Intelligent data processing method and system based on trend quantification

Abstract

This invention discloses an intelligent data processing method and system based on trend quantification. It acquires multi-source time-series data, core flow data, sector anomaly data, and key event data in real time. Based on the multi-source time-series data and core flow data, a preset algorithm quantifies the data trend and momentum strength, generating visualized trend signals and momentum tags, and outputting operation prompts. According to the user-selected analysis period, a corresponding operation interface is matched, displaying the core indicators and analysis signals for that analysis period, and providing historical data backtracking. It monitors sector anomalies, tracks core data flows, and pushes key event reminders in conjunction with the analysis calendar. This system realizes a complete data analysis support chain, from multi-source data collection to trend quantification, multi-period analysis, professional interpretation, intelligent filtering, and user interaction, avoiding functional fragmentation and improving analysis efficiency and accuracy.

Description

Technical Field

[0001] This invention relates to the field of data processing and analysis technology, and in particular to an intelligent data processing method and system based on trend quantification. Background Technology

[0002] In scenarios involving massive amounts of data processing, analysts typically rely on basic statistical indicators and visual charts for decision-making. Traditional data processing tools suffer from the following problems:

[0003] 1. There is a lack of systematic quantitative tools for "trend perception" of data, making it difficult to transform subjective data perception into verifiable and reusable analytical signals;

[0004] 2. The data monitoring and backtracking functions for different time periods (short-term / medium-term) are fragmented, failing to form a complete analytical loop;

[0005] 3. The integration of key data flow, sector anomalies, and other core information with personalized analysis strategies is insufficient, resulting in low analysis efficiency;

[0006] 4. The lack of professional interpretation and intelligent indicator visualization makes it difficult for ordinary analysts to quickly understand complex data signals.

[0007] Therefore, there is an urgent need for an intelligent data processing method and system that can integrate trend quantification, multi-period monitoring and backtracking, core data tracking and professional interpretation, as a decision support tool for analysts. Summary of the Invention

[0008] The purpose of this invention is to provide an intelligent data processing method and system based on trend quantification to solve the above-mentioned problems existing in the prior art.

[0009] In a first aspect, embodiments of the present invention provide an intelligent data processing method based on trend quantification, comprising:

[0010] Real-time acquisition of multi-source time-series data, core flow data, sector anomaly data, and key event data;

[0011] Based on the multi-source time-series data and core flow data, the data trend and momentum strength are quantified and calculated using a preset algorithm to generate visual trend signals and momentum labels, and output operation prompts.

[0012] Based on the analysis period selected by the user, the corresponding operation interface is matched to display the core indicators and analysis signals under that analysis period, and historical data backtracking data is provided.

[0013] Monitor sector anomalies, track the flow of core data, and combine analysis with calendar push notifications for key events.

[0014] Optionally, the method further includes:

[0015] By integrating professional analysis live stream content and overlaying corresponding interpretation information at key signal nodes, the system achieves simultaneous presentation of indicator signals and professional interpretations.

[0016] Based on the trend optimization model, combined with core data flow and sector popularity factors, a phased screening list is generated, and users can customize the screening conditions.

[0017] Secondly, embodiments of the present invention provide an intelligent data processing system based on trend quantification, used to perform the steps of the above method, the system comprising:

[0018] The data acquisition module is used to acquire multi-source time-series data, core flow data, sector anomaly data, and key event data in real time;

[0019] The trend quantification module, connected to the data acquisition module, is used to quantify the data trend and momentum strength based on the multi-source time-series data and core flow data using a preset algorithm, generate visual trend signals and momentum labels, and output operation prompts.

[0020] The multi-period analysis module, connected to the trend quantification module, is used to match the corresponding monitoring / review interface according to the analysis period selected by the user, display the core indicators and analysis signals under that period, and provide historical data backtracking function;

[0021] The data dynamics module, connected to the data acquisition module, is used to monitor sector anomalies, track the flow of core data, and combine analysis with calendar push notifications for key events.

