An intelligent control method and system for a self-propelled platform

Abstract

The present invention relates to the field of self - walking platform control, and particularly to an intelligent control method and system for a self - walking platform. The method includes the following steps: obtaining a satellite remote sensing image of the user's real - time position based on satellite sensing, and performing three - dimensional scene modeling to construct a user position scene structure model; performing emergency situation analysis on the user position scene structure model according to the multi - functional emergency module, and performing self - walking platform mode switching to generate an adaptive emergency requirement mode switching strategy; obtaining a panoramic monitoring image of the self - walking platform, and performing environmental structure type recognition and platform movement constraint analysis to generate real - time terrain movement constraint features; performing intelligent path planning according to the real - time terrain movement constraint features and the user position scene structure model to generate an initial planned path. The present invention performs intelligent path control adjustment based on the dynamic changes of the real - time scene, improving the control quality and stability of the self - walking platform.

Description

Technical Field

[0001] The present invention relates to the field of self - walking platform control, and particularly to an intelligent control method and system for a self - walking platform. Background Art

[0002] With the increasing complexity of emergency response and rescue tasks, traditional emergency self - walking platforms often suffer from problems such as low efficiency, poor emergency response ability, and insufficient adaptability. Especially in complex environments, how to ensure that the platform can quickly and accurately reach the target location and avoid potential obstacles is a challenging task. In recent years, with the rapid development of artificial intelligence, robotics, and sensor technologies, intelligent emergency platforms have gradually become an effective means to solve this problem.

[0003] In this context, a multi - functional emergency self - walking platform intelligent navigation control method based on user needs has emerged. This method can not only make autonomous decisions and path planning according to the user's real - time needs, but also flexibly switch working modes in emergency situations to cope with changes in different environments and tasks. By integrating high - precision sensors, advanced algorithms, and an adaptive control system, the intelligent navigation platform can real - time acquire and analyze environmental information, judge the optimal path, and dynamically adjust the traveling route of the platform. This enables the emergency platform to maintain high mobility in complex terrains and maximize the task execution efficiency.

[0004] However, although there are currently some navigation control methods based on preset routes, most of them do not fully consider the particularity of emergency situations and the variability of user needs. During long - term operation, the platform may face uncertain factors such as equipment failures, environmental mutations, or complex terrains. Existing methods often have difficulty providing sufficient coping strategies in such situations. At the same time, traditional navigation systems mostly rely on manual operation and monitoring, lacking sufficient intelligence and adaptability, resulting in the system being unable to quickly adjust its working state in case of emergencies, and there are significant risks and efficiency bottlenecks.

[0005] Therefore, with the increasing requirements of emergency tasks for speed, accuracy, and reliability, it is particularly urgent to develop a multi - functional emergency self - walking platform intelligent navigation control method based on user needs. Summary of the Invention

[0006] To solve the above - mentioned technical problems, the present invention proposes an intelligent control method and system for a self - walking platform to solve at least one of the above - mentioned technical problems.

[0007] To achieve the above object, the present invention provides an intelligent control method for a self - walking platform, including the following steps:

[0008] Step S1: Obtain the satellite remote sensing image of the user's real-time position based on satellite sensing, and perform 3D scene modeling to construct the user position scene structure model;

[0009] Step S2: Conduct emergency situation analysis on the user position scene structure model according to the multi-functional emergency module, and perform self-walking platform mode switching to generate an adaptive emergency demand mode switching strategy;

[0010] Step S3: Obtain the panoramic monitoring image of the self-walking platform, and perform environmental structure type recognition and platform movement constraint analysis to generate real-time terrain movement constraint features;

[0011] Step S4: Perform intelligent path planning according to the real-time terrain movement constraint features and the user position scene structure model to generate an initial planned path;

[0012] Step S5: Conduct dynamic path cruising based on the initial planned path, and then perform dynamic avoidance path adjustment to construct an intelligent path optimization strategy;

[0013] Step S6: Perform full-cycle platform intelligent control according to the adaptive emergency demand mode switching strategy and the intelligent path optimization strategy, and perform iterative control optimization to construct an intelligent control optimization model.

[0014] The present invention combines satellite remote sensing images with satellite positioning technology (GPS / GLONASS / Beidou, etc.), so that the platform can accurately obtain the real-time location of the user. In complex terrain or confined areas, errors may occur when relying on traditional methods (such as GPS), while remote sensing images provide a wider and more accurate coverage. Three-dimensional modeling based on satellite images can generate a highly accurate structural model of the user's surrounding environment. Compared with traditional two-dimensional maps, three-dimensional models better reflect the ups and downs of the terrain, the specific locations of buildings and obstacles, so that the platform can accurately identify passable and impassable areas in complex urban or post-disaster environments. By constructing a three-dimensional scene structure model of the user's location, the platform can perceive the specific environmental changes around the user in real time and form a real-time updated dynamic environmental data map, which provides a reliable basis for subsequent path planning and emergency decision-making. The platform can dynamically analyze the emergency features in the current environment according to different emergency scenarios (such as fire, earthquake, natural disasters, etc.), and activate the corresponding emergency function modules (such as emergency evacuation, material delivery, wounded rescue, etc.). This means that the platform can flexibly switch its emergency mode according to the scene, user needs and environmental changes. When it detects changes in user needs or emergency situations, the platform can switch its working mode immediately. For example, when a user requests a quick evacuation from a fire area, the platform can switch to the "quick evacuation mode" and readjust the path; when supplies need to be delivered, the platform can quickly switch to the "supply distribution mode". This dynamic switching ensures that the platform can respond quickly to various emergency situations. The adaptive emergency demand mode switching strategy provides a guarantee for the platform's emergency response capability, avoids the fixed response mode of traditional platforms, and can effectively improve the ability to respond in complex and dynamic emergency environments. Through panoramic monitoring images, the platform can perceive obstacles, terrain height, slope, slippery road surface and other features in the surrounding environment in real time. Especially in post-disaster environments or complex terrains, the platform can identify special environments such as ruins and sloping ground, and generate corresponding terrain movement constraint features. Based on image data and sensor feedback, the platform updates the movement constraint model in real time. For example, if there are narrow passages or rugged ground in the environment, the platform will automatically generate a "difficult to pass" constraint feature and take corresponding measures (such as reducing speed, optimizing the travel path, etc.). By analyzing the terrain movement constraints, the platform can more accurately formulate subsequent path planning, avoid difficulties in passing due to terrain problems, and improve the success rate and safety of navigation. Combining the real-time terrain movement constraint characteristics and the scene structure model of the user's location, the platform can formulate an initial path that meets the environmental characteristics and user needs. By comprehensively considering user needs, environmental constraints and terrain characteristics, the platform can avoid choosing unsuitable paths (such as roads that are too narrow or highly unsuitable), ensuring that the planned path is both safe and efficient.Path planning not only considers geographical locations but also gives priority to emergency needs. For example, it presets priorities for emergency evacuation paths to ensure that users can reach safe areas as soon as possible and reduce potential risks. As the user's location and environment change, the platform can dynamically adjust the path planning in real time according to new constraint information and scenario models, avoiding the limitations of fixed paths and making the platform more flexible in emergency situations. During the actual execution process, the platform can cruise and adjust the path in real time. This means that the platform can continuously monitor the surrounding environment and automatically adjust the path according to new obstacles or emergencies. For example, when new obstacles or road closures are detected, the platform can change the travel route in a timely manner. The platform adopts real-time path adjustment and dynamic avoidance algorithms, which can automatically optimize the path during driving and reduce the impact of obstacles. For example, if a certain section of the path has poor passability, the platform will automatically find a detour route to avoid delays or increased risks. The intelligent path optimization strategy enables the platform to make quick and accurate adjustments when facing complex emergency situations, ensuring that users can complete tasks in the safest and shortest time. Through full-cycle control, the platform can continuously optimize its emergency response strategy throughout the entire task process. Whether in the initial planning, path cruising, or emergency response, the platform can make dynamic adjustments based on real-time data to ensure the smooth completion of emergency tasks. The platform conducts response analysis based on immediate feedback, can identify potential problems or deficiencies in system operation, and perform real-time optimization. For example, if it is found that a certain path selection always encounters obstacles, the platform will continuously improve the path planning through iterative optimization algorithms to avoid the same problem. Through continuous learning and iterative optimization, the intelligent control optimization model of the platform will be gradually improved, enabling the platform to more accurately predict user needs, respond to emergencies, and respond more efficiently to environmental changes during long-term operation.

[0015] In this specification, an intelligent control system for a self-propelled platform is provided, which is used to execute the intelligent control method for a self-propelled platform as described above, including:

[0016] A three-dimensional scene module, which is used to obtain satellite remote sensing images of the user's real-time position based on satellite sensing, and perform three-dimensional scene modeling to construct a user position scene structure model;

[0017] An adaptive mode switching module, which is used to perform emergency situation analysis on the user position scene structure model according to the multifunctional emergency module, and perform self-propelled platform mode switching to generate an adaptive emergency demand mode switching strategy;

[0018] A mobile constraint analysis module, which is used to obtain panoramic monitoring images of the self-propelled platform, and perform environmental structure type recognition and platform mobile constraint analysis to generate real-time terrain mobile constraint features;

[0019] A path planning module for performing intelligent path planning based on real-time terrain movement constraint features and the user location scenario structure model to generate an initial planned path;

[0020] A path adjustment module for performing dynamic path cruising based on the initial planned path, and then performing dynamic avoidance path adjustment to construct an intelligent path optimization strategy;

[0021] An iterative control optimization module for performing full-cycle platform intelligent control according to the adaptive emergency demand mode switching strategy and the intelligent path optimization strategy, and performing iterative control optimization to construct an intelligent control optimization model.

