48 results about "Adaptive environment" patented technology

This application provides a method and apparatus for autonomous obstacle avoidance of unmanned aerial vehicles (UAVs) based on lidar, relating to the field of UAV technology. By extracting the optical features of the surface material of the current area and matching the optimal band interval based on a pre-built feature fingerprint database, the emission wavelength of the lidar can adapt to the main constituent materials of the environment, significantly improving the signal-to-noise ratio and feature recognition of the echo signal. A programmable gain circuit is introduced at the receiving end to dynamically compensate for signal strength changes caused by band switching or different distances, improving the reliability and consistency of point cloud data generation. This ensures that the generated "optimized obstacle avoidance path" not only effectively avoids marked obstacle points but also better conforms to actual environmental constraints, significantly improving the perception reliability and obstacle recognition accuracy of the UAV lidar in complex environments containing various heterogeneous materials, thereby comprehensively enhancing the safety of autonomous obstacle avoidance and the environmental adaptability of the overall system.

The application belongs to the field of artificial intelligence security, and specifically discloses a defense method for specific target forgetting attack on vertical federated learning, which comprises the following steps: each client trains a student model by using a locally stored local data set to generate embedding features, and uploads the embedding features to a server; the server receives the embedding features from each client, calculates the gradient of the loss on the embedding features based on a preset global task loss, and generates a targeted feature according to the gradient, and distributes the targeted feature to each client; each client receives the targeted feature, and updates the student model by minimizing the feature alignment loss between the embedding features generated by the student model and the received targeted feature; when the client obtains a new data set, the client performs incremental learning by using the new data set to update the student model. The application can dynamically adapt to environmental changes and effectively resist the defense mechanism of the specific target forgetting attack with strong concealment.

The present disclosure provides an intelligent autonomous remote control underwater robot ARV and its control system, and relates to the technical field of machine learning, which comprises: a body environment perception and risk assessment system that collects environment risk information and current operation execution information and constructs a current risk vector; an adaptive boundary generation and optimization system that stores at least two risk boundary vectors and calculates a similarity index between the current risk vector; a control mode decision and switching system that determines a target control mode according to the comparison result of the similarity index and a preset threshold value and executes; an intelligent performance evaluation and feedback optimization system that collects key performance indicators to determine a task score and judges whether to update the risk boundary vector according to the task score, and when updating, the adaptive boundary generation and optimization system generates a candidate boundary vector based on the task score and replaces the original risk boundary vector after verification. The scheme solves the problem that the ARV control mode switching in the prior art relies on manual operation and is difficult to adapt to environmental changes.

A novel approach is presented that enables human rescuers and human operators to explore and navigate treacherous and challenging environments safely and efficiently by providing an integrated, practical, flexible, and robust system. Leveraging advanced sensor deployment, adaptive communication networks, and real-time subject detection with AR-enabled navigation to enhance safety and real-time usability, a single practical solution is disclosed that is flexible, scalable, robust and error resilient, secure, energy-efficient, sustainable, and configured to optimize sensor placement and robot behavior to adapt to changes in the environment. Key innovations include federated learning for privacy-preserving model updates and multi-modal sensing for superior accuracy.

The application relates to the technical field of network security, and discloses an Azure virtual desktop micro-isolation monitoring method and system based on a zero-trust architecture, which comprises the following steps: collecting and analyzing user operation logs in real time to construct a behavior baseline model and mode distribution; quantifying the operation deviation degree through behavior fluctuation analysis, identifying abnormalities and determining a risk level; detecting session environment threats in combination with device security data; generating a session credibility score and control instruction based on weighted analysis of user behavior and device state; dynamically adjusting access permissions according to the credibility score, and updating security policies in real time to adapt to environmental changes; continuously monitoring and cyclically executing abnormality analysis and permission adjustment to realize dynamic maintenance of system security. The method can effectively deal with internal behavior abnormalities and external threats, and improve the accuracy and real-time performance of overall security protection.

This invention relates to the field of environmental control technology, and discloses an AI-based predictive adaptive environmental regulation method and system for prefabricated cabins. The method includes calculating the equivalent temperature and humidity within the prefabricated cabin; acquiring predicted temperature and humidity sequences over a time range t; determining the start-up and shutdown timing of the equipment based on a comparison of the predicted temperature and humidity sequences with environmental parameter limits; and continuing to acquire predicted data after the equipment completes the start-up and shutdown commands. This invention avoids conflicting operation and ineffective start-up and shutdown by constructing a dynamic control mechanism for the equipment; simultaneously, it achieves predictive proactive control to prevent environmental parameters within the prefabricated cabin from exceeding safety thresholds. Combined with dual operating modes, it flexibly switches control strategies according to the type of equipment within the prefabricated cabin, effectively reducing environmental fluctuations and suppressing condensation within the cabin, thereby reducing the equipment failure rate.

