CN111859969B - Data analysis method and device, electronic equipment and storage medium - Google Patents
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Abstract
The disclosure provides a data analysis method and device, electronic equipment and a storage medium, and relates to the technical field of data processing. The data analysis method comprises the following steps: responding to an analysis instruction of a case to be processed, and acquiring a pre-constructed data analysis knowledge graph; determining a data analysis micro-service corresponding to the to-be-processed case according to the analysis instruction based on the data analysis knowledge graph; acquiring associated data corresponding to the to-be-processed case through the data analysis micro-service; and carrying out connection processing on the associated data according to the data analysis knowledge graph to generate an analysis result corresponding to the analysis instruction, and returning the analysis result. According to the technical scheme, high-efficiency and high-accuracy processing of business data such as massive civil affairs, administrative affairs, criminal case information, judgment documents and the like can be achieved, analysis collaboration and automatic association among various types of data are achieved, and working efficiency of staff is improved.
Description
Technical Field
The present disclosure relates to the field of data processing technology, and in particular, to a data analysis method, a data analysis device, an electronic apparatus, and a computer readable storage medium.
Background
With the rapid development of internet technology, the number and types of information data to be processed are increasing, so how to rapidly and accurately analyze and process massive data is an important point of attention.
The inspection authorities perform comprehensive analysis on the data of the approved and checked case handling information and the judge document for legal supervision services such as civil inspection, administrative inspection and the like, so as to exercise legal supervision responsibility for the civil and administrative case judgment.
At present, in the technical scheme of related data analysis, a special business auxiliary analysis case handling system and a special business data model are built for specific businesses, or a general case information analysis technology is provided, and comprehensive analysis is performed by inspector manpower. In the first scheme, a special auxiliary case handling software system is built for a specific inspection service, service logic is solidified, the application range is narrow, different types of data cannot be communicated, and the difficulty of analysis and retrieval is increased; in the second scheme, the inspector has more data volume and complex structure in retrieval analysis, so that the working efficiency is low, and the data analysis result is inaccurate.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of an embodiment of the present disclosure is to provide a data analysis method, a data analysis device, an electronic apparatus, and a computer readable storage medium, so as to overcome problems of difficult case data analysis, low efficiency, and low accuracy in related technical schemes at least to some extent.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to a first aspect of embodiments of the present disclosure, there is provided a data analysis method, including: responding to an analysis instruction of a case to be processed, and acquiring a pre-constructed data analysis knowledge graph; determining a data analysis micro-service corresponding to the to-be-processed case according to the analysis instruction based on the data analysis knowledge graph; acquiring associated data corresponding to the to-be-processed case through the data analysis micro-service; and carrying out connection processing on the associated data according to the data analysis knowledge graph to generate an analysis result corresponding to the analysis instruction, and returning the analysis result.
In some example embodiments of the present disclosure, based on the foregoing solution, before determining, according to the analysis instruction, a data analysis micro service corresponding to the to-be-processed case, the method further includes: determining the case service type; and constructing data analysis micro-services corresponding to different case service types according to the different case service types.
In some example embodiments of the present disclosure, based on the foregoing aspect, before acquiring the pre-constructed data analysis knowledge-graph, the method further includes: constructing an initial knowledge graph according to the idiom dictionary; acquiring command parameters corresponding to the data analysis microservices of different case service types and result parameters corresponding to the analysis commands; and mapping the initial knowledge graph, the analysis command parameters and the result parameters to construct the data analysis knowledge graph.
In some example embodiments of the present disclosure, based on the foregoing solution, the determining, based on the data analysis knowledge graph, a data analysis micro service corresponding to the to-be-processed case according to the analysis instruction includes: analyzing command parameters corresponding to the analysis instructions; and carrying out retrieval analysis processing from the data analysis knowledge graph according to the command parameters based on the data analysis knowledge graph so as to determine the data analysis micro-service corresponding to the to-be-processed case.
In some example embodiments of the present disclosure, based on the foregoing solution, obtaining, by the data analysis micro service, association data corresponding to the to-be-processed case includes: acquiring associated data corresponding to the to-be-processed case from a big data platform through the data analysis micro-service based on a preset data analysis tool; wherein the data analysis tool comprises one or more of a template matching tool, an entity recognition tool, an electronic document recognition tool and a deep learning-based key event recognition tool.
In some example embodiments of the present disclosure, based on the foregoing solution, the acquiring, by the data analysis micro service, association data corresponding to the to-be-processed case from a big data platform, the data analysis tool includes: based on a preset data analysis tool, acquiring structured data corresponding to the case service type from a big data platform through the data analysis micro-service; and acquiring associated data related to the to-be-processed case from the structured data according to the analysis instruction.
In some example embodiments of the disclosure, based on the foregoing scheme, the method further comprises: continuously acquiring actual data from the big data platform through the data analysis micro-service; and supplementing and updating the data analysis knowledge graph according to the actual data.
