regulation timeliness change data set, and constructing a language model for identifying law and regulation timeliness change; the step S2 specifically includes:
s2.1, extracting sample data from a law and regulation library, introducing the sample data into a marking tool, marking the modified or abolished law and regulation specified in the law and regulation, and taking the sample data as a law and regulation timeliness change marking data set;
s2.2, randomly dividing the data marked in the step S2.1 into a training data set and a test data set according to a proportion;
s2.3, importing the training data set obtained in the step S2.2 into a model training program to perform law and regulation timeliness change identification model training to obtain a model which can identify that law and regulation timeliness change is required in a given text and is used as a model A;
s2.4, testing the model A by using the test data set obtained in the step S2.2, comparing the identification result of the model A with the reference test evaluation result of the basic model, if the identification result of the model A does not reach the reference test effect, adjusting model training program parameters, repeating the steps S2.2 and S2.3 to correspondingly obtain a model N until the identification result of the model N reaches the model reference test result, and ending the step S2 to obtain a final model N;
s3, according to law and regulation timeliness change trigger rules, constructing an timeliness change trigger processing module; the rule of triggering the time-based change of laws and regulations is as follows:
s3.1, retrieving legal regulations with implementation date larger than the current date and timeliness not being 'not yet effective' from a legal regulation library, modifying the timeliness of the legal regulations to 'not yet effective', and adding a 'not yet effective pending list';
s3.2, retrieving laws and regulations with implementation date being current date and timeliness being equal to 'not effective' from a laws and regulations library, modifying timeliness into 'current effective', and adding a 'current effective pending list';
s4, searching data in the legal and legal library according to the rule in the S3 and screening out a current valid list to be processed;
s5, identifying legal data in the current effective to-be-processed list by using the model obtained in the S2 to obtain a 'time-dependent change to-be-processed list';
s6, sending the timeliness change pending list to manual checking processing, and executing change by the submitting system after the manual checking is finished.
2. The method for dynamically updating the time-dependent legal regulations database according to claim 1, wherein said step S1 specifically comprises:
s1.1, acquiring full-quantity legal regulation text data from a legal regulation database of public authorities;
s1.2, analyzing attribute information of the full-scale legal and legal text data, wherein the attribute information comprises: the legal name, release date, implementation date, and level of efficacy;
s1.3, carrying out segmentation analysis on the full-scale legal and legal text data to obtain legal and legal codes, chapters, sections, strips, money and items.
3. The method for dynamically updating the time-dependent legal regulations database according to claim 1, wherein said step S4 specifically comprises:
s4.1, triggering a processing module through timeliness change of the S3, and periodically acquiring a legal rule that timeliness needs to be changed into current validity, namely a current valid list to be processed;
s4.2, identifying each legal rule in the current valid list to be processed, moving the model content obtained in the S2 to the timeliness change list to be processed, and simultaneously adding the identified legal rule for timeliness change into the timeliness change list.
4. The method for dynamically updating a legal regulation database according to claim 3, wherein after the model identifies the "time-dependent change pending list", the terms belonging to the same legal regulation in the list are first divided into the same group, and then the grouped list is sent to a manual auditing process.
5. A system for implementing the method for dynamic updating of the legal rules database according to any of claims 1-4, characterized in that it comprises:
the legal and regulation data acquisition and module is used for acquiring legal and regulation full-quantity data from the public authority legal database;
the legal regulation content analysis module is used for carrying out attribute information analysis and strip analysis on the full-scale legal regulation text data;
the law and rule timeliness change trigger processing module is used for retrieving law and rule that the implementation date is larger than the current date and timeliness is not effective yet from the law and rule library according to the law and rule timeliness change trigger rule, modifying the timeliness of the law and rule into an effective yet to be processed list, retrieving law and rule that the implementation date is the current date and timeliness is equal to the effective yet from the law and rule library, modifying the timeliness of the law and rule into an effective yet to be processed list, and adding the effective yet to be processed list;
the law and rule timeliness change identification model is used for identifying each law and rule in the current effective to-be-processed list and moving the law and rule to the timeliness change to-be-processed list, and meanwhile, the identified law and rule needing timeliness change is added into the timeliness change to-be-processed list;
the manual auditing module is used for manually auditing;
and the timeliness change execution module is used for executing the change.
6. An electronic device comprising a processor, a memory and a program or instruction stored on the memory and executable on the processor, the program or instruction when executed by the processor implementing the steps of the method of time-dependent dynamic updating of a legal rules database as defined in any one of claims 1 to 4.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH08263490A (en) * | 1995-03-24 | 1996-10-11 | Hitachi Ltd | Legal document updating system |
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