Financial data processing method, device, equipment, storage medium and program product

Abstract

The application relates to a financial data processing method and device, computer equipment, a storage medium and a computer program product, and relates to the technical field of big data. The method comprises the following steps: determining corresponding data attributes and a data processing mode according to a user's financial data analysis requirement; determining whether target financial data meeting the data attributes exist in a data warehouse; if the target financial data exist, processing the target financial data through the data processing mode to obtain a financial data analysis result corresponding to the financial data analysis requirement; if the target financial data do not exist, generating prompt information for prompting the data warehouse to access corresponding financial data according to the data attributes, triggering detection of the financial data access; and in response to the financial data access of the data warehouse, acquiring the target financial data and processing the target financial data through the data processing mode to obtain the financial data analysis result. The method can improve the financial data analysis efficiency and guarantee the accuracy and objectivity of the analysis result.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a financial data processing method, apparatus, computer equipment, storage medium, and computer program product. Background Technology

[0002] With the development of information technology, the amount of financial business data that financial institutions need to process has exploded. To maximize the value of this data, financial institutions typically conduct statistical analysis on the collected financial business data and generate data reports. To better manage business data and improve analytical efficiency, financial institutions often use data warehouses to collect, store, and process the data. Analysts then select relevant data from the data warehouse for analysis based on their specific needs.

[0003] However, due to the large amount of complex data in data warehouses, analysts often need to spend a significant amount of time determining which data to select. Furthermore, because data selection during data analysis relies on the analyst's experience, different analysts may choose different data for the same analysis task, leading to different results and affecting the accuracy and objectivity of the analysis. Summary of the Invention

[0004] Therefore, it is necessary to provide a financial data processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product to address the aforementioned technical problems.

[0005] Firstly, this application provides a financial data processing method. The method includes:

[0006] Based on the user's financial data analysis needs, determine the corresponding data attributes and data processing methods;

[0007] Determine whether target financial data matching the stated data attributes exists in the data warehouse;

[0008] If it exists, the target financial data is processed using the data processing method described above to obtain financial data analysis results corresponding to the financial data analysis requirements.

[0009] If not, a prompt message is generated based on the data attributes to indicate that the corresponding financial data should be accessed to the data warehouse, triggering a detection of the financial data access; in response to the financial data access to the data warehouse, the target financial data is obtained, and the target financial data is processed using the data processing method to obtain the financial data analysis result.

[0010] In one embodiment, determining whether target financial data matching the data attributes exists in the data warehouse includes:

[0011] The data warehouse is queried according to the data attribute to determine whether the data warehouse contains first financial data that matches the data attribute; if it exists, the first financial data is used as the target financial data; if the first financial data does not exist, the data warehouse is determined to contain second financial data that matches the data transformation attribute corresponding to the data attribute; if the second financial data exists in the data warehouse, the second financial data is transformed according to the data attribute and the data transformation attribute, and the transformed second financial data is used as the target financial data.

[0012] In one embodiment, determining the corresponding data attributes and data processing methods based on the user's financial data analysis needs includes:

[0013] Based on the user's financial data analysis needs, the system queries the data warehouse's preset needs list; if a preset need matching the financial data analysis needs exists in the preset needs list, the system obtains the preset data attributes and preset data processing methods corresponding to the preset needs; the system displays the preset data attributes and preset data processing methods; based on the user's first feedback operation on the preset data attributes and preset data processing methods, the system determines the data attributes and data processing methods corresponding to the financial data analysis needs.

[0014] In one embodiment, the method further includes: if there is no preset requirement matching the financial data analysis requirement in the preset requirement list, then parsing the user's financial data analysis requirement to generate corresponding predictive data attributes and predictive data processing methods; displaying the predictive data attributes and predictive data processing methods; and determining the data attributes and data processing methods corresponding to the financial data analysis requirement based on the user's second feedback operation on the predictive data attributes and predictive data processing methods.

[0015] In one embodiment, the method further includes: in response to the user's login operation, obtaining the user's login information; determining the predictive analysis needs corresponding to the user based on the login information; displaying the predictive analysis needs; and determining the user's financial data analysis needs based on the user's third feedback operation on the predictive analysis needs.

[0016] In one embodiment, the method further includes: obtaining a demand description statement input by the user; parsing the demand description statement to obtain candidate analysis demands; displaying the candidate analysis demands; and determining the user's financial data analysis demand based on the user's fourth feedback operation on the candidate analysis demands.

