CN116860805A - Data processing method, device, computer equipment and storage medium - Google Patents
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Abstract
The embodiment of the application belongs to the field of big data, and relates to a data processing method, which comprises the following steps: judging whether a data calculation request triggered by a user is received or not; if yes, acquiring the current time; determining a data acquisition strategy corresponding to the current time, and acquiring basic service data corresponding to a preset service type based on the data acquisition strategy; marking the basic service data to obtain marked target data; acquiring a target calculation rule corresponding to target data from a preset rule database; and calculating the target data based on the target calculation rule to generate calculation data corresponding to the target data. The application also provides a data processing device, computer equipment and a storage medium. In addition, the present application relates to blockchain technology in which computational data can be stored. The application can realize automatic, rapid and accurate calculation processing of the acquired service data based on the data acquisition strategy and the rule database.
Description
Technical Field
The present application relates to the field of big data technology and the field of financial technology, and in particular, to a data processing method, a data processing device, a computer device, and a storage medium.
Background
With the rapid development of internet finance, many financial and technological companies, such as insurance companies, develop a number of business systems for conducting business processes, such as insurance systems, banking systems, transaction systems, etc. For insurance systems, reinsurance contract computation is increasingly referred to as a common business. In the existing reinsurance contract calculation process, service personnel usually inquire related service data by themselves and then consult related calculation modes to manually count the service data so as to finish calculation of the reinsurance contract. However, this manual-based business data processing method requires a lot of manpower time, which results in low processing efficiency of business data.
Disclosure of Invention
The embodiment of the application aims to provide a data processing method, a device, computer equipment and a storage medium, so as to solve the technical problem that the existing manual-based business data processing mode consumes more manpower time, thereby causing low processing efficiency of business data.
In order to solve the above technical problems, an embodiment of the present application provides a data processing method, which adopts the following technical schemes:
Judging whether a data calculation request triggered by a user is received or not;
if yes, acquiring the current time;
determining a data acquisition strategy corresponding to the current time, and acquiring basic service data corresponding to a preset service type based on the data acquisition strategy;
marking the basic service data to obtain marked target data;
acquiring a target calculation rule corresponding to the target data from a preset rule database;
and carrying out calculation processing on the target data based on the target calculation rule, and generating calculation data corresponding to the target data.
Further, the step of determining a data acquisition policy corresponding to the current time and acquiring basic service data corresponding to a preset service type based on the data acquisition policy specifically includes:
judging whether the current time is a month knot time or not;
if the current time is the month time, determining a first data acquisition strategy corresponding to the month time;
acquiring basic service data corresponding to the preset service type based on the first data acquisition strategy;
if the current time is not the month ending time, determining a second data acquisition strategy corresponding to the current time;
And acquiring basic service data corresponding to the preset service type based on the second data acquisition strategy.
Further, the step of obtaining the target calculation rule corresponding to the target data from a preset rule database specifically includes:
acquiring tag data corresponding to the target data;
invoking the rule database;
inquiring the rule database based on the tag data, and inquiring a specified calculation rule corresponding to the tag data from the rule database;
and taking the specified calculation rule as the target calculation rule.
Further, before the step of acquiring the target calculation rule corresponding to the target data from the preset rule database, the method further includes:
receiving a rule configuration request triggered by a designated user;
displaying a preset rule configuration interface;
receiving rule configuration data input by the appointed user in the rule configuration interface;
generating corresponding calculation rules based on the rule configuration data;
and storing the calculation rule into the rule database.
Further, after the step of performing calculation processing on the target data based on the target calculation rule to generate calculation data corresponding to the target data, the method further includes:
Acquiring first address information of a service system;
generating corresponding accounting information based on the calculation data;
based on the first address information, sending the accounting information to the service system so as to perform accounting processing on the target data based on the accounting information through the service system and generate a corresponding accounting result;
receiving the accounting result returned by the service system;
and adjusting the calculation data based on the calculation result to obtain adjusted calculation data.
Further, after the step of performing calculation processing on the target data based on the target calculation rule to generate calculation data corresponding to the target data, the method further includes:
acquiring second address information of a service statistics system;
generating corresponding credential generation information based on the calculation data;
based on the second address information, the credential generation information is sent to the business statistics system to process the calculation data through the business statistics system to generate corresponding target credential data;
receiving the target credential data returned by the service statistics system;
and generating corresponding sign report data based on the target credential data.
