Data interaction method and system of financial service platform

Abstract

The embodiment of the invention provides a data interaction method and system of a financial service platform, and belongs to the technical field of data visualization. The method comprises the following steps: collecting to-be-interacted data of each data source, and preprocessing each to-be-interacted data; classifying the preprocessed to-be-interacted data, and matching a corresponding data interaction scheme based on a classification result; based on the data interaction scheme, determining a visualization scheme and a pushing rule of each classification data; and based on the visualization scheme of each piece of classification data, correspondingly executing visualization arrangement of each piece of classification data, and based on a pushing rule, executing visualization arrangement result pushing. According to the scheme, the problem that a data interaction scheme of an existing financial service platform cannot meet diversified requirements of users is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data visualization, and in particular to a data interaction method and a data interaction system of a financial service platform. Background Art

[0002] With the rapid development of big data, cloud computing and artificial intelligence technologies, energy supply chain financial service platforms are playing an increasingly important role in connecting the energy industry with financial capital. The main function of such platforms is to improve operational efficiency, reduce risks, and optimize resource allocation through data management and analysis. However, existing energy supply chain financial service platforms still have some significant problems in data visualization, which limits their effectiveness and user experience in practical applications.

[0003] The data visualization display of existing solutions mainly relies on traditional reports and static charts. Although this method can assist decision-making to a certain extent, its intuitiveness and interactivity are poor. It is difficult for users to quickly understand complex data relationships, and it often takes extra time and effort to parse information. For example, static charts cannot dynamically reflect real-time data changes. Users find it difficult to obtain the latest business dynamics and key indicators when viewing data, which affects decision-making efficiency. Existing platforms lack adaptability to user personalized needs. Different business scenarios and user roles have different requirements for data display. However, traditional solutions often cannot provide customized display functions, resulting in users having to find information in fixed views, which reduces the convenience of operation. This lack of flexibility in design has a serious impact on user experience, especially in a rapidly changing market environment, where users need to be able to quickly adjust views to adapt to new decision-making needs. Existing solutions lack real-time performance in data processing and analysis. The data processing capabilities of many platforms cannot meet large-scale, high-frequency data requests, resulting in delays and reduced accuracy in data display. In situations where rapid response to market changes and business decisions is required, delayed data display may lead to unnecessary economic losses or decision-making errors.

[0004] Furthermore, the existing visualization tools lack interactivity, and the interaction experience between users and data is poor. Users cannot deeply analyze data through simple operations, and lack effective exploration and discovery functions, which hinders the mining of data's potential value. This limitation makes it impossible for users to fully play the guiding role of data when using the platform, thus limiting business innovation and development.

[0005] It can be seen that the existing data interaction solutions for financial service platforms have problems such as low display intuitiveness and poor interactivity, and cannot meet the diverse needs of users. Based on this, it is necessary to propose a new data interaction solution suitable for financial service platforms. Summary of the invention

[0006] The purpose of the embodiments of the present invention is to provide a data interaction method and system for a financial service platform, so as to at least solve the problem that the data interaction solution for the financial service platform cannot meet the diverse needs of users.

[0007] In order to achieve the above-mentioned purpose, the first aspect of the present invention provides a data interaction method of a financial service platform, the method comprising: collecting data to be interacted from various data sources, and performing preprocessing on each data to be interacted; performing classification on each preprocessed data to be interacted, and matching the corresponding data interaction scheme based on the classification result; determining the visualization scheme and push rules of each classified data based on the data interaction scheme; performing visualization arrangement of each classified data based on the visualization scheme of each classified data, and performing visualization arrangement result push based on the push rule. .

[0008] Optionally, preprocessing is performed on each data to be interacted, including: performing data cleaning on each data to be interacted, and performing data conversion on each cleaned data to be interacted to obtain data to be interacted in a unified data format; performing data indicator extraction on each data to be interacted after data conversion, and sorting the data based on the extraction result, and using the data sorting result as the preprocessing result.

