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CN116739673A - Prediction method, device, equipment and storage medium for financial products to be recommended - Google Patents

Prediction method, device, equipment and storage medium for financial products to be recommended Download PDF

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Publication number
CN116739673A
CN116739673A CN202310689915.7A CN202310689915A CN116739673A CN 116739673 A CN116739673 A CN 116739673A CN 202310689915 A CN202310689915 A CN 202310689915A CN 116739673 A CN116739673 A CN 116739673A
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data
financial product
prediction
financial
recommended
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张全龙
程梦琴
李文兵
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Bank of China Ltd
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Bank of China Ltd
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Abstract

The embodiment of the application discloses a prediction method, a device, equipment and a storage medium for financial products to be recommended, which can be applied to the financial field or other fields. In the method, training sample data is acquired; inputting training sample data into a cellular automaton model based on a convolutional neural network for training to obtain a financial product prediction model after training; acquiring characteristic data of a target user; and taking the characteristic data as input data of a financial product prediction model, and predicting through the financial product prediction model to obtain a prediction result. Therefore, by utilizing the scheme provided by the embodiment of the application, the prediction capability of the cellular automaton is improved by training the cellular automaton model based on the convolutional neural network and characterizing the learning capability through the convolutional neural network, so that the prediction capability of the financial product satisfactory to a customer is improved, the recommendation efficiency of the financial product is improved, and the work efficiency of bank financial service personnel is improved.