According to a second aspect of embodiments of the present disclosure, there is provided a data analysis apparatus comprising: the knowledge graph acquisition module is used for responding to the analysis instruction of the case to be processed and acquiring a pre-constructed data analysis knowledge graph; the micro-service determining module is used for determining the data analysis micro-service corresponding to the to-be-processed case according to the analysis instruction based on the data analysis knowledge graph; the associated data acquisition module is used for acquiring associated data corresponding to the to-be-processed case through the data analysis micro-service; and the analysis result returning module is used for carrying out connection processing on the associated data according to the data analysis knowledge graph so as to generate an analysis result corresponding to the analysis instruction and returning the analysis result.
In an exemplary embodiment of the present disclosure, based on the foregoing aspect, the data analysis apparatus further includes a data analysis micro service construction unit configured to: determining the case service type; and constructing data analysis micro-services corresponding to different case service types according to the different case service types.
In an exemplary embodiment of the present disclosure, based on the foregoing aspect, the data analysis apparatus further includes a data analysis knowledge graph construction unit configured to: constructing an initial knowledge graph according to the idiom dictionary; acquiring command parameters corresponding to the data analysis microservices of different case service types and result parameters corresponding to the analysis commands; and mapping the initial knowledge graph, the analysis command parameters and the result parameters to construct the data analysis knowledge graph.
In an exemplary embodiment of the present disclosure, based on the foregoing, the micro-service determination module is further configured to: analyzing command parameters corresponding to the analysis instructions; and carrying out retrieval analysis processing from the data analysis knowledge graph according to the command parameters based on the data analysis knowledge graph so as to determine the data analysis micro-service corresponding to the to-be-processed case.
In an exemplary embodiment of the disclosure, based on the foregoing scheme, the association data acquisition module is further configured to: acquiring associated data corresponding to the to-be-processed case from a big data platform through the data analysis micro-service based on a preset data analysis tool; wherein the data analysis tool comprises one or more of a template matching tool, an entity recognition tool, an electronic document recognition tool and a deep learning-based key event recognition tool.
In an exemplary embodiment of the disclosure, based on the foregoing scheme, the association data acquisition module is further configured to: based on a preset data analysis tool, acquiring structured data corresponding to the case service type from a big data platform through the data analysis micro-service; and acquiring associated data related to the to-be-processed case from the structured data according to the analysis instruction.
In an exemplary embodiment of the present disclosure, based on the foregoing aspect, the data analysis apparatus further includes a data analysis knowledge-graph updating unit configured to: continuously acquiring actual data from the big data platform through the data analysis micro-service; and supplementing and updating the data analysis knowledge graph according to the actual data.
According to a third aspect of embodiments of the present disclosure, there is provided an electronic device, comprising: a processor; and a memory having stored thereon computer readable instructions which when executed by the processor implement the data analysis method of any of the above.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a data analysis method according to any one of the above.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects:
According to the data analysis method in the example embodiment of the disclosure, a pre-built data analysis knowledge graph is obtained, a data analysis micro service corresponding to a to-be-processed case is determined from the data analysis knowledge graph according to an analysis instruction, associated data corresponding to the to-be-processed case is obtained through the data analysis micro service, then the associated data is subjected to connection processing according to the data analysis knowledge graph to generate an analysis result corresponding to the analysis instruction, and the analysis result is returned. On the one hand, the related data corresponding to the to-be-processed case is obtained according to the data analysis micro-services of different types, so that the to-be-analyzed data can be rapidly positioned, the analyzed data can be obtained in a targeted manner, the difficulty of data analysis is reduced, the data obtaining efficiency is improved, and the accuracy of the data can be improved; on the other hand, determining data analysis micro-services corresponding to the to-be-processed case from the data analysis knowledge graph according to the analysis instruction, and managing different data analysis micro-services through data analysis knowledge graph fusion to realize fusion among different types of data, so that the data analysis efficiency is further improved; on the other hand, the associated data are connected according to the data analysis knowledge graph, so that the analysis result is convenient for a user to arrange, the analysis efficiency of the user on the analysis result is improved, the data analysis knowledge graph is updated through the supplement of the analysis result, and the timeliness and the integrity of the data analysis knowledge graph are ensured.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort. In the drawings:
FIG. 1 schematically illustrates a flow diagram of a data analysis method according to some embodiments of the present disclosure;
FIG. 2 schematically illustrates a flow diagram of building data analysis knowledge-graph, in accordance with some embodiments of the present disclosure;
FIG. 3 schematically illustrates an ontology diagram of a data analysis knowledge-graph, according to some embodiments of the present disclosure;
FIG. 4 schematically illustrates a framework diagram of an application system corresponding to a data analysis method according to some embodiments of the present disclosure;
FIG. 5 schematically illustrates a schematic diagram of a data analysis device according to some embodiments of the present disclosure;
FIG. 6 schematically illustrates a structural schematic diagram of a computer system of an electronic device, in accordance with some embodiments of the present disclosure;
Fig. 7 schematically illustrates a schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure.
