CN113971612A - Service data processing method, device, equipment and storage medium - Google Patents
Service data processing method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the field of artificial intelligence, and discloses a method, a device, equipment and a storage medium for processing service data, which are used for the accuracy of service data processing. The method comprises the following steps: acquiring numerical data and date data from a database, assembling the numerical data and the date data to obtain assembled product data, acquiring a fixed-investment date and fixed-investment money of a fixed-investment plan of a user, and determining a target business product according to the assembled product data; acquiring product financial data of a target business product, calculating the data trend of the target business product according to a target investment model and the product financial data, and generating a purchase instruction of the target business product according to a target investment rule and the data trend; inputting the buying instruction into the batch model for analysis to obtain an analysis result; and if the analysis result is deduction, buying the target business product and generating target position data. In addition, the invention also relates to a block chain technology, and the target position taking data can be stored in the block chain node.
Description
Technical Field
The present invention relates to the field of artificial intelligence, and in particular, to a method, an apparatus, a device, and a storage medium for processing service data.
Background
At present, common service management participants are more and more, and while service management is more and more flattened, common service managers are difficult to master correct service time points in time, and can buy the services at a high point of the market and sell the services at a low point of the market. If a batch buying method is adopted, the defect that only one time point is selected for buying and putting is overcome, and the cost can be balanced, namely the fixed-batch method.
The existing scheme is usually one-time processing based on some simple analyses, but the influence of a service manager on subjective judgment of the approach opportunity cannot be avoided, so that the risk of service processing is great, the service processing mode is single, more service information reference contents cannot be provided for a user, and the accuracy of the existing scheme is low.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for processing service data, which are used for the accuracy of the service data processing.
A first aspect of the present invention provides a method for processing service data, where the method for processing service data includes: receiving a fixed-delivery request of a user, and acquiring a fixed-delivery protocol in a preset service application program based on the fixed-delivery request; matching a transaction type fixed-investment model corresponding to the fixed-investment protocol according to the fixed-investment protocol to obtain a target fixed-investment model; acquiring numerical data and date data of each business product from a preset database, assembling the numerical data and the date data of each business product to obtain assembled product data corresponding to each business product, acquiring a fixed-investment date and fixed-investment money of a fixed-investment plan of a user, and determining a target business product corresponding to the fixed-investment request according to the assembled product data; acquiring product financial data of the target business product, calculating data trend of the target business product according to the target investment model and the product financial data, and generating a purchase instruction of the target business product according to a preset investment rule and the data trend; inputting the purchase instruction into a preset batch model, and analyzing the purchase instruction through the batch model to obtain an analysis result; and if the analysis result is deduction, updating the service product data corresponding to the target service product.
Optionally, in a first implementation manner of the first aspect of the present invention, the receiving a request for a fixed-subscription service from a user, and acquiring a fixed-subscription protocol in a preset service application based on the request for the fixed-subscription service includes: receiving a fixed-delivery request of a user based on a preset time period, wherein the fixed-delivery request comprises account information and personal information of the user; inquiring a fixed-delivery protocol in a preset business application program according to the account information and the personal information to obtain the fixed-delivery protocol corresponding to the account information and the personal information; and taking a fixed-drop protocol corresponding to the account information and the personal information as a fixed-drop protocol.
Optionally, in a second implementation manner of the first aspect of the present invention, the matching, according to the fixed-dose protocol, a transactional fixed-dose model corresponding to the fixed-dose protocol to obtain a target fixed-dose model includes: carrying out data analysis on the fixed-projection protocol to obtain target protocol data; and matching a transaction type fixed-investment model corresponding to the fixed-investment protocol based on the mapping relation between the target protocol data and a preset fixed-investment model base to obtain a target fixed-investment model.
Optionally, in a third implementation manner of the first aspect of the present invention, the obtaining the numerical data and the date data of each business product from a preset database, assembling the numerical data and the date data of each business product to obtain the assembled product data corresponding to each business product, obtaining a fixed-investment date and a fixed-investment amount of a fixed-investment plan of a user, and determining a target business product corresponding to the fixed-investment request according to the assembled product data includes: acquiring numerical data of each business product and a data table name corresponding to date data from a preset database; acquiring numerical data and date data corresponding to each business product according to the data table name and a preset query rule; data assembly is carried out on numerical data and date data corresponding to each business product according to a preset data assembly rule, and assembled product data corresponding to each business product are obtained; and acquiring the fixed investment date and the fixed investment amount of the fixed investment plan of the user, and determining the target business product corresponding to the fixed investment request according to the assembled product data.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the acquiring product financial data of the target business product, calculating a data trend of the target business product according to the target bidding model and the product financial data, and generating a purchase instruction of the target business product according to a preset bidding rule and the data trend includes: crawling product financial data corresponding to the target business product through a preset crawler; inputting the component stocks and the bond assets into a preset target fixed investment model to perform data trend calculation to obtain data trends, wherein the data trends are descending and rising; and if the trend of the data is rising, generating a purchase instruction of the target service product according to a preset fixed-investment rule.
Optionally, in a fifth implementation manner of the first aspect of the present invention, the inputting the purchase instruction into a preset batch model, and analyzing the purchase instruction through the batch model to obtain an analysis result includes: inputting the buy instruction into a preset batch model, wherein the batch model comprises a logistic regression classifier; and performing logistic regression operation on the purchase instruction through the logistic regression classifier to generate an analysis result, wherein the analysis result is a deduction and a non-deduction.