Description

Prediction method, device, equipment and storage medium for financial products to be recommended
Technical Field
The application relates to the field of finance, in particular to a prediction method, a device, equipment and a storage medium for financial products to be recommended.
Background
The demand for financial products is increasing due to the increase of people's financial consciousness, however, banking financial service personnel cannot accurately provide satisfactory financial products for clients, or recommend financial products to clients according to asset conditions, browsing records and consultation conditions of financial clients.
Therefore, how to predict financial products recommended to customers to achieve satisfactory financial products recommended to customers, and further improve the working efficiency of banking financial service personnel is needed to be solved by the technicians in the field.
Disclosure of Invention
The embodiment of the application provides a prediction method, a device, equipment and a storage medium for financial products to be recommended, which are used for predicting the financial products to be recommended, so that satisfactory financial products are recommended to customers, and further the working efficiency of bank financial service personnel is improved.
The first aspect of the present application provides a method for predicting a financial product to be recommended, including:
acquiring training sample data;
inputting the training sample data into a cellular automaton model based on a convolutional neural network for training to obtain a financial product prediction model after training;
acquiring characteristic data of a target user;
and taking the characteristic data as input data of the financial product prediction model, and predicting through the financial product prediction model to obtain a prediction result.
Optionally, the method further comprises:
collecting financial data of a financial product to be recommended;
and preprocessing the financial data to obtain the sample training sample data, wherein the preprocessing comprises missing value processing and data noise reduction processing.
Optionally, the acquiring training sample data includes:
a unit for determining the training sample data according to the attribute of the financial data;
and determining an input characteristic set of the convolutional neural network based on the cellular automaton model of the convolutional neural network according to the unit.
Optionally, the method further comprises:
performing super-parameter setting on the input feature set of the cellular automaton model based on the convolutional neural network and a preset cellular automaton to obtain an optimized input feature set;
and constructing the cellular automaton model based on the convolutional neural network based on the optimized input feature set, the preset cellular automaton and the convolutional neural network.
Optionally, the predicting by the financial product prediction model using the feature data as input data of the financial product prediction model includes:
the characteristic data is used as input data of the financial product prediction model, and prediction is carried out through the financial product prediction model to obtain prediction data of the financial product to be recommended;
and determining a prediction result according to the prediction data.
Optionally, the determining a prediction result according to the prediction data includes:
calculating the probability of customer selection corresponding to the financial product to be recommended according to the prediction data through an input layer of the convolutional neural network;
and determining a target recommended financial product according to the probability, wherein the target recommended financial product is the prediction result.
The second aspect of the present application provides a predicting device for a financial product to be recommended, including:
the acquisition unit is used for acquiring training sample data;
the training unit is used for inputting the training sample data into a cellular automaton model based on a convolutional neural network for training, so as to obtain a financial product prediction model after training;
the acquisition unit is also used for acquiring the characteristic data of the target user;
and the prediction unit is used for taking the characteristic data as input data of the financial product prediction model, and predicting through the financial product prediction model to obtain a prediction result.
Optionally, the acquiring unit is specifically configured to:
collecting financial data of a financial product to be recommended;
and preprocessing the financial data to obtain the sample training sample data, wherein the preprocessing comprises missing value processing and data noise reduction processing.
The third aspect of the present application provides a predicting device for a financial product to be recommended, including:
one or more processors;
a memory having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of predicting a financial product to be recommended as described in any one of the above.
A fourth aspect of the present application provides a computer storage medium storing a program which, when executed, is operable to implement a method of predicting a financial product to be recommended as described in any one of the above.
The embodiment of the application discloses a prediction method, a device, equipment and a storage medium for financial products to be recommended. In the method, training sample data is acquired; inputting training sample data into a cellular automaton model based on a convolutional neural network for training to obtain a financial product prediction model after training; acquiring characteristic data of a target user; and taking the characteristic data as input data of a financial product prediction model, and predicting through the financial product prediction model to obtain a prediction result. Therefore, by utilizing the scheme provided by the embodiment of the application, the prediction capability of the cellular automaton is improved by training the cellular automaton model based on the convolutional neural network and characterizing the learning capability through the convolutional neural network, so that the prediction capability of the financial product satisfactory to a customer is improved, the recommendation efficiency of the financial product is improved, and the work efficiency of bank financial service personnel is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to the drawings without inventive effort to those skilled in the art.
Fig. 1 is a flow chart of a prediction method for financial products to be recommended according to an embodiment of the present application;
fig. 2 is a flow chart of a processing method of financial data according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a predicting device for a financial product to be recommended according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an apparatus according to an embodiment of the present application.
Detailed Description
The embodiment of the application provides a prediction method, a device, equipment and a storage medium for financial products to be recommended, which are used for predicting the financial products to be recommended, so that satisfactory financial products are recommended to customers, and further the working efficiency of bank financial service personnel is improved.
For easy understanding, an application scenario of the embodiment of the present application will be described first.
Society is rapidly developed, social economy is increasingly prosperous, people gradually have financial consciousness, but bank financial service personnel are difficult to accurately provide satisfactory financial products for financial clients when recommending the financial products to the clients, and financial product recommendation can not be rapidly carried out for the clients according to asset conditions, browsing records, consultation conditions and the like of the financial clients.
Therefore, the embodiment of the application provides a prediction method for financial products to be recommended, which predicts the financial products recommended to financial clients through a prediction model, so as to recommend satisfactory financial products to the clients, and further improve the working efficiency of banking financial service personnel.
Referring to fig. 1, the flow chart of a prediction method for a financial product to be recommended according to an embodiment of the present application is shown. The prediction method for the financial products to be recommended provided by the embodiment of the application can be realized through the following steps S101-S104.
S101: training sample data is obtained.
