CN116843481A - Knowledge graph analysis method, device, equipment and storage medium - Google Patents
Knowledge graph analysis method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to an artificial intelligence technology in the field of financial science and technology, and discloses a knowledge graph analysis method, which comprises the following steps: constructing a clause policy library, carrying out data standardization processing based on the clause policy library to obtain a standard data set, obtaining a historical service data set, constructing a service knowledge graph library based on the historical service data set and the standard data set, training a pre-constructed data analysis model based on the service knowledge graph library to obtain a service analysis model, and carrying out data analysis on service data to be analyzed by utilizing the service analysis model to obtain a data analysis result. The present invention also relates to blockchain techniques, and the data analysis results may be stored in nodes of the blockchain. The invention further provides a knowledge graph analysis device, electronic equipment and a readable storage medium. The invention can improve the accuracy and efficiency of data analysis, for example in the field of financial risk production, and can improve the accuracy and efficiency of analysis of the verification data.
Description
Technical Field
The present invention relates to the field of financial science and technology and artificial intelligence technology, and in particular, to a knowledge graph analysis method, a knowledge graph analysis device, an electronic device, and a readable storage medium.
Background
With the development of artificial intelligence, the data volume is continuously increased, and data analysis in different fields is becoming more and more important, for example, in the danger-producing scene in the financial field, a underwriting person is required to analyze underwriting policies, terms, external data and the like, and an analysis result is given.
In the prior art, data analysis becomes more and more intelligent, however, under the scene of frequent data updating and complex data association, a large number of manual audit analysis conditions still exist, so that the accuracy and the efficiency of data analysis are lower. For example, in the financial insurance production and verification business, the group wealth relates to the classification of numerous products such as enterprises, projects, freight, ships and responsibilities, and the like, and the standards, pricing factors and risk assessment of each product are large in variability, so that verification policies are various, and the total issuing of the verification policy files to institutions is performed, and the institutions refine the verification policy files into quantifiable rules after specific interpretation according to policy information, and the establishment is completed in a system so as to support risk interception or underwriting condition reminding in the quotation polling process. Two types of problems exist according to the current management mode of the underwriting policy, one is that underwriting person experience may influence the underwriting policy to be read into a specific system rule, so that the accuracy and the efficiency of underwriting data analysis are lower; another is that the rules need to be maintained and the preceding system rules are flat, so that omission may exist in inconvenient maintenance, and the possibility of leakage risks in the presence of the problems, which affect the check and guarantee judgment, and result in lower data analysis accuracy.
Disclosure of Invention
The invention provides a knowledge graph analysis method, a knowledge graph analysis device, electronic equipment and a readable storage medium, and mainly aims to improve the accuracy and efficiency of data analysis.
In order to achieve the above object, the present invention provides a knowledge graph analysis method, including:
constructing a clause policy library, and performing data standardization processing based on the clause policy library to obtain a standardization data set;
acquiring a historical service data set, and constructing a service knowledge graph base based on the historical service data set and the standard data set;
training a pre-constructed data analysis model based on the business knowledge graph library to obtain a business analysis model;
and carrying out data analysis on the business data to be analyzed by utilizing the business analysis model to obtain a data analysis result.
Optionally, the constructing a clause policy repository includes:
acquiring business policy data and business clause data, and carrying out data extraction on the business policy data and the business clause data according to a preset data structure to obtain structured policy data;
and summarizing all the structured policy data to obtain the clause policy library.
Optionally, the data normalization processing is performed based on the term policy library to obtain a normalized data set, including:
acquiring a historical structured data set, and performing data cleaning processing on the historical structured data set and data in the clause policy library to obtain a cleaning data set;
and carrying out data standardization processing on the data in the cleaning data set based on a preset service standard and a service standard to obtain a standard data set.