Optionally, in a sixth implementation manner of the first aspect of the present invention, if the analysis result is a deduction, updating the service product data corresponding to the target service product includes: if the analysis result is deduction, generating a target purchase amount according to the purchase instruction; and updating the service product data corresponding to the target service product based on the target purchase amount.
A second aspect of the present invention provides a service data processing apparatus, including: the system comprises a receiving module, a sending module and a processing module, wherein the receiving module is used for receiving a fixed-delivery request of a user and acquiring a fixed-delivery protocol in a preset service application program based on the fixed-delivery request; the matching module is used for matching a transaction type fixed-investment model corresponding to the fixed-investment protocol according to the fixed-investment protocol to obtain a target fixed-investment model; the assembling module is used for acquiring numerical data and date data of each business product from a preset database, assembling the numerical data and the date data of each business product to obtain the assembled product data corresponding to each business product, acquiring the fixed-investment date and fixed-investment amount of a fixed-investment plan of a user, and determining a target business product corresponding to the fixed-investment request according to the assembled product data; the processing module is used for acquiring product financial data of the target business product, calculating the data trend of the target business product according to the target investment model and the product financial data, and generating a purchase instruction of the target business product according to a preset investment rule and the data trend; the analysis module is used for inputting the purchase instruction into a preset batch model and analyzing the purchase instruction through the batch model to obtain an analysis result; and the generating module is used for updating the business product data corresponding to the target business product if the analysis result is deduction.
Optionally, in a first implementation manner of the second aspect of the present invention, the receiving module is specifically configured to: receiving a fixed-delivery request of a user based on a preset time period, wherein the fixed-delivery request comprises account information and personal information of the user; inquiring a fixed-delivery protocol in a preset business application program according to the account information and the personal information to obtain the fixed-delivery protocol corresponding to the account information and the personal information; and taking a fixed-drop protocol corresponding to the account information and the personal information as a fixed-drop protocol.
Optionally, in a second implementation manner of the second aspect of the present invention, the matching module is specifically configured to: carrying out data analysis on the fixed-projection protocol to obtain target protocol data; and matching a transaction type fixed-investment model corresponding to the fixed-investment protocol based on the mapping relation between the target protocol data and a preset fixed-investment model base to obtain a target fixed-investment model.
Optionally, in a third implementation manner of the second aspect of the present invention, the assembly module further includes: the acquisition unit is used for acquiring numerical data of each business product and a data table name corresponding to the date data from a preset database; the query unit is used for acquiring numerical data and date data corresponding to each business product according to the data table name and a preset query rule; the assembling unit is used for carrying out data assembling on the numerical data and the date data corresponding to each business product according to a preset data assembling rule to obtain the assembled product data corresponding to each business product; and the configuration unit is used for acquiring the fixed investment date and the fixed investment amount of the fixed investment plan of the user and determining the target business product corresponding to the fixed investment request according to the assembled product data.
Optionally, in a fourth implementation manner of the second aspect of the present invention, the processing module is specifically configured to: crawling product financial data corresponding to the target business product through a preset crawler; inputting the component stocks and the bond assets into a preset target fixed investment model to perform data trend calculation to obtain data trends, wherein the data trends are descending and rising; and if the trend of the data is rising, generating a purchase instruction of the target service product according to a preset fixed-investment rule.
Optionally, in a fifth implementation manner of the second aspect of the present invention, the parsing module is specifically configured to: inputting the buy instruction into a preset batch model, wherein the batch model comprises a logistic regression classifier; and performing logistic regression operation on the purchase instruction through the logistic regression classifier to generate an analysis result, wherein the analysis result is a deduction and a non-deduction.
Optionally, in a sixth implementation manner of the second aspect of the present invention, the generating module is specifically configured to: if the analysis result is deduction, generating a target purchase amount according to the purchase instruction; and updating the service product data corresponding to the target service product based on the target purchase amount.
A third aspect of the present invention provides a service data processing device, including: a memory and at least one processor, the memory having instructions stored therein; the at least one processor calls the instructions in the memory to cause the service data processing device to execute the service data processing method.
A fourth aspect of the present invention provides a computer-readable storage medium having stored therein instructions, which, when run on a computer, cause the computer to execute the above-mentioned service data processing method.
According to the technical scheme provided by the invention, a transaction type fixed-investment model corresponding to a fixed-investment protocol is matched according to the fixed-investment protocol to obtain a target fixed-investment model; acquiring numerical data and date data of each business product from a preset database, assembling the numerical data and the date data of each business product to obtain assembled product data corresponding to each business product, acquiring a fixed-investment date and a fixed-investment amount of a fixed-investment plan of a user, and determining a target business product corresponding to a fixed-investment request according to the assembled product data; acquiring product financial data of a target business product, calculating the data trend of the target business product according to a target investment model and the product financial data, and generating a purchase instruction of the target business product according to a preset investment rule and the data trend; inputting the purchase instruction into a preset batch model, and analyzing the purchase instruction through the batch model to obtain an analysis result; and if the analysis result is deduction, updating the service product data corresponding to the target service product. According to the invention, fund data is analyzed through the target investment model to obtain the optimal target business product, and then the target business product is input into the batch model to be processed to obtain the target position data, so that the position data generation accuracy is improved, and further the business data processing accuracy is improved.
