CN117217933A - Data multidimensional analysis method and device for insurance industry - Google Patents
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Abstract
The invention provides a data multidimensional analysis method and a device for insurance industry, wherein the method comprises the following steps: acquiring all dimension information of risk analysis in the insurance industry; acquiring all index information of risk measurement in the insurance industry; correlating the dimension information and the index information with entity table relations in a database through a knowledge graph, and constructing a fact table based on correlation results; based on the dimension information, the index information and the fact table, establishing a metadata model, wherein the metadata model is an entity relation model; inputting dimension information and index information to be risk analyzed into the metadata model to obtain a fact table with minimum query cost; and obtaining an optimal query path meeting the query condition and a query result corresponding to the optimal query path based on the fact table with the minimum query cost. The method can realize the multi-dimensional analysis and quick response of big data and improve the user experience.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a data multidimensional analysis method and device for insurance industry.
Background
The service quality monitoring and risk analysis are important concerns of statistical analysis in the insurance industry, and are important points of exploration of big data application scenes of insurance companies by timely providing flexible and various fine-grained analysis view angles and calculating risk indexes such as multi-dimensional odds, rates, risk rates and the like in real time so as to perform risk early warning. In the prior art, conventional multidimensional analysis has the following problems: firstly, the data sets are mutually independent, and the phenomenon of non-uniform dimension and index caliber exists among the data sets. And secondly, flexible multidimensional analysis of large data volume cannot be supported. When the insurance industry performs risk pricing analysis, the required data granularity is fine, basically reaches the policy granularity, the time span is large, the data is 5-10 years, the detail data basically reaches billions of data volume, and the traditional multidimensional analysis can not meet the flexible multidimensional analysis requirement of the business based on large data volume.
Based on the scheme, a model with unified dimension indexes needs to be established, and the requirement of flexible multidimensional analysis on risk related indexes of the insurance industry is met by using a distributed computing technology.
Disclosure of Invention
The invention provides a data multidimensional analysis method and a data multidimensional analysis device for the insurance industry, which are used for carrying out rapid statistical analysis and more comprehensive monitoring on insurance business risks.
The invention provides a data multidimensional analysis method for insurance industry, comprising the following steps:
acquiring all dimension information of risk analysis in the insurance industry, wherein the dimension information comprises dimension categories and dimension level information corresponding to the dimension categories;
acquiring all index information of risk measurement in the insurance industry, wherein the index information comprises index categories and index coding information corresponding to the index categories;
correlating the dimension information and the index information with entity table relations in a database through a knowledge graph, and constructing a fact table based on correlation results;
based on the dimension information, the index information and the fact table, establishing a metadata model, wherein the metadata model is an entity relation model;
inputting dimension information and index information to be risk analyzed into the metadata model to obtain a fact table with minimum query cost; and obtaining an optimal query path meeting the query condition and a query result corresponding to the optimal query path based on the fact table with the minimum query cost.
According to the data multidimensional analysis method for the insurance industry provided by the invention, all dimension information of risk analysis of the insurance industry is obtained, and before the dimension information comprises dimension types and dimension level information corresponding to the dimension types, the method comprises the following steps:
unifying the codes of the dimension categories in the database, wherein one dimension category corresponds to the unique code;
storing the dimension category and the dimension category code into a dimension list, wherein the dimension list also comprises a dimension corresponding database table name, a dimension associated fact table name and a dimension description;
unifying the codes of the dimension levels in the database, wherein one dimension level corresponds to the unique code;
and storing the dimension level and the dimension level code into a level list, wherein the level list also comprises a database table name corresponding to the dimension level and an ID of the level list.
According to the data multidimensional analysis method for the insurance industry provided by the invention, all index information of the insurance industry for measuring risk is obtained, wherein the index information comprises index categories and index coding information corresponding to the index categories, and the method comprises the following steps:
uniformly coding index categories in a database, wherein one index category corresponds to a unique code;
storing the index category and the index category code into an index list, wherein the index list also comprises index common database table names, index units, whether indexes are calculated or not, and whether indexes are obtained or not.