In the embodiment of the application, training sample data is obtained by collecting banking data, wherein the banking data refers to data containing characteristic attributes of various items of banking data.
In one implementation manner of the embodiment of the application, financial data of a financial product to be recommended is collected; and preprocessing financial data to obtain sample training sample data, wherein the preprocessing comprises missing value processing and data noise reduction processing. In the embodiment of the application, redundant data is removed by preprocessing financial data, so that generalization of the acquired data set is ensured, the authenticity of model training is increased, and the acquired data set is enabled to be effective.
S102: and inputting training sample data into a cellular automaton model based on a convolutional neural network for training.
In the embodiment of the application, training sample data is input into a cellular automaton model based on a convolutional neural network for training, and a financial product prediction model after training is obtained.
The cellular automaton is a model discrete in space, time and state, the use of the cellular automaton has the characteristics of simple and flexible algorithm and high calculation efficiency, the convolutional neural network has strong characteristic learning capability, and the input information can be subjected to translation invariant classification according to the hierarchical structure of the convolutional neural network, so that the simulation prediction capability of the cellular automaton can be improved.
In one implementation manner of the embodiment of the application, a unit of training sample data is determined according to the attribute of the financial data; an input feature set of a convolutional neural network based on a cellular automaton model of the convolutional neural network is determined from the cells.
Specifically, firstly, an input feature set of a neural network is determined, and then a random variable is introduced into a convolutional neural network model to obtain a cellular automaton model based on the convolutional neural network.
Determining an input feature set of the neural network:
for each cell (cell), there are h attributes, the input feature set is:
x m ＝[x 1 (a,t),x 2 (a,t),…,x h (a,t)] T ；
where a represents one unit composed of h attributes, T represents time, T represents transpose, and m represents the m-th layer of the neural network.
The convolutional neural network model is as follows:
y n ＝f(∑ m a mn *x m +b n )；
wherein x is m Representing the feature set of the mth layer, x when m=1 m Representing an input feature set of a convolutional neural network; y is n A feature set representing an nth layer; a, a mn Representing a convolution between the m-th layer and the n-th layer; b n A bias term representing the nth layer; f is an activation function; * Is a convolution operator.
Introducing a random variable into the convolutional neural network model, wherein a random term R is expressed as:
R＝1+(-lnβ) γ ；
wherein, beta is a random number in the range of [0,1], and gamma is a parameter for controlling the size of a random variable;
finally, a cellular automaton model based on a convolutional neural network is obtained, and the cellular automaton model is specifically expressed as follows:
y n ＝R×f(∑ m a mn *x m +b n )；
the random term is a cellular automaton model in the embodiment of the application, and the embodiment of the application obtains a financial product prediction model by combining the cellular automaton model on the basis of a convolutional neural network model, so that the prediction result is more accurate.
Referring to fig. 2, fig. 2 is a flow chart of a processing method of financial data according to an embodiment of the present application. Firstly, collecting financial data of a financial product to be recommended, and providing data for subsequent model training and prediction. Secondly, data preprocessing is carried out on the acquired financial data, wherein the data preprocessing specifically comprises data noise reduction processing and missing value processing. And then, inputting the financial data subjected to data preprocessing into a pre-constructed model for model training to obtain a financial product prediction model. And finally, transmitting the financial data subjected to data preprocessing into a financial product prediction model, and calculating by the financial product prediction model according to the financial data subjected to data preprocessing and the characteristic data of the target user to obtain a prediction result.
In one implementation manner of the embodiment of the application, the input feature set of the cellular automaton model based on the convolutional neural network is subjected to super-parameter setting based on the convolutional neural network and a preset cellular automaton to obtain an optimized input feature set; and constructing a cellular automaton model based on the convolutional neural network based on the optimized input feature set, the preset cellular automaton and the convolutional neural network. In the embodiment of the application, the optimal prediction model parameters are obtained by performing super-parameter setting on the input feature set of the model, so that the prediction effect of the prediction model is improved.
S103: and acquiring the characteristic data of the target user.
In the embodiment of the application, the characteristic data of the target user are acquired, wherein the characteristic data comprise asset conditions, browsing records, consultation conditions and the like of the target user.
S104, predicting through a financial product prediction model to obtain a prediction result.
In the embodiment of the application, the characteristic data is used as the input data of the financial product prediction model, and the prediction is carried out through the financial product prediction model to obtain a prediction result.
In one implementation manner of the embodiment of the application, the characteristic data is used as input data of a financial product prediction model, and prediction is carried out through the financial product prediction model to obtain prediction data of a financial product to be recommended; and determining a prediction result according to the prediction data.
In one implementation manner of the embodiment of the application, the probability of customer selection corresponding to the financial product to be recommended is calculated according to the prediction data through an input layer of the convolutional neural network; and determining a target recommended financial product according to the probability, wherein the target recommended financial product is a prediction result.
Specifically, multiple circulation operation is performed according to characteristic data of a target user through a financial product prediction model, probabilities of corresponding M financial products are calculated through an input layer of a neural network, the probabilities of the M financial products are compared, and therefore the probability of which financial product is selected by a customer is maximum, the financial product with the maximum probability is determined to be the target recommended financial product, wherein the calculation formula of the probability F is as follows:
F＝softmax(y n )；
wherein y is n The calculation result of the cellular automaton model based on the convolutional neural network in step S103. Where Softmax is the activation function, a numerical vector may be normalized to a probability distribution vector, and the sum of the probabilities is 1. The Softmax function is used for outputting multi-classification problems, and can display multi-classification results in the form of probability.
The embodiment of the application discloses a prediction method, a device, equipment and a storage medium for financial products to be recommended. In the method, training sample data is acquired; inputting training sample data into a cellular automaton model based on a convolutional neural network for training to obtain a financial product prediction model after training; acquiring characteristic data of a target user; and taking the characteristic data as input data of a financial product prediction model, and predicting through the financial product prediction model to obtain a prediction result. Therefore, by utilizing the scheme provided by the embodiment of the application, the prediction capability of the cellular automaton is improved by training the cellular automaton model based on the convolutional neural network and characterizing the learning capability through the convolutional neural network, so that the prediction capability of the financial product satisfactory to a customer is improved, the recommendation efficiency of the financial product is improved, and the work efficiency of bank financial service personnel is improved.