Drawings
Fig. 1 is a schematic diagram of an embodiment of a service data processing method in an embodiment of the present invention;
fig. 2 is a schematic diagram of another embodiment of a service data processing method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an embodiment of a business data processing apparatus according to an embodiment of the present invention;
fig. 4 is a schematic diagram of another embodiment of a service data processing apparatus according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an embodiment of a service data processing device in the embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a method, a device and equipment for processing service data and a storage medium, which are used for the accuracy rate of service data processing. The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of understanding, a specific flow of the embodiment of the present invention is described below, and referring to fig. 1, a first embodiment of a service data processing method according to the embodiment of the present invention includes:
101. receiving a fixed-delivery request of a user, and acquiring a fixed-delivery protocol in a preset service application program based on the fixed-delivery request;
it is to be understood that the executing subject of the present invention may be a service data processing apparatus, and may also be a terminal or a server, which is not limited herein. The embodiment of the present invention is described by taking a server as an execution subject. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), and a big data and artificial intelligence platform.
The embodiment of the invention can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result. The artificial intelligence infrastructure generally includes 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 the like.
Specifically, the server receives a fixed-delivery request of a user, and queries a preset fixed-delivery protocol in a preset business application program based on the fixed-delivery request, wherein the preset business application program comprises numerical data of each business product and query address information of date data, and the server further comprises a fund database, and the fund database comprises numerical data of financial products and date data. The numerical data refers to the original total value or the balance obtained by subtracting the accumulated total value from the reset total value, which shows the current depreciation amount of the financial product, and the like, and comprises the total investment cost of the financial product, the filling rate of the financial product, the dividend amount of the financial product, the total share of the financial product and the like; the date data refers to a time node at which a financial price change occurs in the financial product production.
102. Matching a transaction type fixed-investment model corresponding to the fixed-investment protocol according to the fixed-investment protocol to obtain a target fixed-investment model;
specifically, the server matches a transaction type fixed-investment model corresponding to a fixed-investment protocol according to the fixed-investment protocol to obtain a target fixed-investment model, the date, fund and amount of deduction per month are set by the transaction type fixed-investment model, namely purchase is not expanded, after the first fixed investment is started, a service application program starts recorded fixed-investment actions, when the next deduction date is future, software can pre-judge net value (inaccurate net value) when buying, if the net value of buying at this time is in a relatively low position, a buying instruction is sent, and if the net value of buying at this time is in a relatively high position, the instruction of not buying is sent.
103. Acquiring numerical data and date data of each business product from a preset database, assembling the numerical data and the date data of each business product to obtain assembled product data corresponding to each business product, acquiring a fixed-investment date and a fixed-investment amount of a fixed-investment plan of a user, and determining a target business product corresponding to a fixed-investment request according to the assembled product data;
specifically, the server performs data analysis on the fixed-delivery protocol to obtain target protocol data, and the server queries a preset database and obtains numerical data and date data of each service product according to preset query rules, which specifically includes: the server queries a preset database by calling a pre-configured calculation engine according to a preset data table name and acquires numerical data and date data of each service product. The pre-configured computing engine corresponds to a corresponding configuration process, and the configuration process is as follows: the server creates a project and introduces a framework for providing a package of request response services. The server creates the object for calculation when starting the service, and encapsulates the object in a class, and the server inherits the class, thereby completing the pre-configuration of the calculation engine. When a query request of the numerical data of the fund and the date data comes in each time, the server calls an SQL method, and the SQL of the query request is transmitted to calculate so as to start the pre-configured calculation engine to inquire the preset database.
104. Acquiring product financial data of a target business product, calculating the data trend of the target business product according to a target investment model and the product financial data, and generating a purchase instruction of the target business product according to a preset investment rule and the data trend;
specifically, the server selects a target business product with net profit growth speed far exceeding the main business profit growth by 25% per year for a fund according to the product financial data of the fund, including component stocks and bonds or other assets, and the current component assets show and closing prices in the corresponding stock market, and if other businesses can still keep fast development, the future development speed of the company is very good. The server sets the deduction date of each month, other funds and other time do not accord with the transaction rules, and two days before the deduction date is determined, the server sends a transaction instruction to deduct money or not deduct money.
105. Inputting the purchase instruction into a preset batch model, and analyzing the purchase instruction through the batch model to obtain an analysis result;
specifically, the server inputs the purchase instruction into a preset batch model, analyzes the purchase instruction through the batch model to obtain an analysis result, performs logistic regression operation on the purchase instruction through a logistic regression classifier to generate an analysis result, and analyzes the purchase instruction through the batch model to obtain an analysis result, wherein the analysis result comprises a deduction and a non-deduction.
106. And if the analysis result is deduction, updating the service product data corresponding to the target service product.
Specifically, if the analysis result is deduction, the service product data corresponding to the target service product is updated, wherein before the target position data is due for physical delivery or cash delivery, the investor can voluntarily decide to buy or sell the futures contract according to market conditions and personal wishes. The investor (doing more or doing nothing) does not do reverse operation (selling or buying) with equal delivery months and quantity, holds the futures contract and is called position taking, and when the user buys the target service product, the numerical data of the target service product is the position taking data of the user.
