AI position moving system for dynamically generating route and formation

Abstract

The invention discloses an AI position moving system, method and device for dynamically generating a route and a formation and a storage medium. The system comprises a music feature analysis module used for extracting rhythm, pitch, intensity and emotion features of music; the path generation engine establishes a mapping relationship between music and spatial motion parameters by means of a pre-training neural network algorithm, and generates a multi-target collaborative motion path and a formation transformation sequence; the three-dimensional visualization platform dynamically renders path and formation changes; the user interaction interface supports operations such as file uploading, condition setting and scheme editing. By means of the system, the problems that traditional multi-target position movement planning is poor in flexibility and low in manual arrangement efficiency can be solved, and intelligence and automation of movement control are achieved. The method can be applied to scenes such as stage performance, unmanned aerial vehicle formation flight and virtual reality, related equipment and a storage medium guarantee operation and implementation of the technical scheme, and the efficiency and creativity of multi-target position movement control are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence and automation control, and particularly to an intelligent position movement system integrating music feature analysis, group path planning, and three-dimensional dynamic rendering, which is applicable to scenarios such as stage performance choreography, unmanned aerial vehicle formation control, and virtual character animation generation. Background Art

[0002] In the prior art, the group movement path and formation choreography mainly rely on manual design or fixed program control, with the following defects: High cost: Traditional methods for generating routes and formations often rely on manual pre-design and choreography, which are time-consuming and laborious. Poor dynamic adaptability: Traditional systems cannot adjust the path plan in real time according to dynamic elements such as music rhythm and emotion. Low collaboration efficiency: Optimizing multi-target movement trajectories requires manually setting collision avoidance rules and physical constraints, which is time-consuming and error-prone. Summary of the Invention

[0003] Object of the Invention This application aims to solve the problems of poor flexibility, insufficient adaptability in multi-target position movement planning, and low efficiency of manual choreography in the prior art. By constructing an AI position movement system including a music feature analysis module, a path generation engine, a three-dimensional visualization platform, and a user interface, a mapping relationship between music features and spatial movement parameters is established using a pre-trained neural network algorithm. In this way, the system can automatically generate multi-target collaborative movement paths and formation transformation sequences according to the features of the input music. Users can also flexibly set and adjust through the interaction interface, and the three-dimensional visualization platform can intuitively display the generated paths and formations. Thus, the technical effects of improving the efficiency and creativity of multi-target position movement control, enhancing the flexibility and adaptability of the system, and reducing the workload of manual choreography are achieved.

[0004] Technical Solution This system includes the following core modules and their interaction relationships.

[0005] Music Feature Analysis Module: This module is used to extract the rhythm, pitch, intensity, and emotion features of the input music file. Specifically, a convolutional neural network is used to extract the music time-frequency map features, and an LSTM network is combined to identify the music emotion tendency, outputting the emotion intensity value and the beat phase matrix. The music feature database includes the spectral features of music elements, the timestamp of beat points, and emotion labels, where the emotion labels include four basic modes: exciting, soothing, sad, and cheerful.

[0006] Path generation engine: The mapping relationship between the music feature database and the spatial motion parameter library is established through a pre-trained neural network algorithm, and based on this mapping relationship, a multi-target collaborative motion path and formation transformation sequence are generated. The spatial motion parameter library includes the coordinate range, speed threshold, formation topology and obstacle avoidance rules of the moving target. The path generation engine integrates the group kinematics model and generates the path through the following steps: dividing the movement phase according to the music beat; matching the formation transformation frequency based on the emotional intensity value; and calculating the optimal solution for the multi-target motion trajectory in combination with the constraints input by the user. The engine supports both offline batch processing and real-time streaming processing modes, and can be connected to third-party motion control systems through an API interface.

[0007] 3D visualization platform: used for dynamically rendering the generated paths and formation change process. Supports timeline-based path animation preview and pause / playback control; multi-view observation mode switching and motion trajectory heat map display; formation collision detection warning and path smoothness score feedback.