[0022] Optionally, the system further includes:

[0023] The professional interpretation module, connected to the trend quantification module and the multi-period analysis module, is used to integrate the professional analysis live broadcast content and overlay corresponding interpretation information at key signal nodes to achieve synchronous presentation of indicator signals and professional interpretation.

[0024] The intelligent filtering module, connected to the trend quantification module and the data dynamic module, is used to generate a phased filtering list based on the trend optimization model, combined with core data flow and sector popularity factors, and supports user-defined filtering conditions.

[0025] The user interaction module connects to the above modules and is used to display data, input user actions, and personalize settings.

[0026] Optionally, the multi-period analysis module includes a short-term market monitoring module, a short-term market review module, a medium-term market monitoring module, and a medium-term market review module:

[0027] The short-term monitoring module is used to provide time-based data refresh, core data change alerts, and quick node marking functions;

[0028] The short-term review module is used to perform one-click backtracking and comparison of short-term analysis data for the day;

[0029] The mid-term monitoring module is used to provide periodic-level trend monitoring and signal early warning functions;

[0030] The mid-term review module is used to perform trend attribution and pattern mining on mid-term historical data.

[0031] Optionally, the data dynamics module includes:

[0032] The plate anomaly monitoring unit is used to identify abnormal fluctuations in the data of each plate and calculate the intensity of the anomaly;

[0033] The core flow tracking unit is used to visualize the flow direction and scale of core data between different modules;

[0034] The event push unit is used to associate key events with corresponding data fluctuation periods and push reminder information.

[0035] Optionally, the professional interpretation module includes:

[0036] The live streaming access unit is used to acquire real-time live streaming content and historical live streaming clips from professional analysts.

[0037] The signal linkage unit is used to match the signals generated by the trend quantification module with the live content by timestamp, and automatically switch to the corresponding interpretation segment at key signal nodes.

[0038] The text interpretation unit is used to overlay text interpretation content corresponding to the current signal on the visualization interface.

[0039] Optionally, the intelligent filtering module includes:

[0040] The factor calculation unit is used to calculate multi-dimensional factors such as trend signal score, core data net inflow ratio, and sector gains.

[0041] The ranking generation unit is used to generate screening rankings for different periods, such as quarterly rankings and monthly rankings, based on the multi-dimensional factors.

[0042] A customizable filter unit is used to receive user-inputted filter criteria and perform precise filtering of candidate objects.

[0043] Optionally, the user interaction module includes:

[0044] The top search bar is designed to help users search for data objects or professional analysts;

[0045] The function entry unit provides quick access to functions such as trend selection, expert circle, sector anomaly, market monitoring and review, and analysis calendar;

[0046] The bottom navigation bar provides the core entry points for data overview, customizable items, homepage, filter pool, and personal center;

[0047] The personalization settings section allows users to configure analysis cycles, notification preferences, and interface display styles.

[0048] Optionally, the system further includes:

[0049] The feedback optimization module, connected to the user interaction module, trend quantification module, and intelligent filtering module, is used to collect user operation behavior data, iteratively optimize the trend quantification algorithm and intelligent filtering model, and improve system adaptability and recommendation accuracy.

[0050] Compared with the prior art, the present invention achieves the following beneficial effects:

[0051] Trend Quantification and Visualization: Transforms abstract data "trend perception" into intuitive trend signals and momentum labels, reducing the difficulty for analysts to perceive data.

[0052] Complete closed-loop analysis: Integrates short-term / medium-term monitoring, review, and screening functions to form a complete auxiliary closed loop from data monitoring to analysis output.

[0053] Professional Interpretation and Empowerment: The professional analysis live broadcasts are linked with intelligent indicators to achieve dual assistance of "data + interpretation" and improve the decision-making and cognition of ordinary analysts.

[0054] Personalized adaptation: Supports switching between multiple analysis cycles and custom filtering to meet the needs of analysts with different styles.

[0055] Efficiency Improvement: Through features such as sector anomalies and core data tracking, data hotspots can be quickly identified, reducing information filtering time. Attached Figure Description

[0056] Figure 1 This is a flowchart of an intelligent data processing method based on trend quantification provided in an embodiment of the present invention. Detailed Implementation

[0057] The present invention will now be described in detail with reference to the accompanying drawings.

