CN112364004B - Data warehouse-based policy data processing method, device and storage medium - Google Patents
Data warehouse-based policy data processing method, device and storage medium Download PDFInfo
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Abstract
The present invention relates to the field of data processing technologies, and in particular, to a policy data processing method and apparatus based on a data warehouse, and a storage medium. According to the processing method, the index to be analyzed is calculated based on the policy data analysis request, the service calculation data obtained through calculation is generated into the fact table, the dimension is generated into the dimension table, and then the association relation between the fact table and the dimension table is established, so that the generated policy association data table can be called by the touch platform, the data consistency of the calculation task, the policy data model and the touch platform is ensured, the policy data model is stored in the form of the policy association data table, the data extraction speed is improved, and the space occupation of data is reduced.
Description
[ field of technology ]
The present invention relates to the field of data processing technologies, and in particular, to a policy data processing method and apparatus based on a data warehouse, and a storage medium.
[ background Art ]
In the prior art, the data of a computing task, a policy data model and a touch platform lack of consistency, the label pool data of the touch platform depend on a policy image model wide table, service data is derived from a database, related service data is required to be called from the database when the computing task is executed, the data in the policy image model wide table is not source data but is data obtained through computation or statistics, fields in the policy image model wide table are continuously increased along with the increase of the service labels, repeated comparison fields of data query are increased, and when policy data analysis is required, data required by modeling of the policy data model are obtained from the policy image model wide table, and the data speed is slow to pull the data from the policy image model wide table due to the large data quantity of the policy image model wide table. Moreover, due to the lack of consistency of data of the computing tasks, the policy data model and the touch platform, redundant storage of data is caused.
[ invention ]
The invention aims to provide a data warehouse-based policy data processing method, a data warehouse-based policy data processing device and a storage medium, so as to solve the technical problem of lack of consistency of data in the prior art.
The technical scheme of the invention is as follows: provided is a policy data processing method based on a data warehouse, comprising:
receiving a policy data analysis request sent by a user side, wherein the policy data analysis request comprises policy data model information and data calling information, the policy data model information comprises indexes to be analyzed and dimensions, and the data calling information comprises a service database identifier and a service data identifier;
acquiring service data matched with the service data identification from a service database matched with the service database identification according to the data calling information;
calculating or counting the index to be analyzed according to the service data to obtain service calculation data matched with the dimension;
carrying out data modeling on the service calculation data according to the policy data model information, generating a fact table and a dimension table, and establishing an association relation between the fact table and the dimension table to obtain a policy association data table matched with the policy data model information;
And sending the policy association data table to the user side so that the user side displays the policy association data table according to a preset display form.
Preferably, the policy data model information further includes a model structure, and the model structure is a constellation model; the step of carrying out data modeling on the service calculation data according to the policy data model information, generating a fact table and a dimension table, and establishing an association relation between the fact table and the dimension table to obtain a policy association data table matched with the policy data model information, comprising the following steps:
dividing the index to be analyzed into at least two types;
for each type, establishing a fact table according to service calculation data of indexes to be analyzed of the type and the dimension, wherein the indexes to be analyzed are primary key fields of the fact table, and the dimension is a foreign key field of the fact table;
establishing a dimension table for each dimension, wherein the dimension is a primary key field of the dimension table;
and connecting the foreign key field of the fact table with the corresponding primary key field of the dimension table, so as to establish the association relation between the fact table and the dimension table, and obtain a policy association data table matched with the policy data model.
Preferably, the data warehouse comprises an application layer, an intermediate layer and a base layer, wherein the intermediate layer comprises an integration layer and a summary layer;
the step of calculating or counting the index to be analyzed according to the service data to obtain service calculation data matched with the dimension, and the step of further comprises the following steps:
calculating or counting the index to be analyzed according to the dimension according to the service data to obtain service calculation data;
storing the business calculation data in the base layer, wherein the business calculation data is stored in a data table form;
the step of carrying out data modeling on the service calculation data according to the policy data model information, generating a fact table and a dimension table, and establishing an association relation between the fact table and the dimension table to obtain a policy association data table matched with the policy data model information, comprising the following steps:
the service calculation data is sent to the integration layer, and cleaning, transcoding and field name standardization processing are carried out on the service calculation data through the integration layer, so that processed service calculation data is generated;
transmitting the processed service calculation data to the summarization layer, generating a fact table and a dimension table by the summarization layer based on the policy data model information, and establishing an association relation for the fact table and the dimension table to obtain a policy association data table matched with the policy data model;
And sending the policy association data table to an application layer.
Preferably, after the sending the policy association data table to the application layer, the method further includes:
receiving a policy data query request sent by a user side, wherein the policy data query request comprises a policy data model, and the policy data model comprises indexes and dimensions;
searching a policy associated data table matched with the policy data model in an application layer of the data warehouse, wherein the policy associated data table comprises a fact table and a dimension table which have an association relation;
when the application layer does not have the policy associated data table matched with the policy data model, searching a fact table matched with the index and a dimension table matched with the dimension in a summarization layer of the data warehouse;
when the dimension exists in the fact table, connecting a foreign key field of the dimension in the fact table with a primary key field of a corresponding dimension in the dimension table, and establishing an association relationship between the fact table and the dimension table to obtain a policy association data table matched with the policy data model;
and when the dimension does not exist in the fact table, writing the dimension into the fact table as an external key, connecting an external key field of the dimension in the fact table with a main key field of the corresponding dimension in the dimension table, and establishing an association relationship between the fact table and the dimension table to obtain a policy association data table matched with the policy data model.