[0008] User interaction interface: supports music file upload, motion constraint setting, path plan editing and visual feedback interaction. Provides music file format compatibility detection and beat calibration tools; dynamic adjustment of the number of motion targets, initial positions and physical parameters; drag and drop editing of path keyframes and overlay application of formation templates; vector drawing tools for manually drawing paths, supporting Bezier curve editing; import / export function of custom formation template library; synchronous export interface of path generation plan and original music file. Beneficial Effects

[0009] The present invention realizes the generation of intelligent paths and formations based on music features, greatly improves the efficiency and creativity of multi-target position movement control, and reduces the workload of manual arrangement.

[0010] The system is highly flexible and adaptable, and can generate a variety of exercise plans in real time according to different music characteristics and user-set constraints to meet the needs of different scenarios.

[0011] The design of the 3D visualization platform and user interaction interface allows users to intuitively view and adjust the generated paths and formations, improving user experience and operational convenience.

[0012] The system has broad application prospects and can be expanded to many fields, such as the coordinated control of actors' movements and lighting changes in stage performances, the automatic generation and real-time correction of drone formation flight paths, and the group motion simulation of digital characters in virtual reality environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the accompanying drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0014] Figure 1 This is the system architecture diagram provided by the embodiments of the present invention.

[0015] Figure 2 This is the method step diagram provided by the embodiments of the present invention. Detailed implementation manners

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0017] An AI position movement system for dynamically generating routes and formations described in the embodiments of the present invention mainly consists of four key parts: a music feature analysis module, a path generation engine, a 3D visualization platform, and a user interaction interface, as Figure 1 shown.

[0018] The music feature analysis module described in the embodiments of the present invention. The core function of this module is to comprehensively and deeply extract features from the input music files, covering multiple dimensions such as rhythm, pitch, intensity, and emotion.

[0019] In the actual operation process, it innovatively adopts an advanced technical solution that combines a convolutional neural network (CNN) and a long short-term memory network (LSTM). With its powerful feature extraction ability, CNN can efficiently extract key spectral features and other information from the time-frequency diagram of music.

[0020] LSTM is excellent in processing time series data, can deeply identify the emotional tendency of music, and finally output a quantified emotional intensity value and a detailed beat phase matrix. These rich and accurate data provide a solid foundation for subsequent path planning and formation design, enabling the system to perform in-depth motion planning based on the inherent features of music.

[0021] The path generation engine described in the embodiments of the present invention. Through a pre-trained neural network algorithm, this engine cleverly builds a bridge between the music feature database and the spatial motion parameter database, establishing a close mapping relationship between the two.

[0022] The music feature database details key information such as the spectral features of music elements, precise beat timestamps, and diverse emotion tags; the spatial motion parameter library comprehensively covers important parameters such as the coordinate range of moving targets, reasonable speed thresholds, diverse formation topologies, and rigorous obstacle avoidance rules. Based on this stable mapping relationship, the engine further integrates a crowd kinematics model to generate multi-target collaborative motion paths and complex and variable formation transformation sequences. The specific implementation steps are as follows.

[0023] (1) According to the unique law of music beats, the entire music process is carefully divided into different motion stages. For example, for common 4 / 4 time music, it can be reasonably divided according to the strong and weak characteristics of the beats.

[0024] (2) According to the change of emotion intensity value, flexibly match the corresponding formation transformation frequency. When the music shows an exciting emotion, appropriately increase the formation transformation frequency and increase the motion speed to create a lively atmosphere. When the music is relatively soothing, adopt a gradual path transition method and maintain a symmetrical and stable formation to show a peaceful and harmonious feeling.

[0025] (3) Fully combine various constraint conditions input by the user in the interaction interface and use advanced optimization algorithms (such as particle swarm optimization algorithm) to accurately calculate the optimal solution of the multi-target motion trajectory, so as to ensure that the moving targets do not collide during the motion process and can perfectly fit the rhythm and emotion changes of the music, realizing safe, efficient and creative motion planning.

[0026] In the three-dimensional visualization platform described in the embodiment of the present invention, its main function is to vividly and intuitively perform dynamic rendering and display on the generated path and formation change process.

[0027] In practical applications, it provides users with a path animation preview and pause / playback control function based on the timeline. Users can easily and casually view the path and formation states at different times, just like operating a video player, which is convenient for detailed observation and analysis of the entire motion process.

