Information query method and device, electronic equipment and storage medium

Abstract

The invention discloses an information query method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring historical ten thousand revenue data of a target financial product; decomposing the historical ten thousands of revenue data to obtain a first data component and a second data component; determining a first prediction result of ten thousand earnings of the target financial management product in a future time period according to the first data component through a first type of earnings prediction model; determining a second prediction result of ten thousand earnings of the target financial management product in the future time period according to the second data component through a second type of earnings prediction model; and carrying out fusion processing on the first prediction result and the second prediction result to obtain a final ten thousand revenue prediction result, and feeding the final ten thousand revenue prediction result back to the user. By adopting the method and the device, the accuracy of predicting the ten thousand revenue can be improved, the predicted ten thousand revenue is fed back to the user as a query result, and the user can query the ten thousand revenue of the financial product in the future time.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an information query method, device, electronic device and storage medium. Background Art

[0002] With the development of financial technology, cash management products are popular among customers due to their low risk, high liquidity and stable returns. Cash management products do not have expected returns and investment periods, and the issuer does not promise fixed returns. The returns are shown through seven-day annualized or per-thousand-share returns. The level of per-thousand-share returns determines the risk and return of financial products, and is also an important factor for investors to choose financial products.

[0003] At present, for most financial products, customers can usually only view the historical per-thousand-share income data of financial products, but cannot query the forecast results of per-thousand-share income of financial products in the future time period. Therefore, how to enable customers to query the forecast results of per-thousand-share income of financial products in the future time period has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present invention provides an information query method, device, electronic equipment, storage medium and computer program product.

[0005] According to one aspect of the present invention, there is provided an information query method, comprising:

[0006] In response to a user's query request for the return per 10,000 shares of a target financial product in a future time period, obtaining historical return per 10,000 shares data of the target financial product;

[0007] Decomposing the historical per-10,000-share return data to obtain a first data component and a second data component;

[0008] Determine, by using the pre-trained first type of income prediction model, a first prediction result of the income per 10,000 shares of the target financial product in the future time period according to the first data component;

[0009] Determine, by using the pre-trained second type of income prediction model, a second prediction result of the income per 10,000 shares of the target financial product in the future time period according to the second data component;

[0010] The first prediction result and the second prediction result are fused to obtain the final prediction result of the revenue per 10,000 shares, and the final prediction result of the revenue per 10,000 shares is fed back to the user.

[0011] Optionally, the historical per-10,000-share income data is decomposed to obtain a first data component and a second data component, including:

[0012] Perform wavelet transform on the historical per-10,000-share earnings data to obtain high-frequency components and low-frequency components;

[0013] The low-frequency component is used as the first data component, and the high-frequency component is used as the second data component.

[0014] Optionally, the first type of revenue prediction model is a first gated recurrent neural network model pre-trained using 10,000 revenue sample data;

[0015] Through the pre-trained first type of income prediction model, according to the first data component, a first prediction result of the income per 10,000 shares of the target financial product in the future time period is determined, including:

[0016] Through the first gated recurrent neural network model, the first prediction result of the ten thousand share yield of the target financial product in the future time period is determined according to the low-frequency components in the historical ten thousand share yield data.

[0017] Optionally, the second type of revenue prediction model includes a second gated recurrent neural network model and a gradient boosting decision tree model pre-trained using 10,000 revenue sample data;

[0018] Through the pre-trained second type of income prediction model, according to the second data component, the second prediction result of the income per ten thousand shares of the target financial product in the future time period is determined, including:

[0019] Through the second gated recurrent neural network model, based on the high-frequency components in the historical 10,000-share return data, a third prediction result of the 10,000-share return of the target financial product in the future time period is determined;

[0020] Through the gradient boosting decision tree model, according to the high-frequency components in the historical ten thousand share income data, the fourth prediction result of the ten thousand share income of the target financial product in the future time period is determined;

[0021] The third prediction result and the fourth prediction result are fused by means of covariance cross-fusion to obtain the second prediction result of the return per ten thousand shares of the target financial product in the future time period.

[0022] Optionally, the third prediction result and the fourth prediction result are fused by covariance cross fusion to obtain a second prediction result of the return per 10,000 shares of the target financial product in the future time period, including

[0023] Obtaining a first estimated variance and a first fusion weight of a prediction error of a second gated recurrent neural network model determined during the model training process;

[0024] Obtaining a second estimated variance and a second fusion weight of a prediction error of a gradient boosting decision tree model determined during model training;

[0025] According to the first estimated variance, the first fusion weight, the second estimated variance, the second fusion weight, the third prediction result and the fourth prediction result, a second prediction result of the yield per ten thousand shares of the target financial product in the future time period is determined.