In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the disclosed aspects may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
Moreover, the drawings are only schematic illustrations and are not necessarily drawn to scale. The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
At present, for legal supervision services such as civil investigation, administrative investigation and the like, the inspection authorities perform comprehensive analysis on data of the judge case handling information and the judge document which are already examined and validated, so that legal supervision responsibilities for the judge work of civil and administrative cases are exercised. The false litigation case clues are found from the civil cases, and the false litigation illegal criminal behavior is judged, so that the false litigation case clues are one of the key works of civil law supervision, and are also important contents for cooperation between civil inspection and criminal inspection, court case setting judgment and other works. Therefore, there is a technical need for professional analysis of massive business data such as civil affairs, administration, criminal case information, and documents, and there is a need for accurate data analysis results and high performance.
Since the inspection business like false litigation supervision requires a lot of case information and document types to be collected and comprehensively analyzed, the case information comprises criminal cases and civil cases (different cases adopt different analysis methods); the case data analysis methods of different cases are different, and when analysis results are integrated and associated, the problem of semantic consistency of the data exists. There is a need for process collaboration, result automatic correlation, for case business data analysis between different inspection and supervision businesses, different inspection authorities, inspection authorities and courts.
The applicant found that in solving the above-mentioned problems, in one technical solution, a general case information analysis technique is provided, and a inspector performs comprehensive analysis to determine whether a legal problem, such as a false litigation, exists in a civil judgment case. However, in this scheme, the inspector performs search analysis on the case handling information and the referee document according to the existing general case information analysis system, and has dimensions including, but not limited to, the following: information such as the name and identity card of the litigation party, information of the litigation agent, the relationship of the litigation party, information of the business condition of the reported enterprise, the relationship among different case litigation times, whether appeal special cases exist in the case, the situation of the litigation party in the criminal case, and the like. The data volume required to be retrieved and analyzed by a inspector is large and complex, so that the working efficiency is low, and the data analysis is inaccurate.
In another technical scheme, a special business auxiliary analysis case handling system and a special business data model are built for businesses like false litigation inspection and supervision. However, in the scheme, on one hand, the definition (dimension description and information semantics) of the case handling data acquisition and analysis results is not unified, so that the data of different inspection services, the data of different inspection authorities and the data between inspection hospitals and courts cannot be automatically integrated and analyzed intuitively by a machine; on the other hand, a special auxiliary case handling software system is built for a specific inspection service, service logic is solidified, and the requirements of case handling service development and different types of services cannot be rapidly met. Repeated developments exist to assist in data analysis techniques in the case handling tools that cannot be used by more types of case handling business.
In view of one or more of the above-mentioned problems, in the present exemplary embodiment, a data analysis method is provided first, where the data analysis method may be applied to a terminal device or may be applied to a server, and the present exemplary embodiment is not limited thereto, and a method performed by the terminal device is described below as an example. Fig. 1 schematically illustrates a flow diagram of a data analysis method according to some embodiments of the present disclosure. Referring to fig. 1, the data analysis method may include the steps of:
step S110, responding to an analysis instruction of a case to be processed, and acquiring a pre-constructed data analysis knowledge graph;
Step S120, based on the data analysis knowledge graph, determining a data analysis micro-service corresponding to the to-be-processed case according to the analysis instruction;
step S130, obtaining associated data corresponding to the to-be-processed case through the data analysis micro-service;
And step 140, performing connection processing on the associated data according to the data analysis knowledge graph to generate an analysis result corresponding to the analysis instruction, and returning the analysis result.
According to the data analysis method in the embodiment, on one hand, the related data corresponding to the to-be-processed case is obtained according to the data analysis micro-services of different types, so that the to-be-analyzed data can be rapidly positioned, the data with analysis can be obtained in a targeted manner, the data obtaining efficiency is improved, and the data accuracy can be improved; on the other hand, determining data analysis micro-services corresponding to the to-be-processed case from the data analysis knowledge graph according to the analysis instruction, and managing different data analysis micro-services through data analysis knowledge graph fusion to realize fusion among different types of data, so that the data analysis efficiency is further improved; on the other hand, the associated data are connected according to the data analysis knowledge graph, so that the analysis result is convenient for a user to arrange, the analysis efficiency of the user on the analysis result is improved, the data analysis knowledge graph is updated through the supplement of the analysis result, and the timeliness and the integrity of the data analysis knowledge graph are ensured.
Next, a data analysis method in the present exemplary embodiment will be further described.
In step S110, in response to an analysis instruction of a case to be processed, a pre-constructed data analysis knowledge graph is acquired.
In an example embodiment of the present disclosure, the case to be processed may refer to a case that needs to be subjected to data analysis, and the analysis instruction may refer to an instruction that is sent by a user through a front end (HTML 5 or an application program) or other micro services through a data interface and includes a keyword or a command parameter corresponding to the case to be processed.