[0028] At the same time, the platform also supports the switching of multi-view observation modes, including various practical views such as top view, side view, and follow view. Users can flexibly select different views according to their own needs and observation focuses to comprehensively evaluate the rationality and aesthetics of the path.

[0029] The platform can display a heat map of the motion trajectory. Through the intuitive change of color depth, it clearly shows the frequency of moving targets appearing in different regions, helping users quickly grasp the motion hot spots.

[0030] The platform also has a powerful formation collision detection and warning function. Once it detects a possible collision risk, it will immediately issue an alarm in a timely manner to remind the user to make adjustments. And it provides feedback on the path smoothness score, in an intuitive percentage form, so that the user can clearly understand the smoothness of the path, and thus have an accurate judgment on the quality of the generated path.

[0031] In the embodiment of the present invention, the user interaction interface module provides rich and convenient operation functions for the user, supporting core operations such as music file upload, motion constraint condition setting, path scheme editing, and visual feedback interaction.

[0032] In actual use, it is equipped with a music file format compatibility detection and beat calibration tool, which can automatically and quickly identify common music file formats, such as MP3, WAV, etc. For music files with inaccurate beats, the user can use the beat calibration tool to perform precise calibration through simple manual operations.

[0033] The interface is provided with a vector drawing tool for manually drawing paths, supporting the user to freely create personalized paths through ways such as Bezier curve editing to meet the creative needs of the user. The user can also easily and dynamically adjust the number of motion targets, initial positions, and physical parameters on the interface. For example, when setting up the formation flight of drones, key parameters such as the number of drones, take-off positions, and flight speeds can be conveniently adjusted.

[0034] The user can flexibly adjust the generated path manually through the function of dragging and editing the key frames of the path, or select a rich formation template preset by the system to overwrite the currently generated formation with one key, realizing rapid formation transformation and optimization.

[0035] In the music feature database described in the embodiment of the present invention, the spectral features can accurately reflect the energy distribution in a specific frequency range. For example, in a piece of exciting rock music, the energy distribution characteristics in the high-frequency part will show obvious peak changes, reflecting strong rhythms and rich instrument expressiveness.

[0036] The beat point timestamps are like the "time coordinates" of music, accurately recording the specific time points when each beat appears, which is crucial for subsequent precise matching with the motion phases to ensure perfect synchronization between the motion and the music rhythm.

[0037] The emotion tags can be further subdivided according to the style characteristics of the music. In addition to common types such as exciting and soothing, there may also be more detailed emotion classifications such as lively and sad, to more accurately describe the emotional connotations conveyed by the music.

[0038] In terms of the spatial motion parameter library, the coordinate range clearly defines the activity space boundaries of the motion targets.

[0039] In a stage performance scenario, the movement range of actors may be restricted to a specific area of the stage. The speed threshold reasonably limits the maximum and minimum movement speeds of moving targets to ensure the safety and rationality of movement. When drones fly in formation, a suitable speed threshold needs to be set according to their performance and flight environment.

[0040] The formation topology structure details the relative position relationships among moving targets. Common formations include circular, square, diamond, etc. Different topology structures can create different visual effects.

[0041] The obstacle avoidance rule is the key to ensuring movement safety. Through preset algorithms and sensor data, it ensures that moving targets can timely and accurately avoid obstacles during movement, avoiding the occurrence of collision accidents.

[0042] In the convolutional neural network (CNN) described in the embodiments of the present invention, when extracting the music time-frequency map features, through carefully designed multi-layer convolutional layers and pooling layers, the time-frequency map is processed step by step in depth.

[0043] In the first convolutional layer, some simple basic features are usually extracted, such as edge features in the music signal or basic frequency change patterns; as the number of network layers increases, subsequent convolutional layers can extract more complex and abstract features, which can more comprehensively and deeply reflect the internal structure and style characteristics of music.

[0044] When the LSTM network identifies the emotional tendency of music, it gives full play to its powerful analysis ability for time series data. It can deeply learn the changing laws of music features over time under different emotional patterns. For example, exciting music often has a more compact rhythm, more drastic pitch changes, and greater intensity. Through the time series analysis of these features, the LSTM network can accurately capture these changes, thereby accurately judging the emotional tendency of music and providing key emotional basis for subsequent path and formation generation.