[0026] Optionally, before decomposing the historical per-10,000-share return data, the method further includes:

[0027] The historical per-thousand-share return data is cleaned, and the missing values ​​identified during the cleaning process are supplemented by the mean completion method.

[0028] Optionally, the first prediction result and the second prediction result are fused to obtain a final prediction result of the revenue per 10,000 shares, and the final prediction result of the revenue per 10,000 shares is fed back to the user, including:

[0029] The first prediction result and the second prediction result are added and merged at the corresponding time points to obtain the final per-thousand-share revenue prediction result;

[0030] The final per-thousand-share profit forecast results are visualized and displayed to users.

[0031] According to another aspect of the present invention, there is provided an information query device, comprising:

[0032] A data acquisition module, for responding to a user's query request for the return per 10,000 shares of a target financial product in a future time period, and acquiring historical return per 10,000 shares data of the target financial product;

[0033] A decomposition processing module, used for decomposing the historical 10,000-share income data to obtain a first data component and a second data component;

[0034] A first prediction module is used to determine a first prediction result of the return per ten thousand shares of the target financial product in a future time period according to the first data component by using a pre-trained first type of return prediction model;

[0035] A second prediction module is used to determine a second prediction result of the return per ten thousand of the target financial product in a future time period according to the second data component by using a pre-trained second type of return prediction model;

[0036] The fusion feedback module is used to fuse the first prediction result and the second prediction result to obtain the final 10,000-share revenue prediction result, and feed back the final 10,000-share revenue prediction result to the user.

[0037] Optionally, the decomposition processing module is specifically used for:

[0038] Perform wavelet transform on the historical per-10,000-share earnings data to obtain high-frequency components and low-frequency components;

[0039] The low-frequency component is used as the first data component, and the high-frequency component is used as the second data component.

[0040] Optionally, the first type of revenue prediction model is a first gated recurrent neural network model pre-trained using 10,000 revenue sample data;

[0041] The first prediction module is specifically used for:

[0042] Through the first gated recurrent neural network model, the first prediction result of the ten thousand share yield of the target financial product in the future time period is determined according to the low-frequency components in the historical ten thousand share yield data.

[0043] Optionally, the second type of revenue prediction model includes a second gated recurrent neural network model and a gradient boosting decision tree model pre-trained using 10,000 revenue sample data;

[0044] The second prediction module includes:

[0045] The first prediction unit is used to determine a third prediction result of the return per 10,000 shares of the target financial product in a future time period according to the high-frequency components in the historical return per 10,000 shares data through the second gated recurrent neural network model;

[0046] The second prediction unit is used to determine the fourth prediction result of the ten thousand share income of the target financial product in the future time period according to the high-frequency components in the historical ten thousand share income data by using the gradient boosting decision tree model;

[0047] The fusion unit is used to fuse the third prediction result and the fourth prediction result by means of covariance cross fusion to obtain a second prediction result of the yield per ten thousand of the target financial product in the future time period.

[0048] Optionally, the fusion unit is specifically used for:

[0049] Obtaining a first estimated variance and a first fusion weight of a prediction error of a second gated recurrent neural network model determined during the model training process;

[0050] Obtaining a second estimated variance and a second fusion weight of a prediction error of a gradient boosting decision tree model determined during model training;

[0051] According to the first estimated variance, the first fusion weight, the second estimated variance, the second fusion weight, the third prediction result and the fourth prediction result, a second prediction result of the yield per ten thousand shares of the target financial product in the future time period is determined.

[0052] Optionally, the device further comprises:

[0053] The cleaning processing module is used to clean the historical per-ten-thousand-share return data before decomposing the data, and to supplement the missing values ​​determined in the cleaning process by the mean completion method.

[0054] Optionally, the fusion feedback module is specifically used for:

[0055] The first prediction result and the second prediction result are added and merged at the corresponding time points to obtain the final per-thousand-share revenue prediction result;

[0056] The final per-thousand-share profit forecast results are visualized and displayed to users.

[0057] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0058] at least one processor; and

[0059] a memory communicatively connected to at least one processor; wherein,

[0060] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor can execute the information query method of the embodiment of the present invention.

[0061] According to another aspect of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions. The computer instructions are used to enable a processor to implement the information query method of an embodiment of the present invention when executed.

[0062] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, and when the computer program is executed by a processor, the steps in the above method are implemented.

[0063] The technical solution of the embodiment of the present invention utilizes multiple deep learning-based profit prediction models to predict different components of the historical per-thousand-share profit data of the target wealth management product, and integrates multiple prediction results to obtain the final per-thousand-share profit prediction result, thereby improving the accuracy of predicting the per-thousand-share profit in future time periods, and feeding back the predicted per-thousand-share profit to the user as a query result, thereby enabling the user to query the per-thousand-share profit of the wealth management product in the future time period.