[0022] The present invention can obtain the user's geographic location in real time by acquiring high-precision satellite remote sensing images, and build an accurate three-dimensional scene model based on this information. This provides a solid foundation for subsequent emergency response, path planning and other modules. Using satellite images, geographic information on a global scale can be quickly obtained, avoiding the limitations of traditional manual measurement and significantly improving the efficiency and accuracy of scene modeling. Real-time acquisition of satellite images and scene models provides real-time geographic information support for other modules, ensuring the timeliness and reliability of emergency response. According to the scene model, situation analysis can be performed to identify different emergency needs, such as natural disasters, sudden accidents, etc., so as to generate an adaptive emergency demand mode switching strategy. The module can intelligently switch the operation mode of the platform, such as switching from normal mode to emergency mode, to ensure that the platform can respond quickly and make appropriate operations in various emergency situations. Adaptive mode switching can automatically adjust the behavior of the platform according to the current situation, reduce manual intervention, and improve the overall emergency response efficiency of the system. Real-time images of the environment where the platform is located are obtained through panoramic monitoring images, and terrain types, obstacles, dangerous areas, etc. can be identified, which helps to provide accurate terrain information for path planning. Mobile constraint analysis is performed based on environmental recognition information to generate mobile restriction features of real-time terrain. For example, it analyzes whether the road surface is suitable for the platform to drive, whether there are obstacles that need to be avoided, etc., to provide strong data support for subsequent path planning. Through a comprehensive analysis of the environment, it can ensure that the platform can drive safely in complex terrain and reduce the risks caused by unknown terrain. According to the terrain characteristics and real-time constraints, a safe and efficient driving path is automatically planned. This path not only takes into account the shortest distance, but also incorporates the feasibility analysis of the terrain to avoid potential dangerous areas. In a complex and dynamically changing environment, path planning can be adjusted according to real-time information to ensure that the platform always moves along the optimal route. By combining satellite remote sensing images and terrain constraint information, the accuracy of path planning is greatly improved, reducing the deviation in platform operation. During the platform driving process, the path adjustment module can perceive environmental changes in real time, automatically adjust the path to avoid obstacles or temporary dangerous areas, and ensure the smooth progress of the platform. Based on real-time feedback during the cruise process, the initial planned path is adjusted to avoid falling into dead ends or entering inappropriate areas, and improve the intelligence and adaptability of the path. Through continuous path adjustment, the platform's driving path can be optimized, operating efficiency can be improved, and energy consumption can be reduced. The iterative control optimization module can periodically optimize the platform's intelligent control by continuously monitoring the platform status and external environment changes to ensure the platform's stability and operating efficiency. Based on real-time emergency mode switching strategies and path optimization strategies, the platform can adaptively adjust the control strategy to respond to different emergency situations and terrain changes. This enables the platform to have the ability to self-learn and adapt in complex and dynamic environments, thereby improving its performance and reliability in long-term operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic flow chart of the steps of an intelligent control method for a self - walking platform of the present invention;

[0024] Figure 2 It is a schematic detailed implementation step flow chart of step S1;

[0025] Figure 3 It is a schematic detailed implementation step flow chart of step S2;

[0026] Figure 4 It is a schematic detailed implementation step flow chart of step S3. Specific implementation manners

[0027] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0028] The embodiments of the present application provide an intelligent control method and system for a self - walking platform. The execution subjects of the method and system include but are not limited to: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that carry the system and can be regarded as general computing nodes of the present application. The data processing platform includes but is not limited to at least one of: audio - image management systems, information management systems, and cloud - end data management systems.

[0029] Please refer to Figures 1 to 4 , the present invention provides an intelligent control method for a self - walking platform. The intelligent control method for a self - walking platform includes the following steps:

[0030] Step S1: Based on satellite sensing, obtain a satellite remote - sensing image of the user's real - time position, and perform three - dimensional scene modeling to construct a user - position scene structure model;

[0031] Step S2: According to the multi - function emergency module, perform emergency situation analysis on the user - position scene structure model, and perform self - walking platform mode switching to generate an adaptive emergency - requirement mode - switching strategy;

[0032] Step S3: Obtain a panoramic monitoring image of the self - walking platform, and perform environmental structure type recognition and platform movement constraint analysis to generate real - time terrain movement constraint features;

[0033] Step S4: According to the real - time terrain movement constraint features and the user - position scene structure model, perform intelligent path planning to generate an initial planned path;

[0034] Step S5: Based on the initial planned path, perform dynamic path cruising, and then perform dynamic avoidance path adjustment to construct an intelligent path optimization strategy;

[0035] Step S6: Perform full-cycle platform intelligent control according to the adaptive emergency demand mode switching strategy and the intelligent path optimization strategy, and conduct iterative control optimization to build an intelligent control optimization model.

[0036] The present invention combines satellite remote sensing images with satellite positioning technology (GPS / GLONASS / Beidou, etc.), so that the platform can accurately obtain the real-time location of the user. In complex terrain or confined areas, errors may occur when relying on traditional methods (such as GPS), while remote sensing images provide a wider and more accurate coverage. Three-dimensional modeling based on satellite images can generate a highly accurate structural model of the user's surrounding environment. Compared with traditional two-dimensional maps, three-dimensional models better reflect the ups and downs of the terrain, the specific locations of buildings and obstacles, so that the platform can accurately identify passable and impassable areas in complex urban or post-disaster environments. By constructing a three-dimensional scene structure model of the user's location, the platform can perceive the specific environmental changes around the user in real time and form a real-time updated dynamic environmental data map, which provides a reliable basis for subsequent path planning and emergency decision-making. The platform can dynamically analyze the emergency features in the current environment according to different emergency scenarios (such as fire, earthquake, natural disasters, etc.), and activate the corresponding emergency function modules (such as emergency evacuation, material delivery, wounded rescue, etc.). This means that the platform can flexibly switch its emergency mode according to the scene, user needs and environmental changes. When it detects changes in user needs or emergency situations, the platform can switch its working mode immediately. For example, when a user requests a quick evacuation from a fire area, the platform can switch to the "quick evacuation mode" and readjust the path; when supplies need to be delivered, the platform can quickly switch to the "supply distribution mode". This dynamic switching ensures that the platform can respond quickly to various emergency situations. The adaptive emergency demand mode switching strategy provides a guarantee for the platform's emergency response capability, avoids the fixed response mode of traditional platforms, and can effectively improve the ability to respond in complex and dynamic emergency environments. Through panoramic monitoring images, the platform can perceive obstacles, terrain height, slope, slippery road surface and other features in the surrounding environment in real time. Especially in post-disaster environments or complex terrains, the platform can identify special environments such as ruins and sloping ground, and generate corresponding terrain movement constraint features. Based on image data and sensor feedback, the platform updates the movement constraint model in real time. For example, if there are narrow passages or rugged ground in the environment, the platform will automatically generate a "difficult to pass" constraint feature and take corresponding measures (such as reducing speed, optimizing the travel path, etc.). By analyzing the terrain movement constraints, the platform can more accurately formulate subsequent path planning, avoid difficulties in passing due to terrain problems, and improve the success rate and safety of navigation. Combining the real-time terrain movement constraint characteristics and the scene structure model of the user's location, the platform can formulate an initial path that meets the environmental characteristics and user needs. By comprehensively considering user needs, environmental constraints and terrain characteristics, the platform can avoid choosing unsuitable paths (such as roads that are too narrow or highly unsuitable), ensuring that the planned path is both safe and efficient.Path planning not only considers geographical locations but also gives priority to emergency requirements. For example, it presets priorities for emergency evacuation routes to ensure that users can reach safe areas as soon as possible and reduce potential risks. As the user's location and environment change, the platform can dynamically adjust path planning in real time according to new constraint information and scenario models, avoiding the limitations of fixed paths and making the platform more flexible in emergency situations. During actual execution, the platform can cruise and adjust the path in real time. This means that the platform can continuously monitor the surrounding environment and automatically adjust the path according to new obstacles or emergencies. For example, when new obstacles or road closures are detected, the platform can change the travel route in a timely manner. The platform adopts real-time path adjustment and dynamic avoidance algorithms, which can automatically optimize the path during driving and reduce the impact of obstacles. For example, if a certain section of the path has poor passability, the platform will automatically find a detour route to avoid delays or increased risks. The intelligent path optimization strategy enables the platform to make quick and accurate adjustments in the face of complex emergency situations, ensuring that users can complete tasks in the safest and shortest time. Through full-cycle control, the platform can continuously optimize its emergency response strategy throughout the entire task process. Whether in initial planning, path cruising, or emergency response, the platform can make dynamic adjustments based on real-time data to ensure the smooth completion of emergency tasks. The platform conducts response analysis based on instant feedback, can identify potential problems or deficiencies in system operation, and perform real-time optimization. For example, if it is found that a certain path selection always encounters obstacles, the platform will continuously improve path planning through iterative optimization algorithms to avoid the same problem. Through continuous learning and iterative optimization, the intelligent control optimization model of the platform will be gradually improved, enabling the platform to more accurately predict user needs, respond to emergencies, and make more efficient responses to environmental changes during long-term operation.

[0037] In an embodiment of the present invention, referring to Figure 1 , it is a schematic flowchart of the steps of an intelligent control method for a self-propelled platform according to the present invention. In this example, the steps of the intelligent control method for the self-propelled platform include:

[0038] Step S1: Obtain satellite remote sensing images of the user's real-time position based on satellite sensing and perform three-dimensional scene modeling to construct a user position scene structure model;

