CN109658126B - Data processing method, device, equipment and storage medium based on product popularization - Google Patents
Data processing method, device, equipment and storage medium based on product popularization Download PDFInfo
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Abstract
The invention discloses a data processing method, a device, equipment and a storage medium based on product popularization, wherein the method comprises the following steps: receiving a data submitting request sent by a client, and acquiring user identification information and product popularization data in the data submitting request; calculating commission data for the user in combination with the commission rate and the product extension data; encrypting the commission data according to a preset encryption mode, and sending the obtained ciphertext data to a payment platform so that the payment platform carries out online payment according to the ciphertext data; carrying out big data analysis on the user identification information, the commission data and the product promotion data to obtain product promotion task information, wherein the product promotion task information comprises product promotion tasks customized for users in a personalized way; and sending the product popularization task information to the client corresponding to the user identification information. The technical scheme of the invention is used for solving the problems of low efficiency and low automation level of data management in the product popularization process.
Description
Technical Field
The present invention relates to the field of information processing, and in particular, to a data processing method, apparatus, device, and storage medium based on product promotion.
Background
The popularization of insurance products depends on the efforts of the mass business personnel. Each insurance company is a requirement of the exhibition industry, a plurality of product popularization modes are provided, and besides the exhibition industry potential of personnel inside the company is developed, a proper incentive mechanism is provided to help the company to promote the insurance products by personnel outside the company, and the personnel outside the company are commonly called pushers.
The promotion mode of the products based on the podcasts is suitable for staff with part-time or people with vein resources. The client with the intention of ensuring is only required to be pushed to a business special staff in the company by the client, and the business special staff is responsible for the light business. If the business talks, a corresponding commission is provided to the pushers. The promotion mode not only increases the service promotion range, but also brings convenience to the three parties, and is a win-win product promotion mode.
For the pusher, whether the commission is paid and whether the payment is convenient, quick and safe is a primary consideration of the pusher, which also affects the intention and sales of the pusher for product popularization. The number of the pushing guests is huge, but the fee paid for the pushing guests is mostly calculated by a simple computer, and a manual report mode is adopted. This makes the commission compensation process cumbersome and error-prone. Meanwhile, the data in the whole product popularization process is difficult to trace and maintain, the data management efficiency is low, and the automation level is low.
Disclosure of Invention
The embodiment of the invention provides a data processing method, device, equipment and storage medium based on product popularization, which are used for solving the problems of low efficiency and low automation level of data management in the product popularization process.
A data processing method based on product popularization comprises the following steps:
receiving a data submitting request sent by a client, and acquiring user identification information in the data submitting request;
if the user identification information is consistent with the registered user identification information in the preset user database, acquiring product popularization data in the data submitting request;
obtaining a commission rate from a preset commission configuration table, and calculating by combining the commission rate and the product promotion data to obtain commission data of a user;
encrypting the commission data according to a preset encryption mode to obtain ciphertext data, and sending the ciphertext data to a payment platform so that the payment platform carries out online payment according to the ciphertext data;
carrying out big data analysis on the user identification information, the commission data and the product promotion data to obtain product promotion task information, wherein the product promotion task information comprises product promotion tasks customized for users in a personalized manner;
And sending the product promotion task information to the client corresponding to the user identification information.
A data processing apparatus based on product promotion, comprising:
the receiving module is used for receiving a data submitting request sent by a client and acquiring user identification information in the data submitting request;
the login verification module is used for acquiring product popularization data in the data submitting request if the user identification information is consistent with registered user identification information in a preset user database;
a commission calculation module, configured to obtain a commission percentage from a preset commission configuration table, and calculate a commission percentage in combination with the commission percentage and the product promotion data, so as to obtain commission data of a user;
the encryption module is used for encrypting the commission data according to a preset encryption mode to obtain ciphertext data, and sending the ciphertext data to a payment platform so that the payment platform carries out online payment according to the ciphertext data;
the data analysis module is used for carrying out big data analysis on the user identification information, the commission data and the product promotion data to obtain product promotion task information, wherein the product promotion task information comprises product promotion tasks customized for users in a personalized way;
And the sending module is used for sending the product promotion task information to the client corresponding to the user identification information.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the data processing method based on product promotion as described above when executing the computer program.
A computer readable storage medium storing a computer program which, when executed by a processor, implements the data processing method based on product promotion described above.
