Stock volume and price visual analysis method based on three-dimensional space-time mapping

Abstract

The application belongs to the technical field of financial data processing, and discloses a stock volume and price visual analysis method based on three-dimensional space-time mapping. The method solves the problem of information dimension fragmentation in traditional two-dimensional charts by establishing a fixed mapping relationship between time, price, trading volume and three-dimensional orthogonal coordinate system X, Y and Z axis. The steps include: data acquisition and cleaning, dimension normalization processing; according to the preset primitive mapping rule, the normalized data is converted into three-dimensional geometric primitives to generate a three-dimensional visualization model; the model is rendered and displayed in real time; in response to user instructions, the perspective transformation, time slicing and dynamic backtracking interactive analysis are performed; and based on the preset volume and price relationship analysis rule, the model is automatically labeled with market patterns such as volume and price deviation and trading intensive area. The application realizes the integrated and intuitive presentation of the volume and price space-time relationship, provides a dedicated three-dimensional interactive analysis tool, and improves the depth, efficiency and scientificity of market analysis and decision-making.

Description

Technical Field

[0001] This invention relates to the field of financial data processing technology, and in particular to a stock volume and price visualization analysis method based on three-dimensional spatiotemporal mapping. Background Technology

[0003] Currently, mainstream securities analysis techniques, especially price trend visualization tools, still heavily rely on the traditional two-dimensional candlestick chart system originating from the West. They present trading volume, crucial to stock price fluctuations, only as a supplementary chart. The three core trading elements—time, price, and volume—are separated into different charts or supplementary charts, failing to achieve a unified presentation. Users must manually correlate data from multiple charts, which can easily lead to cognitive biases and make it difficult to quickly capture the spatiotemporal synergy between price and volume. This results in hidden risk signals that require complex indicator calculations or experience-based judgment to identify. Summary of the Invention

[0004] The present invention aims to provide a stock volume and price visualization analysis method based on three-dimensional spatiotemporal mapping to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A stock volume and price visualization analysis method based on three-dimensional spatiotemporal mapping includes the following steps:

[0007] S1. Establish a three-dimensional coordinate mapping relationship: Construct a three-dimensional orthogonal coordinate system, and map the time dimension of stock trading data to the X-axis of the coordinate system, the price dimension to the Y-axis, and the trading volume dimension to the Z-axis.

[0008] S2. Data Acquisition and Preprocessing: Acquire the original transaction time series data of the target stock, clean the data to remove invalid and abnormal data, and based on the coordinate axis range of the three-dimensional orthogonal coordinate system constructed in step S1, normalize the cleaned data in the three dimensions of time, price and trading volume respectively to obtain a normalized three-dimensional data point set.

[0009] S3. 3D model generation: According to the preset primitive mapping rules, the normalized 3D data point set obtained in step S2 is converted into 3D geometric primitives, and the corresponding time-price-volume 3D visualization model is generated in the 3D orthogonal coordinate system constructed in step S1.

[0010] S4. Model rendering and display: The 3D visualization model obtained in step S3 is rendered in real time and presented on a display device.

[0011] S5. Perform interactive analysis: Based on the 3D visualization model rendered in step S4, perform at least one of the following analysis operations:

[0012] Perspective transformation analysis: In response to user commands, the three-dimensional visualization model is rotated, scaled, and translated to observe the spatiotemporal relationship between quantity and price from different angles;

[0013] Time slice analysis: In response to user commands, select a time interval along the X-axis, perform rendering and analysis processing only on the 3D model data within that interval, and generate corresponding statistical summaries;

[0014] Dynamic backtracking analysis: Responding to user commands, the three-dimensional visualization model is controlled to evolve continuously along the time axis, dynamically reproducing the historical transaction process;

[0015] S6. Perform intelligent annotation analysis: Based on the preset volume-price relationship analysis rules, analyze the spatiotemporal distribution characteristics of transaction volume and price in the three-dimensional visualization model, and automatically annotate the areas or marks representing specific market patterns on the three-dimensional visualization model.

