Trajectory data visualization method and system

Abstract

The present invention provides a trajectory data visualization method and system. Specifically, the method parses the timestamp information of trajectory points based on a user-selected trajectory dataset to obtain a dynamic timeline, annotates key event nodes on the dynamic timeline to generate a timeline, maps the geographic location information in the trajectory dataset, and utilizes visual coding technology to enhance the recognition of different speed sections and stop points to generate an enhanced trajectory visualization. The method responds to user interactions and dynamically adjusts trajectory display parameters to achieve a personalized display effect. Multi-dimensional data analysis tools are integrated to respond to users' instant queries and statistical analysis requests, generating and directly presenting analysis results on a visualization interface. The technical solution provided by the present invention improves the efficiency and accuracy of data analysis and enhances the user experience, enabling even non-professional users to easily extract useful information from large amounts of trajectory data.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of trajectory data visualization, and in particular to a trajectory data visualization method. Background Art

[0002] In the field of trajectory data visualization, with the popularization of mobile devices and positioning technologies (such as GPS), more and more application scenarios generate a large amount of trajectory data. This data not only contains time and location information, but may also include other sensor information, which can be used in traffic management, logistics distribution optimization, personal travel analysis, and many other aspects.

[0003] Some existing trajectory data visualization methods have limitations. For example, they lack effective time dimension expression and fail to clearly display the changing characteristics of trajectories over time; the mapping of geographic location information is inaccurate, resulting in deviations between the generated trajectory map and the actual geographical situation; visual encoding is insufficient, and important information such as different speed sections and stop points is marked with simple colors or symbols, which is difficult to identify and easily causes visual confusion; interactivity is poor, and the interactive functions provided are limited, making it difficult to customize the display effect according to the user's personalized needs; data analysis integration is low, and interactive data analysis tools are rarely directly integrated, which affects decision-making efficiency. Summary of the Invention

[0004] Embodiments of the present invention provide a trajectory data visualization method and system to address the problems in the prior art, such as the lack of effective time dimension expression, inaccurate mapping of geographic location information, insufficient visual encoding, difficulty in identification and easy visual confusion, poor interactivity, limited interactive functions, low data analysis integration, and few direct integration of interactive data analysis tools, which affect decision-making efficiency.

[0005] In a first aspect, an embodiment of the present invention provides a trajectory data visualization method, comprising:

[0006] receiving real-time data streams from different sources, wherein the real-time data streams include structured data and unstructured data;

[0007] According to the trajectory dataset selected by the user, the timestamp information of the trajectory points is parsed and processed to obtain a dynamic timeline, and key event nodes are marked on the dynamic timeline to generate a timeline reflecting the characteristics of the trajectory changing over time;

[0008] Based on the dynamic timeline and geographic information system, the geographic location information in the trajectory dataset is mapped to obtain a trajectory map with geographic location information, and visual coding technology is used to enhance the recognition of different speed sections and stop points to generate an enhanced trajectory visualization map;

[0009] By utilizing the enhanced trajectory visualization graph, the user's interactive operations are responded to and trajectory display parameters are dynamically adjusted to obtain personalized display effects that meet different analysis needs;

[0010] Based on the personalized display effect, multi-dimensional data analysis tools are integrated to respond to users' instant queries and statistical analysis requests, calculate the trajectory density and average speed in a specific area, and generate and directly present the analysis results on the visual interface.

[0011] Optionally, based on the dynamic timeline and geographic information system, the geographic location information in the trajectory dataset is mapped to obtain a trajectory map with geographic location information, and visual coding technology is used to enhance the recognition of different speed sections and stop points to generate an enhanced trajectory visualization map, including:

[0012] The geographic location information in the trajectory dataset is mapped using a dynamic timeline and geographic information system to obtain a preliminary trajectory map with precise geographic location information;

[0013] Performing context-aware optimization on the preliminary trajectory map according to topographic features and environmental factors to generate an optimized trajectory map with environmental adaptability;

[0014] Introducing a machine learning algorithm to identify and classify characteristic patterns of different speed sections and stop points, establishing an intelligent identification rule library, and using the intelligent identification rule library to analyze the optimized trajectory map to generate a characteristic trajectory map after intelligent identification;

[0015] According to the user's historical interaction behavior and preference settings, a personalized visual encoding scheme is customized for the characteristic trajectory graph, and an enhanced trajectory visualization graph is obtained by adjusting visual elements.

[0016] Optionally, context-aware optimization is performed on the preliminary trajectory map according to topographic features and environmental factors to generate an optimized trajectory map with environmental adaptability, including:

[0017] Use high-resolution geographic information system data and remote sensing images to analyze and process the topographic features of the track points in the preliminary track map to generate track point data with detailed topographic information;

[0018] Based on the trajectory point data with detailed terrain information, multi-dimensional context-aware processing is performed on the preliminary trajectory map in combination with environmental factors to obtain an intermediate trajectory map after calculating the environmental impact;

[0019] Using a physical simulation algorithm, based on the terrain information and environmental factors of each trajectory point in the intermediate trajectory map after the influence of the computing environment, the behavior pattern of the object's movement under different terrain conditions is simulated to generate an optimized trajectory map;

[0020] According to the specific application scenario requirements of the user, based on the optimized trajectory map, a customized optimization strategy is designed, and at the same time, the optimized trajectory map is adapted to the application field using the optimization strategy to obtain an application scenario optimized trajectory map;

[0021] By integrating virtual reality technology and combining the optimized trajectory map of the application scenario with a three-dimensional geographic model, an immersive visualization experience is created to generate an optimized trajectory map with environmental adaptability.

[0022] Optionally, a machine learning algorithm is introduced to identify and classify characteristic patterns of different speed sections and stop points, establish an intelligent identification rule base, and use the intelligent identification rule base to analyze the optimized trajectory map to generate a characteristic trajectory map after intelligent identification, including:

[0023] Use a deep learning framework to train the trajectory dataset to build a multi-layer neural network model;

[0024] Utilizing the multi-layer neural network model to perform real-time analysis on the multi-dimensional features in the optimization trajectory graph to generate feature classification results;

[0025] Dynamically updating the intelligent identification rule base according to the characteristic classification result to obtain an updated intelligent identification rule base;

[0026] The updated intelligent identification rule base is used to re-evaluate and mark the speed sections and stop points of the optimized trajectory diagram to generate a characteristic trajectory diagram with intelligent identification features.

[0027] Optionally, the multi-layer neural network model is used to perform real-time analysis on the multi-dimensional features in the optimization trajectory graph to generate feature classification results, including:

[0028] Using a deep reinforcement learning framework, combined with historical behavior patterns and environmental feedback from trajectory datasets, we design and train intelligent agents with autonomous learning capabilities.

