CN113723525A - Product recommendation method, device, equipment and storage medium based on genetic algorithm - Google Patents
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Abstract
The embodiment of the invention relates to the field of artificial intelligence, and discloses a product recommendation method, a product recommendation device, product recommendation equipment and a storage medium based on a genetic algorithm, wherein the method comprises the following steps: acquiring data stream information, wherein the data stream information comprises product information and customer information; inputting the data flow information into a pre-trained cash flow prediction model to obtain a prediction result of cash flow data in the data flow information; acquiring a first sample training data set, inputting the first sample training data set into a preset genetic algorithm model for training to obtain an investment portfolio recommendation model; the data flow information and the prediction result are input into the investment portfolio recommendation model, the target recommended product is determined, and the target recommended product is sent to the client terminal, so that the flow direction situation of the cash flow data of the client can be accurately predicted, the investment product is intelligently recommended to the client, and the accuracy and efficiency of product recommendation are improved. The present invention relates to blockchain techniques, such as data streams may be written into blockchains for use in scenarios such as data forensics.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a product recommendation method, a product recommendation device, product recommendation equipment and a storage medium based on a genetic algorithm.
Background
At present, few software is used for accurately predicting future cash flow conditions of enterprises through a big data model in the market, and meanwhile, in different product recommendation scenes, because product data on a big data platform are very much, the product recommendation technology is difficult to realize, so that less software is used for intelligently recommending investment product service schemes for the enterprises. Therefore, how to realize efficient, accurate and intelligent product recommendation is a significant issue.
Disclosure of Invention
The embodiment of the invention provides a product recommendation method, a product recommendation device, product recommendation equipment and a storage medium based on a genetic algorithm, which can accurately predict the flow direction condition of cash flow data of a client, realize intelligent investment product recommendation for the client and improve the accuracy and efficiency of product recommendation.
In a first aspect, an embodiment of the present invention provides a product recommendation method based on a genetic algorithm, including:
acquiring data flow information, wherein the data flow information comprises product information and customer information, and the data flow information is acquired from one or more of an enterprise big data platform, an external data platform and a bank big data platform and is associated with each product;
inputting the data flow information into a pre-trained cash flow prediction model to obtain a prediction result of cash flow data in the data flow information;
acquiring a first sample training data set, wherein the first sample training data set comprises historical investment data of a plurality of products, and inputting the first sample training data set into a preset genetic algorithm model for training to obtain an investment portfolio recommendation model;
and inputting the data flow information and the prediction result into an investment portfolio recommendation model, determining a target recommended product, and sending the target recommended product to a client terminal.
Further, before the inputting the data flow information into the pre-trained cash flow prediction model and obtaining the prediction result of the cash flow data in the data flow information, the method further includes:
obtaining a second sample training data set, wherein the second sample training data set comprises a plurality of historical cash flow data;
inputting the second sample training data set into a pre-trained time sequence prediction model to obtain a loss function value;
when the loss function value does not meet the preset condition, adjusting the model parameters of the time series prediction model, and inputting the second sample training data set into the time series prediction model after the model parameters are adjusted for iterative training;
and when the loss function value obtained by iterative training meets a preset condition, determining to obtain the cash flow prediction model.
Further, after the obtaining the second sample training data set, the method further includes:
preprocessing each historical cash flow data in the second sample training data set, and performing stationarity detection on the preprocessed second sample training data set;
if the stationarity detection does not pass, performing difference processing on the preprocessed second sample training data set;
when the stationarity detection passes after the differential processing is executed for a plurality of times, performing white noise inspection processing on the second sample training data set after the differential processing;
when the white noise test is not passed, performing autocorrelation calculation and partial autocorrelation calculation on the second sample training data set after the difference processing to obtain first result data of the autocorrelation calculation and second result data of the partial autocorrelation calculation;
inputting the second sample training data set into a pre-trained time series prediction model to obtain a loss function value, wherein the method comprises the following steps:
and inputting the second sample training data set, the difference processing times, the first result data and the second result data into a pre-trained time series prediction model to obtain the loss function value.
Further, before the inputting the data flow information into the pre-trained cash flow prediction model and obtaining the prediction result of the cash flow data in the data flow information, the method further includes:
inputting the second sample training data set into a pre-trained business rule prediction model to obtain a loss function value;
when the loss function value does not meet the preset condition, adjusting the model parameters of the business rule prediction model, and inputting the second sample training data set into the business rule prediction model with the adjusted model parameters for iterative training;
and when the loss function value obtained by iterative training meets a preset condition, determining to obtain the cash flow prediction model.
Further, before the inputting the data flow information into the pre-trained cash flow prediction model and obtaining the prediction result of the cash flow data in the data flow information, the method further includes:
fusing the time series prediction model and the service rule prediction model according to a preset fusion rule to obtain a fusion prediction model;
inputting the second sample training data set into the fusion prediction model to obtain a loss function value;
when the loss function value does not meet the preset condition, adjusting the model parameters of the fusion prediction model, and inputting the second sample training data set into the fusion prediction model with the adjusted model parameters for iterative training;
and when the loss function value obtained by iterative training meets a preset condition, determining to obtain the cash flow prediction model.
