CN112613853A - Data aggregation method and device, computer equipment and readable storage medium - Google Patents
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Abstract
The invention discloses a data collection method, which specifically comprises the following steps: when a data collection instruction is detected, target service data is obtained; screening out service data corresponding to a target service node from the target service data, wherein the service data corresponding to the target service node comprises inquiry data, policy data and/or invoice data; acquiring service rule configuration of a target service node; extracting target service elements with an affiliation from corresponding service data according to the service rule configuration of the target service node; and collecting the extracted target business elements. In addition, the invention also relates to a block chain technology.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a data collection method, a data collection device, computer equipment and a computer readable storage medium.
Background
The customer identification is the basis of customer operation analysis and service fine management. In the current large customer identification process, data such as customer group properties, stock control relationship, total separation relationship and the like are generally obtained through the industrial and commercial registration information, external investment information and the like disclosed by customers, the data are collected, cleaned, collected and analyzed by taking the data as a rule, and finally a mapping relationship between the large customer and a single legal customer is formed, and meanwhile, the mapping relationship can be used as a basis for further performing service analysis and management to refine the business management granularity of a company.
However, in an actual service management scenario, a user finds that due to the complexity of actual service development and the particularity of part of services, the mapping relationship between clients collected according to the rules is far from the actual situation, and the service fine management is hindered.
Aiming at the technical problems that the data collection is inaccurate in the existing collection scheme, and great obstacles are caused to user data analysis, service fine management and cost reduction, an effective solution is not provided at present.
Disclosure of Invention
The invention aims to provide a data collection method, a data collection device, computer equipment and a computer readable storage medium, which can solve the technical problems that data collection is inaccurate in the prior art, and great obstacles are caused to user data analysis, service fine management and cost reduction.
One aspect of the present invention provides a data aggregation method, including: when a data collection instruction is detected, target service data is obtained; screening out service data corresponding to a target service node from the target service data, wherein the service data corresponding to the target service node comprises inquiry data, policy data and/or invoice data; acquiring the service rule configuration of the target service node; extracting target service elements with an affiliation from corresponding service data according to the service rule configuration of the target service node; and collecting the extracted target service elements.
Optionally, the target service node includes a query node, the service data corresponding to the target service node includes query data, and the step of extracting the target service element having an affiliation from the corresponding service data according to the service rule configuration of the target service node includes: screening price inquiry data which completes the price quoted, belongs to a specific service type and belongs to a specific product type from the price inquiry data, and marking the price inquiry data as target price inquiry data; and extracting inquiry customer information from the target inquiry data as target main customer information, and extracting customer information belonging to the inquiry customers as target sub customer information.
Optionally, the target service node includes an policy node, the service data corresponding to the target service node includes policy data, and the step of extracting the target service element having an affiliation from the corresponding service data according to the service rule configuration of the target service node includes: screening out policy data which belong to a specific product type, have the policy quantity purchased by the same insuring client greater than or equal to a first threshold value and have the guaranteed client quantity belonging to the insuring client greater than or equal to a second threshold value from the policy data and recording the policy data as target policy data; and extracting the information of the insuring client from the target policy data as the information of the target main client, and extracting the information of the corresponding insured client as the information of the target sub-client.
Optionally, the target service node includes an invoice node, the service data corresponding to the target service node includes invoice data, and the step of extracting the target service element having an affiliation from the corresponding service data according to the service rule configuration of the target service node includes: and if the number of the invoice heads of the customers applying for invoicing is larger than or equal to a third threshold value, extracting the customer information from the invoice data and recording the customer information as target main customer information, and extracting the customer information corresponding to the invoice heads and recording the customer information as target sub-customer information, wherein the extracted customer with the invoice heads belongs to the customer applying for invoicing.
Optionally, the step of collecting the extracted target elements includes: generating a record according to each target host client information and the target sub client information related to the target host client information; traversing all target main client information, and combining records with repeated target client information; and/or traversing all the target sub-client information, and merging the records with the repeated target sub-client information.
Optionally, the step of merging the records in which the duplication target sub-client information exists includes: if the target sub-client information of any two records has an inclusion relationship, combining the two records; or if the coverage rate between the target sub-client information of any two records is larger than or equal to a fourth threshold value, combining the two records.
Optionally, before the step of acquiring the target service data when the data aggregation instruction is detected, the method further includes: and dividing the historical service data according to the service time and the preset time length of the historical service data to obtain N target service data, wherein N is an integer greater than or equal to 1.