According to the data multidimensional analysis method for insurance industry provided by the invention, the relationship between the dimension information and the index information and the entity table in the database is related through the knowledge graph, and the fact table is constructed based on the related result, comprising the following steps:
combining the dimension list and the hierarchy list corresponding to the dimension information, the index list corresponding to the index information and the entity table in the database according to different combination modes, and storing the combination in the corresponding fact table;
the fact table also comprises a fact table query cost, and the fact table accesses ip and ports; and the fact table query cost is calculated according to the fact table record number, the fact table gradient and the machine performance of the fact table.
According to the data multidimensional analysis method for insurance industry provided by the invention, dimension information and index information to be risk analyzed are input into the metadata model to obtain a fact table with minimum query cost, and the method comprises the following steps:
and searching a minimum cost fact table which can meet the aggregation condition of the dimension index in the metadata model.
According to the data multidimensional analysis method for insurance industry provided by the invention, an optimal query path meeting query conditions and a query result corresponding to the optimal query path are obtained based on the fact table with the minimum query cost, and the data multidimensional analysis method comprises the following steps:
and taking the fact table with the minimum query cost as a query entity table, carrying out data statistics, and returning a multidimensional query result.
The invention also provides a data multidimensional analysis device for insurance industry, comprising:
the acquiring module is used for acquiring dimension information for risk analysis in the insurance industry, wherein the dimension information comprises dimension categories and dimension level information corresponding to the dimension categories;
the acquisition module is further used for acquiring all index information of the risk measurement in the insurance industry, wherein the index information comprises index categories and index coding information corresponding to the index categories;
the construction module is used for associating the dimension information, the index information and the entity table relation in the database through a knowledge graph and constructing a fact table based on the association result;
the construction module is further configured to establish a metadata model based on the dimension information, the index information and the fact table, where the metadata model is an entity relationship model;
the determining module is used for inputting dimension information and index information to be risk analyzed into the metadata model to obtain a fact table with minimum query cost;
the determining module is further configured to obtain an optimal query path that meets a query condition and a query result corresponding to the optimal query path based on the fact table with the minimum query cost.
According to the data multidimensional analysis device for insurance industry provided by the invention, the determination module further comprises:
and the multidimensional analysis engine module is used for inputting the dimensional information and the index information to be risk analyzed into the metadata model so as to obtain multidimensional analysis data query results.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the data multidimensional analysis method for insurance industry as described in any of the above when executing the program.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a data multidimensional analysis method for the insurance industry as described in any of the above.
The invention also provides a computer program product comprising a computer program which when executed by a processor implements a data multidimensional analysis method for the insurance industry as described in any of the above.
The data multidimensional analysis method and device for the insurance industry provided by the invention adopt a distributed technology to construct a dimension index analysis metadata model of the insurance industry, establish multidimensional analysis metadata by using entity relationship modeling ideas on all business analysis dimensions and risk related indexes, and the multidimensional analysis engine analyzes user inquiry based on the multidimensional analysis metadata to find an optimal inquiry path, dynamically forms inquiry language and returns inquiry results, thereby realizing the rapid response of large data multidimensional analysis, providing bottom data support for business quality monitoring and risk management in the insurance industry and providing technical support for multidimensional rapid inquiry. The invention also adopts a distributed micro-service architecture, provides multidimensional analysis query through an API interface, not only can disperse query pressure, but also can provide unified interface service standards for various client requests, and applies a distributed column database, a distributed cache technology and distributed micro-service to multidimensional analysis query, thereby improving data access efficiency and query efficiency and user experience on the premise of least using storage.
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In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a data multidimensional analysis method for insurance industry according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a data multidimensional analysis device for insurance industry according to an embodiment of the present invention;
fig. 3 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, the "plurality" typically includes at least two, but does not exclude the case of at least one.
It should be understood that the term "and/or" as used herein is merely one relationship describing the association of the associated objects, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a product or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such product or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a commodity or system comprising such elements.
The embodiment of the invention provides a data multidimensional analysis method for insurance industry, which utilizes a distributed technology to carry out multidimensional analysis on data so as to carry out rapid statistical analysis and more comprehensive monitoring on insurance business risks.
The data multidimensional analysis method and apparatus for insurance industry of the present invention are described below with reference to fig. 1-3.
