CN113901034A - Method for automatically identifying administrative non-complaint execution case source - Google Patents
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Abstract
The invention discloses a method for automatically identifying administrative non-complaint execution case sources, which comprises the following steps: step one, compiling rules; step two, automatically studying and judging; step three, action processing; and step four, front-end application. The method comprises the steps of marking an early warning case source in a labeling mode after administrative examination case source data including official documents and administrative penalty decision books are researched and judged by an administrative non-complaint execution automation engine, and displaying the administrative non-complaint execution early warning case source data, legal basis, supervision key points, brain pictures, case handling guidance and other data to a case handling examiner in a visual mode through links such as model processing, case source pushing and the like. The method can be applied to the field of administrative inspection and supervision, assists an inspector in making a case handling decision, improves the case handling efficiency, improves the source discovery capability of the administrative inspection case, saves judicial resources, maintains normal judicial order, and improves judicial authority and judicial public credibility.
Description
Technical Field
The invention relates to an automatic identification method, in particular to a method for automatically identifying administrative non-complaint execution case sources.
Background
The administrative non-prosecution execution refers to a system that citizens, legal persons or other organizations do not mention administrative reefs or administrative litigation within a legal period and do not fulfill obligations determined by effective specific administrative behaviors, and the citizen court takes mandatory execution measures according to execution applications of authorities or authorities determined by the administrative authorities or the specific administrative behaviors so as to realize the specific administrative behaviors of the administrative authorities. Administrative non-complaint execution supervision refers to legal supervision of administrative non-complaint execution activities by the people's inspection institute according to relevant legal provisions.
With the continuous development of the law and construction in China, the unsound execution supervision mechanism of the administrative complaint causes the problems of irregular and non-uniform supervision and the problems of procedural supervision and selective supervision. The law and regulations related to administrative inspection are various, the specialization is strong, the professional requirements on the inspector are very high, in the past, case sources are found from mass data all manually, a great amount of time and energy are spent on the inspector, and the effect is very little.
Disclosure of Invention
In order to solve the defects of the technology, the invention provides a method for automatically identifying an administrative non-complaint execution case source, which applies a big data processing technology to an administrative non-complaint execution service, realizes legal logic and algorithm data processing through an interactive data development tool, automatically identifies the administrative non-complaint execution early warning case source, pushes early warning case source data to an administrative non-complaint execution big data application platform through a convergence platform data pipeline, highlights and positions violation condition monitoring key points, presents legal bases, case handling guides and the like in a brain picture mode, presents early warning case source information in a visual mode, assists a scout officer to quickly analyze case conditions, judges cases and improves the case handling efficiency of the scout officer.
In order to solve the technical problems, the invention adopts the technical scheme that: a method for automatically identifying the source of an administrative non-prosecution case, comprising the steps of:
step one, compiling rules;
step two, automatically studying and judging;
step three, action processing;
and step four, front-end application.
Preferably, the step one: and extracting metadata and classifying the document data of the referee document and the administrative penalty decision-making data according to the administrative non-complaint execution service characteristics, performing special data modeling aiming at the administrative non-complaint execution illegal supervision type, compiling the extracted structural elements and the studying and judging logic, and storing the compiled structural elements and studying and judging logic in a server.
Preferably, the specific operation steps of the first step are as follows:
l001, processing metadata;
step L002, classifying document data;
l003, determining the illegal situation of the special item;
l004, extracting structural elements;
l005, designing a study and judgment logic;
l006, modeling by processing special data;
and L007, compiling a study and judgment rule through a tool.
Preferably, in the first step, metadata extraction is performed on the official document data according to the service features of the administrative non-complaint execution, and the administrative non-complaint execution official document data is classified into: administrative official books and executive official books; and the administrative punishment decision book performs metadata extraction according to law enforcement subjects, case types and fields, and stores the classified data in an administrative inspection data set to form an administrative non-complaint execution supervision special direction.