[0017] Secondly, this application also provides a financial data processing apparatus. The apparatus includes:

[0018] The information determination module is used to determine the corresponding data attributes and data processing methods based on the user's financial data analysis needs;

[0019] The data determination module is used to determine whether target financial data that matches the data attributes exists in the data warehouse;

[0020] The first processing module is used to process the target financial data through the data processing method if it exists, and obtain financial data analysis results corresponding to the financial data analysis requirements.

[0021] The second processing module is used to generate a prompt message based on the data attributes to prompt the data warehouse to access the corresponding financial data if the data does not exist, thereby triggering the detection of the access to the financial data; in response to the access to the financial data in the data warehouse, the module obtains the target financial data and processes the target financial data through the data processing method to obtain the financial data analysis result.

[0022] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0023] Based on the user's financial data analysis needs, the corresponding data attributes and data processing methods are determined; it is determined whether target financial data matching the data attributes exists in the data warehouse; if it exists, the target financial data is processed using the data processing methods to obtain financial data analysis results corresponding to the financial data analysis needs; if it does not exist, a prompt message is generated based on the data attributes to prompt the data warehouse to access the corresponding financial data, triggering the detection of the financial data access; in response to the financial data access of the data warehouse, the target financial data is obtained, and the target financial data is processed using the data processing methods to obtain the financial data analysis results.

[0024] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0025] Based on the user's financial data analysis needs, the corresponding data attributes and data processing methods are determined; it is determined whether target financial data matching the data attributes exists in the data warehouse; if it exists, the target financial data is processed using the data processing methods to obtain financial data analysis results corresponding to the financial data analysis needs; if it does not exist, a prompt message is generated based on the data attributes to prompt the data warehouse to access the corresponding financial data, triggering the detection of the financial data access; in response to the financial data access of the data warehouse, the target financial data is obtained, and the target financial data is processed using the data processing methods to obtain the financial data analysis results.

[0026] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0027] Based on the user's financial data analysis needs, the corresponding data attributes and data processing methods are determined; it is determined whether target financial data matching the data attributes exists in the data warehouse; if it exists, the target financial data is processed using the data processing methods to obtain financial data analysis results corresponding to the financial data analysis needs; if it does not exist, a prompt message is generated based on the data attributes to prompt the data warehouse to access the corresponding financial data, triggering the detection of the financial data access; in response to the financial data access of the data warehouse, the target financial data is obtained, and the target financial data is processed using the data processing methods to obtain the financial data analysis results.

[0028] The aforementioned financial data processing methods, apparatus, computer equipment, storage media, and computer program products determine the corresponding data attributes and data processing methods based on the user's financial data analysis needs. When it is determined that target financial data matching the data attributes exists in the data warehouse, the target financial data is processed through the data processing method to obtain financial data analysis results corresponding to the financial data analysis needs. When the target financial data does not exist, a prompt message is generated based on the data attributes to indicate that the corresponding financial data should be accessed to the data warehouse, triggering a detection of financial data access. Further, in response to the financial data access from the data warehouse, the target financial data is acquired and then processed through the data processing method to obtain financial data analysis results. In this process, determining the data attributes and data processing methods based on the user's financial data analysis needs allows for standardized processing of the user's analysis needs, effectively avoiding the impact of subjective differences in the analysis needs of different analysts on the analysis results, and ensuring the accuracy and objectivity of the analysis results. Furthermore, determining whether target financial data exists in the data warehouse based on data attributes, and processing the target financial data using the determined data processing method when the target financial data exists, can effectively save analysts' time in selecting data from the data warehouse and improve data analysis efficiency. On the other hand, if the target financial data is not present in the data warehouse, the system prompts the user to access the corresponding financial data and triggers a data access check. Once accessed, the target financial data is retrieved and processed. This can help analysts access data that meets their analytical needs more effectively to the data warehouse and can efficiently provide data analysis results after accessing new data, further improving data analysis efficiency. Attached Figure Description

[0029] Figure 1 This is an application environment diagram of a financial data processing method in one embodiment;

[0030] Figure 2 This is a flowchart illustrating a financial data processing method in one embodiment;

[0031] Figure 3 This is a flowchart illustrating the steps for determining whether target financial data matching the data attributes exists in a data warehouse, as shown in one embodiment.