According to the data processing method, the device, the equipment and the storage medium based on the product promotion, the product promotion data are obtained from the data submitting request submitted by the registered user, and the commission data of the user are calculated according to the product promotion data and the commission calculation rate in the preset commission configuration table, so that the online submitting of the product promotion data and the timely calculation of the commission of the user are realized; the commission data is encrypted and synchronized to the payment platform, so that online payment can be safely and quickly realized; the user identification information, the commission data and the product promotion data are packaged into user data and sent to a big data platform, the product promotion tasks are customized for the user by utilizing the computing capacity of the big data platform, and the personalized product promotion tasks accord with the actual situation of the user, so that the user can finish the product promotion tasks; meanwhile, product promotion data and commission data in the whole product promotion process can be traced and analyzed, and the efficiency and automation level of data management are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic view of an application environment of a data processing method based on product promotion according to an embodiment of the present invention;
FIG. 2 is a flow chart of a data processing method based on product promotion in an embodiment of the invention;
FIG. 3 is a flow chart of processing a failed login client in accordance with one embodiment of the present invention;
FIG. 4 is a flowchart of step S5 in a data processing method based on product promotion according to an embodiment of the present invention;
FIG. 5 is a flowchart of step S51 in a data processing method based on product promotion according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a data processing apparatus based on product promotion in an embodiment of the present invention;
FIG. 7 is a schematic diagram of a computer device in accordance with an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The data processing method based on product popularization can be applied to an application environment as shown in fig. 1, wherein a server side is computer equipment for providing data processing service based on product popularization, and the server side can be a server or a server cluster; the client is a computer terminal device including, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, etc.; the client and the server are connected through a network, and the network can be a finite network or a wireless network. The data processing method based on product popularization provided by the embodiment of the invention is applied to the server.
In an embodiment, as shown in fig. 2, a data processing method based on product promotion is provided, and a specific implementation flow of the method includes the following steps:
s1: and receiving a data submitting request sent by the client, and acquiring user identification information in the data submitting request.
The data submission request is a data entry request issued by the user to the server through the client.
The data in the data submitting request comprises specific data for actually completing the product popularization task under the condition that a user is online, specifically, the data submitting request comprises user identification information, wherein the user identification information is a unique identification of the user identity, and specifically, the user identification information can be an id (identification) number and a verification password matched with the id number.
Specifically, the client can submit data through a Web page or a mobile phone APP. If the client submits through the Web page, namely the client fills the user identification information into the form in the Web page, then submits form data, and the server can obtain the user identification information from the form data; if the client performs data submission through the mobile phone APP, the server interfaces with the back end API of the mobile phone APP, and user identification information in the data submission request can be obtained.
S2: and if the user identification information is consistent with the registered user identification information in the preset user database, acquiring the product popularization data in the data submitting request.
The preset user database is a database for managing data of users. The user who issues the data submitting request is a registered user registered in advance in a preset user database. Specifically, databases include, but are not limited to: MS-SQL, oracle, mySQL, sybase, DB, redis, mongodDB, hbase, etc.
Product promotion data is data related to product promotion generated after a user completes a product promotion task on line, including but not limited to type of product, type of customer, customer base information, time of completion of product promotion, sales data, and the like.
The server side performs identity verification on the user sending the data submitting request, namely, the user identification information is compared with registered user identification information in a preset user database, and if the user identification information is consistent with the registered user identification information, product popularization data in the data submitting request are obtained. Product promotion data is sent to a server together with a data submitting request.
Specifically, if the running environment of the server is Java, the server uses the user identification information obtained from the client as a query condition, queries the preset user database through JDBC, and compares the user identification information with the registered user identification information in the preset user database. If the two are consistent, the server acquires the product promotion data in the data submitting request.
Among them, JDBC (Java DataBase Connectivity, java database connection) is a Java API for executing SQL statements, which can provide unified access to various relational databases, and is composed of a set of classes and interfaces written in Java language. JDBC provides a benchmark from which higher level tools and interfaces can be built to enable database developers to write database applications. The interface program written by the database developer through the JDBC can be suitable for different databases, and the interface program is not required to be written for the different databases, so that the development efficiency is greatly improved.
S3: obtaining a commission rate from a preset commission configuration table, and calculating the commission rate and product popularization data to obtain commission data of the user.
The preset commission configuration table is a data table defining commission calculation for various product popularizers in connection with product types.
The preset commission configuration table comprises product types, product popularizers at different levels and commission percentage. The product types include, but are not limited to, various insurance or financial products such as accident insurance, car insurance, life insurance, consumption credit, business credit and the like; the product popularizers are classified into different levels such as channel specialists, common agents, advanced agents, pushers and the like.
Commission rate is a method of calculating commissions for product promoters. The commission percentage may be a percentage of an amount, such as different product types or different percentages owned by different levels of product promoters; alternatively, the commission rate may be a calculation, for example, a commission calculation for an automotive mortgage product is:
commission = sales%amount-management fee
The percentage of the amount and the specific amount of the management fee may vary from product popularization level to product popularization level.