[0045] In the path generation step described in the embodiments of the present invention, when dividing the movement stages according to the music beats, different types of music have their own unique beat characteristics, and the division methods are also correspondingly different.

[0046] For classical music, its rhythm and rhyme are often more complex, and more detailed and accurate divisions will be made according to multiple factors such as the rhythm changes of the movement, the ups and downs of the melody, and the conversion of harmony; while pop music may be more divided according to obvious style differences such as the chorus and verse of the song to highlight the theme and emotional changes of the music.

[0047] When matching the formation transformation frequency based on the emotional intensity value, when the emotional intensity value is high, it indicates that the music emotion is exciting. At this time, the formation transformation frequency can be set to perform a large-scale transformation every 10 - 15 seconds, while increasing the movement speed to enhance the vitality and impact of the performance. When the emotional intensity value is low, that is, when the music is relatively soothing, a fine-tuning formation transformation is performed every 30 - 60 seconds to maintain a relatively stable and harmonious visual effect.

[0048] When calculating the optimal solution of the multi-objective motion trajectory in combination with the constraint conditions input by the user, the constraint conditions can cover various physical parameters and motion limitations of the moving object, such as the maximum acceleration, minimum turning radius, etc.

[0049] Based on fully considering these constraint conditions, the optimization algorithm uses mathematical models and search algorithms to find the optimal motion trajectory on the premise of meeting the music characteristics and user needs, ensuring the smoothness, safety and efficiency of the motion.

[0050] Regarding the user interface interaction function described in the embodiments of the present invention, the music file format compatibility detection tool can quickly and accurately determine whether the file is a format supported by the system by analyzing key features such as the file header information and encoding format of the file.

[0051] The beat calibration tool displays the music in the form of an intuitive waveform diagram. The user only needs to accurately click on the beat points on the waveform diagram, and the system can automatically perform precise beat calibration according to the time interval between clicks and the beat type (such as 2 / 4 beat, 3 / 4 beat, etc.) preset by the user.

[0052] The vector drawing tool for manually drawing paths provides users with rich drawing functions, including basic tools such as brushes and erasers. Users can freely draw paths with the brush and modify errors in the drawing process in a timely manner with the eraser.

[0053] This tool supports Bezier curve editing. Users can flexibly create various complex and unique path shapes by adjusting the control points of the curve to meet the personalized path design requirements.

[0054] The adjustment of the number of moving objects can be achieved through a simple input box. The user can directly input the specific number value to complete the setting. The adjustment of the initial position is carried out in the three-dimensional visualization interface. The user only needs to easily drag the icon of the moving object with the mouse to intuitively determine its initial position.

[0055] The adjustment of physical parameters is completed in a specially designed parameter setting panel. For example, when setting parameters such as the weight and maximum flight height of the drone, the user inputs the corresponding values in the panel, and the system can update the settings in real time, which is convenient and fast.

[0056] Regarding the three-dimensional visualization platform function described in the embodiment of the present invention, the timeline-based path animation preview and pause / playback control are implemented through an intuitive slider design. By dragging the slider, the user can quickly locate different time points on the timeline, making it convenient to view the path and formation status corresponding to that moment.

[0057] The multi-view observation mode switching is operated through clearly marked buttons on the interface, and each button corresponds to a specific view.

[0058] For example, the bird's-eye view can be used to grasp the overall layout of the path and the distribution of moving targets. The side view can clearly show the height changes and vertical movement trajectory of the moving target. The following view allows users to observe the movement process from the perspective of the moving target, enhancing the sense of immersion.

[0059] The color mapping of the motion trajectory heat map adopts a gradient from blue to red. The blue area indicates the area where the moving target passes less frequently, and the red area indicates the hot spot area where the moving target passes more frequently. Through this intuitive color change, users can quickly understand the activity patterns and hot spot distribution of the moving target.

[0060] The formation collision detection warning monitors the position information of moving targets in real time and uses collision detection algorithms to continuously calculate the distance between moving targets. When the distance between moving targets is detected to be less than the pre-set safety distance, the system immediately issues an alarm to remind users to adjust the path or formation in time to avoid collision accidents.