[0032] Figure 4 This is a flowchart illustrating the steps for determining the corresponding data attributes and data processing methods in one embodiment;

[0033] Figure 5 This is a flowchart illustrating the steps for determining the corresponding data attributes and data processing methods in another embodiment;

[0034] Figure 6This is a flowchart illustrating the steps for determining a user's financial data analysis needs in one embodiment.

[0035] Figure 7 This is a flowchart illustrating the steps for determining a user's financial data analysis needs in another embodiment;

[0036] Figure 8 This is a structural block diagram of a financial data processing device in one embodiment;

[0037] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0039] The financial data processing method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. Server 104 can be used to execute the financial data processing method of this application and provide users with information such as financial data analysis results through terminal 102. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0040] In one embodiment, such as Figure 2 As shown, a financial data processing method is provided, which can be applied to... Figure 1 Taking server 104 as an example, the following steps are included:

[0041] Step S201: Determine the corresponding data attributes and data processing methods based on the user's financial data analysis needs.

[0042] Specifically, financial data analysis needs can include the object of analysis and the analysis methods. For example, the object of analysis could be "housing prices in region A," and the analysis method could be "weekly trend prediction." For instance, financial data analysis needs can be confirmed by the user through a checkbox on terminal 102, or they can be obtained through analysis based on the requirement description entered by the user through terminal 102.

[0043] Based on the financial data analysis requirements, the specific data attributes needed for the financial data, as well as the specific data processing methods, can be determined by referring to the records of the analysis requirements in financial industry documents. For example, for the financial data analysis requirement of "predicting the weekly trend of housing prices in region A," the required data attributes can be determined to include: region A, housing prices, set data granularity, set quantity, set historical time span, etc. The corresponding data processing method is to input housing prices in region A at multiple historical time points that meet the data attributes into a housing price regression model.

[0044] For example, server 104 may store mapping relationships between common financial data analysis needs and corresponding data attributes and data processing methods, so that according to a user's financial data analysis needs, the corresponding data attributes and data processing methods can be queried. In other embodiments, server 104 may also input financial data analysis needs into an analysis model pre-trained using financial domain corpus to obtain the data attributes and data processing methods corresponding to the financial data analysis needs.

[0045] Step S202: Determine whether the target financial data that matches the data attributes exists in the data warehouse.

[0046] Step S203: If it exists, the target financial data is processed through data processing methods to obtain financial data analysis results corresponding to the financial data analysis needs.

[0047] Specifically, after determining the data attributes corresponding to the financial data analysis requirements in step S201, step S202 can query the data warehouse based on the data attributes to find the target financial data that matches those attributes. If the target financial data is found to exist in the data warehouse, step S203 can directly process the target financial data according to the determined data processing method to obtain the corresponding financial data analysis results.

[0048] For example, for the financial data analysis requirement of "predicting the weekly trend of housing prices in region A", the data warehouse can be queried step by step according to each dimension of the data attribute. First, check whether there is data in the data warehouse with the region identifier "region A". Then, determine whether the data is "housing price" data. Then, further determine whether the granularity, time and quantity of the data in the system that meet the above two conditions meet the set values ​​of the data attribute.

[0049] If the target financial data is confirmed to exist in the data warehouse, housing price data for region A from multiple historical time points can be extracted from the data warehouse. Following the data processing method, these data can be input into the housing price regression model to obtain the prediction results of the housing price trend in region A for the next week.

[0050] Step S204: If the data does not exist, generate a prompt message based on the data attributes to indicate that the corresponding financial data has been accessed to the data warehouse, triggering the detection of financial data access; in response to the financial data access of the data warehouse, obtain the target financial data, process the target financial data through data processing methods, and obtain the financial data analysis results.

[0051] Specifically, if the target data does not exist in the data warehouse, server 104 can generate a corresponding prompt message based on the data attributes and display the prompt message to the user through terminal 102 to prompt the user to access the corresponding financial data from the data warehouse. For example, if housing price data for region A exists in the data warehouse, but the set historical time period is not met, the prompt message can be used to prompt the user to access the housing price data for region A in the corresponding historical time period.

[0052] Simultaneously, server 104 can also trigger detection of financial data access to the data warehouse. Whenever new data is detected being accessed, it re-determines whether the data warehouse already contains target financial data that matches the data attributes. Therefore, once the data warehouse accesses the corresponding financial data, it can promptly acquire and process the target financial data to obtain financial data analysis results.