[0016] Preferably, in step S1, the construction of the three-dimensional orthogonal coordinate system specifically includes:

[0017] Define a multi-granularity time scale based on linear scale for the X-axis;

[0018] Define a display range for the Y-axis that is dynamically determined based on the historical extreme values ​​of the target stock's prices;

[0019] Define a display range for the Z-axis that is dynamically determined based on the extreme values ​​of the target stock's historical trading volume.

[0020] Preferably, in step S2, the data cleaning includes rule filtering and statistical filtering:

[0021] The rule filtering is used to remove data from suspension days and obviously erroneous data;

[0022] The statistical filtering is used to identify and remove abnormal abrupt changes in transaction volume based on historical data distribution.

[0023] Preferably, in step S2, the normalization process uses a multidimensional linear normalization algorithm to independently map the data values ​​of the three dimensions of time, price, and trading volume to the same numerical space range.

[0024] Preferably, in step S3, the primitive mapping rule is as follows:

[0025] For daily candlestick data, a cuboid is used as the basic graphic element, where the length of the cuboid is fixed, the height maps to the price fluctuation range, and the width or thickness maps to the trading volume.

[0026] For intraday tick data or minute-level data, points, cylinders, or prisms are used as basic graphic primitives for representation.

[0027] Preferably, in step S5, the rotation angle constraint of the rotation operation is ±90 degrees on the X-axis and ±360 degrees on the Y-axis, and the scaling ratio constraint is 0.1 to 5 times.

[0028] Preferably, in step S5, during the time slice analysis, the time interval supports selection accurate to the day or minute level, and the statistical summary includes at least one of average price, peak trading volume, and volume-price correlation coefficient.

[0029] Preferably, in step S5, the dynamic backtracking analysis supports the evolution of three adjustable playback speed control models: 1x speed, 5x speed, and 10x speed, and provides playback control command interfaces for start, pause, fast forward, and rewind.

[0030] Preferably, in step S6, the preset quantity-price relationship analysis rules include:

[0031] First rule: When the price sequence shows a predetermined upward trend and the trading volume sequence shows a predetermined downward trend, or when the price sequence shows a predetermined downward trend and the trading volume sequence shows a predetermined downward trend, it is judged as a volume-price divergence state.

[0032] The second rule is: when the density of three-dimensional data points within a certain price range exceeds a preset threshold, it is determined to be a densely traded area and support and resistance levels are marked.

[0033] Preferably, the intelligent annotation analysis in step S6 and the interactive analysis in step S5 work together: when the perspective change analysis or time slice analysis in step S5 is performed, the intelligent annotation analysis in step S6 is re-executed based on the currently displayed 3D visualization model area to update the annotation area or markers, so that the annotation information is synchronized with the model display status in real time.

[0034] The beneficial effects of this technical solution compared to existing technologies are as follows:

[0035] (1) This technical solution constructs a unified three-dimensional orthogonal coordinate system by fixing time to the X-axis, price to the Y-axis, and trading volume to the Z-axis. Within this space, each transaction or period's trading data is integrated into a data point with spatial coordinates, solving the problem of time, price, and trading volume information being forcibly displayed separately in traditional two-dimensional stock analysis charts, thus forming a clear three-dimensional trajectory of the entire trading history. This integrated presentation of "volume, price, time, and space" allows analysts to perceive the entire market simultaneously and intuitively without switching between multiple windows, reducing the cognitive load of information integration and improving analytical efficiency and the completeness of information acquisition.

[0036] (2) Through multi-dimensional data cleaning rules and multi-dimensional linear normalization processing, invalid and abnormal transaction data can be eliminated. At the same time, the time, price and volume data are adapted to the numerical space of the three-dimensional coordinate system, ensuring the accuracy of the data foundation. Combined with the pre-set primitive mapping rules of stock transaction data characteristics, differentiated three-dimensional geometric primitives are matched to transaction data of different time granularities, so that the visualization model is highly consistent with the data characteristics and accurately restores the volume and price fluctuation status of different trading cycles. In addition, the multi-granularity time scale and dynamic extreme value display interval design of the three-dimensional coordinate system allow the model to be adapted to stock transaction data of different targets and different cycles, improving the versatility and scenario adaptability of the method.