Preferably, after the sending the policy association data table to the application layer, the method further includes:
acquiring image information to be analyzed, wherein the image information to be analyzed comprises a plurality of labels;
establishing a wide table according to the portrait information to be analyzed, wherein fields of the wide table respectively correspond to labels in the portrait information to be analyzed;
searching a fact table matched with the tag in a summary layer of the data warehouse, wherein the tag corresponds to an index of the fact table;
and updating the measurement value of the index in the fact table to the row of the corresponding field matched with the index in the wide table, and outputting the wide table as the image to be analyzed after updating is finished.
Preferably, after the sending the policy association data table to the application layer, the method further includes:
obtaining calculation results of all calculation tasks, and storing the calculation results in the base layer, wherein the calculation results are stored in a data table form;
sending the calculation result to the summarizing layer, and generating a first fact table through the summarizing layer, wherein an index corresponding to the calculation result is a main key of the first fact table;
Receiving a policy data query request sent by a user side, wherein the policy data query request comprises a policy data model, and the policy data model comprises indexes and dimensions;
searching a first fact table matched with the index and a dimension table matched with the dimension in a summarizing layer of the data warehouse;
and writing the dimension into the first fact table as an external key, connecting an external key field of the dimension in the first fact table with a main key field of the corresponding dimension in the dimension table, and establishing an association relationship between the first fact table and the dimension table to obtain a policy association data table matched with the policy data model.
Preferably, before receiving the policy data analysis request sent by the user side, the method further includes:
receiving a policy data analysis instruction input by a user at a user side, wherein the policy data analysis instruction comprises an index to be analyzed and a dimension;
generating policy data model information according to the index to be analyzed and the dimension;
determining an algorithm of the index to be analyzed according to the index to be analyzed, determining service data to be called according to the algorithm, and generating data calling information of the service data to be called;
And generating and sending the policy data analysis request according to the policy data model information and the data call information.
The other technical scheme of the invention is as follows: there is provided a policy data processing apparatus based on a data warehouse, comprising:
the system comprises a request receiving module, a request processing module and a service data processing module, wherein the request receiving module is used for receiving a policy data analysis request sent by a user side, the policy data analysis request comprises policy data model information and data calling information, the policy data model information comprises indexes to be analyzed and dimensions, and the data calling information comprises a service database identifier and a service data identifier;
the data acquisition module is used for acquiring service data matched with the service data identifier from a service database matched with the service database identifier according to the data calling information;
the calculation module is used for calculating or counting the index to be analyzed according to the service data to obtain service calculation data matched with the dimension;
the analysis module is used for carrying out data modeling on the service calculation data according to the policy data model information, generating a fact table and a dimension table, and establishing an association relation between the fact table and the dimension table to obtain a policy association data table matched with the policy data model;
And the feedback display module is used for sending the policy association data table to the user side so that the user side displays the policy association data table according to a preset display form.
The other technical scheme of the invention is as follows: there is provided a data warehouse-based policy data processing apparatus comprising a processor, and a memory coupled to the processor, the memory storing program instructions; the processor is configured to execute the program instructions stored in the memory to perform the data warehouse-based policy data processing method described above.
The other technical scheme of the invention is as follows: there is provided a storage medium storing program instructions which, when executed by a processor, implement the above-described data warehouse-based policy data processing method.
The invention has the beneficial effects that: according to the processing method, the index to be analyzed is calculated based on the policy data analysis request, the service calculation data obtained through calculation is generated into the fact table, the dimension is generated into the dimension table, and then the association relation between the fact table and the dimension table is established, so that the generated policy association data table can be called by the touch platform, the data consistency of the calculation task, the policy data model and the touch platform is ensured, the policy data model is stored in the form of the policy association data table, the data extraction speed is improved, and the space occupation of data is reduced.
[ description of the drawings ]
FIG. 1 is a flowchart of a policy data processing method based on a data warehouse according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a policy data processing method based on a data warehouse according to a second embodiment of the present invention;
FIG. 3 is a flowchart of a policy data processing method based on a data warehouse according to a third embodiment of the present invention;
FIG. 4 is a flowchart of a policy data processing method based on a data warehouse according to a fourth embodiment of the present invention;
FIG. 5 is a schematic diagram of a policy data processing device based on a data warehouse according to a fifth embodiment of the present invention;
FIG. 6 is a schematic diagram of a data warehouse-based policy data processing apparatus according to a sixth embodiment of the present invention;
fig. 7 is a schematic structural diagram of a storage medium according to a seventh embodiment of the present invention.
[ detailed description ] of the invention
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. 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 terms "first," "second," "third," and the like in this disclosure are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", and "a third" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise. All directional indications (such as up, down, left, right, front, back … …) in embodiments of the present invention are merely used to explain the relative positional relationship, movement, etc. between the components in a particular gesture (as shown in the drawings), and if the particular gesture changes, the directional indication changes accordingly. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Fig. 1 is a flowchart of a policy data processing method based on a data warehouse according to a first embodiment of the present invention. It should be noted that, if there are substantially the same results, the method of the present invention is not limited to the flow sequence shown in fig. 1. As shown in fig. 1, the policy data processing method based on the data warehouse includes the steps of:
s101, receiving a policy data analysis request sent by a user side, wherein the policy data analysis request comprises policy data model information and data calling information, the policy data model information comprises indexes and dimensions to be analyzed, and the data calling information comprises a service database identifier and a service data identifier.
In this embodiment, the policy data model information is used to characterize a policy data model to be constructed, and in order to facilitate understanding, the policy data model is introduced first, where the policy data model is a database model based on a fact table and a dimension table, which is constructed to meet the requirement of a user for data analysis from multiple angles and multiple levels, and the policy data model information further includes a model structure, for example, a constellation model for dimension modeling may be used to perform data modeling on service calculation data representing an index to be analyzed, which is obtained through calculation, according to dimensions.