Another aspect of the present invention provides a data aggregation apparatus, comprising: the first acquisition module is used for acquiring target service data when a data collection instruction is detected; the screening module is used for screening out service data corresponding to a target service node from the target service data, wherein the service data corresponding to the target service node comprises inquiry data, policy data and/or invoice data; the second acquisition module is used for acquiring the service rule configuration of the target service node; the extraction module is used for extracting the target business elements with the affiliation from the corresponding business data according to the business rule configuration of the target business node; and the collecting module is used for collecting the extracted target service elements.
Yet another aspect of the present invention provides a computer apparatus, comprising: the data aggregation system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the data aggregation method of any one of the above embodiments.
Yet another aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the data aggregation method of any of the above embodiments. Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The data aggregation method provided by the invention is not used for aggregating data according to the business registration information and the external investment information disclosed by the client as in the traditional scheme, the accuracy of the data aggregation result is lower due to inaccurate public information, but the data aggregation method is based on the actual business data and aggregates the business elements with the affiliation from the actual business data, and compared with the traditional data, the data aggregation accuracy is higher. Moreover, because the service data volume is huge, different service rule configurations are preset aiming at different nodes, and when the service elements are extracted, the corresponding service rule configurations are screened according to the service nodes, so that the service element extraction speed can be increased, and the data collection speed is increased. By the data collection method provided by the invention, the business management capability of enterprises is improved, the granularity of management is refined, the management cost and the labor cost are reduced, and the technical problems that the data collection is inaccurate, and great obstacles are caused to the user data analysis, the fine business management and the cost reduction in the prior art are solved.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 schematically shows a flow chart of a data aggregation method according to a first embodiment of the invention;
FIG. 2 schematically shows a block diagram of a data aggregation apparatus according to a second embodiment of the present invention;
fig. 3 schematically shows a block diagram of a computer device adapted to implement the data aggregation method according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Example one
The embodiment discloses a data collection method, which can be applied to electronic equipment such as a mobile phone, a notebook computer, a tablet computer or a desktop computer. As shown in fig. 1, fig. 1 schematically shows a flowchart of a data aggregation method according to a first embodiment of the present invention, where the data aggregation method may include steps S1 to S5, specifically:
and step S1, when the data collection instruction is detected, acquiring the target service data.
And data grouping means that the same data set is stored together.
The data collecting instruction is used for triggering the electronic device to execute the data collecting method, wherein the data collecting instruction can be sent by the electronic device at regular time, and can also be sent by a user through operating the electronic device, such as voice operation, touch operation or VR gesture operation.
The target service data may be service data counted according to time, such as yearly, quarterly, or monthly. For example, the target service data is all service data of 2019. The service data described in this embodiment may be insurance service data, and specifically may be life insurance service data, heavy insurance service data, vehicle insurance service data, endowment insurance service data, and the like.
Optionally, before step S1, the data aggregation method may further include:
and dividing the historical service data according to the service time and a preset time period of the historical service data to obtain N target service data, wherein N is an integer greater than or equal to 1.
The service time may be a creation time of the historical service data, an effective time of the historical service data, or an invalid time of the historical service data, and may be specifically selected according to an actual service requirement, which is not limited in this embodiment.
The preset time length may be a length in years, a length in quarters, a length in months, or the like, which is not limited in this embodiment.
For example, if the service time is a valid time, the preset time length is a length of year, and the valid time of the historical service data is 2010-2019, the historical service data 2010 may be divided together, the historical service data 2011 may be divided together, …, and the historical service data 2019 may be divided together. The divided annual historical business data can be called target business data.
Step S2, screening out service data corresponding to a target service node from the target service data, where the service data corresponding to the target service node includes price inquiry data, policy data and/or invoice data.
The target service data comprises a plurality of service nodes, such as an inquiry node, a policy node, an invoice node, a claim settlement node, a termination node and the like, wherein each service node corresponds to its own service data, and the service data corresponding to all the service nodes of the target service data form the target service data.
The target service node may be one or more of a plurality of service nodes comprised by the target service data. If the target service node is a claim settlement node, the service data corresponding to the target service node may be referred to as claim settlement data.
Step S3, obtaining the service rule configuration of the target service node.
In order to ensure data security, the business rule configuration may be stored in the blockchain in advance.
And step S4, extracting the target business element with the dependency relationship from the corresponding business data according to the business rule configuration of the target business node.