Optionally, the building a business knowledge graph base based on the historical business data set and the canonical data set includes:
identifying knowledge graph information in the historical service data set and the standard data set by utilizing a pre-constructed entity identification model;
and carrying out entity association on the entities in the knowledge graph information based on an ontology reasoning technology, and summarizing knowledge graph information after all the entities are associated to obtain the business knowledge graph base.
Optionally, the entity association of the entity in the knowledge-graph information based on the ontology inference technology includes:
classifying the entities in the knowledge graph information, and labeling the entities according to the classification result to obtain labeled entities;
and carrying out entity association on the associated labeling entities based on a preset labeling association rule.
Optionally, training the pre-constructed data analysis model based on the business knowledge graph library to obtain a business analysis model, including:
performing entity analysis on the data in the business knowledge graph base by using the data analysis model to obtain an entity analysis result;
and carrying out iterative training on the data analysis model based on the entity analysis result to obtain the business analysis model.
Optionally, after training the pre-constructed data analysis model based on the business knowledge graph library to obtain a business analysis model, the method further includes:
acquiring a historical business analysis data set, and performing entity analysis on business data in the historical business analysis data set by utilizing the business analysis model to obtain a historical entity analysis result;
and calculating analysis accuracy by using the historical entity analysis result and the historical business analysis data set, and carrying out optimization adjustment on the business analysis model based on the analysis accuracy to obtain an optimized business analysis model.
In order to solve the above problems, the present invention also provides a knowledge graph analysis device, the device comprising:
the data standardization module is used for constructing a clause policy library, carrying out data standardization processing based on the clause policy library, and obtaining a standardization data set;
the knowledge graph construction module is used for acquiring a historical service data set and constructing a service knowledge graph base based on the historical service data set and the standard data set;
the data analysis module is used for training a pre-constructed data analysis model based on the business knowledge graph library to obtain a business analysis model, and carrying out data analysis on business data to be analyzed by using the business analysis model to obtain a data analysis result.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one computer program; a kind of electronic device with high-pressure air-conditioning system
And the processor executes the computer program stored in the memory to realize the knowledge graph analysis method.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned knowledge-graph analysis method.
According to the invention, the business knowledge graph library is constructed based on the data of the clause policy library, different data can be associated based on the knowledge graph technology, then a pre-constructed data analysis model is trained through the business knowledge graph library to obtain a business analysis model, the business analysis model is utilized to carry out data analysis on business data to be analyzed, the efficiency, namely the accuracy of data analysis is improved, for example, in the financial field, the verification policy, clause information, past insurance policy data, claim settlement data, external purchase data and the like are integrated into the business knowledge graph library, the verification policy model is constructed based on the knowledge graph, a verification person is not required to manually configure one product by one policy, the time consumed by the verification person policy interpretation and rule configuration is reduced, and each item of associated information is also required to be queried and counted by oneself during verification quotation, so that the accuracy and the efficiency of data analysis are greatly improved. Therefore, the knowledge graph analysis method, the knowledge graph analysis device, the electronic equipment and the computer readable storage medium can improve the accuracy and the efficiency of data analysis.
Drawings
Fig. 1 is a flow chart of a knowledge graph analysis method according to an embodiment of the invention;
FIG. 2 is a functional block diagram of a knowledge-graph analysis device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for implementing the knowledge-graph analysis method according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention provides a knowledge graph analysis method. The main execution body of the knowledge graph analysis method includes, but is not limited to, at least one of a server, a terminal and the like, which can be configured to execute the method provided by the embodiment of the invention. In other words, the knowledge-graph analysis method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (ContentDelivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a knowledge graph analysis method according to an embodiment of the invention is shown. In this embodiment, the knowledge graph analysis method includes the following steps S1 to S4:
s1, constructing a clause policy library, and performing data standardization processing based on the clause policy library to obtain a standardization data set.
In the embodiment of the invention, the term policy library is composed of data such as policy data and term data of the standard business. For example, in the financial field, the term policy repository may be a underwriting policy repository, including underwriting policy data, underwriting term data, and the like.