Fig. 1 is a flow chart of a data multidimensional analysis method for insurance industry according to an embodiment of the present invention, as shown in fig. 1, the method includes:
step 101: acquiring all dimension information of risk analysis in the insurance industry, wherein the dimension information comprises dimension categories and dimension level information corresponding to the dimension categories;
step 102: acquiring all index information of risk measurement in the insurance industry, wherein the index information comprises index categories and index coding information corresponding to the index categories;
step 103: correlating the dimension information and the index information with entity table relations in a database through a knowledge graph, and constructing a fact table based on correlation results;
step 104: based on the dimension information, the index information and the fact table, establishing a metadata model, wherein the metadata model is an entity relation model;
step 105: inputting dimension information and index information to be risk analyzed into the metadata model to obtain a fact table with minimum query cost; and obtaining an optimal query path meeting the query condition and a query result corresponding to the optimal query path based on the fact table with the minimum query cost.
The above steps are described in detail with reference to specific examples.
Step 101: acquiring all dimension information of risk analysis in the insurance industry, wherein the dimension information comprises dimension categories and dimension level information corresponding to the dimension categories;
in this step, a plurality of subject dimension information of the insurance industry is obtained, wherein the dimension information includes, but is not limited to: dimension information such as car insurance (car, person, product, insurance mode, insurance policy) and related information such as agricultural insurance (target classification, cultivation type, product classification, sales channel), and related information such as property insurance (industry, inventory, organization, product, channel).
In particular, insurance industry risks are associated with a number of dimensional information, such as risks of vehicle insurance, which can be analyzed from multiple dimensions of issuing authorities, products, sales channels, applicant ages, vehicle ages, number of times of undelivered insurance, number of medium traffic, vehicle prices, vehicle types, vehicle systems, and the like. The risk of property risk may be analyzed from multiple dimensions of institutions, products, sales channels, inventory, industry, target addresses, building structures, scenes, etc. In practical application, a user can arbitrarily select a plurality of dimension combinations, and some limiting conditions are added for analysis. The invention takes all dimension information and dimension level information as dimension metadata to be stored in a database, the inquiry dimension information of the user can be any dimension combination, and if the data of the combined dimension does not exist in a preprocessing data set, the system automatically processes summarized data from a list set, thereby meeting the analysis of the data of any dimension combination of the user.
And carrying out dimension coding and dimension hierarchy coding on the dimension information, identifying dimension information with different names and substantially the same, and storing unified dimension information and dimension hierarchy metadata in a database in a two-dimensional table mode. The dimension unified coding mode includes a mechanism dimension code A001A, a product dimension code A002A and a target dimension code A003A. The dimension level unified coding mode includes provincial organization code L001L, city organization code L002L, county branch organization code L003L, product risk class one L004L, product risk class two L005L and the like; and storing all the dimension information into a two-dimensional table, wherein the table content comprises the information of dimension names, dimension codes, dimension corresponding database table names, field names used by dimension associated fact table names, dimension description and the like. And storing all the dimension level information into a two-dimensional table, wherein the table content comprises a level name, a level code, a level corresponding database table field name and a dimension table ID to which the level belongs.
Step 102: acquiring all index information of risk measurement in the insurance industry, wherein the index information comprises index categories and index coding information corresponding to the index categories;
in this step, after all the dimension and level information of risk analysis of the insurance industry is obtained, all the index information of risk measurement of the insurance industry is obtained, wherein the index information includes but is not limited to information such as premium income, earned premium, approved claim, uncore, pay rate, risk rate, effective report number and the like, and the index information is uniformly coded and identified, such as premium income M001M, earned premium M002M and the like. For example, all the index information may be stored in a two-dimensional table, where the table contents include index codes, index names, index common database table field names, index units, whether to calculate an index, whether to time the index, and the like.
Specifically, the insurance industry measures a lot of index information of risks, such as odds, earned premium, approved claim, uncore, earned insurance amount, loss rate, etc. The user performs risk analysis by combining the dimensional information acquired in step 101 and the index information of the present stage. The method is characterized in that all index information is used as index metadata to be stored in a database, the user query information can be any combination of dimension indexes, and if the combined data does not exist in a preprocessing data set, the system automatically processes summarized data from a list set, so that analysis of any dimension combination data of the user is met. And uniformly coding and identifying index information, such as premium income M001M, earned premium M002M and the like. All index information is stored in a two-dimensional table, and the table contents comprise index codes, index names, field names of an index common database table, index units, whether indexes are calculated, whether indexes are obtained at the time point, and the like.