Each target service node corresponds to a preset service rule configuration, the service rule configuration is used for defining which condition the service element with the dependency relationship is met is extracted from service data corresponding to the target service node, namely the target service element is extracted, wherein the service element can be client information, price information and/or vehicle information. Taking the target business element as the target client information as an example, the target business element with the dependency relationship is, for example: and a star-to-home and a star-to-theater, wherein the star-to-home and the star-to-theater are both affiliated with the star-to-home group.
The present embodiment takes a target business element as an example of target customer information, and details step S4 with reference to the following three schemes, where the target customer information may include target primary customer information and target secondary customer information, and these customer information may include customer name, customer location, customer identity, and the like. In addition, the following three schemes may exist alone or in combination with each other.
Scheme one
The target service node includes a query node, the service data corresponding to the target service node includes query data, and step S4 may include step a1 and step a2, where:
step A1, screening the price inquiry data which is completed, belongs to the specific service type and belongs to the specific product type from the price inquiry data, and recording as the target price inquiry data.
Step A2, extracting inquiry customer information from the target inquiry data as target main customer information, and extracting customer information belonging to the inquiry customers as target sub customer information.
And (4) making a quote, wherein the quote is inquired and then the inquired party returns a specific price. The service type can comprise a common inquiry type and a general inquiry type, wherein the common inquiry type refers to the inquiry of a single client; the general price inquiry type refers to inquiring prices together for a plurality of related clients. For the data of the general inquiry type, general marks are preset, and which inquiry data belong to the general inquiry type can be determined through the general marks. The specific service type may be a predetermined service type, such as a general inquiry type. Accordingly, the specific product type may also be a predetermined product type, for example, a product type under a benefit-and-benefit business. The specific service type and the specific product type may be set according to actual service requirements, and the above embodiments are only for explanation and do not play a limiting role.
The price inquiring customer information may be transaction customer information such as customer information for purchasing a policy. The customer information associated with the inquiring customer may be the subsidiaries of the inquiring customer. If the customers to be inquired are the group, the customers related to the customers to be inquired can be the supermarkets, the properties, the cinemas and the like under the group.
In order to facilitate uniform management, after the inquiry customer information and the customer information belonging to the inquiry customer are extracted, the inquiry customer information can be used as target main customer information and the customer information belonging to the inquiry customer can be used as target sub-customer information, wherein the target main customer information and the target sub-customer information have an association relationship.
The following is a concrete example analysis.
For example, from all price inquiring data corresponding to price inquiring nodes, price inquiring data meeting the following three conditions is screened out as target price inquiring data: the system comprises inquiry data for completing the quote, inquiry data containing the general mark and inquiry data belonging to the type of the general service product. Further, inquiring customer information in the target inquiring data is extracted as target main customer information, customer information belonging to the inquiring customer is extracted as target sub customer information, and then the extracted target main customer information, the target sub customer information and the association relationship of the target main customer information and the target sub customer information are gathered into a temporary group customer list.
Scheme two
The target service node includes a policy node, the service data corresponding to the target service node includes policy data, and step S4 may include step B1 and step B2, where:
and step B1, screening out the policy data which belongs to the specific product type, has the policy quantity purchased by the same insuring client more than or equal to a first threshold value and has the guaranteed client quantity belonging to the insuring client more than or equal to a second threshold value, and recording the policy data as target policy data.
And step B2, extracting the information of the insuring client from the target policy data as the information of the target main client, and extracting the information of the corresponding insured client as the information of the target sub-client.
In this embodiment, the salesman can initiate multiple price enquiries for the same price enquiring client, each price enquiry corresponds to one price enquiry number, and each price enquiry number corresponds to one price enquiring client. When the inquiry clients successfully purchase the insurance policy, the inquiry clients are also called as insurance clients, and the inquiry clients corresponding to the inquiry numbers are also called as insurance clients corresponding to the inquiry numbers. That is, step B1 may be: and screening out policy data which belong to a specific product type, have the related policy quantity of the same inquiry number greater than or equal to a first threshold value and have the related guaranteed customer quantity of the inquiry number greater than or equal to a second threshold value from the policy data, and recording the policy data as target policy data.
The target policy data may include data for a plurality of policies, each policy may correspond to an insuring client and an insured client, each insuring client may associate a plurality of insured clients by purchasing a plurality of policies, and accordingly, each insured client may associate a plurality of insuring clients by a plurality of policies.
In order to facilitate uniform management, after extracting the insuring client information and the insured client information, the insuring client information can be used as target main client information and the insured client information can be used as target sub-client information, wherein the target main client information and the target sub-client information have an association relationship.
The following is a concrete example analysis.