[0029] Based on the intelligent agent, multimodal fusion technology is used to integrate and process data from different sensors and external information sources to form a multimodal data stream and obtain context information;

[0030] Implementing context-aware strategy optimization based on the context information, calculating the changing trends and background context within a preset time, and generating a context-optimized intelligent agent;

[0031] The context-optimized intelligent agent is used to perform real-time perception and interactive exploration of the multi-dimensional features in the optimization trajectory graph. Through a continuous trial-and-error learning process, the decision path that is most conducive to feature classification is identified, generating a feature classification result with high adaptability and accuracy.

[0032] Optionally, the enhanced trajectory visualization diagram is used to respond to user interactions and dynamically adjust trajectory display parameters to obtain personalized display effects that meet different analysis needs, including:

[0033] The trajectory display parameters are processed in real time by the user's interactive operations on the interface to obtain an interactive interface with instant feedback;

[0034] Based on the instant feedback interactive interface, and in combination with user interaction behavior and historical preference settings, an intelligent recommendation algorithm is used to predict and process preset analysis needs to generate intelligent preset display effects;

[0035] Utilize natural language processing technology to parse user text or voice commands, perform semantic conversion processing on unstructured user intentions, generate customized display parameter adjustment commands that meet the user's description based on the intelligent preset display effect, and update them to the interactive interface to obtain an optimized interactive interface;

[0036] Combined with virtual reality technology to create a three-dimensional interactive environment, based on the user's interaction with the trajectory data through gestures or eye tracking, the trajectory display parameters are processed for an immersive experience based on the optimized interactive interface to generate personalized display effects that meet different analysis needs;

[0037] An adaptive learning mechanism is introduced to continuously optimize the intelligent recommendation algorithm and semantic parsing model based on each user interaction operation and its resulting results. Based on the personalized display effect in the previous step, it ensures that the system can continuously improve its ability to understand and respond to user needs over time, achieving a continuously evolving personalized display effect.

[0038] Optionally, based on the personalized display effect, a multi-dimensional data analysis tool is integrated to respond to users' instant queries and statistical analysis requests, calculate the trajectory density and average speed in a specific area, and generate and directly present the analysis results on the visualization interface, including:

[0039] Using the trajectory segments selected by the user in the personalized display effect and combining them with the key event nodes on the timeline, the trajectory data within a specific time period is filtered to obtain the trajectory segments within the target time period;

[0040] Based on the trajectory segments within the target time period, the study area in the geographic information system is finely segmented using spatial gridding technology. According to the distribution of trajectory points, the trajectory density within each grid is calculated to generate a high-resolution trajectory density map, which is then integrated into the existing personalized display effect.

[0041] According to the trajectory density map, a dynamic window algorithm is used to track the speed changes of the trajectory segments in real time, the instantaneous speed at different positions is calculated, and the average speed in a specific area is calculated by a cumulative statistical method to obtain accurate speed distribution information, which is updated to the existing display interface to obtain an updated display interface;

[0042] According to the user's instant query and statistical analysis requests, combined with customized analysis conditions, based on the trajectory density map and speed distribution information in the updated display interface, customized query processing is performed using the interactive query interface to generate analysis results that meet the user's needs and provide real-time feedback to the user.

[0043] In a second aspect, an embodiment of the present invention provides a trajectory data visualization system, including:

[0044] A parsing module is used to parse the timestamp information of the trajectory points according to the trajectory dataset selected by the user to obtain a dynamic timeline, and mark key event nodes on the dynamic timeline to generate a timeline reflecting the characteristics of the trajectory changing over time;

[0045] a mapping module for mapping the geographic location information in the trajectory dataset based on the dynamic timeline and the geographic information system to obtain a trajectory map with geographic location information, and using visual coding technology to enhance the recognition of different speed sections and stop points to generate an enhanced trajectory visualization map;

[0046] An adjustment module is used to respond to user interactions using the enhanced trajectory visualization graph and dynamically adjust trajectory display parameters to obtain personalized display effects that meet different analysis requirements;

[0047] A generation module is used to integrate multi-dimensional data analysis tools based on the personalized display effect, respond to users' instant queries and statistical analysis requests, calculate the trajectory density and average speed in a specific area, and generate and directly present the analysis results on the visualization interface.

[0048] In a third aspect, an embodiment of the present invention provides a computing device, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a trajectory data visualization method as described in any one of the first aspects.

[0049] In a fourth aspect, an embodiment of the present invention provides a computer storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement a trajectory data visualization method as described in any one of the first aspects.

[0050] In an embodiment of the present invention, based on a trajectory dataset selected by a user, the timestamp information of the trajectory points is parsed to obtain a dynamic timeline, and key event nodes are marked on the dynamic timeline to generate a timeline reflecting the characteristics of the trajectory over time; based on the dynamic timeline and a geographic information system, the geographic location information in the trajectory dataset is mapped to obtain a trajectory map with geographic location information, and visual coding technology is used to enhance the recognition of different speed sections and stop points to generate an enhanced trajectory visualization map; using the enhanced trajectory visualization map, the trajectory display parameters are dynamically adjusted to respond to user interactions, and a personalized display effect that adapts to different analysis needs is obtained; based on the personalized display effect, a multi-dimensional data analysis tool is integrated to respond to the user's instant query and statistical analysis requests, calculate the trajectory density and average speed in a specific area, generate and directly present the analysis results on the visualization interface. The technical solution provided by the present invention generates a timeline reflecting the characteristics of the trajectory over time by parsing the timestamp information of the trajectory points, constructing a dynamic timeline, and marking key event nodes on the timeline. This method allows users to clearly see the activity patterns or sequence of events within a specific time period, solving the problem of unclear time features in existing methods. Based on a dynamic timeline and geographic information system (GIS), the geographic location information in the trajectory dataset is mapped and processed to obtain a trajectory map with geographic location information. GIS technology is used to ensure the authenticity and reliability of the trajectory map, avoiding the position deviation problem that may exist in traditional methods. Visual coding technology is used to enhance the recognition of different speed sections and stop points to generate an enhanced trajectory visualization map. This not only improves the readability and recognizability of important trajectory features, but also reduces the possibility of visual confusion and improves the user's understanding experience. By responding to user interactions, the trajectory display parameters are dynamically adjusted to achieve personalized display effects that adapt to different analysis needs. This method supports users to customize the display content according to their own needs, enhancing the flexibility and applicability of the system. Based on the personalized display effect, the integration of multi-dimensional data analysis tools can directly respond to users' instant queries and statistical analysis requests, and quickly calculate and present information such as trajectory density and average speed in a specific area. This integration greatly improves data analysis efficiency and provides users with instant and accurate data support.