[0058] Example 1

[0059] like Figure 1 As shown, this embodiment of the invention provides an intelligent data processing method based on trend quantification, including:

[0060] S101: Real-time acquisition of multi-source time-series data, core flow data, sector anomaly data, and key event data.

[0061] S102: Based on the multi-source time-series data and core flow data, the data trend and kinetic strength are quantified and calculated using a preset algorithm to generate a visual trend signal and kinetic energy label, and operation prompts are output.

[0062] S103: Based on the analysis period selected by the user, match the corresponding operation interface, display the core indicators and analysis signals under that analysis period, and provide historical data backtracking data.

[0063] S104: Monitor sector anomalies, track core data flows, and combine analysis with calendar push notifications for key events.

[0064] By adopting the above solutions, a closed-loop data processing system is achieved: from multi-source data collection to trend quantification, multi-period analysis, professional interpretation, intelligent filtering, and user interaction, forming a complete data analysis support chain. This avoids functional fragmentation, improves analysis efficiency and accuracy, and enhances the effectiveness of supporting analysts in making decisions.

[0065] As another optional implementation, the intelligent data processing method based on trend quantification provided in this embodiment of the invention further includes:

[0066] S105: Integrates professional analysis live content, overlays corresponding interpretation information at key signal nodes, and achieves synchronous presentation of indicator signals and professional interpretation.

[0067] S106: Based on the trend optimization model, combined with core data flow and sector popularity factors, it generates a phased screening list and supports user-defined screening conditions.

[0068] By adopting the above approach, time-series data, flow data, anomaly data, and event data are processed uniformly, achieving coordinated support through "data-signal-interpretation-filtering," providing users with more comprehensive decision-making support. It also offers automated filtering criteria, providing users with personalized auxiliary data.

[0069] As another optional implementation, this application provides an intelligent data processing assistance method based on trend quantification, which includes the following steps:

[0070] Data acquisition steps: Real-time acquisition of multi-source time-series data, core flow data, sector anomaly data, and key event data.

[0071] Trend quantification step: Based on the multi-source time series data and core flow data, the data trend and momentum strength are quantified and calculated using a preset algorithm to generate a visual trend signal and momentum label, and output operation prompts.

[0072] Multi-period adaptation steps: Based on the analysis period selected by the user, the corresponding monitoring / review interface is matched, displaying the core indicators and analysis signals for that period, and providing historical data backtracking function.

[0073] Data dynamic monitoring steps: Monitor sector anomalies, track the flow of core data, and combine analysis with calendar push notifications for key events.

[0074] Professional Interpretation and Linkage Steps: Integrate professional analysis live broadcast content, overlay corresponding interpretation information at key signal nodes, and achieve synchronous presentation of indicator signals and professional interpretation.

[0075] Intelligent filtering process: Based on the trend optimization model, combined with core data flow and sector popularity factors, a phased filtering list is generated, and users can customize the filtering conditions.

[0076] Interaction and feedback steps: Display data and analysis results through the user interface, receive user input, and collect user behavior data to optimize the algorithm model.

[0077] By adopting the above solution, a closed-loop data processing system can be achieved across the entire chain: from multi-source data collection to trend quantification, multi-period analysis, professional interpretation, intelligent filtering, and user interaction, forming a complete data analysis support chain, avoiding functional fragmentation, and improving analysis efficiency. Each module is independently designed yet interconnected, facilitating functional iteration or module replacement for specific scenarios and adapting to the data processing needs of different industries. It also achieves multi-dimensional information fusion: unifying the processing of time-series data, flow data, anomaly data, and event data to realize the coordinated support of "data-signal-interpretation-filtering," providing users with more comprehensive decision-making support.

[0078] The trend quantification step specifically includes: calculating the trend direction and fluctuation momentum intensity of the data; generating two trend types, "large waterfall" and "medium waterfall," and two momentum labels, "strengthening" and "weakening," based on the trend direction and momentum intensity; and outputting two operation prompts, "diverge" or "escape," based on the combined state of the trend and momentum.