The commission data includes the amount of commission due to the user after completing the product promotion task.
The preset commission configuration table may be specifically represented as a text file deployed on the server, such as a JSON-format file. Among them, JSON (JavaScript Object Notation, JS object profile) is a lightweight data exchange format. The JSON file is stored in ASCII encoding mode, is independent of the operating system, can be quickly read or generated by a computer program, and is a popular data exchange format.
Specifically, the server side may read a local preset commission configuration table by using a json parsing tool of a third party to obtain a commission calculation formula; substituting the product promotion data into a commission calculation formula to obtain commission data of the user.
S4: encrypting the commission data according to a preset encryption mode to obtain ciphertext data, and sending the ciphertext data to the payment platform so that the payment platform performs online payment according to the ciphertext data.
The payment platform is a computer terminal device connected with the server and can be a server or a server cluster. And the payment platform receives the commission data sent by the server and realizes an online payment function for the user.
When the server sends the commission data to the payment platform, the commission data is encrypted according to a preset encryption mode, so that the commission amount is prevented from being tampered in the transmission process.
The preset encryption modes include, but are not limited to: symmetric encryption algorithms, asymmetric encryption algorithms, custom information scrambling algorithms, and the like. Ciphertext data is obtained by encrypting commission data.
Specifically, the server performs Hash calculation on the commission data to be sent by using an MD5 algorithm to obtain a summary value, and then sends the Hash value to the payment platform. The MD5 is called Message-Digest algorism 5, which is a Message-Digest Algorithm that allows large-volume information to be "compressed" into a secure format, i.e., a string of bytes of arbitrary length is transformed into a large integer of certain length, before signing a private key with digital signature software.
For example, a user id number 0001, a commission amount of 800 rennet, and ciphertext data obtained after encryption calculation by MD5 may be 2529a968bc5e9ca86d1dd51d76d97e9e.
S5: and carrying out big data analysis on the user identification information, the commission data and the product popularization data to obtain product popularization task information, wherein the product popularization task information comprises product popularization tasks customized for the user in a personalized way.
The big data analysis is a process of mining and analyzing mass data by depending on a big data platform to acquire valuable information in the mass data. The big data platform comprises a series of software and hardware architectures consisting of computer equipment. Big data analysis involves many technical aspects such as collection, storage, modeling analysis, machine learning, etc. of data.
The product popularization task information is data of product popularization tasks which are obtained through big data analysis, customized for users and pushed to the users by the server side. The product promotion tasks are various product promotion or sales tasks which are developed off-line. Product promotion task information includes, but is not limited to, a brief introduction of a new product, a commission proposal allocation scheme for a new product, and the like.
The server collects user identification information, commission data and product promotion data of each user, takes the data of each user as a data sample, counts the total performance amount, performance efficiency, the type of products promoted by the user and the like of the user, and comprehensively analyzes a certain number of user data samples within a certain range by combining the commission data to obtain product promotion task information suitable for the specific user. Wherein, the total performance amount is the total amount of the product popularization tasks completed by the user; performance efficiency is the number of product popularization tasks completed by a user within a certain time range; the types of the products promoted by the users are the types of the products, such as financial class, insurance class and the like, and each type of the products can be subdivided into more subclasses, such as insurance class is divided into car insurance class, accident insurance class and the like; and the statistics of the commission data can provide data reference for product popularization cost planning and the like, and the incentive commission is set more flexibly.
The server side can analyze the user identification information, the commission data and the product promotion data based on Hadoop, namely, takes the user identification information, the commission data and the product promotion data as input data, and processes the input data by utilizing a Hadoop data modeling analysis engine, such as Spark, a machine learning algorithm, such as naive Bayesian, logistic regression and the like, so as to obtain the product promotion task information for a single user. Hadoop is a distributed system infrastructure developed by the Apache foundation. Based on Hadoop, a user can develop a distributed program without knowing the details of the distributed bottom layer. The power of the clusters is fully utilized to perform high-speed operation and storage; spark, apache Spark, is a fast and versatile computational engine designed for large-scale data processing.
Specifically, the server firstly packages user identification information, commission data and product promotion data of a single user into a user sample data object, and the user sample data object is used as input data to be imported into Hadoop; then, a product promotion model is established for a user by utilizing a calculation engine Spark, and analysis is carried out by utilizing a logistic regression algorithm to predict product promotion task information suitable for the user; the product promotion model logically abstracts the process of promoting the product by the user from multiple dimensions such as product type, customer type, sales data, promotion period, commission expense and the like.