[0051] Furthermore, the preliminary trajectory map is context-awarely optimized based on topographical features and environmental factors to generate an optimized trajectory map with environmental adaptability. This process takes into account the influence of the actual geographical environment, making the trajectory map closer to reality and improving the realism and credibility of the visualization results. At the same time, a machine learning algorithm is introduced to identify and classify the characteristic patterns of different speed sections and stop points, establish an intelligent identification rule base, and use this rule base to analyze the optimized trajectory map to generate an intelligently identified characteristic trajectory map. This method not only improves the accuracy of trajectory analysis but also automatically discovers and marks meaningful behavioral patterns, providing strong support for subsequent in-depth analysis. Finally, based on the user's historical interaction behavior and preference settings, the characteristic trajectory map is personalized with a visual encoding scheme, and by adjusting the visual elements, an enhanced trajectory visualization map is obtained. This approach ensures that each user's visualization experience is tailored to maximize the satisfaction of the specific needs and preferences of different users.

[0052] These and other aspects of the present invention will become more readily apparent from the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 A flowchart of a big data processing method for implementing hybrid data analysis provided by an embodiment of the present invention;

[0055] Figure 2 A schematic diagram of the structure of a big data processing system for implementing hybrid data analysis provided by an embodiment of the present invention;

[0056] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0058] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] Figure 1 A flow chart of a trajectory data visualization method is provided for an embodiment of the present invention, such as Figure 1 As shown, the method includes:

[0061] Step 101: according to the trajectory data set selected by the user, the timestamp information of the trajectory points is parsed and processed to obtain a dynamic timeline, and key event nodes are marked on the dynamic timeline to generate a timeline reflecting the characteristics of the trajectory changing over time;

[0062] In this step, the timestamp information for each trajectory point in the user-selected trajectory dataset—data that records the exact time—is parsed and processed to extract an accurate time series. These timestamps are used to construct a dynamic timeline that displays and adjusts data over time, allowing users to view activity patterns or the sequence of events within a specific time period. Key event nodes, such as starting points, end points, or stops, are annotated on the dynamic timeline, generating a timeline that reflects the temporal characteristics of the trajectory, allowing users to intuitively understand the temporal distribution of the trajectory.

[0063] Step 102: Based on the dynamic timeline and the geographic information system, the geographic location information in the trajectory dataset is mapped to obtain a trajectory map with the geographic location information, and visual coding technology is used to enhance the recognition of different speed sections and stop points to generate an enhanced trajectory visualization map;

[0064] In this step, the geographic location information in the trajectory dataset is mapped based on the dynamic timeline and geographic information system (GIS) to obtain a preliminary trajectory map with geographic location information. Geographic Information System is an application tool for capturing, storing, managing and displaying all forms of geographic data. Subsequently, visual coding technology is used to color-code or perform other visual processing on different speed segments (such as fast movement, slow movement) and stop points to enhance recognition. Ultimately, an enhanced trajectory visualization map is generated, allowing users to more clearly distinguish different parts of the trajectory and their characteristics.

[0065] Step 103: Utilizing the enhanced trajectory visualization graph, responding to user interactions, and dynamically adjusting trajectory display parameters to obtain personalized display effects adapted to different analysis requirements;

[0066] In this step, the enhanced trajectory visualization dynamically adjusts trajectory display parameters—factors that influence how the trajectory is presented, including line width, color, and transparency—in response to user interactions such as clicks, dragging, or zooming. The trajectory visualization is updated instantly after each interaction, ensuring that the effects of the adjustments are immediately apparent. Furthermore, based on the user's historical interactions and preferences, a variety of personalized display options are provided, such as filtering trajectories by time and focusing on specific areas, resulting in a personalized display that suits different analysis needs.

[0067] Step 104: Based on the personalized display effect, a multi-dimensional data analysis tool is integrated to respond to the user's instant query and statistical analysis request, calculate the trajectory density and average speed in a specific area, and generate and directly present the analysis results on the visualization interface;

[0068] In this step, multi-dimensional data analysis tools are integrated based on personalized display effects. These tools provide multiple ways to explore and understand data, including statistical analysis, trend forecasting, and other functions. Users' immediate queries and statistical analysis requests—that is, questions about data or information that needs to be calculated—require prompt responses, directly receiving and quickly processing these requests. The trajectory density and average speed within a specific area (i.e., the number of trajectories per unit area within a selected geographic area and the average travel speed of these trajectories) are calculated and presented directly on the visual interface, making it easy for users to view and interpret the analysis results.

[0069] Based on this, the present invention provides a specific embodiment. Step 102 maps the geographic location information in the trajectory dataset based on the dynamic timeline and the geographic information system to obtain a trajectory map with geographic location information. Visual coding technology is used to enhance the recognition of different speed sections and stop points to generate an enhanced trajectory visualization map. The specific steps include:

[0070] Step 201: Mapping the geographic location information in the trajectory dataset using a dynamic timeline and a geographic information system to obtain a preliminary trajectory map with precise geographic location information;

[0071] In this step, the timestamp and coordinate information of each record in the trajectory dataset is parsed. This is combined with the dynamic timeline, a tool that can display events in a time series, and the Geographic Information System (GIS), an application used to capture, store, manage, analyze, and display all forms of geographic data, to convert the geographic location information into a specific location on the map. The result of this process is a preliminary trajectory map with precise geographic location information, which intuitively shows how the trajectory changes over time and space.

[0072] Step 202: performing context-aware optimization on the preliminary trajectory map according to topographic features and environmental factors to generate an optimized trajectory map with environmental adaptability;

[0073] In this step, context-aware optimization is performed based on the preliminary trajectory map, taking into account topographic features such as mountains, rivers, and urban layout, as well as environmental factors such as weather conditions and light intensity. This involves adjusting the trajectory representation to better suit the actual environmental conditions, such as changing the color or width of the path to reflect visibility or traffic flow in different environments. Ultimately, an optimized trajectory map with environmental adaptability is generated, allowing users to more accurately understand the real-world context of the trajectory.

