Track data visualization method and system

Abstract

The invention provides a track data visualization method and system, and the method comprises the steps: carrying out the analysis of the timestamp information of a track point according to a track data set selected by a user, obtaining a dynamic time axis, marking a key event node on the dynamic time axis, and generating a timeline, the method comprises the following steps: acquiring a track data set, mapping geographic position information in the track data set, enhancing the identification degree of different speed sections and staying points by utilizing a visual coding technology, generating an enhanced track visualization graph, responding to interactive operation of a user, dynamically adjusting track display parameters, and obtaining a personalized display effect. The integrated multi-dimensional data analysis tool responds to the instant query and statistical analysis request of the user, and the analysis result is generated and directly presented on the visual interface, so that the data analysis efficiency and accuracy are improved, the user experience is enhanced, and the user experience is improved. Even a non-professional user can easily extract useful information from a large amount 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. These data not only contain time and location information, but may also include other sensor information, which can be used in traffic management, logistics distribution optimization, personal travel analysis, etc.

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

[0004] The embodiments of the present invention provide a trajectory data visualization method and system to solve the problems in the prior art such as lack of effective time dimension expression, inaccurate mapping of geographic location information, insufficient visual coding, difficulty in identification and easy visual confusion, poor interactivity, limited interactive functions, low data analysis integration, and few directly integrated 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, the real-time data streams containing structured data and unstructured data;

[0007] 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;

[0008] Based on the dynamic timeline and geographic information system, the geographic location information in the trajectory data set 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 using the enhanced trajectory visualization diagram, the interactive operation of the user is responded to, and the trajectory display parameters are dynamically adjusted to obtain a personalized display effect adapted to different analysis requirements;

[0010] 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 a visual interface.

[0011] Optionally, based on the dynamic timeline and the geographic information system, the geographic location information in the trajectory data set 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 the 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 diagram to generate a characteristic trajectory diagram 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, the preliminary trajectory map is context-aware optimized according to terrain 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 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 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 application scenario optimized trajectory map 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, an intelligent identification rule base is established, and the optimized trajectory diagram is analyzed using the intelligent identification rule base to generate a characteristic trajectory diagram after intelligent identification, including:

[0023] The trajectory dataset is trained using a deep learning framework to build a multi-layer neural network model;

[0024] 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;

[0025] According to the characteristic classification result, the intelligent identification rule base is dynamically updated 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 a feature classification result, including:

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

[0029] Based on the intelligent agent, the data from different sensors and external information sources are integrated and processed using multimodal fusion technology to form a multimodal data stream and obtain context information;

[0030] Implementing situation-aware strategy optimization processing based on the context information, calculating the change trend and background situation within a preset time, and generating a situation-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 interaction operations and dynamically adjust trajectory display parameters to obtain personalized display effects that meet different analysis requirements, including:

[0033] The trajectory display parameters are processed in real time by using 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, generating an intelligent preset display effect;

[0035] Utilize natural language processing technology to parse the user's text or voice instructions, 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] Combine virtual reality technology to create a three-dimensional interactive environment, and perform immersive experience processing on trajectory display parameters based on the optimized interactive interface according to the user's interaction with trajectory data through gestures or line of sight tracking, so as 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. According to 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, and achieve 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 the user's instant query and statistical analysis request, calculate the trajectory density and average speed in a specific area, generate and directly present the analysis results on the visualization interface, including:

[0039] By 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 spatial grid division technology is used to perform fine-grained segmentation of the research area in the geographic information system, and the trajectory density in each grid is calculated according to the distribution of trajectory points 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 change of the trajectory segment 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, and the information is updated to the existing display interface to obtain an updated display interface;

[0042] According to 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.