[0064] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0066] Figure 1 It is a flowchart of an information query method provided by an embodiment of the present invention;

[0067] Figure 2 is a flow chart of another information query method provided by an embodiment of the present invention;

[0068] Figure 3a is a flow chart of another information query method provided by an embodiment of the present invention;

[0069] Figure 3b It is a logic block diagram of an information query method provided by an embodiment of the present invention;

[0070] Figure 4 is a structural schematic diagram of an information query device provided by an embodiment of the present invention;

[0071] Figure 5 It is a structural schematic diagram of an electronic device for implementing the information query method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0072] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0073] Embodiment 1

[0074] Figure 1 A flowchart of an information query method is provided for an embodiment of the present invention. This embodiment can be applicable to a scenario in which the per-thousand-share yield of a financial product is predicted. The method can be executed by an information query device, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device.

[0075] like Figure 1 As shown, the information query method includes:

[0076] S101. In response to a user's query request for the return per ten thousand shares of a target financial product in a future time period, historical return per ten thousand shares data of the target financial product is obtained.

[0077] In the embodiment of the present invention, the target financial product may be a cash management financial product, which refers to a financial product with low risk, high liquidity and stable income. The future time period refers to a number of days starting from tomorrow with the current time as a reference, such as the next 3 days, the next 7 days, etc. The specific number of days can be determined by the user according to his own query needs. In order to facilitate the user's query, the user can be presented with a query interface for the income per 10,000 shares of the financial product, so that the corresponding query request can be generated according to the product name / product code of the target financial product entered by the user in the text input box of the query interface, and the specific future time period; or, the corresponding query request can be generated according to the product name / product code of the target financial product entered by the user through voice, and the specific future time period. In response to the user's query request for the income per 10,000 shares of the target financial product in the future time period, the historical income per 10,000 shares data of the target financial product can be obtained from a pre-specified financial data platform. Exemplarily, the historical income per 10,000 shares data can be the income per 10,000 shares data of the target financial product in the past period of time, for example, it can be the income per 10,000 shares data of the past week or the past month. It can be understood that the historical income per 10,000 shares data is essentially time series data.

[0078] S102, decomposing the historical ten thousand share return data to obtain a first data component and a second data component.

[0079] In an embodiment of the present invention, the historical per-thousand-share revenue data obtained through step S101 is data with a relatively high overall dimension and complexity, and contains information of various frequencies and properties. If a revenue prediction model is used to directly predict the historical per-thousand-share revenue data, there will be a problem of low prediction accuracy. Based on this, the present invention proposes to first decompose the historical per-thousand-share revenue data to obtain a first data component and a second data component. Exemplarily, any of the following methods can be used to decompose the historical per-thousand-share revenue data: a wavelet transform method, a principal component analysis method, and a singular value decomposition method. It can be understood that by decomposing the historical per-thousand-share revenue data, the complexity of the first data component and the second data component obtained by the decomposition is reduced, and when predictions are subsequently made based on the low-complexity data components, the accuracy of the prediction results can be guaranteed.

[0080] S103. Determine a first prediction result of the yield per ten thousand shares of the target financial product in a future time period according to the first data component through a pre-trained first type of yield prediction model.

[0081] In an embodiment of the present invention, the first type of income prediction model is a pre-trained deep learning model that can predict the income per ten thousand shares based on the first data component. When predicting, the first data component can be input into the first type of income prediction model, and the first prediction result of the income per ten thousand shares of the target financial product in the future time period can be determined according to the model output; wherein the first prediction result is also time series data. It should be noted that the number of the first type of income prediction models can be one or more. In the case where there are multiple first type of income prediction models, the output results of each model can be fused to obtain the final first prediction result.

[0082] S104. Determine a second prediction result of the return per ten thousand of the target financial product in a future time period according to the second data component through the pre-trained second type of return prediction model.

[0083] In an embodiment of the present invention, the second type of income prediction model is a pre-trained deep learning model that can predict the income per ten thousand shares based on the second data component. When predicting, the second data component can be input into the second type of income prediction model, and the second prediction result of the income per ten thousand shares of the target financial product in the future time period can be determined according to the model output; wherein the second prediction result is also time series data. It should be noted that the number of the second type of income prediction models can be one or more. In the case where there are multiple second type of income prediction models, the output results of each model can be fused to obtain the final second prediction result.

[0084] S105: The first prediction result and the second prediction result are integrated to obtain a final prediction result of the revenue per 10,000 shares, and the final prediction result of the revenue per 10,000 shares is fed back to the user.