[0074] Step 203: Introducing a machine learning algorithm to identify and classify characteristic patterns of different speed sections and stop points, establishing an intelligent identification rule base, and using the intelligent identification rule base to analyze the optimized trajectory map to generate a characteristic trajectory map after intelligent identification;

[0075] In this step, machine learning algorithms—a type of technology that automatically analyzes patterns from data and uses these patterns to predict unknown data—are used to identify and classify different speed segments (fast movement, slow movement, stationary, etc.) and stop points (locations where the vehicle remains for a longer period of time). An intelligent identification rule base is established, which is a collection of rules and models that guide how to define and classify trajectory behaviors based on specific speed patterns and other characteristics. This rule base is then applied to conduct an in-depth analysis of the optimized trajectory map, generating a characteristic trajectory map after intelligent identification, providing a more detailed explanation of trajectory behavior.

[0076] Step 204: performing personalized visual encoding scheme customization processing on the characteristic trajectory graph based on the user's historical interaction behavior and preference settings, and obtaining an enhanced trajectory visualization graph by adjusting visual elements;

[0077] In this step, machine learning algorithms—a type of technology that automatically analyzes patterns from data and uses these patterns to predict unknown data—are used to identify and classify different speed segments (fast movement, slow movement, stationary, etc.) and stop points (locations where the vehicle remains for a longer period of time). An intelligent identification rule base is established, which is a collection of rules and models that guide how to define and classify trajectory behaviors based on specific speed patterns and other characteristics. This rule base is then applied to conduct an in-depth analysis of the optimized trajectory map, generating a characteristic trajectory map after intelligent identification, providing a more detailed explanation of trajectory behavior.

[0078] Based on this, the present invention provides a specific embodiment, wherein step 202 performs context-aware optimization on the preliminary trajectory map based on topographic features and environmental factors to generate an optimized trajectory map with environmental adaptability, specifically comprising the following steps:

[0079] Step 301: Analyze and process the topographic features of the track points in the preliminary track map using high-resolution geographic information system data and remote sensing images to generate track point data with detailed topographic information;

[0080] In this step, by integrating high-resolution Geographic Information System (GIS) data and the latest remote sensing imagery, we can accurately obtain detailed terrain information surrounding each track point. This includes, but is not limited to, natural environmental factors such as altitude, slope, and vegetation cover. Through this analysis and processing, we can generate track point data with detailed terrain information, providing a foundation for subsequent environmental adaptability optimization.

[0081] Step 302: Based on the trajectory point data with detailed terrain information, the preliminary trajectory map is processed with multi-dimensional context awareness in combination with environmental factors to obtain an intermediate trajectory map after calculating environmental impacts;

[0082] In this step, the preliminary trajectory map is processed using multi-dimensional context-awareness using trajectory point data with detailed terrain information, taking into account various environmental factors such as weather conditions, lighting conditions, and time of day. The goal is to assess how these factors influence the activities or movement patterns along the trajectory and adjust the trajectory map accordingly, resulting in an intermediate trajectory map that accounts for environmental influences. This process helps improve the trajectory map's adaptability and accuracy to actual environmental changes.

[0083] Step 303: Using a physical simulation algorithm, based on the terrain information and environmental factors of each trajectory point in the intermediate trajectory graph after the calculation of the environmental influence, simulate the behavior pattern of the object's movement under different terrain conditions to generate an optimized trajectory graph;

[0084] In this step, physical simulation algorithms are used to simulate the movement patterns of objects (such as vehicles and pedestrians) under different terrain conditions and environmental factors. This may involve simulating the changes in speed when driving on a slope, the changes in gait when walking on wet ground, etc. By simulating these behavior patterns, the trajectory map can be further optimized to ensure that it not only reflects the changes in geographical location, but also reflects the actual laws of object movement in the real world.

[0085] Since many traditional trajectory visualization methods ignore the influence of terrain and environmental factors on the trajectory, the generated trajectory may be inaccurate and inconsistent with the actual situation. By introducing a physical simulation algorithm, the generated trajectory not only looks more natural but also conforms to the actual physical laws, ensuring that the trajectory is closer to the real physical behavior.

[0086] Among them, the calculation method of the physical simulation algorithm is as follows:

[0087] P final (t) = P intermediate (t)+α·(Δ terrain (t)+Δ environment (t))

[0088] Among them, P final (t) represents the final optimized trajectory point position at time t, which is the result obtained by the formula calculation, which combines the initial trajectory point position and other influencing factors. In order to ensure that the generated trajectory is closer to the reality, the influence of terrain and environmental factors on the object movement is considered, making the visualization result not only beautiful but also more accurate; intermediate(t) represents the position of the trajectory point at time t in the intermediate trajectory map that has been preliminarily calculated or generated by other algorithms. It is usually the position of the trajectory point generated based on the original data set or other preliminary processing steps. It serves as the starting point and provides basic trajectory information. Subsequent optimization adjustments are performed on this basis to ensure that the adjusted trajectory still maintains the original basic path characteristics; α represents the adjustment factor, which is used to control the influence of terrain and environmental factors on the adjustment of the trajectory point position. It can be set according to actual needs, such as through experimental determination or based on user input. The introduction of the adjustment factor is to balance the importance of different factors, allowing flexible adjustment according to specific application scenarios, and improving the adaptability and flexibility of the method. In addition, it also provides a mechanism to avoid certain factors from excessively affecting the trajectory and causing unreasonable offsets; Δ terrain (t) represents the terrain change vector at time t, reflecting the impact of terrain characteristics (such as slope, altitude, geological structure, etc.) on the position of the trajectory point. It is obtained based on high-resolution geographic information system (GIS) data and remote sensing image analysis. Terrain characteristics are one of the important factors affecting the movement of objects, especially for ground transportation such as vehicles and pedestrians. Accurately reflecting these characteristics can significantly improve the realism of trajectory simulation. High-resolution GIS data and remote sensing images provide detailed terrain information, making this adjustment more accurate; Δ environment (t) represents the additional position adjustment vector caused by environmental factors at time t. It can include factors such as wind speed, temperature, humidity, etc., which may affect the movement of the object. External information sources such as meteorological data and sensor data, as well as environmental factors such as weather conditions and light intensity, can also affect the movement of the object. Taking these factors into consideration can make the trajectory better adapt to the physical behavior patterns under actual conditions. For example, strong winds may cause the aircraft to deviate from the planned route, and low temperatures may cause icing on the road, affecting the vehicle's speed.

[0089] The overall design of this formula aims to create a trajectory optimization model that can adapt to complex real-world conditions. By incorporating topographic features and environmental factors, this not only improves the accuracy and authenticity of trajectory data visualization, but also provides users with more practical information. This approach is particularly suitable for applications requiring precise trajectory analysis, such as traffic management, logistics distribution, and personal travel planning. Furthermore, by introducing an adjustment factor α, the formula also provides a degree of flexibility to adapt to different application scenarios and technical requirements.