Preferably, step two: reading and analyzing the compiled study and judgment rules, and performing automatic study and judgment engine through administrative non-complaint execution to make early warning identification on the project source data which accords with logic in a labeling mode and store the early warning identification in a database.
Preferably, the specific operation steps of step two are as follows:
step S001, preparing an interpreter;
step S002, restarting the interpreter;
step S003, running a special Note;
step S004, connecting a database;
step S005, reading data meeting the conditions;
step S006, identifying structural elements;
step S007, when the dictionary needs to be called, a dictionary interface is called; when the dictionary does not need to be called, the step S009 is directly carried out;
step S008, returning to dictionary items;
step S009, judging, namely skipping over the case source data which do not accord with the judging logic, and re-entering the step S006 to process the next data;
and S010, carrying out early warning labeling identification on the case meeting the research and judgment rules in the research and judgment logic.
Preferably, step three: establishing a corresponding special index model at the rear end of an administrative non-complaint execution application platform, configuring a data pipeline and importing and exporting data items through a data management platform convergence subsystem, and pushing early warning case source data to a front-end application database and an Elasticissearch distributed search engine through the pipeline for storage.
Preferably, the specific operation steps of step three are as follows:
a001, constructing an index data model in an application background;
step A002, creating an index;
step A003, constructing brain map data;
step A004, preparing a data pipeline;
step A005, preparing sending information;
step A006, preparing receiving information;
step A007, pushing data;
step A008, receiving data by an application;
and step A009, storing the early warning case source.
Preferably, step four: early warning case source information case information, illegal situations, supervision key points and brain map data pushed to an administrative non-complaint execution application platform are displayed in a visual mode through a front end vue layer technology.
Preferably, the specific operation steps of step four are as follows:
f001, reading special classification data;
f002, calling early warning case source data;
step F003, assembling visual brain map data;
step F004, assembling visual highlight data;
step F005, assembling and monitoring key points;
step F006, push to vue front end presentation.
The invention has the following beneficial effects:
(1) the administrative non-complaint execution supervision direction, legal basis, typical case and inspection official case experience are combed through legal research, and the research and judgment rules identified by the computer are arranged according to the model methodology.
(2) The advanced technologies such as semantic analysis technology, big data cleaning, conversion and loading are introduced to be deeply fused with the administrative inspection service, the discovery of the administrative non-complaint execution case source is converted into computer recognition by manpower, and the case handling efficiency is greatly improved.
(3) The technical problem of case source discovery of non-complaint execution is solved, the inspection and supervision capacity is improved, judicial resources are saved, normal judicial order is maintained, and meanwhile, judicial authority and judicial credibility are improved, so that the method has a very positive effect on promoting the law administration of administrative authorities and the judicial notarization of the national institutes of people.
Drawings
Fig. 1 is an overall architecture diagram of the present invention.
FIG. 2 is a flowchart of the operation of rule making in step one of the present invention.
FIG. 3 is a flow chart of the automatic determination in step two of the present invention.
FIG. 4 is a flowchart of the process of step three.
FIG. 5 is a flowchart of the operation of the front-end application of step four of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 shows a method for automatically identifying an executive case source of an administrative non-complaint, which comprises four steps of rule making, automatic study and judgment, action processing and front-end application. According to laws and regulations, typical cases and inspection officer case experience, by analyzing mass data, administrative non-complaint execution illegal situations, supervision key points and study rules are extracted, the study rules are compiled, then are presented in a visual highlight positioning and brain map mode and the like at the application front end through an automatic study engine and action processing, and after the four steps of processing, the method for automatically identifying the administrative non-complaint execution case source is formed, and the method specifically comprises the following steps:
the method comprises the following steps: extracting metadata and classifying the document data of the referee document and the administrative penalty decision-making data according to the administrative non-complaint execution service characteristics, performing special data modeling aiming at the administrative non-complaint execution illegal supervision type, compiling the extracted structural elements and the studying and judging logic, and storing the compiled structural elements and studying and judging logic in a server;
and (3) extracting metadata of the official document data aiming at the service characteristics of the administrative non-complaint execution, and classifying the administrative non-complaint execution official document data into: administrative official books and executive official books; and the administrative punishment decision book performs metadata extraction according to law enforcement subjects, case types and fields, and stores the classified data in an administrative inspection data set to form an administrative non-complaint execution supervision special direction.