In detail, the constructing a clause policy library includes:
acquiring business policy data and business clause data, and carrying out data extraction on the business policy data and the business clause data according to a preset data structure to obtain structured policy data;
and summarizing all the structured policy data to obtain the clause policy library.
In an alternative embodiment of the present invention, for example, in a risk scenario in the financial field, after acquiring data of a warranty policy, a warranty term, etc., the warranty policy, the warranty term, etc. are extracted as identifiable structured data according to a preset data structure, so as to form a term policy library.
In detail, the data normalization processing is performed based on the term policy library to obtain a normalized data set, which includes:
acquiring a historical structured data set, and performing data cleaning processing on the historical structured data set and data in the clause policy library to obtain a cleaning data set;
and carrying out data standardization processing on the data in the cleaning data set based on a preset service standard and a service standard to obtain a standard data set.
In an optional embodiment of the present invention, in the risk production in the financial field, the history structured data set may include history underwriting data, history claim settlement data, history wind investigation data, and the accuracy of analysis of insurance business data may be improved by performing data processing and cleaning on existing structured policy data, claim settlement data, customer information, and other data, and the core policy and core term information, and forming a normative data set based on insurance industry standard and normative integration data.
S2, acquiring a historical service data set, and constructing a service knowledge graph base based on the historical service data set and the standard data set.
In the embodiment of the invention, the historical service information comprises external information of different service bets, for example, in the financial field risk scenario, the historical service data set comprises external data such as customer data, vehicles, weather and the like of different enterprises.
In detail, the constructing a business knowledge graph base based on the historical business data set and the specification data set includes:
identifying knowledge graph information in the historical service data set and the standard data set by utilizing a pre-constructed entity identification model;
and carrying out entity association on the entities in the knowledge graph information based on an ontology reasoning technology, and summarizing knowledge graph information after all the entities are associated to obtain the business knowledge graph base.
Further, the entity association of the entity in the knowledge-graph information based on the ontology reasoning technology comprises:
classifying the entities in the knowledge graph information, and labeling the entities according to the classification result to obtain labeled entities;
and carrying out entity association on the associated labeling entities based on a preset labeling association rule.
In the embodiment of the present invention, the pre-constructed entity recognition model may be a model such as BRET, transformer, and the knowledge graph information includes information such as an entity, an attribute, a relationship, and a category. The ontology reasoning technology is to automatically classify, label and associate the entities in the knowledge graph information by using a preset business rule, so that the integration of data is realized, and the association between different data is improved. For example, in a financial insurance scenario, the target entity of the insurance may be closely related to weather, and the marked insurance entity and weather entity are related.
And S3, training a pre-constructed data analysis model based on the business knowledge graph library to obtain a business analysis model.
In the embodiment of the invention, the pre-constructed data analysis model can be a machine learning model, including decision trees, neural networks, XGBoost and the like.
In detail, training the pre-constructed data analysis model based on the business knowledge graph library to obtain a business analysis model, including:
performing entity analysis on the data in the business knowledge graph base by using the data analysis model to obtain an entity analysis result;
and carrying out iterative training on the data analysis model based on the entity analysis result to obtain the business analysis model.
In the embodiment of the invention, the entity analysis refers to classifying, labeling and associating the entities in the business knowledge graph base by using a data analysis model, and matching the entities with the data in the business knowledge graph base to give a corresponding entity analysis result. For example, in the financial risk production field, classifying, labeling and associating entities in a business knowledge graph base through a machine learning algorithm; auditing, screening and classifying different risk levels of the insurance targets; and judging the information of the client, completing the matching of the client characteristics and the underwriting knowledge base, and giving out a corresponding underwriting suggestion analysis result.
In another optional embodiment of the present invention, after training the pre-constructed data analysis model based on the service knowledge graph library to obtain a service analysis model, the method further includes:
acquiring a historical business analysis data set, and performing entity analysis on business data in the historical business analysis data set by utilizing the business analysis model to obtain a historical entity analysis result;
and calculating analysis accuracy by using the historical entity analysis result and the historical business analysis data set, and carrying out optimization adjustment on the business analysis model based on the analysis accuracy to obtain an optimized business analysis model.