[0085] In the embodiment of the present invention, through steps S103 and S104, different data components of the historical ten thousand share income data are used to perform predictions respectively, and the first prediction result and the second prediction result which are essentially time series data are obtained. Subsequently, only the first prediction result and the second prediction result obtained in the two steps need to be added and fused at the corresponding time points to obtain the final ten thousand share income prediction result, and the final ten thousand share income prediction result is fed back to the user as a query result, thereby completing a user's query on the prediction result of the ten thousand share income of the financial product in the future time period. It should be noted that the final ten thousand share income prediction result is also a time series data, and this time series data can be directly fed back to the user as a query result, or this time series data can be visualized (for example, a line graph is drawn according to the time series data), and the visualization result is fed back to the user as a query result.

[0086] The present invention utilizes multiple deep learning-based profit prediction models to predict different components of the historical per-thousand-share profit data of the target financial product, and integrates multiple prediction results to obtain the final per-thousand-share profit prediction result, thereby improving the accuracy of predicting the per-thousand-share profit in future time periods, and feeding back the predicted per-thousand-share profit to the user as a query result, thereby enabling the user to query the per-thousand-share profit of the financial product in the future time period.

[0087] Embodiment 2

[0088] Figure 2 The present invention provides a flowchart of an information query method. Figure 2 , the method comprises the following steps:

[0089] S201. In response to a user's query request for the return per ten thousand shares of a target financial product in a future time period, historical return per ten thousand shares data of the target financial product is obtained.

[0090] S202. Perform wavelet transform processing on the historical per-ten-thousand-share return data to obtain high-frequency components and low-frequency components; use the low-frequency components as the first data component and the high-frequency components as the second data component.

[0091] In the embodiment of the present invention, in order to reduce the complexity of the historical 10,000-share income data, the wavelet transform method is used to decompose the historical 10,000-share income data to obtain high-frequency components and low-frequency components; wherein the low-frequency component represents the long-term trend in the historical 10,000-share income data, and reflects the overall trend of the income of the financial product over a long period of time. The high-frequency component contains the short-term fluctuation information in the historical 10,000-share income data. The low-frequency component is used as the first data component, and the high-frequency component is used as the second data component.

[0092] In the embodiment of the present invention, in order to predict the per-thousand-share income of the target financial product in the future time period, two types of income prediction models are pre-selected and trained, wherein the first type of income prediction model is a first gated recurrent neural network model pre-trained with per-thousand-share income sample data; the number of the second type of income prediction models is two, namely, a second gated recurrent neural network model pre-trained with per-thousand-share income sample data and a gradient boosting decision tree model. To train the first gated recurrent neural network model, first construct a gated recurrent neural network, and determine the number of input nodes, hidden nodes, and output nodes, for example, 256 hidden nodes and 1 output node; use the Relu function as the activation function in the hidden layer; and set the model training times to 1000 times. In the embodiment of the present invention, the structure of the second gated recurrent neural network model is the same as the network structure of the first gated recurrent neural network model. The gradient boosting decision tree model is composed of a gradient boosting decision tree network, and the number of training times of the model is also 1000 times. On this basis, the first gated recurrent neural network model, the second gated recurrent neural network model, and the gradient boosting decision tree model can be trained simultaneously. The training process includes: firstly, obtaining the historical per-ten-thousand-share income data of the financial product, and performing wavelet transform on the historical per-ten-thousand-share income data to obtain high-frequency components and low-frequency components; wherein, the high-frequency components and low-frequency components are essentially time series data; for the low-frequency components, constructing multiple per-ten-thousand-share income sample data through the window method, for example, the low-frequency components are the following time series data: [(t1, x1), (t2, x2)…(t n , x n )]; where t n Indicates time, x nRepresents the low-frequency component of ten thousand shares of revenue; the low-frequency component of the 1st to 5th elements in the time series data is taken as a ten thousand share of revenue sample, and the low-frequency component of the 6th to 8th elements is taken as the actual result corresponding to the ten thousand share of revenue sample; the low-frequency component of the 2nd to 6th elements in the time series data is taken as a ten thousand share of revenue sample, and the low-frequency component of the 7th to 9th elements is taken as the actual result corresponding to the ten thousand share of revenue sample; and so on, multiple ten thousand share of revenue training samples based on low-frequency components can be obtained; similarly, according to the above method, multiple ten thousand share of revenue samples based on high-frequency components can be constructed. On this basis, the ten thousand share of revenue training samples based on low-frequency components are input into the first gated recurrent neural network model, and the loss between the output of the first gated recurrent neural network model and the actual result corresponding to the ten thousand share of revenue training samples is calculated; the parameters of the first gated recurrent neural network model are adjusted inversely according to the loss; when the number of training times reaches the preset number, the training is stopped to obtain the first type of revenue prediction model of the present invention. Similarly, for the second gated recurrent neural network model and the gradient boosting decision tree model, ten thousand profit training samples based on high-frequency components are input into the second gated recurrent neural network model and the gradient boosting decision tree model respectively, and the first loss between the output of the second gated recurrent neural network model and the actual results corresponding to the ten thousand profit training samples is calculated, and the second loss between the output of the gradient boosting decision tree model and the actual results corresponding to the ten thousand profit training samples is calculated; the parameters of the second gated recurrent neural network model are reversely adjusted according to the first loss, and the parameters of the gradient boosting decision tree model are reversely adjusted according to the second loss; when the number of training times reaches the preset number, the training is stopped to obtain two second-category profit prediction models.