[0039] In this embodiment, a suitable satellite remote sensing image source is selected to ensure that the selected satellite can provide image data with high resolution (such as 1 meter to 3 meters). Commonly used satellites include WorldView, GeoIQ, etc. When selecting, the acquisition frequency and coverage of the images need to be considered. Configure the image acquisition parameters, including the band selection of the image (such as visible light, infrared) and the coverage area of the image, to ensure that the specific image of the user's current location can be obtained. Download the required remote sensing images through a satellite data service platform (such as NASA, ESA, etc.). During the download process, record the acquisition time, coordinate range, and image resolution of the image. Preprocess the acquired image, including radiometric correction, geometric correction, and atmospheric correction, to eliminate noise and distortion in the image. Use standardized correction parameters to improve the quality and accuracy of the image. After the processing is completed, perform a quality check on the image to ensure that the clarity and contrast of the image meet the requirements of subsequent analysis. The inspection content includes the resolution, color balance, and visible features of the image. Record the results of the quality check, mark or re-acquire the unqualified images to ensure that the finally used images for modeling have high quality. Obtain the user's geographical location in real time through a user device (such as a smartphone or GPS device), and record its longitude, latitude, and altitude information. Set the acquisition frequency (such as updating once per second) to ensure that the dynamic position of the user can be reflected in a timely manner. Ensure the accuracy of the position data, use technologies such as differential GPS to improve the positioning accuracy, and usually require the accuracy to be at the meter level (±1 meter). Store the acquired user position data in a database to ensure the structured management of the data. Record the timestamp, user ID, and their corresponding geographical coordinates for subsequent analysis and use. Set up a data cleaning mechanism to regularly clean up the outdated data and maintain the efficiency and real-time nature of the database. Perform quality verification on the stored user position data to ensure the rationality and accuracy of the data. Check whether the data is within a reasonable geographical range and eliminate duplicate data. Record the verification results for subsequent analysis and correction of potential data problems. After obtaining high-quality satellite remote sensing images and the user's real-time position, prepare for 3D scene modeling. Select a suitable 3D modeling software (such as Blender, SketchUp, ArcGIS, etc.), and select the supported functional modules according to the requirements. Set the modeling parameters, including the scale of the model (such as 1:1000) and the level of detail, to ensure that the generated 3D model can reflect the structure of the actual environment. Use image processing technologies (such as computer vision algorithms) to extract ground features from satellite images, including buildings, roads, vegetation, etc. Apply edge detection and region growing algorithms to identify the boundaries and shapes of the ground features. Record the parameters of the extracted features, such as the extraction accuracy and the number of features, for subsequent analysis and modeling. Based on the extracted feature data, construct a 3D scene model. Convert the ground features into 3D graphic elements, and combine the user position data to mark the user's location in the model.During the modeling process, conduct visual inspections regularly to ensure the accuracy and details of the model, and record the modeling results and parameter settings at each stage. Integrate the constructed 3D model with the real-time location data of the user to generate a user location scene structure model. The model should accurately reflect the environmental characteristics around the user, including relative positions and spatial structures. Set the attribute parameters of the model, such as structure type, size, and material, for subsequent path planning and intelligent control.

[0040] Step S2: Conduct an emergency situation analysis on the user location scene structure model according to the multi-functional emergency module, and perform a self-walking platform mode switch to generate an adaptive emergency requirement mode switching strategy;

[0041] In this embodiment, the self-propelled platform is equipped with a multi-functional emergency module, including environmental monitoring sensors (such as temperature, humidity, gas sensors), cameras, lidar, etc. Configure the emergency module to ensure that it can work under various environmental conditions, set the sampling frequency (such as once per second) and data processing capabilities of the sensors to improve the real-time nature of emergency response. Ensure that the software system of the emergency module has data fusion and analysis functions, and can integrate data from different sensors in real time to form a comprehensive environmental perception ability. Load the user location scenario structure model generated in step S1 into the control system of the emergency module. Ensure that the data format of the model is compatible with the emergency module for subsequent situation analysis. Record the main structural features and environmental parameters (such as building height, road width) of the model to provide basic data support for emergency situation analysis. Set relevant situation parameters according to common scenarios of emergency requirements (such as fire, flood, equipment failure, etc.). These parameters include the degree of urgency, potential risks, emergency response time, etc. for subsequent analysis and mode switching. Set the weights and priorities of emergency scenarios. For example, the priority of the fire scenario can be set to the highest (weight 1.0), while the priority of the equipment failure can be lower (weight 0.5). Start the monitoring function of the emergency module to collect environmental data and user location data in real time. According to the set sampling frequency, collect environmental change information (such as temperature, humidity, gas composition) and the dynamic location of the user. Record the data of each monitoring, including the timestamp, sensor readings, and user location information for subsequent situation analysis. Process and analyze the real-time monitoring data to extract key emergency situation features. For example, use the threshold detection method to identify an abnormal increase in temperature (such as exceeding 50°C) which may indicate an emergency such as a fire. Set the parameters for feature extraction, such as the threshold of temperature change, the rate of humidity change, etc. to ensure the accuracy and timeliness of situation recognition. Match the extracted situation features with the preset emergency scenarios to identify the type of emergency situation in the current environment. For example, by comparing the real-time temperature and humidity with the preset values, identify a possible fire situation. Record the recognition results, including the matched situation type, recognition time, and relevant parameters for subsequent decision-making and response. According to the identified type of emergency situation, design corresponding mode switching strategies. Set specific behavior modes, such as switching to the "emergency evacuation" mode in case of fire, and switching to the "fault troubleshooting" mode in case of equipment failure. Set the parameters (such as speed, navigation strategy, obstacle avoidance method) in each mode to ensure that the behavior after switching can effectively respond to the current situation. After identifying the emergency situation, immediately execute the mode switching strategy. Adjust the control system of the self-propelled platform in real time to ensure that the platform can quickly respond to the current environmental changes. Record the process of mode switching, including the switching time, original mode, target mode, and changes in relevant parameters for subsequent analysis and optimization. After mode switching, continuously monitor the response effect of the platform to evaluate whether the new mode can effectively respond to the current emergency situation.For example, monitor the moving speed, driving direction, and obstacle detection of the monitoring platform. Based on the monitoring results, make real-time feedback adjustments. If it is found that the current mode cannot meet the emergency requirements, the strategy can be further adjusted or switched to other preset modes. Evaluate the implemented mode switching strategy and analyze its effectiveness in different emergency scenarios. For example, evaluate the success rate of the strategy by collecting data such as response time, user feedback, and environmental changes. Set evaluation criteria, such as the response time should be less than 3 seconds and the successful obstacle avoidance rate should be higher than 90%, to ensure the practicality and effectiveness of the strategy. Generate an adaptive emergency requirement mode switching strategy based on the evaluation results and real-time monitoring data. Establish a strategy decision tree to ensure that the appropriate emergency mode can be quickly selected in different scenarios. Record the generated strategy, including the corresponding scenario type, switching conditions, and operation steps, for subsequent implementation and adjustment.

[0042] Step S3: Obtain the panoramic monitoring image of the self-walking platform, perform environmental structure type recognition and platform movement constraint analysis, and generate real-time terrain movement constraint features;

[0043] In this embodiment, the self-propelled platform is equipped with a multi-lens panoramic camera (such as a 360-degree panoramic camera) to ensure that panoramic images of the surrounding environment can be collected in real time during driving. Configure the resolution (e.g., 4000x3000 pixels) and frame rate (e.g., 30 frames per second) of the camera to ensure that the image quality meets the analysis requirements. Ensure that the camera is stable during the acquisition process to avoid image blurring caused by vibrations. Anti-shake technology or a fixed bracket can be used to improve the stability of image acquisition. Start the panoramic monitoring system and regularly collect panoramic images during the driving of the self-propelled platform. Set the acquisition frequency (e.g., collect once every 5 seconds) to ensure that each key point of the path is covered by the images. Record the acquisition time, location, and camera status of each image for subsequent analysis. Store the collected images in real time on a local storage or a cloud server to ensure the security and accessibility of the data. Regularly check the collected image data to ensure that the image quality meets the requirements. Mark the blurred or distorted images for subsequent elimination. Set the quality inspection standard, for example, the image clarity should reach more than 80%. Preprocess the obtained panoramic monitoring images, including denoising, color correction, and image enhancement, to improve the accuracy of subsequent recognition. Use image enhancement techniques (such as histogram equalization) to improve the contrast of the images. Apply an edge detection algorithm (such as Canny edge detection) to extract the obvious edges in the images for subsequent structure recognition. Apply computer vision and deep learning algorithms (such as convolutional neural networks) to identify the environmental structure types of the preprocessed images. The model needs to be pre-trained using a labeled structure feature dataset (such as buildings, roads, vegetation, etc.). Set the confidence threshold for recognition (e.g., 0.7) to ensure that only high-confidence structure types are marked, reducing the situation of misrecognition. Define the movement constraint features of the platform under different environmental conditions according to the recognized environmental structure types. For example, the features of steep slopes, obstacles (such as buildings, trees), and narrow channels. Set the parameters of the constraint features, such as the maximum climbing angle (e.g., 20 degrees), the minimum passing width (e.g., 0.5 meters), to ensure the accuracy of constraint analysis. Analyze the recognized environmental structures and evaluate their impact on the platform movement. For example, use geometric analysis methods to evaluate the passability of different terrains and calculate the passing capacity of each path. Record the analysis results of each constraint feature, including the impact degree, passing conditions, and potential risks, for subsequent dynamic path planning. Summarize the analyzed movement constraint features to generate a real-time terrain movement constraint feature report. The report should include the influencing factors of each structural feature and their corresponding constraint conditions. Record the generated constraint feature data for subsequent real-time path planning and the implementation of intelligent control strategies.

[0044] Step S4: Perform intelligent path planning based on the real-time terrain movement constraint features and the user location scene structure model to generate an initial planned path;

[0045] In this embodiment, the real-time terrain movement constraint features obtained from step S3 are sorted out to ensure that all relevant data (such as terrain features, obstacle positions, and passage conditions) can be stored in the database in a structured manner. Each feature should include parameters such as the degree of influence and passage restrictions. Set the data format to ensure that the constraint features can match the input format required by the subsequent path planning algorithm. For example, represent the terrain constraints in a grid form, where each grid cell contains its passage ability and obstacle information. Load the user location scenario structure model generated in step S1 into the path planning system. Ensure that the model can reflect the user's current environment and includes important geographical features (such as buildings, roads, natural obstacles, etc.). Perform necessary simplification and optimization on the model to ensure high processing efficiency and accuracy in subsequent path planning. Polygon simplification techniques can be used to reduce the computational complexity. According to the actual requirements and environmental characteristics, select a suitable path planning algorithm, such as the A* algorithm, Dijkstra algorithm, or RRT (Rapidly-Exploring Random Tree) algorithm. Consider the environmental complexity, real-time requirements, and passability when selecting the algorithm. Set algorithm parameters, such as the selection of heuristic functions, search range, and node expansion strategy, to ensure that the algorithm can effectively handle path finding in complex environments. Adjust the parameters in the selected path planning algorithm to adapt to different environments and emergency situations. For example, in complex environments, it may be necessary to increase the search depth or the number of path search expansions to ensure finding the optimal path. Record the basis and results of each parameter adjustment for subsequent optimization and improvement. Start the path planning algorithm and perform path search based on the user's current location, real-time terrain movement constraint features, and the user location scenario structure model. The algorithm will evaluate each possible path, considering obstacles and terrain constraints. During the search process, update the path status in real-time to ensure that the algorithm can adapt to the dynamically changing environment. For example, if new obstacles or changing terrain features are encountered, the algorithm should be able to re-evaluate the path. Evaluate the searched path and calculate the total cost of the path (such as total distance, passage time, etc.). Set evaluation criteria, such as minimizing the path length and the shortest passage time. Optimize the path according to the evaluation results and perform path replanning if necessary. Record the key parameters and changes during the optimization process to ensure the effectiveness of the final path. Output the optimized path as the initial planned path and record each key node (such as the starting point, ending point, and waypoints) of the path and their corresponding geographical coordinates. Generate a visualization graph of the path, marking the starting and ending positions of the path and the environmental features passed through for easy user understanding and use.