Further, the inputting the first sample training data set into a preset genetic algorithm model for training to obtain an investment portfolio recommendation model includes:
acquiring combined investment proportion data among products and individual investment proportion data of each product from each historical investment data in the first sample training data set;
converting the combined investment proportion data and the individual investment proportion data of each product to obtain converted investment data;
merging the historical investment data and the converted investment data to obtain merged investment data, and sequencing the merged investment data;
and selecting one or more target investment data according to the sorted combined investment data, and inputting the one or more target investment data into the preset genetic algorithm model for training to obtain the investment portfolio recommendation model.
Further, the inputting the data flow information and the prediction result into a portfolio recommendation model to determine a target recommended product includes:
acquiring attribute information of product information in the data stream information, and classifying each product according to the attribute information;
and inputting the classified data flow information and the prediction result into the investment portfolio recommendation model to determine the target recommended product.
In a second aspect, an embodiment of the present invention provides a product recommendation device based on a genetic algorithm, including:
the system comprises an acquisition unit, a processing unit and a display unit, wherein the acquisition unit is used for acquiring data flow information, the data flow information comprises product information and customer information, and the data flow information is acquired from one or more of an enterprise big data platform, an external data platform and a bank big data platform and is associated with each product;
the prediction unit is used for inputting the data flow information into a pre-trained cash flow prediction model to obtain a prediction result of cash flow data in the data flow information;
the training unit is used for acquiring a first sample training data set, wherein the first sample training data set comprises historical investment data of a plurality of products, and the first sample training data set is input into a preset genetic algorithm model for training to obtain an investment portfolio recommendation model;
and the recommending unit is used for inputting the data flow information and the prediction result into an investment portfolio recommending model, determining a target recommended product and sending the target recommended product to a client terminal.
In a third aspect, an embodiment of the present invention provides a computer device, including a processor and a memory, where the memory is used to store a computer program, and the computer program includes a program, and the processor is configured to call the computer program to execute the method of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, which stores a computer program, where the computer program is executed by a processor to implement the method of the first aspect.
The embodiment of the invention can acquire data flow information, wherein the data flow information comprises product information and customer information, and the data flow information is acquired from one or more of an enterprise big data platform, an external data platform and a bank big data platform and is associated with each product; inputting the data flow information into a pre-trained cash flow prediction model to obtain a prediction result of cash flow data in the data flow information; acquiring a first sample training data set, wherein the first sample training data set comprises historical investment data of a plurality of products, and inputting the first sample training data set into a preset genetic algorithm model for training to obtain an investment portfolio recommendation model; and inputting the data flow information and the prediction result into an investment portfolio recommendation model, determining a target recommended product, and sending the target recommended product to a client terminal. By the implementation mode, the flow direction condition of the cash flow data of the client can be accurately predicted, the investment products are intelligently recommended to the client, and the accuracy and efficiency of product recommendation are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a product recommendation method based on genetic algorithm according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a product recommendation device based on a genetic algorithm according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of a computer device provided by an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The product recommendation method based on the genetic algorithm provided by the embodiment of the invention can be applied to a product recommendation device based on the genetic algorithm, and in some embodiments, the product recommendation device based on the genetic algorithm is arranged in computer equipment. In certain embodiments, the computer device includes, but is not limited to, one or more of a smartphone, tablet, laptop, and the like.
The embodiment of the invention can acquire data flow information, wherein the data flow information comprises product information and customer information, and the data flow information is acquired from one or more of an enterprise big data platform, an external data platform and a bank big data platform and is associated with each product; inputting the data flow information into a pre-trained cash flow prediction model to obtain a prediction result of cash flow data in the data flow information; acquiring a first sample training data set, wherein the first sample training data set comprises historical investment data of a plurality of products, and inputting the first sample training data set into a preset genetic algorithm model for training to obtain an investment portfolio recommendation model; and inputting the data flow information and the prediction result into an investment portfolio recommendation model, determining a target recommended product, and sending the target recommended product to a client terminal. By the implementation mode, the flow direction condition of the cash flow data of the client can be accurately predicted, the investment products are intelligently recommended to the client, and the accuracy and efficiency of product recommendation are improved.
The embodiment of the application can acquire and process related data (such as data flow information) 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.
The embodiment of the application can be applied to various fields, such as: the field of medical product recommendation, the field of financial product recommendation, and the like.
In one possible implementation, in the field of medical product recommendation, the data may be medical data associated with the medical product, such as examination data, assay data, and the like associated with the medical product.
The product recommendation method based on genetic algorithm provided by the embodiment of the invention is schematically illustrated with reference to fig. 1.
Referring to fig. 1, fig. 1 is a schematic flow chart of a product recommendation method based on a genetic algorithm according to an embodiment of the present invention, as shown in fig. 1, the method may be performed by a product recommendation device based on a genetic algorithm, and the product recommendation device based on a genetic algorithm is disposed in a computer device. Specifically, the method of the embodiment of the present invention includes the following steps.