[0093] On the basis of the above, a validation set including 10,000 income training samples based on high-frequency components is constructed, and the 10,000 income training samples in the validation set are input into the second gated recurrent neural network model and the gradient boosting decision tree model in sequence, and the estimated variance of the prediction error of the two models is calculated. Specifically, it can be calculated according to the following formula: Among them, t is the time step, i is the model number, and n is the number of samples in the validation set. is the actual result corresponding to 10,000 income training samples, The model outputs the prediction results based on 10,000 income samples; p i is the estimated variance of the model with serial number i. For example, the serial number of the second gated recurrent neural network model is 1, and the serial number of the gradient boosting decision tree model is 2, then p1 is the calculated first estimated variance, and p2 is the calculated second estimated variance. Further, the covariance of all models is calculated based on the estimated variance of the prediction error of each model, which can be specifically performed according to the following formula: Among them, p -1 is the covariance, m is the number of models, w iRepresents the fusion weight of the i-th model. Since the minimum covariance means that the estimated value and the stabilization effect are balanced, the calculation of the covariance is converted into an optimization problem, and the covariance is optimized using sequential least squares to obtain the optimal first fusion weight corresponding to the second gated recurrent neural network model and the optimal second fusion weight corresponding to the gradient boosting decision tree model. Subsequently, the prediction results of the two models can be cross-fused by covariance according to the first estimated variance and first fusion weight of the prediction error of the second gated recurrent neural network model, and the second estimated variance and second fusion weight of the prediction error of the gradient boosting decision tree model.

[0094] On this basis, the first prediction result of the target financial product's return per 10,000 shares in the future time period is determined according to the first data component through the pre-trained first type of return prediction model, which can be performed according to step S203. The second prediction result of the target financial product's return per 10,000 shares in the future time period is determined according to the second data component through the pre-trained second type of return prediction model, which can be performed according to steps S204-S206.

[0095] S203. Determine a first prediction result of the ten thousand share yield of the target financial product in a future time period based on the low-frequency components in the historical ten thousand share yield data by using the first gated recurrent neural network model.

[0096] In an embodiment of the present invention, the low-frequency component in the historical per-ten-thousand-share yield data can be input into the first gated recurrent neural network model, and the first prediction result of the per-ten-thousand-share yield of the target financial product in the future time period can be determined based on the output of the first gated recurrent neural network model.

[0097] In the embodiment of the present invention, since the second type of revenue prediction model includes a second gated recurrent neural network model and a gradient boosting decision tree model pre-trained using 10,000 revenue training sample data, it is necessary to use these two models to perform predictions respectively, and fuse the prediction results to obtain a second prediction result. See steps S204-S206 for details.

[0098] S204. Determine, through the second gated recurrent neural network model, a third prediction result of the return per ten thousand shares of the target financial product in the future time period according to the high-frequency components in the historical return per ten thousand shares data.

[0099] In an embodiment of the present invention, the high-frequency components in the historical per-ten-thousand-share yield data can be input into the second gated recurrent neural network model, and the third prediction result of the per-ten-thousand-share yield of the target wealth management product in the future time period can be determined based on the output of the second gated recurrent neural network model.

[0100] S205. Determine the fourth prediction result of the return per ten thousand shares of the target financial product in the future time period according to the high-frequency components in the historical return per ten thousand shares data through the gradient boosting decision tree model.

[0101] In an embodiment of the present invention, the high-frequency components in the historical per-ten-thousand-share yield data can be input into a gradient boosting decision tree model, and the fourth prediction result of the per-ten-thousand-share yield of the target financial product in the future time period can be determined based on the output of the gradient boosting decision tree model.

[0102] S206. The third prediction result and the fourth prediction result are integrated by means of covariance cross-fusion to obtain a second prediction result of the return per ten thousand of the target financial product in the future time period.

[0103] In an optional implementation scheme, first, obtain the first estimated variance and the first fusion weight of the prediction error of the second gated recurrent neural network model determined in the model training process; obtain the second estimated variance and the second fusion weight of the prediction error of the gradient boosting decision tree model determined in the model training process; wherein, the determination process of the first estimated variance, the first fusion weight, the second estimated variance, and the second fusion weight can refer to the above description. Then, according to the first estimated variance, the first fusion weight, the second estimated variance, the second fusion weight, the third prediction result, and the fourth prediction result, determine the second prediction result of the ten thousand shares of the target financial product in the future time period. Exemplarily, first calculate the inverse of the first estimated variance, the first fusion weight, and the product of the third prediction result, and then calculate the inverse of the second estimated variance, the second fusion weight, and the product of the fourth prediction result; finally, add the two product results to obtain the second prediction result of the ten thousand shares of the target financial product in the future time period.