Further, after the step of performing calculation processing on the target data based on the target calculation rule to generate calculation data corresponding to the target data, the method further includes:
acquiring tag data corresponding to the target data;
screening target storage sub-blocks matched with the tag data from a plurality of storage sub-blocks contained in a preset block chain;
packaging the target data and the calculation data to obtain packaged data;
and storing the packed data to the target storage sub-block.
In order to solve the above technical problems, the embodiment of the present application further provides a data processing apparatus, which adopts the following technical scheme:
the judging module is used for judging whether a data calculation request triggered by a user is received or not;
the first acquisition module is used for acquiring the current time if yes;
the second acquisition module is used for determining a data acquisition strategy corresponding to the current time and acquiring basic service data corresponding to a preset service type based on the data acquisition strategy;
the first processing module is used for performing marking processing on the basic service data to obtain marked target data;
The third acquisition module is used for acquiring a target calculation rule corresponding to the target data from a preset rule database;
and the second processing module is used for carrying out calculation processing on the target data based on the target calculation rule and generating calculation data corresponding to the target data.
In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:
judging whether a data calculation request triggered by a user is received or not;
if yes, acquiring the current time;
determining a data acquisition strategy corresponding to the current time, and acquiring basic service data corresponding to a preset service type based on the data acquisition strategy;
marking the basic service data to obtain marked target data;
acquiring a target calculation rule corresponding to the target data from a preset rule database;
and carrying out calculation processing on the target data based on the target calculation rule, and generating calculation data corresponding to the target data.
In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:
Judging whether a data calculation request triggered by a user is received or not;
if yes, acquiring the current time;
determining a data acquisition strategy corresponding to the current time, and acquiring basic service data corresponding to a preset service type based on the data acquisition strategy;
marking the basic service data to obtain marked target data;
acquiring a target calculation rule corresponding to the target data from a preset rule database;
and carrying out calculation processing on the target data based on the target calculation rule, and generating calculation data corresponding to the target data.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
the embodiment of the application firstly judges whether a data calculation request triggered by a user is received or not; if yes, acquiring the current time; then determining a data acquisition strategy corresponding to the current time, and acquiring basic service data corresponding to a preset service type based on the data acquisition strategy; then, marking the basic service data to obtain marked target data; subsequently, obtaining a target calculation rule corresponding to the target data from a preset rule database; and finally, calculating the target data based on the target calculation rule to generate calculation data corresponding to the target data. According to the embodiment of the application, after receiving the data calculation request triggered by the user, basic service data corresponding to the preset service type is automatically acquired based on the data acquisition strategy corresponding to the current time, after marking the basic service data to obtain target data, the target calculation rule corresponding to the target data is intelligently acquired from the preset rule database, and further calculation processing is performed on the target data based on the target calculation rule, so that the calculation data corresponding to the target data is rapidly and accurately generated. The embodiment of the application can automatically calculate the acquired service data based on the use of the data acquisition strategy and the rule database so as to quickly and accurately generate corresponding calculation data, effectively reduce the manual intervention processing data of the service, simplify the service operation flow and improve the processing efficiency of service data calculation.
Drawings
In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a data processing method according to the present application;
FIG. 3 is a schematic diagram of one embodiment of a data processing apparatus according to the present application;
FIG. 4 is a schematic structural diagram of one embodiment of a computer device in accordance with the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
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 application. 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.
In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the data processing method provided by the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the data processing apparatus is generally disposed in the server/terminal device.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow chart of one embodiment of a data processing method according to the present application is shown. The data processing method comprises the following steps:
step S201, determining whether a data calculation request triggered by a user is received.