For example, if the quarter product promotion performance of a user is 10 ten thousand yuan, the product category for completing promotion mainly includes insurance products, the commission is 15% of sales, and the service end predicts that the product promotion task suitable for the user is a certain car insurance product, the incentive commission suitable for the user is 16% by comprehensively analyzing the performance efficiency, the successful promotion product category, the commission expense cost and the most fire sales product in the quarter of each month of the user.
S6: and sending the product popularization task information to the client corresponding to the user identification information.
And the server packages the product popularization task information and sends the product popularization task information to the client corresponding to the user identification information.
Specifically, the server may package the product promotion task information into a JSON file, and then send the JSON file to the client corresponding to the user identification information. Wherein the keys in the JSON file include user identification information, product name, product type, product promotion deadline, incentive commission, and the like. A JSON file containing product promotion task information can be expressed as:
in the embodiment, product promotion data are obtained from data submission requests submitted by registered users, and the commission data of the users are calculated according to the product promotion data and the commission calculation rate in a preset commission configuration table, so that online submission of the product promotion data and timely calculation of commissions of the users are realized; the commission data is encrypted and synchronized to the payment platform, so that online payment can be safely and quickly realized; the user identification information, the commission data and the product promotion data are packaged into user data and sent to a big data platform, the product promotion tasks are customized for the user by utilizing the computing capacity of the big data platform, and the personalized product promotion tasks accord with the actual situation of the user, so that the user can finish the product promotion tasks; meanwhile, product promotion data and commission data in the whole product promotion process can be traced and analyzed, and the efficiency and automation level of data management are improved.
Further, in an embodiment, as shown in fig. 3, after step S2 and before step S3, that is, after the step of acquiring the product promotion data in the data submitting request if the user identification information is consistent with the registered user identification information in the preset user database, and before the step of acquiring the commission rate from the preset commission configuration table and calculating by combining the commission rate and the product promotion data, the data processing method based on product promotion further includes the following steps:
s7: if the user identification information is inconsistent with the registered user identification information in the preset user database, sending a login rejection message to the client, and recording the login failure times of the client.
The login rejection message is used for prompting the reason of the login failure of the client, and the login rejection message can be customized login failure alarm information.
If the user identification information is inconsistent with the registered user identification information in the preset user database, the server side sends out a login rejection message to the client side, and meanwhile, the characteristic information of the client side is obtained, and the number of login failure times corresponding to the client side is stored. The characteristic information of the client is identification information for distinguishing different clients, and the characteristic information of the client can include, but is not limited to, an IP address of the client, a browser version used by the client, an operating system and version information used by the client, and the like.
Specifically, if the user identification information is inconsistent with the registered user identification information in the preset user database, the server sends a login rejection message to the client, and obtains the version information character of the current browser through a function navigator. For example, taking the IE10 browser as an example, the browser version number obtained by calling a function in JavaScript is "IE/10.1098.12351.0". Alternatively, the server inserts the third party tool URL address for obtaining the IP address into the Web page for the user to log in, for example, in the script tag of the Web page. The server saves the acquired browser version number, IP of the client and other information as the characteristic information of the client into a local file, and adds the login failure times of the client to the characteristic information of the client in the local file.
S8: if the number of login failures of the client exceeds a preset number threshold, refusing to receive the data submitting request sent by the client within a preset time.
The preset frequency threshold is a preset value of the server side and is used for judging whether the client side has malicious login behaviors or not. For example, the preset number of times threshold may be 10 times. The preset time is a time interval preset by the server and is used for shielding the data submitting request of the client in the time interval. For example, the preset time may be 5 minutes.
Specifically, the server reads a local file for storing the login failure times of the client, and if the login failure times of a certain client reach 10 times, the server acquires the characteristic information of the client as target characteristic information; when a client sends a login request, the server compares the characteristic information of the current client with the target characteristic information, if the characteristic information of the current client is consistent with the target characteristic information, the connection with the current client is closed, a timer with a time interval of 5 minutes is started, and the data submitting request sent by the client is refused to be received until the time of 5 minutes is over.
In this embodiment, the server records the number of login failures of the client, refuses to receive the data submitting request sent by the client whose number of login failures exceeds the preset number threshold in the preset time, prevents the user from maliciously attempting to login to the server, causes resource waste of the server, and improves the security of the server.
Further, in an embodiment, after step S4 and before step S5, that is, after the step of encrypting the commission data according to the preset encryption manner to obtain ciphertext data and sending the ciphertext data to the payment platform, so that the payment platform performs online payment according to the ciphertext data and before the step of performing big data analysis on the user identification information, the commission data and the product promotion data to obtain the product promotion task information, the data processing method based on product promotion further includes the following steps:
And packaging the user identification information, the commission data and the product popularization data into user data, and storing the user data into a preset user database, wherein the preset user database is used for a client to perform data query.