[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 data set selected by the user, 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 is used to map the geographic location information in the trajectory data set based on the dynamic timeline and the geographic information system to obtain a trajectory map with 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;

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

[0047] The 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, 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, including 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, according to the trajectory data set selected by the user, the timestamp information of the trajectory point 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 the geographic information system, the geographic location information in the trajectory data set is mapped and processed to obtain a trajectory map with geographic location information, and the 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, respond to the user's interactive operation, dynamically adjust the trajectory display parameters, and obtain a personalized display effect that adapts to different analysis needs; based on the personalized display effect, integrate a multi-dimensional data analysis tool, 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. The technical solution provided by the present invention parses the timestamp information of the trajectory point, constructs a dynamic timeline, and marks key event nodes on the timeline to generate a timeline reflecting the characteristics of the trajectory changing over time. This method allows users to clearly see the activity patterns or event sequences within a specific time period, solving the problem of unclear time features in existing methods. Based on the 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. The use of GIS technology ensures the authenticity and reliability of the trajectory map, avoiding the location 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, by performing context-aware optimization on the preliminary trajectory map according to the topographic features and environmental factors, an optimized trajectory map with environmental adaptability is generated. 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 the rule base to analyze the optimized trajectory map to generate a characteristic trajectory map after intelligent identification. This method not only improves the accuracy of trajectory analysis, but also automatically discovers and marks meaningful behavior patterns, providing strong support for subsequent in-depth analysis. Finally, according to the user's historical interaction behavior and preference settings, the characteristic trajectory map is customized with a personalized visual encoding scheme, and the enhanced trajectory visualization map is obtained by adjusting the visual elements. This method ensures that each user's visualization experience is tailored to the specific needs and preferences of different users to the greatest extent possible.

[0052] These and other aspects of the present invention will become more 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 briefly introduces 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 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 by 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 executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish 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., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are 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 of each trajectory point in the trajectory dataset selected by the user - that is, the data recording the exact time - is parsed and processed to extract the accurate time series. With these timestamps, a dynamic timeline is constructed, which can display and adjust the data according to the time change, allowing to view the activity pattern or the order of events in a specific time period. Key event nodes, such as starting points, end points or stop points, are marked on the dynamic timeline to generate a timeline reflecting the characteristics of the trajectory over time, so that users can intuitively understand the time distribution of the trajectory.

[0063] Step 102: Based on the dynamic timeline and the geographic information system, the geographic location information in the trajectory data set is mapped to obtain a trajectory map with geographic location information, and different speed sections and stop points are enhanced in recognition by using visual coding technology 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 other visually process different speed sections (such as fast movement, slow movement) and stop points to enhance recognition. Finally, an enhanced trajectory visualization map is generated, allowing users to more clearly distinguish different parts of the trajectory and their characteristics.

[0065] Step 103: using the enhanced trajectory visualization graph, responding to the user's interactive operation, dynamically adjusting the trajectory display parameters, and obtaining a personalized display effect that meets different analysis requirements;

[0066] In this step, the enhanced trajectory visualization is used to respond to user interactions, such as clicks, drags, or zooms, and the trajectory display parameters—factors that affect how the trajectory is presented, including line width, color, transparency, etc.—are dynamically adjusted. After each interaction, the trajectory visualization is updated instantly to ensure that the adjustment effect is immediately visible. In addition, based on the user's historical interaction behavior and preference settings, a variety of personalized display options are provided, such as filtering trajectories by time, focusing on specific areas, etc., so as to obtain personalized display effects that meet 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, generate and directly present the analysis results on the visualization interface;

[0068] In this step, based on the personalized display effect, multi-dimensional data analysis tools are integrated. These tools provide multiple ways to explore and understand data, including statistical analysis, trend prediction and other functions. For users' instant queries and statistical analysis requests, that is, questions raised by users about data or information that needs to be calculated, prompt responses are required to directly receive and quickly process these requests. The trajectory density and average speed in a specific area refer to the number of trajectories per unit area and the average travel speed of these trajectories within the selected geographic range. The calculations are performed and the results are directly presented on the visual interface, making it convenient for users to view and interpret the analysis results.