[0104] S207: Fusing the first prediction result and the second prediction result to obtain a final prediction result of the revenue per 10,000 shares, and feeding back the final prediction result of the revenue per 10,000 shares to the user.

[0105] Optionally, the first prediction result and the second prediction result are added and fused at corresponding time points to obtain a final prediction result of the revenue per 10,000 shares, and the final prediction result of the revenue per 10,000 shares is fed back to the user as a query result.

[0106] In an embodiment of the present invention, multiple deep learning models are used to predict different components of the historical per-thousand-share return data, and then the prediction results are integrated to obtain the final prediction result, which can ensure the accuracy of the prediction result.

[0107] Embodiment 3

[0108] Figure 3a The present invention provides a flowchart of an information query method. Figure 3a, the method comprises the following steps:

[0109] S301. In response to a user's query request for the return per ten thousand shares of a target financial product in a future time period, historical return per ten thousand shares data of the target financial product is obtained.

[0110] S302. Clean the historical per-10,000-share return data and supplement the missing values ​​identified during the cleaning process using the mean completion method.

[0111] In the embodiment of the present invention, the historical ten thousand share income data is time series data. The cleaning process of the historical ten thousand share income data mainly includes: identifying abnormal values ​​in the historical ten thousand share income data, and deleting or replacing the identified abnormal values; or, identifying missing values ​​in the historical ten thousand share income data, and supplementing the missing values, for example, supplementing the missing values ​​determined in the cleaning process by the mean completion method. It can be understood that by cleaning the acquired ten thousand share historical income data, the integrity and accuracy of the data can be guaranteed.

[0112] S303, decompose and process the historical ten thousand share return data to obtain a first data component and a second data component.

[0113] S304. Determine a first prediction result of the yield per ten thousand of the target financial product in a future time period according to the first data component through the pre-trained first type of yield prediction model.

[0114] S305. Determine a second prediction result of the return per ten thousand of the target financial product in a future time period according to the second data component through the pre-trained second type of return prediction model.

[0115] In the embodiment of the present invention, the specific implementation process of steps S303-S305 can refer to the description of the above embodiment and will not be repeated here.

[0116] S306: Add and merge the first prediction result and the second prediction result at corresponding time points to obtain the final prediction result of the revenue per 10,000 shares.

[0117] Since the first prediction result and the second prediction result are essentially time series data, the final prediction result of the revenue per 10,000 shares can be obtained by adding and fusing the two prediction results at corresponding time points. It can be understood that the final prediction result of the revenue per 10,000 shares is also time series data.

[0118] S307: Visualize the final per-thousand-share revenue forecast result and present the visualization result to the user.

[0119] In the embodiment of the present invention, the final per-thousand-share profit forecast result is visualized, that is, the per-thousand-share profit forecast result belonging to the numerical time series data is converted into a chart (such as a line chart or a bar chart, etc.). Exemplarily, a visualization conversion instruction is constructed, and the visualization conversion instruction and the final per-thousand-share profit forecast result are input into the large language model, and the visualization result (including the per-thousand-share profit forecast value chart) is obtained according to the output of the large language model; wherein, the visualization conversion instruction can be exemplarily: "This data is the per-thousand-share profit forecast data of XX financial product in the next X days, and your task is to convert the per-thousand-share profit forecast data into a visualized chart".

[0120] When presenting the visualization processing results to the user, the following method can be used: embed the charts included in the visualization results into a specified web page, and then automatically jump to the web page to present the forecast results of the revenue per 10,000 shares in the form of charts to the user, or send the web link of the web page to the user so that the user can click the web link to view the forecast results of the revenue per 10,000 shares in the form of charts. Alternatively, the charts included in the visualization results are inserted into the relevant report document, and accompanied by corresponding text descriptions to explain the content displayed by the visualization chart and the key information of the forecast results, so as to present it to the user in the form of a formal report.

[0121] In the embodiment of the present invention, by visualizing and displaying the final per-thousand-share revenue forecast result, compared with directly viewing the per-thousand-share revenue forecast result in the form of a time series, the user can intuitively understand the possible future changing trend of the per-thousand-share revenue.