The specific operation steps are as follows:
l001, processing metadata;
step L002, classifying document data;
l003, determining the illegal situation of the special item;
l004, extracting structural elements;
l005, designing a study and judgment logic;
l006, modeling by processing special data;
and L007, compiling a study and judgment rule through a tool.
Step two: reading and analyzing the compiled study and judgment rules, using an administrative non-complaint execution automatic study and judgment engine to make early warning identification of the project source data which accords with the logic in a labeling mode, and storing the early warning identification in a database;
and sending the extracted case structural elements into an administrative non-complaint execution automatic studying and judging engine, wherein the automatic studying and judging engine acquires feature dictionary items through an enabling platform aiming at different specials and sends back the feature dictionary items to the studying and judging engine for further studying and judging, and after the automatic engine studies and judges, performing tagging identification on early warning case source data meeting studying and judging rules.
The specific operation steps are as follows:
step S001, preparing an interpreter;
step S002, restarting the interpreter;
step S003, running a special Note;
step S004, connecting a database;
step S005, reading data meeting the conditions;
step S006, identifying structural elements;
step S007, when the dictionary needs to be called, a dictionary interface is called; when the dictionary does not need to be called, the step S009 is directly carried out;
step S008, returning to dictionary items;
step S009, judging, namely skipping over the case source data which do not accord with the judging logic, and re-entering the step S006 to process the next data;
and S010, carrying out early warning labeling identification on the case meeting the research and judgment rules in the research and judgment logic.
Step three: establishing a corresponding special index model at the rear end of an administrative non-complaint execution application platform, configuring a data pipeline and importing and exporting data items through a data management platform convergence subsystem, and pushing early warning case source data to a front-end application database and an Elasticissearch distributed search engine through the pipeline for storage;
aiming at special items, an index data model is created on a management platform for administrative non-complaint execution, a data pipeline is established in a convergence system in a data management platform, and convergence and circulation of multi-source heterogeneous data are realized. And pushing the labeled early warning case source data to an administrative non-complaint execution application platform server side with the established corresponding index data model through a convergence platform pipeline for storage.
The specific operation steps are as follows:
a001, constructing an index data model in an application background;
step A002, creating an index;
step A003, constructing brain map data;
step A004, preparing a data pipeline;
step A005, preparing sending information;
step A006, preparing receiving information;
step A007, pushing data;
step A008, receiving data by an application;
and step A009, storing the early warning case source.
Step four: displaying early warning case source information case information, illegal situations, supervision key points and brain map data which are pushed to an administrative non-complaint execution application platform in a visual mode by a front end vue layer technology;
the administrative non-complaint execution application platform arranges the received early-warning case source data, highlights, positions and displays the administrative non-complaint execution illegal condition supervision key points, displays the special data model, the laws and regulations and the case handling guide data in a brain map mode, and renders the data in a visual mode through vue front-end technology.
The specific operation steps are as follows:
f001, reading special classification data;
f002, calling early warning case source data;
step F003, assembling visual brain map data;
step F004, assembling visual highlight data;
step F005, assembling and monitoring key points;
step F006, push to vue front end presentation.
The present invention will be described in further detail with reference to examples.