The data analysis knowledge graph may refer to a knowledge graph including data of case entities, entity attributes, entity relationships, and the like, for example, an ontology of the data analysis knowledge graph may include the following entity classes: case information entities and social entities such as inspection authorities, judgment courts, principals, agents, witness, case setting time, judging validation time, case law, crime names, disputed focuses, judging results, evidence and the like; the entity attributes of the ontology of the data analysis knowledge-graph may include: text attributes such as name, identification card number, legal name, enterprise name, business license number and numerical attributes; the entity relationship of the ontology of the data analysis knowledge-graph may include: the social relationship of various case relationships and entities such as a principal relationship, a principal-agent relationship, a principal-witter relationship, a criminal incidental civil case relationship, a couple, a parent-child, and a stakeholder of the same enterprise. The data analysis microservices required for different inspection and case handling businesses can be described by data analysis knowledge maps in the present example embodiment.
Further, the data analysis knowledge graph can be used for describing the process and the result of the data analysis micro service required by the inspection and case handling business by mapping the body of the data analysis knowledge graph with the service interface (command and parameter) of the data analysis micro service required by the case to be processed.
Specifically, before the pre-constructed data analysis knowledge-graph is obtained, the data analysis knowledge-graph may be constructed through the steps in fig. 2, as shown with reference to fig. 2:
step S210, constructing an initial knowledge graph according to the idiom dictionary;
Step S220, command parameters corresponding to the data analysis micro-services of different case service types and result parameters corresponding to the analysis commands are obtained;
And step S230, mapping the initial knowledge graph with the analysis command parameters and the result parameters to construct the data analysis knowledge graph.
The professional term dictionary may be a pre-constructed dictionary containing unified names and terms in judicial fields, and through the professional term dictionary, structured (semi-structured) information or key information or spoken information in the referee document may be extracted and unified. The functional description of the inspection and case handling data analysis, the description of the entity in the case and the description of the case element information all adopt unified names and terms in the judicial field, and the relationship and the attribute of the data analysis knowledge graph are constructed according to the original relationship and the attribute of the information in the judicial case, so that the semantic consistency and the correlation of functions and results of the data analysis micro-services corresponding to different inspection and case handling services are ensured.
The initial knowledge graph can refer to a knowledge graph which is preliminarily established according to data, data analysis micro-services and professional language dictionaries in large data platform data, command parameters and result parameters can refer to service interfaces corresponding to the data analysis micro-services of different case service types, the command parameters, the result parameters and other service interfaces of the data analysis micro-services are mapped with an ontology in the initial knowledge graph to obtain the data analysis knowledge graph, so that the process and the result of the data analysis micro-services are described by the data analysis knowledge graph, and the entity, the attribute, the relation and other actual data of the data analysis knowledge graph of the inspection case are continuously acquired through the data analysis micro-services required by the inspection case service, thereby realizing the overall integration of the data analysis of the inspection case by using the data analysis knowledge graph of the inspection case.
In an example embodiment of the present disclosure, command parameters corresponding to the analysis instructions may be parsed, based on the data analysis knowledge graph, search analysis processing may be performed from the data analysis knowledge graph according to the command parameters to determine data analysis micro-services corresponding to the case to be processed. The retrieval analysis processing can be used for retrieving and determining the processing process of the data analysis micro-service to be called from the data analysis knowledge graph according to the command parameters, and the intelligent calling of the data analysis micro-service of the case service type corresponding to the case to be processed is realized by analyzing the command parameters in the command, so that the data retrieval efficiency is improved.
The data analysis micro-service is described by using the data analysis knowledge graph corresponding to the inspection case, so that the standardization and semanteme of the functions, input data and analysis result description of the data analysis micro-service of different case handling businesses are solved; and the relation description between analysis results of the same case handling data analysis micro-service and the relation description between analysis results of different case handling data analysis micro-service are solved. Therefore, the technical requirement of analyzing and integrating a plurality of case handling business data according to different cases is realized, and the functions of the case handling data analysis micro-service and the analysis result are described by using the judicial case handling knowledge graph of the inspection agency, and the effects of the cooperation of the analysis functions and the semantic consistency and relevance of the analysis result are also realized.
In step S120, based on the data analysis knowledge graph, determining a data analysis micro service corresponding to the to-be-processed case according to the analysis instruction.
In an example embodiment of the present disclosure, a micro-service may refer to a software architecture, where the micro-service is designed to dismember traffic so that services can run independently, for example, a large single application and service may be split into tens of supporting micro-services by the micro-service, and a policy of the micro-service may make the work easier, and it may extend a single component rather than the entire application stack, so as to satisfy a service level agreement. The data analysis micro-service can refer to a micro-service for performing professional analysis on the judicial business data of different inspection authorities, and based on a pre-constructed data analysis knowledge graph, the data analysis micro-service to be invoked can be quickly searched through command parameters in an analysis command corresponding to a to-be-processed case.