[0090] Step 304: Customize and design an optimization strategy based on the optimized trajectory map according to the specific application scenario requirements of the user, and use the optimization strategy to adapt the optimized trajectory map to the application field to obtain an optimized trajectory map for the application scenario;

[0091] In this step, customized optimization strategies are designed to meet the specific needs of different user scenarios, such as logistics distribution, travel planning, and emergency rescue. This may involve route selection, speed limits, and stop point settings. By applying these optimization strategies, the optimized trajectory map can be better adapted to specific application scenarios, improving its practical value and service quality.

[0092] Step 305: Integrate virtual reality technology, create an immersive visualization experience by combining the application scenario optimized trajectory map with a three-dimensional geographic model, and generate an optimized trajectory map with environmental adaptability;

[0093] In this step, all previous optimization work is integrated and combined with virtual reality (VR) technology to create an immersive visualization experience based on a 3D geographic model. This system allows users to "walk" through their trajectory from a first-person perspective, viewing the details of their surroundings and even simulating changes in the landscape under different time of day and weather conditions. This not only enhances the user experience but also helps professionals more intuitively understand and analyze complex trajectory data, leading to better decision-making.

[0094] Based on this, the present invention provides a specific embodiment, wherein step 203 introduces a machine learning algorithm to identify and classify characteristic patterns of different speed sections and stop points, establishes an intelligent identification rule base, and uses the intelligent identification rule base to analyze the optimized trajectory map to generate a characteristic trajectory map after intelligent identification, specifically comprising the following steps:

[0095] Step 401: Using a deep learning framework to train the trajectory dataset to build a multi-layer neural network model;

[0096] In this step, a deep learning framework suitable for processing spatiotemporal sequence data (such as TensorFlow or PyTorch) is selected and trained using a large dataset of labeled trajectories. These datasets contain multi-dimensional features such as location, timestamp, speed, and direction. Through an iterative training process, a multi-layer neural network model is constructed that can learn and recognize complex patterns in trajectories, such as different types of mobility (walking, running, driving), unusual activities, and rest points. During training, model parameters are optimized to improve prediction accuracy, and measures are taken to prevent overfitting.

[0097] Step 402: Utilize the multi-layer neural network model to perform real-time analysis on the multi-dimensional features in the optimization trajectory graph to generate a feature classification result;

[0098] In this step, the trained multi-layer neural network model is applied to the optimized trajectory data for real-time analysis. The model extracts multi-dimensional features from the trajectory, including but not limited to position changes, speed fluctuations, and direction changes, and generates a feature classification based on these features. The classification results can be different attributes or behavioral patterns of the trajectory, such as fast-moving sections, slow-moving sections, and stop points. This step is critical to ensuring that the model can operate efficiently in real-world applications and provide immediate and accurate classification results.

[0099] Step 403: Dynamically updating the intelligent identification rule base according to the characteristic classification result to obtain an updated intelligent identification rule base;

[0100] In this step, the intelligent identification rule base is dynamically updated based on the feature classification results obtained from the multi-layer neural network model. The intelligent identification rule base is a collection of predefined rules used to identify specific behavioral patterns or characteristics. As new data is added and the model learns, the rule base needs to be continuously updated to maintain its accuracy and relevance. The dynamic update process may include adding new rules, adjusting the weights or thresholds of existing rules, and deleting rules that are no longer applicable. The updated intelligent identification rule base can better reflect the latest behavioral patterns and environmental changes, thereby improving the accuracy of subsequent analysis.

[0101] Step 404: re-evaluate and mark the speed sections and stop points of the optimized trajectory map using the updated intelligent identification rule library to generate a characteristic trajectory map with intelligent identification features;

[0102] In this step, the updated intelligent recognition rule base is applied to re-evaluate and mark the speed sections and stop points in the optimized trajectory map. Specifically, different types of movement behaviors (such as fast movement, slow movement, long stays) and other significant features are identified and marked through the rules in the intelligent recognition rule base. The final generated characteristic trajectory map not only contains the original location information, but also has intelligently identified feature labels, making it easier for users to understand and analyze these trajectory data. This characteristic trajectory map helps to enhance the application value of trajectory data analysis and support more complex and accurate services or decisions.

[0103] Based on this, the present invention provides a specific embodiment, wherein step 402 uses the multi-layer neural network model to perform real-time analysis on the multi-dimensional features in the optimization trajectory graph to generate a feature classification result, specifically including the following steps:

[0104] Step 501: Using a deep reinforcement learning framework, combined with historical behavior patterns and environmental feedback in the trajectory dataset, an intelligent agent with autonomous learning capabilities is designed and trained;

[0105] In this step, the Deep Reinforcement Learning (DRL) framework is employed to design and train an intelligent agent capable of autonomous learning by integrating historical behavioral patterns recorded in trajectory datasets (e.g., past movement paths, stop points, etc.) with feedback from the environment (e.g., traffic conditions, weather changes, etc.). This intelligent agent automatically adjusts its internal parameters based on different trajectory characteristics to adapt to new data analysis requirements and continuously optimizes its decision-making strategies through interaction with the environment. The DRL framework allows the intelligent agent to learn optimal behavioral strategies through trial and error without explicit programming instructions.

[0106] Step 502: Based on the intelligent agent, using multimodal fusion technology, integrate and process data from different sensors and external information sources to form a multimodal data stream and obtain context information;

[0107] In this step, multimodal fusion techniques are applied to the trained intelligent agent, integrating data from various sensors (such as GPS, accelerometers, and gyroscopes) and external information sources (such as weather forecasts and traffic flow reports). This data is fused into a single multimodal data stream, providing comprehensive and accurate contextual information. This contextual information includes not only location and timestamps, but also the state of the surrounding environment and other relevant factors, enabling the intelligent agent to make more informed decisions within a richer context.

[0108] Step 503: Implementing a context-aware strategy optimization process based on the context information, calculating the change trend and background context within a preset time, and generating a context-optimized intelligent agent;

[0109] In this step, context-aware policy optimization is implemented based on the contextual information obtained in the previous step. This involves analyzing trends within a predefined time period (e.g., traffic flow changes at different times of the day) and contextual circumstances (e.g., activity patterns during holidays and special events). By comprehensively considering these factors, the intelligent agent's decision-making logic is adjusted to achieve optimal performance in different scenarios. The resulting intelligent agent is not only able to respond to the current situation but also anticipate potential future changes, allowing it to prepare in advance.