In this embodiment, the model structure may include a star model, a snowflake model, and a constellation model. The index to be analyzed is generally a value with practical meaning, and the index to be analyzed can include an insurance information index, a user behavior index and a service index, for example, the insurance information index can include a policy renewal probability, a pay-by-date insurance rate and a claim risk assessment value, the user behavior index can include a telemarketing acceptance, an AI intelligent outbound fee acceptance and an owned policy number, and the service index can include an insurance sales increase rate, an A product sales amount and a B product claim rate. The dimension (also referred to as dimension) is the angle from which the data is viewed, and may include time, place, product type, etc. Based on policy data analysis purposes, it is determined which dimensions to analyze and which metrics to measure. For example, taking the policy renewal as an example, the policy renewal status can be analyzed from different dimensions such as product type, customer occupation, area where the customer is located, insurance agent, etc., while the policy renewal quantity and the policy renewal rate can intuitively reflect the policy renewal status, and the policy renewal quantity and the policy renewal rate can be selected as the indexes to be analyzed for measurement.
In this embodiment, the index to be analyzed needs to be calculated or counted according to the service data, for example, the warranty renewal rate, and the ratio of the number of the warranties of the products to the total number of the warranties of the products needs to be calculated. Thus, the policy data analysis requires the invocation of service data, the service database identifier is used for characterizing a database in which the service data to be invoked is stored, the service database identifier includes a database name and a database server name, and the service data identifier includes a service data name and a policy type or a policy number. The service database comprises a Oracle, mysql, mpp, hbase, hive, HDFS relational or non-relational database. In the application scenario of the embodiment, the service data may be stored in the same service database, for example, the service data of each product required for performing policy renewal probability calculation is stored in the K1 database; of course, the service data may be stored in different service databases, for example, the service data of the health class policy is stored in the K2 database, the service data of the investment classification policy is stored in the K3 database, the service data of the health class policy needs to be called from the K2 database to calculate the renewal probability of each policy respectively, and the service data of the investment classification policy needs to be called from the K3 database to calculate the renewal probability of each policy respectively.
S102, acquiring service data matched with the service data identification from a service database matched with the service database identification according to the data calling information.
In step S102, a service database matching with the service database identifier is connected according to the service database identifier, and service data matching with the service data identifier is queried and obtained in the service database according to the service data identifier.
And S103, calculating or counting the index to be analyzed according to the service data to obtain service calculation data matched with the dimension.
In step S103, for each index to be analyzed, the corresponding service data is substituted into the algorithm of the index to calculate, so as to obtain service calculation data for measuring the index. For example, the index to be analyzed comprises a policy renewal probability and an AI intelligent outbound fee acceptance, and service data required for calculating the policy renewal rate is substituted into the policy renewal probability algorithm aiming at the policy renewal probability to obtain service calculation data for measuring the policy renewal rate; and substituting the service data required by calculating the AI intelligent outbound fee acceptances into an AI intelligent outbound fee acceptances algorithm aiming at the AI intelligent outbound fee acceptances to obtain service calculation data for measuring the AI intelligent outbound fee acceptances.
S104, carrying out data modeling on the service calculation data according to the policy data model information, generating a fact table and a dimension table, and establishing an association relation between the fact table and the dimension table to obtain a policy association data table matched with the policy data model information.
In step S104, the table in which the index to be analyzed is located is referred to as a fact table. The fact table is used to record specific events, contains specific elements of each event, and specific occurrences. The key feature of the fact table is to include numerical data (facts) that can be statistically summarized to provide information about policy state changes or business state changes. The fact data stored in the fact table typically contains a large number of data rows. A table containing dimensions is called a dimension table, which is descriptive information of elements of an event in a fact table. The dimension table contains characteristics describing fact records in the fact table. The specific description information of the dimension is recorded in the dimension table, and the dimension attribute in the fact table is only one key associated with the dimension table, and no specific information is recorded. Of course, there may also be different levels of detail for a particular angle (i.e., a dimension) of the observation data, the different levels of detail for these dimensions being at the level of the dimension. One dimension often has multiple levels. For example, when describing the time dimension, it may be described from different levels of month, quarter, year, etc., then month, quarter, year, etc. are the levels of the time dimension.
In step S104, in the present embodiment, the policy data model is a third-paradigm relationship model, where the third paradigm (Third Normal Form, abbreviated as 3 NF) refers to that all data elements in the table are not only uniquely identified by the primary key, but also must be independent from each other, and no other functional relationship exists between them.
Taking a constellation model as an example, describing the step S104 in detail, wherein the constellation model belongs to the expansion of a star model or a snowflake model, and is constructed based on a plurality of fact tables, and a connection relationship is established between the fact table 1 and the dimension table 1; dimension table 1 and fact table 2 may establish a connection relationship.
The step S104 specifically includes: s1041, dividing the index to be analyzed into at least two types; s1042, for each type, establishing a fact table according to the business calculation data of the index and the dimension, wherein the index is a primary key field of the fact table, and the dimension is a foreign key field of the fact table; s1043, establishing a dimension table for each dimension, wherein the dimension is a primary key field of the dimension table; s1044, connecting the foreign key field of the fact table with the corresponding primary key field of the dimension table, thereby establishing the association relationship between the fact table and the dimension table to obtain a policy association data table matched with the policy data model.