[0037] (3) By setting up three types of interactive analysis operations—viewpoint transformation, time slicing, and dynamic backtracking—users are given the ability to explore the three-dimensional visualization model from multiple dimensions. The viewpoint transformation operation allows users to observe the spatiotemporal relationship between volume and price from any angle and uncover the market structure characteristics hidden under the traditional fixed perspective; the time slicing operation can accurately focus on any key time interval, realize in-depth analysis of micro-trading periods and generate exclusive statistical summaries, and improve the precision of the analysis; the dynamic backtracking analysis supports the reproduction of historical market conditions with multiple speed adjustments, allowing users to intuitively observe the continuous evolution of volume and price trends, clearly grasp the causal logic of market formation, and solve the problem that traditional static visualization is difficult to trace the evolution of market conditions.

[0038] (4) By setting up pre-defined volume-price relationship analysis rules, automatic intelligent labeling of key market patterns such as volume-price divergence and dense trading areas is achieved, eliminating the need for manual identification and judgment and reducing analysis costs. At the same time, intelligent labeling and interactive analysis are coordinated in real time. When the perspective changes or time slices are used, intelligent labeling is re-executed and the labels are updated based on the currently displayed model area, ensuring the synchronization of labeling information and model display status. This allows users to quickly obtain accurate key market signals from any analysis perspective and at any time interval. Through the automation and linkage of intelligent labeling, the efficiency of stock volume-price analysis is improved, providing investors and analysis institutions with more scientific and accurate decision-making basis. Attached Figure Description

[0039] Figure 1 This is a flowchart of the method of the present invention; Detailed Implementation

[0040] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments:

[0041] like Figure 1 This paper presents a stock volume and price visualization analysis method based on three-dimensional spatiotemporal mapping. Taking the daily data analysis of an A-share stock as an example, the complete implementation process of this method is as follows:

[0042] S1. Establish three-dimensional coordinate mapping relationship

[0043] Initialize a 3D graphics scene and define a right-handed or left-handed 3D orthogonal coordinate system. Within this coordinate system, establish and solidify immutable mapping rules: the X-axis is mapped and used only to represent transaction time series; the Y-axis is mapped and used only to represent stock price series; and the Z-axis is mapped and used only to represent stock trading volume series.

[0044] Set a linearly scaled time ruler for the X-axis, supporting label display at different granularities such as year, month, week, day, and minute. Display range for the Y-axis. Dynamically determined based on the historical price extremes of the target stock within a selected time range, for example:

[0045] ,

[0046] in and These are the historical lowest and highest prices, multiplied by a coefficient to allow for visual margins.

[0047] Z-axis display area The target stock is dynamically determined based on historical extreme trading volumes within a selected time range, for example:

[0048] ,

[0049] in, This is the highest trading volume in history.

[0050] S2. Data Acquisition and Preprocessing

[0051] Obtain raw trading time-series data for the target stock within a selected time period. This data can be obtained through conventional financial data interfaces, databases, or file systems. The data must include at least a timestamp t, a price p (which may include the opening price open, closing price close, highest price high, and lowest price low), and a trading volume v.

[0052] First, perform the data cleaning steps:

[0053] Rule filtering: Based on the publicly available suspension calendar, remove all data records for suspension dates; remove erroneous data records where the price p or trading volume v is zero, negative, or significantly exceeds a reasonable range (e.g., the price fluctuation in a single day exceeds the daily price limit stipulated by the securities market).

[0054] Statistical filtering: Calculate the N-day moving average of the trading volume series. and standard deviation Transaction volume Data points that meet the following conditions are identified as anomalous mutation points and removed:

[0055]

[0056] in, For example, a preset threshold coefficient. For time points caused by data removal or missing source data, linear interpolation can be used, utilizing adjacent valid data points. and Fill in:

[0057]

[0058] Then, the cleaned data is subjected to dimensional linear normalization to map it to a unified numerical space. :

[0059] Time dimension normalization:

[0060]

[0061] Price normalization (using closing price as an example):

[0062]

[0063] Normalization of trading volume:

[0064]

[0065] in, , , , , , These represent the minimum and maximum values ​​for the corresponding dimensions in the cleaned dataset.

[0066] Thus, the normalized three-dimensional data point set is obtained. ,in This represents the total number of data points.

[0067] S3, 3D Model Generation

[0068] Based on the preset primitive mapping rules, the normalized data point set is... Convert to three-dimensional geometric primitives.