A method for automatically identifying a source of a non-complaint execution case specifically comprises the following steps: the law and regulation, typical cases and inspection officer case experience required by the execution, inspection and supervision of administrative non-complaints are deeply analyzed, a multi-case type law violation supervision and early warning special model is formed by combining a large amount of data such as administrative official documents, administrative penalty determinants and the like, supervision key point extraction rules and study and judgment logics are formed, the supervision key points and the study and judgment logics are compiled and realized through interactive data development tools such as Zeppelin (Apache open source framework) and the like, then an administrative non-complaint automatic study and judgment engine is responsible for explaining and running, and after the study and judgment of the automatic study and judgment engine, case sources meeting the study and judgment logics in mass data are labeled and processed.
The specific implementation steps comprise: rule making, automatic study and judgment, action processing and front-end application.
Step one, rule compilation
The rule compilation is a key precondition for automatically identifying and executing case source administrative non-complaints, and lays a foundation for subsequent automatic study and judgment, as shown in FIG. 2:
l001, processing metadata; metadata processing is a standard and standardized processing mode for performing unified data definition on multiple data sources and multiple types of heterogeneous data, and is a key for ensuring data quality. Carrying out metadata extraction processing on data such as referee documents, administrative penalty determinants and the like to form a plurality of universal data sets, wherein the metadata items comprise: case number, case name, application executor, court of law, judge date, law enforcement, penalty date, law violation fact and other hundreds of items.
Step L002, classifying document data; after the official document data and the administrative penalty decision document data are processed by metadata, cases such as civil affairs, administration, criminal affairs and execution are mixed and placed in a plurality of data sets according to administrative divisions, cases such as civil affairs, criminal affairs and administrative litigation are stripped according to characteristics of administrative non-complaint execution cases, cases such as case numbers, case names and applicant executor types in the metadata, only administrative official documents and executive official documents which are executed by administrative non-complaints are reserved, and document data classification is completed.
L003, determining the illegal situation of the special item; according to laws and regulations, typical cases and inspection official case experience, illegal point situations of administrative non-complaint execution supervision are extracted to form a special supervision direction which can supervise a court or an administrative organ; and determining whether the link belongs to a checking and deciding link or an execution link in the links of administrative non-complaint execution business, and forming a supervised legal basis, a case handling guide and the like.
L004, extracting structural elements; the inspection and supervision need to design the extraction rule of the structured elements and design and supervise key points according to laws and regulations, the legal basis of carding and by researching and reading a large amount of document data. Extracting the structural elements is usually a regular expression, and is composed of keywords combined with regular symbols, and can be combined with qualifiers and meta characters: , +,? { n }, { n, }, { n, m }, [0-9] $, x | y, x & y, etc., as follows: the penalty date extraction rules are: from the administrative penalty decision book, "make | give down & (penalty decision | process decision | decision on administrative levy compensation)" in & (\\ d {2,4} year (\ d {1,2} day) {0,1}) {0,1}) & (make | give down) & (penalty decision | process decision | administrative levy compensation) ".
L005, designing a study and judgment logic; when structural elements and monitoring key points are designed, study and judgment logics are designed, namely study and judgment rules for describing illegal situations through legal languages. Such as: the law violation situation that the law administration and administration of the court is not to complain and execute the case overdue is judged according to the study logic formed by combining the legal logic and the structural elements as follows: and screening cases with the date of judgment (referee) to the date of filing (filing date) of more than 30 days in an administrative official document data with classified referee documents, wherein the data is 1 (which represents the data set field type in the Mongdob database, 1 represents the administrative official document, and 2 represents the execution of the referee document) to perform early warning identification.
L006, modeling by processing special data; for each illegal supervision special item executed by the administrative non-complaints, modeling needs to be completed in a data set executed by the administrative non-complaints according to structural elements and supervision key points, and field attribute names, data types, modes (pictures, texts, administrative partitions, timestamps and the like), whether the special item is an array, whether an energized dictionary is quoted or not and the like corresponding to the special item are configured.