The following description will be made with a specific case, for example, the index includes a policy renewal probability, pay on schedule a policy, a risk assessment value for a claim, a telemarketing acceptance, an AI intelligent outbound fee acceptance, a policy possession amount, an insurance sales increase rate, a product sales amount, and a product claim rate; dimensions include product type, region, customer occupation, and agent. In step S1041, the multiple indexes are divided into insurance information types including policy renewal probability, pay-to-date policy rates, and claim risk assessment values, user behavior types including telemarketing acceptance, AI intelligent outbound fee acceptance, and number of owning policies, and business parameter types including insurance sales increase rate, product sales amount, and product claim rate. In step S1042, a first fact table is established for the insurance information type, wherein three fields including the policy renewal probability, the on-demand payment of the policy, and the risk assessment value of claims are used as the primary keys, and four fields including the product type, the region, the customer occupation, and the agent are used as the foreign keys; a second fact table is established aiming at the user behavior type, three fields of the telephone marketing acceptance, the AI intelligent outbound fee acceptance and the number of the warranty owners are used as a main key, and four fields of the product type, the region, the client occupation and the agent are used as an external key; and a third fact table is established for the service parameter type, three fields of insurance sales increase rate, product sales amount and product odds are used as a main key, and four fields of product type, region, customer occupation and agent are used as external keys. In step S1042, a first dimension table, a second dimension table, a third dimension table and a fourth dimension table are respectively established for the product type, the region, the client occupation and the agent, wherein the product type is the primary key of the first dimension table, the region is the primary key of the second dimension table, the client occupation is the primary key of the third dimension table, and the agent is the primary key of the fourth dimension table. In step S1044, an association relationship is established between the first fact table and the first, second, third and fourth dimension tables, respectively, between the second fact table and the first, second, third and fourth dimension tables, respectively, and between the third fact table and the first, second, third and fourth dimension tables, respectively.
S105, the policy association data table is sent to the user side, so that the user side displays the policy association data table according to a preset display form.
In the embodiment, a calculation task is directly generated to acquire service calculation data of an index to be analyzed when the policy data analysis is performed, a policy data model is established and then is called by a touch platform, the data consistency of the calculation task, the policy data model and the touch platform is ensured, the policy data model is stored in a form of a policy associated data table, the data extraction speed is improved, and the space occupation of data is reduced; fact tables in the policy association data table may also be used for modeling of other policy data models.
In an optional embodiment, step S101 further includes a step of generating a policy data analysis request:
s001, receiving a policy data analysis instruction input by a user at a user terminal, wherein the policy data analysis instruction comprises an index to be analyzed and a dimension;
s002, generating policy data model information according to the index to be analyzed and the dimension;
s003, determining an algorithm of the index to be analyzed according to the index to be analyzed, determining service data to be called according to the algorithm, and generating data call information of the service data to be called;
S004, generating and sending the policy data analysis request according to the policy data model information and the data call information.
Step S001 to step S004 of the present embodiment are applied to a user terminal, where the user needs to generate a policy data analysis request based on the policy data analysis and send the policy data analysis request to the server, where the user terminal may be an electronic device, and the user terminal is connected to the server in a wireless manner.
In another alternative embodiment, the data warehouse includes an application layer (APP layer), an intermediate layer (ODS layer) including an integration layer (DWD layer) and a summary layer (DWA layer); then, in step S103, calculating or counting the index to be analyzed according to the dimension according to the service data, to obtain service calculation data; and storing the service calculation data in the base layer, wherein the service calculation data is stored in the form of a data table.
In step S104, the service calculation data is sent to the integration layer, and the service calculation data is cleaned, transcoded and normalized by the integration layer, so as to generate processed service calculation data; transmitting the processed service calculation data to the summarization layer, generating a fact table and a dimension table by the summarization layer based on the policy data model information, and establishing an association relation for the fact table and the dimension table to obtain a policy association data table matched with the policy data model; and sending the policy association data table to an application layer.
In another optional implementation manner, the policy data processing method of the present embodiment may be further used to perform policy contract management, and establish a policy contract model, where the dimension table includes time, place, customer source and agent where each service node occurs, and the fact table includes detail facts including basic information of an insurance contract, receipt and payment details of the insurance contract, labeling information of the insurance contract, detailed information of the insurance contract, attachment information of the insurance contract, and approval information of the insurance contract.
Fig. 2 is a flowchart of a policy data processing method based on a data warehouse according to a second embodiment of the present invention. It should be noted that, if there are substantially the same results, the method of the present invention is not limited to the flow sequence shown in fig. 2. As shown in fig. 2, the policy data processing method based on the data warehouse includes the steps of:
s201, receiving a policy data analysis request sent by a user side, wherein the policy data analysis request comprises policy data model information and data calling information, the policy data model information comprises indexes and dimensions to be analyzed, and the data calling information comprises a service database identifier and a service data identifier.
S202, acquiring service data matched with the service data identification from a service database matched with the service database identification according to the data calling information.
And S203, calculating or counting the index to be analyzed according to the service data to obtain service calculation data matched with the dimension.
S204, carrying out data modeling on the service calculation data according to the policy data model information, generating a fact table and a dimension table, and establishing an association relation between the fact table and the dimension table to obtain a policy association data table matched with the policy data model information.
S205, the policy association data table is sent to the user side, so that the user side displays the policy association data table according to a preset display form.
Steps S201 to S205 correspond to steps S101 to S105 of the first embodiment, respectively, see the description of the first embodiment in detail.
S206, receiving a policy data query request sent by a user terminal, wherein the policy data query request comprises a policy data model, and the policy data model comprises indexes and dimensions.
S207, searching a policy association data table matched with the policy data model in an application layer of the data warehouse, wherein the policy association data table comprises a fact table and a dimension table with association relation.