[0009] Optionally, the data indicator extraction is performed on each data to be interacted after the data conversion, and the data is sorted based on the extraction result, and the data sorting result is used as the preprocessing result, including: identifying the business type of the current user, and determining the target data indicator corresponding to each data to be interacted based on the business type; wherein the target data indicator is any one or more of a real-time value, an average value, an extreme value and a total amount; determining the sorting rule corresponding to each interactive data based on the business type, and performing data sorting on the extraction result based on the determined sorting rule; wherein the sorting rule is a time sorting rule or a numerical value sorting rule.

[0010] Optionally, the pre-processed data to be interacted with are classified, and corresponding data interaction schemes are matched based on the classification results, including: identifying the business type of the current user, and pre-screening the pre-processed data to be interacted with based on the business type to obtain a matching business data set; classifying the data to be interacted with in the business data set based on data features of the data to be interacted with, and determining display requirements of the data to be interacted with based on each data feature class; matching visualization schemes of the data to be interacted with based on the display requirements of the data to be interacted with; determining push rules for the data to be interacted with based on a request signal of the data interaction request initiated by the current user; and generating a data interaction scheme based on the visualization schemes of the data to be interacted with and the push rules for the data to be interacted with.

[0011] Optionally, classifying each data to be interacted with based on data features of each data to be interacted with in the business data set, and determining the display requirements of each data to be interacted with based on each data feature class, including: traversing the business data set to identify data features of each data to be interacted with; wherein the data features are any one or more of data type features, data range features, data distribution features and data correlation features; based on the data feature identification result, treating data with the same data features as the same type of data and classifying each data to be interacted with; determining the display requirements of each data to be interacted with based on the data display target and data display method matched by each data feature class.

[0012] Optionally, the push rules for the interaction data are determined based on the request signal of the data interaction request initiated by the current user, including: taking the requesting end that initiates the data interaction request as the first push target end; taking the display end associated with the current business type as the second push target end; determining the data transmission protocol and the data transmission path based on the communication links between the first push target end and the second push target end, respectively; and generating push rules based on the corresponding data transmission protocol and data transmission path for each push target end.

[0013] Optionally, based on the visualization scheme of each classified data, visualization organization of each classified data is performed accordingly, including: based on the visualization scheme of each classified data, executing visualization data generation for each data to be interacted with under the corresponding classification; based on each target end to be pushed for each data to be interacted with, performing visualization data rendering adapted to the display interface of each push target end, and obtaining rendering data corresponding to each push target end as the visualization organization result.

[0014] Optionally, the push of visualization results based on push rules includes: based on the data transmission protocol between the corresponding push target terminals, encrypting and packaging the rendering data of the corresponding push target terminal to obtain packaged data; transmitting the packaged data to the corresponding push target terminal based on the data transmission path of the corresponding push target terminal, so that the corresponding push target terminal can perform data visualization display.

[0015] A second aspect of the present invention provides a data interaction system for a financial service platform, the system comprising: a collection unit, used to collect data to be interacted from various data sources, and perform preprocessing on each data to be interacted; a classification unit, used to classify each preprocessed data to be interacted, and match the corresponding data interaction plan based on the classification result; a plan determination unit, used to determine the visualization plan and push rules for each classified data based on the data interaction plan; an execution unit, used to execute the visualization arrangement of each classified data based on the visualization plan of each classified data, and execute the push of the visualization arrangement result based on the push rule.

[0016] On the other hand, the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed on a computer, enables the computer to execute the data interaction method of the above-mentioned financial service platform.

[0017] Through the above technical scheme, the present invention improves the efficiency and effect of data processing and visualization through systematic steps. First, the data to be interacted from multiple data sources are collected and preprocessed to ensure the accuracy and timeliness of the data, laying a solid foundation for subsequent analysis. Secondly, by classifying the preprocessed data, the system can match the most suitable interaction scheme according to different data characteristics, which not only improves the efficiency of data organization, but also makes information transmission more targeted and personalized. Next, based on the matching interaction scheme, the visualization scheme and push rules of each classified data are clarified to ensure that key information can be effectively conveyed during the display process. This process improves the user's response speed and accuracy when making decisions. Finally, by performing visual collation of each classified data and pushing results according to the push rules, users can obtain the most relevant data display in real time, which significantly improves the user experience.