[0069] Based on this, the present invention provides a specific embodiment, wherein the step 102 maps the geographic location information in the trajectory data set based on the dynamic timeline and the geographic information system to obtain a trajectory map with geographic location information, and uses visual coding technology to enhance the recognition of different speed sections and stop points to generate an enhanced trajectory visualization map, which specifically includes the following steps:

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

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

[0072] Step 202: Context-aware optimization is performed on the preliminary trajectory map according to terrain 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 means adjusting the representation of the trajectory to better adapt to the actual environmental conditions, such as changing the color or width of the path to reflect visibility or traffic flow in different environments. Finally, 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 diagram to generate a characteristic trajectory diagram after intelligent identification;

[0075] In this step, machine learning algorithms are used, a type of technology that can automatically analyze patterns from data and use these patterns to predict unknown data, to identify and classify different speed sections (fast movement, slow movement, stationary, etc.) and stop points (locations where the stop time is longer). 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 features. This rule base is then applied to conduct an in-depth analysis of the optimized trajectory map to generate a characteristic trajectory map after intelligent identification, providing a more detailed explanation of the trajectory behavior.

[0076] Step 204: performing a personalized visual encoding scheme customization process on the characteristic trajectory graph according to 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 are used, a type of technology that can automatically analyze patterns from data and use these patterns to predict unknown data, to identify and classify different speed sections (fast movement, slow movement, stationary, etc.) and stop points (locations where the stop time is longer). 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 features. This rule base is then applied to conduct an in-depth analysis of the optimized trajectory map to generate a characteristic trajectory map after intelligent identification, providing a more detailed explanation of the 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 according to terrain 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 images, detailed terrain information around each track point can be accurately obtained. This includes but is not limited to natural environmental factors such as altitude, slope, and vegetation cover type. Through this analysis and processing, track point data with detailed terrain information can be generated, providing a basis 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 impact;

[0082] In this step, the preliminary trajectory map is processed with multi-dimensional context awareness using trajectory point data with detailed terrain information and considering various environmental factors such as weather conditions, lighting conditions, time period, etc. The purpose is to evaluate how these factors affect the activities or movement patterns on the trajectory, and adjust the trajectory map accordingly to obtain an intermediate trajectory map after calculating the environmental impact. This process helps to improve the adaptability and accuracy of the trajectory map 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 map after the calculation of the environmental influence, simulate the behavior pattern of the object movement under different terrain conditions to generate an optimized trajectory map;

[0084] In this step, physical simulation algorithms are used to simulate the movement behavior patterns of objects (such as vehicles and pedestrians) under different terrain conditions and environmental factors. This may involve simulating the change in speed of objects when driving on a slope, the change 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. The present invention introduces a physical simulation algorithm to make the generated trajectory not only look more natural, but also more consistent with 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 position of the trajectory point at time t after the final optimization. This is the result calculated by the formula, which combines the initial trajectory point position and other influencing factors. In order to ensure that the generated trajectory is closer to the actual situation, the influence of terrain and environmental factors on the movement of the object is considered, making the visualization result not only beautiful but also more accurate; P intermediate(t) represents the position of the trajectory point at time t in the intermediate trajectory map after preliminary calculation 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 made 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 determined by experiments or based on user input. The purpose of introducing 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 deviations; Δ terrain (t) represents the terrain change vector at time t, reflecting the influence of terrain features (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 features are one of the important factors affecting the movement of objects, especially for ground transportation such as vehicles and pedestrians. Accurately reflecting these features can significantly improve the authenticity 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, which may include factors such as wind speed, temperature, humidity, etc., which may affect the movement of objects. External information sources such as meteorological data and sensor data, environmental factors such as weather conditions and light intensity will also affect the movement of objects. 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 ice on the road, affecting the vehicle's driving speed;

[0089] The overall design of this formula aims to create a trajectory optimization model that can adapt to complex real-world conditions. By combining the influence of topographic features and environmental factors, it can not only improve the accuracy and authenticity of trajectory data visualization, but also provide users with more practical information. This method is particularly suitable for application scenarios that require precise trajectory analysis, such as traffic management, logistics distribution, personal travel planning, etc. At the same time, by introducing the adjustment factor α, the formula also has a certain degree of flexibility and can adapt to different application scenarios and technical requirements.