And S208, when the policy associated data table matched with the policy data model does not exist in the application layer, searching a fact table matched with the index and a dimension table matched with the dimension in a summary layer of the data warehouse.
And S209, when the dimension exists in the fact table, connecting a foreign key field in which the dimension exists in the fact table with a primary key field in which the corresponding dimension exists in the dimension table, and establishing an association relationship between the fact table and the dimension table to obtain a policy association data table matched with the policy data model.
And S210, when the dimension does not exist in the fact table, writing the dimension into the fact table as an external key, connecting an external key field of the dimension in the fact table with a main key field of a corresponding dimension in the dimension table, and establishing an association relationship between the fact table and the dimension table to obtain a policy association data table matched with the policy data model.
The difference between this embodiment and the first embodiment is that the method further includes a step of querying policy data, in steps S206 to S210, the policy data may be queried according to a policy data model, and the query result is a policy association data table composed of a fact table and a dimension table with association relation, and when the fact table and the dimension table corresponding to the policy data model have established association relation, the policy association data table may be directly obtained from the application layer; when the association relation between the fact table and the dimension table corresponding to the policy data model is not established, the fact table and the dimension table corresponding to the policy data model are acquired first, and then the acquired fact table and dimension table are output after the association relation is established.
Fig. 3 is a flowchart of a policy data processing method based on a data warehouse according to a third embodiment of the present invention. It should be noted that, if there are substantially the same results, the method of the present invention is not limited to the flow sequence shown in fig. 3. As shown in fig. 3, the policy data processing method based on the data warehouse includes the steps of:
s301, receiving a policy data analysis request sent by a user side, wherein the policy data analysis request comprises policy data model information and data calling information, the policy data model information comprises indexes and dimensions to be analyzed, and the data calling information comprises a service database identifier and a service data identifier.
S302, acquiring service data matched with the service data identification from a service database matched with the service database identification according to the data calling information.
And S303, calculating or counting the index to be analyzed according to the service data to obtain service calculation data matched with the dimension.
S304, carrying out data modeling on the service calculation data according to the policy data model information, generating a fact table and a dimension table, and establishing an association relation between the fact table and the dimension table to obtain a policy association data table matched with the policy data model information.
S305, the policy association data table is sent to the user side, so that the user side displays the policy association data table according to a preset display form.
Steps S301 to S305 correspond to steps S101 to S105 of the first embodiment, respectively, specifically, see the description of the first embodiment.
S306, obtaining image information to be analyzed, wherein the image information to be analyzed comprises a plurality of labels.
S307, a wide table is established according to the portrait information to be analyzed, wherein fields of the wide table respectively correspond to labels in the portrait information to be analyzed.
S308, searching a fact table matched with the tag in a summary layer of the data warehouse, wherein the tag corresponds to an index of the fact table.
S309, updating the measurement value of the index in the fact table to the row of the corresponding field matched with the index in the wide table, and outputting the wide table as the image to be analyzed after updating.
The difference between this embodiment and the first embodiment is that the method further includes a step of generating an image to be analyzed, in steps S306 to S309, according to each tag in the image information to be analyzed, a fact table matched with the tag is searched in the data warehouse, the fact table obtained by searching is used to update the wide table, the fact table is directly used to establish the image to be analyzed, and the generation speed of the policy image is increased without calling source data. In the embodiment, the image to be analyzed can be a policy image, so that the data consistency of a computing task, a policy data model, a touch platform and the policy image is further realized, and the policy image is generated more quickly.
Further, after step S309, the following steps are further included:
s310, uploading the image to be analyzed to a blockchain, so that the blockchain stores the image to be analyzed in an encrypted mode.
In step S310, corresponding digest information is obtained based on the image to be analyzed, specifically, the digest information is obtained by performing a hash process on the image to be analyzed, for example, by using a sha256S algorithm. Uploading summary information to the blockchain can ensure its security and fair transparency to the user. The user device may download the summary information from the blockchain to verify whether the representation to be analyzed has been tampered with. The blockchain referred to in this example is a novel mode of application for computer technology such as distributed data storage, point-to-point transmission, consensus mechanisms, encryption algorithms, 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.
Fig. 4 is a flowchart of a policy data processing method based on a data warehouse according to a fourth embodiment of the present invention. It should be noted that, if there are substantially the same results, the method of the present invention is not limited to the flow sequence shown in fig. 4. As shown in fig. 4, the policy data processing method based on the data warehouse includes the steps of:
s401, receiving a policy data analysis request sent by a user side, wherein the policy data analysis request comprises policy data model information and data calling information, the policy data model information comprises indexes and dimensions to be analyzed, and the data calling information comprises a service database identifier and a service data identifier.
S402, acquiring service data matched with the service data identification from a service database matched with the service database identification according to the data calling information.
S403, calculating or counting the index to be analyzed according to the service data to obtain service calculation data matched with the dimension.
S404, carrying out data modeling on the service calculation data according to the policy data model information, generating a fact table and a dimension table, and establishing an association relation for the fact table and the dimension table to obtain a policy association data table matched with the policy data model information.
S405, the policy association data table is sent to the user side, so that the user side displays the policy association data table according to a preset display form.
Steps S401 to S405 correspond to steps S101 to S105 of the first embodiment, respectively, specifically, see the description of the first embodiment.
S406, obtaining calculation results of all calculation tasks, and storing the calculation results in the base layer, wherein the calculation results are stored in a data table form.
S407, sending the calculation result to the summarizing layer, and generating a first fact table through the summarizing layer, wherein an index corresponding to the calculation result is a primary key of the first fact table.