[0046] Step S5: Based on the initial planned path, perform dynamic path cruising, and then perform dynamic avoidance path adjustment to construct an intelligent path optimization strategy;

[0047] In this embodiment, the self-propelled platform starts the dynamic path cruising mode. According to the initial planned path generated in step S4, the path information is loaded into the navigation system of the platform. Ensure that the system can receive and process environmental data from sensors in real time, such as position, speed, and obstacle information. Set the cruising speed (for example, 0.5 meters per second) and the cruising interval (such as updating the path status every 5 seconds) to ensure the effective acquisition of image and sensor data. During the cruising process, the platform tracks the initial planned path in real time and continuously monitors the deviation between its position and the path. Use GPS and inertial navigation system (INS) to calculate the current position in real time and compare it with the planned path. Record the key parameters during the cruising process, including the current position, the distance from the path deviation, speed, etc., for subsequent analysis and optimization. During the cruising process, the platform uses the panoramic camera and lidar carried to collect the image and depth information of the surrounding environment in real time. Set the image acquisition frequency (such as 2 frames per second) and the scanning frequency of the lidar (such as 10 times per second) to ensure rich environmental data is obtained. Store the collected environmental data in the local or cloud server in real time to ensure the integrity and traceability of the data for subsequent dynamic avoidance analysis. During the cruising process, monitor and identify obstacles ahead in real time. Use lidar and camera data to automatically detect obstacles in the surrounding environment through computer vision algorithms (such as YOLO or RANSAC) to ensure that the dynamically changing environment can be quickly identified. Set the threshold for obstacle detection (such as an object with a height ≥ 0.3 meters is regarded as an obstacle), and record the results of each detection, including the position, type, and confidence of the obstacle. Once an obstacle is detected, the platform should immediately evaluate its impact on the current path. Use the identified obstacle information and apply path planning algorithms (such as A* or RRT algorithms) to re-plan the dynamic path. Set the parameters for re-planning, such as the maximum allowable deviation (for example, re-plan the path within 5 meters) to ensure that the platform can effectively bypass the obstacle and maintain a safe distance. According to the re-planned path, adjust the driving direction and speed of the platform in real time. Set a new path tracking algorithm (such as a PID controller) to ensure that the platform can drive smoothly and accurately towards the new target path. Record each key step during the path adjustment process, including the original path, the adjusted path, the adjustment time, and the reason for adjustment, for subsequent analysis and optimization. Collect all the data generated during the dynamic cruising process, including obstacle detection results, path deviation data, path adjustment records, etc., for comprehensive analysis. Evaluate the success rate and efficiency of each path adjustment, and set evaluation indicators (such as the successful adjustment rate ≥ 90%). Use data analysis methods (such as regression analysis) to identify the key factors affecting the path adjustment effect, such as the type of obstacle, the detection time, and the path deviation. Based on the data analysis results, design an intelligent path optimization strategy. The strategy should include dynamic adjustment rules, obstacle handling priorities, and path selection criteria.For example, for mobile obstacles (such as pedestrians or vehicles), higher priorities can be set for evasion. Record the specific implementation steps and parameter settings of the optimization strategy to ensure that the strategy can be flexibly applied under different environmental conditions.

[0048] Step S6: Perform full-cycle platform intelligent control according to the adaptive emergency demand mode switching strategy and the intelligent path optimization strategy, and perform iterative control optimization to build an intelligent control optimization model.

[0049] In this embodiment, start the intelligent control system of the full-cycle platform and load the adaptive emergency demand mode switching strategy and intelligent path optimization strategy generated in step S5. Ensure that the system can respond to environmental changes and user needs in real time. Configure the control parameters of the system, including the control frequency (such as 10 Hz) and the sensor data processing delay (such as ≤100 milliseconds), to ensure that the system can quickly respond to the dynamic environment. Integrate the adaptive emergency demand mode switching strategy with the intelligent path optimization strategy to form a comprehensive control strategy. Set priority rules, for example, give priority to the emergency strategy in case of emergency, and give priority to the path optimization strategy under normal circumstances. Record every detail of the integrated strategy, including the switching conditions and execution order in different scenarios, for subsequent analysis and optimization. Start the real-time data acquisition system to collect environmental data and platform status information from sensors (such as GPS, IMU, lidar, etc.). Set the data sampling frequency (such as 10 times per second) to ensure the integrity and accuracy of the data. Monitor the running state of the platform, including position, speed, acceleration, and sensor status, to ensure that the system can detect abnormalities in time and trigger corresponding control strategies. During normal cruising, the platform executes navigation according to the optimized path, tracks the path in real time, and monitors the status. If an obstacle is detected or a preset emergency situation occurs, immediately switch to the emergency demand mode. Set a path tracking algorithm (such as a PID controller) to ensure that the platform can accurately follow the optimized path, adjust the driving speed and steering angle, and record the time and parameters of each adjustment. During driving, analyze the sensor data in real time and dynamically determine the control strategy according to the current environment and status. For example, when encountering an obstacle, the system will automatically evaluate the nature and position of the obstacle and decide whether to detour or stop. Record the basis and execution results of each decision, including the algorithms used, input data, and output instructions, for subsequent analysis and optimization. While implementing the control, continuously monitor the control effect, evaluate the response speed of the system and the accuracy of path execution. For example, set the maximum allowable deviation (such as ±0.5 meters) to ensure that the performance of the platform on the predetermined path meets the standards. Collect user feedback and environmental change information, analyze whether the control effect meets the expectations, and record the monitoring results for subsequent adjustment and improvement. Analyze the real-time collected data to identify problems and deficiencies in the control process. For example, check the reasons for path deviation, and analyze whether it is due to environmental changes, unrecognized obstacles in time, or improper control parameter settings. Set data analysis indicators, such as the path deviation rate (should be less than 5%) and the emergency response time (should be less than 3 seconds), and identify the key factors affecting the control effect through statistical analysis methods (such as regression analysis). Based on the data analysis results, build an intelligent control optimization model. The model should consider various factors affecting the control effect (such as environmental complexity, obstacle characteristics, user needs), and set corresponding weights.Continuously optimize the control model using machine learning techniques (such as reinforcement learning), train the model with historical data to enable it to adaptively adjust control strategies in different scenarios. Verify the constructed intelligent control optimization model, test it with new real-time data, and evaluate the prediction accuracy and control effect of the model. Record the key parameters and results during the verification process to ensure the practicality of the model.

[0050] In this embodiment, refer to Figure 2 , which is a schematic diagram of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0051] Obtain the monitoring log of the self-propelled platform;

[0052] Locate the real-time position of the user based on the monitoring log of the self-propelled platform;

[0053] Based on the real-time position of the user, obtain the satellite remote sensing image of the user's real-time position through satellite sensing;

[0054] Perform 3D scene modeling based on the satellite remote sensing image of the user's real-time position to construct a user position scene structure model.

[0055] In this embodiment, the monitoring logs are parsed to extract the real-time location information of the user. A data processing tool (such as Python, MATLAB, etc.) is used to read the log file and convert the data into a usable format. An algorithm (such as Kalman filtering) is used to process the GPS signal to reduce noise and errors and improve the accuracy of location estimation. Ensure that the processed location information has high reliability. Based on the processed data, the current location of the user is calculated in real time. By combining GPS data with IMU data, a fusion algorithm (such as sensor fusion technology) is used to further improve the positioning accuracy. After determining the current location of the user, the location is marked on the map through a visualization tool (such as a map API) for real-time monitoring and tracking. By comparing with known locations, the accuracy of the user's real-time location is verified. If a deviation is found, appropriate adjustments are made, such as recalculating the location or correcting the sensor data. The results of each location calculation are recorded, including calculation errors, signal strength, and data sources, for subsequent analysis and optimization of the algorithm. A suitable satellite remote sensing image data source is selected. Commonly used data sources include Sentinel, Landsat, WorldView, etc. These satellites provide high-resolution surface images. The satellite image data is accessed through the corresponding API or data platform (such as GoogleEarth Engine, NASA Earthdata, etc.) to ensure that the latest images near the user's real-time location can be obtained. According to the user's real-time GPS coordinates, the satellite remote sensing images of the corresponding area are queried and downloaded. The time range for obtaining the images is set (such as within the past 24 hours) to ensure the timeliness of the images. The obtained images are preprocessed, including radiometric correction, atmospheric correction, and geometric correction, to improve the image quality and accuracy. Ensure that the images can truly reflect the surface conditions. According to the user's location, the obtained satellite images are cut to extract the image area where the user is located. The resolution and range of the cut are set to meet the needs of subsequent modeling. The processed remote sensing images are stored in a local database or a cloud server to ensure the accessibility and security of the images for subsequent 3D modeling use. A suitable 3D modeling software (such as Blender, SketchUp, Unity, etc.) is selected. These software can process satellite remote sensing images and generate 3D scene models. Ensure that the selected software supports importing remote sensing images and can perform terrain modeling and scene construction to realize the visualization of the user's location. The processed satellite remote sensing images are imported into the 3D modeling software for terrain modeling. According to the terrain elevation data and image information, a 3D geomorphic model is constructed to ensure that the model conforms to the actual surface conditions. A mark of the user's location is added to the model, and the scene is dynamically updated according to the user's real-time status (such as walking, staying, etc.) to ensure that the scene can reflect the user's behavior and location in real time. The constructed 3D scene model is verified by comparing it with the actual terrain and satellite images to ensure the accuracy and rationality of the model.