The server newly builds a user performance table in a preset user database and is used for storing user identification information, commission data and product popularization data, so that the product popularization record of each user is stored. Fields in the user performance table correspond to user identification information, commission data, product promotional data, including, but not limited to:
the user identification information can be used for inquiring basic information of the push in the push database;
customer information for storing customer name, ID card number, contact information, intention insurance product name, etc.;
recording time, and submitting client information by a user;
product information including product name, product category, product description, commission amount;
and the promotion progress state is used for representing the progress stage of the current business. The development stage comprises initial examination, review, paying-off stage, completion and the like. The user can inquire the progress condition of the service at any time according to the state value, and if the service is in a completed state, the promotion can apply for commission extraction.
Specifically, if the running environment of the server is Java, the server stores the user identification information, the commission data and the product popularization data into the performance data table through JDBC.
In this embodiment, the server stores the user identification information, commission data and product promotion data in association with a preset user database, so that the user can conveniently perform real-time data information related to the user, and at the same time, further data analysis is performed to provide a data source.
Further, in an embodiment, after step S2 and before step S3, that is, after the step of acquiring the product promotion data in the data submission request if the user identification information is consistent with the registered user identification information in the preset user database, and before the step of acquiring the commission rate from the preset commission configuration table and calculating by combining the commission rate and the product promotion data, the data processing method based on product promotion further includes the steps of:
and carrying out validity check on the product popularization data, and if the product popularization data exceeds a preset numerical range, carrying out account freezing treatment on a user submitting the product popularization data.
The preset numerical range is a data input range set by the server for each item of data in the product popularization data, so that the situations of false report, unreal and the like of the data input by a user are prevented. For example, a definition is made of the character length of the customer name in the product promotion data, such as no more than 10 bytes, etc.; practical limits are made on the product popularization completion, such as the current system time is not exceeded; restrictions are placed on the upper limit of sales data, such as no more than 100 ten thousand yuan.
Specifically, the server checks each item of data in the product promotion data, and if the value of more than half of data items in the product promotion data exceeds a preset numerical value set numerical value range, the user submitting the product promotion data is marked as an abnormal user in a preset user database. The permissions of the account marked as an abnormal user will be limited, such as inability to log in for a preset time, inability to submit data, etc.
In the embodiment, the server performs validity check on the product popularization data to prevent users from false reporting of the unreal data, and simultaneously, the server performs freezing treatment on the account number of the user without passing the validity check, so that manual intervention is reduced in the data auditing process, the authenticity of the data is ensured, and the entry of the unreal data is reduced.
Further, in an embodiment, as shown in fig. 4, for step S5, that is, large data analysis is performed on the user identification information, the commission data and the product promotion data, so as to obtain product promotion task information, the method specifically includes the following steps:
s51: and extracting user data of different users from a preset user database at preset time intervals to obtain a user sample data set.
The preset time interval is a time period adopted when analyzing data in a preset user database. For example, it may be in units of weeks, months, quarters or years.
When the server extracts the user data of different users, the server can freely select the user data in a certain range. For example, regions such as province, city, district and the like are taken as selection conditions; or selecting user data registered from 2015, 1 month and 1 day by taking the user registration time as a selection condition; or user data between 25 and 35 years of age, etc. with the user age as a selection condition.
The user sample data set is a data record set obtained from a preset user database. Specifically, the user sample data set may be data acquired from a preset user database according to a preset extraction condition.
For example, if the preset time interval is 1 month, the preset extraction condition is to analyze and classify data for the user with the age of 25 to 35 years, the server uses the time interval of 1 month as the trigger condition of the timer, uses the value range of the user age field between 25 and 35 as the search condition, and extracts the data record from the preset user database, the extracted data record is the user sample data set.
Specifically, the server may call a system timer () function to start a timer in month units, use a user selection condition as a search condition of the SQL statement, obtain user data meeting the search condition from a preset user database, and store the obtained data record in a newly built data table, thereby obtaining a set of user sample data.
S52: and carrying out cluster analysis on the user sample data set to obtain different user groups.
The cluster analysis refers to an analysis process of grouping a collection of physical or abstract objects into a plurality of classes composed of similar objects, and can automatically classify a batch of data by utilizing the cluster analysis without classifying the batch of data in advance, so that actions of manually presetting classification standards are reduced. The algorithm of the cluster analysis comprises a systematic clustering method, a decomposition method, an addition method, a dynamic clustering method, ordered sample clustering, overlapping clustering, fuzzy clustering and the like.