For example, from all insurance data corresponding to the insurance node, insurance data satisfying the following three conditions is screened out as target insurance data: insurance data which belongs to the type of the benefits-closing business products, the number of the insurance policies purchased by the same insurance client is more than or equal to 2, and the number of the insured clients affiliated to the insurance client is more than or equal to 2. Further, extracting insuring client information in the target insurance data as target main client information, extracting insured client information as target sub-client information, and then converging the extracted target main client information, the target sub-client information and the incidence relation of the target main client information and the target sub-client information into a temporary group client list.
Scheme three
The target service node includes an invoice node, the service data corresponding to the target service node includes invoice data, and step S4 may include:
and if the number of the invoice heads of the customers applying for invoicing is larger than or equal to a third threshold value, extracting the customer information from the invoice data and recording the customer information as target main customer information, and extracting the customer information corresponding to the invoice heads and recording the customer information as target sub-customer information, wherein the extracted customer with the invoice heads belongs to the customer applying for invoicing.
Specifically, in all invoice data corresponding to the invoice nodes, whether the number of new heads of the invoices corresponding to any client applying for invoicing is larger than or equal to a third threshold value or not is judged, if yes, the information of the client is extracted to be the target main client information, and the client information corresponding to the new heads of the invoices is extracted to be the target sub-client information. And then the extracted target main customer information, target sub customer information and the incidence relation of the two can be converged into a temporary group customer list.
It should be noted that, in order to ensure the accuracy and normalization of the extracted target service element field, after the target service data is obtained, all service elements in the target service data may be subjected to standard processing in advance, such as unified de-spacing, full half-angle and odd character processing, to standardize a unified recognition standard.
And step S5, collecting the extracted target service elements.
In this embodiment, the target service elements extracted by any one or more of the first scheme, the second scheme and the third scheme may be collected.
Alternatively, step S5 may include step S51 and step S52, or step S51 and step S53, or step S51 to step S53, in which:
in step S51, a record is generated based on each target host client information and its associated target sub-client information.
Step S52, traverse all target host client information, merge records with duplicate target client information.
Step S53, traverse all the target sub-client information, merge the records with the repeated target sub-client information.
In this embodiment, when collecting the data, a record may be generated for each piece of target host client information and its associated target sub-client information, and then the generated records may be stored. For example, each target main customer information and its associated target sub-customer information may be obtained from a temporary group customer list, and then a record may be generated based on the obtained information. For each record, canonical word processing may be performed, e.g., the standard fields of each record are as follows: the target client number, the target main client information and the target sub-client information, wherein: the target client number is null; if the target main customer information is null, manual maintenance can be carried out; the target sub-client information contains information of all clients associated with the target main client information. And in the target main customer information and the target sub-customer information related to the target main customer information, all the target sub-customers belong to the target main customer. In addition, in order to ensure the tidiness of the data, the generated records can be subjected to deduplication processing, specifically, records in which duplicate target client information exists can be merged, and/or records in which duplicate target sub-client information exists can be merged. Target customer numbers in the deduplicated records can then be automatically generated, and the complete records are further used as a large customer pool for subsequent business analysis.
Further, since one target main client information may be associated with a plurality of target sub client information, the method of step S531 or step S532 may be performed when records in which there is duplicate target sub client information are merged, wherein:
step S531, if the target sub-client information of any two records has a containing relationship, merging the two records.
For example, if the target sub-client information of record 1 is A, B, C, D, E, F and the target sub-client information of record 2 is A, B, C, the target sub-client information of record 1 includes the target sub-client information of record 2, and record 1 and record 2 can be merged.
In step S532, if the coverage between the target sub-client information of any two records is greater than or equal to the fourth threshold, the two records are merged.
For example, if the fourth threshold is 50%, the target sub-client information of record 1 is A, B, C, D, E, F, G, and the target sub-client information of record 3 is A, B, C, D, the coverage ratio of the target sub-client information of record 1 and the target sub-client information of record 2 is 50%, and record 1 and record 3 may be merged.
Note that, in order to ensure data security, the deduplicated records may be stored in the blockchain.
The invention sets different business rule attributes for the business data of different nodes from the actual business, and can automatically configure and filter target business elements according to the business rules and collect the business elements when data collection is needed, thereby improving the business management capability of enterprises, refining the granularity of management and reducing the management cost and the labor cost. In a specific example, taking a target service element as a client as an example, setting a plurality of screening mechanisms meeting service requirements, establishing a logical relationship between a main client and a sub-client, and definitely subdividing the logical relationship into a total hierarchy, a branch hierarchy, an organization hierarchy and the like to provide a data basis for further service fine management, process management, quality management and continuous guarantee management, so that systematization and fine management are realized. Meanwhile, in view of the particularity of the scene, namely the diversity of the sub-clients, a merging mechanism is further arranged, and the neatness and the accuracy of data collection are further improved.