And L007, compiling a study and judgment rule through a tool. The method comprises the steps of setting visual requirements according to designed structured extraction rules and monitoring Key points, compiling a studying and judging logic through interactive data development tools such as Zeppelin and the like, storing the compiled codes in notes (the notes are a record of the Zeppelin and store Python program codes) in a Zeppelin Server (the Zeppelin is a high-performance and high-availability distributed Key-Value storage platform) by adopting Python language design of the studying and judging logic, waiting for the administrative non-complaint execution of automatic studying and judging engine loading and calling, and realizing automatic studying and judging. After a special supervision and study logic is compiled, the process can return to the link of the step L003 again to carry out the compilation of the next special.
Step two, automatic study and judgment
The method for automatically identifying the source of the administrative non-complaint execution case needs to automatically judge and identify the early-warning case source data which accords with the judging logic, and needs to finish data classification and compile judging rules in Zeppelin before automatic judgment, as shown in FIG. 3:
step S001, preparing an interpreter; the version and output data size of a Python Interpreter (Interpreter) are configured at a Zeppelin management end, and the like are as follows:
zeppelin.python＝/usr/bin/python3.5
zeppelin.interpreter.output.limit＝10240000
zeppelin.ipython.grpc.message_size＝33554432
step S002, restarting the interpreter; after the interpreter related parameters are configured, the "restart" is clicked to validate the configuration.
Step S003, running a special Note; and starting an automatic studying and judging engine according to the studying and judging rule which is compiled in the step L007 and exists in the special item of the illegal situation in the Note in the Zeppelin Sever.
Step S004, connecting a database; because a large amount of data needs to be processed, the data base needs to be connected firstly, the MongoDB data base which is based on distributed file storage, expandable and high performance is adopted in the invention, and Phtyon is connected with an administrative non-complaint execution data set through a MongoClient API (open interface of the MongoDB data base), so that the data query and data update in subsequent links are prepared.
Step S005, reading data meeting the conditions;
according to the judging rule, the data set meeting the conditions is searched through database linkage, such as: only for the administrative official in a certain administrative district, the data retrieval range can be set as follows: param { "data.citcode": 330702, "data.judgmenttype": 1"} (where citcode represents an administrative division code and judgmentType represents that data is an administrative cutting book), and a target data set is obtained by find (find is a query function of MongoDB data), as follows: fine (param, no _ current _ timeout ═ True) (this expression is a method of MongoDB to retrieve a specific parameter, and no _ current _ timeout ═ True indicates that the cursor of the database is not timed out).
Step S006, identifying structural elements; after the data set meeting the preliminary screening condition is obtained, each record in the data set needs to be analyzed for research and judgment. According to the structured element rule in the Note, analyzing the specific content of the structured element from the text of the data record and updating the specific content into the data field corresponding to the special data model, namely: and saving the data items generated in the data processing process.
Step S007, when the dictionary needs to be called, a dictionary interface is called; when the dictionary does not need to be called, the step S009 is directly carried out; for data characteristics shared by a plurality of illegal supervision types, a dictionary API interface can be called for obtaining, and the dictionary API is called to save development efficiency and make study and judgment rule codes easier to maintain. The dictionary call scenario is as follows: calling a dictionary API through the place name of the case to obtain a specific administrative division code; by means of the party information, calling the dictionary API to know which type of administrative law enforcement agent belongs to, the codes referring to the dictionary API are as follows:
dictionary address: http:// xx. com/muscle/api/v2/dict/getItem
The parameter types are as follows: dictionary _ key _ xx & key _ yy
The returned dictionary entry is obtained through a request.get or a request.post (a get or post request initiated by Python and a method return value is obtained through a returned Response object).
Step S008, returning to dictionary items; and sending the dictionary item value returned by the dictionary API into the study and judgment rule to continue running according to the study and judgment rule.
Step S009, judging, namely skipping over the case source data which do not accord with the judging logic, and re-entering the step S006 to process the next data; and (4) transmitting the extracted structural elements, dictionary characteristic data and the like into a study and judgment logic, skipping the case source data which do not conform to the study and judgment logic, and re-entering the step S006 to process the next piece of data.