[0018] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0020] Figure 1 It is a flowchart of the steps of a data interaction method of a financial service platform provided by an embodiment of the present invention;

[0021] Figure 2 It is a system structure diagram of a data interaction system of a financial service platform provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0022] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0023] Figure 1 1 is a method flow chart of a data interaction method of a financial service platform provided by an embodiment of the present invention. Figure 1 As shown, an embodiment of the present invention provides a data interaction method for a financial service platform, the method comprising:

[0024] Step S10: Collect the data to be interacted from each data source, and perform preprocessing on each data to be interacted.

[0025] Specifically, it supports the connection of multiple data sources, including relational databases, NoSQL databases, and API interfaces. This wide compatibility ensures that data from different platforms and can be smoothly integrated, providing users with rich and diverse data. This feature is particularly important for data integration in diversified operations of enterprises, which enhances the breadth and depth of information acquisition.

[0026] Preferably, preprocessing is performed on each data to be interacted, including: performing data cleaning on each data to be interacted, and performing data conversion on each cleaned data to be interacted to obtain data to be interacted in a unified data format; performing data indicator extraction on each data to be interacted after data conversion, and sorting the data based on the extraction result, and using the data sorting result as the preprocessing result.

[0027] In an embodiment of the present invention, after data collection, all data to be interacted are pre-processed comprehensively. Data cleaning is a key link in this process, and the quality of data is ensured by removing duplicate, erroneous and irrelevant data. Data cleaning not only improves the accuracy of subsequent analysis, but also reduces the risk of decision-making errors caused by erroneous data. The cleaned data will be converted and unified into data to be interacted in a standard format. This process ensures that data from different sources can be seamlessly connected, which is convenient for subsequent processing and analysis. For example, date formats, numerical units, etc. will be standardized so that data will not be troubled by inconsistent formats when analyzing. After the data conversion is completed, indicators will be extracted for each data. This process aims to identify key data points and important indicators to help users quickly obtain the deep information behind the data. Then, data is sorted based on the extraction results to ensure that users can quickly find the most concerned information according to the preset priority. This orderly data presentation method greatly improves the user's operating efficiency and makes the decision-making process faster and more efficient. The results of a series of pre-processing will be used as interactive data, and users can conduct in-depth analysis and real-time decision-making on this basis. The realization of this technical effect not only enhances the utilization value of data, but also lays a solid foundation for the visualization of the energy supply chain financial service platform.

[0028] Based on the scheme of the present invention, through the collection and preprocessing process, the present invention significantly improves the flexibility and accuracy of data processing, ensuring that users can obtain more insightful information support when interacting with data. This data-driven approach can effectively optimize enterprise resource allocation, reduce operational risks, improve decision-making efficiency, and provide strong support for users in complex business environments.

[0029] Furthermore, the data indicator extraction is performed on each data to be interacted after the data conversion, and the data is sorted based on the extraction result, and the data sorting result is used as the preprocessing result, including: identifying the business type of the current user, and determining the target data indicator corresponding to each data to be interacted based on the business type; wherein the target data indicator is any one or more of a real-time value, an average value, an extreme value and a total amount; determining the sorting rule corresponding to each interactive data based on the business type, and performing data sorting on the extraction result based on the determined sorting rule; wherein the sorting rule is a time sorting rule or a numerical value sorting rule.

[0030] In an embodiment of the present invention, in the process of extracting data indicators for each data to be interacted after data conversion, the business type of the current user is first identified. This step is crucial because different business scenarios may have different focus and analysis requirements on data. For example, the financial industry may pay more attention to real-time values ​​and extreme values, while supply chain management may pay more attention to average values ​​and total amounts. By identifying the user's business type, the required target data indicators can be accurately determined to ensure the relevance and effectiveness of data analysis.