[0079] Abstract data trends are standardized using visual labels such as "Large Waterfall / Medium Waterfall," "Strengthening / Weakening," and "Diverging / Escape," reducing comprehension differences among users and improving the consistency and reusability of analysis results. These concise label combinations allow users to quickly identify the strength of data trends and their operational direction, grasping core data patterns without complex calculations. Directly linking trend status with operational prompts reduces the cost of secondary judgment for users and improves the responsiveness of real-time decisions.

[0080] The specific multi-period adaptation steps include: receiving the user's selected short-term or medium-term analysis period; if a short-term period is selected, the short-term monitoring / review module is invoked to display intraday data and signals; if a medium-term period is selected, the medium-term monitoring / review module is invoked to display period-level trends and historical backtesting data.

[0081] By combining the judgments of "continuous decline / reversal rebound" and "increasing / decreasing momentum," the system can accurately capture key inflection points in data trends, avoiding misjudgments of gentle fluctuations. It distinguishes between "large waterfall" and "medium waterfall" levels, adapting to scenarios with different fluctuation intensities, covering both extreme anomalies and mild trends, thus improving the signal's scenario adaptability. The quantitative logic is transparent, with each label corresponding to clear data change characteristics, facilitating subsequent attribution analysis and algorithm optimization, and enhancing user trust in the system.

[0082] In this application, the professional interpretation of the linkage steps specifically includes:

[0083] Acquire real-time live stream content and historical live stream clips from professional analysts. Match the signals generated during the trend quantification process with the timestamps of the live stream content.

[0084] In this way, corresponding live broadcast segments or text interpretations are automatically overlaid at key signal nodes to help users understand the signal logic. It also supports short-term (time-sharing) and medium-term (cycle-based) market monitoring / review, meeting the dual needs of real-time monitoring and historical pattern mining, forming a complete closed loop of "monitoring-review-optimization". Different cycles correspond to independent sub-modules, and the interface and indicators automatically adapt, avoiding users switching between multiple tools and reducing learning costs and operational complexity. Short-term signals can be verified for historical validity in medium-term reviews, and medium-term trends can guide the key directions of short-term monitoring, improving the reliability of analysis results.

[0085] Optionally, the intelligent filtering steps specifically include:

[0086] The system calculates multi-dimensional factors such as trend signal scores, net inflow ratio of core data, and sector gains. Based on these factors, it generates filtering lists for different periods, such as quarterly and monthly rankings. It also accepts user-defined filtering conditions, precisely filters candidates, and updates the lists accordingly.

[0087] By calculating the intensity of anomalies, we objectively identify data anomalies at the sector level, avoiding the subjective oversight of hotspots or risk sources. We intuitively present the flow direction and scale of core data between sectors, helping users quickly understand the transmission logic of "local anomalies - global impact." We link key events with corresponding data fluctuation periods in our push notifications, assisting users in quickly attributing events and improving the timeliness and relevance of our analysis.

[0088] In this embodiment of the application, the intelligent data processing method based on trend quantification further includes:

[0089] Feedback and optimization steps: Collect user operation behavior data, iteratively optimize trend quantification algorithms and intelligent filtering models, and improve system adaptability and recommendation accuracy.

[0090] Example 2

[0091] Based on the aforementioned trend-quantification-based intelligent data processing method, this application provides a trend-quantification-based intelligent data processing system for executing the steps of the method described in claim 1, the system comprising:

[0092] The data acquisition module is used to acquire multi-source time-series data, core flow data, sector anomaly data, and key event data in real time;

[0093] The trend quantification module, connected to the data acquisition module, is used to quantify the data trend and momentum strength based on the multi-source time-series data and core flow data using a preset algorithm, generate visual trend signals and momentum labels, and output operation prompts.

[0094] The multi-period analysis module, connected to the trend quantification module, is used to match the corresponding monitoring / review interface according to the analysis period selected by the user, display the core indicators and analysis signals under that period, and provide historical data backtracking function;

[0095] The data dynamics module, connected to the data acquisition module, is used to monitor sector anomalies, track the flow of core data, and combine analysis with calendar push notifications for key events.