Based on the method provided by the embodiment, the embodiment of the application also provides a predicting device for the financial product to be recommended, and the predicting device for the financial product to be recommended is described below with reference to the accompanying drawings.
Referring to fig. 3, the structure diagram of a predicting device for a financial product to be recommended according to an embodiment of the present application is shown.
The prediction device 300 for a financial product to be recommended provided by the embodiment of the application comprises: an acquisition unit 301, a training unit 302 and a prediction unit 303.
An acquiring unit 301, configured to acquire training sample data;
the training unit 302 is configured to input training sample data into a cellular automaton model based on a convolutional neural network for training, so as to obtain a financial product prediction model after training is completed;
the acquiring unit 301 is further configured to acquire feature data of a target user;
the prediction unit 303 is configured to use the feature data as input data of the financial product prediction model, and predict the feature data through the financial product prediction model to obtain a prediction result.
In one possible implementation, the obtaining unit 301 is specifically configured to:
collecting financial data of a financial product to be recommended;
and preprocessing financial data to obtain sample training sample data, wherein the preprocessing comprises missing value processing and data noise reduction processing.
In one possible implementation, the apparatus further includes:
a determining unit for determining training sample data according to the attribute of the financial data; an input feature set of a convolutional neural network based on a cellular automaton model of the convolutional neural network is determined from the cells.
In one possible implementation, the apparatus further includes:
the construction unit is used for performing super-parameter setting on an input feature set of a cellular automaton model based on the convolutional neural network and a preset cellular automaton to obtain an optimized input feature set; and constructing a cellular automaton model based on the convolutional neural network based on the optimized input feature set, the preset cellular automaton and the convolutional neural network.
In one possible implementation, the prediction unit 303 is specifically configured to:
the characteristic data are used as input data of a financial product prediction model, and prediction is carried out through the financial product prediction model to obtain prediction data of the financial product to be recommended;
and determining a prediction result according to the prediction data.
In one possible implementation, the prediction unit 303 is specifically configured to:
calculating the probability of customer selection corresponding to the financial product to be recommended according to the prediction data through an input layer of the convolutional neural network;
and determining a target recommended financial product according to the probability, wherein the target recommended financial product is a prediction result.
Since the apparatus 300 is an apparatus corresponding to the prediction method of the financial product to be recommended provided in the above method embodiment, the specific implementation of each unit of the apparatus 300 is the same as the above method embodiment, so the description of the prediction method of the financial product to be recommended with reference to the above method embodiment will not be repeated here.
The prediction method, the prediction device, the prediction equipment and the storage medium of the financial products to be recommended can be used in the financial field or other fields, for example, the prediction method, the prediction device, the prediction equipment and the storage medium of the financial products to be recommended can be used in the application scene of financial product recommendation in the financial field. Other fields are any field other than the financial field, for example, the computer field. The foregoing is merely an example, and the application fields of the prediction method, the device, the equipment and the storage medium for the financial product to be recommended provided by the present application are not limited.
The embodiment of the application also provides a prediction device for the financial product to be recommended, which comprises: a processor and a memory;
a memory for storing instructions;
and a processor for executing the instructions in the memory, and executing the prediction method of the financial product to be recommended, which is executed by the analysis device and is mentioned in the above embodiment.
It should be noted that, in the predicting device for financial products to be recommended provided in the embodiment of the present application, the hardware structure may be the structure shown in fig. 4, and fig. 4 is a schematic structural diagram of a device provided in the embodiment of the present application.
Referring to fig. 4, an apparatus 400 includes: a processor 410, a communication interface 420, and a memory 430. Where the number of processors 410 in device 400 may be one or more, one processor is illustrated in fig. 4. In an embodiment of the present application, processor 410, communication interface 420, and memory 430 may be connected by a bus system or otherwise, wherein FIG. 4 illustrates a connection via bus system 440.
The processor 410 may be a central processing unit (central processing unit, CPU), a Network Processor (NP) or a combination of CPU and NP. The processor 410 may further comprise a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (programmable logic device, PLD), or a combination thereof. The PLD may be a complex programmable logic device (complexprogrammable logic device, CPLD), a field-programmable gate array (field-programmable gate array, FPGA), general-purpose array logic (generic array logic, GAL), or any combination thereof.
Memory 430 may include volatile memory (English) such as random-access memory (RAM); the memory 430 may also include a nonvolatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (HDD) or a Solid State Drive (SSD); memory 430 may also include a combination of the above types of memory.
Memory 430 optionally stores an operating system and programs, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the programs may include various operational instructions for performing various operations. The operating system may include various system programs for implementing various underlying services and handling hardware-based tasks. The processor 410 may read the program in the memory 430 to implement the prediction method for the financial product to be recommended according to the embodiment of the present application.
The bus system 440 may be a peripheral component interconnect (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus system 440 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 4, but not only one bus or one type of bus.
The embodiment of the application also provides a computer readable storage medium, which comprises instructions that when run on a computer, cause the computer to execute the prediction method of the financial product to be recommended mentioned in the above embodiment.
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," and "having," and any variations 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 but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
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.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, e.g., the division of units is merely a logical service division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each service unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software business units.
The integrated units, if implemented in the form of software business 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 application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several 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 methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those skilled in the art will appreciate that in one or more of the examples described above, the services described herein may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, the services may be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The objects, technical solutions and advantageous effects of the present application have been described in further detail in the above embodiments, and it should be understood that the above are only embodiments of the present application.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application 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 of the application.