And S010, carrying out early warning labeling identification on the case meeting the research and judgment rules in the research and judgment logic. And carrying out early warning labeling identification on cases meeting the research and judgment rules in the research and judgment logic, updating corresponding fields of the database model, and then waiting for an action processing link to push the cases to a front-end application of administrative non-complaint execution.
Step three, action processing
Action processing realizes pushing and storing case source data which is automatically researched and judged as an early warning mark to an administrative non-complaint execution front-end application, the data storage of an administrative non-complaint execution application platform is a database combined with an elastic search (also called ES), the case source data is a distributed full-text search engine with multi-user capability, the early warning case source data needs to be stored and accessed in an index structure mode, and the engine enables mass data to be accessed more quickly, as shown in fig. 4:
a001, constructing an index data model in an application background; the administrative non-complaint execution application platform needs to correspondingly construct an index data model for a special item of illegal behavior supervision on the background of the administrative non-complaint execution application platform, and the index is a separate and physical storage structure for sequencing one or more columns of values in a database table. Establishing an index data model according to the model in the step L006, wherein the index data model comprises: field names, field types, arrays, aggregate queries, dictionaries, whether controls are used, and the like.
Step A002, creating an index; after the index data model is configured, a formal index is created to form a formal ES index table, and in order to ensure data security, the structure of the created index table is not allowed to be modified. And creating an index, namely, providing a space for storing the early warning case source data.
Step A003, constructing brain map data; the brain map is one of important visual components for the source of the administrative non-complaint execution early-warning plan, the brain map data needs to be configured separately for a special model, and the data presented by the brain map comprises: the brain map data can be automatically loaded through the model number, and the brain map data can be presented to a scout in a visual mode after being rendered on a front-end application page.
Step A004, preparing a data pipeline; and configuring an independent data mapping relation in a data management platform convergence subsystem, and pushing the early warning case source data to a front-end application through a pipeline. The pipeline configuration is to perform data relation mapping configuration on a data model of the early warning case source and an index data model applied at the front end, such as: caseNO in the data model is mapped with data (caseNO of referee document case) in the ES model, and the type is character string and non-array.
Step A005, preparing sending information; the method comprises the steps of configuring source information, and configuring a pipeline to be directly connected with a database of an early warning case source or to be connected with an interface to acquire source data. The pipeline configuration can push heterogeneous and different source data to a target server in a universal metadata form. The source of the pipeline supports MongDB direct connection, Excel file uploading, Ftp (file transfer protocol) and the like. After the connection mode is determined, specific project configuration is performed, such as: firstly selecting MongDB direct connection, then configuring: host, port, username, password, database, collection, etc.
Step A006, preparing receiving information;
configuring information of a receiver, wherein the receiver supports restful, ftp, kafka (three receiving modes, restful is a design style and a development mode of a network application program, ftp is a set of standard protocol for file transmission on the network, kafka is a high-throughput distributed publishing and subscribing message system) and the like, and the method mainly adopts restful and configures a receiving address of http (hypertext transfer protocol), namely: the administrative non-prosecution application back end receives url (network address), and the format is as follows: http:// ip: port/smarttase/api/data/importDataBatch.
Step A007, pushing data; and (3) packing and exporting the special early warning case source in a json (data exchange format) format in batches by using query statements of MongDB to obtain a zip-format compressed file package, and selecting the file in a pipeline to upload to a Redis (remote dictionary service) of an application server.
Step A008, receiving data by an application; the control class of the application background which is responsible for receiving data is in an ApiDataController (data controller), and the receiving method comprises the following steps: the method calls apiDataBatch, the json data is analyzed from a zip compressed file, and then the json data is stored in a Redis cache container.
And step A009, storing the early warning case source.