Example two
The second embodiment of the present invention provides a data aggregation device, which corresponds to the first embodiment, and corresponding technical features and technical effects are not described in detail in this embodiment, and reference may be made to the first embodiment for related points. Specifically, fig. 2 schematically shows a block diagram of a data aggregation apparatus according to a second embodiment of the present invention, and as shown in fig. 2, the data aggregation apparatus 200 may include a first obtaining module 201, a screening module 202, a second obtaining module 203, an extracting module 204, and an aggregation module 205, where:
a first obtaining module 201, configured to obtain target service data when a data aggregation instruction is detected;
a screening module 202, configured to screen out service data corresponding to a target service node from the target service data, where the service data corresponding to the target service node includes inquiry data, policy data, and/or invoice data;
a second obtaining module 203, configured to obtain a service rule configuration of the target service node;
an extracting module 204, configured to extract a target service element having an affiliation from corresponding service data according to the service rule configuration of the target service node;
a collecting module 205, configured to collect the extracted target business elements.
Optionally, the target service node includes a query node, the service data corresponding to the target service node includes query data, and the extracting module is further configured to: screening price inquiry data which completes the price quoted, belongs to a specific service type and belongs to a specific product type from the price inquiry data, and marking the price inquiry data as target price inquiry data; and extracting inquiry customer information from the target inquiry data as target main customer information, and extracting customer information belonging to the inquiry customers as target sub customer information.
Optionally, the target service node includes an policy node, the service data corresponding to the target service node includes policy data, and the extraction module is further configured to: screening out policy data which belong to a specific product type, have the policy quantity purchased by the same insuring client greater than or equal to a first threshold value and have the guaranteed client quantity belonging to the insuring client greater than or equal to a second threshold value from the policy data and recording the policy data as target policy data; and extracting the information of the insuring client from the target policy data as the information of the target main client, and extracting the information of the corresponding insured client as the information of the target sub-client.
Optionally, the target service node includes an invoice node, the service data corresponding to the target service node includes invoice data, and the extraction module is further configured to: and if the number of the invoice heads of the customers applying for invoicing is larger than or equal to a third threshold value, extracting the customer information from the invoice data and recording the customer information as target main customer information, and extracting the customer information corresponding to the invoice heads and recording the customer information as target sub-customer information, wherein the extracted customer with the invoice heads belongs to the customer applying for invoicing.
Optionally, the collection module is further configured to: generating a record according to each target host client information and the target sub client information related to the target host client information; traversing all target main client information, and combining records with repeated target client information; and/or traversing all the target sub-client information, and merging the records with the repeated target sub-client information.
Optionally, the collecting module, when executing the step of merging records in which the repeat target sub-client information exists, is further configured to: combining any two records under the condition that the target sub-client information of the two records has an inclusion relationship; or, in the case that the coverage rate between the target sub-client information of any two records is greater than or equal to the fourth threshold, the two records are merged.
Optionally, the apparatus further comprises: and the dividing module is used for dividing the historical service data according to the service time and the preset time length of the historical service data to obtain N target service data before the step of obtaining the target service data when the data collection instruction is detected, wherein N is an integer greater than or equal to 1.
EXAMPLE III
Fig. 3 schematically shows a block diagram of a computer device adapted to implement the data aggregation method according to a third embodiment of the present invention. In this embodiment, the computer device 300 may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server, or a rack server (including an independent server or a server cluster composed of a plurality of servers), and the like that execute programs. As shown in fig. 3, the computer device 300 of the present embodiment includes at least but is not limited to: a memory 301, a processor 302, a network interface 303, which may be communicatively coupled to each other via a system bus. It is noted that FIG. 3 only shows computer device 300 having components 301 and 303, but it is understood that not all of the shown components are required and that more or fewer components may be implemented instead.
In this embodiment, the memory 303 includes at least one type of computer-readable storage medium, which includes flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 301 may be an internal storage unit of the computer device 300, such as a hard disk or a memory of the computer device 300. In other embodiments, the memory 301 may also be an external storage device of the computer device 300, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 300. Of course, the memory 301 may also include both internal and external storage devices for the computer device 300. In the present embodiment, the memory 301 is generally used for storing an operating system installed in the computer device 300 and various types of application software, such as program codes of a data aggregation method, and the like. In addition, the memory 301 may also be used to temporarily store various types of data that have been output or are to be output.