S101: and acquiring data stream information, wherein the data stream information comprises product information and customer information.
In the embodiment of the invention, the product recommendation device based on the genetic algorithm can acquire data flow information, wherein the data flow information comprises product information and customer information. In some embodiments, the data flow information may be data information associated with individual products obtained from one or more of an enterprise big data platform, an external data platform, a bank big data platform. In certain embodiments, the data flow information includes, but is not limited to, enterprise production and operation data, marketing data, banking data, and the like.
S102: and inputting the data flow information into a pre-trained cash flow prediction model to obtain a prediction result of cash flow data in the data flow information.
In the embodiment of the invention, the product recommendation device based on the genetic algorithm can input the data flow information into a pre-trained cash flow prediction model to obtain the prediction result of the cash flow data in the data flow information. In some embodiments, the predicted outcome includes a predicted surplus or shortage of cash, an amount of surplus or shortage, etc. for each product over a future period of time.
In one embodiment, the product recommendation device based on genetic algorithm may obtain a second sample training data set before inputting the data stream information into the pre-trained cash stream prediction model to obtain the prediction result of the cash stream data in the data stream information, wherein the second sample training data set comprises a plurality of historical cash stream data; inputting the second sample training data set into a pre-trained time sequence prediction model to obtain a loss function value; when the loss function value does not meet the preset condition, adjusting the model parameters of the time series prediction model, and inputting the second sample training data set into the time series prediction model after the model parameters are adjusted for iterative training; and when the loss function value obtained by iterative training meets a preset condition, determining to obtain the cash flow prediction model. In some embodiments, the time series prediction model may be an arima differential integrated moving average autoregressive model.
In one embodiment, the product recommendation device based on genetic algorithm may process the plurality of historical cash flow data in the second sample training data set before inputting the second sample training data set into the pre-trained time series prediction model.
In one embodiment, when the product recommendation device based on the genetic algorithm processes the plurality of historical cash flow data in the second sample training data set, operations such as missing filling and abnormal detection may be performed on each historical cash flow data to fill up the missing data, process or delete the abnormal data, and the like.
In one embodiment, the product recommendation device based on genetic algorithm may perform trend analysis on the historical cash flow data when processing the multi-historical cash flow data in the second sample training data set, and determine a long-term trend, a seasonal trend, a periodic trend, an irregular variation trend, and the like according to the historical cash flow data.
In some embodiments, a long-term trend refers to a certain continuously rising or continuously falling variation that a time series exhibits over a long period of time, and may be linear or non-linear; seasonal trends refer to regular periodic variations that occur with seasonal changes over the course of a year; the periodic trend refers to regular variation of the wave form presented by a period of several years (non-fixed period); the irregular variation tendency refers to an irregular follow-up variation, and includes two types of variations that are strictly random variations and irregular sudden influences are large.
In one embodiment, after acquiring the second sample training data set, the product recommendation device based on the genetic algorithm may pre-process each historical cash flow data in the second sample training data set, and perform stationarity detection on the pre-processed second sample training data set; if the stationarity detection does not pass, performing difference processing on the preprocessed second sample training data set; when the stationarity detection passes after the differential processing is executed for a plurality of times, performing white noise inspection processing on the second sample training data set after the differential processing; and when the white noise test is not passed, performing autocorrelation calculation and partial autocorrelation calculation on the second sample training data set after the difference processing to obtain first result data of the autocorrelation calculation and second result data of the partial autocorrelation calculation.
In one embodiment, the autocorrelation calculation is calculated as shown in equation (1) below:
wherein the covariance γ (s, t) is cov (x)s,xt)＝E[(xs-μs)(xt-μt) As can be seen from the Cauchy-Schwarz inequality, rho (s, t) is not less than 1 and not more than 1.
Let s be t + h, then γ (t + h, t) be cov (x)t+h,xt) If the time series x is stationary, the covariance is independent of the time t.
cov(xt+h,xt)＝cov(xh,x0) When the second parameter is generally omitted, denoted as γ (h), the autocorrelation function of the stationary sequence can be simplified as shown in the following equation (2):
in one embodiment, the partial autocorrelation is different from the autocorrelation in that the autocorrelation is a direct calculation of xtAnd xt+hThe partial auto-correlation is the calculation of xtAnd xt+hCorrelation between the two, but removing { x between the twot+h,…,xt+h-1Of linear dependence ofInfluence. Wherein, the calculation formula of the partial autocorrelation calculation is shown as the following formula (3):
φ11＝corr(x1,x0)＝ρ(1)
wherein byhhA partial auto-correlation is represented which,
is { x1,x2,…,xh-1With respect to x0The regression of (a) the estimated value of (b),
is { x1,x2,…,xh-1With respect to x0Is estimated.