S408, receiving a policy data query request sent by a user terminal, wherein the policy data query request comprises a policy data model, and the policy data model comprises indexes and dimensions.
And S409, searching a first fact table matched with the index and a dimension table matched with the dimension in a summarization layer of the data warehouse.
And S410, writing the dimension into the first fact table as an external key, connecting an external key field of the dimension in the first fact table with a main key field of the corresponding dimension in the dimension table, and establishing an association relationship between the first fact table and the dimension table to obtain a policy association data table matched with the policy data model.
The difference between this embodiment and the first embodiment is that the method further includes a result processing step of calculating the task, and in steps S406 to S410, after the calculation task is completed, a first fact table is generated and stored, where the first fact table is free of dimension foreign keys; and then matching the first event table with a pre-established dimension table according to the policy data model, adding a dimension external key in the first event table, and establishing an association relationship between the first event table and the dimension table so as to accelerate the policy data query speed. In this embodiment, when there is no insurance data modeling requirement, a fact table is built for the settlement result of the calculation task for subsequent inquiry or modeling call, so as to further realize the data consistency of the calculation task, the policy data model and the touch platform.
Fig. 5 is a schematic structural diagram of a policy data processing device based on a data warehouse according to a fifth embodiment of the present invention. As shown in fig. 5, the data warehouse-based policy data processing apparatus 50 includes a request receiving module 51, a data obtaining module 52, a calculating module 53, an analyzing module 54 and a feedback displaying module 55, where the request receiving module 51 is configured to receive a policy data analysis request sent by a user side, where the policy data analysis request includes policy data model information and data call information, the policy data model information includes an index to be analyzed and a dimension, and the data call information includes a service database identifier and a service data identifier; a data acquisition module 52, configured to acquire, according to the data call information, service data matched with the service data identifier from a service database matched with the service database identifier; the calculating module 53 is configured to calculate or count the index to be analyzed according to the service data, so as to obtain service calculation data matched with the dimension; the analysis module 54 is configured to perform data modeling on the service calculation data according to the policy data model information, generate a fact table and a dimension table, and establish an association relationship between the fact table and the dimension table, so as to obtain a policy association data table matched with the policy data model information; and the feedback display module 55 is configured to send the policy association data table to the user side, so that the user side displays the policy association data table according to a preset display form.
Further, the policy data model information further includes a model structure, and the analysis module 54 is configured to divide the index to be analyzed into at least two types; for each type, establishing a fact table according to service calculation data of indexes to be analyzed of the type and the dimension, wherein the indexes to be analyzed are primary key fields of the fact table, and the dimension is a foreign key field of the fact table; establishing a dimension table for each dimension, wherein the dimension is a primary key field of the dimension table; and connecting the foreign key field of the fact table with the corresponding primary key field of the dimension table, and establishing an association relationship between the fact table and the dimension table to obtain a policy association data table matched with the policy data model.
Further, the data warehouse-based policy data processing device 50 further includes a request generation module, configured to receive a policy data analysis instruction input by a user at a user end, where the policy data analysis instruction includes an index to be analyzed and a dimension; generating policy data model information according to the index to be analyzed and the dimension; determining an algorithm of the index to be analyzed according to the index to be analyzed, determining service data to be called according to the algorithm, and generating data calling information of the service data to be called; and generating and sending the policy data analysis request according to the policy data model information and the data call information.
Further, the data warehouse-based policy data processing device 50 further includes a query module, configured to receive a policy data query request sent by a user, where the policy data query request includes a policy data model, and the policy data model includes an index and a dimension; searching a policy associated data table matched with the policy data model in an application layer of the data warehouse, wherein the policy associated data table comprises a fact table and a dimension table which have an association relation; when the application layer does not have the policy associated data table matched with the policy data model, searching a fact table matched with the index and a dimension table matched with the dimension in a summarization layer of the data warehouse; when the dimension exists in the fact table, connecting a foreign key field of the dimension in the fact table with a primary key field of a corresponding dimension in the dimension table, and establishing an association relationship between the fact table and the dimension table to obtain a policy association data table matched with the policy data model; and when the dimension does not exist in the fact table, writing the dimension into the fact table as an external key, connecting an external key field of the dimension in the fact table with a main key field of the corresponding dimension in the dimension table, and establishing an association relationship between the fact table and the dimension table to obtain a policy association data table matched with the policy data model.
Further, the data warehouse-based policy data processing device 50 further includes a to-be-analyzed portrait generation module, configured to obtain to-be-analyzed portrait information, where the to-be-analyzed portrait information includes a plurality of tags; establishing a wide table according to the portrait information to be analyzed, wherein fields of the wide table respectively correspond to labels in the portrait information to be analyzed; searching a fact table matched with the tag in a summary layer of the data warehouse, wherein the tag corresponds to an index of the fact table; and updating the measurement value of the index in the fact table to the row of the corresponding field matched with the index in the wide table, and outputting the wide table as the image to be analyzed after updating is finished.
Further, the data warehouse-based policy data processing device 50 further includes a calculation result processing module, configured to obtain a calculation result of each calculation task, and store the calculation result in the base layer, where the calculation result is stored in a form of a data table; sending the calculation result to the summarizing layer, and generating a first fact table through the summarizing layer, wherein an index corresponding to the calculation result is a main key of the first fact table; receiving a policy data query request sent by a user side, wherein the policy data query request comprises a policy data model, and the policy data model comprises indexes and dimensions; searching a first fact table matched with the index and a dimension table matched with the dimension in a summarizing layer of the data warehouse; and writing the dimension into the first fact table as an external key, connecting an external key field of the dimension in the first fact table with a main key field of the corresponding dimension in the dimension table, and establishing an association relationship between the first fact table and the dimension table to obtain a policy association data table matched with the policy data model.