And the administrative non-complaint execution application back end polls the Redis through an ApiDataTask and ApiDataTaskThreadPool automatic task thread, starts a doImport data method when finding early warning case source data to be put in storage, calls a redisuits.rightPop ('api: data: import _ data') to obtain data from a cache window in the Redis, stores the early warning case source in an index of an ES according to a set pipeline model mapping relation, completes data receiving work and prepares for front-end application.
Step four, front end application
The front-end application of the administrative non-complaint execution is an important process for visually presenting the illegal situation of the early-warning case source, laws and regulations, key points of supervision, highlight components, brain pictures and other component data arranged at the back end to the inspector, as shown in fig. 5:
f001, reading special classification data; the administrative non-complaint execution is divided into a supervision court, a supervision administrative organ, a key field and an auxiliary case handling module according to the service, and the special items are obtained and classified by calling a background menuList method to form illegal supervision category json format data which is presented by applying a front page at the front end.
F002, calling early warning case source data; when the early warning case source data of a certain classification is checked, a getIndexInfo method is called to acquire an early warning case source data list aiming at the index identification corresponding to the special classification identification.
Step F003, assembling visual brain map data; encapsulating the brain map data, encapsulating the brain map data into json, and including the following data items: legal and legal rules, the bottom code fragment of brain picture, the data of handling case guide, supervising key points and the like, and the brain picture generation is assisted.
Step F004, assembling visual highlight data; packaging visual highlight data, in order to quickly position and supervise key points, performing object packaging on character strings corresponding to the key points in a text, setting highlighted attributes as { "highright": "true" }, and enabling corresponding highlight index elements to appear in pairs with key structural elements, such as: the model field of the judge date is: JudgmentDate, the highlight officiating date field for which the pair is right is: judgmentDate _ highlight.
Step F005, assembling and monitoring key points;
the key point of assembly supervision is that according to an illegal supervision party, the well processed structural elements are packaged into a { "name": keys "," type ": keyinfo", "name": data.
Step F006, push to vue front end presentation. Rendering packaged component data transmitted from the rear end by utilizing vue front-end technology, presenting in a highlight visualization mode, dynamically creating an orange semi-transparent background on characters corresponding to the early warning case source text, marking by using a highlight positioning < highlight > </highlight > mark, and setting the character background attribute as ' background ' # ff9125 '; and (3) brain graph visualization, wherein the types of brain graph components required to be in page configuration are set as follows: the value of kmdata must take data as a prefix, and the content is json format data at the bottom of the brain map. The value of cover must use data as prefix, content is page display brain picture cover picture, the format can be png or jpg. The visual technology can assist the inspector to quickly position and supervise key points, quickly know the case and improve the case analysis efficiency.
The invention can be applied to the field of administrative inspection and supervision, assists an inspector in making a case, improves the case handling efficiency, improves the source discovery capability of the administrative inspection cases, improves the inspection and supervision capability of the inspection authorities, improves the judicial authority and the judicial credibility while saving judicial resources and maintaining normal judicial order, and plays a very positive role in promoting the law-enforcement administration of the administrative authorities and the judicial notarization of the national institutes.
While the system mechanism is perfected, the administrative inspection needs to actively integrate the modern technology represented by big data and the like with the depth of the administrative inspection work by means of strong technological assistance. The method comprises the steps of carrying out language analysis on characters in data such as official documents, administrative penalty determinants and the like, extracting critical attributes and characteristics of study and judgment rules, and then realizing study and judgment logic through technical tools so as to screen illegal situation supervision key points in administrative non-complaint execution cases.
The above embodiments are not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make variations, modifications, additions or substitutions within the technical scope of the present invention.
Claims (10)
1. A method for automatically identifying the source of an administrative non-complaint execution case is characterized in that: the method comprises the following steps:
step one, compiling rules;
step two, automatically studying and judging;
step three, action processing;
and step four, front-end application.