Specifically, each data analysis micro-service is a micro-service capable of independently carrying out data analysis on structured case information and judicial documents, and various data in a large data platform for existence inspection are subjected to information extraction, retrieval, statistical analysis and association analysis through interfaces for providing data analysis on other micro-services and front ends (HTML 5 or mobile APP). Each data analysis micro-service can be used as an independent subsystem to carry out retrieval analysis through the input item and the selection item of the front-end interface, and can also be used for carrying out retrieval analysis through inputting data and information through the micro-service programming interface.
Specifically, before determining the data analysis micro-service corresponding to the to-be-processed case according to the analysis instruction, the data analysis micro-service may be pre-constructed by:
determining the case service type;
and constructing data analysis micro-services corresponding to different case service types according to the different case service types.
The data analysis micro-service may be built with a case service type as a center, for example, a data analysis micro-service corresponding to criminal case data analysis may be built, data analysis of civil cases of different cases may be subdivided into different data analysis micro-services, a data analysis micro-service corresponding to administrative case data analysis may be built, a data analysis micro-service corresponding to standing case service data analysis may be built, and a data analysis micro-service corresponding to control and application service data analysis may be built, which is not limited in this example embodiment. By constructing the data analysis micro-service in this way, not only the complexity of data analysis can be reduced, but also the data analysis micro-service is operated on a lightweight cloud (which can be the operation environment of the data analysis micro-service) to realize distributed computation, and different numbers of data analysis micro-services can be started according to the data volume of different service types, so that the system performance is improved.
The data analysis micro-service corresponding to different case service types is different in that different text analysis, information retrieval, data summarization, statistical analysis and association analysis are performed for different case service types to obtain different result information and data.
For example, criminal names (and synonyms) are used as keywords to analyze criminal case information and file documents, corresponding data analysis micro-services gather associated data of the criminal names, which can include case information, criminal documents, subsidiary civil case documents and the like, and extract structured information such as names, identification numbers, legal names and courts in principal information, and all data are gathered by taking principal or court as a first dimension to be structured (or semi-structured, such as Json document data) and stored in a centralized manner; searching a civil referee document by using a specified civil referee document, analyzing the civil referee document by using corresponding data, extracting structural information such as principal information (name, identity card number, enterprise name, business license number and the like), witness information, court of trial and the like in the document, gathering all data by using the principal information or court as a first dimension, and carrying out structural (or semi-structural, such as Json document data) centralized storage; the method comprises the steps of searching a civil case dispute focus, searching corresponding data analysis micro-services for gathering civil case judge documents of the same dispute focus, searching the case processing time, gathering documents with similar processing time characteristics by the corresponding data analysis micro-services, searching the same characteristics of the civil case parties, such as enterprises with non-bearing bonds, searching the special relations of the civil case parties, such as relations of couples, parents, children, stakeholders of the same enterprises, and the like, analyzing the micro-services by the corresponding data and extracting structured information of party information (names, identification numbers, enterprise names, business license numbers and the like), witness information, judging courts, conference focuses, party relations and the like in the documents, gathering all data by taking the party information or courts as a first dimension, and carrying out structuring (or semi-structuring, such as Json document data) centralized storage.
Specifically, each of the transaction data analysis microservices' functions (commands) may include, but are not limited to: and (3) an instruction for data analysis, an instruction for obtaining analysis result information and data, an instruction for obtaining the current data analysis progress and the like are obtained, and services are provided to the outside through a micro-service interface (REST ful and the like).
In step S130, the association data corresponding to the to-be-processed case is obtained through the data analysis micro-service.
In one example embodiment of the present disclosure, the associated data may be data corresponding to an analysis instruction of a to-be-processed case, which is acquired from a big data platform by the data analysis micro service, for example, the to-be-processed case may be a criminal case, the analysis instruction may be a criminal name (or synonym), and then the associated data may be all data related to the criminal name, such as case information, principal information, criminal document, and incidental civil case document.
Furthermore, based on a preset data analysis tool, the associated data corresponding to the to-be-processed case can be obtained from the big data platform through the data analysis micro-service. The data analysis tool may be a preset tool for acquiring data from a big data platform, for example, the data analysis tool may include a template matching tool, an entity recognition tool, an electronic document recognition tool, a key event recognition model based on deep learning, and other tools based on big data, natural language processing, and other artificial intelligence technologies, which are not limited in particular in this example embodiment.
Specifically, based on a preset data analysis tool, structured data corresponding to the case service type can be obtained from a large data platform through a data analysis micro-service; and acquiring associated data related to the to-be-processed case from the structured data according to the analysis instruction. The structured data may be data that the data analysis microservice aggregates all data corresponding to the case service type with the key information as the first dimension, and performs structured (or semi-structured) centralized storage, for example, the structured data may be Json document data or OEM (Object exchange Model, semi-structured data model) data, which is not limited in this example embodiment.
In step S140, the association data is subjected to connection processing according to the data analysis knowledge graph to generate an analysis result corresponding to the analysis instruction, and the analysis result is returned.
