CN113971612B - Service data processing method, device, equipment and storage medium - Google Patents
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Abstract
The invention relates to the field of artificial intelligence, and discloses a business data processing method, a device, equipment and a storage medium, which are used for the accuracy of business 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 casting date and a fixed casting amount of a fixed casting plan of a user, and determining a target service product according to the assembled product data; acquiring product financial data of a target business product, calculating data trend of the target business product according to a target projection model and the product financial data, and generating a buying instruction of the target business product according to a fixed projection rule and the data trend; inputting the buying instruction into the running batch model for analysis to obtain an analysis result; and if the analysis result is deduction, buying the target service product and generating target warehouse holding data. In addition, the invention also relates to a blockchain technology, and target holding data can be stored in a blockchain 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, more and more common service management participants are involved, and the service management is flattened, and meanwhile, the common service manager is difficult to timely master the correct service time point, and the common service manager can buy at a high point in the market and sell at a low point in 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, namely a fixed throwing method, can be balanced.
The existing scheme is usually one-time processing based on some simple analysis, but because influence of a business manager on subjective judgment of approach opportunity cannot be avoided, the risk of business processing is high, the business processing mode is single, more business information reference content cannot be provided for a user, namely, the accuracy of the existing scheme is low.
Disclosure of Invention
The invention provides a service data processing method, a device, equipment and a storage medium, which are used for the accuracy of service data processing.
The first aspect of the present invention provides a service data processing method, where the service data processing method includes: receiving a fixed casting request of a user, and acquiring a fixed casting protocol from a preset service application program based on the fixed casting request; matching a transaction type fixed casting model corresponding to the fixed casting protocol according to the fixed casting protocol to obtain a target fixed casting model; acquiring 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 assembled product data corresponding to each business product, acquiring a fixed casting date and fixed casting amount of a fixed casting plan of a user, and determining a target business product corresponding to the fixed casting 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 fixed casting model and the product financial data, and generating a buying instruction of the target business product according to a preset fixed casting rule and the data trend; inputting the buying instruction into a preset running model, and analyzing the buying instruction through the running model to obtain an analysis result; and if the analysis result is deduction, updating the business product data corresponding to the target business product.
Optionally, in a first implementation manner of the first aspect of the present invention, the receiving a fixed-cast request of a user, and acquiring a fixed-cast protocol in a preset service application program based on the fixed-cast request includes: receiving a fixed-throw request of a user based on a preset time period, wherein the fixed-throw request comprises account information and personal information of the user; inquiring a fixed-throwing protocol in a preset service application program according to the account information and the personal information to obtain a fixed-throwing protocol corresponding to the account information and the personal information; and taking the fixed-throwing protocol corresponding to the account information and the personal information as the fixed-throwing protocol.
Optionally, in a second implementation manner of the first aspect of the present invention, the matching, according to the fixed-throw protocol, the transaction fixed-throw model corresponding to the fixed-throw protocol to obtain the target fixed-throw model includes: carrying out data analysis on the fixed casting protocol to obtain target protocol data; and matching the transaction type fixed casting model corresponding to the fixed casting protocol based on the mapping relation between the target protocol data and a preset fixed casting model library to obtain a target fixed casting model.
Optionally, in a third implementation manner of the first aspect of the present invention, the obtaining numerical data and date data of each service product from a preset database, and assembling the numerical data and date data of each service product to obtain assembled product data corresponding to each service product, and obtaining a fixed throw date and fixed throw amount of a fixed throw plan of a user, and determining a target service product corresponding to the fixed throw request according to the assembled product data includes: acquiring the data table name corresponding to the numerical data and the date data of each business product from a preset database; acquiring numerical data and date data corresponding to each business product according to the data table names and through a preset query rule; carrying out data assembly on the numerical data and the date data corresponding to each business product according to a preset data assembly rule to obtain assembled product data corresponding to each business product; and acquiring a fixed casting date and a fixed casting amount of a fixed casting plan of the user, and determining a target service product corresponding to the fixed casting request according to the assembled product data.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the obtaining product financial data of the target service product, calculating a data trend of the target service product according to the target fixed-throw model and the product financial data, and generating a purchase instruction of the target service product according to a preset fixed-throw 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 throwing model to calculate data trend, so as to obtain data trend, wherein the data trend is falling and rising; and if the data trend is rising, generating a buying instruction of the target business product according to a preset fixed throwing rule.
Optionally, in a fifth implementation manner of the first aspect of the present invention, inputting the purchase instruction into a preset running batch model, and analyzing the purchase instruction through the running batch model to obtain an analysis result, including: inputting the buying instruction into a preset running batch model, wherein the running batch model comprises a logistic regression classifier; and carrying out logistic regression operation on the buying instruction through the logistic regression classifier and generating an analysis result, wherein the analysis result is deduction or non-deduction.