[0053] In the aforementioned financial data processing method, based on the user's financial data analysis needs, corresponding data attributes and data processing methods are determined. When it is determined that target financial data matching the data attributes exists in the data warehouse, the target financial data is processed using the data processing method to obtain financial data analysis results corresponding to the financial data analysis needs. When the target financial data does not exist, a prompt message is generated based on the data attributes to indicate that the corresponding financial data should be accessed to the data warehouse, triggering a detection of financial data access. Further, in response to the financial data access from the data warehouse, the target financial data is acquired and then processed using the data processing method to obtain the financial data analysis results. In this process, determining data attributes and data processing methods based on the user's financial data analysis needs allows for standardized processing of user analysis requirements, effectively avoiding the impact of subjective differences in the analysis needs of different analysts on the analysis results, and ensuring the accuracy and objectivity of the analysis results. Furthermore, determining whether target financial data exists in the data warehouse based on data attributes, and processing the target financial data using the determined data processing method when the target financial data exists, can effectively save analysts' time in selecting data from the data warehouse and improve data analysis efficiency. On the other hand, if the target financial data is not present in the data warehouse, the system prompts the user to access the corresponding financial data and triggers a data access check. Once accessed, the target financial data is retrieved and processed. This can help analysts access data that meets their analytical needs more effectively to the data warehouse and can efficiently provide data analysis results after accessing new data, further improving data analysis efficiency.

[0054] In one embodiment, such as Figure 3 As shown, step S202 above, determining whether target financial data matching the data attributes exists in the data warehouse, includes:

[0055] Step S301: Query the data warehouse based on the data attributes to determine whether the data warehouse contains first financial data that matches the data attributes.

[0056] Step S302: If it exists, then the first financial data is used as the target financial data.

[0057] Step S303: If the first financial data does not exist, determine whether the data warehouse has second financial data that matches the data transformation attribute corresponding to the data attribute.

[0058] Step S304: If the data warehouse contains second financial data, the second financial data is transformed according to the data attributes and data transformation attributes, and the transformed second financial data is used as the target financial data.

[0059] Specifically, in this embodiment, the data warehouse can first be queried to see if there is first financial data that can directly match the data attributes. If so, it can be used as the target financial data and directly used for subsequent processing and analysis. For example, for the financial data analysis requirement of "prediction of the weekly trend of housing prices in region A", if it is determined that there is housing price data in region A in the data warehouse that matches the corresponding set conditions such as historical duration, quantity, and granularity, then this data can be directly used as the target financial data.

[0060] On the other hand, if matching primary financial data cannot be directly retrieved from the data warehouse based on data attributes, the dimensions of the data attributes can be analyzed to determine the data transformation attributes that secondary financial data, which can be transformed into primary financial data, should possess. Then, based on these transformation attributes, the existence of corresponding secondary financial data in the data warehouse can be checked. For example, for the financial data analysis requirement of "weekly price trend prediction for region A," the region dimension "region A" in the data attributes can be further broken down into multiple sub-regions under region A, such as "region A1," "region A2," etc. Based on the data transformation attributes, the existence of corresponding price data for region A1 and region A2 in the data warehouse can be checked. For example, when a data attribute is limited to discrete data in a certain dimension, the corresponding data transformation attribute can include the relevant dimension in the form of continuous data. For instance, for the financial data analysis requirement of "statistics on real estate transaction volume for each age group", the data attribute includes the age group dimension and is limited to "youth", "middle-aged", and "elderly". The corresponding data transformation attribute can include the age dimension or the birth date dimension. Based on the data transformation attribute, it is possible to query whether there is real estate transaction data in the data warehouse that is labeled with a specific age or birth date.

[0061] Furthermore, for the second financial data obtained, the corresponding conversion processing method can be determined according to the relationship between its corresponding data conversion attributes and the data attributes of the target financial data, so that the second financial data can be converted and processed to obtain the target financial data.

[0062] For example, for data transformation attributes obtained by further granular splitting based on the dimensions of data attributes, the second financial data can be aggregated on that dimension to obtain target data that conforms to the data attributes. For example, the housing prices in regions A1 and A2 can be summed to obtain the housing price in region A. For related dimensions that are continuous data obtained based on the data dimensions that are required to be discrete data in the data attributes, they can be discretized to obtain target data that conforms to the data attributes. For example, the corresponding age range can be obtained based on age or birth date.