Further, the server stores the target taken position data in a block chain database, which is not limited herein.
In the embodiment of the invention, a transaction type fixed-investment model corresponding to a fixed-investment protocol is matched according to the fixed-investment protocol to obtain a target fixed-investment model; acquiring numerical data and date data of each business product from a preset database, assembling the numerical data and the date data of each business product to obtain assembled product data corresponding to each business product, acquiring a fixed-investment date and a fixed-investment amount of a fixed-investment plan of a user, and determining a target business product corresponding to a fixed-investment request according to the assembled product data; acquiring product financial data of a target business product, calculating the data trend of the target business product according to a target investment model and the product financial data, and generating a purchase instruction of the target business product according to a preset investment rule and the data trend; inputting the purchase instruction into a preset batch model, and analyzing the purchase instruction through the batch model to obtain an analysis result; and if the analysis result is deduction, updating the service product data corresponding to the target service product. According to the invention, fund data is analyzed through the target investment model to obtain the optimal target business product, and then the target business product is input into the batch model to be processed to obtain the target position data, so that the position data generation accuracy is improved, and further the business data processing accuracy is improved.
Referring to fig. 2, a second embodiment of the service data processing method according to the embodiment of the present invention includes:
201. receiving a fixed-delivery request of a user, and acquiring a fixed-delivery protocol in a preset service application program based on the fixed-delivery request;
optionally, the server receives a user's request for making a subscription based on a preset time period, where the request for making a subscription includes account information and personal information of the user; the server inquires a fixed-delivery protocol in a preset business application program according to the account information and the personal information to obtain the fixed-delivery protocol corresponding to the account information and the personal information; and the server takes a fixed-drop protocol corresponding to the account information and the personal information as a fixed-drop protocol.
Specifically, the server receives a fixed-delivery request of a user based on a preset time period, wherein the fixed-delivery request comprises account information and personal information of the user, and the preset time period is two days before a transaction day; the server inquires a fixed-delivery protocol in a preset business application program according to the account information and the personal information to obtain the fixed-delivery protocol corresponding to the account information and the personal information, wherein the account information comprises account data, password data and position data of a user, and the personal information comprises personal operation records, personal preference data and the like of the user; the server takes a fixed-throw protocol corresponding to the account information and the personal information as a fixed-throw protocol, the server inquires a fixed-throw mode preferred by the user from the business application program according to the account information of the user, and the server determines the corresponding fixed-throw protocol, namely the fixed-throw protocol according to the fixed-throw mode.
202. Matching a transaction type fixed-investment model corresponding to the fixed-investment protocol according to the fixed-investment protocol to obtain a target fixed-investment model;
optionally, the server performs data analysis on the fixed-throw protocol to obtain target protocol data; and the server matches a transaction type fixed-investment model corresponding to the fixed-investment protocol based on the mapping relation between the target protocol data and a preset fixed-investment model base to obtain a target fixed-investment model.
Specifically, the server analyzes data of the fixed-throw protocol to obtain target protocol data; and the server matches a transaction type fixed-investment model corresponding to the fixed-investment protocol based on the mapping relation between the target protocol data and a preset fixed-investment model base to obtain a target fixed-investment model. The server sets No. 5 or No. 20 of each month as a deduction day, other funds and other time do not accord with transaction rules, and the server sends a transaction instruction to deduct money or not to deduct money 2 days before the deduction day.
203. Acquiring numerical data of each business product and a data table name corresponding to date data from a preset database;
specifically, the server performs data analysis on the fixed-delivery protocol to obtain target protocol data, and the server queries a preset database and obtains numerical data and date data of each service product according to preset query rules, which specifically includes: the server queries a preset database by calling a pre-configured calculation engine according to a preset data table name and acquires numerical data and date data of each service product.
204. Acquiring numerical data and date data corresponding to each business product according to the data table name and a preset query rule;
the method specifically comprises the following steps: comparing the user's drop-on date with date data in a key-value data format; if the user's fixed-delivery date does not correspond to the date data in the key-value data format, natural day shift is carried out according to the date data in the key-value data format to determine the numerical data of the fixed-delivery product corresponding to the fixed-delivery date, and the corresponding target business product is determined according to the determined numerical data of the fixed-delivery product.
205. Data assembly is carried out on numerical data and date data corresponding to each business product according to a preset data assembly rule, and assembled product data corresponding to each business product are obtained;
specifically, the server acquires numerical data and a data table name corresponding to date data of each business product from a preset database, acquires the numerical data and the date data corresponding to each business product according to the data table name and a preset query rule, and takes the numerical data of each business product as a key in a key-value data format; the server takes the date data of each business product as a value corresponding to a key in a key-value data format; and the server assembles the numerical data and the date data corresponding to each business product into data in a key-value data format according to the key and the value, wherein the key-value data format refers to a key-value pair data format, the key is used as an index of an element, and the value represents the stored and read data to obtain the assembled product data.