[0122] Further, in order to explain the process of the present invention in detail, see Figure 3b , which shows the logic block diagram of the information query method. The specific prediction process includes: obtaining the historical per-thousand-share income data of the target financial product; cleaning the historical per-thousand-share income data and supplementing the missing values ​​in the cleaning process; performing wavelet transform processing on the cleaned historical per-thousand-share income data to obtain high-frequency components and low-frequency components; inputting the low-frequency components into the first gated recurrent neural network model for prediction, and simultaneously inputting the high-frequency components into the second gated recurrent neural network model and the gradient boosting decision tree model for prediction; performing covariance cross fusion on the prediction results output by the second gated recurrent neural network model and the gradient boosting decision tree model; performing addition fusion of the covariance cross fusion results and the prediction results output by the first gated recurrent neural network model at the corresponding time to obtain the final per-thousand-share income prediction results, and displaying them to the user in a visual way, thereby completing the query of the per-thousand-share income of the financial product in the future time period.

[0123] Embodiment 4

[0124] Figure 4The structure diagram of an information query device provided by an embodiment of the present invention is shown in FIG. This embodiment is applicable to the scenario of predicting the return per 10,000 shares of financial products. The device can execute any information query method of the present invention. Figure 4 As shown, the information query device includes:

[0125] The data acquisition module 401 is used to respond to a user's query request for the return per 10,000 shares of the target financial product in a future time period, and acquire the historical return per 10,000 shares data of the target financial product;

[0126] Decomposition processing module 402, used to decompose the historical per-10,000-share income data to obtain a first data component and a second data component;

[0127] The first prediction module 403 is used to determine a first prediction result of the return per 10,000 shares of the target financial product in a future time period according to the first data component by using a pre-trained first type of return prediction model;

[0128] The second prediction module 404 is used to determine a second prediction result of the return per 10,000 shares of the target financial product in a future time period according to the second data component by using the pre-trained second type of return prediction model;

[0129] The fusion feedback module 405 is used to fuse the first prediction result and the second prediction result to obtain the final prediction result of the revenue per 10,000 shares, and feed back the final prediction result of the revenue per 10,000 shares to the user.

[0130] In some embodiments, the decomposition processing module 402 is specifically used to:

[0131] Perform wavelet transform on the historical per-10,000-share earnings data to obtain high-frequency components and low-frequency components;

[0132] The low-frequency component is used as the first data component, and the high-frequency component is used as the second data component.

[0133] In some embodiments, the first type of revenue prediction model is a first gated recurrent neural network model pre-trained using 10,000 revenue sample data;

[0134] The first prediction module 403 is specifically used for:

[0135] Through the first gated recurrent neural network model, the first prediction result of the ten thousand share yield of the target financial product in the future time period is determined according to the low-frequency components in the historical ten thousand share yield data.

[0136] In some embodiments, the second type of revenue prediction model includes a second gated recurrent neural network model and a gradient boosting decision tree model pre-trained using 10,000 revenue sample data;

[0137] The second prediction module 404 includes:

[0138] The first prediction unit is used to determine a third prediction result of the return per 10,000 shares of the target financial product in a future time period according to the high-frequency components in the historical return per 10,000 shares data through the second gated recurrent neural network model;

[0139] The second prediction unit is used to determine the fourth prediction result of the ten thousand share income of the target financial product in the future time period according to the high-frequency components in the historical ten thousand share income data by using the gradient boosting decision tree model;

[0140] The fusion unit is used to fuse the third prediction result and the fourth prediction result by means of covariance cross fusion to obtain a second prediction result of the yield per ten thousand of the target financial product in the future time period.

[0141] In some embodiments, the fusion unit is specifically used to:

[0142] Obtaining a first estimated variance and a first fusion weight of a prediction error of a second gated recurrent neural network model determined during the model training process;

[0143] Obtaining a second estimated variance and a second fusion weight of a prediction error of a gradient boosting decision tree model determined during model training;

[0144] According to the first estimated variance, the first fusion weight, the second estimated variance, the second fusion weight, the third prediction result and the fourth prediction result, a second prediction result of the yield per ten thousand shares of the target financial product in the future time period is determined.

[0145] In some embodiments, the apparatus further comprises:

[0146] The cleaning processing module is used to clean the historical per-ten-thousand-share return data before decomposing the data, and to supplement the missing values ​​determined in the cleaning process by the mean completion method.

[0147] In some embodiments, the fusion feedback module 405 is specifically used to:

[0148] The first prediction result and the second prediction result are added and merged at the corresponding time points to obtain the final per-thousand-share revenue prediction result;

[0149] The final per-thousand-share profit forecast results are visualized and displayed to users.

[0150] The information query device provided by the embodiment of the present invention can execute the information query method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0151] According to an embodiment of the present invention, the present invention also provides an electronic device, a readable storage medium and a computer program product.

[0152] Embodiment 5

[0153] Figure 5 The components, their connections and relationships, and their functions shown herein are merely examples and are not intended to limit implementation of the invention described and / or claimed herein.