In an example embodiment of the present disclosure, the analysis result may refer to presentation data formed by connecting association data acquired by the data analysis microservices through a data analysis knowledge graph, for example, the analysis result may be a knowledge graph formed by the association data, or may be triplet data or table data formed by the association data, which is not limited in particular.
Optionally, the actual data may be continuously obtained from the big data platform through the data analysis microservice; and supplementing and updating the data analysis knowledge graph according to the actual data. The actual data may refer to relevant data of real cases corresponding to different case service types updated in real time in the big data platform, the actual data may be continuously obtained from the big data platform through the data analysis micro service, and the data analysis knowledge graph may be supplemented and updated according to the obtained actual data. The actual data such as the entity, the attribute, the relation and the like of the data analysis knowledge graph are continuously acquired through the data analysis micro service required by the inspection and case handling business, so that the overall integration of the inspection and case handling data analysis by using the inspection and case handling data analysis knowledge graph is realized.
Based on the data analysis knowledge graph, on one hand, the fusion of the micro service results of the data analysis of different inspection and case handling services can be realized, and the multiplexing of the micro service of the data analysis of various inspection and case handling services can be effectively realized. The method can also carry out different combinations and recombination on the calling of the existing inspection and case-handling business data analysis micro-service according to the different requirements of different inspection and case-handling on the data analysis result and the change of business logic, thereby realizing the multiplexing of the inspection and case-handling business data analysis micro-service which is already developed and successfully applied, improving the development efficiency of data analysis software of different inspection and case handling, reducing the research and development time and cost, and achieving the continuous accumulation and enhancement of the inspection and case-handling data analysis capability. On the other hand, the interactive intelligent data analysis can be realized to comprehensively analyze the data convergence result, the calling requirement for some micro-service content is automatically formed according to the correlation analysis of information and data of the check case handling, the intelligent case feature knowledge system analysis system is used for cooperatively using the general dimension analysis micro-service, and the analysis result is subjected to data convergence.
Fig. 3 schematically illustrates an ontology diagram of a data analysis knowledge-graph, according to some embodiments of the present disclosure.
Referring to fig. 3, the entity classes of the data analysis knowledge graph 301 may include a case information objective entity class (for example, may be a trial entity, a principal entity, a related person entity, etc.), a case element entity class (for example, may be a criminal element, a mitigation scenario, a criminal intention, a criminal behavior, a weight criminal scenario, a special criminal situation, a contract dispute, a property dispute, a company resolution dispute, etc.), and a data analysis entity class (for example, may be a criminal case analysis, a principal analysis, a case element analysis, etc.). The case information objective entity class and the case element entity class are consistent with the terms, standards and habits of the judicial authorities, and the names of the data analysis entity class and the entity correspond to the function names (determined by the service interface names of the data analysis micro-service) of the inspection and case handling data analysis (the tag character strings of the specific entity relationship of the knowledge graph are used for matching management).
Fig. 4 schematically illustrates a framework diagram of an application system corresponding to a data analysis method according to some embodiments of the present disclosure.
Referring to fig. 4, the basic architecture of the data analysis system may include a database layer 401, a micro-service layer 402, and a review transaction data analysis application layer 403. The database layer 401 may include various databases required by the system, for example, a graph database, a relational database, a document database, and the like; the micro service layer 402 may be formed by a micro service running environment (such as a micro service resource pool constructed by DockerKubernetes technologies) and each micro service corresponding to different case service types, where the micro services are designed and developed according to requirements of different case service, and each micro service realizes a data analysis function through service interface parameters; the inspection case data analysis application layer 403 may include an inspection case data analysis knowledge graph application system and other inspection case application systems, where other inspection case application systems may directly call a required data analysis micro service (may be one or more) through a front end (HTML 5 or a mobile application program) to meet the requirements of an inspection case service, and the inspection case data analysis knowledge graph application system may include a subsystem for performing search analysis on an inspection case data knowledge graph, a knowledge graph, an engine of a knowledge graph driving micro service, and the like, where the system may perform operations on the micro service by searching, reasoning, collecting case information in the knowledge graph, thereby obtaining analysis and statistics on case information and referee documents, and collecting case information and case elements by the knowledge graph, so as to provide full-scale information and data for an inspection office.
In general, by the data analysis method in the present exemplary embodiment, the data analysis microservices corresponding to the case service types can be constructed according to the case service needs by performing the judicial case handling data analysis work of the inspection authorities, and the data analysis microservices are described by using the data analysis knowledge graphs, and in the application of the inspection authorities, the purpose of collaborative case handling is achieved by integrating the knowledge graphs of the data analysis microservices of the respective case handling services; the knowledge graph of each data analysis micro-service is constructed by using judicial concepts, terms and key information (case element information) in the data, so that the fusion of data analysis instructions and business data information can be ensured. The data analysis method not only has the effect of intelligent fusion and association of the service data and the service analysis result, but also can realize the professional analysis function of each inspection service data and the effective multiplexing of the service, thereby improving the value and the efficiency of the inspection service data analysis.