If p and q are determined, the next step is to determine the coefficients of the terms
θ1,…,qAnd
specifically, the following formula (4) shows:
the least square estimation and the maximum likelihood estimation are commonly used as methods for parameter estimation, wherein the least square estimation is to find a set of estimation values to minimize the distance between an actual value and the estimation values, and it is troublesome to mathematically find the minimum value due to an absolute value, and the common method is to use the least square sum of the difference between the actual value and the estimation value, so that the least square is formed, as shown in the following formula (5):
in solving, the above square sum formula is usually used to derive the parameters, and the first derivative is made to be zero, so as to obtain the value of the optimal parameter.
Maximum likelihood estimation maximizes the probability of a sample occurring by finding a set of parameters. Because the joint probability of all the observations of the sample is maximized, the method is in the form of a continuous product, and can be changed into a linear summation form by taking a logarithm, as shown in the following formula (6):
in solving, the parameter is usually derived by summing the above linear sums, and the first derivative is made zero to obtain the value of the optimal parameter.
In one embodiment, when the second sample training data set is input to the pre-trained time series prediction model to obtain the loss function value, the product recommendation apparatus based on the genetic algorithm may input the second sample training data set, the number of differential processes, the first result data, and the second result data to the pre-trained time series prediction model to obtain the loss function value.
In one example, assuming that the number of differential processing is d, the first result data obtained by autocorrelation calculation is p, the second result data obtained by partial autocorrelation calculation is q, and the time series prediction model is an ARIMA model, wherein ARIMA (p, d, q) is determined based on two basic models ar (p) and ma (q).
Wherein ar (p) is represented by the following formula (7):
MA (q) is shown in the following formula (8):
xt＝wt+θ1wt-1+θ2wt-2+…+θqwt-q (8)
combining AR (p) and MA (q) is ARMA (p, q), as shown in equation (9):
in one embodiment, the product recommendation device based on genetic algorithm may input the second sample training data set into the pre-trained business rule prediction model to obtain the loss function value before inputting the data flow information into the pre-trained cash flow prediction model to obtain the prediction result of cash flow data in the data flow information; when the loss function value does not meet the preset condition, adjusting the model parameters of the business rule prediction model, and inputting the second sample training data set into the business rule prediction model with the adjusted model parameters for iterative training; and when the loss function value obtained by iterative training meets a preset condition, determining to obtain the cash flow prediction model.
In one embodiment, the product recommendation device based on the genetic algorithm may perform fusion processing on the time series prediction model and the business rule prediction model according to a preset fusion rule before inputting the data stream information into a pre-trained cash stream prediction model to obtain a prediction result of cash stream data in the data stream information, so as to obtain a fusion prediction model; inputting the second sample training data set into the fusion prediction model to obtain a loss function value; when the loss function value does not meet the preset condition, adjusting the model parameters of the fusion prediction model, and inputting the second sample training data set into the fusion prediction model with the adjusted model parameters for iterative training; and when the loss function value obtained by iterative training meets a preset condition, determining to obtain the cash flow prediction model.
In an embodiment, the preset fusion rule may be the formula C ═ w a + (1-w) B, where w is a weight, a is a time-series prediction model, and B is a business rule prediction model, and the product recommendation device based on the genetic algorithm may obtain the fusion prediction model according to the formula C ═ w a + (1-w) B when the fusion processing is performed on the time-series prediction model and the business rule prediction model according to the preset fusion rule.
S103: the method comprises the steps of obtaining a first sample training data set, wherein the first sample training data set comprises historical investment data of a plurality of products, inputting the first sample training data set into a preset genetic algorithm model for training, and obtaining an investment portfolio recommendation model.
In the embodiment of the invention, a product recommending device based on a genetic algorithm can obtain a first sample training data set, the first sample training data set comprises historical investment data of a plurality of products, and the first sample training data set is input into a preset genetic algorithm model for training to obtain an investment portfolio recommending model.
In one embodiment, when the product recommendation device based on the genetic algorithm inputs the first sample training data set into a preset genetic algorithm model for training to obtain an investment portfolio recommendation model, combined investment proportion data among products and individual investment proportion data of each product can be obtained from historical investment data in the first sample training data set; converting the combined investment proportion data and the individual investment proportion data of each product to obtain converted investment data; merging the historical investment data and the converted investment data to obtain merged investment data, and sequencing the merged investment data; and selecting one or more target investment data according to the sorted combined investment data, and inputting the one or more target investment data into the preset genetic algorithm model for training to obtain the investment portfolio recommendation model.
In one embodiment, the product recommendation apparatus based on genetic algorithm may determine a congestion distance between the respective consolidated investment data based on the sorted consolidated investment data when selecting one or more target investment data based on the sorted consolidated investment data, and select one or more target investment data based on the congestion distance. In some embodiments, the crowd distance refers to a density of the merged investment data within a specified area, and a greater crowd distance for a certain merged investment data represents a lesser amount of other merged investment data surrounding it.
In an embodiment, when the product recommendation device based on the genetic algorithm inputs the first sample training data set into a preset genetic algorithm model for training to obtain the investment portfolio recommendation model, the product recommendation device based on the genetic algorithm can be obtained based on markov's mean square error investment theory and a preset genetic algorithm for training.