[0063] This embodiment focuses on data attributes. First, it queries the data warehouse for matching first financial data. If the first financial data does not exist, it queries the data warehouse for second financial data that can be transformed to match the data attributes based on the corresponding data transformation attributes and transforms it into target data. This allows for better utilization of the data in the data warehouse, effectively saves the time waiting for data access, and improves the efficiency of financial data analysis.

[0064] In one embodiment, such as Figure 4 As shown, step S201 above, based on the user's financial data analysis needs, determines the corresponding data attributes and data processing methods, including:

[0065] Step S401: Based on the user's financial data analysis needs, query the data warehouse's preset requirement list.

[0066] Step S402: If there is a preset requirement in the preset requirement list that matches the financial data analysis requirement, then obtain the preset data attributes and preset data processing methods corresponding to the preset requirement.

[0067] Step S403: Display the preset data attributes and preset data processing methods.

[0068] Step S404: Based on the user's first feedback operation on the preset data attributes and preset data processing methods, determine the data attributes and data processing methods corresponding to the financial data analysis needs.

[0069] Specifically, server 104 can store a list of preset requirements, which may include multiple preset requirements along with their corresponding preset data attributes and preset data processing methods. These preset requirements may include data analysis requirements commonly used in the financial field, as well as records of data analysis requirements generated by analysts from financial institutions when using this data warehouse for data analysis in the past. For data analysis requirements commonly used in the financial field, their corresponding data attributes and data processing methods can be obtained through analysis of textual materials in the financial field; while for data analysis requirements obtained from analysts' historical analysis activities, more reliable data attributes and data processing methods can be obtained by combining the specific analytical operations of multiple analysts.

[0070] Therefore, in step S401, the preset requirement list can be queried according to the user's financial data analysis needs. When a matching preset requirement is stored in the list, the corresponding preset data attributes and preset data processing methods can be obtained in step S402.

[0071] Then, in step S403, the acquired preset data attributes and preset data processing methods can be sent to terminal 102 for display to the user. The user can perform a first feedback operation on the displayed preset data attributes and preset data processing methods through interaction with terminal 102, such as clicking, pressing, or inputting. The first feedback operation may include confirming the displayed content or adjusting the displayed content.

[0072] In step S404, the server 104, based on the first feedback operation received from the terminal 102, can ultimately determine the data attributes and data processing methods corresponding to the financial data analysis requirements based on preset data attributes and preset data processing methods.

[0073] This embodiment can quickly determine the preset data attributes and preset data processing methods corresponding to the user's financial data analysis needs by querying the preset requirements table. Furthermore, considering that the data attributes and data processing methods corresponding to the current financial data analysis needs may have changed compared to the contents recorded in the list, it also allows users to adjust them through feedback operations, which can effectively improve the accuracy of the analysis results.

[0074] In one embodiment, such as Figure 5 As shown, the above method also includes:

[0075] Step S501: If there is no preset requirement in the preset requirement list that matches the financial data analysis requirement, then the user's financial data analysis requirement is parsed to generate the corresponding predictive data attributes and predictive data processing methods.

[0076] Specifically, when a user's financial data analysis needs cannot be matched by a preset requirement from the preset requirement list, the user's financial data analysis needs can be analyzed to predict the corresponding data attributes and data processing methods.

[0077] For example, based on the user's financial data analysis needs, the similarity between each preset need in the preset need list and the current financial data analysis need can be obtained. If there is a preset need with a similarity higher than a set threshold, then the data attributes and data processing methods of the user's financial data analysis need can be predicted based on the preset data attributes and preset data processing methods of that preset need. For instance, for the financial data analysis need of "predicting the weekly trend of housing prices in region A", if the analysis reveals a preset need "predicting the weekly trend of housing prices in region B" with a high similarity in the preset need list, then the predicted data attributes and predicted data processing methods corresponding to "predicting the weekly trend of housing prices in region A" can be generated based on its corresponding preset data attributes and preset data processing methods.

[0078] In other embodiments, users' financial data analysis needs can be input into an analysis model pre-trained using financial domain corpus, and the model can output the predicted data attributes and predicted data processing methods corresponding to the financial data analysis needs.

[0079] Step S502: Display the predicted data attributes and the predicted data processing method.

[0080] Specifically, in this step, the generated prediction data attributes and prediction data processing methods can be sent to terminal 102 for display to the user.

[0081] Step S503: Based on the user's second feedback operation on the predicted data attributes and predicted data processing methods, determine the data attributes and data processing methods corresponding to the financial data analysis needs.