[0069] For daily candlestick chart data: the data for each trading day is converted into a cuboid primitive. The coordinates of the center point of this cuboid on the X-axis represent the corresponding trading day. The value, its length along the X-axis. It is fixed at one unit (representing one time period). Its height along the Y-axis. Determined by the price fluctuation range of that trading day:

[0070]

[0071] in, and These are the normalized values ​​of the highest and lowest prices of the day, respectively. The top surface is located Its width (or thickness) along the Z-axis. Normalized trading volume of that trading day Decide:

[0072]

[0073] in, This is a preset scaling factor used to control the visual scaling of trading volume. The color of this cuboid can be rendered using a "red for increases, green for decreases" principle (e.g., red for a closing price higher than the opening price, and green for a closing price lower than the opening price), and based on... The value adjusts the brightness or transparency of the color. ,For example This results in the candlestick pattern with higher trading volume being darker or less transparent.

[0074] For high-frequency data at the minute or tick level: each data point can be converted into a point primitive (e.g., radius of ). A sphere, or a cylindrical / prismatic primitive. The coordinates of its center point (or the center point of its base) are... For a cylinder, its radius... and height It can be set to a fixed value, or slightly correlated with trading volume, for example... ,in It is a constant.

[0075] All generated graphic elements together constitute a complete three-dimensional visualization model of time, price, and trading volume in the three-dimensional coordinate system defined in step S1. .

[0076] S4. Model rendering and display.

[0077] Visualizing 3D models using graphics rendering libraries (such as OpenGL, WebGL, DirectX) Real-time rendering is performed. The rendering pipeline configures ambient and directional lighting to enhance the sense of depth and enables depth testing (Z-Buffer) to ensure correct spatial occlusion relationships. The rendered image frames are then output to display devices such as computer monitors and mobile device screens to complete the initial presentation of the 3D visualization model.

[0078] S5, Perform interactive analysis

[0079] Enter interactive mode to respond to user commands.

[0080] Viewpoint transformation analysis: Users can change the perspective of the model by dragging with the mouse (PC) or swiping with their finger (mobile). Perform rotation operations; zoom operations can be performed using the mouse wheel or pinch-to-zoom; translation operations can be performed by dragging or using specific key combinations or gestures. To improve interaction stability and user experience, constraints can be imposed on the operations: during rotation operations, the rotation angle around the X-axis (pitch angle) can be limited. Constraints The rotation angle around the Y-axis (yaw angle) within the interval. Constraints or Within the range; scaling factor for scaling operations Constraints Within the range.

[0081] Time slice analysis: Users can specify a time interval by using the timeline slider in the graphical interface or by directly selecting a box. Based on this interval, set a clipping plane along the X-axis, applicable only to the model. China satisfies The primitives of the conditions are rendered, where and This is the normalized value corresponding to the timestamp. Meanwhile, the original data (prices) within this slice interval... and trading volume Perform calculations to generate statistical summaries, for example:

[0082] Average price:

[0083] Peak trading volume:

[0084] Quantity-price correlation coefficient:

[0085] in, This represents the number of data points within the slice interval. For covariance, The value is the standard deviation. Summary information is displayed in a floating window or sidebar.

[0086] Dynamic backtracking analysis: The dynamic backtracking process begins when the user clicks the play control button. During this process, a "current time pointer" is maintained. The pointer starts from the beginning time. Start at the preset speed (For example, 1x speed corresponds to 1 trading day / second in reality) Incrementing. Simultaneously, a fixed "backtracking window length" is defined. (e.g., 20 trading days). In each rendered frame, dynamically determine the current time window to be displayed:

[0087]

[0088] Then, only for the model Primitives whose timestamps fall within this dynamic window are rendered. This is done by updating frame by frame. It also re-selects and renders primitives to visually create the effect of a model continuously evolving along the time axis (X-axis). It supports multiple playback speed levels (such as...). It also provides playback control commands such as start, pause, fast forward, and rewind.

[0089] S6. Perform intelligent annotation analysis

[0090] The intelligent annotation analysis step is performed, which analyzes and marks the currently displayed 3D visualization model area based on preset quantity-price relationship analysis rules.