In an alternative embodiment of the invention, for example, in the financial insurance production field, the model is tested by applying the historical underwriting data, and the accuracy of the model is confirmed by analyzing the underwriting proposal provided by the model, the actual underwriting proposal of the historical insurance policy and the historical odds data, so that the model is optimized and adjusted to obtain the optimized business analysis model. Meanwhile, as business products are continuously updated, data are continuously increased, risks are continuously changed, and models can be corrected through existing data, so that accuracy of model analysis is continuously improved.
And S4, carrying out data analysis on the service data to be analyzed by utilizing the service analysis model to obtain a data analysis result.
In an optional embodiment of the present invention, in the field of financial insurance production, the service data to be analyzed may be price inquiring data for a check, for example, a user of a warehouse enterprise applies for insurance, a target address is located in a non-flooding water disaster area, after the service analysis model is applied to perform data analysis, the model may give an underwriting suggestion based on historical reimbursement data of an insurance company, suggest additional terms or improve conditional underwriting such as reimbursement free amount, so as to reduce possible loss, and greatly improve accuracy and efficiency of data analysis. By integrating the underwriting policy, clause information, past insurance policy data, claim settlement data, external purchase data and the like into a business knowledge graph base, a underwriting policy model is constructed based on the knowledge graph, the underwriting person is not required to manually configure the underwriting policy one by one product by one policy, the time consumed by the underwriting person for policy interpretation and rule configuration is reduced, and each item of associated information is also required to be automatically inquired and counted during underwriting quotation, so that the accuracy rate, namely the efficiency of data analysis is greatly improved.
According to the invention, the business knowledge graph library is constructed based on the data of the clause policy library, different data can be associated based on the knowledge graph technology, then a pre-constructed data analysis model is trained through the business knowledge graph library to obtain a business analysis model, the business analysis model is utilized to carry out data analysis on business data to be analyzed, the efficiency, namely the accuracy of data analysis is improved, for example, in the financial field, the verification policy, clause information, past insurance policy data, claim settlement data, external purchase data and the like are integrated into the business knowledge graph library, the verification policy model is constructed based on the knowledge graph, a verification person is not required to manually configure one product by one policy, the time consumed by the verification person policy interpretation and rule configuration is reduced, and each item of associated information is also required to be queried and counted by oneself during verification quotation, so that the accuracy and the efficiency of data analysis are greatly improved. Therefore, the knowledge graph analysis method provided by the invention can improve the accuracy and efficiency of data analysis.
Fig. 2 is a functional block diagram of a knowledge-graph analysis device according to an embodiment of the present invention.
The knowledge-graph analysis apparatus 100 of the present invention may be installed in an electronic device. The knowledge-graph analysis device 100 may include a data specification module 101, a knowledge-graph construction module 102, and a data analysis module 103 according to the implemented functions. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the data normalization module 101 is configured to construct a clause policy library, and perform data normalization processing based on the clause policy library to obtain a normalized data set;
the knowledge graph construction module 102 is configured to obtain a historical service data set, and construct a service knowledge graph base based on the historical service data set and the canonical data set;
the data analysis module 103 is configured to train a pre-constructed data analysis model based on the service knowledge graph library to obtain a service analysis model, and perform data analysis on service data to be analyzed by using the service analysis model to obtain a data analysis result.
In detail, the specific embodiments of the modules of the knowledge-graph analysis device 100 are as follows:
step one, constructing a clause policy library, and carrying out data standardization processing based on the clause policy library to obtain a standardization data set.
In the embodiment of the invention, the term policy library is composed of data such as policy data and term data of the standard business. For example, in the financial field, the term policy repository may be a underwriting policy repository, including underwriting policy data, underwriting term data, and the like.