[0082] Specifically, regarding the predicted data attributes and processing methods displayed on terminal 102, users can interact with terminal 102 through methods such as clicking, pressing, or inputting, thereby performing a second feedback operation. This second feedback operation may include confirming or adjusting the displayed content. Based on the second feedback operation received from terminal 102, server 104 can directly use the predicted data attributes and processing methods as the data attributes and processing methods corresponding to the financial data analysis requirements, or it can adjust them to ultimately obtain the data attributes and processing methods corresponding to the financial data analysis requirements.

[0083] In this embodiment, when there is no preset requirement matching the financial data analysis requirement in the preset requirement list, the corresponding predictive data attributes and predictive data processing methods are generated by parsing the financial data analysis requirement. This can provide users with effective reference information and reduce the impact of users' subjective judgment on the analysis results.

[0084] In one embodiment, such as Figure 6 As shown, the above method also includes:

[0085] Step S601: In response to the user's login operation, obtain the user's login information.

[0086] Step S602: Determine the predictive analysis needs corresponding to the user based on the login information.

[0087] Specifically, a user's login information can include user ID, login time, etc. Based on the user's login information, one can query the data analysis operations the user has previously performed using the data warehouse, as well as the common data analysis needs of the analysis department to which the user belongs. Based on the login time, the corresponding financial business analysis scenario can be determined, such as end-of-day or end-of-quarter. Therefore, by combining the above information, the user's current desired data analysis needs can be predicted, and their corresponding predictive analysis requirements can be determined.

[0088] Step S603: Show the predictive analytics requirements.

[0089] Specifically, in this step, the predictive analysis requirements determined in step S602 can be sent to terminal 102 for display to the user.

[0090] Step S604: Based on the user's third feedback operation regarding predictive analysis needs, determine the user's financial data analysis needs.

[0091] Specifically, users can interact with terminal 102, such as clicking, pressing, or inputting, to provide third-party feedback on the displayed predictive analysis needs. This third-party feedback can include confirming or adjusting the displayed content, or providing new data analysis needs. In this step, server 104 can ultimately determine the user's corresponding financial data analysis needs based on the third-party feedback received from terminal 102.

[0092] This embodiment can provide users with targeted predictive analysis services based on their login information, offering useful reference information, saving users time in determining their financial data analysis needs, and improving analysis efficiency.

[0093] In one embodiment, such as Figure 7 As shown, the above method also includes:

[0094] Step S701: Obtain the user's input description of the requirements.

[0095] Specifically, server 104 can receive a user-inputted request description statement from terminal 102. This request description statement can be a statement in which the user describes their financial data analysis needs using natural language.

[0096] Step S702: Parse the requirement description statement to obtain candidate analysis requirements.

[0097] Specifically, server 104 can use a pre-trained natural language processing model to parse the requirement description statement and output one or more candidate analysis requirements. This natural language processing model can be trained using a combination of general corpora and financial domain corpora, thus accurately identifying standardized financial data analysis requirements from the requirement description statement.

[0098] Step S703: Show the candidate analysis requirements.

[0099] Specifically, in this step, the candidate analysis requirements obtained in step S702 can be sent to terminal 102 for display to the user.

[0100] Step S704: Based on the user's fourth feedback operation on the candidate analysis requirements, determine the user's financial data analysis requirements.

[0101] Specifically, users can interact with terminal 102, such as clicking, pressing, or inputting, to provide a fourth feedback operation on the displayed predictive analysis requirements. This fourth feedback operation can include confirming or adjusting the displayed content, or providing new data analysis requirements. In this step, server 104 can ultimately determine the user's corresponding financial data analysis requirements based on the fourth feedback operation received from terminal 102.

[0102] This embodiment allows users to describe their analysis needs using natural language without having to manually input standardized financial data analysis requirements. Users can easily conduct data analysis without having to remember standardized requirement names, thus improving the convenience of using data warehouses for analysis.

[0103] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0104] Based on the same inventive concept, this application also provides a financial data processing apparatus for implementing the financial data processing method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more financial data processing apparatus embodiments provided below can be found in the limitations of the financial data processing method described above, and will not be repeated here.

[0105] In one embodiment, such as Figure 8 As shown, a financial data processing apparatus is provided, the apparatus 800 may include:

[0106] The information determination module 801 is used to determine the corresponding data attributes and data processing methods based on the user's financial data analysis needs;

[0107] Data determination module 802 is used to determine whether target financial data that matches the data attributes exists in the data warehouse;

[0108] The first processing module 803 is used to process the target financial data through the data processing method if it exists, and obtain financial data analysis results corresponding to the financial data analysis requirements.