206. Acquiring a fixed-investment date and a fixed-investment amount of a fixed-investment plan of a user, and determining a target business product corresponding to the fixed-investment request according to the assembled product data;
specifically, the server stores numerical data and date data by a plurality of key-value pairs in a preset set. The fixed delivery date of the fixed delivery product selected by the user is not necessarily the optimal filling date of the fixed delivery product, numerical data and date data of the fixed delivery product are recorded in a key-value data format, namely the optimal filling date of the fixed delivery product is recorded in the key-value data format, the fixed delivery date of the fixed delivery product selected by the user is not the optimal filling date recorded in the key-value data format, natural day offset is carried out on the fixed delivery date according to the key-value data format, and the target business product which is most consistent with the fixed delivery date of the user is selected.
207. Acquiring product financial data of a target business product, calculating the data trend of the target business product according to a target investment model and the product financial data, and generating a purchase instruction of the target business product according to a preset investment rule and the data trend;
optionally, the server crawls product financial data corresponding to the target business product through a preset crawler; the server inputs the financial data of the product into a preset target fixed-projection model to carry out data trend calculation to obtain the data trend, wherein the data trend is a drop and an increase; and if the trend of the data is rising, the server generates a purchase instruction of the target service product according to a preset fixed-investment rule.
Specifically, the server crawls product financial data corresponding to a target business product through a preset crawler; the server inputs the financial data of the product into a preset target fixed-projection model to carry out data trend calculation to obtain the data trend, wherein the data trend is a drop and an increase; and if the data trend is rising, the server generates a purchase instruction of the target service product according to a preset fixed-investment rule. The trading method is characterized in that trading decision is that buying and dropping are not bought, after the first decision is started, trading software of a fund company starts to record the decision action of the trading software, when the next deduction day is the future, the software can pre-judge the net value of buying, if the net value of buying is relatively low, a buying instruction is sent, otherwise, if the net value of buying is relatively high, a non-buying instruction is sent, and the desire of buying and dropping but not bought is realized through analysis of a target decision model, so that more fund shares are obtained at a fixed cost, and more profit is realized when the fund rises.
208. Inputting the purchase instruction into a preset batch model, and analyzing the purchase instruction through the batch model to obtain an analysis result;
optionally, the server inputs the purchase instruction into a preset batch model, wherein the batch model comprises a logistic regression classifier; and the server performs logistic regression operation on the buying instruction through the logistic regression classifier and generates an analysis result, wherein the analysis result is a deduction and a non-deduction.
Specifically, the server inputs a purchase instruction into a preset batch model, and the batch model comprises a logistic regression classifier; and the server performs logistic regression operation on the buying instruction through the logistic regression classifier and generates an analysis result, wherein the analysis result is a deduction and a non-deduction. When the batch model is tested through a preset test data set, the data in the test data set are divided into 5 grades, namely a first grade, a second grade, a third grade, a fourth grade and a fifth grade, according to the probability value output by the batch model from large to small. And if the yield of the data of the first file divided by the batch model is the highest, recording the test result corresponding to the batch model as 1, and otherwise, recording the test result as 0. The server tests the batch model through a test data set acquired from a time sequence of a fixed time point (such as No. 1 monthly in one year) of a plurality of years, marks the test result corresponding to the batch model according to the principle, and carries out logistic regression operation on the purchase instruction through the logistic regression classifier to generate an analysis result, wherein the analysis result is a deduction and a non-deduction.
209. And if the analysis result is deduction, updating the service product data corresponding to the target service product.
Optionally, if the analysis result is deduction, the server generates a target purchase amount according to the purchase instruction; and the server purchases the target business product based on the target purchase amount and updates position holding data of the target business product to obtain target position holding data.
Specifically, if the analysis result is deduction, the server generates a target purchase amount according to a purchase instruction, wherein the purchase instruction carries the size of the purchase amount of the target service product, and when the purchase instruction sent by the target investment model is received, the batch running model buys the target service product according to the purchase amount in the purchase instruction, and the purchase is accompanied with deduction of account balance; and the server purchases the target service product based on the target purchase amount and updates position holding data of the target service product to obtain target position holding data, and after the batch running model responds to the purchase instruction, the server updates account balance data in the current state, namely the target position holding data.
Further, the server stores the target taken position data in a block chain database, which is not limited herein.
In the embodiment of the invention, a transaction type fixed-investment model corresponding to a fixed-investment protocol is matched according to the fixed-investment protocol to obtain a target fixed-investment model; acquiring numerical data and date data of each business product from a preset database, assembling the numerical data and the date data of each business product to obtain assembled product data corresponding to each business product, acquiring a fixed-investment date and a fixed-investment amount of a fixed-investment plan of a user, and determining a target business product corresponding to a fixed-investment request according to the assembled product data; acquiring product financial data of a target business product, calculating the data trend of the target business product according to a target investment model and the product financial data, and generating a purchase instruction of the target business product according to a preset investment rule and the data trend; inputting the purchase instruction into a preset batch model, and analyzing the purchase instruction through the batch model to obtain an analysis result; and if the analysis result is deduction, updating the service product data corresponding to the target service product. According to the invention, fund data is analyzed through the target investment model to obtain the optimal target business product, and then the target business product is input into the batch model to be processed to obtain the target position data, so that the position data generation accuracy is improved, and further the business data processing accuracy is improved.