Step 103: correlating the dimension information and the index information with entity table relations in a database through a knowledge graph, and constructing a fact table based on correlation results;
in the step, after all dimension and level information of risk analysis of the insurance industry and all index information of risk measurement of the insurance industry are obtained, fact table data comprising a plurality of dimension levels and a plurality of index combinations are established.
Step 104: based on the dimension information, the index information and the fact table, establishing a metadata model, wherein the metadata model is an entity relation model;
in this step, all the metadata information of the fact table is stored in a two-dimensional table, the table contents include the name of the fact table, the description of the fact table, the dimension list contained in the fact table, the hierarchy list contained in the fact table, the index list contained in the fact table, the query cost of the fact table, and the access ip and port of the fact table. The dimension list, the hierarchy list and the index list are stored in the form of coding + ',' and the like. For example, fact tables include organization, product, channel dimensions, then the dimension list is stored as "A001A, A002A, A003A". All list codes are stored in the coding sequence. The fact table query cost is calculated according to the fact table record number, the fact table gradient, the machine performance of the fact table and other factors, so that an entity relation model, namely a metadata model, is constructed.
Step 105: inputting dimension information and index information to be risk analyzed into the metadata model to obtain a fact table with minimum query cost; and obtaining an optimal query path meeting the query condition and a query result corresponding to the optimal query path based on the fact table with the minimum query cost.
In this step, through the unified dimension index metadata model, a fact table with the minimum cost and capable of providing the dimension set and index set of the required query is retrieved, metadata information of the fact table is used to automatically generate a query language, the query language is submitted to a server API where the fact table is located, the query language is executed, and a query result is returned. The dimension index combination of each query request is recorded in the background, and a new fact table is automatically generated by the model periodically according to the request times, so that the fact table with smaller cost for the dimension index combination of frequent query is realized to support the query, and the self-adaptive function is realized.
Specifically, the dimension information and the index information can be input variables of multidimensional analysis. The dimension combination information, the hierarchy combination information, the screening condition and the index combination information are input into a multidimensional analysis engine, the multidimensional analysis engine returns an optimal query statement, the statement is executed, and a query result is returned. The dimension combination information can be any dimension combination related to the insurance industry risk, the hierarchy combination information can be any dimension hierarchy combination related to the insurance industry risk, and the index combination information can be any index combination related to the insurance industry risk.
Further, converting the dimension combination, the dimension level combination and the index combination into internal coding identifiers, and finding out fact table information with minimum query cost meeting the combination condition in a metadata model; by analyzing the fact table metadata, a query language is automatically generated, a query sentence is executed on a corresponding server, and a query result is returned.
It is worth noting that if no matching fact table is found, generating a summary fact table meeting the query condition according to the clean-level metadata model, and updating the newly generated fact table into the fact table metadata to meet the query requirement.
Furthermore, the dimension combinations and index combinations used by the users are periodically checked, and the fact table with lower newly built query cost meets the requirement of the dimension combinations and index combinations used at high frequency. And adding the newly built fact table into the metadata model, and improving the multidimensional analysis efficiency in a self-adaptive mode.
It can be understood that metadata information can be stored in a distributed cache database, fact table and dimension table data are stored in a column type distributed database, and recently accessed data results are also stored in the distributed cache database, so that the multidimensional analysis and retrieval efficiency is improved.
In the whole, firstly converting dimension combination information into dimension code combinations, and separating the dimension code combinations through a' + matching symbol after sequencing; and converting the dimension level combination information into dimension level coding combination, and separating by the' + matching symbol after sequencing. And then converting the index combination information into index coding combination, and separating by using a ',' plus match symbol after sequencing. And matching the ordered dimension code combination, dimension level code combination and index code combination on the fact table metadata, searching all the fact tables meeting the conditions, and selecting the fact table with the minimum query cost from the fact tables as the fact table used by the query language. Finally, automatically generating query sentences according to standard query language specifications by automatically analyzing the relations between the fact table metadata and the dimension table metadata, between the dimension level table metadata and the index table metadata. Submitting the query statement to a server where the fact table is located for execution, and returning a query result in a jason format for front-end display.
If no matching fact table is found in the steps, automatically establishing the fact table from the list layer, maintaining the fact table metadata into metadata, and participating in the calculation of a later analysis engine.