[0091] First rule: Identifying volume-price divergence

[0092] Price series corresponding to the current visible model region and trading volume sequence Perform trend analysis. Calculate the short-term (e.g., trend) of the price series. (Period) Simple Moving Average and the concurrent moving average of the trading volume series Calculate the moving average in the most recent The slope within a given period. A price-volume divergence is determined if any of the following conditions are met:

[0093] Top divergence: and , lasting at least One cycle.

[0094] Bottom divergence: and , lasting at least One cycle.

[0095] in, A preset small positive threshold is used to determine the significance of the trend. Regions identified as divergent are marked in the 3D model with a striking semi-transparent highlight block (e.g., red for top divergence, green for bottom divergence) or a dynamic icon.

[0096] Rule Two: Identification of High-Trading Area Status

[0097] Divide the current visible model area along the Y-axis (price axis) into There are three equal-width intervals, with an interval width of [missing value]. For the j-th price range Calculate the number of data points falling within this interval. (i.e., spatial density). Calculate the average density across all intervals. If a certain interval The density satisfies:

[0098]

[0099] in For a preset density threshold (e.g.) If so, then determine the price range. This is a high-volume trading area. In the 3D model, a semi-transparent strip-shaped plane is drawn and highlighted at the horizontal spatial position corresponding to this price range. It can be marked as a "support level" or "resistance level" based on historical price breakout behavior near this range.

[0100] Collaborative Mechanism: When a user performs perspective change analysis (changing the observation angle) or time slice analysis (changing the data range), the intelligent annotation analysis step is re-executed. This step, based on the 3D model portion actually displayed on the screen after triggering (i.e., the model area visible from the new perspective, or the model data within the new slice interval), re-runs the aforementioned volume-price divergence identification rules and transaction density area identification rules, and immediately updates the annotation graphics and text on the screen, ensuring that all annotation information remains precisely synchronized with the user's current observation focus. Dynamic backtracking analysis typically does not trigger this recalculation to ensure the visual continuity of the animation playback.

[0101] The axial order (X, Y, Z) of the three-dimensional orthogonal coordinate system can be arbitrarily defined, but the fixed mapping relationship between time, price, trading volume, and the X, Y, and Z axes is the core feature of this invention, and this relationship cannot be arbitrarily interchanged. In addition to linear normalization, logarithmic normalization or Z-score standardization can be used for stocks with extremely large price ranges. Basic geometric primitives are not limited to cuboids, spheres, and cylinders; they can be replaced with pyramids, capsules, custom mesh models, etc., according to visualization aesthetic requirements. Parameters in the intelligent annotation rules (such as trend judgment period, moving average period, and density threshold) can be empirically optimized or have a configuration interface provided for user adjustment based on different financial market characteristics, trading instruments, or user risk preferences. The method of this invention can be packaged as a standalone software application (desktop, mobile), a cloud service providing APIs, or integrated as a functional plugin / module into existing professional stock trading and analysis platforms.

[0102] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A stock volume-price visualization analysis method based on three-dimensional spatiotemporal mapping, characterized in that, Includes the following steps: S1. Establish a three-dimensional coordinate mapping relationship: Construct a three-dimensional orthogonal coordinate system, and map the time dimension of stock trading data to the X-axis of the coordinate system, the price dimension to the Y-axis, and the trading volume dimension to the Z-axis. S2. Data Acquisition and Preprocessing: Acquire the original transaction time series data of the target stock, clean the data to remove invalid and abnormal data, and based on the coordinate axis range of the three-dimensional orthogonal coordinate system constructed in step S1, normalize the cleaned data in the three dimensions of time, price and trading volume respectively to obtain a normalized three-dimensional data point set. S3. 3D model generation: According to the preset primitive mapping rules, the normalized 3D data point set obtained in step S2 is converted into 3D geometric primitives, and the corresponding time-price-volume 3D visualization model is generated in the 3D orthogonal coordinate system constructed in step S1. S4. Model rendering and display: The 3D visualization model obtained in step S3 is rendered in real time and presented on a display device. S5. Perform interactive analysis: Based on the 3D visualization model rendered in step S4, perform at least one of the following analysis operations: Perspective transformation analysis: In response to user commands, the three-dimensional visualization model is rotated, scaled, and translated to observe the spatiotemporal relationship between quantity and price from different angles; Time slice analysis: In response to user commands, select a time interval along the X-axis, perform rendering and analysis processing only on the 3D model data within that interval, and generate corresponding statistical summaries; Dynamic backtracking analysis: Responding to user commands, the three-dimensional visualization model is controlled to evolve continuously along the time axis, dynamically reproducing the historical transaction process; S6. Perform intelligent annotation analysis: Based on the preset volume-price relationship analysis rules, analyze the spatiotemporal distribution characteristics of transaction volume and price in the three-dimensional visualization model, and automatically annotate the areas or marks representing specific market patterns on the three-dimensional visualization model.