2. The method of automatically identifying the source of an administrative non-prosecution case as recited in claim 1, wherein: the first step is as follows: and extracting metadata and classifying the document data of the referee document and the administrative penalty decision-making data according to the administrative non-complaint execution service characteristics, performing special data modeling aiming at the administrative non-complaint execution illegal supervision type, compiling the extracted structural elements and the studying and judging logic, and storing the compiled structural elements and studying and judging logic in a server.
3. The method of automatically identifying the source of an administrative non-prosecution case as recited in claim 2, wherein: the specific operation steps of the first step are as follows:
l001, processing metadata;
step L002, classifying document data;
l003, determining the illegal situation of the special item;
l004, extracting structural elements;
l005, designing a study and judgment logic;
l006, modeling by processing special data;
and L007, compiling a study and judgment rule through a tool.
4. The method of automatically identifying the source of an administrative non-prosecution case as recited in claim 3, wherein: in the first step, metadata extraction is performed on the official document data aiming at the service characteristics of the administrative non-complaint execution, and the administrative non-complaint execution official document data is classified into: administrative official books and executive official books; and the administrative punishment decision book performs metadata extraction according to law enforcement subjects, case types and fields, and stores the classified data in an administrative inspection data set to form an administrative non-complaint execution supervision special direction.
5. The method of automatically identifying the source of an administrative non-prosecution case as recited in claim 1, wherein: the second step is as follows: reading and analyzing the compiled study and judgment rules, and performing automatic study and judgment engine through administrative non-complaint execution to make early warning identification on the project source data which accords with logic in a labeling mode and store the early warning identification in a database.
6. The method of automatically identifying the source of an administrative non-prosecution case as recited in claim 5, wherein: the second step comprises the following specific operation steps:
step S001, preparing an interpreter;
step S002, restarting the interpreter;
step S003, running a special Note;
step S004, connecting a database;
step S005, reading data meeting the conditions;
step S006, identifying structural elements;
step S007, when the dictionary needs to be called, a dictionary interface is called; when the dictionary does not need to be called, the step S009 is directly carried out;
step S008, returning to dictionary items;
step S009, judging, namely skipping over the case source data which do not accord with the judging logic, and re-entering the step S006 to process the next data;
and S010, carrying out early warning labeling identification on the case meeting the research and judgment rules in the research and judgment logic.
7. The method of automatically identifying the source of an administrative non-prosecution case as recited in claim 1, wherein: the third step is that: establishing a corresponding special index model at the rear end of an administrative non-complaint execution application platform, configuring a data pipeline and importing and exporting data items through a data management platform convergence subsystem, and pushing early warning case source data to a front-end application database and an Elasticissearch distributed search engine through the pipeline for storage.
8. The method of automatically identifying the source of an administrative non-prosecution case as recited in claim 7, wherein: the third step comprises the following specific operation steps:
a001, constructing an index data model in an application background;
step A002, creating an index;
step A003, constructing brain map data;
step A004, preparing a data pipeline;
step A005, preparing sending information;
step A006, preparing receiving information;
step A007, pushing data;
step A008, receiving data by an application;
and step A009, storing the early warning case source.
9. The method of automatically identifying the source of an administrative non-prosecution case as recited in claim 1, wherein: the fourth step is that: early warning case source information case information, illegal situations, supervision key points and brain map data pushed to an administrative non-complaint execution application platform are displayed in a visual mode through a front end vue layer technology.
10. The method of automatically identifying the source of an administrative non-prosecution case as recited in claim 9, wherein: the specific operation steps of the fourth step are as follows:
f001, reading special classification data;
f002, calling early warning case source data;
step F003, assembling visual brain map data;
step F004, assembling visual highlight data;
step F005, assembling and monitoring key points;
step F006, push to vue front end presentation.
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| CN115511668B (en) * | 2022-10-12 | 2023-09-08 | 金华智扬信息技术有限公司 | Case supervision method, device, equipment and medium based on artificial intelligence |
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