In detail, the constructing a clause policy library includes:
acquiring business policy data and business clause data, and carrying out data extraction on the business policy data and the business clause data according to a preset data structure to obtain structured policy data;
and summarizing all the structured policy data to obtain the clause policy library.
In an alternative embodiment of the present invention, for example, in a risk scenario in the financial field, after acquiring data of a warranty policy, a warranty term, etc., the warranty policy, the warranty term, etc. are extracted as identifiable structured data according to a preset data structure, so as to form a term policy library.
In detail, the data normalization processing is performed based on the term policy library to obtain a normalized data set, which includes:
acquiring a historical structured data set, and performing data cleaning processing on the historical structured data set and data in the clause policy library to obtain a cleaning data set;
and carrying out data standardization processing on the data in the cleaning data set based on a preset service standard and a service standard to obtain a standard data set.
In an optional embodiment of the present invention, in the risk production in the financial field, the history structured data set may include history underwriting data, history claim settlement data, history wind investigation data, and the accuracy of analysis of insurance business data may be improved by performing data processing and cleaning on existing structured policy data, claim settlement data, customer information, and other data, and the core policy and core term information, and forming a normative data set based on insurance industry standard and normative integration data.
Step two, acquiring a historical service data set, and constructing a service knowledge graph base based on the historical service data set and the standard data set.
In the embodiment of the invention, the historical service information comprises external information of different service bets, for example, in the financial field risk scenario, the historical service data set comprises external data such as customer data, vehicles, weather and the like of different enterprises.
In detail, the constructing a business knowledge graph base based on the historical business data set and the specification data set includes:
identifying knowledge graph information in the historical service data set and the standard data set by utilizing a pre-constructed entity identification model;
and carrying out entity association on the entities in the knowledge graph information based on an ontology reasoning technology, and summarizing knowledge graph information after all the entities are associated to obtain the business knowledge graph base.
Further, the entity association of the entity in the knowledge-graph information based on the ontology reasoning technology comprises:
classifying the entities in the knowledge graph information, and labeling the entities according to the classification result to obtain labeled entities;
and carrying out entity association on the associated labeling entities based on a preset labeling association rule.
In the embodiment of the present invention, the pre-constructed entity recognition model may be a model such as BRET, transformer, and the knowledge graph information includes information such as an entity, an attribute, a relationship, and a category. The ontology reasoning technology is to automatically classify, label and associate the entities in the knowledge graph information by using a preset business rule, so that the integration of data is realized, and the association between different data is improved. For example, in a financial insurance scenario, the target entity of the insurance may be closely related to weather, and the marked insurance entity and weather entity are related.
Training a pre-constructed data analysis model based on the business knowledge graph library to obtain a business analysis model.
In the embodiment of the invention, the pre-constructed data analysis model can be a machine learning model, including decision trees, neural networks, XGBoost and the like.
In detail, training the pre-constructed data analysis model based on the business knowledge graph library to obtain a business analysis model, including:
performing entity analysis on the data in the business knowledge graph base by using the data analysis model to obtain an entity analysis result;
and carrying out iterative training on the data analysis model based on the entity analysis result to obtain the business analysis model.
In the embodiment of the invention, the entity analysis refers to classifying, labeling and associating the entities in the business knowledge graph base by using a data analysis model, and matching the entities with the data in the business knowledge graph base to give a corresponding entity analysis result. For example, in the financial risk production field, classifying, labeling and associating entities in a business knowledge graph base through a machine learning algorithm; auditing, screening and classifying different risk levels of the insurance targets; and judging the information of the client, completing the matching of the client characteristics and the underwriting knowledge base, and giving out a corresponding underwriting suggestion analysis result.
In another optional embodiment of the present invention, after training the pre-constructed data analysis model based on the service knowledge graph library to obtain a service analysis model, the method further includes:
acquiring a historical business analysis data set, and performing entity analysis on business data in the historical business analysis data set by utilizing the business analysis model to obtain a historical entity analysis result;
and calculating analysis accuracy by using the historical entity analysis result and the historical business analysis data set, and carrying out optimization adjustment on the business analysis model based on the analysis accuracy to obtain an optimized business analysis model.