[0110] Step 504: Utilize the context-optimized intelligent agent to perform real-time perception and interactive exploration of the multi-dimensional features in the optimization trajectory graph, identify the decision path most conducive to feature classification through a continuous trial-and-error learning process, and generate a highly adaptable and accurate feature classification result;

[0111] In this step, a context-optimized intelligent agent performs real-time perception and interactive exploration of multi-dimensional features (such as speed changes, dwell times, and path selection) in the optimized trajectory graph. Through a continuous trial-and-error learning process, the intelligent agent attempts different decision paths and identifies the most effective solution for feature classification. This dynamic learning mechanism ensures that the intelligent agent can quickly adapt to emerging situations and generate highly adaptable and accurate feature classification results. Ultimately, these results can be used to further improve trajectory data analysis and service quality, providing users with more personalized and efficient solutions.

[0112] Based on this, the present invention provides a specific embodiment. Step 103 utilizes the enhanced trajectory visualization graph to respond to user interactions and dynamically adjust trajectory display parameters to obtain personalized display effects that meet different analysis requirements. The embodiment specifically includes the following steps:

[0113] Step 601: utilizing the user's interactive operation on the interface to perform real-time response processing on the trajectory display parameters, thereby obtaining an interactive interface with instant feedback;

[0114] In this step, by capturing user interactions on the interface (such as clicking, dragging, and zooming), the system can immediately respond to these actions and adjust the trajectory display parameters, such as changing the trajectory color, width, or transparency. This real-time response processing ensures that users can immediately see the changes brought about by their actions, generating an interactive interface with instant feedback, and enhancing users' sense of participation and control.

[0115] Step 602: Based on the instant feedback interactive interface, and in combination with the user's interactive behavior and historical preference settings, an intelligent recommendation algorithm is used to predict and process the preset analysis needs, and generate an intelligent preset display effect;

[0116] In this step, an intelligent recommendation algorithm is used to predict the user's potential analysis needs based on the user's interactive interface with instant feedback, historical interaction behavior (such as past query patterns and preferences), and current operations. The goal of this step is to prepare corresponding display parameter configurations in advance to provide intelligent preset display effects, allowing users to see expected preliminary results before beginning data exploration, thereby improving analysis efficiency.

[0117] Step 603: Analyze the user's text or voice instructions using natural language processing technology, perform semantic conversion on the unstructured user intention, generate customized display parameter adjustment commands that meet the user's description based on the intelligent preset display effect, and update them to the interactive interface to obtain an optimized interactive interface;

[0118] In this step, natural language processing (NLP) technology is applied to parse the user's text or voice commands, converting unstructured user intent into specific display parameter adjustment commands. This process involves semantic understanding and conversion, ensuring that the system accurately understands the user's request. Based on the intelligent preset display effect in the previous step, the display parameters are further customized to more accurately reflect the user's description, and these adjustments are immediately applied to the interactive interface, generating an optimized interactive interface that efficiently realizes the user's intent.

[0119] Step 604: Create a three-dimensional interactive environment using virtual reality technology. Based on the optimized interactive interface, perform immersive experience processing on the trajectory display parameters according to the user's interaction with the trajectory data through gestures or eye tracking, and generate personalized display effects that meet different analysis requirements.

[0120] In this step, virtual reality (VR) or augmented reality (AR) technologies are integrated to create a three-dimensional interactive environment, allowing users to directly interact with trajectory data through gestures or eye tracking. Based on the optimized interactive interface, the system dynamically adjusts trajectory display parameters based on user interaction, providing an immersive visualization experience. This approach not only enhances the realism and intuitiveness of the user experience but also automatically generates personalized presentations based on different analysis needs, enabling deeper data exploration.

[0121] Step 605: Introduce an adaptive learning mechanism to continuously optimize the intelligent recommendation algorithm and semantic parsing model based on each user interaction and its resulting results. Based on the personalized display effect in the previous step, this ensures that the system can continuously improve its ability to understand and respond to user needs over time, achieving a continuously evolving personalized display effect.

[0122] In this step, an adaptive learning mechanism is introduced to continuously optimize the intelligent recommendation algorithm and semantic parsing model by recording and analyzing each user interaction and its resulting results. As user usage increases, the system gradually improves its ability to understand and respond to user needs, ensuring that each update better meets the user's personalized needs. This approach enables the system to evolve, ensuring increasingly accurate and personalized presentations over time, improving overall service quality and user experience.

[0123] Based on this, the present invention provides a specific embodiment. Step 104 integrates a multi-dimensional data analysis tool based on the personalized display effect, responds to users' instant queries and statistical analysis requests, calculates the trajectory density and average speed within a specific area, and generates and directly presents the analysis results on a visual interface. The specific steps include:

[0124] Step 701: Using the trajectory segment selected by the user in the personalized display effect and combining it with the key event nodes on the timeline, the trajectory data within a specific time period is filtered to obtain the trajectory segment within the target time period;

[0125] In this step, starting from the trajectory segment selected by the user through the interactive interface, the trajectory data within a specific time period is accurately filtered by combining key event nodes on the dynamic timeline (such as starting point, end point, and stop points). This process aims to extract trajectory segments within the time period that matches the user's interest, ensuring that subsequent analysis and visualization can focus on the specific time period of interest, thereby generating more targeted trajectory segments within the target time period.

[0126] Step 702: Based on the trajectory segments within the target time period, the study area in the geographic information system is finely segmented using spatial gridding technology. The trajectory density within each grid is calculated based on the distribution of trajectory points, and a high-resolution trajectory density map is generated. The trajectory density map is then integrated into the existing personalized display effect.

[0127] In this step, the study area in the Geographic Information System (GIS) is subdivided into multiple small grids using spatial gridding techniques, based on the trajectory segments within the target time period. The trajectory density within each grid is calculated by analyzing the number and distribution of trajectory points. This step results in a high-resolution trajectory density map that visually demonstrates the density of trajectories within different areas. This trajectory density map is then integrated into existing personalized display effects, providing users with a visual interface containing detailed density information, enhancing their understanding of the spatial distribution of trajectory data.

[0128] Step 703: Based on the trajectory density map, a dynamic window algorithm is used to track the speed changes of the trajectory segments in real time, calculate the instantaneous speed at different positions, and calculate the average speed in a specific area using a cumulative statistical method to obtain accurate speed distribution information. The information is then updated to the existing display interface to obtain an updated display interface.

[0129] In this step, based on the generated trajectory density map, a dynamic window algorithm is used to track speed changes of trajectory segments in real time. This algorithm continuously monitors and records instantaneous speed during movement, ensuring accurate capture of speed changes even in complex and changing environments. Cumulative statistical methods are used to further calculate the average speed within a specific area, providing precise speed distribution information. This information is instantly updated to the existing display interface, allowing users to immediately see trends and patterns in speed changes, providing a more comprehensive and dynamic data view, namely the updated display interface.