Optionally, in a sixth implementation manner of the first aspect of the present invention, if the analysis result is deduction, updating service product data corresponding to the target service product includes: if the analysis result is deduction, generating a target buying amount according to the buying instruction; and updating the business product data corresponding to the target business product based on the target buying amount.
A second aspect of the present invention provides a service data processing apparatus, comprising: the receiving module is used for receiving a fixed casting request of a user and acquiring a fixed casting protocol from a preset service application program based on the fixed casting request; the matching module is used for matching the transaction type fixed casting model corresponding to the fixed casting protocol according to the fixed casting protocol to obtain a target fixed casting model; the assembly module is used for acquiring the numerical data and the date data of each service product from a preset database, assembling the numerical data and the date data of each service product to obtain the assembled product data corresponding to each service product, acquiring the fixed casting date and the fixed casting amount of a fixed casting plan of a user, and determining a target service product corresponding to the fixed casting request according to the assembled product data; the processing module is used for acquiring the product financial data of the target business product, calculating the data trend of the target business product according to the target fixed-throwing model and the product financial data, and generating a buying instruction of the target business product according to a preset fixed-throwing rule and the data trend; the analysis module is used for inputting the buying instruction into a preset running batch model, and analyzing the buying instruction through the running 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-throw request of a user based on a preset time period, wherein the fixed-throw request comprises account information and personal information of the user; inquiring a fixed-throwing protocol in a preset service application program according to the account information and the personal information to obtain a fixed-throwing protocol corresponding to the account information and the personal information; and taking the fixed-throwing protocol corresponding to the account information and the personal information as the fixed-throwing 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 casting protocol to obtain target protocol data; and matching the transaction type fixed casting model corresponding to the fixed casting protocol based on the mapping relation between the target protocol data and a preset fixed casting model library to obtain a target fixed casting 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 the data table names corresponding to the numerical data and the date data of each business product 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 names and through a preset query rule; the assembling unit is used for carrying out data assembly on the numerical data and the date data corresponding to each business product according to a preset data assembling rule to obtain assembled product data corresponding to each business product; the configuration unit is used for acquiring the fixed casting date and the fixed casting amount of the fixed casting plan of the user and determining the target service product corresponding to the fixed casting 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 throwing model to calculate data trend, so as to obtain data trend, wherein the data trend is falling and rising; and if the data trend is rising, generating a buying instruction of the target business product according to a preset fixed throwing rule.
Optionally, in a fifth implementation manner of the second aspect of the present invention, the parsing module is specifically configured to: inputting the buying instruction into a preset running batch model, wherein the running batch model comprises a logistic regression classifier; and carrying out logistic regression operation on the buying instruction through the logistic regression classifier and generating an analysis result, wherein the analysis result is deduction or 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 buying amount according to the buying instruction; and updating the business product data corresponding to the target business product based on the target buying amount.
A third aspect of the present invention provides a service data processing apparatus, comprising: 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 traffic data processing apparatus to perform the traffic data processing method described above.
A fourth aspect of the present invention provides a computer readable storage medium having instructions stored therein which, when run on a computer, cause the computer to perform the business data processing method described above.
According to the technical scheme, a transaction type fixed casting model corresponding to the fixed casting protocol is matched according to the fixed casting protocol, so that a target fixed casting model is obtained; acquiring 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 assembled product data corresponding to each business product, acquiring a fixed casting date and fixed casting amount of a fixed casting plan of a user, and determining a target business product corresponding to a fixed casting request according to the assembled product data; acquiring product financial data of a target service product, calculating data trend of the target service product according to a target projection model and the product financial data, and generating a buying instruction of the target service product according to a preset projection rule and the data trend; inputting the buying instruction into a preset running batch model, and analyzing the buying instruction through the running 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, the fund data is analyzed through the target casting model to obtain the optimal target service product, and then the target service product is input into the running model for processing to obtain the target holding data, so that the accuracy of holding data generation is improved, and the accuracy of service data processing is further improved.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a method for processing service data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of another embodiment of a method for processing service data according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an embodiment of a service 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 according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a service data processing method, a device, equipment and a storage medium, which are used for the accuracy of service data processing. The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, 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 or inherent to such process, method, article, or apparatus.