In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the data processing method operates may acquire the data calculation request through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connection, wiFi connection, bluetooth connection, wiMAX connection, zigbee connection, UWB (ultra wideband) connection, and other now known or later developed wireless connection. The data processing method provided by the application can be applied to the service scene of reinsurance contract calculation in insurance service. The data calculation request may specifically be a request triggered by a user to perform data calculation on service data in the insurance system, such as reinsurance contract data. The reinsurance contract data may specifically be a reinsurance fee. Reinsurance is also known as "partial insurance". Based on the original insurance contract, the insurer performs insurance actions on other insurers through signing an insurance contract and carrying out partial risks and responsibilities of the insurance contract. The basis of reinsurance is original insurance, and the generation of reinsurance is based on the need of scattered risks in the operation of original insurers. In the reinsurance transaction, the business-separating company is called an original insurer or a business-separating company, and the business-receiving company is called a reinsurer or a business-separating recipient or a business-separating company. The reinsurance transfers the premium paid by the risk responsibility to pay the premium or reinsurance; the original insurer pays a certain fee in the process of soliciting business, and the fee paid to the original insurer by the reinsurer is called as sub-insurance commission or sub-insurance commission. Reinsurance can be categorized into proportional reinsurance and non-proportional reinsurance. The proportional reinsurance is that the original insurer and the reinsurer contract with reinsurer, and the responsibility is shared according to the insurance amount and the agreed proportion. For insurance business in appointed proportion, the original insurer has obligation and timely distributes, the reinsurer has obligation to accept, and both sides have no option. The proportional reinsurance is divided into a number reinsurance, a premium reinsurance, a number and a premium mixed reinsurance. Non-proportional reinsurance is classified as excess claim reinsurance and excess odds reinsurance.
Step S202, if yes, obtaining the current time.
In this embodiment, the current time may refer to current date data, at least as accurate as a unit of day.
Step S203, determining a data acquisition policy corresponding to the current time, and acquiring basic service data corresponding to a preset service type based on the data acquisition policy.
In this embodiment, the foregoing determining the data acquisition policy corresponding to the current time, and acquiring, based on the data acquisition policy, a specific implementation process of the basic service data corresponding to the preset service type, which will be described in further detail in the following specific embodiments, which will not be described herein.
And step S204, marking the basic service data to obtain marked target data.
In this embodiment, the marking process may be an automatic machine marking process. The label types of the marking process can include new, present, transfer, international co-insurance, temporary, principal co-insurance, etc. The marking machine can be constructed and generated according to the actual marking requirements.
Step S205, obtaining a target calculation rule corresponding to the target data from a preset rule database.
In this embodiment, the specific implementation process of obtaining the target calculation rule corresponding to the target data from the preset rule database is described in further detail in the following specific embodiment, which is not described herein.
Step S206, performing calculation processing on the target data based on the target calculation rule, and generating calculation data corresponding to the target data.
In this embodiment, the target data may be filled into a calculation formula included in the target calculation rule based on the calculation formula to perform an operation, so as to generate calculation data corresponding to the target data.
Firstly, judging whether a data calculation request triggered by a user is received or not; if yes, acquiring the current time; then determining a data acquisition strategy corresponding to the current time, and acquiring basic service data corresponding to a preset service type based on the data acquisition strategy; then, marking the basic service data to obtain marked target data; subsequently, obtaining a target calculation rule corresponding to the target data from a preset rule database; and finally, calculating the target data based on the target calculation rule to generate calculation data corresponding to the target data. According to the method and the device, after a data calculation request triggered by a user is received, basic service data corresponding to a preset service type is automatically acquired based on a data acquisition strategy corresponding to the current time, after the basic service data is marked to obtain target data, target calculation rules corresponding to the target data are intelligently acquired from a preset rule database, and further calculation processing is carried out on the target data based on the target calculation rules, so that calculation data corresponding to the target data are rapidly and accurately generated. The application can automatically calculate the acquired service data based on the data acquisition strategy and the rule database so as to quickly and accurately generate corresponding calculation data, effectively reduce the manual intervention processing data of the service, simplify the service operation flow and improve the processing efficiency of service data calculation.
In some alternative implementations, step S203 includes the steps of:
and judging whether the current time is the month ending time or not.
In this embodiment, the monthly time may be a time point of settlement of the monthly business set according to the business requirement, and is generally set as the last day of the month.
And if the current time is the month time, determining a first data acquisition strategy corresponding to the month time.
In this embodiment, if the current time is the lunar time, the corresponding first data acquisition policy is: and acquiring business basic data corresponding to the preset business type from the data warehouse of the big data platform storing the business data.
And acquiring basic service data corresponding to the preset service type based on the first data acquisition strategy.