[0061] The path smoothness score is achieved by accurately calculating the curvature change rate of the path. The smaller the curvature change rate, the smoother the path. The system will intuitively display the calculation results to the user in the form of a percentage to help the user evaluate the quality and smoothness of the path.

[0062] The extended application of the system described in the embodiment of the present invention is that in a stage performance scene, the system and the stage lighting control system are seamlessly connected through a carefully designed API interface.

[0063] When the actors perform according to the path generated by the system, the lighting control system can quickly and accurately adjust the brightness, color and angle of the lights according to the real-time position information of the actors. When the actors gather in the center of the stage for the climax performance, the brightness of the lights is automatically increased and the colors are switched to warm tones to highlight the key points of the performance and create a warm atmosphere. When the actors move in different places to show different scenes, the lighting control system turns on multiple sets of auxiliary lights. Through clever lighting layout and changes, it creates dynamic and rich light and shadow effects, which perfectly match the performances of the actors and enhance the artistic appeal of the stage performances.

[0064] In the scenario of UAV formation flight, the precise flight path data generated by the path generation engine is transmitted to the UAV flight control system through an efficient API interface. The UAV flight control system accurately controls the flight attitude and movement trajectory of each UAV according to the received path data to ensure that the UAVs fly stably along the preset path. Meanwhile, the 3D visualization platform monitors the flight status of the UAVs in real time, including key information such as position, speed, and battery level. Once abnormal situations such as the UAV deviating from the predetermined path, abnormal speed, or too low battery level are detected, the system immediately issues an alarm and sends a correction instruction to the UAV flight control system through the API interface to timely adjust the flight parameters of the UAVs to ensure the safety and stability of the UAV formation flight.

[0065] In the virtual reality environment scenario, the motion paths and formations of the digital character groups generated by the system are applied to the virtual reality engine. The digital characters in the virtual reality environment can perform natural and smooth group motion simulations according to music or other set conditions, such as the development of the plot, scene changes, etc. For example, in a virtual large-scale celebration scenario, the digital characters can neatly change various formations along with the lively music rhythm, perform dances and interactions, vividly presenting the lively celebration atmosphere, greatly enhancing the immersion and realism of the virtual reality scene, and bringing a richer and more vivid virtual experience to users.

[0066] Regarding the AI position movement method for dynamically generating routes and formations described in the embodiments of the present invention, as Figure 2 shown, the specific steps are as follows.

[0067] (1) This method first uses the music feature analysis module to comprehensively extract features from the input music file. The music feature analysis module adopts the technology of combining convolutional neural network and long short-term memory network to extract key features such as rhythm, pitch, intensity, and emotion from the music file, and these features will provide an important data basis for subsequent path and formation generation.

[0068] (2) Based on the mapping relationship between the music feature database and the spatial motion parameter database established by the pre-trained neural network algorithm, the path generation engine starts to generate multi-objective collaborative motion paths and formation transformation sequences. The path generation engine integrates the group kinematics model, divides the motion stages according to the music beats, matches the formation transformation frequency according to the emotion intensity value, and combines the constraint conditions input by the user, and uses the optimization algorithm to calculate the optimal solution of the multi-objective motion trajectory, so as to generate reasonable and efficient paths and formations.

[0069] (3) With the help of a 3D visualization platform, the generated path and the formation change process are dynamically rendered and displayed. The 3D visualization platform supports functions such as timeline-based path animation preview and pause / playback control, multi-view observation mode switching, display of movement trajectory heat maps, formation collision detection warnings, and path smoothness score feedback, enabling users to intuitively and comprehensively view the generation results.

[0070] (4) Users can perform operations such as uploading music files, setting movement constraint conditions, editing path plans, and visual feedback interactions through the user interface. The user interface provides tools for music file format compatibility detection and beat calibration, vector drawing tools for manually drawing paths, dynamic adjustment functions for the number of movement targets, initial positions, and physical parameters, drag-and-drop editing of path key frames, and overlay application functions for formation templates, facilitating users to flexibly control and finely adjust the entire generation process.

[0071] Regarding the electronic device described in the embodiments of the present invention, it mainly consists of a processor, a memory, and a computer program stored in the memory and executable on the processor.