For easy understanding, the following describes a specific flow of an embodiment of the present invention, referring to fig. 1, and a first embodiment of a service data processing method in an embodiment of the present invention includes:
101. Receiving a fixed casting request of a user, and acquiring a fixed casting protocol from a preset service application program based on the fixed casting request;
It will be appreciated that the execution body 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 invention is described by taking a server as an execution main body as an example. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
The embodiment of the invention can acquire and process the related data based on the artificial intelligence technology. Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results. Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
Specifically, the server receives a fixed-cast request of a user, and queries a preset fixed-cast protocol in a preset business application program based on the fixed-cast request, wherein the preset business application program comprises numerical data of each business product and query address information of date data, and further comprises a fund database, and the fund database comprises the numerical data and the date data of financial products. The numerical data refers to the original total value of the financial product or the balance obtained by subtracting the accumulated depreciation amount of the financial product from the reset total value, and the like, and the numerical data comprises the total investment cost of the financial product, the anti-interference rate of the financial product, the amount of the financial product and the total share of the financial product, and the like; date data refers to the time node at which the financial product produced the change in financial price.
102. Matching a transaction type fixed casting model corresponding to the fixed casting protocol according to the fixed casting protocol to obtain a target fixed casting model;
Specifically, the server matches a transaction type fixed-throw model corresponding to the fixed-throw protocol according to the fixed-throw protocol to obtain a target fixed-throw model, the transaction type fixed-throw model sets the date, the fund and the amount of deduction in each month, the transaction type fixed-throw model is used for buying, buying is not expanded, after the first fixed-throw is started, the business application program starts the recorded fixed-throw action, the software can pre-judge the net value (inaccurate net value) when buying in the future on the next deduction day, if the net value purchased at the time is in a relatively low position, a buying instruction is sent, and otherwise, if the net value purchased at the time is in a relatively high position, the buying instruction is sent.
103. Acquiring 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 assembled product data corresponding to each business product, acquiring a fixed casting date and fixed casting amount of a fixed casting plan of a user, and determining a target business product corresponding to a fixed casting request according to the assembled product data;
Specifically, the server performs data analysis on the fixed casting protocol to obtain target protocol data, and queries a preset database through a preset query rule and obtains numerical data and date data of each service product, which specifically includes: the server inquires a preset database by calling a preset calculation engine according to the preset data table name, and acquires numerical value 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 an item, imports a framework, and provides packages for request response services. The server creates objects for computation at the start of the service and encapsulates them in a class, and the server completes the pre-configuration of the compute engine by inheriting this class. When the server has the query request of the numerical data and the date data of the fund each time, the server can call the SQL method, and the SQL of the query request is transmitted to calculate so as to start the pre-configured calculation engine to query the pre-set database.
104. Acquiring product financial data of a target service product, calculating data trend of the target service product according to a target projection model and the product financial data, and generating a buying instruction of the target service product according to a preset projection rule and the data trend;
Specifically, the server selects a target business product with net profit growth rate far exceeding that of the main business according to the product financial data of the fund through a target casting model, wherein the product financial data comprises component stocks and bonds or other assets, the current component assets are expressed in corresponding stock markets and closing prices, the net profit growth rate is 25% of that of the main business, and the target business product is a good expansion rate for a fund every year, if other businesses still can be rapidly developed, the future development rate of the company is good. The server sets the deduction day of each month, other funds and other times do not accord with the transaction rules, and the first two days of the fixed deduction day can send transaction instructions to deduct money or not.
105. Inputting the buying instruction into a preset running batch model, and analyzing the buying instruction through the running batch model to obtain an analysis result;
Specifically, the server inputs the buying instruction into a preset running model, analyzes the buying instruction through the running model to obtain an analysis result, performs logistic regression operation on the buying instruction through a logistic regression classifier to generate an analysis result, analyzes the buying instruction through the running model to obtain the analysis result, and the analysis result comprises deduction and 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 business product data corresponding to the target business product is updated, wherein the target warehouse holding data can voluntarily determine to buy or sell futures contracts according to market quotation and personal will before the real object delivery or cash delivery expires. The investors (doing more or making blank) do not do the reverse operations (selling or buying) with equal delivery months and quantity, and hold futures contracts, which is called holding, and when the user buys the target business product, the numerical data of the target business product is the holding data of the user.
Further, the server stores the targeted holding data in a blockchain database, as not limited herein.
In the embodiment of the invention, a transaction type fixed casting model corresponding to a fixed casting protocol is matched according to the fixed casting protocol to obtain a target fixed casting model; acquiring 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 assembled product data corresponding to each business product, acquiring a fixed casting date and fixed casting amount of a fixed casting plan of a user, and determining a target business product corresponding to a fixed casting request according to the assembled product data; acquiring product financial data of a target service product, calculating data trend of the target service product according to a target projection model and the product financial data, and generating a buying instruction of the target service product according to a preset projection rule and the data trend; inputting the buying instruction into a preset running batch model, and analyzing the buying instruction through the running 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, the fund data is analyzed through the target casting model to obtain the optimal target service product, and then the target service product is input into the running model for processing to obtain the target holding data, so that the accuracy of holding data generation is improved, and the accuracy of service data processing is further improved.