On one hand, data analysis micro-services required by different inspection and case handling services are constructed, and information required by specific case handling services is acquired from structured case information and judge documents by using big data, natural language processing and other artificial intelligence technologies. The complexity problem and the accuracy problem of information extraction and analysis required in case handling can be solved; the performance problem of large-scale data analysis is solved through the elastic expansion capability of the micro service in the lightweight cloud.
On the other hand, the data analysis micro-service required by different inspection and case handling businesses is described by using an inspection and case handling data analysis knowledge graph. The functional description of the analysis of the inspection and case handling data, the description of the entity in the case and the description of the case element information all adopt unified names and terms in the judicial field, and the relationship and the attribute of the knowledge graph are constructed according to the original relationship and the attribute of the information in the judicial case, so that the semantic consistency and the correlation of functions and results of the micro-service of the analysis of the different inspection and case handling business data are achieved.
On the other hand, based on the data analysis knowledge graph, the fusion of data analysis micro-service results of different case service types can be realized, and multiplexing of various data analysis micro-services can also be realized. According to different requirements of different inspection and transaction cases on data analysis results and changes of business logic, different combinations and recombination can be carried out on the call of the existing data analysis micro-service, so that multiplexing of the developed and successfully applied data analysis micro-service is realized, the development efficiency of data analysis software of different inspection and transaction cases is improved, and the development time and the development cost are reduced; and the continuous accumulation and enhancement of the analysis capability of the inspection and case handling data can be achieved.
It should be noted that although the steps of the methods of the present disclosure are illustrated in a particular order in the figures, this does not require or imply that the steps must be performed in that particular order or that all of the illustrated steps must be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
Further, in the present exemplary embodiment, a data analysis apparatus is also provided. Referring to fig. 5, the data analysis device 500 includes: the system comprises a knowledge graph acquisition module 510, a micro-service determination module 520, a correlation data acquisition module 530 and an analysis result return module 540. Wherein: the knowledge graph acquisition module 510 is configured to acquire a pre-constructed data analysis knowledge graph in response to an analysis instruction of a case to be processed; the micro-service determining module 520 is configured to determine, based on the data analysis knowledge graph, a data analysis micro-service corresponding to the to-be-processed case according to the analysis instruction; the associated data obtaining module 530 is configured to obtain associated data corresponding to the to-be-processed case through the data analysis microservice; the analysis result returning module 540 is configured to perform connection processing on the associated data according to the data analysis knowledge graph to generate an analysis result corresponding to the analysis instruction, and return the analysis result.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the data analysis apparatus 500 further includes a data analysis micro service construction unit configured to: determining the case service type; and constructing data analysis micro-services corresponding to different case service types according to the different case service types.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the data analysis apparatus 500 further includes a data analysis knowledge graph construction unit configured to: constructing an initial knowledge graph according to the idiom dictionary; acquiring command parameters corresponding to the data analysis microservices of different case service types and result parameters corresponding to the analysis commands; and mapping the initial knowledge graph, the analysis command parameters and the result parameters to construct the data analysis knowledge graph.
In an exemplary embodiment of the present disclosure, based on the foregoing, the micro-service determination module 520 is further configured to: analyzing command parameters corresponding to the analysis instructions; and carrying out retrieval analysis processing from the data analysis knowledge graph according to the command parameters based on the data analysis knowledge graph so as to determine the data analysis micro-service corresponding to the to-be-processed case.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the association data acquisition module 530 is further configured to: acquiring associated data corresponding to the to-be-processed case from a big data platform through the data analysis micro-service based on a preset data analysis tool; wherein the data analysis tool comprises one or more of a template matching tool, an entity recognition tool, an electronic document recognition tool and a deep learning-based key event recognition tool.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the association data acquisition module 530 is further configured to: based on a preset data analysis tool, acquiring structured data corresponding to the case service type from a big data platform through the data analysis micro-service; and acquiring associated data related to the to-be-processed case from the structured data according to the analysis instruction.
In an exemplary embodiment of the present disclosure, based on the foregoing aspect, the data analysis apparatus 500 further includes a data analysis knowledge-graph updating unit configured to: continuously acquiring actual data from the big data platform through the data analysis micro-service; and supplementing and updating the data analysis knowledge graph according to the actual data.
The specific details of each module of the data analysis device are described in detail in the corresponding data analysis method, so that the details are not repeated here.
It should be noted that although in the above detailed description several modules or units of the data analysis device are mentioned, this division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above data analysis method is also provided.
Those skilled in the art will appreciate that the various aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
An electronic device 600 according to such an embodiment of the present disclosure is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is merely an example and should not be construed as limiting the functionality and scope of use of the disclosed embodiments.
As shown in fig. 6, the electronic device 600 is in the form of a general purpose computing device. Components of electronic device 600 may include, but are not limited to: the at least one processing unit 610, the at least one memory unit 620, a bus 630 connecting the different system components (including the memory unit 620 and the processing unit 610), a display unit 640.