[0130] Step 704: Based on the user's instant query and statistical analysis request, combined with the customized analysis conditions, based on the trajectory density map and speed distribution information in the updated display interface, the interactive query interface is used to perform customized query processing, generate analysis results that meet the user's needs, and provide real-time feedback to the user.

[0131] In this step, in response to users' immediate queries and statistical analysis requests, the interactive query interface performs customized query processing on the trajectory density map and speed distribution information in the updated display interface, combined with user-defined analysis conditions (such as time range, geographic location, speed threshold, etc.). This approach allows users to flexibly adjust query parameters according to their specific needs and quickly obtain analysis results that meet their needs. The system will immediately feedback these results to users, ensuring that users can quickly obtain the required information and support more efficient decision-making and analysis processes. This process not only improves the user experience, but also enhances the interactivity and practicality of the system.

[0132] Figure 2 A schematic diagram of the structure of a trajectory data visualization system is provided in an embodiment of the present invention. Figure 2 As shown, the system includes:

[0133] The parsing module 21 is used to parse the timestamp information of the trajectory points according to the trajectory dataset selected by the user to obtain a dynamic timeline, and mark the key event nodes on the dynamic timeline to generate a timeline reflecting the characteristics of the trajectory changing over time;

[0134] A mapping module 22 is configured to map the geographic location information in the trajectory dataset based on the dynamic timeline and the geographic information system to obtain a trajectory map with the geographic location information, and to enhance the recognition of different speed sections and stop points using visual coding technology to generate an enhanced trajectory visualization map;

[0135] An adjustment module 23 is configured to utilize the enhanced trajectory visualization graph to respond to user interactions and dynamically adjust trajectory display parameters to obtain personalized display effects that meet different analysis requirements;

[0136] The generation module 24 is used to integrate multi-dimensional data analysis tools based on the personalized display effect, respond to users' instant queries and statistical analysis requests, calculate the trajectory density and average speed in a specific area, and generate and directly present the analysis results on the visualization interface.

[0137] Figure 2 The trajectory data visualization system can be executed Figure 1The implementation principle and technical effects of the trajectory data visualization method described in the illustrated embodiment will not be elaborated here. The specific manner in which each module and unit performs operations in the trajectory data visualization system in the above embodiment has been described in detail in the embodiment of the method and will not be elaborated here.

[0138] In one possible design, Figure 2 A trajectory data visualization system of the illustrated embodiment may be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0139] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0140] The processing component 32 is used to parse the timestamp information of the trajectory points according to the trajectory dataset selected by the user to obtain a dynamic timeline, and mark key event nodes on the dynamic timeline to generate a timeline reflecting the characteristics of the trajectory changing over time;

[0141] Based on the dynamic timeline and geographic information system, the geographic location information in the trajectory dataset is mapped to obtain a trajectory map with geographic location information, and visual coding technology is used to enhance the recognition of different speed sections and stop points to generate an enhanced trajectory visualization map;

[0142] By utilizing the enhanced trajectory visualization graph, the user's interactive operations are responded to and trajectory display parameters are dynamically adjusted to obtain personalized display effects that meet different analysis needs;

[0143] Based on the personalized display effect, multi-dimensional data analysis tools are integrated to respond to users' instant queries and statistical analysis requests, calculate the trajectory density and average speed in a specific area, and generate and directly present the analysis results on the visual interface.

[0144] The processing component 32 may include one or more processors to execute computer instructions to perform all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0145] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0146] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0147] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0148] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0149] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0150] The embodiment of the present invention further provides a computer storage medium storing a computer program, which can achieve the above-mentioned Figure 1 A trajectory data visualization method according to the illustrated embodiment.

[0151] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0152] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0153] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A trajectory data visualization method, characterized in that: include: According to the trajectory dataset selected by the user, the timestamp information of the trajectory points is parsed and processed to obtain a dynamic timeline, and key event nodes are marked on the dynamic timeline to generate a timeline reflecting the characteristics of the trajectory changing over time; Based on the dynamic timeline and geographic information system, the geographic location information in the trajectory dataset is mapped to obtain a trajectory map with geographic location information, and visual coding technology is used to enhance the recognition of different speed sections and stop points to generate an enhanced trajectory visualization map; By utilizing the enhanced trajectory visualization graph, the user's interactive operations are responded to and trajectory display parameters are dynamically adjusted to obtain personalized display effects that meet different analysis needs; Based on the personalized display effect, a multi-dimensional data analysis tool is integrated to respond to users' instant queries and statistical analysis requests, calculate the trajectory density and average speed in a specific area, and generate and present the analysis results directly on the visual interface; Based on the dynamic timeline and geographic information system, the geographic location information in the trajectory dataset is mapped to obtain a trajectory map with geographic location information. Visual coding technology is used to enhance the recognition of different speed sections and stop points to generate an enhanced trajectory visualization map, including: The geographic location information in the trajectory dataset is mapped using a dynamic timeline and geographic information system to obtain a preliminary trajectory map with precise geographic location information; Performing context-aware optimization on the preliminary trajectory map according to topographic features and environmental factors to generate an optimized trajectory map with environmental adaptability; Introducing a machine learning algorithm to identify and classify characteristic patterns of different speed sections and stop points, establishing an intelligent identification rule library, and using the intelligent identification rule library to analyze the optimized trajectory map to generate a characteristic trajectory map after intelligent identification; According to the user's historical interaction behavior and preference settings, a personalized visual encoding scheme is customized for the characteristic trajectory graph, and an enhanced trajectory visualization graph is obtained by adjusting visual elements.

2. The method according to claim 1, characterized in that The preliminary trajectory map is optimized with context awareness based on topographic features and environmental factors to generate an optimized trajectory map with environmental adaptability, including: Use high-resolution geographic information system data and remote sensing images to analyze and process the topographic features of the track points in the preliminary track map to generate track point data with detailed topographic information; Based on the trajectory point data with detailed terrain information, multi-dimensional context-aware processing is performed on the preliminary trajectory map in combination with environmental factors to obtain an intermediate trajectory map after calculating the environmental impact; Using a physical simulation algorithm, based on the terrain information and environmental factors of each trajectory point in the intermediate trajectory map after the influence of the computing environment, the behavior pattern of the object's movement under different terrain conditions is simulated to generate an optimized trajectory map; According to the specific application scenario requirements of the user, based on the optimized trajectory map, a customized optimization strategy is designed, and at the same time, the optimized trajectory map is adapted to the application field using the optimization strategy to obtain an application scenario optimized trajectory map; By integrating virtual reality technology and combining the optimized trajectory map of the application scenario with a three-dimensional geographic model, an immersive visualization experience is created to generate an optimized trajectory map with environmental adaptability.