That is, when multidimensional analysis is performed on insurance industry risks, not only all dimensional information and dimensional hierarchy combination information but also index combination information are acquired. Specifically, dimension combination information, dimension level combination information and index combination information are input into an established insurance industry dimension index metadata model to obtain fact table metadata information capable of supporting dimension indexes, a query language is automatically generated according to the fact table metadata information, query operation is completed, and a query result is returned. Based on the scheme, various analysis systems and various data marts existing in a company are unified in one dimension index metadata model by establishing the dimension index metadata model of the self-adaptive insurance industry, and the unified dimension index standard of the insurance industry is formed by utilizing the calculation power of hardware resources distributed in each analysis system and each data mart, so that the multidimensional analysis efficiency is remarkably improved.
The data multidimensional analysis device for the insurance industry provided by the invention is described below, and the data multidimensional analysis device for the insurance industry described below and the data multidimensional analysis method for the insurance industry described above can be correspondingly referred to each other.
Fig. 2 is a schematic structural diagram of a data multidimensional analysis device for insurance industry according to an embodiment of the present invention, as shown in fig. 2, the device includes:
the acquiring module 21 is configured to acquire dimension information for risk analysis in an insurance industry, where the dimension information includes a dimension category and dimension level information corresponding to the dimension category;
the acquiring module 21 is further configured to acquire all index information of the risk measured by the insurance industry, where the index information includes an index category and index coding information corresponding to the index category;
a construction module 22, configured to associate the dimension information and the index information with the entity table relationship in the database through a knowledge graph, and construct a fact table based on the association result;
the building module 22 is further configured to build a metadata model based on the dimension information, the index information, and the fact table, where the metadata model is an entity relationship model;
the determining module 23 is configured to input dimension information and index information to be risk analyzed into the metadata model, so as to obtain a fact table with minimum query cost;
the determining module 23 is further configured to obtain an optimal query path that satisfies a query condition and a query result corresponding to the optimal query path based on the fact table with the minimum query cost.
Optionally, the acquiring module 21 is specifically configured to:
unifying the codes of the dimension categories in the database, wherein one dimension category corresponds to the unique code;
storing the dimension category and the dimension category code into a dimension list, wherein the dimension list also comprises a dimension corresponding database table name, a dimension associated fact table name and a dimension description;
unifying the codes of the dimension levels in the database, wherein one dimension level corresponds to the unique code;
and storing the dimension level and the dimension level code into a level list, wherein the level list also comprises a database table name corresponding to the dimension level and an ID of the level list.
Optionally, the acquiring module 21 is specifically further configured to:
uniformly coding index categories in a database, wherein one index category corresponds to a unique code;
storing the index category and the index category code into an index list, wherein the index list also comprises index common database table names, index units, whether indexes are calculated or not, and whether indexes are obtained or not.
Optionally, the construction module 22 is specifically configured to:
combining the dimension list and the hierarchy list corresponding to the dimension information, the index list corresponding to the index information and the entity table in the database according to different combination modes, and storing the combination in the corresponding fact table;
the fact table also comprises a fact table query cost, and the fact table accesses ip and ports; and the fact table query cost is calculated according to the fact table record number, the fact table gradient and the machine performance of the fact table.
Optionally, the determining module 23 is specifically configured to:
and searching a minimum cost fact table which can meet the aggregation condition of the dimension index in the metadata model.
Optionally, the determining module 23 is specifically configured to:
the fact table is used as an entity table for inquiring, data statistics is carried out, and a multidimensional analysis result is returned.
The apparatus of the present embodiment may be used to execute the method of any one of the foregoing electronic device side method embodiments, and specific implementation processes and technical effects of the apparatus are similar to those of the electronic device side method embodiments, and specific details of the electronic device side method embodiments may be referred to in the detailed description of the electronic device side method embodiments, which are not repeated herein.
The data multidimensional analysis method and device for the insurance industry provided by the embodiment of the invention adopt the distributed technology, apply the distributed column database, the distributed cache technology and the distributed microservice to multidimensional analysis query for the first time, improve the query efficiency and improve the user experience; the dimension table and the fact table are used for establishing multidimensional analysis metadata by using entity relation modeling ideas, and aiming at dimension information, business significance is more concerned, so that system attributes related to query such as slicing, authority control, data cutting and the like are set; aiming at index information, business meaning is focused more, so that relevant system attributes such as units, update frequency, whether number statistics is carried out, whether time points are carried out, physical storage positions and the like are inquired, and all index sets of risk analysis are covered.