[0072] When the processor executes this computer program, it can implement the AI position movement method for dynamically generating routes and formations described in claim 8. In practical applications, a high-performance multi-core CPU can be selected as the processor, and its powerful computing power can quickly and efficiently process complex computing tasks, ensuring that the system can generate paths and formations in real time and accurately.

[0073] The memory can use a high-speed solid-state drive, which has the characteristics of fast reading and writing, and can quickly store and read a large amount of data such as music files, feature data, path, and formation information, providing strong support for the efficient operation of the system.

[0074] Regarding the computer-readable storage medium described in the embodiments of the present invention, it is used to store a computer program, which can implement the AI position movement method for dynamically generating routes and formations described in claim 8 when the program is executed by the processor.

[0075] Common computer-readable storage media include optical discs, USB flash drives, hard disks, etc. These storage media have characteristics such as large capacity and stable storage, facilitating users to store and transfer program files. Users only need to connect the medium storing the program to the electronic device, and the processor can read the program therein and execute the corresponding method steps to implement the function of dynamically generating routes and formations.

[0076] Through in-depth analysis and intelligent processing of music features, combined with innovative system architecture and interaction design, the present invention realizes the dynamic generation of multi-target position movement paths and formations, providing an advanced and efficient solution for the development of related fields. In practical applications, the present invention can significantly improve work efficiency, enhance creative expression, and bring a better and more convenient experience to users.

[0077] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An AI position movement system for dynamically generating routes and formations, characterized in that, It includes a music feature analysis module, a path generation engine, a 3D visualization platform, and a user interaction interface; The music feature analysis module is used to extract the rhythm, pitch, intensity, and emotional features of the input music file; The path generation engine establishes a mapping relationship between the music feature database and the spatial motion parameter library through a pre-trained neural network algorithm, and generates a multi-objective collaborative motion path and a formation transformation sequence based on this mapping relationship; The 3D visualization platform is used to dynamically render the generated path and the formation change process; The user interaction interface supports music file upload, motion constraint condition setting, path scheme editing, and visualization feedback interaction.

2. The system according to claim 1, wherein The music feature database includes the spectral features of music elements, the beat point timestamps, and emotional labels, and the spatial motion parameter library includes the coordinate range of motion targets, speed thresholds, formation topological structures, and obstacle avoidance rules.

3. The system according to claim 1, wherein The music feature analysis module extracts the music time-frequency map features using a convolutional neural network, and combines an LSTM network to identify the music emotional tendency, and outputs the emotional intensity value and the beat phase matrix.

4. The system according to claim 1, wherein The path generation engine integrates a group kinematics model and generates a path according to the following steps: dividing the motion stage according to the music beat; matching the formation transformation frequency based on the emotional intensity value; combining the constraint conditions input by the user to calculate the optimal solution of the multi-objective motion trajectory.

5. The system according to claim 1, characterized in that, The user interaction interface has a music file format compatibility detection and beat calibration tool, a vector drawing tool for manually drawing paths, a dynamic adjustment function for the number of motion targets, initial positions, and physical parameters, a drag-and-drop editing of path key frames, and an overlay application function of formation templates.

6. The system according to claim 1, wherein The 3D visualization platform supports path animation preview and pause / playback control based on the time axis, multi-view observation mode switching, display of motion trajectory heat maps, formation collision detection warnings, and path smoothness score feedback.

7. The system according to claim 1, wherein The system can be extended and applied to the collaborative control of actor positions and lighting changes in stage performances, the automatic generation and real-time correction of UAV formation flight paths, and the group motion simulation of digital characters in virtual reality environments.

8. An AI position movement method for dynamically generating routes and formations, characterized in that, It includes the following steps: Using the music feature analysis module to extract the rhythm, pitch, intensity, and emotional features of the input music file; through the path generation engine, generating a multi-objective collaborative motion path and a formation transformation sequence based on the mapping relationship between the music feature database and the spatial motion parameter library established by a pre-trained neural network algorithm; using the 3D visualization platform to dynamically render the generated path and the formation change process; realizing music file upload, motion constraint condition setting, path scheme editing, and visualization feedback interaction through the user interaction interface.

9. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the AI position movement method for dynamically generating routes and formations as described in claim 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the AI position movement method for dynamically generating routes and formations as described in claim 8.

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