[0056] In this embodiment, the specific steps of performing three-dimensional scene modeling based on the satellite remote sensing image of the user's real-time position and constructing the user position scene structure model are as follows:

[0057] Perform cloud cover fuzzy detection on the satellite remote sensing image of the user's real-time position, and extract the cloud fuzzy area image;

[0058] Perform quantitative analysis of the clouds on the cloud fuzzy area image to obtain the cloud thickness and moving direction;

[0059] Predict the future situation of the clouds based on the cloud thickness and moving direction to generate a cloud movement situation prediction map;

[0060] Evaluate the occlusion degree of the cloud movement situation prediction map to generate an occlusion degree value;

[0061] Calculate the continuous occlusion time based on the cloud movement situation prediction map to obtain the cloud continuous occlusion time;

[0062] Perform a comprehensive evaluation of the difficulty of cloud fuzzy elimination based on the occlusion degree value and the cloud continuous occlusion time to obtain a cloud fuzzy elimination evaluation value;

[0063] Make a decision on the cloud fuzzy elimination evaluation value based on a preset elimination difficulty threshold. When the cloud fuzzy elimination evaluation value is greater than the preset elimination difficulty threshold, it is determined as severe occlusion, and the historical cloudless image at the same position is obtained for reconstruction; when the cloud fuzzy elimination evaluation value is less than or equal to the preset elimination difficulty threshold, it is determined as slight occlusion, and perform light transmission compensation on the cloud fuzzy area image to obtain a cloud fuzzy elimination remote sensing image;

[0064] Perform user real-time scene structure analysis on the cloud fuzzy elimination remote sensing image to generate user real-time scene structure data;

[0065] Perform depth image semantic segmentation on the cloud fuzzy elimination remote sensing image to extract multi-environment elements;

[0066] Perform three-dimensional scene modeling based on the user real-time scene structure data and multi-environment elements to construct the user position scene structure model.

[0067] In this embodiment, the acquired satellite remote sensing images are preprocessed to ensure radiometric correction and geometric correction of the images, so as to eliminate data noise and improve image quality. Standardization parameters are set, such as the coefficients for radiometric correction and the reference points for geometric correction. Image enhancement techniques (such as histogram equalization) are used to improve the contrast of the images, making the boundaries of the clouds more obvious and facilitating subsequent blur detection. Blur detection algorithms (such as the Canny algorithm based on edge detection or the blur metric algorithm) are used to identify the cloud blur regions in the images. A threshold is set to ensure effective discrimination between clear regions and blurred regions. The blurred regions are marked, and the coordinate and range information of the cloud blur regions are output for subsequent analysis and processing. The marked cloud blur regions are extracted from the original image to generate a dedicated blur region image. This image will be used for subsequent quantitative analysis and situation prediction. A remote sensing inversion model (such as a model based on radiative transfer) is used to analyze the thickness of the extracted cloud blur regions. The optical thickness of the clouds is calculated using spectral reflectance information, and calculation parameters (such as the wavelength range) are set. Combining meteorological data (such as temperature, humidity), a regression analysis method is used to establish a relationship model between cloud thickness and optical thickness to improve the accuracy of thickness estimation. By analyzing the time series images of the cloud blur regions, the moving direction of the clouds is determined. The optical flow method or feature point tracking algorithm is used to calculate the displacement of the clouds between different time points. The moving speed and direction of the clouds are recorded to generate a path map of cloud movement, facilitating subsequent situation prediction. Based on the cloud thickness and moving direction data, a future situation prediction model of the clouds is constructed. Time series analysis (such as the ARIMA model) or machine learning algorithms (such as random forest or neural network) can be used for prediction. Model parameters are set, including the size of the historical data window, feature variables (such as thickness, speed, etc.), and the prediction time period (such as the next 1 hour, 3 hours, etc.). The prediction model is run to generate a future cloud movement situation map, showing the expected positions and thickness changes of the clouds in the future time period. The prediction results are recorded, including the cloud positions, thicknesses, and their change trends at each time point, to support subsequent occlusion degree assessment. According to the generated future cloud movement situation prediction map, the occlusion degrees of different regions are evaluated. The occlusion ratio calculation formula can be used to compare the cloud thickness with the ground reflectance to generate an occlusion degree value (between 0 and 1). Evaluation parameters are set, such as the occlusion threshold, to ensure that the evaluation results can effectively reflect the impact of the clouds on ground observations. According to the moving speed and thickness change of the clouds, the continuous occlusion time of different regions is calculated. A simple time integration method is used to record the occlusion duration of each region. An occlusion time distribution map is generated, indicating the occlusion times of different regions, to support subsequent decision-making. Based on the occlusion degree value and the continuous occlusion time, a comprehensive evaluation model is constructed. Weight parameters are set to generate a difficulty evaluation value for eliminating cloud blur by combining the two factors, and to judge the difficulty of eliminating cloud blur. Threshold criteria are set to determine the evaluation criteria for slight, moderate, and severe occlusion based on historical data and expert experience.Judge according to the evaluation value and the preset occlusion elimination difficulty threshold. If the evaluation value is greater than the threshold, it is determined as severe occlusion, and the historical cloudless image is called for reconstruction; if the evaluation value is less than or equal to the threshold, it is determined as slight occlusion, and light transmission compensation is performed on the blurred area. Record the decision result and generate the corresponding processing plan to support subsequent image processing. For slightly occluded areas, image processing algorithms (such as adaptive histogram equalization) are used for light transmission compensation to enhance the visibility of the image. Record the parameter settings of the compensation (such as enhancement intensity). Ensure the image quality during the compensation process and avoid loss of image details. Based on the remote sensing image with cloud blur removed, perform real-time user scene structure analysis. Use image segmentation techniques (such as K-means clustering or superpixel segmentation) to extract building, tree, and other environmental elements in the scene. Generate real-time user scene structure data and record the characteristics of the extracted environmental elements, such as location, size, and shape. On the basis of the remote sensing image with blur removed, perform semantic segmentation of the depth image, and use deep learning models such as convolutional neural networks (CNNs) to extract multiple environmental elements. Record the category of each environmental element and its spatial location information to provide data support for subsequent modeling. Based on the real-time user scene structure data and the extracted multiple environmental elements, use 3D modeling software (such as Blender or Unity) to build a 3D scene model of the user's location. Ensure the accuracy and details of the model, and record the parameter settings and environmental characteristics during the modeling process for subsequent analysis and optimization.

[0068] In this embodiment, refer to Figure 3 , which is a schematic diagram of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0069] Extract real-time user instructions based on the monitoring log of the self-driving platform;

[0070] Conduct current user demand analysis on the real-time user instructions to generate real-time user demand characteristics;

[0071] Conduct emergency situation analysis based on the real-time user demand characteristics and the user location scene structure model to obtain the real-time user emergency demand situation;

[0072] Perform function module corresponding matching processing on the real-time user emergency demand situation according to the multi-functional emergency module, and perform self-driving platform mode switching to generate an adaptive emergency demand mode switching strategy.

[0073] In this embodiment, the self-propelled platform is equipped with a voice recognition module and a user input interface to receive user instructions in real time. These instructions can be input via voice, touch screen, or mobile application. Set the data collection frequency to ensure that each instruction can be immediately recorded after being received and stored in the real-time log. The recorded content includes information such as timestamp, instruction content, and user ID. Use natural language processing (NLP) techniques to parse the instructions input by the user. By using methods such as word segmentation, part-of-speech tagging, and named entity recognition, convert the instructions into structured data. Classify the instructions according to preset instruction categories (such as navigation, path adjustment, stop, emergency help, etc.), and extract key features such as verbs, nouns, and demand types. Store the parsed user instructions in the database to ensure the traceability and real-time update of the instructions. Design the data structure for subsequent analysis and retrieval. Regularly clean up outdated instruction records to maintain the efficiency and response speed of the database, ensuring that the system can respond to new user needs in a timely manner. Based on the extracted real-time user instructions, use feature extraction techniques to identify the user's demand characteristics. This can include the urgency, type, and expected operation results of the demand. Use machine learning algorithms (such as decision trees or support vector machines) to analyze the demand and generate a user demand feature vector. These features can include time features (such as morning, evening) and spatial features (such as the environmental features of the current location). Convert the user demand characteristics into a quantifiable data model for subsequent emergency situation analysis. Set the weights and influencing factors of the demand characteristics. For example, the weight of the demand urgency can be set to 0.6, and the environmental adaptability is set to 0.4. Record the historical data of different user demands to support the training and optimization of the model and ensure the accuracy of demand analysis. The system continuously monitors the changes in user demand characteristics and updates dynamically according to real-time instructions. Establish a feedback mechanism to adjust the demand analysis model in a timely manner. According to the real-time user demand characteristics, construct an emergency situation analysis model. The model should consider various environmental factors, such as the user's current location, the safety and accessibility of the surrounding environment, etc. Combine historical data and use clustering analysis methods (such as K-means) to identify similar emergency situations and generate a situation feature library. By real-time monitoring the user demand characteristics and matching with the situation feature library, identify the user's current emergency demand situation. Set fuzzy logic rules for situation reasoning when the user's demand is unclear (such as the meaning of the voice instruction is ambiguous) to ensure that the system can perform effective emergency responses. Record the identified emergency situation information in the database to ensure the data integrity and traceability of the situation. The recorded content includes situation type, occurrence time, relevant instructions, etc. Regularly analyze the emergency situation data to identify common emergency scenarios and user demands, providing data support for subsequent module matching processing. The self-propelled platform is equipped with multiple emergency function modules, such as navigation guidance, automatic obstacle avoidance, emergency help, environmental monitoring, etc. Configure the corresponding function modules according to the user demand characteristics.Set the trigger conditions and priorities for each functional module. For example, when the user issues a help request, the emergency help module is activated preferentially. Design a module matching algorithm to dynamically select the most suitable functional module for response based on the real-time demand characteristics of the user and the emergency situation information. Adopt a weighted matching method to calculate the matching degree according to the weights of the demand characteristics to ensure that the most suitable functional module can be selected. According to the matching result, perform mode switching of the self-driving platform. For example, when the user needs navigation, switch to the navigation mode; when the user needs emergency help, switch to the emergency mode. Generate an adaptive emergency demand mode switching strategy, and record the time, mode type, and user feedback during the switching process for subsequent optimization. Implement the generated emergency demand mode switching strategy, and monitor the status of the platform and the user feedback in real time. If the user is not satisfied with the current mode, it can be adjusted through the feedback mechanism. Record the effect data after switching, including user satisfaction, response time, and success rate, etc., to evaluate the effectiveness of the strategy. Regularly analyze the historical data of emergency demand mode switching to identify common switching modes and user preferences to optimize the matching algorithm and switching strategy. According to the user feedback and environmental changes, adjust the module configuration and priorities to ensure that the system can efficiently respond to user demands. Generate a summary report of emergency demand mode switching, record the situation of each switching, user feedback, and effect evaluation, and provide a basis for subsequent decision-making. Integrate the data in the report with other systems (such as the user behavior analysis system) to support comprehensive decision-making analysis.