In this embodiment, the data aggregation method stored in the memory 301 may be further divided into one or more program modules and executed by one or more processors (in this embodiment, the processor 302) to complete the present invention.
The network interface 303 may comprise a wireless network interface or a wired network interface, and the network interface 303 is typically used to establish communication links between the computer device 300 and other computer devices. For example, the network interface 303 is used to connect the computer device 300 to an external terminal via a network, establish a data transmission channel and a communication link between the computer device 300 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), or Wi-Fi.
Example four
The fourth embodiment further provides a computer-readable storage medium, including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, which when executed by a processor implements a data aggregation method. Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
It will be apparent to those skilled in the art that the modules or steps of the embodiments of the invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, embodiments of the invention are not limited to any specific combination of hardware and software.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method of data aggregation, the method comprising:
when a data collection instruction is detected, target service data is obtained;
screening out service data corresponding to a target service node from the target service data, wherein the service data corresponding to the target service node comprises inquiry data, policy data and/or invoice data;
acquiring the service rule configuration of the target service node;
extracting target service elements with an affiliation from corresponding service data according to the service rule configuration of the target service node;
and collecting the extracted target service elements.
2. The method of claim 1, wherein the target service node comprises a query node, wherein the service data corresponding to the target service node comprises query data, and wherein extracting the target service element with an affiliation from the corresponding service data according to the service rule configuration of the target service node comprises:
screening price inquiry data which completes the price quoted, belongs to a specific service type and belongs to a specific product type from the price inquiry data, and marking the price inquiry data as target price inquiry data;
and extracting inquiry customer information from the target inquiry data as target main customer information, and extracting customer information belonging to the inquiry customers as target sub customer information.
3. The method according to claim 1, wherein the target service node comprises a policy node, the service data corresponding to the target service node comprises policy data, and the step of extracting the target service element with affiliation from the corresponding service data according to the service rule configuration of the target service node comprises:
screening out policy data which belong to a specific product type, have the policy quantity purchased by the same insuring client greater than or equal to a first threshold value and have the guaranteed client quantity belonging to the insuring client greater than or equal to a second threshold value from the policy data and recording the policy data as target policy data;
and extracting the information of the insuring client from the target policy data as the information of the target main client, and extracting the information of the corresponding insured client as the information of the target sub-client.
4. The method according to claim 1, wherein the target service node comprises an invoice node, the service data corresponding to the target service node comprises invoice data, and the step of extracting the target service element with affiliation from the corresponding service data according to the service rule configuration of the target service node comprises:
and if the number of the invoice heads of the customers applying for invoicing is larger than or equal to a third threshold value, extracting the customer information from the invoice data and recording the customer information as target main customer information, and extracting the customer information corresponding to the invoice heads and recording the customer information as target sub-customer information, wherein the extracted customer with the invoice heads belongs to the customer applying for invoicing.
5. The method according to any one of claims 2 to 4, wherein the step of collecting the extracted target elements comprises:
generating a record according to each target host client information and the target sub client information related to the target host client information; and
traversing all target main client information, and combining records with repeated target client information; and/or
And traversing all the target sub-client information, and combining the records with the repeated target sub-client information.
6. The method of claim 5, wherein the step of merging records for which duplicate target sub-client information exists comprises:
if the target sub-client information of any two records has an inclusion relationship, combining the two records; or
And if the coverage rate between the target sub-client information of any two records is larger than or equal to a fourth threshold value, merging the two records.
7. The method of claim 1, wherein before the step of obtaining target business data upon detecting a data aggregation instruction, the method further comprises:
and dividing the historical service data according to the service time and the preset time length of the historical service data to obtain N target service data, wherein N is an integer greater than or equal to 1.
8. A data aggregation apparatus, the apparatus comprising:
the first acquisition module is used for acquiring target service data when a data collection instruction is detected;
the screening module is used for screening out service data corresponding to a target service node from the target service data, wherein the service data corresponding to the target service node comprises inquiry data, policy data and/or invoice data;
the second acquisition module is used for acquiring the service rule configuration of the target service node;
the extraction module is used for extracting the target business elements with the affiliation from the corresponding business data according to the business rule configuration of the target business node;
and the collecting module is used for collecting the extracted target service elements.
9. A computer device, the computer device comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1 to 7.
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