[0096] The above system aims to achieve the following objectives:

[0097] A complete data processing loop: From multi-source data collection to trend quantification, multi-period analysis, professional interpretation, intelligent filtering, and user interaction, a complete data analysis support loop is formed, avoiding functional fragmentation and improving analysis efficiency. Modular decoupling and scalability: Each module is independently designed yet interconnected, facilitating functional iteration or module replacement for specific scenarios and adapting to the data processing needs of different industries. Multi-dimensional information fusion: Time-series data, flow data, anomaly data, and event data are processed uniformly to achieve coordinated support of "data-signal-interpretation-filtering," providing users with more comprehensive decision-making support.

[0098] Optionally, the system further includes:

[0099] The professional interpretation module, connected to the trend quantification module and the multi-period analysis module, is used to integrate the professional analysis live broadcast content and overlay corresponding interpretation information at key signal nodes to achieve synchronous presentation of indicator signals and professional interpretation.

[0100] The intelligent filtering module, connected to the trend quantification module and the data dynamic module, is used to generate a phased filtering list based on the trend optimization model, combined with core data flow and sector popularity factors, and supports user-defined filtering conditions.

[0101] The user interaction module connects to the above modules and is used to display data, input user actions, and personalize settings.

[0102] In this application, the visualized trend signals generated by the trend quantification module include two trend types: "large waterfall" and "medium waterfall". The momentum labels include two status identifiers: "strengthening" and "weakening". The operation prompts include two instruction identifiers: "diverge" and "escape".

[0103] The trend quantification module is specifically used for:

[0104] When data continues to decline and the volatility momentum increases, it is marked as a "weakening waterfall" or "weakening mid-waterfall" signal; when the data trend reverses and the rebound momentum increases, it is marked as a "strengthened waterfall" or "strengthened mid-waterfall" signal; based on the combination of trend and momentum, the corresponding "break" or "escape" operation prompts are output.

[0105] In this application, abstract data trends are unified into visual labels such as "Large Waterfall / Medium Waterfall," "Strengthening / Weakening," and "Divergence / Escape," reducing comprehension differences among different users and improving the consistency and reusability of analysis results. Intuitive information delivery: Through concise label combinations, users can quickly identify the strength of data trends and the direction of action, grasping core data patterns without complex calculations. Contextualized instruction prompts: Trend states are directly linked to operational prompts, reducing the cost of secondary judgment for users and improving the responsiveness of real-time decisions.

[0106] Optionally, the multi-period analysis module includes a short-term market monitoring module, a short-term market review module, a medium-term market monitoring module, and a medium-term market review module:

[0107] The short-term monitoring module is used to provide time-based data refresh, core data change alerts, and quick node marking functions;

[0108] The short-term review module is used to perform one-click backtracking and comparison of short-term analysis data for the day;

[0109] The mid-term monitoring module is used to provide periodic-level trend monitoring and signal early warning functions;

[0110] The mid-term review module is used to perform trend attribution and pattern mining on mid-term historical data.

[0111] Optionally, the data dynamics module includes:

[0112] The plate anomaly monitoring unit is used to identify abnormal fluctuations in the data of each plate and calculate the intensity of the anomaly;

[0113] The core flow tracking unit is used to visualize the flow direction and scale of core data between different modules;

[0114] The event push unit is used to associate key events with corresponding data fluctuation periods and push reminder information.

[0115] Optionally, the professional interpretation module includes:

[0116] The live streaming access unit is used to acquire real-time live streaming content and historical live streaming clips from professional analysts.

[0117] The signal linkage unit is used to match the signals generated by the trend quantification module with the live content by timestamp, and automatically switch to the corresponding interpretation segment at key signal nodes.

[0118] The text interpretation unit is used to overlay text interpretation content corresponding to the current signal on the visualization interface.