[0154] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0155] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0156] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as executing the information query method.

[0157] In some embodiments, the information query method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the information query method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the information query method in any other appropriate manner (e.g., by means of firmware).

[0158] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0159] The computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable information query device, so that when the computer programs are executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0160] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0161] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0162] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0163] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0164] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0165] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An information query method, characterized in that: include: In response to a user's query request for the return per 10,000 shares of a target financial product in a future time period, obtaining historical return per 10,000 shares data of the target financial product; Decomposing the historical per-10,000-share revenue data to obtain a first data component and a second data component; Determine, by using a pre-trained first type of yield prediction model, a first prediction result of the yield per ten thousand shares of the target financial product in a future time period according to the first data component; Determine, by using the pre-trained second type of income prediction model, a second prediction result of the income per ten thousand shares of the target financial product in a future time period according to the second data component; The first prediction result and the second prediction result are fused to obtain a final prediction result of the revenue per 10,000 shares, and the final prediction result of the revenue per 10,000 shares is fed back to the user.

2. The method according to claim 1, characterized in that The decomposition process of the historical 10,000-share income data to obtain the first data component and the second data component includes: Perform wavelet transformation on the historical per-thousand-share return data to obtain high-frequency components and low-frequency components; The low-frequency component is used as the first data component, and the high-frequency component is used as the second data component.

3. The method according to claim 2, characterized in that The first type of revenue prediction model is a first gated recurrent neural network model pre-trained using 10,000 revenue sample data; The first type of pre-trained yield prediction model determines, according to the first data component, a first prediction result of the yield per ten thousand shares of the target financial product in a future time period, including: Through the first gated recurrent neural network model, a first prediction result of the ten thousand share yield of the target financial product in a future time period is determined according to the low-frequency components in the historical ten thousand share yield data.

4. The method according to claim 2, characterized in that: The second type of revenue prediction model includes a second gated recurrent neural network model and a gradient boosting decision tree model pre-trained using 10,000 revenue sample data; The second type of pre-trained yield prediction model determines, according to the second data component, a second prediction result of the yield per ten thousand of the target financial product in a future time period, including: Determine, by the second gated recurrent neural network model, a third prediction result of the ten thousand share return of the target financial product in a future time period according to the high-frequency components in the historical ten thousand share return data; Determine, by means of the gradient boosting decision tree model, a fourth prediction result of the ten thousand share return of the target financial product in a future time period according to the high-frequency components in the historical ten thousand share return data; The third prediction result and the fourth prediction result are fused by means of covariance cross fusion to obtain a second prediction result of the yield per ten thousand of the target financial product in the future time period.

5. The method according to claim 4, characterized in that The third prediction result and the fourth prediction result are merged by means of covariance cross fusion to obtain a second prediction result of the return per 10,000 shares of the target financial product in the future time period, including Obtaining a first estimated variance and a first fusion weight of a prediction error of the second gated recurrent neural network model determined during a model training process; Obtaining a second estimated variance and a second fusion weight of the prediction error of the gradient boosting decision tree model determined during the model training process; A second prediction result of the yield per ten thousand shares of the target financial product in a future time period is determined based on the first estimated variance, the first fusion weight, the second estimated variance, the second fusion weight, the third prediction result and the fourth prediction result.

6. The method according to claim 1, characterized in that Before decomposing the historical per-10,000-share earnings data, the method further includes: The historical per-10,000-share return data is cleaned, and the missing values ​​determined during the cleaning process are supplemented by the mean completion method.

7. The method according to claim 1, characterized in that The fusing the first prediction result and the second prediction result to obtain a final prediction result of the revenue per 10,000 shares, and feeding back the final prediction result of the revenue per 10,000 shares to the user, comprises: Adding and fusing the first prediction result and the second prediction result at corresponding time points to obtain a final per-10,000-share revenue prediction result; The final per-thousand-share revenue forecast result is visualized and the visualization result is presented to the user.

8. An information query device, characterized in that: include: A data acquisition module, configured to respond to a user's query request for the return per 10,000 shares of a target financial product in a future time period, and acquire historical return per 10,000 shares data of the target financial product; A decomposition processing module, used for decomposing the historical 10,000-share revenue data to obtain a first data component and a second data component; A first prediction module, used to determine a first prediction result of the return per 10,000 shares of the target financial product in a future time period according to the first data component by using a pre-trained first type of return prediction model; A second prediction module is used to determine a second prediction result of the return per 10,000 shares of the target financial product in a future time period according to the second data component by using a pre-trained second type of return prediction model; A fusion feedback module is used to fuse the first prediction result and the second prediction result to obtain a final prediction result of the revenue per 10,000 shares, and feed back the final prediction result of the revenue per 10,000 shares to the user.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method according to any one of claims 1 to 7 when executed.

11. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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