The invention also builds a dimension index analysis metadata model of the insurance industry, realizes the multidimensional analysis and quick response of big data, and provides a technical guarantee of bottom data support and multidimensional quick query for monitoring the service quality of the insurance industry and risk management.
The invention also utilizes a multidimensional analysis engine, adopts a self-adaptive optimization model strategy, analyzes the user query based on multidimensional analysis metadata, searches an optimal query path, dynamically forms a query language and returns a query result; the distributed micro-service architecture is adopted, and multidimensional analysis query is provided through an API interface, so that the query pressure can be dispersed, and unified interface service standards can be provided for various client requests; the multidimensional analysis engine performs pre-aggregation on the data according to the user query habit and the autonomous optimization model, and improves the data access efficiency and the user experience on the premise of least using storage.
Fig. 3 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention, where, as shown in fig. 3, the electronic device may include: processor 310, communication interface (Communications Interface) 320, memory 330 and communication bus 340, wherein processor 310, communication interface 320, memory 330 accomplish communication with each other through communication bus 340. The processor 310 may invoke logic instructions in the memory 330 to perform a data multidimensional analysis method for the insurance industry, the method comprising: acquiring all dimension information of risk analysis in the insurance industry, wherein the dimension information comprises dimension categories and dimension level information corresponding to the dimension categories; acquiring all index information of risk measurement in the insurance industry, wherein the index information comprises index categories and index coding information corresponding to the index categories; correlating the dimension information and the index information with entity table relations in a database through a knowledge graph, and constructing a fact table based on correlation results; based on the dimension information, the index information and the fact table, establishing a metadata model, wherein the metadata model is an entity relation model; inputting dimension information and index information to be risk analyzed into the metadata model to obtain a fact table with minimum query cost; and obtaining an optimal query path meeting the query condition and a query result corresponding to the optimal query path based on the fact table with the minimum query cost.
Further, the logic instructions in the memory 330 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product comprising a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of performing the data multidimensional analysis method for insurance industry provided by the above methods, the method comprising: acquiring all dimension information of risk analysis in the insurance industry, wherein the dimension information comprises dimension categories and dimension level information corresponding to the dimension categories; acquiring all index information of risk measurement in the insurance industry, wherein the index information comprises index categories and index coding information corresponding to the index categories; correlating the dimension information and the index information with entity table relations in a database through a knowledge graph, and constructing a fact table based on correlation results; based on the dimension information, the index information and the fact table, establishing a metadata model, wherein the metadata model is an entity relation model; inputting dimension information and index information to be risk analyzed into the metadata model to obtain a fact table with minimum query cost; and obtaining an optimal query path meeting the query condition and a query result corresponding to the optimal query path based on the fact table with the minimum query cost.
In yet another aspect, the present invention further provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a data multidimensional analysis method for insurance industry provided by the above methods, the method comprising: acquiring all dimension information of risk analysis in the insurance industry, wherein the dimension information comprises dimension categories and dimension level information corresponding to the dimension categories; acquiring all index information of risk measurement in the insurance industry, wherein the index information comprises index categories and index coding information corresponding to the index categories; correlating the dimension information and the index information with entity table relations in a database through a knowledge graph, and constructing a fact table based on correlation results; based on the dimension information, the index information and the fact table, establishing a metadata model, wherein the metadata model is an entity relation model; inputting dimension information and index information to be risk analyzed into the metadata model to obtain a fact table with minimum query cost; and obtaining an optimal query path meeting the query condition and a query result corresponding to the optimal query path based on the fact table with the minimum query cost.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of multidimensional analysis of data for use in the insurance industry, comprising:
acquiring all dimension information of risk analysis in the insurance industry, wherein the dimension information comprises dimension categories and dimension level information corresponding to the dimension categories;
acquiring all index information of risk measurement in the insurance industry, wherein the index information comprises index categories and index coding information corresponding to the index categories;
correlating the dimension information and the index information with entity table relations in a database through a knowledge graph, and constructing a fact table based on correlation results;
based on the dimension information, the index information and the fact table, establishing a metadata model, wherein the metadata model is an entity relation model;
inputting dimension information and index information to be risk analyzed into the metadata model to obtain a fact table with minimum query cost; and obtaining an optimal query path meeting the query condition and a query result corresponding to the optimal query path based on the fact table with the minimum query cost.