[0031] The target data indicator can be any one or more of real-time values, average values, extreme values, and total values. This flexibility enables the specific needs of different users to be met. Users can respond quickly to real-time changes or make long-term decisions based on historical data trends. Next, based on the business type, the sorting rules corresponding to each interactive data will be determined. These sorting rules may include time sorting rules (such as sorting by timestamp) or numerical size sorting rules (such as arranging from high to low or from low to high). This flexible sorting capability allows users to quickly locate the required information according to specific needs.

[0032] Based on the solution of the present invention, by extracting data indicators related to the user's business type and applying appropriate sorting rules, users can obtain key information more efficiently, thereby supporting the rapid and accurate decision-making process. In addition, this method can also enhance the user experience, improve the availability and comprehensibility of interactive data, and ultimately achieve optimization of business operations and rational allocation of resources.

[0033] Step S20: Classify the pre-processed data to be interacted with, and match the corresponding data interaction plan based on the classification result.

[0034] Specifically, the business type of the current user is identified, and pre-screening is performed on the pre-processed data to be interacted based on the business type to obtain a matching business data set; each data to be interacted is classified in the business data set based on the data characteristics of each data to be interacted, and the display requirements of each data to be interacted are determined based on each data feature class; the visualization scheme of each data to be interacted is matched based on the display requirements of each data to be interacted; the push rules of this interaction data are determined based on the request signal of the data interaction request initiated by the current user; and the data interaction scheme is generated based on the visualization scheme of each data to be interacted and the push rules of this interaction data.

[0035] Furthermore, the business data set classifies each data to be interacted based on its data features, and determines the display requirements of each data to be interacted based on each data feature class, including: traversing the business data set to identify the data features of each data to be interacted; wherein the data features are any one or more of data type features, data range features, data distribution features and data correlation features; based on the data feature identification result, treating data with the same data features as the same type of data and classifying each data to be interacted; and determining the display requirements of each data to be interacted based on the data display target and data display method matched by each data feature class.

[0036] Furthermore, the push rules for the interactive data are determined based on the request signal of the data interaction request initiated by the current user, including: taking the requesting end that initiates the data interaction request as the first push target end; taking the display end associated with the current business type as the second push target end; determining the data transmission protocol and the data transmission path based on the communication links between the first push target end and the second push target end respectively; and generating push rules based on the corresponding data transmission protocol and data transmission path for each push target end.

[0037] In an embodiment of the present invention, by identifying the business type of the current user, the foundation for subsequent data processing is laid. Different business fields have different data requirements. For example, industries such as finance, medical care, and retail have different data concerns and analysis objectives. Therefore, accurately identifying the user's business type can ensure the relevance of data processing. By analyzing the user's background information, historical operation behavior, and current business needs, the business field to which the user belongs can be quickly determined. After identifying the user's business type, pre-screening is performed on the pre-processed data to be interacted with, so as to obtain a matching business data set. This process not only improves the efficiency of data processing, but also reduces redundant information in subsequent analysis. By screening out data that is closely related to the current business type, users can focus on the most valuable information and quickly grasp business dynamics.

[0038] Furthermore, after obtaining the business data set, the data features of each data to be interacted with will be analyzed. These features include data type features (such as numeric type, character type), data range features (such as the maximum and minimum values ​​of data values), data distribution features (such as normal distribution, uniform distribution) and data correlation features (such as the degree of correlation between variables). By traversing the business data set, these data features can be identified and classified, so that data with the same features can be classified into the same category for subsequent processing. Based on the classification results, the display requirements for each type of data to be interacted with are determined. Different data features correspond to different display methods. For example, real-time monitoring data may require charts or dashboards to display, while historical data may be suitable for tables or graphical reports. By combining with user business needs, personalized display requirements can be generated, allowing users to more easily obtain key information.