[0119] By calculating the intensity of anomalies, we objectively identify data anomalies at the sector level, avoiding the subjective oversight of hotspots or risk sources. We intuitively present the flow direction and scale of core data between sectors, helping users quickly understand the transmission logic of "local anomalies - global impact." We link key events with corresponding data fluctuation periods in our push notifications, assisting users in quickly attributing events and improving the timeliness and relevance of our analysis.

[0120] As a further step, the intelligent filtering module includes:

[0121] The factor calculation unit is used to calculate multi-dimensional factors such as trend signal score, core data net inflow ratio, and sector gains.

[0122] The ranking generation unit is used to generate screening rankings for different periods, such as quarterly rankings and monthly rankings, based on the multi-dimensional factors.

[0123] A customizable filter unit is used to receive user-inputted filter criteria and perform precise filtering of candidate objects.

[0124] By matching timestamps, the system automatically switches to corresponding live stream segments or text interpretations at key signal nodes, achieving simultaneous presentation of "data signals + business logic" and solving the pain point of "understanding charts but not logic." It binds analyst interpretations to the signal system, forming a reusable analytical knowledge base and lowering the professional knowledge threshold for ordinary users. It also supports both live video and text interpretations, balancing real-time interaction with lightweight browsing to meet the needs of different use cases.

[0125] Optionally, the user interaction module includes:

[0126] The top search bar is designed to help users search for data objects or professional analysts;

[0127] The function entry unit provides quick access to functions such as trend selection, expert circle, sector anomaly, market monitoring and review, and analysis calendar;

[0128] The bottom navigation bar provides the core entry points for data overview, customizable items, homepage, filter pool, and personal center;

[0129] The personalization settings section allows users to configure analysis cycles, notification preferences, and interface display styles.

[0130] By integrating multi-dimensional factors such as trend signals, core flows, and sector popularity, the system avoids screening biases caused by single indicators, improving the comprehensiveness and accuracy of the ranking results. It provides filtering results for different periods, such as quarterly and monthly rankings, adapting to different needs of medium- to long-term strategies and short-term tracking. It supports user-defined conditions to meet the analytical needs of different business scenarios and preferences, enhancing the system's flexibility.

[0131] Optionally, the system further includes:

[0132] The feedback optimization module, connected to the user interaction module, trend quantification module, and intelligent filtering module, is used to collect user operation behavior data, iteratively optimize the trend quantification algorithm and intelligent filtering model, and improve system adaptability and recommendation accuracy.

[0133] The layered design of top search, quick access, and bottom navigation allows users to quickly locate target functions and data, improving operational efficiency.

[0134] Personalized experience optimization: Supports customizable settings for analysis cycles, notification preferences, and interface styles to adapt to different users' habits and improve user satisfaction.

[0135] Unified interaction paradigm: All function entry points follow consistent interaction logic, reducing user learning costs and improving system usability.

[0136] A layered design with top search, quick access, and bottom navigation allows users to quickly locate target functions and data, improving operational efficiency. Customizable settings for analysis cycles, notification preferences, and interface styles cater to different user habits, enhancing user satisfaction. All function entry points follow consistent interaction logic, reducing user learning costs and improving system usability. By collecting user operation data, closed-loop optimization of trend quantification algorithms and filtering models is achieved, allowing the system to continuously adapt to business changes. Based on real user behavior feedback, algorithm deviations are gradually corrected, improving the accuracy of signal recognition and filtering results. User operations directly impact system optimization, forming a positive cycle of "user usage - system optimization - better user service," increasing user stickiness.

[0137] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0138] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0139] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the apparatus according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

Claims

1. A method for intelligent data processing based on trend quantification, characterized in that, include: Real-time acquisition of multi-source time-series data, core flow data, sector anomaly data, and key event data; Based on the multi-source time-series data and core flow data, the data trend and momentum strength are quantified and calculated using a preset algorithm to generate visual trend signals and momentum labels, and output operation prompts. Based on the analysis period selected by the user, the corresponding operation interface is matched to display the core indicators and analysis signals under that analysis period, and historical data backtracking data is provided. Monitor sector anomalies, track the flow of core data, and combine analysis with calendar push notifications for key events.