2. The method for multidimensional analysis of data in an insurance industry according to claim 1, wherein the step of obtaining all dimension information of risk analysis in the insurance industry, before the dimension information includes a dimension category and dimension level information corresponding to the dimension category, includes:
unifying the codes of the dimension categories in the database, wherein one dimension category corresponds to the unique code;
storing the dimension category and the dimension category code into a dimension list, wherein the dimension list also comprises a dimension corresponding database table name, a dimension associated fact table name and a dimension description;
unifying the codes of the dimension levels in the database, wherein one dimension level corresponds to the unique code;
and storing the dimension level and the dimension level code into a level list, wherein the level list also comprises a database table name corresponding to the dimension level and an ID of the level list.
3. The method for multidimensional analysis of data in an insurance industry according to claim 1, wherein acquiring all index information of risk measured by the insurance industry, wherein before the index information includes an index category and index coding information corresponding to the index category, the method comprises:
uniformly coding index categories in a database, wherein one index category corresponds to a unique code;
storing the index category and the index category code into an index list, wherein the index list also comprises index common database table names, index units, whether indexes are calculated or not, and whether indexes are obtained or not.
4. The method for multidimensional analysis of data in insurance industry according to claim 1, wherein associating the dimensional information, the index information and the entity table relationship in the database by a knowledge graph, and constructing a fact table based on the association result comprises:
combining the dimension list and the hierarchy list corresponding to the dimension information, the index list corresponding to the index information and the entity table in the database according to different combination modes, and storing the combination in the corresponding fact table;
the fact table also comprises a fact table query cost, and the fact table accesses ip and ports; and the fact table query cost is calculated according to the fact table record number, the fact table gradient and the machine performance of the fact table.
5. The method for multidimensional analysis of data in insurance industry according to claim 1, wherein inputting dimension information and index information to be risk analyzed into the metadata model to obtain a fact table with minimum query cost comprises:
and searching a minimum cost fact table which can meet the aggregation condition of the dimension index in the metadata model.
6. The method for multidimensional analysis of data in insurance industry according to claim 1, wherein obtaining an optimal query path satisfying a query condition and a query result corresponding to the optimal query path based on the fact table with minimum query cost comprises:
the fact table is used as an entity table for inquiring, data statistics is carried out, and a multidimensional analysis result is returned.
7. A data multidimensional analysis device for use in the insurance industry, comprising:
the acquiring module is used for acquiring dimension information for risk analysis in the insurance industry, wherein the dimension information comprises dimension categories and dimension level information corresponding to the dimension categories;
the acquisition module is further used for acquiring all index information of the risk measurement in the insurance industry, wherein the index information comprises index categories and index coding information corresponding to the index categories;
the construction module is used for associating the dimension information, the index information and the entity table relation in the database through a knowledge graph and constructing a fact table based on the association result;
the construction module is further configured to establish a metadata model based on the dimension information, the index information and the fact table, where the metadata model is an entity relationship model;
the determining module is used for inputting dimension information and index information to be risk analyzed into the metadata model to obtain a fact table with minimum query cost;
the determining module is further configured to obtain an optimal query path that meets a query condition and a query result corresponding to the optimal query path based on the fact table with the minimum query cost.
8. The data multidimensional analysis device for the insurance industry of claim 7, wherein the determination module further includes:
and the multidimensional analysis engine module is used for inputting the dimensional information and the index information to be risk analyzed into the metadata model so as to obtain multidimensional analysis data query results.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements a data multidimensional analysis method for the insurance industry as claimed in any of claims 1 to 6 when the program is executed by the processor.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements a data multidimensional analysis method for the insurance industry as claimed in any of claims 1 to 6.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117709804A (en) * | 2024-02-05 | 2024-03-15 | 杭州研趣信息技术有限公司 | Index calculation method, device, equipment and medium based on block matrix |
| CN117709804B (en) * | 2024-02-05 | 2024-05-07 | 杭州研趣信息技术有限公司 | Index calculation method, device, equipment and medium based on block matrix |
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