In an alternative embodiment of the invention, for example, in the financial insurance production field, the model is tested by applying the historical underwriting data, and the accuracy of the model is confirmed by analyzing the underwriting proposal provided by the model, the actual underwriting proposal of the historical insurance policy and the historical odds data, so that the model is optimized and adjusted to obtain the optimized business analysis model. Meanwhile, as business products are continuously updated, data are continuously increased, risks are continuously changed, and models can be corrected through existing data, so that accuracy of model analysis is continuously improved.
And fourthly, carrying out data analysis on the service data to be analyzed by utilizing the service analysis model to obtain a data analysis result.
In an optional embodiment of the present invention, in the field of financial insurance production, the service data to be analyzed may be price inquiring data for a check, for example, a user of a warehouse enterprise applies for insurance, a target address is located in a non-flooding water disaster area, after the service analysis model is applied to perform data analysis, the model may give an underwriting suggestion based on historical reimbursement data of an insurance company, suggest additional terms or improve conditional underwriting such as reimbursement free amount, so as to reduce possible loss, and greatly improve accuracy and efficiency of data analysis. By integrating the underwriting policy, clause information, past insurance policy data, claim settlement data, external purchase data and the like into a business knowledge graph base, a underwriting policy model is constructed based on the knowledge graph, the underwriting person is not required to manually configure the underwriting policy one by one product by one policy, the time consumed by the underwriting person for policy interpretation and rule configuration is reduced, and each item of associated information is also required to be automatically inquired and counted during underwriting quotation, so that the accuracy rate, namely the efficiency of data analysis is greatly improved.
According to the invention, the business knowledge graph library is constructed based on the data of the clause policy library, different data can be associated based on the knowledge graph technology, then a pre-constructed data analysis model is trained through the business knowledge graph library to obtain a business analysis model, the business analysis model is utilized to carry out data analysis on business data to be analyzed, the efficiency, namely the accuracy of data analysis is improved, for example, in the financial field, the verification policy, clause information, past insurance policy data, claim settlement data, external purchase data and the like are integrated into the business knowledge graph library, the verification policy model is constructed based on the knowledge graph, a verification person is not required to manually configure one product by one policy, the time consumed by the verification person policy interpretation and rule configuration is reduced, and each item of associated information is also required to be queried and counted by oneself during verification quotation, so that the accuracy and the efficiency of data analysis are greatly improved. Therefore, the knowledge graph analysis device provided by the invention can improve the accuracy and efficiency of data analysis.
Fig. 3 is a schematic structural diagram of an electronic device for implementing the knowledge-graph analysis method according to an embodiment of the present invention.
The electronic device may comprise a processor 10, a memory 11, a communication interface 12 and a bus 13, and may further comprise a computer program, such as a knowledge-graph analysis program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various types of data, such as codes of a knowledge-graph analysis program, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing Unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules (e.g., a knowledge-graph analysis program, etc.) stored in the memory 11, and calling data stored in the memory 11.
The communication interface 12 is used for communication between the electronic device and other devices, including network interfaces and user interfaces. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
The bus 13 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus 13 may be classified into an address bus, a data bus, a control bus, and the like. The bus 13 is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
Further, the electronic device may also include a network interface, optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the electronic device may further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The knowledge-graph analysis program stored in the memory 11 in the electronic device is a combination of instructions that, when executed in the processor 10, may implement:
constructing a clause policy library, and performing data standardization processing based on the clause policy library to obtain a standardization data set;
acquiring a historical service data set, and constructing a service knowledge graph base based on the historical service data set and the standard data set;
training a pre-constructed data analysis model based on the business knowledge graph library to obtain a business analysis model;
and carrying out data analysis on the business data to be analyzed by utilizing the business analysis model to obtain a data analysis result.