[0074] In this embodiment, refer to Figure 4 , which is a schematic diagram of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0075] Obtain the panoramic monitoring image of the self-driving platform;

[0076] Perform terrain height analysis on the panoramic monitoring image of the self-driving platform to generate terrain height information;

[0077] Identify the environmental structure type according to the panoramic monitoring image of the self-driving platform to obtain the environmental structure type;

[0078] Perform terrain structure mining based on the environmental structure type and terrain height information to generate terrain structure features;

[0079] Perform platform movement constraint analysis according to the terrain structure features to generate real-time terrain movement constraint features.

[0080] In this embodiment, the self-propelled platform is equipped with a multi-lens panoramic camera, which can achieve 360-degree panoramic image acquisition. Configure the camera parameters, including resolution (such as 4000x3000 pixels), frame rate (such as 30 frames per second), and field of view (such as 180 degrees). Ensure the stability of the camera during the acquisition process to avoid image blurring caused by vibration. Anti-shake technology or a fixed bracket can be used to improve the image quality. Start the panoramic monitoring system and set the timed acquisition mode, for example, acquire a panoramic image every 10 seconds. Ensure that each acquired image can completely cover the surrounding environment. Record the acquisition time, location, and camera status of each image for subsequent analysis and processing. Store the acquired images in real-time on the local storage of the platform or a cloud server. Regularly check the acquired image data to ensure that the image quality meets the requirements. Mark the blurred or distorted images for subsequent elimination. Preprocess the acquired panoramic monitoring images, including color correction, denoising, and image enhancement, to improve the accuracy of subsequent high-resolution analysis. Use histogram equalization technology to improve the contrast of the images, making the terrain features more obvious in the images. Adopt structured light or stereo vision technology, and compare two or more images with different perspectives to calculate the depth information of the terrain. During this process, set the parallax threshold and the minimum / maximum depth range to improve the accuracy of the analysis. Use a 3D reconstruction algorithm (such as Triangulation) to convert the image data into a 3D point cloud and generate the height information of the terrain. Record the density of the generated point cloud (such as the number of points per square meter) and the height accuracy (such as ±5 cm). Visualize the parsed terrain height information to generate a height map. Use pseudo-color coding technology to facilitate the intuitive display of different height regions. Overlay the height information with the original panoramic image to form a composite image, providing rich visual information for subsequent analysis. Based on the panoramic monitoring images, apply image segmentation techniques (such as superpixel segmentation or region growing algorithm) to extract different structural features (such as buildings, roads, vegetation, etc.) in the environment. Set the segmentation parameters, such as the number of superpixels and the similarity threshold, to ensure the accurate identification of different environmental structures. Use machine learning or deep learning algorithms (such as convolutional neural network CNN) to classify the extracted structural features. When training the model, use the labeled environmental images as the training set and set the training parameters (such as learning rate, batch size). During the classification process, record the recognition accuracy and recall rate of each environmental structure to evaluate the performance of the model. Generate a map of the environmental structure types according to the classification results and mark the structure types of different regions. Record the occurrence frequency and spatial distribution information of each type for subsequent analysis. Combine the environmental structure type information with the height information to provide basic data for subsequent terrain structure mining. Based on the environmental structure type and terrain height information, conduct feature mining of the terrain structure. Use terrain analysis tools (such as GIS software) to calculate features such as the slope, aspect, and curvature of the terrain.Set feature extraction parameters, such as the resolution of slope calculation (e.g., 1 meter) and the range of curvature analysis, to ensure the accuracy of the extraction results. Model the extracted terrain structure features to generate a three-dimensional terrain structure model to visually display the spatial features of the environment. Record the detailed parameters of the model, such as the resolution of the three-dimensional grid and the number of polygons, for subsequent performance optimization. Analyze the generated terrain structure model to identify key terrain features (such as steep slopes, gullies, etc.) and evaluate their impact on the movement of the self-propelled platform. Record the results of the feature analysis, including the spatial position, impact degree, and potential risks of each feature, for subsequent decision-making support. Analyze the movement constraints of the platform under different geographical conditions based on the terrain structure features. For example, identify steep slope areas, obstacle distributions, and their impact on path selection. Set the parameters of the constraint analysis, such as the maximum climbing angle (e.g., 20 degrees) and the minimum passing width (e.g., 0.5 meters), to ensure the operability of the analysis results. During the operation of the self-propelled platform, monitor the impact of environmental changes on the movement constraint features in real time. Use sensor data (such as lidar, GPS, etc.) for comparative analysis to dynamically adjust the movement strategy. Record the changes in the real-time constraint features to support subsequent path optimization and decision-making. Output the analyzed real-time terrain movement constraint features as decision-making support data, recording the impact degree and specific location of each feature. Provide data support for the path planning system of the self-propelled platform to ensure that the platform can move safely and efficiently under complex terrain conditions.

[0081] In this embodiment, step S4 includes the following steps:

[0082] Identify the real-time position of the self-propelled platform;

[0083] Calculate the three-dimensional spatial coordinates of the real-time position and the user's real-time position;

[0084] Perform spatial distance calculation based on the three-dimensional spatial coordinates to obtain spatial distance parameters;

[0085] Perform intelligent path planning on the user position scene structure model according to the real-time terrain movement constraint features and the accurate spatial distance parameters to generate an initial planned path.

[0086] In this embodiment, the self-propelled platform is equipped with a high-precision positioning system, which usually includes GPS, IMU (Inertial Measurement Unit), and ground base station signals. Configure the system to ensure accurate positioning data can be obtained in various environments (such as cities, mountains, forests, etc.). Set the update frequency of GPS (for example, update once per second) and the sampling rate of IMU (such as 100Hz) to improve the accuracy of real-time position. Start the real-time positioning system to collect the position information of the platform. Record the timestamp, longitude, latitude, and altitude (such as in the WGS84 coordinate system) of each data point to ensure data integrity. Adopt multi-source data fusion technology to combine GPS data with IMU data, and use the Kalman filtering algorithm to eliminate noise and errors, generating a more accurate real-time position. Regularly verify the obtained real-time position data to ensure its accuracy. For example, use known reference points for comparison and check whether the deviation range is within the acceptable range (such as ±5 meters). Record the results of each verification, including time, position data, and deviation, for subsequent analysis and optimization. Obtain the user's real-time position through the client device (such as a smartphone, tablet). This can also be achieved through user input or other sensors. Similarly, record the longitude, latitude, and altitude information of the user's position and ensure that the data update frequency matches that of the self-propelled platform's positioning system. Convert the obtained longitude and latitude information into three-dimensional space coordinates. Use the conversion formula from geographic coordinates to Cartesian coordinates to convert longitude, latitude, and altitude into three-dimensional coordinates (X, Y, Z). Set conversion parameters, such as the radius of the earth (for example, 6371 kilometers), to ensure accuracy during the conversion process. Verify the converted three-dimensional coordinates to ensure the validity of the coordinates. For example, check whether the coordinates fall within the valid geographical range and record the coordinate values and timestamps of each calculation. Store the three-dimensional coordinates of the self-propelled platform and the user in the database respectively for subsequent path planning. Adopt the three-dimensional space distance formula to calculate the space distance between the position of the self-propelled platform and the user's position. Use the obtained three-dimensional coordinates and substitute them into the formula for accurate space distance calculation. Record each coordinate value used in the calculation process to ensure the transparency of the calculation. Set the calculation accuracy parameter, for example, retain two decimal places, to ensure the reliability of the calculation result. Output the calculation result, generate an accurate space distance parameter, and store it in the system database, recording the calculation time and the coordinate data involved in the calculation. Generate a distance report based on the distance result to provide necessary information for subsequent path planning. Use the real-time terrain movement constraint features obtained from the previous analysis to evaluate the impact of the current environment on path planning. The constraint features can include slope, obstacle position, and ground type, etc. Set the constraint evaluation criteria, such as the maximum allowable slope (such as 15 degrees), the minimum passing width (such as 1 meter), to ensure that the path planning follows the principles of safety and feasibility. Select a suitable path planning algorithm, such as the A* algorithm, Dijkstra algorithm, or RRT (Rapidly-Exploring Random Tree) algorithm, which can effectively handle path finding in complex environments.Set algorithm parameters, such as heuristic functions, search ranges, and priorities, to ensure the efficiency and accuracy of path planning. Based on the user's location, platform location, and environmental constraint characteristics, run the path planning algorithm to generate an initial planned path. The path should be as short as possible and avoid obstacles to ensure safety. Record the characteristics of the generated path, including path length, key points passed through, and corresponding terrain information, for subsequent optimization and adjustment.

[0087] In this embodiment, step S5 includes the following steps:

[0088] Perform dynamic path cruising based on the initial planned path and obtain path cruising images;

[0089] Perform global brightness enhancement on the path cruising images to generate globally brightness-optimized cruising images;

[0090] Perform real-time visual recognition of path obstacles on the globally brightness-optimized cruising images and mark path obstacle nodes;

[0091] Perform dynamic road section structure change analysis based on the globally brightness-optimized cruising images to generate dynamic road section structure characteristics;

[0092] Perform real-time road section passability assessment based on the dynamic road section structure characteristics to obtain real-time road section passability assessment results;

[0093] Perform dynamic avoidance path adjustment on the initial planned path according to the path obstacle nodes and real-time road section passability assessment results, and construct an intelligent path optimization strategy.