S104: and inputting the data flow information and the prediction result into an investment portfolio recommendation model, determining a target recommended product, and sending the target recommended product to a client terminal.
In the embodiment of the invention, the product recommending device based on the genetic algorithm can input the data stream information and the prediction result into an investment portfolio recommending model, determine a target recommended product and send the target recommended product to the client terminal.
In one embodiment, when the product recommendation device based on the genetic algorithm inputs the data stream information and the prediction result into an investment portfolio recommendation model to determine a target recommended product, the product recommendation device may acquire attribute information of product information in the data stream information and classify each product according to the attribute information; and inputting the classified data flow information and the prediction result into the investment portfolio recommendation model to determine the target recommended product.
In some embodiments, the classification result obtained by classifying each product may be an investment product, wherein the investment product may be classified into a short term, a medium term and a medium term according to an investment term, and may be classified into a conservative type, a robust type, an access type and the like according to an investment risk.
In the embodiment of the invention, a product recommendation device based on a genetic algorithm can acquire data flow information, wherein the data flow information comprises product information and customer information, and the data flow information is acquired from one or more of an enterprise big data platform, an external data platform and a bank big data platform and is associated with each product; inputting the data flow information into a pre-trained cash flow prediction model to obtain a prediction result of cash flow data in the data flow information; acquiring a first sample training data set, wherein the first sample training data set comprises historical investment data of a plurality of products, and inputting the first sample training data set into a preset genetic algorithm model for training to obtain an investment portfolio recommendation model; and inputting the data flow information and the prediction result into an investment portfolio recommendation model, determining a target recommended product, and sending the target recommended product to a client terminal. By the implementation mode, the flow direction condition of the cash flow data of the client can be accurately predicted, the investment products are intelligently recommended to the client, and the accuracy and efficiency of product recommendation are improved.
The embodiment of the invention also provides a product recommending device based on the genetic algorithm, which is used for executing the unit of the method in any one of the preceding items. Specifically, referring to fig. 2, fig. 2 is a schematic block diagram of a product recommendation device based on a genetic algorithm according to an embodiment of the present invention. The product recommendation device based on the genetic algorithm of the embodiment comprises: an acquisition unit 201, a prediction unit 202, a training unit 203, and a recommendation unit 204.
An obtaining unit 201, configured to obtain data flow information, where the data flow information includes product information and customer information, and the data flow information is data information associated with each product, and is obtained from one or more of an enterprise big data platform, an external data platform, and a bank big data platform;
the prediction unit 202 is configured to input the data flow information into a pre-trained cash flow prediction model to obtain a prediction result of cash flow data in the data flow information;
the training unit 203 is configured to obtain a first sample training data set, where the first sample training data set includes historical investment data of multiple products, and input the first sample training data set into a preset genetic algorithm model for training to obtain an investment portfolio recommendation model;
and the recommending unit 204 is configured to input the data stream information and the prediction result into an investment portfolio recommendation model, determine a target recommended product, and send the target recommended product to a client terminal.
Further, the predicting unit 202 inputs the data flow information into a pre-trained cash flow prediction model, and before obtaining a prediction result of cash flow data in the data flow information, is further configured to:
obtaining a second sample training data set, wherein the second sample training data set comprises a plurality of historical cash flow data;
inputting the second sample training data set into a pre-trained time sequence prediction model to obtain a loss function value;
when the loss function value does not meet the preset condition, adjusting the model parameters of the time series prediction model, and inputting the second sample training data set into the time series prediction model after the model parameters are adjusted for iterative training;
and when the loss function value obtained by iterative training meets a preset condition, determining to obtain the cash flow prediction model.
Further, after the prediction unit 202 obtains the second sample training data set, it is further configured to:
preprocessing each historical cash flow data in the second sample training data set, and performing stationarity detection on the preprocessed second sample training data set;
if the stationarity detection does not pass, performing difference processing on the preprocessed second sample training data set;
when the stationarity detection passes after the differential processing is executed for a plurality of times, performing white noise inspection processing on the second sample training data set after the differential processing;
when the white noise test is not passed, performing autocorrelation calculation and partial autocorrelation calculation on the second sample training data set after the difference processing to obtain first result data of the autocorrelation calculation and second result data of the partial autocorrelation calculation;
inputting the second sample training data set into a pre-trained time series prediction model to obtain a loss function value, wherein the method comprises the following steps:
and inputting the second sample training data set, the difference processing times, the first result data and the second result data into a pre-trained time series prediction model to obtain the loss function value.
Further, the predicting unit 202 inputs the data flow information into a pre-trained cash flow prediction model, and before obtaining a prediction result of cash flow data in the data flow information, is further configured to:
inputting the second sample training data set into a pre-trained business rule prediction model to obtain a loss function value;
when the loss function value does not meet the preset condition, adjusting the model parameters of the business rule prediction model, and inputting the second sample training data set into the business rule prediction model with the adjusted model parameters for iterative training;
and when the loss function value obtained by iterative training meets a preset condition, determining to obtain the cash flow prediction model.