In this embodiment, the above-mentioned big data platform data warehouse may be queried to obtain therefrom basic service data corresponding to the preset service type in the time period of the month first arrival T-1 of the current month.
And if the current time is not the month ending time, determining a second data acquisition strategy corresponding to the current time.
In this embodiment, if the current time is not the month node time, the corresponding second data acquisition policy is: and acquiring month knot adjustment data corresponding to the preset service type and the basic data of the previous day based on inquiring a big data platform data warehouse storing the service data.
And acquiring basic service data corresponding to the preset service type based on the second data acquisition strategy.
In this embodiment, the foregoing big data platform data repository may be queried to obtain, from the big data platform data repository, month adjustment data corresponding to the preset service type of the current month and base data of the previous day.
Judging whether the current time is the moon time or not; if the current time is the month time, determining a first data acquisition strategy corresponding to the month time; then, acquiring basic service data corresponding to the preset service type based on the first data acquisition strategy; if the current time is not the month ending time, determining a second data acquisition strategy corresponding to the current time; and acquiring basic service data corresponding to the preset service type based on the second data acquisition strategy. According to the application, the time of a data calculation request initiated by a user is analyzed to intelligently determine the data acquisition strategy corresponding to the current time, and the basic service data corresponding to the preset service type is acquired by utilizing the data acquisition strategy, so that the basic service data required to be calculated is rapidly and conveniently acquired, and the acquisition efficiency and the acquisition intelligence of the basic service data are improved.
In some alternative implementations of the present embodiment, step S205 includes the steps of:
and acquiring tag data corresponding to the target data.
In this embodiment, the label corresponding to the basic service data is generated by performing the marking process on the basic service data.
And calling the rule database.
In this embodiment, the rule database is a pre-constructed database storing calculation rules corresponding to various tag types.
And carrying out query processing on the rule database based on the tag data, and querying a specified calculation rule corresponding to the tag data from the rule database.
In this embodiment, a target tag that matches the tag data may be determined from the rule database, and then a specified calculation rule that matches the target tag may be queried from the rule database.
And taking the specified calculation rule as the target calculation rule.
The method comprises the steps of obtaining tag data corresponding to the target data; then calling the rule database; and then, carrying out query processing on the rule database based on the tag data, querying a specified calculation rule corresponding to the tag data from the rule database, and taking the specified calculation rule as the target calculation rule. According to the method and the device for obtaining the target calculation rule, the label data corresponding to the target data are obtained, so that the appointed calculation rule corresponding to the label data can be quickly and accurately inquired from the rule database by utilizing the label data, the required target calculation rule is obtained, and the obtaining efficiency and the obtaining intelligence of the target calculation rule are effectively improved.
In some alternative implementations, after step S205, the electronic device may further perform the following steps:
a rule configuration request is received specifying a user trigger.
In this embodiment, the specified user may be an operation and maintenance person. The configuration function of the calculation rule is preset to support the operation and maintenance personnel to configure the calculation index rule corresponding to different types of service data in the service.
And displaying a preset rule configuration interface.
In this embodiment, the rule configuration page is a page for configuring a calculation rule created according to an actual calculation rule construction service requirement.
And receiving rule configuration data input by the appointed user in the rule configuration interface.
In this embodiment, the rule configuration data may include a calculation formula. In particular, the rule configuration data may be a procedure matching process applied to the reinsurance contract business scenario. Specifically, the computation rules for contract matching are different from one another, for example: the number Mao Fei is configured according to dangerous seed or dangerous seed responsibility, the number Mao Fei is matched according to professional level, the age and sex social insurance is needed or part of special contracts are needed, the number unit insurance is matched with the responsibility insurance coverage ratio, the information acquisition rate such as age and sex is matched for carrying out the calculation of the sub-insurance policy, the overflow is matched with the self-insurance coverage and the maximum overflow according to the insurance coverage of the responsibility major class or the insurance coverage of the responsibility fine item, and the related sub-insurance configuration information is acquired; the calculation of the fractional share ratio is directly matched with the calculation of the fractional share ratio, the premium is required to judge the rate of the fractional share to be divided, the fractional share of the premium is calculated according to the contract, and the like.
And generating corresponding calculation rules based on the rule configuration data.
In this embodiment, the rule configuration data may be processed by calling a rule engine, and the calculation rule corresponding to the rule configuration data may be generated by integrating the rule configuration data input by the specified user and performing the rule configuration processing.