The server takes the user sample data set as input, and analyzes the user sample data set by using a cluster analysis algorithm, so that the users are divided into different user groups.
Specifically, the server takes the record in the obtained data table as input, and transmits the record to the SPSS (statistical product and service solution, statistical Product and Service Solutions) for cluster analysis operation, so that the users corresponding to the user data are divided into different user groups. Among them, SPSS software is a generic term for a series of software products and related services for statistical analysis operations, data mining, predictive analysis and decision support tasks, which are proposed by IBM corporation. The SPSS may be configured to compare user data between different users, i.e., perform cluster analysis on the input user data, and divide users corresponding to the user data into a plurality of user groups to obtain different user group identification information.
For example, users with ages between 25 and 35 are classified into insurance type preference user groups, financial type preference user groups, sales primary user groups, sales advanced user groups, and the like, each user group having a unique id number.
S53: and taking the user group of the user corresponding to the user identification information as a target user group, acquiring product information matched with the target user group from a preset product promotion database, and determining product promotion task information according to the product information.
The preset product promotion database is used for storing product information in a product promotion stage. For example, the product promotion activity is promoted for the catering to mid-autumn festival, wherein the name class and special price information of a certain product are provided.
The server acquires product information from a preset product promotion database, packages the product information into product promotion task information, and sends the product promotion task information to a user with the capability of promoting the product. Different user groups are suitable for popularizing different products, namely, the user groups and the product information have a mapping relation. For example, the user in the sales high-level user group can obtain the information of recommending new products preferentially, and the server side pushes the newly pushed product information to the user in the sales high-level user group preferentially; and the server side pushes the product information of the financial class to the users in the financial preference user group preferentially.
Specifically, the server takes a user group where a user corresponding to the user identification information is located as a target user group, acquires an id number of each user group, acquires product information matched with the user id number from a preset product promotion database according to the mapping relation between the user group id number and the product information, and packages the user identification information and the product information to obtain product promotion task information matched with the user identification information.
In this embodiment, the server periodically extracts user data from a preset user database according to a certain user data selection condition, and classifies users into different user groups according to the user data; according to the mapping relation between the user group and the product information, the product information matched with the user capacity is obtained from a preset product promotion database, and the product promotion task information is determined, so that the product promotion task can be customized for the user more finely.
Further, in an embodiment, as shown in fig. 5, for step S51, user data of different users are extracted from a preset user database at preset time intervals to obtain a user sample data set, which specifically includes the following steps:
s511: splitting a data table in a preset user database to obtain a plurality of small data tables.
The server splits the data table, and splits the data table with data records reaching millions into a plurality of small data tables.
Specifically, the server side is divided into a plurality of small tables on average according to the sequence of data records in the data tables. For example, if the number of records in the table is 100 ten thousand, the table is to be split into 10 small tables, and the number of data records in each small table is 10 ten thousand, records with record id numbers between 1 and 10 ten thousand in the table can be used as the first small data table, and the like, until the whole table is split into 10 small data tables.
Or, the server may split the whole table at certain intervals by using a hash function when splitting, so that the id numbers of each table are discontinuous and relatively uniformly distributed. For example, taking 100 as a module, performing modulo operation on id numbers recorded in the table, taking the record with the remainder of 1 after modulo operation as a group, i.e. the record with id numbers of 1, 101, 201, 301, etc., to form a small data table, and pushing the same until the whole table is split.
S512: and creating a processing thread, and calling the processing thread to extract records in a plurality of small data tables to obtain a user sample data set.
The thread is the minimum unit of program execution flow, is an entity in the process, and is the basic unit independently scheduled and allocated by the system.
The server establishes multiple threads, so that each thread processes one small data table, and a plurality of small data tables can be processed at the same time.
Specifically, the server may determine the number of processing threads to be built according to the number of cores of the local CPU, if the server CPU is 16 cores, call the newthread () method to build 16 processing threads, each thread is responsible for a small data table, and the tasks of each processing thread are the same, i.e., the processing threads match each record in the small data table according to a preset extraction condition, so as to obtain the user sample data set.
In this embodiment, since the data size in the preset user database is very large, the query and comparison operation on the large table is involved, the server splits the data table in the preset user database into a plurality of small data tables, then enables multiple threads, and each thread reads a record in one small data table and completes matching according to the preset extraction condition, thereby improving the speed of acquiring the user sample data set.
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 invention.