With reference to fig. 3, the service data processing method in the embodiment of the present invention is described above, and a service data processing apparatus in the embodiment of the present invention is described below, where a first embodiment of the service data processing apparatus in the embodiment of the present invention includes:
a receiving module 301, configured to receive a fixed-delivery request of a user, and obtain a fixed-delivery protocol in a preset service application program based on the fixed-delivery request;
a matching module 302, configured to match, according to the fixed-investment protocol, a transactional fixed-investment model corresponding to the fixed-investment protocol, so as to obtain a target fixed-investment model;
the assembling module 303 is configured to obtain numerical data and date data of each business product from a preset database, assemble the numerical data and date data of each business product to obtain assembled product data corresponding to each business product, obtain a fixed-investment date and fixed-investment amount of a fixed-investment plan of a user, and determine a target business product corresponding to the fixed-investment request according to the assembled product data;
a processing module 304, configured to obtain product financial data of the target business product, calculate a data trend of the target business product according to the target investment model and the product financial data, and generate a purchase instruction of the target business product according to a preset investment rule and the data trend;
the analysis module 305 is configured to input the purchase instruction into a preset batch model, and analyze the purchase instruction through the batch model to obtain an analysis result;
a generating module 306, configured to update the service product data corresponding to the target service product if the analysis result is a deduction.
In the embodiment of the invention, a transaction type fixed-investment model corresponding to a fixed-investment protocol is matched according to the fixed-investment protocol to obtain a target fixed-investment model; acquiring numerical data and date data of each business product from a preset database, assembling the numerical data and the date data of each business product to obtain assembled product data corresponding to each business product, acquiring a fixed-investment date and a fixed-investment amount of a fixed-investment plan of a user, and determining a target business product corresponding to a fixed-investment request according to the assembled product data; acquiring product financial data of a target business product, calculating the data trend of the target business product according to a target investment model and the product financial data, and generating a purchase instruction of the target business product according to a preset investment rule and the data trend; inputting the purchase instruction into a preset batch model, and analyzing the purchase instruction through the batch model to obtain an analysis result; and if the analysis result is deduction, updating the service product data corresponding to the target service product. According to the invention, fund data is analyzed through the target investment model to obtain the optimal target business product, and then the target business product is input into the batch model to be processed to obtain the target position data, so that the position data generation accuracy is improved, and further the business data processing accuracy is improved.
Referring to fig. 4, a second embodiment of a service data processing apparatus according to the embodiment of the present invention includes:
a receiving module 301, configured to receive a fixed-delivery request of a user, and obtain a fixed-delivery protocol in a preset service application program based on the fixed-delivery request;
a matching module 302, configured to match, according to the fixed-investment protocol, a transactional fixed-investment model corresponding to the fixed-investment protocol, so as to obtain a target fixed-investment model;
the assembling module 303 is configured to obtain numerical data and date data of each business product from a preset database, assemble the numerical data and date data of each business product to obtain assembled product data corresponding to each business product, obtain a fixed-investment date and fixed-investment amount of a fixed-investment plan of a user, and determine a target business product corresponding to the fixed-investment request according to the assembled product data;
a processing module 304, configured to obtain product financial data of the target business product, calculate a data trend of the target business product according to the target investment model and the product financial data, and generate a purchase instruction of the target business product according to a preset investment rule and the data trend;
the analysis module 305 is configured to input the purchase instruction into a preset batch model, and analyze the purchase instruction through the batch model to obtain an analysis result;
a generating module 306, configured to update the service product data corresponding to the target service product if the analysis result is a deduction.
Optionally, the receiving module 301 is specifically configured to:
receiving a fixed-delivery request of a user based on a preset time period, wherein the fixed-delivery request comprises account information and personal information of the user; inquiring a fixed-delivery protocol in a preset business application program according to the account information and the personal information to obtain the fixed-delivery protocol corresponding to the account information and the personal information; and taking a fixed-drop protocol corresponding to the account information and the personal information as a fixed-drop protocol.
Optionally, the matching module 302 is specifically configured to: carrying out data analysis on the fixed-projection protocol to obtain target protocol data; and matching a transaction type fixed-investment model corresponding to the fixed-investment protocol based on the mapping relation between the target protocol data and a preset fixed-investment model base to obtain a target fixed-investment model.
Optionally, the assembly module 303 further includes:
an obtaining unit 3031, configured to obtain, from a preset database, a data table name corresponding to numerical data and date data of each business product;
the query unit 3032 is configured to obtain numerical data and date data corresponding to each business product according to the data table name and through a preset query rule;
an assembling unit 3033, configured to perform data assembly on the numerical data and the date data corresponding to each business product according to a preset data assembly rule, so as to obtain assembled product data corresponding to each business product;
the configuration unit 3034 is configured to obtain a fixed-investment date and a fixed-investment amount of a fixed-investment plan of a user, and determine a target business product corresponding to the fixed-investment request according to the assembled product data.
Optionally, the processing module 304 is specifically configured to:
crawling product financial data corresponding to the target business product through a preset crawler; inputting the component stocks and the bond assets into a preset target fixed investment model to perform data trend calculation to obtain data trends, wherein the data trends are descending and rising; and if the trend of the data is rising, generating a purchase instruction of the target service product according to a preset fixed-investment rule.
Optionally, the parsing module 305 is specifically configured to:
inputting the buy instruction into a preset batch model, wherein the batch model comprises a logistic regression classifier; and performing logistic regression operation on the purchase instruction through the logistic regression classifier to generate an analysis result, wherein the analysis result is a deduction and a non-deduction.