And storing the calculation rule into the rule database.
In this embodiment, an applicable service tag type corresponding to the calculation rule may be obtained, a data correspondence between the service tag type and the calculation rule may be regenerated, and the calculation rule may be stored in the rule database based on the data correspondence, so that the corresponding calculation rule may be quickly queried from the rule database based on the service tag.
The method and the device are characterized by receiving a rule configuration request triggered by a designated user; then displaying a preset rule configuration interface; then receiving rule configuration data input by the appointed user in the rule configuration interface; generating corresponding calculation rules based on the rule configuration data; and finally, storing the calculation rule into the rule database. After receiving the rule configuration request triggered by the user, the method and the device intelligently construct the corresponding calculation rule according to the rule configuration data input by the user in the rule configuration interface, so that the flexible configuration of the calculation rule according to the personal business requirement of the user is realized, the processing efficiency of the calculation rule configuration is effectively improved, the generation efficiency of the calculation rule is improved, and the use experience of the user is improved. In addition, the calculation rules are stored in a rule database, so that the intelligent storage of the calculation rules is realized, the required calculation rules can be quickly searched from the rule database later, and the efficiency of calculation rule extraction is improved.
In some alternative implementations, after step S206, the electronic device may further perform the following steps:
and acquiring first address information of the service system.
In this embodiment, the service system may be an insurance system, a banking system, a transaction system, an order system, and the like. The first address information refers to a communication address of the service system.
And generating corresponding accounting information based on the calculation data.
In this embodiment, an accounting information template may be obtained, and then the calculation data is filled into the accounting information template to generate corresponding accounting information. The accounting information template is constructed and generated according to actual data accounting requirements.
And sending the accounting information to the service system based on the first address information so as to perform accounting processing on the target data based on the accounting information through the service system and generate a corresponding accounting result.
In this embodiment, after receiving the accounting information, the service system invokes an internal accounting rule to perform accounting processing on the target data to generate a corresponding accounting result.
And receiving the accounting result returned by the service system.
In the present embodiment, the accounting result includes checking pass or checking fail.
And adjusting the calculation data based on the calculation result to obtain adjusted calculation data.
In this embodiment, if the calculation result is that the calculation is not passed, it indicates that the calculation data is erroneous, and the calculation data may be adjusted according to the calculation result to obtain adjusted normal calculation data. And if the calculation result is that the calculation is passed, the calculation data is indicated to be normal data, and the calculation data does not need to be processed.
The application obtains the first address information of the service system; then generating corresponding accounting information based on the calculation data; then, based on the first address information, the accounting information is sent to the service system, so that the service system performs accounting processing on the target data based on the accounting information, and a corresponding accounting result is generated; subsequently receiving the accounting result returned by the service system; and finally, adjusting the calculation data based on the accounting result to obtain adjusted calculation data. According to the application, after the target data is calculated based on the target calculation rule to generate the calculation data corresponding to the target data, the calculation data is intelligently interacted with the service system to perform calculation processing on the calculation data, and then the calculation data is adjusted according to the calculation result of the calculation processing, so that the data accuracy of the calculation data can be ensured.
In some optional implementations of this embodiment, after step S206, the electronic device may further perform the following steps:
and acquiring second address information of the service statistics system.
In this embodiment, the service statistics system may be a financial system of a service system. The second address information refers to a communication address of the service system.
And generating corresponding credential generation information based on the calculation data.
In this embodiment, an accounting information template may be obtained, and then the computing data is filled into the credential generation information template to generate corresponding credential generation information. The credential generation information template is constructed and generated according to the actual credential generation requirements.
And based on the second address information, sending the credential generation information to the service statistics system to process the calculation data through the service statistics system to generate corresponding target credential data.
In this embodiment, after receiving the credential generation information, the service statistics system performs credential generation processing on the computing data based on the credential generation information to generate corresponding target credential data. The target credential data may specifically be payable credential data.
And receiving the target credential data returned by the business statistics system.
And generating corresponding sign report data based on the target credential data.
In this embodiment, after receiving the target credential data, the device generates corresponding sign report data based on the target credential data. The sign-up data may specifically be eoa sign-up for making approval payments. In addition, after the payment is successful, the business statistics system can be informed to pay for the receipt of the payable data.