[0109] The second processing module 804 is used to generate a prompt message based on the data attributes to prompt the access of the corresponding financial data to the data warehouse if the data does not exist, thereby triggering the detection of the access of the financial data; in response to the access of the financial data to the data warehouse, the module obtains the target financial data and processes the target financial data through the data processing method to obtain the financial data analysis result.

[0110] In one embodiment, the data determination module 802 is configured to query the data warehouse according to the data attribute to determine whether the data warehouse contains first financial data matching the data attribute; if it exists, the first financial data is used as the target financial data; if the first financial data does not exist, the data warehouse is configured to determine whether the data warehouse contains second financial data matching the data transformation attribute corresponding to the data attribute; if the data warehouse contains the second financial data, the second financial data is transformed according to the data attribute and the data transformation attribute, and the transformed second financial data is used as the target financial data.

[0111] In one embodiment, the information determination module 801 is configured to query the preset requirement list of the data warehouse according to the user's financial data analysis needs; if there is a preset requirement in the preset requirement list that matches the financial data analysis needs, then obtain the preset data attributes and preset data processing methods corresponding to the preset requirements; display the preset data attributes and preset data processing methods; and determine the data attributes and data processing methods corresponding to the financial data analysis needs based on the user's first feedback operation on the preset data attributes and preset data processing methods.

[0112] In one embodiment, the information determination module 801 is configured to, if there is no preset requirement in the preset requirement list that matches the financial data analysis requirement, parse the user's financial data analysis requirement, generate corresponding predictive data attributes and predictive data processing methods, display the predictive data attributes and predictive data processing methods, and determine the data attributes and data processing methods corresponding to the financial data analysis requirement based on the user's second feedback operation on the predictive data attributes and predictive data processing methods.

[0113] In one embodiment, the information determination module 801 is configured to, in response to the user's login operation, obtain the user's login information; determine the predictive analysis needs corresponding to the user based on the login information; display the predictive analysis needs; and determine the user's financial data analysis needs based on the user's third feedback operation on the predictive analysis needs.

[0114] In one embodiment, the information determination module 801 is used to obtain the user's input demand description statement; parse the demand description statement to obtain candidate analysis demands; display the candidate analysis demands; and determine the user's financial data analysis demand based on the user's fourth feedback operation on the candidate analysis demands.

[0115] Each module in the aforementioned financial data processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0116] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data such as financial data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a financial data processing method.

[0117] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0118] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0119] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0120] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0121] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0122] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0123] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0124] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A financial data processing method, characterized in that, The method includes: Based on the user's financial data analysis needs, determine the corresponding data attributes and data processing methods; Query the data warehouse based on the data attributes to determine whether the data warehouse contains first financial data that matches the data attributes; If it exists, then the first financial data will be used as the target financial data; If the first financial data does not exist, then determine whether the data warehouse has a second financial data that matches the data transformation attribute corresponding to the data attribute; wherein, the data transformation attribute is obtained by fine-grained splitting based on the data dimension of the data attribute, or, when the data attribute includes a data dimension in discrete data form, the data transformation attribute includes the data dimension in continuous data form; If the second financial data exists in the data warehouse, the second financial data is transformed according to the data attributes and the data transformation attributes, and the transformed second financial data is used as the target financial data; wherein, when the data transformation attributes are obtained by fine-grained splitting based on the data dimensions of the data attributes, the transformation process includes aggregation processing; when the data attributes include data dimensions in discrete data form, and the data transformation attributes include data dimensions in continuous data form, the transformation process includes discretization processing; If the target financial data exists, the target financial data is processed using the data processing method to obtain financial data analysis results corresponding to the financial data analysis requirements; If the target financial data does not exist, a prompt message is generated based on the data attributes to prompt the data warehouse to access the corresponding financial data, triggering the detection of the financial data access; in response to the financial data access of the data warehouse, the target financial data is obtained, and the target financial data is processed through the data processing method to obtain the financial data analysis result.