[0039] In one possible implementation, functions such as data reports, dashboards, data exploration, data reports, and third-party data service docking are integrated to meet diverse business needs. Based on different display requirements, corresponding data visualization methods can be matched. The visualization chart customization tool based on the JAVA development language supports C / S and B / S access, provides independently developed basic charts based on Html5 and novel charts developed based on webgl, and provides a complete interface to introduce the eCharts component library, supporting users to manually and freely configure chart types, styles, interaction methods, etc. according to business needs, combine to generate display boards, and support display on multiple terminals. At the same time, it supports word type data reporting functions and integrates wps components. Users can directly develop word reports in wps, generate monthly reports and quarterly reports that meet the needs, and support download and export.

[0040] Furthermore, after clarifying the display requirements, the corresponding visualization scheme will be matched. The selection of the visualization scheme is based on a comprehensive analysis of user needs and data characteristics. By selecting appropriate chart types, color schemes and layout designs, the display results are ensured to be both beautiful and practical. Effective visualization can help users quickly grasp the core points of the data and improve the efficiency of data analysis. The push rules for this interactive data are determined through the request signal of the data interaction request initiated by the current user. The generation of the push rules is based on the following steps: first, the terminal initiating the request is used as the first push target terminal; second, the display terminal associated with the current business type is used as the second push target terminal. Then, based on the communication link between the first and second push target terminals, the data transmission protocol and path are determined. This process ensures that the data can be efficiently and stably transmitted to the specified target terminal, providing users with timely information feedback. Based on the visualization schemes of each data to be interacted and the push rules of this interactive data, a complete data interaction scheme is generated. This scheme not only covers the source, processing and display methods of the data, but also includes the data transmission mechanism and the feedback mechanism of the target terminal. Through this kind of design, users can get a smoother experience during the interaction process and can follow up data changes in real time.

[0041] Step S30: Based on the data interaction solution, determine the visualization solution and push rules for each category data.

[0042] Specifically, the visualization scheme of each classified data will be personalized according to the characteristics of the data, user needs and business scenarios. Different types of data require different visualization forms. For example, for real-time monitoring data, the use of dynamic charts or dashboards can more intuitively reflect the changing trend of the data, while for historical data analysis, it may be more appropriate to use static tables or charts to facilitate users to conduct in-depth comparison and analysis. By automatically selecting and generating visualization elements, such as chart type, color, layout, etc., it is ensured that the display effect is both beautiful and meets the actual needs of users. At the same time, the formulation of push rules is also crucial. According to the user's interactive behavior, data importance and business type, set the frequency, timing and channel of data push. For example, real-time data of key business indicators may need to be pushed immediately every time the data is updated, while some statistical analysis reports can be set to be pushed regularly. Through this intelligent push mechanism, users can obtain relevant data at the most appropriate time and make timely decisions.

[0043] Step S40: Visualization sorting of each classified data is performed based on the visualization scheme of each classified data, and visualization sorting result is pushed based on the push rule.

[0044] Specifically, based on the visualization scheme of each classified data, visualization data generation is executed for each data to be interacted with under the corresponding classification; based on each target end to be pushed of each data to be interacted with, visualization data rendering is performed to adapt to the display interface of each push target end, and rendering data corresponding to each push target end is obtained as the visualization arrangement result.

[0045] Furthermore, the push of visualization results based on push rules includes: based on the data transmission protocol between the corresponding push target terminals, encrypting and packaging the rendering data of the corresponding push target terminal to obtain packaged data; transmitting the packaged data to the corresponding push target terminal based on the data transmission path of the corresponding push target terminal, so that the corresponding push target terminal can perform data visualization display.

[0046] In an embodiment of the present invention, corresponding chart types, such as pie charts, bar charts, line charts, etc., are used to generate visualized data according to the data characteristics of each category. For example, for time series data, a line chart can well show trend changes; while for proportional relationship data, it is more suitable to use a pie chart to express it. By dynamically generating these visualization elements, users can not only see the absolute value of the data, but also intuitively understand the relative relationship and development trend of the data through graphical means. In the process of visualization generation, a variety of data processing technologies are used to ensure the accuracy and timeliness of the visualization content. This includes real-time data updates, data cleaning, outlier detection, etc., to ensure that users can obtain the latest and most accurate information when viewing data.