Further, the predicting unit 202 inputs the data flow information into a pre-trained cash flow prediction model, and before obtaining a prediction result of cash flow data in the data flow information, is further configured to:
fusing the time series prediction model and the service rule prediction model according to a preset fusion rule to obtain a fusion prediction model;
inputting the second sample training data set into the fusion prediction model to obtain a loss function value;
when the loss function value does not meet the preset condition, adjusting the model parameters of the fusion prediction model, and inputting the second sample training data set into the fusion prediction model with the adjusted model parameters for iterative training;
and when the loss function value obtained by iterative training meets a preset condition, determining to obtain the cash flow prediction model.
Further, the training unit 203 inputs the first sample training data set into a preset genetic algorithm model for training, and when obtaining the investment portfolio recommendation model, is specifically configured to:
acquiring combined investment proportion data among products and individual investment proportion data of each product from each historical investment data in the first sample training data set;
converting the combined investment proportion data and the individual investment proportion data of each product to obtain converted investment data;
merging the historical investment data and the converted investment data to obtain merged investment data, and sequencing the merged investment data;
and selecting one or more target investment data according to the sorted combined investment data, and inputting the one or more target investment data into the preset genetic algorithm model for training to obtain the investment portfolio recommendation model.
Further, the recommending unit 204 inputs the data flow information and the prediction result into an investment portfolio recommendation model, and when determining a target recommended product, is specifically configured to:
acquiring attribute information of product information in the data stream information, and classifying each product according to the attribute information;
and inputting the classified data flow information and the prediction result into the investment portfolio recommendation model to determine the target recommended product.
In the embodiment of the invention, a product recommendation device based on a genetic algorithm can acquire data flow information, wherein the data flow information comprises product information and customer information, and the data flow information is acquired from one or more of an enterprise big data platform, an external data platform and a bank big data platform and is associated with each product; inputting the data flow information into a pre-trained cash flow prediction model to obtain a prediction result of cash flow data in the data flow information; acquiring a first sample training data set, wherein the first sample training data set comprises historical investment data of a plurality of products, and inputting the first sample training data set into a preset genetic algorithm model for training to obtain an investment portfolio recommendation model; and inputting the data flow information and the prediction result into an investment portfolio recommendation model, determining a target recommended product, and sending the target recommended product to a client terminal. By the implementation mode, the flow direction condition of the cash flow data of the client can be accurately predicted, the investment products are intelligently recommended to the client, and the accuracy and efficiency of product recommendation are improved.
Referring to fig. 3, fig. 3 is a schematic block diagram of a computer device provided in an embodiment of the present invention, and in some embodiments, the computer device in the embodiment shown in fig. 3 may include: one or more processors 301; one or more input devices 302, one or more output devices 303, and memory 304. The processor 301, the input device 302, the output device 303, and the memory 304 are connected by a bus 305. The memory 304 is used for storing computer programs, including programs, and the processor 301 is used for executing the programs stored in the memory 304. Wherein the processor 301 is configured to invoke the program to perform:
acquiring data flow information, wherein the data flow information comprises product information and customer information, and the data flow information is acquired from one or more of an enterprise big data platform, an external data platform and a bank big data platform and is associated with each product;
inputting the data flow information into a pre-trained cash flow prediction model to obtain a prediction result of cash flow data in the data flow information;
acquiring a first sample training data set, wherein the first sample training data set comprises historical investment data of a plurality of products, and inputting the first sample training data set into a preset genetic algorithm model for training to obtain an investment portfolio recommendation model;
and inputting the data flow information and the prediction result into an investment portfolio recommendation model, determining a target recommended product, and sending the target recommended product to a client terminal.
Further, the processor 301 inputs the data flow information into a pre-trained cash flow prediction model, and before obtaining a prediction result of cash flow data in the data flow information, is further configured to:
obtaining a second sample training data set, wherein the second sample training data set comprises a plurality of historical cash flow data;
inputting the second sample training data set into a pre-trained time sequence prediction model to obtain a loss function value;
when the loss function value does not meet the preset condition, adjusting the model parameters of the time series prediction model, and inputting the second sample training data set into the time series prediction model after the model parameters are adjusted for iterative training;
and when the loss function value obtained by iterative training meets a preset condition, determining to obtain the cash flow prediction model.
Further, after the processor 301 obtains the second sample training data set, it is further configured to:
preprocessing each historical cash flow data in the second sample training data set, and performing stationarity detection on the preprocessed second sample training data set;
if the stationarity detection does not pass, performing difference processing on the preprocessed second sample training data set;
when the stationarity detection passes after the differential processing is executed for a plurality of times, performing white noise inspection processing on the second sample training data set after the differential processing;
when the white noise test is not passed, performing autocorrelation calculation and partial autocorrelation calculation on the second sample training data set after the difference processing to obtain first result data of the autocorrelation calculation and second result data of the partial autocorrelation calculation;
inputting the second sample training data set into a pre-trained time series prediction model to obtain a loss function value, wherein the method comprises the following steps:
and inputting the second sample training data set, the difference processing times, the first result data and the second result data into a pre-trained time series prediction model to obtain the loss function value.