[0094] In this embodiment, the self-propelled platform is equipped with a high-resolution camera to ensure real-time acquisition of path images during cruising. Set the working parameters of the camera, such as resolution (e.g., 1920x1080 pixels) and frame rate (e.g., 30 frames per second), to ensure image quality. Start the dynamic cruising mode, navigate according to the initial planned path, and set the cruising speed (e.g., 0.5 m / s) to ensure the coordination between image acquisition and moving speed. During driving, record the cruising images in real-time to ensure coverage of each key point of the path. By regularly acquiring images, generate an image sequence of path cruising. Record the timestamp and position of each frame of the image for subsequent analysis and processing to ensure the integrity and accuracy of the data. Preprocess the obtained path cruising images, including denoising and image format conversion, to improve the effect of subsequent brightness enhancement. Use a high-pass filter to reduce background noise while maintaining the edge information of the image to ensure image clarity. Adopt histogram equalization technology to perform global brightness enhancement on the image. Set the equalization parameters to enhance the contrast and brightness of the image, making the image details more obvious. During the processing, record the changes in the brightness histogram before and after enhancement to evaluate the effectiveness of the enhancement effect. Output the images processed by global brightness enhancement to generate globally brightness-optimized cruising images. Ensure that the brightness and contrast of each image meet the requirements of subsequent visual recognition. Store the optimized images and prepare for subsequent obstacle recognition and structural change analysis. Select a suitable visual recognition algorithm (such as YOLO, Faster R-CNN, etc.) for real-time recognition of path obstacles. When training the model, use the labeled obstacle images as the training set and set the training parameters (such as learning rate, batch size). Ensure that the recognition model can handle obstacles under different lighting and environmental conditions to improve the robustness of the model. Input the globally brightness-optimized cruising images into the obstacle recognition model to detect obstacles in the image in real-time and mark the corresponding nodes. Record the timestamp of the recognition and the obstacle category (such as pedestrians, vehicles, obstacles, etc.). Set the confidence threshold of the recognition (e.g., 0.6) to ensure that only the recognition results with high confidence are marked, reducing the impact of misrecognition. Store the recognized obstacle node information (including position, type, and confidence) in the database for subsequent path adjustment and analysis. Record the characteristic information of each obstacle, such as size, shape, and relative position, to support dynamic avoidance path adjustment. Based on the globally brightness-optimized images, analyze the structural changes in the dynamic sections of the path. Use an edge detection algorithm (such as Canny edge detection) to extract the edge features of the sections to identify structural changes. Set the parameters of edge detection, such as the low threshold (e.g., 50) and the high threshold (e.g., 150), to ensure effective identification of section changes. Compare the extracted edge features with the structural features in the previous path images to identify the specific location and nature of the dynamic changes (such as width, shape changes). Record the time and position of the changes for subsequent analysis and decision support.Output the results of dynamic change analysis, generate a dynamic road section structure feature map, and mark the changed areas. These features will be used for subsequent passability assessment. Record the feature information of each dynamic road section to support path optimization and adjustment. According to the dynamic road section structure features, set passability assessment criteria, such as the minimum passing width (e.g., 1 meter), the maximum slope (e.g., 15 degrees), etc., to ensure the effectiveness of the assessment results. Consider the impacts of different obstacles, set corresponding weight parameters to comprehensively evaluate the passability of the path. Conduct real-time passability assessment for each dynamic road section and calculate whether it meets the set assessment criteria. Record the parameter changes and assessment results (such as passable or impassable) during the assessment process. Generate an assessment report, record the passability status and influencing factors of each road section, and provide data support for subsequent path adjustment. According to the identified obstacle nodes and the real-time road section passability assessment results, design a dynamic avoidance path adjustment strategy. Set adjustment rules, such as automatically searching for alternative paths when encountering impassable road sections. Record the logic of path adjustment, including the preferred alternative path types (such as detouring, temporary stay, etc.) and the adjusted parameters. Use path optimization algorithms (such as A* algorithm or Dijkstra algorithm) to dynamically adjust the initial planned path to ensure the safety and feasibility of the path. Set algorithm parameters to improve the optimization efficiency and accuracy. Record each key step during the optimization process, including the path features before and after adjustment and the corresponding environmental constraints. Visualize the generated dynamically adjusted path, showing the starting point, ending point of the path, as well as the obstacles and dynamic road sections passed through. Ensure that users can intuitively understand the results of path adjustment. Verify the optimized path, simulate the platform moving along the new path, observe whether it conforms to the real-time environmental changes, and make necessary adjustments according to the feedback.

[0095] In this embodiment, step S6 includes the following steps:

[0096] Perform full-cycle platform intelligent control according to the adaptive emergency demand mode switching strategy and the intelligent path optimization strategy, and collect instant control feedback information;

[0097] Perform platform movement response recognition on the instant control feedback information, and extract platform movement response information;

[0098] Perform response delay analysis on the platform movement response information to generate self-walking platform response delay parameters;

[0099] Perform abnormal fault detection on the instant control feedback information to generate platform abnormal fault detection data;

[0100] Perform iterative control optimization based on the self-walking platform response delay parameters and the platform abnormal fault detection data, and construct an intelligent control optimization model.

[0101] In this embodiment, the self-propelled platform is equipped with an advanced intelligent control system, which can perform full-cycle control according to the adaptive emergency demand mode switching strategy and the intelligent path optimization strategy. The system includes sensors (such as IMU, GPS, lidar) and actuators (such as motors, steering systems). Set the update frequency of the control system (such as 10 times per second) to ensure that it can respond to user needs and environmental changes in real time. Start the intelligent control system of the platform and perform navigation and motion control of the platform according to the preset strategy. Dynamically adjust the speed, direction, and driving mode to adapt to the current environment and task requirements. During the control process, collect control feedback information in real time, including the position, speed, status (such as driving, stopping) of the platform, etc., to ensure the integrity and accuracy of the data. Store the instant control feedback information in the database to ensure data traceability and accessibility. Mark each piece of data with a timestamp for subsequent analysis. Regularly check the status of data storage, clean up outdated data, and maintain the efficiency of the database to ensure that the system can respond to new control instructions in a timely manner. Analyze the instant control feedback information to extract the mobile response information of the platform. This information can include driving status, speed changes, direction adjustments, etc. Use signal processing techniques (such as FFT transformation) to perform frequency-domain analysis on the extracted data to identify the response characteristics and timing changes of the platform. Classify the extracted mobile response information and record the occurrence frequency and characteristics (such as response time, amplitude, etc.) of each response type. Generate a database of response information to ensure the availability of subsequent analysis. Set the storage structure for easy and quick retrieval and access to ensure that the system can quickly obtain response information when needed. Implement a real-time monitoring mechanism to continuously track the mobile response of the platform and identify abnormal response patterns (such as excessive vibration, stagnation, etc.). Record the results of each monitoring, including response characteristics and monitoring time, for subsequent analysis and optimization. Set the calculation formula for response delay, for example, response delay = control instruction issuance time - actual platform response time. Ensure the accuracy of the timestamp to calculate accurate delay parameters. Record the issuance time of each control instruction and the response time of the platform, and calculate the delay of each response. Perform statistical analysis on the calculated response delay data to generate a distribution map of response delay parameters, including indicators such as average delay, maximum delay, and standard deviation. Set analysis parameters, such as a time window (such as the past 30 minutes), to facilitate observing the delay change trend and identifying potential problems. Design an abnormal fault detection algorithm to identify potential faults of the platform based on the instant control feedback information. This can adopt rule-based detection methods (such as threshold detection) and machine learning-based methods (such as anomaly detection algorithms). Set detection parameters, such as threshold criteria (such as marking as abnormal when the speed fluctuation exceeds ±20%), to ensure the accuracy and timeliness of detection. During the real-time monitoring process, apply the fault detection algorithm to promptly identify abnormal situations and record fault information (such as fault type, occurrence time, relevant status).Generate a fault monitoring report, record the detection results and impact scope of each fault, and ensure the integrity and traceability of the data. Store the generated abnormal fault detection data in the database to ensure that the fault information can be queried and analyzed at any time. Record the occurrence frequency, type, and impact degree of the faults. Regularly summarize and analyze the fault data to identify common fault patterns and potential risks, providing support for subsequent maintenance. Integrate the response delay parameter and the abnormal fault detection data, and analyze the relationship between the two. For example, whether the delay is related to the fault occurrence frequency, or whether the delay affects the control effect of the platform. Set the analysis methods, such as correlation analysis (e.g., Pearson correlation coefficient) and regression analysis, to ensure the scientific nature of the results. Based on the integrated analysis results, construct an intelligent control optimization model. The model should consider the influencing factors (such as response delay, fault type) and their weights to achieve dynamic adjustment of the control strategy. Set the model parameters, such as the optimization objectives (e.g., minimizing the delay and fault occurrence rate), to ensure the practicality and effectiveness of the model. Verify the constructed intelligent control optimization model, test it using historical data, and evaluate the prediction accuracy and control effect of the model. Record the verification results, including the success rate and error range. Iteratively optimize the model according to the verification results, adjust the model parameters and optimization strategies to improve the overall performance and response ability of the system.

[0102] In this embodiment, an intelligent control system for a self-propelled platform is provided, which is used to execute the intelligent control method for a self-propelled platform as described above, and includes:

[0103] A three-dimensional scene module, which is used to obtain the user's real-time position satellite remote sensing image based on satellite sensing, perform three-dimensional scene modeling, and construct a user position scene structure model;

[0104] An adaptive mode switching module, which is used to perform an emergency situation analysis on the user position scene structure model according to the multi-functional emergency module, perform self-propelled platform mode switching, and generate an adaptive emergency demand mode switching strategy;

[0105] A mobile constraint analysis module, which is used to obtain the panoramic monitoring image of the self-propelled platform, perform environmental structure type recognition and platform mobile constraint analysis, and generate real-time terrain mobile constraint features;

[0106] A path planning module, which is used to perform intelligent path planning according to the real-time terrain mobile constraint features and the user position scene structure model, and generate an initial planned path;

[0107] A path adjustment module, which is used to perform dynamic path cruising based on the initial planned path, and then perform dynamic avoidance path adjustment to construct an intelligent path optimization strategy;

[0108] The iterative control optimization module is used to perform full-cycle platform intelligent control according to the adaptive emergency demand pattern switching strategy and the intelligent path optimization strategy, and conduct iterative control optimization to build an intelligent control optimization model.