Wherein the storage unit stores program code that is executable by the processing unit 610 such that the processing unit 610 performs steps according to various exemplary embodiments of the present disclosure described in the above-described "exemplary methods" section of the present specification. For example, the processing unit 610 may perform step S110 shown in fig. 1, and acquire a pre-constructed data analysis knowledge graph in response to an analysis instruction of a case to be processed; step S120, based on the data analysis knowledge graph, determining a data analysis micro-service corresponding to the to-be-processed case according to the analysis instruction; step S130, obtaining associated data corresponding to the to-be-processed case through the data analysis micro-service; and step 140, performing connection processing on the associated data according to the data analysis knowledge graph to generate an analysis result corresponding to the analysis instruction, and returning the analysis result.
The storage unit 620 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 621 and/or cache memory 622, and may further include Read Only Memory (ROM) 623.
The storage unit 620 may also include a program/utility 624 having a set (at least one) of program modules 625, such program modules 625 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 630 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.
The electronic device 600 may also communicate with one or more external devices 670 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and/or any devices (e.g., routers, modems, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 650. Also, electronic device 600 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 660. As shown, network adapter 660 communicates with other modules of electronic device 600 over bus 630. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 600, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible embodiments, the various aspects of the present disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the disclosure as described in the "exemplary methods" section of this specification, when the program product is run on the terminal device.
Referring to fig. 7, a program product 700 for implementing the above-described data analysis method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
Furthermore, the above-described figures are only schematic illustrations of processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (8)
1. A method of data analysis, comprising:
responding to an analysis instruction of a case to be processed, and acquiring a pre-constructed data analysis knowledge graph;
analyzing command parameters corresponding to the analysis instructions, and carrying out retrieval analysis processing from the data analysis knowledge graph according to the command parameters based on the data analysis knowledge graph so as to determine data analysis micro-services corresponding to the to-be-processed cases;
Acquiring associated data corresponding to the to-be-processed case through the data analysis micro-service;
Connecting the associated data according to the data analysis knowledge graph to generate an analysis result corresponding to the analysis instruction, and returning the analysis result;
The process of pre-constructing the data analysis knowledge graph comprises the following steps:
constructing an initial knowledge graph according to the idiom dictionary;
Acquiring command parameters corresponding to the data analysis microservices of different case service types and result parameters corresponding to analysis commands;
and mapping the initial knowledge graph with analysis command parameters and the result parameters to construct the data analysis knowledge graph.
2. The data analysis method according to claim 1, wherein before determining the data analysis microservice corresponding to the case to be processed according to the analysis instruction, the method further comprises:
determining the case service type;
and constructing data analysis micro-services corresponding to different case service types according to the different case service types.
3. The data analysis method according to claim 1, wherein obtaining, by the data analysis micro service, the associated data corresponding to the case to be processed, comprises:
Acquiring associated data corresponding to the to-be-processed case from a big data platform through the data analysis micro-service based on a preset data analysis tool;
Wherein the data analysis tool comprises one or more of a template matching tool, an entity recognition tool, an electronic document recognition tool and a deep learning-based key event recognition tool.
4. The data analysis method according to claim 3, wherein the acquiring, by the data analysis micro service, the associated data corresponding to the to-be-processed case from the large data platform based on the preset data analysis tool includes:
Based on a preset data analysis tool, acquiring structured data corresponding to the case service type from a big data platform through the data analysis micro-service;
and acquiring associated data related to the to-be-processed case from the structured data according to the analysis instruction.
5. A method of data analysis according to claim 3, wherein the method further comprises:
Continuously acquiring actual data from the big data platform through the data analysis micro-service;
And supplementing and updating the data analysis knowledge graph according to the actual data.
6. A data analysis device, comprising:
The knowledge graph acquisition module is used for responding to the analysis instruction of the case to be processed and acquiring a pre-constructed data analysis knowledge graph;
The micro-service determining module is used for analyzing the command parameters corresponding to the analysis instructions, carrying out retrieval analysis processing from the data analysis knowledge graph based on the data analysis knowledge graph according to the command parameters so as to determine the data analysis micro-service corresponding to the to-be-processed case;
the associated data acquisition module is used for acquiring associated data corresponding to the to-be-processed case through the data analysis micro-service;
The analysis result returning module is used for carrying out connection processing on the associated data according to the data analysis knowledge graph so as to generate an analysis result corresponding to the analysis instruction and returning the analysis result;
The data analysis knowledge graph construction unit is used for constructing an initial knowledge graph according to the idiom dictionary; acquiring command parameters corresponding to the data analysis microservices of different case service types and result parameters corresponding to analysis commands; and mapping the initial knowledge graph with analysis command parameters and the result parameters to construct the data analysis knowledge graph.
7. An electronic device, comprising:
A processor; and
A memory having stored thereon computer readable instructions which, when executed by the processor, implement the data analysis method of any of claims 1 to 5.
8. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the data analysis method according to any of claims 1 to 5.
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