[0090] Step 304: according to the specific application scenario requirements of the user, based on the optimized trajectory map, a customized optimization strategy is designed, and the optimized trajectory map is adapted to the application field using the optimization strategy to obtain an application scenario optimized trajectory map;

[0091] In this step, customized optimization strategies are designed according to the specific application scenario requirements of different users, such as logistics distribution, travel planning, emergency rescue, etc. This may involve route selection, speed limit, stop point setting, etc. 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, combined with virtual reality (VR) technology, and based on a three-dimensional geographic model, an immersive visualization experience is created. Such a system allows users to "walk" through their tracks from a first-person perspective, view the details of the surrounding environment, and even simulate changes in the landscape under different time and weather conditions. This not only enhances the user experience, but also helps professionals understand and analyze complex trajectory data more intuitively, thereby making better decisions.

[0094] Based on this, the present invention provides a specific embodiment, wherein the 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 diagram to generate a characteristic trajectory diagram after intelligent identification, which specifically includes the following steps:

[0095] Step 401: Train the trajectory data set using a deep learning framework to build a multi-layer neural network model;

[0096] In this step, a deep learning framework suitable for processing spatiotemporal series data (such as TensorFlow, PyTorch, etc.) is selected and trained with a large number of annotated trajectory datasets. These datasets contain multi-dimensional features such as location, timestamp, speed, direction, etc. Through the 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 behaviors (walking, running, driving), abnormal activities, stop points, etc. During the training process, the model parameters are also optimized to improve the 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 map data for real-time analysis. The model extracts multi-dimensional features in the trajectory, including but not limited to position changes, speed fluctuations, direction changes, etc., and generates characteristic classification results based on these features. The classification results can be different attributes or behavior patterns of the trajectory, such as fast-moving sections, slow-moving sections, stop points, etc. The key to this step is to ensure that the model can run efficiently in the actual application environment and provide instant 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 behavior patterns or features. As new data is added and the model is learned, 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 behavior patterns and environmental changes, thereby improving the accuracy of subsequent analysis.

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

[0102] In this step, the updated intelligent identification rule base is used 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 stay) and other significant features are identified and marked through the rules in the intelligent identification 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 the trajectory data. This characteristic trajectory map helps to improve 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, which specifically includes the following steps:

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

[0105] In this step, the Deep Reinforcement Learning (DRL) framework is used to design and train an intelligent agent with autonomous learning capabilities by integrating historical behavior patterns recorded in the trajectory dataset (such as past movement paths, stop points, etc.) and feedback obtained from the environment (such as traffic conditions, weather changes, etc.). This intelligent agent can automatically adjust its internal parameters according to different trajectory characteristics to adapt to new data analysis needs, and continuously optimize its decision-making strategy through interaction with the environment. The DRL framework allows the intelligent agent to learn the best behavior strategy through trial and error without explicit programming instructions.

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

[0107] In this step, based on the trained intelligent agent, multimodal fusion technology is applied to integrate and process data from different sensors (such as GPS, accelerometer, gyroscope, etc.) and external information sources (such as weather forecasts, traffic flow reports, etc.). These data are fused into a multimodal data stream to provide comprehensive and accurate contextual information. Contextual information includes not only location and timestamp, but also the state of the surrounding environment and other relevant factors, enabling the intelligent agent to make more intelligent decisions in a richer context.

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

[0109] In this step, the context-aware strategy optimization process is implemented based on the context information obtained in the previous step. This involves analyzing the changing trends within the preset time period (such as the changes in traffic flow at different times of the day) and the background context (such as activity patterns during holidays and special events). By comprehensively considering these factors, the decision logic of the intelligent agent is adjusted so that it can perform optimally in different situations. The resulting intelligent agent can not only respond to the current situation, but also foresee possible changes in the future, so as to prepare in advance.