In an embodiment, a data processing device based on product promotion is provided, where the data processing device based on product promotion corresponds to the data processing method based on product promotion in the above embodiment one by one. As shown in fig. 6, the data processing apparatus based on product promotion includes a receiving module 61, a login verifying module 62, a commission calculating module 63, an encrypting module 64, a data analyzing module 65, and a transmitting module 66. The functional modules are described in detail as follows:
a receiving module 61, configured to receive a data submission request sent by a client, and obtain user identification information in the data submission request;
The login verification module 62 is configured to obtain product promotion data in the data submission request if the user identification information is consistent with registered user identification information in the preset user database;
a commission calculation module 63, configured to obtain a commission percentage from a preset commission configuration table, and calculate the commission percentage and product popularization data in combination, so as to obtain commission data of the user;
the encryption module 64 is configured to encrypt the commission data according to a preset encryption manner, obtain ciphertext data, and send the ciphertext data to the payment platform, so that the payment platform performs online payment according to the ciphertext data;
the data analysis module 65 is configured to perform big data analysis on the user identification information, the commission data and the product promotion data to obtain product promotion task information, where the product promotion task information includes a product promotion task personalized customized for a user;
and a sending module 66, configured to send the product promotion task information to the client corresponding to the user identification information.
Further, the data processing device based on product promotion further comprises:
the login tracking module 67 is configured to send a login rejection message to the client if the user identification information is inconsistent with the registered user identification information in the preset user database, and record the number of login failures of the client;
The masking module 68 is configured to reject the data submission request sent by the client within a preset time if the number of login failures of the client exceeds a preset number threshold.
Further, the data processing device based on product promotion further comprises:
and the storage module 69 is configured to store the user identification information, the commission data and the product promotion data in a preset user database in association, where the preset user database is used for the client to perform data query.
Further, the data processing device based on product promotion further comprises:
the validity verification module 610 is configured to perform validity check on the product promotion data, and if the product promotion data exceeds a preset numerical range, perform account freezing processing on a user submitting the product promotion data.
Further, the data analysis module 65 includes:
the sample extraction submodule 651 is used for extracting user data of different users from a preset user database at preset time intervals to obtain a user sample data set;
a grouping sub-module 652, configured to perform cluster analysis on the user sample data set to obtain different user groups;
the data obtaining sub-module 653 is configured to take a user group where a user corresponding to the user identification information is located as a target user group, obtain product information matched with the target user group from a preset product promotion database, and determine product promotion task information according to the product information.
Further, the sample extraction sub-module 651 includes:
a splitting unit 6511, configured to split the data table in the preset user database to obtain a plurality of small data tables;
the extracting unit 6512 is used for creating a processing thread, and calling the processing thread to extract records in the plurality of small data tables, so as to obtain a user sample data set.
For specific limitations of the data processing apparatus based on product promotion, reference may be made to the above limitation of the data processing method based on product promotion, and no further description is given here. The modules in the data processing device based on product promotion can be realized in whole or in part by software, hardware and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a data processing method based on product promotion.
In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the steps of the data processing method based on product promotion in the above embodiment, such as steps S1 to S6 shown in fig. 2. Alternatively, the processor may implement the functions of the respective modules/units of the data processing apparatus based on product promotion in the above embodiment, such as the functions of the modules 61 to 66 shown in fig. 6, when executing the computer program. In order to avoid repetition, a description thereof is omitted.
In an embodiment, a computer readable storage medium is provided, on which a computer program is stored, where the computer program when executed by a processor implements the data processing method based on product promotion in the above method embodiment, or where the computer program when executed by a processor implements the functions of each module/unit in the data processing device based on product promotion in the above device embodiment. In order to avoid repetition, a description thereof is omitted.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (7)
1. The data processing method based on the product popularization is characterized by comprising the following steps of:
receiving a data submitting request sent by a client, and acquiring user identification information in the data submitting request;
If the user identification information is consistent with the registered user identification information in the preset user database, acquiring product popularization data in the data submitting request;
obtaining a commission rate from a preset commission configuration table, and calculating by combining the commission rate and the product promotion data to obtain commission data of a user;
encrypting the commission data according to a preset encryption mode to obtain ciphertext data, and sending the ciphertext data to a payment platform so that the payment platform carries out online payment according to the ciphertext data;
packaging the user identification information, the commission data and the product popularization data into user data, and storing the user data into a preset user database, wherein the preset user database is used for the client to perform data query;
carrying out big data analysis on the user identification information, the commission data and the product promotion data to obtain product promotion task information, wherein the product promotion task information comprises product promotion tasks customized for users in a personalized manner;
transmitting the product promotion task information to the client corresponding to the user identification information;
The big data analysis is performed on the user identification information, the commission data and the product promotion data to obtain product promotion task information, including:
extracting the user data of different users from the preset user database at preset time intervals to obtain a user sample data set;
performing cluster analysis on the user sample data set to obtain different user groups;
taking a user group where a user corresponding to the user identification information is located as a target user group, acquiring product information corresponding to the target user group from a preset product promotion database, and determining the product promotion task information according to the product information;
the step of extracting the user data of different users from the preset user database at preset time intervals to obtain a user sample data set comprises the following steps:
splitting the data table in the preset user database at fixed intervals by utilizing a hash function to obtain a plurality of small data tables;
and creating a processing thread, and calling the processing thread to extract records in the plurality of small data tables to obtain the user sample data set.