3. The method according to claim 1, characterized in that A machine learning algorithm is introduced to identify and classify the characteristic patterns of different speed sections and stop points, and an intelligent identification rule base is established. The optimized trajectory map is analyzed using the intelligent identification rule base to generate a characteristic trajectory map after intelligent identification, including: Use a deep learning framework to train the trajectory dataset to build a multi-layer neural network model; Utilizing the multi-layer neural network model to perform real-time analysis on the multi-dimensional features in the optimization trajectory graph to generate feature classification results; Dynamically updating the intelligent identification rule base according to the characteristic classification result to obtain an updated intelligent identification rule base; The updated intelligent identification rule base is used to re-evaluate and mark the speed sections and stop points of the optimized trajectory diagram to generate a characteristic trajectory diagram with intelligent identification features.

4. The method according to claim 3, characterized in that The multi-layer neural network model is used to perform real-time analysis on the multi-dimensional features in the optimization trajectory graph to generate feature classification results, including: Using a deep reinforcement learning framework, combined with historical behavior patterns and environmental feedback from trajectory datasets, we design and train intelligent agents with autonomous learning capabilities. Based on the intelligent agent, multimodal fusion technology is used to integrate and process data from different sensors and external information sources to form a multimodal data stream and obtain context information; Implementing context-aware strategy optimization based on the context information, calculating the changing trends and background context within a preset time, and generating a context-optimized intelligent agent; The context-optimized intelligent agent is used to perform real-time perception and interactive exploration of the multi-dimensional features in the optimization trajectory graph. Through a continuous trial-and-error learning process, the decision path that is most conducive to feature classification is identified, generating a feature classification result with high adaptability and accuracy.

5. The method according to claim 1, wherein The enhanced trajectory visualization diagram responds to user interactions and dynamically adjusts trajectory display parameters to obtain personalized display effects that meet different analysis needs, including: The trajectory display parameters are processed in real time by the user's interactive operations on the interface to obtain an interactive interface with instant feedback; Based on the instant feedback interactive interface, and in combination with user interaction behavior and historical preference settings, an intelligent recommendation algorithm is used to predict and process preset analysis needs to generate intelligent preset display effects; Utilize natural language processing technology to parse user text or voice commands, perform semantic conversion processing on unstructured user intentions, generate customized display parameter adjustment commands that meet the user's description based on the intelligent preset display effect, and update them to the interactive interface to obtain an optimized interactive interface; Combined with virtual reality technology to create a three-dimensional interactive environment, based on the user's interaction with the trajectory data through gestures or eye tracking, the trajectory display parameters are processed for an immersive experience based on the optimized interactive interface to generate personalized display effects that meet different analysis needs; An adaptive learning mechanism is introduced to continuously optimize the intelligent recommendation algorithm and semantic parsing model based on each user interaction operation and its resulting results. Based on the personalized display effect in the previous step, it ensures that the system can continuously improve its ability to understand and respond to user needs over time, achieving a continuously evolving personalized display effect.

6. The method according to claim 1, characterized in that Based on the personalized display effect, multi-dimensional data analysis tools are integrated to respond to users' instant queries and statistical analysis requests, calculate the trajectory density and average speed in a specific area, and generate and directly present the analysis results on the visual interface, including: Using the trajectory segments selected by the user in the personalized display effect and combining them with the key event nodes on the timeline, the trajectory data within a specific time period is filtered to obtain the trajectory segments within the target time period; Based on the trajectory segments within the target time period, the study area in the geographic information system is finely segmented using spatial gridding technology. According to the distribution of trajectory points, the trajectory density within each grid is calculated to generate a high-resolution trajectory density map, which is then integrated into the existing personalized display effect. According to the trajectory density map, a dynamic window algorithm is used to track the speed changes of the trajectory segments in real time, the instantaneous speed at different positions is calculated, and the average speed in a specific area is calculated by a cumulative statistical method to obtain accurate speed distribution information, which is updated to the existing display interface to obtain an updated display interface; According to the user's instant query and statistical analysis requests, combined with customized analysis conditions, based on the trajectory density map and speed distribution information in the updated display interface, customized query processing is performed using the interactive query interface to generate analysis results that meet the user's needs and provide real-time feedback to the user.

7. A trajectory data visualization system, characterized in that: include: A parsing module is used to parse the timestamp information of the trajectory points according to the trajectory dataset selected by the user to obtain a dynamic timeline, and mark key event nodes on the dynamic timeline to generate a timeline reflecting the characteristics of the trajectory changing over time; a mapping module for mapping the geographic location information in the trajectory dataset based on the dynamic timeline and the geographic information system to obtain a trajectory map with geographic location information, and using visual coding technology to enhance the recognition of different speed sections and stop points to generate an enhanced trajectory visualization map; An adjustment module is used to respond to user interactions using the enhanced trajectory visualization graph and dynamically adjust trajectory display parameters to obtain personalized display effects that meet different analysis requirements; A generation module, which integrates multi-dimensional data analysis tools based on the personalized display effect, responds to users' instant queries and statistical analysis requests, calculates the trajectory density and average speed in a specific area, and generates and presents the analysis results directly on the visualization interface; Based on the dynamic timeline and geographic information system, the geographic location information in the trajectory dataset is mapped to obtain a trajectory map with geographic location information. Visual coding technology is used to enhance the recognition of different speed sections and stop points to generate an enhanced trajectory visualization map, including: The geographic location information in the trajectory dataset is mapped using a dynamic timeline and geographic information system to obtain a preliminary trajectory map with precise geographic location information; Performing context-aware optimization on the preliminary trajectory map according to topographic features and environmental factors to generate an optimized trajectory map with environmental adaptability; Introducing a machine learning algorithm to identify and classify characteristic patterns of different speed sections and stop points, establishing an intelligent identification rule library, and using the intelligent identification rule library to analyze the optimized trajectory map to generate a characteristic trajectory map after intelligent identification; According to the user's historical interaction behavior and preference settings, a personalized visual encoding scheme is customized for the characteristic trajectory graph, and an enhanced trajectory visualization graph is obtained by adjusting visual elements.

8. A computing device, characterized in that The method comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a trajectory data visualization method according to any one of claims 1 to 6.

9. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a trajectory data visualization method according to any one of claims 1 to 6 is implemented.

Images