Referring to fig. 2, a second embodiment of a service data processing method in an embodiment of the present invention includes:
201. receiving a fixed casting request of a user, and acquiring a fixed casting protocol from a preset service application program based on the fixed casting request;
optionally, the server receives a fixed-throw request of the user based on a preset time period, wherein the fixed-throw request comprises account information and personal information of the user; inquiring a fixed-throwing protocol in a preset service application program according to the account information and the personal information by the server to obtain a fixed-throwing protocol corresponding to the account information and the personal information; and the server takes the fixed-throwing protocol corresponding to the account information and the personal information as the fixed-throwing protocol.
Specifically, the server receives a fixed-throw request of a user based on a preset time period, wherein the fixed-throw request comprises account information and personal information of the user, and the preset time period is the first two days of a transaction day; the server inquires a fixed-throwing protocol in a preset service application program according to account information and personal information to obtain the fixed-throwing protocol corresponding to the account information and the personal information, wherein the account information comprises account data and password data of a user and holding data, and the personal information comprises personal operation records, personal preference data and the like of the user; the server takes the fixed-throwing protocol corresponding to the account information and the personal information as the fixed-throwing protocol, the server inquires the fixed-throwing mode of the user preference from the service application program according to the account information of the user, and the server determines the corresponding fixed-throwing protocol, namely the fixed-throwing protocol according to the fixed-throwing mode.
202. Matching a transaction type fixed casting model corresponding to the fixed casting protocol according to the fixed casting protocol to obtain a target fixed casting model;
Optionally, the server performs data analysis on the fixed casting protocol to obtain target protocol data; the server matches the transaction type fixed casting model corresponding to the fixed casting protocol based on the mapping relation between the target protocol data and a preset fixed casting model library to obtain the target fixed casting model.
Specifically, the server performs data analysis on the fixed casting protocol to obtain target protocol data; the server matches the transaction type fixed casting model corresponding to the fixed casting protocol based on the mapping relation between the target protocol data and a preset fixed casting model library to obtain the target fixed casting model. The server sets No. 5 or No. 20 of each month as the deduction day, other funds and other times do not accord with the transaction rule, and the transaction instruction deduction or no deduction is sent in the first 2 days of the fixed deduction day.
203. Acquiring the data table name corresponding to the numerical data and the date data of each business product from a preset database;
Specifically, the server performs data analysis on the fixed casting protocol to obtain target protocol data, and queries a preset database through a preset query rule and obtains numerical data and date data of each service product, which specifically includes: the server inquires a preset database by calling a preset calculation engine according to the preset data table name, and acquires numerical value 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 through a preset query rule;
The method specifically comprises the following steps: comparing the user's posting date with date data in a key-value data format; if the fixed-throw date of the user does not correspond to the date data in the key-value data format, natural day offset is carried out according to the date data in the key-value data format to determine the numerical value data of the fixed-throw product corresponding to the fixed-throw date, and the corresponding target business product is determined according to the determined numerical value data of the fixed-throw product.
205. Carrying out data assembly on the numerical data and the date data corresponding to each business product according to a preset data assembly rule to obtain assembled product data corresponding to each business product;
Specifically, the server acquires the data table name corresponding to the numerical data and the date data of each service product from a preset database, acquires the numerical data and the date data corresponding to each service product according to the data table name and through a preset query rule, and takes the numerical data of each service 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; the server assembles the numerical data and date data corresponding to each business product into data in a key-value data format according to keys and values, wherein the key-value data format refers to a key-value data format, the keys are used as indexes of elements, and the values represent the stored and read data to obtain the assembled product data.
206. Acquiring a fixed throw date and a fixed throw amount of a fixed throw plan of a user, and determining a target service product corresponding to a fixed throw request according to assembled product data;
specifically, the server stores numerical data and date data through a plurality of key-value pairs in a preset set. Because the fixed casting date of the fixed casting product selected by the user is not necessarily the optimal profit and receipt date of the fixed casting product, the numerical data and date data of the fixed casting product are recorded in the key-value data format, namely the optimal profit and receipt date of the fixed casting product is recorded in the key-value data format, the fixed casting date of the fixed casting product selected by the user is not the optimal profit and receipt date recorded in the key-value data format, natural day offset is carried out on the fixed casting date according to the key-value data format, and the target service product which is most consistent with the fixed casting date of the user is selected.