2. The product-promotion-based data processing method of claim 1, wherein after the receiving a data submission request sent by a client obtains user identification information in the data submission request, and after the obtaining a commission rate from a preset commission configuration table, and before calculating in combination with the commission rate and the product promotion data to obtain commission data of a user, the product-promotion-based data processing method further comprises:
If the user identification information is inconsistent with the registered user identification information in the preset user database, sending a login rejection message to the client, and recording the login failure times of the client;
and if the login failure times of the client exceeds a preset time threshold, refusing to receive the data submitting request sent by the client within a preset time.
3. The product extension-based data processing method according to claim 1, wherein the product extension-based data processing method further comprises, after acquiring the product extension data in the data submission request if the user identification information is consistent with registered user identification information in a preset user database, and after acquiring a commission rate from the preset commission configuration table, and calculating in combination with the commission rate and the product extension data, before obtaining the commission data of the user:
and carrying out validity check on the product popularization data, and if the product popularization data exceeds a preset numerical range, carrying out account freezing treatment on a user submitting the product popularization data.
4. Data processing apparatus based on product popularization, characterized in that, data processing apparatus based on product popularization includes:
The receiving module is used for receiving a data submitting request sent by a client and acquiring user identification information in the data submitting request;
the login verification module is used for acquiring product popularization data in the data submitting request if the user identification information is consistent with registered user identification information in a preset user database;
a commission calculation module, configured to obtain a commission percentage from a preset commission configuration table, and calculate a commission percentage in combination with the commission percentage and the product promotion data, so as to obtain commission data of a user;
the encryption module is used for encrypting the commission data according to a preset encryption mode to obtain ciphertext data, and sending the ciphertext data to a payment platform so that the payment platform carries out online payment according to the ciphertext data;
the storage module is used for packaging the user identification information, the commission data and the product promotion data into user data and storing the user data into a preset user database, wherein the preset user database is used for the client to perform data query;
the data analysis module is used for carrying out big data analysis on the user identification information, the commission data and the product promotion data to obtain product promotion task information, wherein the product promotion task information comprises product promotion tasks customized for users in a personalized way;
The sending module is used for sending the product promotion task information to the client corresponding to the user identification information;
the data analysis module comprises:
the sample extraction sub-module is used for extracting the user data of different users from the preset user database at preset time intervals to obtain a user sample data set;
the grouping sub-module is used for carrying out cluster analysis on the user sample data set to obtain different user groups;
the data acquisition sub-module is used for taking a user group where a user corresponding to the user identification information is located as a target user group, acquiring product information corresponding to the target user group from a preset product promotion database, and determining the product promotion task information according to the product information;
the sample extraction submodule includes:
the splitting unit is used for splitting the data table in the preset user database at fixed intervals by utilizing a hash function to obtain a plurality of small data tables;
and the extraction unit is used for creating a processing thread and calling the processing thread to extract records in the plurality of small data tables so as to obtain the user sample data set.
5. The product-promotion-based data processing apparatus of claim 4, further comprising:
The login tracking module is used for sending a login rejection message to the client if the user identification information is inconsistent with the registered user identification information in the preset user database, and recording the login failure times of the client;
and the shielding module is used for refusing to receive the data submitting request sent by the client in preset time if the login failure times of the client exceeds a preset time threshold.
6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the product-popularization-based data processing method according to any of claims 1 to 3 when executing the computer program.
7. A computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the data processing method based on product promotion according to any one of claims 1 to 3.
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| CN111507395A (en) * | 2020-04-15 | 2020-08-07 | 赛诺数据科技(南京)有限公司 | Marketing big data modeling method and platform |
| CN112036928B (en) * | 2020-07-28 | 2024-05-31 | 长沙市到家悠享网络科技有限公司 | Data processing method, device, equipment and storage medium |
| CN112070525B (en) * | 2020-08-05 | 2024-04-23 | 长沙市到家悠享网络科技有限公司 | Data processing method, device, equipment and storage medium |
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