2. The intelligent data processing method based on trend quantification as claimed in claim 1, wherein, The method further includes: By integrating professional analysis live stream content and overlaying corresponding interpretation information at key signal nodes, the system achieves simultaneous presentation of indicator signals and professional interpretations. Based on the trend optimization model, combined with core data flow and sector popularity factors, a phased screening list is generated, and users can customize the screening conditions.

3. An intelligent data processing system based on trend quantification, characterized by, The system for performing the steps of the method of claim 1, comprising: The data acquisition module is used to acquire multi-source time-series data, core flow data, sector anomaly data, and key event data in real time; The trend quantification module, connected to the data acquisition module, is used to quantify the data trend and momentum strength based on the multi-source time-series data and core flow data using a preset algorithm, generate visual trend signals and momentum labels, and output operation prompts. The multi-period analysis module, connected to the trend quantification module, is used to match the corresponding monitoring / review interface according to the analysis period selected by the user, display the core indicators and analysis signals under that period, and provide historical data backtracking function; The data dynamics module, connected to the data acquisition module, is used to monitor sector anomalies, track the flow of core data, and combine analysis with calendar push notifications for key events.

4. The system of claim 3, wherein, The system also includes: The professional interpretation module, connected to the trend quantification module and the multi-period analysis module, is used to integrate the professional analysis live broadcast content and overlay corresponding interpretation information at key signal nodes to achieve synchronous presentation of indicator signals and professional interpretation. The intelligent filtering module, connected to the trend quantification module and the data dynamic module, is used to generate a phased filtering list based on the trend optimization model, combined with core data flow and sector popularity factors, and supports user-defined filtering conditions. The user interaction module connects to the above modules and is used to display data, input user actions, and personalize settings.

5. The system of claim 3, wherein, The multi-period analysis module includes a short-term market monitoring module, a short-term market review module, a medium-term market monitoring module, and a medium-term market review module. The short-term monitoring module is used to provide time-based data refresh, core data change alerts, and quick node marking functions; The short-term review module is used to perform one-click backtracking and comparison of short-term analysis data for the day; The mid-term monitoring module is used to provide periodic-level trend monitoring and signal early warning functions; The mid-term review module is used to perform trend attribution and pattern mining on mid-term historical data.

6. The system of claim 3, wherein, The data dynamic module includes: The plate anomaly monitoring unit is used to identify abnormal fluctuations in the data of each plate and calculate the intensity of the anomaly; The core flow tracking unit is used to visualize the flow direction and scale of core data between different modules; The event push unit is used to associate key events with corresponding data fluctuation periods and push reminder information.

7. The system of claim 4, wherein, The professional interpretation module includes: The live streaming access unit is used to acquire real-time live streaming content and historical live streaming clips from professional analysts. The signal linkage unit is used to match the timestamp of the signal generated by the trend quantification module with the live content, and automatically switch to the corresponding interpretation segment at key signal nodes. The text interpretation unit is used to overlay text interpretation content corresponding to the current signal on the visualization interface.

8. The system of claim 4, wherein, The intelligent filtering module includes: The factor calculation unit is used to calculate multi-dimensional factors such as trend signal score, core data net inflow ratio, and sector increase. The ranking generation unit is used to generate screening rankings for different periods, such as quarterly rankings and monthly rankings, based on the multi-dimensional factors. A customizable filter unit is used to receive user-inputted filter criteria and perform precise filtering of candidate objects.

9. The system of claim 4, wherein, The user interaction module includes: The top search bar is designed to help users search for data objects or professional analysts; The function entry unit provides quick access to functions such as trend selection, expert circle, sector anomaly, market monitoring and review, and analysis calendar; The bottom navigation bar provides the core entry points for data overview, customizable items, homepage, filter pool, and personal center; The personalization settings section allows users to configure analysis cycles, notification preferences, and interface display styles.

10. The system of claim 4, wherein, The system also includes: The feedback optimization module, connected to the user interaction module, trend quantification module, and intelligent filtering module, is used to collect user operation behavior data, iteratively optimize the trend quantification algorithm and intelligent filtering model, and improve system adaptability and recommendation accuracy.

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