207. Acquiring product financial data of a target service product, calculating data trend of the target service product according to a target projection model and the product financial data, and generating a buying instruction of the target service product according to a preset projection 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 throwing model to calculate the data trend, so as to obtain the data trend, wherein the data trend is falling and rising; if the data trend is rising, the server generates a buying instruction of the target service product according to a preset fixed throwing rule.
Specifically, 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 throwing model to calculate the data trend, so as to obtain the data trend, wherein the data trend is falling and rising; and if the data trend is rising, the server generates a buying instruction of the target service product according to a preset fixed throwing rule. The trade formula is thrown surely, namely is buying and falls and does not buy and rise, after the first time is thrown surely, the trade software of the fund company begins to record our and throw the action surely, and is temporary in the future on the next withholding day, the software will judge the net value when buying in advance, if the net value that buys in this time is in the relatively low level, send the instruction of buying, otherwise if is relatively high level, send the instruction of not buying, through the analysis of the model of target throwing surely, realize buying and falling and does not buy and rise the wish, in order to obtain more fund share with fixed cost, in order to have more profit when the fund rises.
208. Inputting the buying instruction into a preset running batch model, and analyzing the buying instruction through the running batch model to obtain an analysis result;
Optionally, the server inputs the purchase instruction into a preset running batch model, wherein the running batch model comprises a logistic regression classifier; the server carries out logistic regression operation on the buying instruction through the logistic regression classifier and generates an analysis result, and the analysis result is deduction or non-deduction.
Specifically, the server inputs a buying instruction into a preset running batch model, and the running batch model comprises a logistic regression classifier; the server carries out logistic regression operation on the buying instruction through the logistic regression classifier and generates an analysis result, and the analysis result is deduction or non-deduction. When the running batch model is tested through a preset test data set, the data in the test data set are divided into 5 gears, namely a first gear, a second gear, a third gear, a fourth gear and a fifth gear from large to small according to the probability value output by the running batch model. If the yield of the first data of the running batch model is highest, the test result corresponding to the running batch model is marked as 1, otherwise, the test result is marked as 0. The server tests the running batch model through a test data set acquired on a time sequence of a fixed time point (such as month 1 in one year) of the past several years, marks the test result corresponding to the running batch model according to the principle, carries out logistic regression operation on the buying instruction through a logistic regression classifier, and generates an analysis result, wherein the analysis result is deduction and 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 buying amount according to the buying instruction; and the server buys the target business product based on the target buying amount and updates the holding data of the target business product to obtain target holding data.
Specifically, if the analysis result is deduction, the server generates a target buying amount according to the buying instruction, wherein the buying instruction carries the buying amount of the target service product, and when receiving the buying instruction sent by the target throwing model, the batch running model performs buying of the target service product according to the buying amount in the buying instruction, and in addition, the buying is accompanied with deduction of account balance; the server buys the target business product based on the target buying amount and updates the holding data of the target business product to obtain target holding data, and after the running batch model responds to the buying instruction, the server updates account balance data in the current state, namely the target holding data.
Further, the server stores the targeted holding data in a blockchain database, as not limited herein.
In the embodiment of the invention, a transaction type fixed casting model corresponding to a fixed casting protocol is matched according to the fixed casting protocol to obtain a target fixed casting model; acquiring 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 assembled product data corresponding to each business product, acquiring a fixed casting date and fixed casting amount of a fixed casting plan of a user, and determining a target business product corresponding to a fixed casting request according to the assembled product data; acquiring product financial data of a target service product, calculating data trend of the target service product according to a target projection model and the product financial data, and generating a buying instruction of the target service product according to a preset projection rule and the data trend; inputting the buying instruction into a preset running batch model, and analyzing the buying instruction through the running 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, the fund data is analyzed through the target casting model to obtain the optimal target service product, and then the target service product is input into the running model for processing to obtain the target holding data, so that the accuracy of holding data generation is improved, and the accuracy of service data processing is further improved.
The method for processing service data in the embodiment of the present invention is described above, and the service data processing device in the embodiment of the present invention is described below, referring to fig. 3, where a first embodiment of the service data processing device in the embodiment of the present invention includes:
the receiving module 301 is configured to receive a fixed-cast request of a user, and obtain a fixed-cast protocol from a preset service application program based on the fixed-cast request;
The matching module 302 is configured to match the transaction type fixed casting model corresponding to the fixed casting protocol according to the fixed casting protocol to obtain a target fixed casting model;
the assembling module 303 is configured to obtain numerical data and date data of each service product from a preset database, assemble the numerical data and date data of each service product to obtain assembled product data corresponding to each service product, obtain a fixed-throw date and a fixed-throw amount of a fixed-throw plan of a user, and determine a target service product corresponding to the fixed-throw request according to the assembled product data;
The processing module 304 is configured to obtain product financial data of the target service product, calculate a data trend of the target service product according to the target fixed-throw model and the product financial data, and generate a purchase instruction of the target service product according to a preset fixed-throw rule and the data trend;
the analysis module 305 is configured to input the purchase instruction into a preset run model, and analyze the purchase instruction through the run model to obtain an analysis result;
And the generating module 306 is configured to update the service product data corresponding to the target service product if the analysis result is deduction.