Optionally, the generating module 306 is specifically configured to:
if the analysis result is deduction, generating a target purchase amount according to the purchase instruction; and updating the service product data corresponding to the target service product based on the target purchase amount.
In the embodiment of the invention, a transaction type fixed-investment model corresponding to a fixed-investment protocol is matched according to the fixed-investment protocol to obtain a target fixed-investment model; acquiring numerical data and date data of each business product from a preset database, assembling the numerical data and the date data of each business product to obtain assembled product data corresponding to each business product, acquiring a fixed-investment date and a fixed-investment amount of a fixed-investment plan of a user, and determining a target business product corresponding to a fixed-investment request according to the assembled product data; acquiring product financial data of a target business product, calculating the data trend of the target business product according to a target investment model and the product financial data, and generating a purchase instruction of the target business product according to a preset investment rule and the data trend; inputting the purchase instruction into a preset batch model, and analyzing the purchase instruction through the batch model to obtain an analysis result; and if the analysis result is deduction, updating the service product data corresponding to the target service product. According to the invention, fund data is analyzed through the target investment model to obtain the optimal target business product, and then the target business product is input into the batch model to be processed to obtain the target position data, so that the position data generation accuracy is improved, and further the business data processing accuracy is improved.
Fig. 3 and fig. 4 describe the service data processing apparatus in the embodiment of the present invention in detail from the perspective of the modular functional entity, and the service data processing apparatus in the embodiment of the present invention is described in detail from the perspective of hardware processing.
Fig. 5 is a schematic structural diagram of a service data processing apparatus according to an embodiment of the present invention, where the service data processing apparatus 500 may generate relatively large differences due to different configurations or performances, and may include one or more processors (CPUs) 510 (e.g., one or more processors) and a memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) for storing applications 533 or data 532. Memory 520 and storage media 530 may be, among other things, transient or persistent storage. The program stored on the storage medium 530 may include one or more modules (not shown), each of which may include a series of instructions operating on the business data processing apparatus 500. Further, the processor 510 may be configured to communicate with the storage medium 530, and execute a series of instruction operations in the storage medium 530 on the service data processing apparatus 500.
The business data processing apparatus 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input-output interfaces 560, and/or one or more operating systems 531, such as Windows server, Mac OS X, Unix, Linux, FreeBSD, and the like. It will be appreciated by those skilled in the art that the business data processing apparatus configuration shown in fig. 5 does not constitute a limitation of the business data processing apparatus and may include more or less components than those shown, or some components may be combined, or a different arrangement of components.
The present invention further provides a service data processing device, where the service data processing device includes a memory and a processor, where the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the service data processing method in the foregoing embodiments.
The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium, and may also be a volatile computer-readable storage medium, having stored therein instructions, which, when executed on a computer, cause the computer to perform the steps of the business data processing method.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A service data processing method is characterized in that the service data processing method comprises the following steps:
receiving a fixed-delivery request of a user, and acquiring a fixed-delivery protocol in a preset service application program based on the fixed-delivery request;
matching a transaction type fixed-investment model corresponding to the fixed-investment protocol according to the fixed-investment protocol to obtain a target fixed-investment model;
acquiring numerical data and date data of each business product from a preset database, assembling the numerical data and the date data of each business product to obtain assembled product data corresponding to each business product, acquiring a fixed-investment date and fixed-investment money of a fixed-investment plan of a user, and determining a target business product corresponding to the fixed-investment request according to the assembled product data;
acquiring product financial data of the target business product, calculating data trend of the target business product according to the target investment model and the product financial data, and generating a purchase instruction of the target business product according to a preset investment rule and the data trend;
inputting the purchase instruction into a preset batch model, and analyzing the purchase instruction through the batch model to obtain an analysis result;
and if the analysis result is deduction, updating the service product data corresponding to the target service product.
2. The business data processing method of claim 1, wherein the receiving a user's request for a fixed-subscription and obtaining a fixed-subscription protocol in a preset business application based on the request for a fixed-subscription comprises:
receiving a fixed-delivery request of a user based on a preset time period, wherein the fixed-delivery request comprises account information and personal information of the user;
inquiring a fixed-delivery protocol in a preset business application program according to the account information and the personal information to obtain the fixed-delivery protocol corresponding to the account information and the personal information;
and taking a fixed-drop protocol corresponding to the account information and the personal information as a fixed-drop protocol.
3. The business data processing method of claim 1, wherein matching a transactional fixed-projection model corresponding to the fixed-projection protocol according to the fixed-projection protocol to obtain a target-projection model comprises:
carrying out data analysis on the fixed-projection protocol to obtain target protocol data;
and matching a transaction type fixed-investment model corresponding to the fixed-investment protocol based on the mapping relation between the target protocol data and a preset fixed-investment model base to obtain a target fixed-investment model.