Further, the processor 301 inputs the data flow information into a pre-trained cash flow prediction model, and before obtaining a prediction result of cash flow data in the data flow information, is further configured to:
inputting the second sample training data set into a pre-trained business rule prediction model to obtain a loss function value;
when the loss function value does not meet the preset condition, adjusting the model parameters of the business rule prediction model, and inputting the second sample training data set into the business rule prediction model with the adjusted model parameters for iterative training;
and when the loss function value obtained by iterative training meets a preset condition, determining to obtain the cash flow prediction model.
Further, the processor 301 inputs the data flow information into a pre-trained cash flow prediction model, and before obtaining a prediction result of cash flow data in the data flow information, is further configured to:
fusing the time series prediction model and the service rule prediction model according to a preset fusion rule to obtain a fusion prediction model;
inputting the second sample training data set into the fusion prediction model to obtain a loss function value;
when the loss function value does not meet the preset condition, adjusting the model parameters of the fusion prediction model, and inputting the second sample training data set into the fusion prediction model with the adjusted model parameters for iterative training;
and when the loss function value obtained by iterative training meets a preset condition, determining to obtain the cash flow prediction model.
Further, the processor 301 inputs the first sample training data set into a preset genetic algorithm model for training, and when obtaining the investment portfolio recommendation model, is specifically configured to:
acquiring combined investment proportion data among products and individual investment proportion data of each product from each historical investment data in the first sample training data set;
converting the combined investment proportion data and the individual investment proportion data of each product to obtain converted investment data;
merging the historical investment data and the converted investment data to obtain merged investment data, and sequencing the merged investment data;
and selecting one or more target investment data according to the sorted combined investment data, and inputting the one or more target investment data into the preset genetic algorithm model for training to obtain the investment portfolio recommendation model.
Further, the processor 301 inputs the data flow information and the prediction result into an investment portfolio recommendation model, and when determining a target recommended product, is specifically configured to:
acquiring attribute information of product information in the data stream information, and classifying each product according to the attribute information;
and inputting the classified data flow information and the prediction result into the investment portfolio recommendation model to determine the target recommended product.
In the embodiment of the invention, computer equipment can acquire data flow information, wherein the data flow information comprises product information and customer information, and the data flow information is acquired from one or more of an enterprise big data platform, an external data platform and a bank big data platform and is associated with each product; inputting the data flow information into a pre-trained cash flow prediction model to obtain a prediction result of cash flow data in the data flow information; acquiring a first sample training data set, wherein the first sample training data set comprises historical investment data of a plurality of products, and inputting the first sample training data set into a preset genetic algorithm model for training to obtain an investment portfolio recommendation model; and inputting the data flow information and the prediction result into an investment portfolio recommendation model, determining a target recommended product, and sending the target recommended product to a client terminal. By the implementation mode, the flow direction condition of the cash flow data of the client can be accurately predicted, the investment products are intelligently recommended to the client, and the accuracy and efficiency of product recommendation are improved.
It should be understood that, in the embodiment of the present invention, the Processor 301 may be a Central Processing Unit (CPU), and the Processor may also be other general processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 302 may include a touch pad, a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.
The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.
In a specific implementation, the processor 301, the input device 302, and the output device 303 described in this embodiment of the present invention may execute the implementation described in the method embodiment shown in fig. 1 provided in this embodiment of the present invention, and may also execute the implementation of the product recommendation apparatus based on a genetic algorithm described in fig. 2 in this embodiment of the present invention, which is not described herein again.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for recommending a product based on a genetic algorithm described in the embodiment corresponding to fig. 1 may be implemented, or the apparatus for recommending a product based on a genetic algorithm according to the embodiment corresponding to fig. 2 may also be implemented, which is not described herein again.
The computer readable storage medium may be an internal storage unit of the genetic algorithm based product recommendation device according to any of the foregoing embodiments, for example, a hard disk or a memory of the genetic algorithm based product recommendation device. The computer readable storage medium may also be an external storage device of the product recommendation device based on genetic algorithm, such as a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like equipped on the product recommendation device based on genetic algorithm. Further, the computer-readable storage medium may also include both an internal storage unit and an external storage device of the genetic algorithm-based product recommendation device. The computer readable storage medium is used for storing the computer program and other programs and data required by the genetic algorithm based product recommendation device. The computer readable storage medium may also be used to temporarily store data that has been output or is to be output.
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 essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a computer-readable storage medium, which includes several instructions for causing a computer device (which may be a personal computer, a terminal, 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 computer-readable storage media comprise: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. 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.
It is emphasized that the data may also be stored in a node of a blockchain in order to further ensure the privacy and security of the data. 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.