In the embodiment of the invention, a transaction type fixed casting model corresponding to a fixed casting protocol is matched according to the fixed casting protocol to obtain a target fixed casting model; acquiring 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 assembled product data corresponding to each business product, acquiring a fixed casting date and fixed casting amount of a fixed casting plan of a user, and determining a target business product corresponding to a fixed casting request according to the assembled product data; acquiring product financial data of a target service product, calculating data trend of the target service product according to a target projection model and the product financial data, and generating a buying instruction of the target service product according to a preset projection rule and the data trend; inputting the buying instruction into a preset running batch model, and analyzing the buying instruction through the running 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, the fund data is analyzed through the target casting model to obtain the optimal target service product, and then the target service product is input into the running model for processing to obtain the target holding data, so that the accuracy of holding data generation is improved, and the accuracy of service data processing is further improved.
Referring to fig. 4, a second embodiment of a service data processing apparatus according to an embodiment of the present invention includes:
the receiving module 301 is configured to receive a fixed-cast request of a user, and obtain a fixed-cast protocol from a preset service application program based on the fixed-cast request;
The matching module 302 is configured to match the transaction type fixed casting model corresponding to the fixed casting protocol according to the fixed casting protocol to obtain a target fixed casting model;
the assembling module 303 is configured to obtain numerical data and date data of each service product from a preset database, assemble the numerical data and date data of each service product to obtain assembled product data corresponding to each service product, obtain a fixed-throw date and a fixed-throw amount of a fixed-throw plan of a user, and determine a target service product corresponding to the fixed-throw request according to the assembled product data;
The processing module 304 is configured to obtain product financial data of the target service product, calculate a data trend of the target service product according to the target fixed-throw model and the product financial data, and generate a purchase instruction of the target service product according to a preset fixed-throw rule and the data trend;
the analysis module 305 is configured to input the purchase instruction into a preset run model, and analyze the purchase instruction through the run model to obtain an analysis result;
And the generating module 306 is configured to update the service product data corresponding to the target service product if the analysis result is deduction.
Optionally, the receiving module 301 is specifically configured to:
Receiving a fixed-throw request of a user based on a preset time period, wherein the fixed-throw request comprises account information and personal information of the user; inquiring a fixed-throwing protocol in a preset service application program according to the account information and the personal information to obtain a fixed-throwing protocol corresponding to the account information and the personal information; and taking the fixed-throwing protocol corresponding to the account information and the personal information as the fixed-throwing protocol.
Optionally, the matching module 302 is specifically configured to: carrying out data analysis on the fixed casting protocol to obtain target protocol data; and matching the transaction type fixed casting model corresponding to the fixed casting protocol based on the mapping relation between the target protocol data and a preset fixed casting model library to obtain a target fixed casting model.
Optionally, the assembling module 303 further includes:
an obtaining unit 3031, configured to obtain, from a preset database, a data table name corresponding to the numerical data and the date data of each service product;
A query unit 3032, configured to obtain numerical data and date data corresponding to each service 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 service product according to a preset data assembly rule, so as to obtain assembled product data corresponding to each service product;
And the configuration unit 3034 is used for acquiring the fixed casting date and the fixed casting amount of the fixed casting plan of the user and determining the target service product corresponding to the fixed casting 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 throwing model to calculate data trend, so as to obtain data trend, wherein the data trend is falling and rising; and if the data trend is rising, generating a buying instruction of the target business product according to a preset fixed throwing rule.
Optionally, the parsing module 305 is specifically configured to:
inputting the buying instruction into a preset running batch model, wherein the running batch model comprises a logistic regression classifier; and carrying out logistic regression operation on the buying instruction through the logistic regression classifier and generating an analysis result, wherein the analysis result is deduction or non-deduction.
Optionally, the generating module 306 is specifically configured to:
If the analysis result is deduction, generating a target buying amount according to the buying instruction; and updating the business product data corresponding to the target business product based on the target buying amount.