[0109] The present invention can obtain the user's geographic location in real time by acquiring high-precision satellite remote sensing images, and build an accurate three-dimensional scene model based on this information. This provides a solid foundation for subsequent emergency response, path planning and other modules. Using satellite images, geographic information on a global scale can be quickly obtained, avoiding the limitations of traditional manual measurement and significantly improving the efficiency and accuracy of scene modeling. Real-time acquisition of satellite images and scene models provides real-time geographic information support for other modules, ensuring the timeliness and reliability of emergency response. According to the scene model, situation analysis can be performed to identify different emergency needs, such as natural disasters, sudden accidents, etc., so as to generate an adaptive emergency demand mode switching strategy. The module can intelligently switch the operation mode of the platform, such as switching from normal mode to emergency mode, to ensure that the platform can respond quickly and make appropriate operations in various emergency situations. Adaptive mode switching can automatically adjust the behavior of the platform according to the current situation, reduce manual intervention, and improve the overall emergency response efficiency of the system. Real-time images of the environment where the platform is located are obtained through panoramic monitoring images, and terrain types, obstacles, dangerous areas, etc. can be identified, which helps to provide accurate terrain information for path planning. Mobile constraint analysis is performed based on environmental recognition information to generate mobile restriction features of real-time terrain. For example, it analyzes whether the road surface is suitable for the platform to drive, whether there are obstacles that need to be avoided, etc., to provide strong data support for subsequent path planning. Through a comprehensive analysis of the environment, it can ensure that the platform can drive safely in complex terrain and reduce the risks caused by unknown terrain. According to the terrain characteristics and real-time constraints, a safe and efficient driving path is automatically planned. This path not only takes into account the shortest distance, but also incorporates the feasibility analysis of the terrain to avoid potential dangerous areas. In a complex and dynamically changing environment, path planning can be adjusted according to real-time information to ensure that the platform always moves along the optimal route. By combining satellite remote sensing images and terrain constraint information, the accuracy of path planning is greatly improved, reducing the deviation in platform operation. During the platform driving process, the path adjustment module can perceive environmental changes in real time, automatically adjust the path to avoid obstacles or temporary dangerous areas, and ensure the smooth progress of the platform. Based on real-time feedback during the cruise process, the initial planned path is adjusted to avoid falling into dead ends or entering inappropriate areas, and improve the intelligence and adaptability of the path. Through continuous path adjustment, the platform's driving path can be optimized, operating efficiency can be improved, and energy consumption can be reduced. The iterative control optimization module can periodically optimize the platform's intelligent control by continuously monitoring the platform status and external environment changes to ensure the platform's stability and operating efficiency. Based on real-time emergency mode switching strategies and path optimization strategies, the platform can adaptively adjust the control strategy to respond to different emergency situations and terrain changes. This enables the platform to have the ability to self-learn and adapt in complex and dynamic environments, thereby improving its performance and reliability in long-term operation.

[0110] Therefore, in any case, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0111] As described above, these are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent control method for a self-propelled platform, characterized in that, The self - walking platform is equipped with a multi - functional emergency module; it includes the following steps: Step S1: Obtain the satellite remote - sensing image of the user's real - time position based on satellite sensing, and conduct 3D scene modeling to construct the user - position scene structure model; Step S2: Conduct emergency situation analysis on the user - position scene structure model according to the multi - functional emergency module, and perform mode switching of the self - walking platform to generate an adaptive emergency - requirement mode - switching strategy; Step S3: Obtain the panoramic monitoring image of the self - walking platform, and conduct environmental structure type recognition and platform - movement constraint analysis to generate real - time terrain movement constraint features; Step S4: Conduct intelligent path planning according to the real - time terrain movement constraint features and the user - position scene structure model to generate an initial planned path; Step S5: Conduct dynamic path cruising based on the initial planned path, and then perform dynamic avoidance path adjustment to construct an intelligent path optimization strategy; Step S6: Conduct full - cycle platform intelligent control according to the adaptive emergency - requirement mode - switching strategy and the intelligent path optimization strategy, and perform iterative control optimization to construct an intelligent control optimization model; Among them, the specific steps of Step S1 are: Obtain the monitoring log of the self - walking platform; Locate the user's real - time position based on the monitoring log of the self - walking platform; Obtain the satellite remote - sensing image of the user's real - time position based on satellite sensing according to the user's real - time position; Conduct 3D scene modeling according to the satellite remote - sensing image of the user's real - time position to construct the user - position scene structure model; among them, the specific steps of conducting 3D scene modeling according to the satellite remote - sensing image of the user's real - time position to construct the user - position scene structure model are: Conduct cloud - cover fuzzy detection on the satellite remote - sensing image of the user's real - time position, and extract the cloud - blurred area image; Conduct quantitative analysis of the cloud on the cloud - blurred area image to obtain the cloud thickness and moving direction; Conduct future trend prediction of the cloud according to the cloud thickness and moving direction to generate a cloud - movement trend prediction map; Conduct occlusion - degree evaluation on the cloud - movement trend prediction map to generate an occlusion - degree value; Conduct continuous occlusion - time calculation according to the cloud - movement trend prediction map to obtain the cloud continuous occlusion time; Conduct comprehensive cloud - blur elimination - difficulty evaluation based on the occlusion - degree value and the cloud continuous occlusion time to obtain a cloud - blur elimination evaluation value; Make a decision on the cloud - blur elimination evaluation value based on the preset elimination - difficulty threshold. When the cloud - blur elimination evaluation value is greater than the preset elimination - difficulty threshold, it is determined as severe occlusion, and obtain the historical cloud - free image at the same position for reconstruction; when the cloud - blur elimination evaluation value is less than or equal to the preset elimination - difficulty threshold, it is determined as slight occlusion, and perform light - transmission compensation on the cloud - blurred area image to obtain a cloud - blur elimination remote - sensing image; Conduct real - time scene structure analysis of the user on the cloud - blur elimination remote - sensing image to generate real - time scene structure data of the user; Conduct depth - image semantic segmentation on the cloud - blur elimination remote - sensing image to extract multi - environmental elements; Conduct 3D scene modeling based on the real - time scene structure data of the user and the multi - environmental elements to construct the user - position scene structure model.

2. The method according to claim 1, characterized in that, The specific steps of Step S2 are: Extract the user's real - time instructions based on the monitoring log of the self - walking platform; Analyze the current user requirements based on the user's real-time instructions to generate user real-time requirement features; Conduct an emergency situation analysis according to the user real-time requirement features and the user location scenario structure model to obtain the user real-time emergency requirement situation; Perform function module corresponding matching processing on the user real-time emergency requirement situation according to the multi-functional emergency module, and perform self-walking platform mode switching to generate an adaptive emergency requirement mode switching strategy.

3. The intelligent control method for a self-propelled platform according to claim 1, characterized in that, The specific steps of step S3 are as follows: Obtain the panoramic monitoring image of the self-walking platform; Analyze the terrain height of the panoramic monitoring image of the self-walking platform to generate terrain height information; Identify the environmental structure type according to the panoramic monitoring image of the self-walking platform to obtain the environmental structure type; Conduct terrain structure excavation based on the environmental structure type and terrain height information to generate terrain structure features; Conduct platform movement constraint analysis according to the terrain structure features to generate real-time terrain movement constraint features.

4. The intelligent control method for a self-propelled platform according to claim 1, characterized in that The specific steps of step S4 are as follows: Identify the real-time position of the self-walking platform; Calculate the three-dimensional space coordinates of the real-time position and the user's real-time position; Conduct spatial distance calculation based on the three-dimensional space coordinates to obtain the spatial distance parameter; Conduct intelligent path planning on the user location scenario structure model according to the real-time terrain movement constraint features and the accurate spatial distance parameter to generate the initial planned path.

5. The intelligent control method for a self-propelled platform according to claim 1, characterized in that, The specific steps of step S5 are as follows: Conduct dynamic path cruising based on the initial planned path and obtain the path cruising image; Enhance the global brightness of the path cruising image to generate a globally brightness-optimized cruising image; Conduct real-time visual recognition of path obstacles on the globally brightness-optimized cruising image and mark the path obstacle nodes; Conduct dynamic road section structure change analysis according to the globally brightness-optimized cruising image to generate dynamic road section structure features; Conduct real-time road section passability evaluation based on the dynamic road section structure features to obtain the real-time road section passability evaluation result; Conduct dynamic avoidance path adjustment on the initial planned path according to the path obstacle nodes and the real-time road section passability evaluation result to construct an intelligent path optimization strategy.

6. The intelligent control method for a self-propelled platform according to claim 1, wherein The specific steps of step S6 are as follows: Conduct full-cycle platform intelligent control according to the adaptive emergency requirement mode switching strategy and the intelligent path optimization strategy, and collect instant control feedback information; Conduct platform movement response recognition on the instant control feedback information and extract the platform movement response information; Conduct response delay analysis on the platform movement response information to generate the self-walking platform response delay parameter; Conduct abnormal fault detection on the instant control feedback information to generate the platform abnormal fault detection data; Conduct iterative control optimization based on the self-walking platform response delay parameter and the platform abnormal fault detection data to construct an intelligent control optimization model.

7. An intelligent control system for a self-propelled platform, characterized in that, For executing the intelligent control method for a self-walking platform as described in claim 1, including: A three-dimensional scene module for obtaining the user real-time position satellite remote sensing image based on satellite sensing and conducting three-dimensional scene modeling to construct the user location scenario structure model; An adaptive mode switching module for conducting emergency situation analysis on the user location scenario structure model according to the multi-functional emergency module and conducting self-walking platform mode switching to generate an adaptive emergency requirement mode switching strategy; A mobile constraint analysis module, which is used to obtain panoramic monitoring images of the self-propelled platform, identify the environmental structure type and analyze the platform movement constraints, and generate real-time terrain movement constraint features; A path planning module, which is used to perform intelligent path planning according to the real-time terrain movement constraint features and the user location scene structure model, and generate an initial planned path; A path adjustment module, which is used to perform dynamic path cruising based on the initial planned path, and then perform dynamic avoidance path adjustment to construct an intelligent path optimization strategy; An iterative control optimization module, which is used to perform full-cycle platform intelligent control according to the adaptive emergency demand mode switching strategy and the intelligent path optimization strategy, and perform iterative control optimization to construct an intelligent control optimization model.

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