In particular, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
constructing a clause policy library, and performing data standardization processing based on the clause policy library to obtain a standardization data set;
acquiring a historical service data set, and constructing a service knowledge graph base based on the historical service data set and the standard data set;
training a pre-constructed data analysis model based on the business knowledge graph library to obtain a business analysis model;
and carrying out data analysis on the business data to be analyzed by utilizing the business analysis model to obtain a data analysis result.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the invention can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A knowledge-graph analysis method, the method comprising:
constructing a clause policy library, and performing data standardization processing based on the clause policy library to obtain a standardization data set;
acquiring a historical service data set, and constructing a service knowledge graph base based on the historical service data set and the standard data set;
training a pre-constructed data analysis model based on the business knowledge graph library to obtain a business analysis model;
and carrying out data analysis on the business data to be analyzed by utilizing the business analysis model to obtain a data analysis result.
2. The knowledge-graph analysis method of claim 1, wherein constructing a clause policy library comprises:
acquiring business policy data and business clause data, and carrying out data extraction on the business policy data and the business clause data according to a preset data structure to obtain structured policy data;
and summarizing all the structured policy data to obtain the clause policy library.
3. The knowledge-graph analysis method of claim 1, wherein the performing data normalization processing based on the term policy repository to obtain a normalized data set comprises:
acquiring a historical structured data set, and performing data cleaning processing on the historical structured data set and data in the clause policy library to obtain a cleaning data set;
and carrying out data standardization processing on the data in the cleaning data set based on a preset service standard and a service standard to obtain a standard data set.
4. The knowledge-graph analysis method as claimed in claim 1, wherein said constructing a business knowledge-graph base based on said historical business data set and said canonical data set comprises:
identifying knowledge graph information in the historical service data set and the standard data set by utilizing a pre-constructed entity identification model;
and carrying out entity association on the entities in the knowledge graph information based on an ontology reasoning technology, and summarizing knowledge graph information after all the entities are associated to obtain the business knowledge graph base.
5. The knowledge-graph analysis method of claim 4, wherein the entity-association of the entities in the knowledge-graph information based on the ontology inference technique comprises:
classifying the entities in the knowledge graph information, and labeling the entities according to the classification result to obtain labeled entities;
and carrying out entity association on the associated labeling entities based on a preset labeling association rule.
6. The knowledge-graph analysis method of claim 1, wherein training a pre-constructed data analysis model based on the business knowledge-graph library to obtain a business analysis model comprises:
performing entity analysis on the data in the business knowledge graph base by using the data analysis model to obtain an entity analysis result;
and carrying out iterative training on the data analysis model based on the entity analysis result to obtain the business analysis model.
7. The knowledge-graph analysis method of claim 6, wherein after training a pre-constructed data analysis model based on the business knowledge-graph library to obtain a business analysis model, the method further comprises:
acquiring a historical business analysis data set, and performing entity analysis on business data in the historical business analysis data set by utilizing the business analysis model to obtain a historical entity analysis result;
and calculating analysis accuracy by using the historical entity analysis result and the historical business analysis data set, and carrying out optimization adjustment on the business analysis model based on the analysis accuracy to obtain an optimized business analysis model.
8. A knowledge-graph analysis device, the device comprising:
the data standardization module is used for constructing a clause policy library, carrying out data standardization processing based on the clause policy library, and obtaining a standardization data set;
the knowledge graph construction module is used for acquiring a historical service data set and constructing a service knowledge graph base based on the historical service data set and the standard data set;
the data analysis module is used for training a pre-constructed data analysis model based on the business knowledge graph library to obtain a business analysis model, and carrying out data analysis on business data to be analyzed by using the business analysis model to obtain a data analysis result.
9. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the knowledge-graph analysis method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the knowledge-graph analysis method according to any one of claims 1 to 7.
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| CN117391313B (en) * | 2023-12-12 | 2024-04-30 | 广东正迪科技股份有限公司 | Intelligent decision method, system, equipment and medium based on AI |
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