The above description is only a part of the embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present invention, and these modifications or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A product recommendation method based on genetic algorithm is characterized by comprising the following steps:
acquiring data flow information, wherein the data flow information comprises product information and customer information, and the data flow information is acquired from one or more of an enterprise big data platform, an external data platform and a bank big data platform and is associated with each product;
inputting the data flow information into a pre-trained cash flow prediction model to obtain a prediction result of cash flow data in the data flow information;
acquiring a first sample training data set, wherein the first sample training data set comprises historical investment data of a plurality of products, and inputting the first sample training data set into a preset genetic algorithm model for training to obtain an investment portfolio recommendation model;
and inputting the data flow information and the prediction result into an investment portfolio recommendation model, determining a target recommended product, and sending the target recommended product to a client terminal.
2. The method of claim 1, wherein before entering the data flow information into a pre-trained cash flow prediction model to obtain a prediction of cash flow data in the data flow information, further comprising:
obtaining a second sample training data set, wherein the second sample training data set comprises a plurality of historical cash flow data;
inputting the second sample training data set into a pre-trained time sequence prediction model to obtain a loss function value;
when the loss function value does not meet the preset condition, adjusting the model parameters of the time series prediction model, and inputting the second sample training data set into the time series prediction model after the model parameters are adjusted for iterative training;
and when the loss function value obtained by iterative training meets a preset condition, determining to obtain the cash flow prediction model.
3. The method of claim 2, wherein after the obtaining the second sample training data set, further comprising:
preprocessing each historical cash flow data in the second sample training data set, and performing stationarity detection on the preprocessed second sample training data set;
if the stationarity detection does not pass, performing difference processing on the preprocessed second sample training data set;
when the stationarity detection passes after the differential processing is executed for a plurality of times, performing white noise inspection processing on the second sample training data set after the differential processing;
when the white noise test is not passed, performing autocorrelation calculation and partial autocorrelation calculation on the second sample training data set after the difference processing to obtain first result data of the autocorrelation calculation and second result data of the partial autocorrelation calculation;
inputting the second sample training data set into a pre-trained time series prediction model to obtain a loss function value, wherein the method comprises the following steps:
and inputting the second sample training data set, the difference processing times, the first result data and the second result data into a pre-trained time series prediction model to obtain the loss function value.
4. The method of claim 2, wherein before entering the data flow information into a pre-trained cash flow prediction model to obtain a prediction of cash flow data in the data flow information, further comprising:
inputting the second sample training data set into a pre-trained business rule prediction model to obtain a loss function value;
when the loss function value does not meet the preset condition, adjusting the model parameters of the business rule prediction model, and inputting the second sample training data set into the business rule prediction model with the adjusted model parameters for iterative training;
and when the loss function value obtained by iterative training meets a preset condition, determining to obtain the cash flow prediction model.
5. The method of claim 4, wherein before entering the data flow information into a pre-trained cash flow prediction model to obtain a prediction of cash flow data in the data flow information, further comprising:
fusing the time series prediction model and the service rule prediction model according to a preset fusion rule to obtain a fusion prediction model;
inputting the second sample training data set into the fusion prediction model to obtain a loss function value;
when the loss function value does not meet the preset condition, adjusting the model parameters of the fusion prediction model, and inputting the second sample training data set into the fusion prediction model with the adjusted model parameters for iterative training;
and when the loss function value obtained by iterative training meets a preset condition, determining to obtain the cash flow prediction model.
6. The method according to claim 1, wherein the training by inputting the first sample training data set into a preset genetic algorithm model, and obtaining the investment portfolio recommendation model comprises:
acquiring combined investment proportion data among products and individual investment proportion data of each product from each historical investment data in the first sample training data set;
converting the combined investment proportion data and the individual investment proportion data of each product to obtain converted investment data;
merging the historical investment data and the converted investment data to obtain merged investment data, and sequencing the merged investment data;
and selecting one or more target investment data according to the sorted combined investment data, and inputting the one or more target investment data into the preset genetic algorithm model for training to obtain the investment portfolio recommendation model.
7. The method of claim 6, wherein the inputting the data flow information and the prediction result into a portfolio recommendation model to determine a target recommended product comprises:
acquiring attribute information of product information in the data stream information, and classifying each product according to the attribute information;
and inputting the classified data flow information and the prediction result into the investment portfolio recommendation model to determine the target recommended product.
8. A product recommendation device based on a genetic algorithm, comprising:
the system comprises an acquisition unit, a processing unit and a display unit, wherein the acquisition unit is used for acquiring data flow information, the data flow information comprises product information and customer information, and the data flow information is acquired from one or more of an enterprise big data platform, an external data platform and a bank big data platform and is associated with each product;
the prediction unit is used for inputting the data flow information into a pre-trained cash flow prediction model to obtain a prediction result of cash flow data in the data flow information;
the training unit is used for acquiring a first sample training data set, wherein the first sample training data set comprises historical investment data of a plurality of products, and the first sample training data set is input into a preset genetic algorithm model for training to obtain an investment portfolio recommendation model;
and the recommending unit is used for inputting the data flow information and the prediction result into an investment portfolio recommending model, determining a target recommended product and sending the target recommended product to a client terminal.
9. A computer device comprising a processor and a memory, wherein the memory is configured to store a computer program and the processor is configured to invoke the computer program to perform the method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which is executed by a processor to implement the method of any one of claims 1-7.
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