[0110] Step 504: using the context-optimized intelligent agent to perform real-time perception and interactive exploration processing on the multi-dimensional features in the optimization trajectory graph, identifying the decision path that is most conducive to feature classification through a continuous trial-and-error learning process, and generating a feature classification result with high adaptability and accuracy;

[0111] In this step, the intelligent agent that has been optimized for the situation is used to perform real-time perception and interactive exploration of the multi-dimensional features in the optimized trajectory graph (such as speed changes, dwell time, path selection, etc.). Through a continuous trial-and-error learning process, the intelligent agent tries different decision paths and identifies the solution that is most conducive to feature classification. This dynamic learning mechanism ensures that the intelligent agent can quickly adapt to new 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, wherein step 103 uses the enhanced trajectory visualization graph to respond to the user's interactive operation, dynamically adjusts the trajectory display parameters, and obtains a personalized display effect that meets different analysis requirements, specifically including 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, and obtaining an interactive interface with instant feedback;

[0114] In this step, by capturing the user's interactive operations on the interface (such as clicking, dragging, zooming, etc.), the system can immediately respond to these operations 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 operations, generating an interactive interface with instant feedback, and enhancing the user's 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, the preset analysis requirements are predicted and processed using an intelligent recommendation algorithm to generate an intelligent preset display effect;

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

[0117] Step 603: parse the user's text or voice instructions using natural language processing technology, perform semantic conversion processing 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 instructions and convert the unstructured user intentions into specific display parameter adjustment commands. This process involves semantic understanding and conversion to ensure 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, thereby generating an optimized interactive interface so that the user's intentions can be efficiently realized.

[0119] Step 604: Create a three-dimensional interactive environment in combination with virtual reality technology, and perform immersive experience processing on the trajectory display parameters based on the optimized interactive interface according to the user's interaction with the trajectory data through gestures or line of sight tracking, so as to generate personalized display effects that meet different analysis requirements;

[0120] In this step, virtual reality (VR) or augmented reality (AR) technology is 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 the trajectory display parameters according to the user's interaction method to provide an immersive visualization experience. This method not only enhances the realism and intuitiveness of the user experience, but also automatically generates personalized display effects according to different analysis needs, supporting more in-depth 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 operation and its generated results. According to the personalized display effect in the previous step, ensure that the system can continuously improve its ability to understand and respond to user needs over time, and achieve 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 results. As the frequency of user use increases, the system can gradually improve its ability to understand and respond to user needs, ensuring that each update can better meet the personalized needs of users. This method realizes the self-evolution of the system, ensuring that users are provided with increasingly accurate and personalized display effects in the long term, and improving the overall service quality and user experience.

[0123] Based on this, the present invention provides a specific embodiment, wherein step 104 integrates a multi-dimensional data analysis tool based on the personalized display effect, responds to the user's instant query and statistical analysis request, calculates the trajectory density and average speed in a specific area, generates and directly presents the analysis results on a visualization interface, and specifically includes the following steps:

[0124] Step 701: using the trajectory segment selected by the user in the personalized display effect and combining 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, combined with the key event nodes on the dynamic timeline (such as starting point, end point, stop point, etc.), the trajectory data within a specific time period is accurately screened. This process aims to extract trajectory segments within the time period that meets the user's interest, ensuring that subsequent analysis and visualization can focus on the specific time period of the user's concern, 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 grid division technology, and the trajectory density in each grid is calculated according to the distribution of trajectory points to generate a high-resolution trajectory density map, and the trajectory density map is 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 technology based on the trajectory fragments within the target time period. The trajectory density in each grid is calculated by analyzing the number and distribution of trajectory points in each grid. The result of this step is a high-resolution trajectory density map that intuitively shows the density of trajectories in different areas. Subsequently, this trajectory density map is integrated into the existing personalized display effect, providing users with a visualization interface with detailed density information, enhancing the understanding of the spatial distribution of trajectory data.

[0128] Step 703: According to the trajectory density map, a dynamic window algorithm is used to track the speed change of the trajectory segment in real time, the instantaneous speed of 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, and the information is 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 the speed changes of the trajectory segments in real time. The dynamic window algorithm can continuously monitor and record the instantaneous speed during movement, ensuring that the speed changes can be accurately captured even in complex and changing environments. Through cumulative statistical methods, the average speed in a specific area can be further calculated to obtain accurate speed distribution information. This information is updated instantly to the existing display interface, allowing users to immediately see the trends and patterns of speed changes, providing a more comprehensive and dynamic data view, that is, 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 the user's instant query and statistical analysis request, combined with the user's customized analysis conditions (such as time range, geographic location, speed threshold, etc.), the trajectory density map and speed distribution information in the updated display interface are customized through the interactive query interface. This method 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 feed back these results to the user to ensure that the user 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 a trajectory data visualization system is provided for 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 data set selected by the user, obtain a dynamic time axis, and mark key event nodes on the dynamic time axis to generate a time line reflecting the characteristics of the trajectory changing over time;