2. The method according to claim 1, characterized in that, The process of determining the corresponding data attributes and data processing methods based on the user's financial data analysis needs includes: Based on the user's financial data analysis needs, query the preset requirements list of the data warehouse; If there is a preset requirement in the preset requirement list that matches the financial data analysis requirement, then obtain the preset data attributes and preset data processing methods corresponding to the preset requirement; The preset data attributes and preset data processing methods are displayed; Based on the user's first feedback operation on the preset data attributes and preset data processing methods, the data attributes and data processing methods corresponding to the financial data analysis requirements are determined.

3. The method according to claim 2, characterized in that, Also includes: If there is no preset requirement matching the financial data analysis requirement in the preset requirement list, then the user's financial data analysis requirement is parsed to generate corresponding predictive data attributes and predictive data processing methods. This demonstrates the attributes of the predicted data and the methods for processing the predicted data; Based on the user's second feedback operation regarding the predicted data attributes and the predicted data processing method, the data attributes and data processing method corresponding to the financial data analysis requirement are determined.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: In response to the user's login operation, obtain the user's login information; Based on the login information, determine the predictive analysis needs corresponding to the user; This demonstrates the aforementioned predictive analytics requirements; Based on the user's third feedback action regarding the predictive analysis requirements, the user's financial data analysis needs are determined.

5. The method according to any one of claims 1 to 3, characterized in that, The method further includes: Obtain the user's input description of the requirements; The candidate analysis requirements are obtained by parsing the requirement description statement; Present the aforementioned candidate analysis requirements; Based on the user's fourth feedback action regarding the candidate analysis requirements, the user's financial data analysis requirements are determined.

6. A financial data processing device, characterized in that, The device includes: The information determination module is used to determine the corresponding data attributes and data processing methods based on the user's financial data analysis needs; A data determination module is used to query a data warehouse based on the data attribute to determine whether the data warehouse contains first financial data matching the data attribute; if it exists, the first financial data is used as the target financial data; if the first financial data does not exist, the module determines whether the data warehouse contains second financial data matching the data transformation attribute corresponding to the data attribute; wherein the data transformation attribute is obtained by fine-grained splitting of the data dimension of the data attribute, or, when the data attribute includes a discrete data dimension, the data transformation attribute includes the continuous data dimension; if the second financial data exists in the data warehouse, the second financial data is transformed according to the data attribute and the data transformation attribute, and the transformed second financial data is used as the target financial data; wherein, when the data transformation attribute is obtained by fine-grained splitting of the data dimension of the data attribute, the transformation process includes aggregation; when the data attribute includes a discrete data dimension and the data transformation attribute includes a continuous data dimension, the transformation process includes discretization. The first processing module is used to process the target financial data through the data processing method if the target financial data exists, and obtain financial data analysis results corresponding to the financial data analysis requirements. The second processing module is used to generate a prompt message based on the data attributes to prompt the data warehouse to access the corresponding financial data if the target financial data does not exist, thereby triggering the detection of the access to the financial data; in response to the access to the financial data in the data warehouse, the module obtains the target financial data and processes the target financial data through the data processing method to obtain the financial data analysis result.

7. The apparatus according to claim 6, characterized in that, The information determination module is used to query the preset requirement list of the data warehouse according to the user's financial data analysis needs; if there is a preset requirement in the preset requirement list that matches the financial data analysis needs, then the preset data attributes and preset data processing methods corresponding to the preset requirement are obtained. Display the preset data attributes and preset data processing methods; based on the user's first feedback operation on the preset data attributes and preset data processing methods, determine the data attributes and data processing methods corresponding to the financial data analysis requirements.

8. The apparatus according to claim 7, characterized in that, The information determination module is used to parse the user's financial data analysis needs if there is no preset need matching the financial data analysis needs in the preset needs list, generate corresponding predicted data attributes and predicted data processing methods; display the predicted data attributes and predicted data processing methods; and determine the data attributes and data processing methods corresponding to the financial data analysis needs based on the user's second feedback operation on the predicted data attributes and predicted data processing methods.

9. The apparatus according to any one of claims 6 to 8, characterized in that, The information determination module is used to respond to the user's login operation, obtain the user's login information; determine the predictive analysis needs corresponding to the user based on the login information; display the predictive analysis needs; and determine the user's financial data analysis needs based on the user's third feedback operation on the predictive analysis needs.

10. The apparatus according to any one of claims 6 to 8, characterized in that, The information determination module is used to acquire the user's input demand description statement; parse the demand description statement to obtain candidate analysis demands; display the candidate analysis demands; and determine the user's financial data analysis demand based on the user's fourth feedback operation on the candidate analysis demands.

11. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

13. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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