In the embodiment of the invention, a transaction type fixed casting model corresponding to a fixed casting protocol is matched according to the fixed casting protocol to obtain a target fixed casting model; acquiring 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 assembled product data corresponding to each business product, acquiring a fixed casting date and fixed casting amount of a fixed casting plan of a user, and determining a target business product corresponding to a fixed casting request according to the assembled product data; acquiring product financial data of a target service product, calculating data trend of the target service product according to a target projection model and the product financial data, and generating a buying instruction of the target service product according to a preset projection rule and the data trend; inputting the buying instruction into a preset running batch model, and analyzing the buying instruction through the running 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, the fund data is analyzed through the target casting model to obtain the optimal target service product, and then the target service product is input into the running model for processing to obtain the target holding data, so that the accuracy of holding data generation is improved, and the accuracy of service data processing is further improved.
The service data processing device in the embodiment of the present invention is described in detail above in terms of modularized functional entities in fig. 3 and fig. 4, and the service data processing apparatus in the embodiment of the present invention is described in detail below in terms of hardware processing.
Fig. 5 is a schematic structural diagram of a service data processing device according to an embodiment of the present invention, where the service data processing device 500 may have a relatively large difference due to different configurations or performances, and may include one or more processors (central processing units, CPU) 510 (e.g., one or more processors) and a memory 520, one or more storage mediums 530 (e.g., one or more mass storage devices) storing application programs 533 or data 532. Wherein memory 520 and storage medium 530 may be transitory or persistent storage. The program stored in the storage medium 530 may include one or more modules (not shown), each of which may include a series of instruction operations in the service data processing apparatus 500. Still further, the processor 510 may be arranged to communicate with a storage medium 530 and to execute a series of instruction operations in the storage medium 530 on the traffic data processing apparatus 500.
The traffic 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 Serve, mac OS X, unix, linux, freeBSD, and the like. It will be appreciated by those skilled in the art that the configuration of the traffic data processing apparatus shown in fig. 5 is not limiting of the traffic data processing apparatus and may include more or less components than those illustrated, or may be combined with certain components, or may be arranged with different components.
The present invention also provides a service data processing device, which includes a memory and a processor, where the memory stores computer readable instructions that, 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, or 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 service 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 from the use of blockchain nodes, and the like.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
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.
Claims (10)
1. A business data processing method, characterized in that the business data processing method comprises:
Receiving a fixed casting request of a user, and acquiring a fixed casting protocol from a preset service application program based on the fixed casting request;
Matching a transaction type fixed casting model corresponding to the fixed casting protocol according to the fixed casting protocol to obtain a target fixed casting model;
acquiring 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 assembled product data corresponding to each business product, acquiring a fixed casting date and fixed casting amount of a fixed casting plan of a user, and determining a target business product corresponding to the fixed casting 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 fixed casting model and the product financial data, and generating a buying instruction of the target business product according to a preset fixed casting rule and the data trend;
Inputting the buying instruction into a preset running model, and analyzing the buying instruction through the running model to obtain an analysis result;
if the analysis result is deduction, updating the business product data corresponding to the target business product;
the receiving the fixed throwing request of the user and acquiring the fixed throwing protocol from a preset service application program based on the fixed throwing request comprises the following steps:
receiving a fixed-throw request of a user based on a preset time period, wherein the fixed-throw request comprises account information and personal information of the user;
inquiring a fixed-throwing protocol in a preset service application program according to the account information and the personal information to obtain a fixed-throwing protocol corresponding to the account information and the personal information;
taking the fixed-throwing protocol corresponding to the account information and the personal information as a fixed-throwing protocol;
the step of matching the transaction type fixed casting model corresponding to the fixed casting protocol according to the fixed casting protocol to obtain a target fixed casting model comprises the following steps:
Carrying out data analysis on the fixed casting protocol to obtain target protocol data;
matching a transaction type fixed casting model corresponding to the fixed casting protocol based on the mapping relation between the target protocol data and a preset fixed casting model library to obtain a target fixed casting model;
The method for 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 the fixed casting date and the fixed casting amount of a fixed casting plan of a user, and determining the target business product corresponding to the fixed casting request according to the assembled product data comprises the following steps:
acquiring the data table name corresponding to the numerical data and the date data of each business product from a preset database;
Acquiring numerical data and date data corresponding to each business product according to the data table names and through a preset query rule;
Carrying out data assembly on the numerical data and the date data corresponding to each business product according to a preset data assembly rule to obtain assembled product data corresponding to each business product;
And acquiring a fixed casting date and a fixed casting amount of a fixed casting plan of the user, and determining a target service product corresponding to the fixed casting request according to the assembled product data.
2. The business data processing method of claim 1, wherein the obtaining product financial data of the target business product, calculating a data trend of the target business product according to the target throwing model and the product financial data, and generating a buying instruction of the target business product according to a preset throwing rule and the data trend, comprises:
crawling product financial data corresponding to the target business product through a preset crawler;
Inputting component stocks and bond assets into a preset target throwing model to perform data trend calculation to obtain data trends, wherein the data trends are falling and rising;
and if the data trend is rising, generating a buying instruction of the target business product according to a preset fixed throwing rule.