2. The stock volume and price visualization analysis method based on three-dimensional spatiotemporal mapping as described in claim 1, characterized in that, In step S1, the construction of the three-dimensional orthogonal coordinate system specifically includes: Define a multi-granularity time scale based on linear scale for the X-axis; Define a display range for the Y-axis that is dynamically determined based on the historical extreme values ​​of the target stock's prices; Define a display range for the Z-axis that is dynamically determined based on the extreme values ​​of the target stock's historical trading volume.

3. The stock volume and price visualization analysis method based on three-dimensional spatiotemporal mapping as described in claim 1, characterized in that, In step S2, the data cleaning includes rule-based filtering and statistical filtering: The rule filtering is used to remove data from suspension days and obviously erroneous data; The statistical filtering is used to identify and remove abnormal abrupt changes in transaction volume based on historical data distribution.

4. The stock volume and price visualization analysis method based on three-dimensional spatiotemporal mapping as described in claim 1, characterized in that, In step S2, the normalization process uses a multidimensional linear normalization algorithm to independently map the data values ​​of the three dimensions of time, price, and trading volume to the same numerical space.

5. The stock volume and price visualization analysis method based on three-dimensional spatiotemporal mapping as described in claim 1, characterized in that, In step S3, the primitive mapping rule is as follows: For daily candlestick data, a cuboid is used as the basic graphic element, where the length of the cuboid is fixed, the height maps to the price fluctuation range, and the width or thickness maps to the trading volume. For intraday tick data or minute-level data, points, cylinders, or prisms are used as basic graphic primitives for representation.

6. The stock volume and price visualization analysis method based on three-dimensional spatiotemporal mapping as described in claim 1, characterized in that, In step S5, the rotation angle constraint of the rotation operation is ±90 degrees on the X-axis and ±360 degrees on the Y-axis, and the scaling ratio constraint is 0.1 to 5 times.

7. The stock volume and price visualization analysis method based on three-dimensional spatiotemporal mapping as described in claim 1, characterized in that, In step S5, during the time slice analysis, the time interval supports selection accurate to the day or minute level, and the statistical summary includes at least one of the following: average price, peak trading volume, and volume-price correlation coefficient.

8. The stock volume and price visualization analysis method based on three-dimensional spatiotemporal mapping as described in claim 1, characterized in that, In step S5, the dynamic backtracking analysis supports the evolution of three adjustable playback speed control models: 1x speed, 5x speed, and 10x speed, and provides playback control command interfaces for start, pause, fast forward, and rewind.

9. The stock volume and price visualization analysis method based on three-dimensional spatiotemporal mapping as described in claim 1, characterized in that, In step S6, the preset quantity-price relationship analysis rules include: First rule: When the price sequence shows a predetermined upward trend and the trading volume sequence shows a predetermined downward trend, or when the price sequence shows a predetermined downward trend and the trading volume sequence shows a predetermined downward trend, it is judged as a volume-price divergence state. The second rule is: when the density of three-dimensional data points within a certain price range exceeds a preset threshold, it is determined to be a densely traded area and support and resistance levels are marked.

10. The stock volume and price visualization analysis method based on three-dimensional spatiotemporal mapping as described in claim 1, characterized in that, The intelligent annotation analysis in step S6 works in synergy with the interactive analysis in step S5: when the viewpoint transformation analysis or time slice analysis in step S5 is performed, the intelligent annotation analysis in step S6 is re-executed based on the currently displayed 3D visualization model area to update the annotation area or markers, so that the annotation information is synchronized with the model display status in real time.

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