The application obtains the second address information of the business statistics system; then generating corresponding credential generation information based on the calculated data; then, based on the second address information, the credential generation information is sent to the business statistics system so as to process the calculation data through the business statistics system to generate corresponding target credential data; subsequently receiving the target credential data returned by the service statistics system; and finally, generating corresponding sign report data based on the target credential data. According to the application, after the target data is calculated based on the target calculation rule to generate the calculation data corresponding to the target data, the calculation data is interacted with a business statistics system intelligently to generate the certificate of the calculation data, and the corresponding sign report data is generated according to the target certificate data obtained by the certificate generation process, so that the sign report data can be used for smoothly carrying out subsequent business process processing.
In some optional implementations of this embodiment, after step S206, the electronic device may further perform the following steps:
and acquiring tag data corresponding to the target data.
In this embodiment, the label corresponding to the basic service data is generated by performing the marking process on the basic service data.
And screening target storage sub-blocks matched with the tag data from a plurality of storage sub-blocks contained in a preset block chain.
In this embodiment, for each service type, the blockchain is divided into a plurality of storage sub-blocks in advance, and each storage sub-block corresponds to a service type, so as to be dedicated for storing service data corresponding to the service type.
And packaging the target data and the calculation data to obtain packaged data.
In this embodiment, the packing manner of the packed data may adopt zip packing.
And storing the packed data to the target storage sub-block.
In this embodiment, the target data and the calculation data are packaged and stored, so that the storage space can be effectively reduced, and the intelligence of data storage can be improved.
The method comprises the steps of obtaining tag data corresponding to the target data; then screening target storage sub-blocks matched with the tag data from a plurality of storage sub-blocks contained in a preset block chain; then, the target data and the calculation data are packaged to obtain packaged data; and storing the packed data to the target storage sub-block. According to the method and the device for calculating the target data, after the target data is calculated based on the target calculation rule to generate the calculation data corresponding to the target data, the calculation data is intelligently stored to the target storage sub-block corresponding to the label data in the block chain according to the label data corresponding to the calculation data, so that the standardization and the intelligence of data storage are effectively improved, the required data can be obtained quickly, and the data obtaining efficiency is improved. In addition, the target data and the calculation data are further packaged and then stored, so that the storage space can be effectively reduced, and the intelligence of data storage is improved.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
It is emphasized that to further ensure the privacy and security of the above-mentioned computing data, the above-mentioned computing data may also be stored in a node of a blockchain.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
With further reference to fig. 3, as an implementation of the method shown in fig. 2 described above, the present application provides an embodiment of a data processing apparatus, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.
As shown in fig. 3, the data processing apparatus 300 according to the present embodiment includes: a judging module 301, a first acquiring module 302, a second acquiring module 303, a first processing module 304, a third acquiring module 305 and a second processing module 306. Wherein:
a judging module 301, configured to judge whether a data calculation request triggered by a user is received;
a first obtaining module 302, configured to obtain a current time if yes;
a second obtaining module 303, configured to determine a data obtaining policy corresponding to the current time, and obtain basic service data corresponding to a preset service type based on the data obtaining policy;
the first processing module 304 is configured to perform marking processing on the basic service data to obtain marked target data;
a third obtaining module 305, configured to obtain a target calculation rule corresponding to the target data from a preset rule database;
and a second processing module 306, configured to perform calculation processing on the target data based on the target calculation rule, and generate calculation data corresponding to the target data.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data processing method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of this embodiment, the second obtaining module 303 includes:
the judging submodule is used for judging whether the current time is the moon time or not;
the first determining submodule is used for determining a first data acquisition strategy corresponding to the moon time if the current time is the moon time;
the first acquisition sub-module is used for acquiring basic service data corresponding to the preset service type based on the first data acquisition strategy;
a second determining submodule, configured to determine a second data acquisition policy corresponding to the current time if the current time is not the lunar junction time;
and the second acquisition sub-module is used for acquiring the basic service data corresponding to the preset service type based on the second data acquisition strategy.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data processing method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of this embodiment, the third obtaining module 305 includes:
A third obtaining sub-module, configured to obtain tag data corresponding to the target data;
a calling sub-module for calling the rule database;
the query sub-module is used for carrying out query processing on the rule database based on the tag data, and querying a specified calculation rule corresponding to the tag data from the rule database;
and the third determining submodule is used for taking the specified calculation rule as the target calculation rule.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data processing method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of this embodiment, the data processing apparatus further includes:
the first receiving module is used for receiving a rule configuration request triggered by a specified user;