Fig. 6 is a schematic structural diagram of a policy data processing device based on a data warehouse according to a sixth embodiment of the present invention. As shown in fig. 6, the data warehouse-based policy data processing device 60 includes a processor 61 and a memory 62 coupled to the processor 61.
The memory 62 stores program instructions for implementing the data warehouse-based policy data processing method of any of the embodiments described above.
The processor 61 is configured to execute program instructions stored in the memory 62 for policy data processing based on the data warehouse.
The processor 61 may also be referred to as a CPU (Central Processing Unit ). The processor 61 may be an integrated circuit chip with signal processing capabilities. Processor 61 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a storage medium according to a seventh embodiment of the present invention. The storage medium according to the embodiment of the present invention stores a program instruction 71 capable of implementing all the policy data processing methods based on the data repository, where the program instruction 71 may be stored in the storage medium in the form of a software product, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the methods 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, an optical disk, or other various media capable of storing program codes, or a terminal device such as a computer, a server, a mobile phone, a tablet, or the like.
In the several embodiments provided in the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
In addition, each functional unit 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 may be implemented in hardware or in software functional units. The foregoing is only the embodiments of the present invention, and the patent scope of the invention is not limited thereto, but is also covered by the patent protection scope of the invention, as long as the equivalent structures or equivalent processes of the present invention and the contents of the accompanying drawings are changed, or the present invention is directly or indirectly applied to other related technical fields.
While the invention has been described with respect to the above embodiments, it should be noted that modifications can be made by those skilled in the art without departing from the inventive concept, and these are all within the scope of the invention.
Claims (10)
1. A policy data processing method based on a data warehouse, comprising:
receiving a policy data analysis request sent by a user side, wherein the policy data analysis request comprises policy data model information and data calling information, the policy data model information comprises indexes to be analyzed and dimensions, and the data calling information comprises a service database identifier and a service data identifier;
acquiring service data matched with the service data identification from a service database matched with the service database identification according to the data calling information;
calculating or counting the index to be analyzed according to the service data to obtain service calculation data matched with the dimension;
carrying out data modeling on the service calculation data according to the policy data model information, generating a fact table and a dimension table, and establishing an association relation between the fact table and the dimension table to obtain a policy association data table matched with the policy data model information;
The policy association data table is sent to the user side, so that the user side displays the policy association data table according to a preset display form;
the policy data model information also comprises a model structure, wherein the model structure is a constellation model; the step of carrying out data modeling on the service calculation data according to the policy data model information, generating a fact table and a dimension table, and establishing an association relation between the fact table and the dimension table to obtain a policy association data table matched with the policy data model information, comprising the following steps:
dividing the index to be analyzed into at least two types;
for each type, establishing a fact table according to service calculation data of indexes to be analyzed of the type and the dimension, wherein the indexes to be analyzed are primary key fields of the fact table, and the dimension is a foreign key field of the fact table;
establishing a dimension table for each dimension, wherein the dimension is a primary key field of the dimension table;
and connecting the foreign key field of the fact table with the corresponding primary key field of the dimension table, and establishing an association relationship between the fact table and the dimension table to obtain a policy association data table matched with the policy data model.
2. The method for processing policy data based on data warehouse as defined in claim 1, further comprising, before receiving the policy data analysis request sent by the user side:
receiving a policy data analysis instruction input by a user at a user side, wherein the policy data analysis instruction comprises an index to be analyzed and a dimension;
generating policy data model information according to the index to be analyzed and the dimension;
determining an algorithm of the index to be analyzed according to the index to be analyzed, determining service data to be called according to the algorithm, and generating data calling information of the service data to be called;
and generating and sending the policy data analysis request according to the policy data model information and the data call information.
3. A policy data processing method based on a data warehouse, comprising:
receiving a policy data analysis request sent by a user side, wherein the policy data analysis request comprises policy data model information and data calling information, the policy data model information comprises indexes to be analyzed and dimensions, and the data calling information comprises a service database identifier and a service data identifier;
Acquiring service data matched with the service data identification from a service database matched with the service database identification according to the data calling information;
calculating or counting the index to be analyzed according to the service data to obtain service calculation data matched with the dimension;
carrying out data modeling on the service calculation data according to the policy data model information, generating a fact table and a dimension table, and establishing an association relation between the fact table and the dimension table to obtain a policy association data table matched with the policy data model information;
the policy association data table is sent to the user side, so that the user side displays the policy association data table according to a preset display form;
the data warehouse comprises an application layer, an intermediate layer and a base layer, wherein the intermediate layer comprises an integration layer and a summarization layer;
the step of calculating or counting the index to be analyzed according to the service data to obtain service calculation data matched with the dimension, and the step of further comprises the following steps:
storing the business calculation data in the base layer, wherein the business calculation data is stored in a data table form;
The step of carrying out data modeling on the service calculation data according to the policy data model information, generating a fact table and a dimension table, and establishing an association relation between the fact table and the dimension table to obtain a policy association data table matched with the policy data model information, comprising the following steps:
the service calculation data is sent to the integration layer, and cleaning, transcoding and field name standardization processing are carried out on the service calculation data through the integration layer, so that processed service calculation data is generated;
transmitting the processed service calculation data to the summarization layer, generating a fact table and a dimension table by the summarization layer based on the policy data model information, and establishing an association relation for the fact table and the dimension table to obtain a policy association data table matched with the policy data model;
and sending the policy association data table to an application layer.