4. The business data processing method of claim 1, wherein the steps of obtaining the numerical data and date data of each business product from a preset database, assembling the numerical data and date data of each business product to obtain the assembled product data corresponding to each business product, obtaining the fixed-investment date and fixed-investment amount of the fixed-investment plan of the user, and determining the target business product corresponding to the fixed-investment request according to the assembled product data comprise:
acquiring numerical data of each business product and a data table name corresponding to date data from a preset database;
acquiring numerical data and date data corresponding to each business product according to the data table name and a preset query rule;
data assembly is carried out on numerical data and date data corresponding to each business product according to a preset data assembly rule, and assembled product data corresponding to each business product are obtained;
and acquiring the fixed investment date and the fixed investment amount of the fixed investment plan of the user, and determining the target business product corresponding to the fixed investment request according to the assembled product data.
5. The business data processing method of claim 1, wherein the obtaining product financial data of the target business product, calculating data trend of the target business product according to the target investment model and the product financial data, and generating a purchase instruction of the target business product according to preset investment rules and the data trend comprises:
crawling product financial data corresponding to the target business product through a preset crawler;
inputting the component stocks and the bond assets into a preset target fixed investment model to perform data trend calculation to obtain data trends, wherein the data trends are descending and rising;
and if the trend of the data is rising, generating a purchase instruction of the target service product according to a preset fixed-investment rule.
6. The business data processing method of claim 1, wherein the inputting the purchase command into a preset batch model and analyzing the purchase command through the batch model to obtain an analysis result comprises:
inputting the buy instruction into a preset batch model, wherein the batch model comprises a logistic regression classifier;
and performing logistic regression operation on the purchase instruction through the logistic regression classifier to generate an analysis result, wherein the analysis result is a deduction and a non-deduction.
7. The service data processing method according to any one of claims 1 to 6, wherein if the analysis result is a deduction, updating the service product data corresponding to the target service product includes:
if the analysis result is deduction, generating a target purchase amount according to the purchase instruction;
and updating the service product data corresponding to the target service product based on the target purchase amount.
8. A service data processing apparatus, characterized in that the service data processing apparatus comprises:
the system comprises a receiving module, a sending module and a processing module, wherein the receiving module is used for receiving a fixed-delivery request of a user and acquiring a fixed-delivery protocol in a preset service application program based on the fixed-delivery request;
the matching module is used for matching a transaction type fixed-investment model corresponding to the fixed-investment protocol according to the fixed-investment protocol to obtain a target fixed-investment model;
the assembling module is used for acquiring numerical data and date data of each business product from a preset database, assembling the numerical data and the date data of each business product to obtain the assembled product data corresponding to each business product, acquiring the fixed-investment date and fixed-investment amount of a fixed-investment plan of a user, and determining a target business product corresponding to the fixed-investment request according to the assembled product data;
the processing module is used for acquiring product financial data of the target business product, calculating the data trend of the target business product according to the target investment model and the product financial data, and generating a purchase instruction of the target business product according to a preset investment rule and the data trend;
the analysis module is used for inputting the purchase instruction into a preset batch model and analyzing the purchase instruction through the batch model to obtain an analysis result;
and the generating module is used for updating the business product data corresponding to the target business product if the analysis result is deduction.
9. A service data processing apparatus, characterized in that the service data processing apparatus comprises: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invokes the instructions in the memory to cause the business data processing apparatus to perform the business data processing method of any one of claims 1-7.
10. A computer-readable storage medium having instructions stored thereon, wherein the instructions, when executed by a processor, implement the business data processing method of any one of claims 1-7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117251713A (en) * | 2023-10-11 | 2023-12-19 | 易方达基金管理有限公司 | Data processing method, device, terminal equipment and medium for buying quantity service |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040054610A1 (en) * | 2001-11-28 | 2004-03-18 | Monetaire | Monetaire wealth management platform |
| US20050278198A1 (en) * | 2004-06-09 | 2005-12-15 | Darren Huxol | Methods and systems for managing a portfolio of insurance products |
| US20080319923A1 (en) * | 2007-06-21 | 2008-12-25 | Copperleaf Technologies Inc | Investment Analysis and Planning System and Method |
| CN107945026A (en) * | 2018-01-05 | 2018-04-20 | 上海宽全智能科技有限公司 | Linearly determine Output-input analysis of Nutrients method, equipment and storage medium |
| CN109615530A (en) * | 2018-12-11 | 2019-04-12 | 平安科技（深圳）有限公司 | Surely calculation method, device, computer equipment and storage medium are thrown |
-
2021
- 2021-10-21 CN CN202111225193.7A patent/CN113971612B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040054610A1 (en) * | 2001-11-28 | 2004-03-18 | Monetaire | Monetaire wealth management platform |
| US20050278198A1 (en) * | 2004-06-09 | 2005-12-15 | Darren Huxol | Methods and systems for managing a portfolio of insurance products |
| US20080319923A1 (en) * | 2007-06-21 | 2008-12-25 | Copperleaf Technologies Inc | Investment Analysis and Planning System and Method |
| CN107945026A (en) * | 2018-01-05 | 2018-04-20 | 上海宽全智能科技有限公司 | Linearly determine Output-input analysis of Nutrients method, equipment and storage medium |
| CN109615530A (en) * | 2018-12-11 | 2019-04-12 | 平安科技（深圳）有限公司 | Surely calculation method, device, computer equipment and storage medium are thrown |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117251713A (en) * | 2023-10-11 | 2023-12-19 | 易方达基金管理有限公司 | Data processing method, device, terminal equipment and medium for buying quantity service |
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