the display module is used for displaying a preset rule configuration interface;
the second receiving module is used for receiving rule configuration data input by the appointed user in the rule configuration interface;
the first generation module is used for generating corresponding calculation rules based on the rule configuration data;
and the first storage module is used for storing the calculation rule into the rule database.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data processing method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of this embodiment, the data processing apparatus further includes:
a fourth obtaining module, configured to obtain first address information of the service system;
the second generation module is used for generating corresponding accounting information based on the calculation data;
the third processing module is used for sending the accounting information to the service system based on the first address information so as to perform accounting processing on the target data based on the accounting information through the service system and generate a corresponding accounting result;
the third receiving module is used for receiving the accounting result returned by the service system;
and the adjusting module is used for adjusting the calculation data based on the accounting result to obtain adjusted calculation data.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data processing method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of this embodiment, the data processing apparatus further includes:
A fifth acquisition module, configured to acquire second address information of the service statistics system;
the third generation module is used for generating corresponding credential generation information based on the calculation data;
the fourth processing module is used for sending the credential generation information to the service statistics system based on the second address information so as to process the calculation data through the service statistics system to generate corresponding target credential data;
a fourth receiving module, configured to receive the target credential data returned by the service statistics system;
and the fourth generation module is used for generating corresponding sign report data based on the target credential data.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data processing method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of this embodiment, the data processing apparatus further includes:
a sixth acquisition module, configured to acquire tag data corresponding to the target data;
the screening module is used for screening target storage sub-blocks matched with the tag data from a plurality of storage sub-blocks contained in a preset block chain;
The fifth processing module is used for packaging the target data and the calculation data to obtain packaged data;
and the second storage module is used for storing the packed data to the target storage sub-block.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data processing method in the foregoing embodiment one by one, and are not described herein again.
In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of a data processing method. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.
The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing the data processing method.
The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
in the embodiment of the application, firstly, whether a data calculation request triggered by a user is received is judged; if yes, acquiring the current time; then determining a data acquisition strategy corresponding to the current time, and acquiring basic service data corresponding to a preset service type based on the data acquisition strategy; then, marking the basic service data to obtain marked target data; subsequently, obtaining a target calculation rule corresponding to the target data from a preset rule database; and finally, calculating the target data based on the target calculation rule to generate calculation data corresponding to the target data. According to the embodiment of the application, after receiving the data calculation request triggered by the user, basic service data corresponding to the preset service type is automatically acquired based on the data acquisition strategy corresponding to the current time, after marking the basic service data to obtain target data, the target calculation rule corresponding to the target data is intelligently acquired from the preset rule database, and further calculation processing is performed on the target data based on the target calculation rule, so that the calculation data corresponding to the target data is rapidly and accurately generated. The embodiment of the application can automatically calculate the acquired service data based on the use of the data acquisition strategy and the rule database so as to quickly and accurately generate corresponding calculation data, effectively reduce the manual intervention processing data of the service, simplify the service operation flow and improve the processing efficiency of service data calculation.
The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the data processing method as described above.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
in the embodiment of the application, firstly, whether a data calculation request triggered by a user is received is judged; if yes, acquiring the current time; then determining a data acquisition strategy corresponding to the current time, and acquiring basic service data corresponding to a preset service type based on the data acquisition strategy; then, marking the basic service data to obtain marked target data; subsequently, obtaining a target calculation rule corresponding to the target data from a preset rule database; and finally, calculating the target data based on the target calculation rule to generate calculation data corresponding to the target data. According to the embodiment of the application, after receiving the data calculation request triggered by the user, basic service data corresponding to the preset service type is automatically acquired based on the data acquisition strategy corresponding to the current time, after marking the basic service data to obtain target data, the target calculation rule corresponding to the target data is intelligently acquired from the preset rule database, and further calculation processing is performed on the target data based on the target calculation rule, so that the calculation data corresponding to the target data is rapidly and accurately generated. The embodiment of the application can automatically calculate the acquired service data based on the use of the data acquisition strategy and the rule database so as to quickly and accurately generate corresponding calculation data, effectively reduce the manual intervention processing data of the service, simplify the service operation flow and improve the processing efficiency of service data calculation.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.