4. A data warehouse-based policy data processing method as defined in claim 3, wherein after said sending the policy-associated data table to the application layer, further comprising:
receiving a policy data query request sent by a user side, wherein the policy data query request comprises a policy data model, and the policy data model comprises indexes and dimensions;
Searching a policy associated data table matched with the policy data model in an application layer of the data warehouse, wherein the policy associated data table comprises a fact table and a dimension table which have an association relation;
when the application layer does not have the policy associated data table matched with the policy data model, searching a fact table matched with the index and a dimension table matched with the dimension in a summarization layer of the data warehouse;
when the dimension exists in the fact table, connecting a foreign key field of the dimension in the fact table with a primary key field of a corresponding dimension in the dimension table, and establishing an association relationship between the fact table and the dimension table to obtain a policy association data table matched with the policy data model;
and when the dimension does not exist in the fact table, writing the dimension into the fact table as an external key, connecting an external key field of the dimension in the fact table with a main key field of the corresponding dimension in the dimension table, and establishing an association relationship between the fact table and the dimension table to obtain a policy association data table matched with the policy data model.
5. A data warehouse-based policy data processing method as defined in claim 3, wherein after said sending the policy-associated data table to the application layer, further comprising:
Acquiring image information to be analyzed, wherein the image information to be analyzed comprises a plurality of labels;
establishing a wide table according to the portrait information to be analyzed, wherein fields of the wide table respectively correspond to labels in the portrait information to be analyzed;
searching a fact table matched with the tag in a summary layer of the data warehouse, wherein the tag corresponds to an index of the fact table;
and updating the measurement value of the index in the fact table to the row of the corresponding field matched with the index in the wide table, and outputting the wide table as the image to be analyzed after updating is finished.
6. A data warehouse-based policy data processing method as defined in claim 3, wherein after said sending the policy-associated data table to the application layer, further comprising:
obtaining calculation results of all calculation tasks, and storing the calculation results in the base layer, wherein the calculation results are stored in a data table form;
sending the calculation result to the summarizing layer, and generating a first fact table through the summarizing layer, wherein an index corresponding to the calculation result is a main key of the first fact table;
Receiving a policy data query request sent by a user side, wherein the policy data query request comprises a policy data model, and the policy data model comprises indexes and dimensions;
searching a first fact table matched with the index and a dimension table matched with the dimension in a summarizing layer of the data warehouse;
and writing the dimension into the first fact table as an external key, connecting an external key field of the dimension in the first fact table with a main key field of the corresponding dimension in the dimension table, and establishing an association relationship between the first fact table and the dimension table to obtain a policy association data table matched with the policy data model.
7. A method for processing policy data based on a data warehouse as defined in claim 3, wherein before receiving the policy data analysis request sent by the user side, the method further comprises:
receiving a policy data analysis instruction input by a user at a user side, wherein the policy data analysis instruction comprises an index to be analyzed and a dimension;
generating policy data model information according to the index to be analyzed and the dimension;
determining an algorithm of the index to be analyzed according to the index to be analyzed, determining service data to be called according to the algorithm, and generating data calling information of the service data to be called;
And generating and sending the policy data analysis request according to the policy data model information and the data call information.
8. A data warehouse-based policy data processing apparatus for implementing the data warehouse-based policy data processing method as claimed in any one of claims 1 to 7, the data warehouse-based policy data processing apparatus comprising:
the system comprises a request receiving module, a request processing module and a service data processing module, wherein the request receiving module is used for receiving a policy data analysis request sent by a user side, the policy data analysis request comprises policy data model information and data calling information, the policy data model information comprises indexes to be analyzed and dimensions, and the data calling information comprises a service database identifier and a service data identifier;
the data acquisition module is used for acquiring service data matched with the service data identifier from a service database matched with the service database identifier according to the data calling information;
the calculation module is used for calculating or counting the index to be analyzed according to the service data to obtain service calculation data matched with the dimension;
the analysis module is used for carrying out data modeling on the service calculation data according to the policy data model information, generating a fact table and a dimension table, establishing an association relation between the fact table and the dimension table, and obtaining a policy association data table matched with the policy data model information;
And the feedback display module is used for sending the policy association data table to the user side so that the user side displays the policy association data table according to a preset display form.
9. A data warehouse-based policy data processing apparatus, the apparatus comprising a processor, and a memory coupled to the processor, the memory storing program instructions; the processor is configured to execute the program instructions stored by the memory to perform the data warehouse-based policy data processing method of any one of claims 1 to 7.
10. A storage medium storing program instructions which, when executed by a processor, implement the data warehouse-based policy data processing method of any one of claims 1 to 7.
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| CN113254544B (en) * | 2021-04-29 | 2023-01-03 | 西安交通大学 | Data processing device and method based on dimension modeling |
| CN113780716A (en) * | 2021-07-09 | 2021-12-10 | 北京鼎立保险经纪有限责任公司 | Business machine account generation system |
| CN113297184B (en) * | 2021-07-23 | 2021-12-28 | 北京达佳互联信息技术有限公司 | Data processing method, device, equipment and computer readable storage medium |
| CN113704365A (en) * | 2021-08-24 | 2021-11-26 | 北京明略昭辉科技有限公司 | Method, system, device and storage medium for intelligently dividing data subjects |
| CN113901004B (en) * | 2021-10-27 | 2023-06-02 | 江苏瑞中数据股份有限公司 | A method for complex real-time calculation of big data in data centralization |
| CN114218309A (en) * | 2021-11-04 | 2022-03-22 | 招银云创信息技术有限公司 | Data processing method, system and computer equipment |
| CN114996368A (en) * | 2022-06-13 | 2022-09-02 | 深圳市和讯华谷信息技术有限公司 | Basic data model processing method based on time series characteristics and related device |
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