[0047] Furthermore, after the visualization data is generated, the visualization data will be rendered according to the display interface of each target terminal to be pushed. This means that the rendering style and layout are automatically adjusted according to the characteristics of different users' devices (such as desktop, mobile or large-screen display). For example, on the mobile terminal, the rendering may adopt a simpler design to adapt to the display of the small screen; on the large screen, the data can be displayed in a richer form so that multiple users can share the view. Through this adaptive rendering, users can get a good visual experience and operational convenience in different usage scenarios. Finally, the rendered data will be prepared to be pushed to the user as a visualization result.

[0048] Furthermore, in the push phase, the push of visualization results will be executed based on the previously set push rules. First, the rendering data is encrypted and packaged according to the data transmission protocol between each push target. Data encryption not only ensures the security of information during transmission, but also protects the privacy of users and prevents sensitive data from being leaked. Next, the packaged data will be transmitted according to the data transmission path of the corresponding push target. In this process, the best network path is selected to ensure that the data can be delivered to the destination as quickly and securely as possible. Whether through API calls, WebSocket connections, or other transmission protocols, these complex logics will be executed in the background to provide a seamless user experience.

[0049] In a possible implementation, in order to meet the display requirements of different push target terminals, multiple scene displays are configured, including:

[0050] 1) Large-screen display: Design and deploy highly customized operational large-screen displays to achieve accurate and timely display of data, interactive operations, beautiful design, and multi-screen linkage functions.

[0051] 2) Cockpit display: A portal-based data cockpit is built on the PC side to display key business indicators in an intuitive way to support quick decision-making.

[0052] 3) Mobile display: Develop applications that are compatible with mobile apps and mini-programs, integrate pages in H5 mode, and realize mobile access and display of data.

[0053] Figure 2 1 is a system structure diagram of a data interaction system of a financial service platform provided by an embodiment of the present invention. Figure 2 As shown, an embodiment of the present invention provides a data interaction system for a financial service platform, the system comprising: a collection unit, used to collect data to be interacted from various data sources, and perform preprocessing on each data to be interacted; a classification unit, used to classify each preprocessed data to be interacted, and match the corresponding data interaction plan based on the classification result; a plan determination unit, used to determine the visualization plan and push rules for each classified data based on the data interaction plan; an execution unit, used to execute the visualization arrangement of each classified data corresponding to the visualization plan of each classified data, and execute the push of the visualization arrangement result based on the push rule.

[0054] The embodiment of the present invention further provides a computer-readable storage medium, on which instructions are stored, which, when executed on a computer, enable the computer to execute the data interaction method of the financial service platform.

[0055] Those skilled in the art will understand that all or part of the steps in the method for implementing the above-mentioned embodiments can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions for making a single-chip microcomputer, a chip or a processor (processor) perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0056] The optional embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the technical concept of the embodiments of the present invention, the technical scheme of the embodiments of the present invention can be subjected to a variety of simple modifications, and these simple modifications all belong to the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the embodiments of the present invention will not further describe various possible combinations.

[0057] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the embodiments of the present invention, they should also be regarded as the contents disclosed in the embodiments of the present invention.

Claims

1. A data interaction method for a financial service platform, characterized in that: The method comprises: Collect the data to be interacted from each data source, and perform preprocessing on each data to be interacted; Classify the pre-processed data to be interacted with, and match the corresponding data interaction plan based on the classification results; Based on the data interaction plan, determine the visualization plan and push rules for each category of data; Based on the visualization scheme of each classified data, the visualization arrangement of each classified data is executed accordingly, and the visualization arrangement result is pushed based on the push rules.