[0134] A mapping module 22 is used to map the geographic location information in the trajectory data set based on the dynamic time axis and the geographic information system to obtain a trajectory map with 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 used to respond to user interaction operations using the enhanced trajectory visualization diagram 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, 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 effect of the trajectory data visualization method described in the embodiment are not described in detail. The specific way 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 described in detail 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 data set selected by the user, 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 data set 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 using the enhanced trajectory visualization diagram, the interactive operation of the user is responded to, and the trajectory display parameters are dynamically adjusted to obtain a personalized display effect adapted to different analysis requirements;

[0143] 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 a visual interface.

[0144] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by 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 storage 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, the 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 may 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 can 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, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0153] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for 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 some 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 embodiments of the present invention.

Claims

1. A trajectory data visualization method, characterized in that: include: 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; Based on the dynamic timeline and geographic information system, the geographic location information in the trajectory data set 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 using the enhanced trajectory visualization diagram, the interactive operation of the user is responded to, and the trajectory display parameters are dynamically adjusted to obtain a personalized display effect adapted to different analysis requirements; 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 a visual interface.

2. The method according to claim 1, characterized in that: Based on the dynamic timeline and geographic information system, the geographic location information in the trajectory data set is mapped to obtain a trajectory map with geographic location information, and the 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 the 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 diagram to generate a characteristic trajectory diagram 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.

3. The method according to claim 2, characterized in that The preliminary trajectory map is context-awarely optimized according to terrain 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 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 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 application scenario optimized trajectory map with a three-dimensional geographic model, an immersive visualization experience is created to generate an optimized trajectory map with environmental adaptability.

4. The method according to claim 2, 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: The trajectory dataset is trained using a deep learning framework to build a multi-layer neural network model; 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; According to the characteristic classification result, the intelligent identification rule base is dynamically updated 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.

5. The method according to claim 4, 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 in trajectory datasets, we design and train intelligent agents with autonomous learning capabilities. Based on the intelligent agent, the 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 situation-aware strategy optimization processing based on the context information, calculating the change trend and background situation within a preset time, and generating a situation-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.

6. The method according to claim 1, characterized in that By using the enhanced trajectory visualization diagram, the user's interactive operation is responded to and the trajectory display parameters are dynamically adjusted to obtain personalized display effects that meet different analysis requirements, including: The trajectory display parameters are processed in real time by using the user's interactive operations on the interface to obtain an interactive interface with instant feedback; Based on the instant feedback interactive interface, combined 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 the user's text or voice instructions, 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; Combine virtual reality technology to create a three-dimensional interactive environment, and perform immersive experience processing on trajectory display parameters based on the optimized interactive interface according to the user's interaction with trajectory data through gestures or line of sight tracking, so as 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. According to 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, and achieve a continuously evolving personalized display effect.

7. The method according to claim 1, characterized in that 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, generate and directly present the analysis results on the visualization interface, including: By 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 spatial grid division technology is used to perform fine-grained segmentation of the research area in the geographic information system, and the trajectory density in each grid is calculated according to the distribution of trajectory points 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 change of the trajectory segment 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, and the information is updated to the existing display interface to obtain an updated display interface; According to 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.

8. 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 data set selected by the user, 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 is used to map the geographic location information in the trajectory data set based on the dynamic timeline and the geographic information system to obtain a trajectory map with 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; An adjustment module, used to respond to user interaction operations using the enhanced trajectory visualization graph, dynamically adjust trajectory display parameters, and obtain personalized display effects that meet different analysis requirements; The 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, generate and directly present the analysis results on the visualization interface.

9. A computing device, characterized in that It 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 as described in any one of claims 1 to 7.

10. 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 7 is implemented.

Images