Claims (10)

1. A method of predicting a financial product to be recommended, the method comprising:
acquiring training sample data;
inputting the training sample data into a cellular automaton model based on a convolutional neural network for training to obtain a financial product prediction model after training;
acquiring characteristic data of a target user;
and taking the characteristic data as input data of the financial product prediction model, and predicting through the financial product prediction model to obtain a prediction result.
2. The method of claim 1, wherein the acquiring training sample data comprises:
collecting financial data of a financial product to be recommended;
and preprocessing the financial data to obtain the training sample data, wherein the preprocessing comprises missing value processing and data noise reduction processing.
3. The method according to claim 1, characterized in that the method further comprises:
a unit for determining the training sample data according to the attribute of the financial data;
and determining an input characteristic set of the convolutional neural network based on the cellular automaton model of the convolutional neural network according to the unit.
4. A method according to claim 3, characterized in that the method further comprises:
performing super-parameter setting on the input feature set of the cellular automaton model based on the convolutional neural network and a preset cellular automaton to obtain an optimized input feature set;
and constructing the cellular automaton model based on the convolutional neural network based on the optimized input feature set, the preset cellular automaton and the convolutional neural network.
5. The method of claim 3, wherein predicting by the financial product prediction model using the feature data as input data to the financial product prediction model comprises:
the characteristic data is used as input data of the financial product prediction model, and prediction is carried out through the financial product prediction model to obtain prediction data of the financial product to be recommended;
and determining a prediction result according to the prediction data.
6. The method of claim 5, wherein said determining a prediction result from said prediction data comprises:
calculating the probability of customer selection corresponding to the financial product to be recommended according to the prediction data through an input layer of the convolutional neural network;
and determining a target recommended financial product according to the probability, wherein the target recommended financial product is the prediction result.
7. A predicting device for a financial product to be recommended, the device comprising:
the acquisition unit is used for acquiring training sample data;
the training unit is used for inputting the training sample data into a cellular automaton model based on a convolutional neural network for training, so as to obtain a financial product prediction model after training;
the acquisition unit is also used for acquiring the characteristic data of the target user;
and the prediction unit is used for taking the characteristic data as input data of the financial product prediction model, and predicting through the financial product prediction model to obtain a prediction result.
8. The apparatus according to claim 6, wherein the acquisition unit is specifically configured to:
collecting financial data of a financial product to be recommended;
and preprocessing the financial data to obtain the sample training sample data, wherein the preprocessing comprises missing value processing and data noise reduction processing.
9. A prediction apparatus for a financial product to be recommended, the apparatus comprising: a processor and a memory;
the memory is used for storing instructions;
the processor being configured to execute the instructions in the memory and to perform the method of any of claims 1-6.
10. A computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the method of any of the preceding claims 1-6.
CN202310689915.7A 2023-06-12 2023-06-12 Prediction method, device, equipment and storage medium for financial products to be recommended Pending CN116739673A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118211733A (en) * 2024-04-17 2024-06-18 东北林业大学 A method for predicting the occurrence of pine wood nematode disease, electronic equipment and storage medium

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118211733A (en) * 2024-04-17 2024-06-18 东北林业大学 A method for predicting the occurrence of pine wood nematode disease, electronic equipment and storage medium

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