2. The method according to claim 1, characterized in that Preprocess each data to be interacted with, including: Performing data cleaning on each data to be interacted with, and performing data conversion on each data to be interacted with after cleaning, so as to obtain the data to be interacted with in a unified data format; After the data conversion, data indicators are extracted for each data to be interacted, and the data is sorted based on the extraction results, and the data sorting results are used as preprocessing results.

3. The method according to claim 2, characterized in that The step of extracting data indicators from each to-be-interacted data after data conversion, sorting the data based on the extraction results, and using the data sorting results as preprocessing results includes: Identify the business type of the current user, and determine the target data indicators corresponding to each data to be interacted based on the business type; wherein, The target data indicator is any one or more of real-time value, average value, extreme value and total value; Determine the sorting rules corresponding to each interactive data based on the business type, and perform data sorting on the extracted results based on the determined sorting rules; wherein, The sorting rule is a time sorting rule or a numerical value sorting rule.

4. The method according to claim 1, characterized in that: The performing classification on each pre-processed data to be interacted with, and matching the corresponding data interaction scheme based on the classification result, includes: Identify the business type of the current user, perform pre-screening on the pre-processed data to be interacted with based on the business type, and obtain a matching business data set; Classifying each data to be interacted with based on the data features of each data to be interacted with in the business data set, and determining the display requirements corresponding to each data to be interacted with based on each data feature class; Match the visualization scheme of each data to be interacted with based on the display requirements of each data to be interacted with; Determine the push rule of this interactive data based on the request signal of the current user initiating the data interaction request; A data interaction plan is generated based on the visualization plan of each data to be interacted with and the push rules of the current interaction data.

5. The method according to claim 4, characterized in that The classifying each data to be interacted with based on the data features of each data to be interacted with in the business data set, and determining the display requirements corresponding to each data to be interacted with based on each data feature class, includes: Traverse the business data set to identify the data features of each data to be interacted with; wherein, The data feature is any one or more of a data type feature, a data range feature, a data distribution feature, and a data correlation feature; Based on the data feature recognition results, data with the same data features are regarded as the same type of data, and each data to be interacted is classified; Determine the display requirements of each interactive data based on the data display target and data display method matched by each data feature class.

6. The method according to claim 4, characterized in that The step of determining the push rule of the current interactive data based on the request signal of the current user initiating the data interaction request includes: The requesting end that initiates the data interaction request is used as the first push target end; The display terminal associated with the current service type is used as the second push target terminal; Determine a data transmission protocol and a data transmission path based on the communication links with the first push target end and the second push target end respectively; Based on each push target end, the corresponding data transmission protocol and data transmission path generate push rules.

7. The method according to claim 6, characterized in that Based on the visualization scheme of each classification data, the visualization arrangement of each classification data is performed accordingly, including: Based on the visualization scheme of each classified data, generate visualization data for each data to be interacted under the corresponding classification; Based on each target end to be pushed of each data to be interacted with, visual data rendering adapted to the display interface of each push target end is performed to obtain rendering data corresponding to each push target end as a visual arrangement result.

8. The method according to claim 7, characterized in that The push of visualization sorting result based on push rules includes: Based on the data transmission protocol between the corresponding push target terminals, encrypt and package the rendering data of the corresponding push target terminal to obtain packaged data; The packaged data is transmitted to the corresponding push target end based on the data transmission path of the corresponding push target end, so that the corresponding push target end can perform data visualization display.

9. A data interaction system for a financial service platform, characterized in that: The system comprises: A collection unit, used for collecting the data to be interacted from each data source, and performing preprocessing on each data to be interacted; A classification unit, used to classify the pre-processed data to be interacted with, and match the corresponding data interaction plan based on the classification result; A scheme determination unit, used to determine the visualization scheme and push rules for each classification data based on the data interaction scheme; The execution unit is used to execute the visualization arrangement of each classified data based on the visualization scheme of each classified data, and to push the visualization arrangement result based on the push rule.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the data interaction method of the financial service platform described in any one of claims 1-8.

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