3. The business data processing method of claim 1, wherein inputting the purchase order into a preset run-batch model, and analyzing the purchase order by the run-batch model to obtain an analysis result, comprises:
inputting the buying instruction into a preset running batch model, wherein the running batch model comprises a logistic regression classifier;
and carrying out logistic regression operation on the buying instruction through the logistic regression classifier and generating an analysis result, wherein the analysis result is deduction or non-deduction.
4. A service data processing method according to any one of claims 1 to 3, wherein if the analysis result is a deduction, updating service product data corresponding to the target service product includes:
if the analysis result is deduction, generating a target buying amount according to the buying instruction;
and updating the business product data corresponding to the target business product based on the target buying amount.
5. A traffic data processing apparatus, characterized in that the traffic data processing apparatus comprises:
the receiving module is used for receiving a fixed casting request of a user and acquiring a fixed casting protocol from a preset service application program based on the fixed casting request;
the matching module is used for matching the transaction type fixed casting model corresponding to the fixed casting protocol according to the fixed casting protocol to obtain a target fixed casting model;
The assembly module is used for acquiring the numerical data and the date data of each service product from a preset database, assembling the numerical data and the date data of each service product to obtain the assembled product data corresponding to each service product, acquiring the fixed casting date and the fixed casting amount of a fixed casting plan of a user, and determining a target service product corresponding to the fixed casting request according to the assembled product data;
The processing module is used for acquiring the product financial data of the target business product, calculating the data trend of the target business product according to the target fixed-throwing model and the product financial data, and generating a buying instruction of the target business product according to a preset fixed-throwing rule and the data trend;
The analysis module is used for inputting the buying instruction into a preset running batch model, and analyzing the buying instruction through the running batch model to obtain an analysis result;
the generation module is used for updating the business product data corresponding to the target business product if the analysis result is deduction;
the receiving module is specifically configured to: receiving a fixed-throw request of a user based on a preset time period, wherein the fixed-throw request comprises account information and personal information of the user; inquiring a fixed-throwing protocol in a preset service application program according to the account information and the personal information to obtain a fixed-throwing protocol corresponding to the account information and the personal information; taking the fixed-throwing protocol corresponding to the account information and the personal information as a fixed-throwing protocol;
The matching module is specifically used for: carrying out data analysis on the fixed casting protocol to obtain target protocol data; matching a transaction type fixed casting model corresponding to the fixed casting protocol based on the mapping relation between the target protocol data and a preset fixed casting model library to obtain a target fixed casting model;
The assembly module further includes:
The acquisition unit is used for acquiring the data table names corresponding to the numerical data and the date data of each business product 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 names and through a preset query rule;
the assembling unit is used for carrying out data assembly on the numerical data and the date data corresponding to each business product according to a preset data assembling rule to obtain assembled product data corresponding to each business product;
The configuration unit is used for acquiring the fixed casting date and the fixed casting amount of the fixed casting plan of the user and determining the target service product corresponding to the fixed casting request according to the assembled product data.
6. The service data processing device according to claim 5, wherein the processing module is specifically configured to: crawling product financial data corresponding to the target business product through a preset crawler; inputting component stocks and bond assets into a preset target throwing model to perform data trend calculation to obtain data trends, wherein the data trends are falling and rising; and if the data trend is rising, generating a buying instruction of the target business product according to a preset fixed throwing rule.
7. The service data processing device according to claim 5, wherein the parsing module is specifically configured to: inputting the buying instruction into a preset running batch model, wherein the running batch model comprises a logistic regression classifier; and carrying out logistic regression operation on the buying instruction through the logistic regression classifier and generating an analysis result, wherein the analysis result is deduction or non-deduction.
8. The service data processing apparatus according to any one of claims 5-7, wherein the generating module is specifically configured to: if the analysis result is deduction, generating a target buying amount according to the buying instruction; and updating the business product data corresponding to the target business product based on the target buying amount.
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 invoking the instructions in the memory to cause the traffic data processing apparatus to perform the traffic data processing method according to any of claims 1-4.
10. A computer readable storage medium having instructions stored thereon, which when executed by a processor, implement the business data processing method of any of claims 1-4.
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| US20050278198A1 (en) * | 2004-06-09 | 2005-12-15 | Darren Huxol | Methods and systems for managing a portfolio of insurance products |
| US20080319811A1 (en) * | 2007-06-21 | 2008-12-25 | Audrey Lynn Casey | System and method for modeling an asset-based business |
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| 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 |
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