Claims (10)
1. A method of data processing comprising the steps of:
judging whether a data calculation request triggered by a user is received or not;
if yes, acquiring the current time;
determining a data acquisition strategy corresponding to the current time, and acquiring basic service data corresponding to a preset service type based on the data acquisition strategy;
marking the basic service data to obtain marked target data;
acquiring a target calculation rule corresponding to the target data from a preset rule database;
and carrying out calculation processing on the target data based on the target calculation rule, and generating calculation data corresponding to the target data.
2. The data processing method according to claim 1, wherein the step of determining a data acquisition policy corresponding to the current time and acquiring basic service data corresponding to a preset service type based on the data acquisition policy specifically comprises:
judging whether the current time is a month knot time or not;
if the current time is the month time, determining a first data acquisition strategy corresponding to the month time;
acquiring basic service data corresponding to the preset service type based on the first data acquisition strategy;
If the current time is not the month ending time, determining a second data acquisition strategy corresponding to the current time;
and acquiring basic service data corresponding to the preset service type based on the second data acquisition strategy.
3. The method for processing data according to claim 1, wherein the step of acquiring the target calculation rule corresponding to the target data from a preset rule database specifically comprises:
acquiring tag data corresponding to the target data;
invoking the rule database;
inquiring the rule database based on the tag data, and inquiring a specified calculation rule corresponding to the tag data from the rule database;
and taking the specified calculation rule as the target calculation rule.
4. The data processing method according to claim 1, further comprising, before the step of acquiring a target calculation rule corresponding to the target data from a preset rule database:
receiving a rule configuration request triggered by a designated user;
displaying a preset rule configuration interface;
receiving rule configuration data input by the appointed user in the rule configuration interface;
Generating corresponding calculation rules based on the rule configuration data;
and storing the calculation rule into the rule database.
5. The data processing method according to claim 1, characterized by further comprising, after the step of performing calculation processing on the target data based on the target calculation rule, generating calculation data corresponding to the target data:
acquiring first address information of a service system;
generating corresponding accounting information based on the calculation data;
based on the first address information, sending the accounting information to the service system so as to perform accounting processing on the target data based on the accounting information through the service system and generate a corresponding accounting result;
receiving the accounting result returned by the service system;
and adjusting the calculation data based on the calculation result to obtain adjusted calculation data.
6. The data processing method according to claim 1, characterized by further comprising, after the step of performing calculation processing on the target data based on the target calculation rule, generating calculation data corresponding to the target data:
Acquiring second address information of a service statistics system;
generating corresponding credential generation information based on the calculation data;
based on the second address information, the credential generation information is sent to the business statistics system to process the calculation data through the business statistics system to generate corresponding target credential data;
receiving the target credential data returned by the service statistics system;
and generating corresponding sign report data based on the target credential data.
7. The data processing method according to claim 1, characterized by further comprising, after the step of performing calculation processing on the target data based on the target calculation rule, generating calculation data corresponding to the target data:
acquiring tag data corresponding to the target data;
screening target storage sub-blocks matched with the tag data from a plurality of storage sub-blocks contained in a preset block chain;
packaging the target data and the calculation data to obtain packaged data;
and storing the packed data to the target storage sub-block.
8. A data processing apparatus, comprising:
The judging module is used for judging whether a data calculation request triggered by a user is received or not;
the first acquisition module is used for acquiring the current time if yes;
the second acquisition module is used for determining a data acquisition strategy corresponding to the current time and acquiring basic service data corresponding to a preset service type based on the data acquisition strategy;
the first processing module is used for performing marking processing on the basic service data to obtain marked target data;
the third acquisition module is used for acquiring a target calculation rule corresponding to the target data from a preset rule database;
and the second processing module is used for carrying out calculation processing on the target data based on the target calculation rule and generating calculation data corresponding to the target data.
9. A computer device comprising a memory having stored therein computer readable instructions which when executed by a processor implement the steps of the data processing method of any of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon computer-readable instructions which, when executed by a processor, implement the steps of the data processing method according to any of claims 1 to 7.
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