This invention discloses a deep learning-based method for VOCs pollution feature analysis and source tracing. The method includes acquiring online monitoring time-series data of VOCs and synchronous meteorological parameters of the monitoring area, constructing a multidimensional input tensor after preprocessing, building a deep neural network model containing a spatiotemporal feature extraction module and a source apportionment regression module, extracting nonlinear chemical coupling features and temporal evolution features between VOCs components using a one-dimensional convolutional neural network and a bidirectional long short-term memory network, and weighting and fusing them using meteorological parameters, and finally decoding and outputting the source component spectrum and temporal contribution rate that conform to physical constraints through the source apportionment regression module to generate a pollution feature map. This invention effectively solves the problem that traditional receptor models struggle to handle nonlinear chemical transformations and complex meteorological couplings, improves the accuracy and physical interpretability of VOCs pollution source tracing, and has online update capabilities to adapt to environmental changes.

This invention discloses a fire-resistant, flame-retardant, and highly weather-resistant integrated marine furniture system, comprising a three-layer architecture: a basic layer, an intelligent layer, and an application layer. The basic layer consists of prefabricated functional units and quick-connect interfaces for rapid installation. The intelligent layer includes an intelligent fire prevention unit and an adaptive environment unit. The intelligent fire prevention unit automatically triggers targeted aerosol fire suppression based on dual-condition logic using temperature and smoke sensors and safety control nodes deployed in key locations, enabling early autonomous fire suppression. The adaptive environment unit learns user habits through sensors and, upon user confirmation, solidifies "context-operation" rules, achieving personalized automatic environmental adjustment. The application layer provides various interactive terminals. This system represents an upgrade from passive alarm to active fire prevention, and from manual operation to intelligent adaptation, significantly improving the safety and comfort of ship cabins.

This invention discloses a method for drone flight control path planning in a power plant setting, comprising: collecting power plant environmental data through multiple sensors to construct a real-time updated environmental model; processing sensor data to form multimodal data input into an AI model to improve the accuracy of environmental understanding; generating and optimizing an initial flight path based on the multimodal data using an improved A* algorithm combined with deep learning; and dynamically adjusting the path during flight based on real-time sensor feedback to cope with obstacles and environmental changes. Dynamic adjustment includes: introducing a reinforcement learning-based path optimization strategy, combining a genetic algorithm to balance flight time, energy consumption, and obstacle avoidance effectiveness, calculating path costs, adjusting the optimization objective through weight coefficients, and performing state transitions to ensure real-time adaptation to environmental changes. This invention, through multimodal data fusion and dynamic optimization algorithms, enables safe and efficient drone flight in complex power plant environments, improves the real-time performance and adaptability of path planning, reduces risks, and increases operational efficiency.

The application discloses a control method for motion and exploration of a snake robot in a narrow irregular environment. The method mainly comprises the following steps: a cordless and non-carrying snake robot is assembled through a P-Y orthogonal linkage method, and a dynamic model is established through the method; a forward motion curve is formulated according to a backbone curve, so that the snake robot can enter a narrow space; in the process of exploration, the robot is not matched with any sensor, the snake robot obtains torque feedback through a steering wheel on a joint, a force feedback model is established, the model can adjust the expected angle of the steering wheel in real time, so that the radius of the motion curve is changed, and the environment can be perfectly adapted; through feedback of position information of the steering wheel, the size of the radius of the narrow space can be analyzed, and exploration of the radius of the space is realized.

This invention discloses a method for health management of emergency rescue equipment based on deep reinforcement learning, belonging to the field of mechanical equipment fault prediction and health management technology. The method includes: collecting and fusing multimodal data from simulation, sensors, and the environment; adaptively extracting health status features using a multi-branch CNN with an attention mechanism; constructing a deep reinforcement learning model with embedded physical constraints in the reward function, and introducing a meta-learning framework to enable it to adapt to environmental changes; using the trained model for real-time fault prediction and remaining service life estimation, and optimizing the model through comparison with physical simulation results. This invention achieves accurate, adaptive, and physically consistent prediction and intelligent health management of the health status of emergency rescue equipment in complex operating environments, significantly improving equipment reliability and mission efficiency.

The application relates to the technical field of bridge health monitoring, in particular to a bridge disease identification method based on a low-altitude unmanned aerial vehicle, which comprises the following steps: acquiring a bridge model and extracting component three-dimensional space and attribute features; dividing global and fine detection areas and configuring constraint parameters; generating an initial inspection route, wherein the fine area comprises a close-to-edge path and multi-angle nodes; controlling the unmanned aerial vehicle to fly and acquire real-time environmental data; fusing and comparing the data with the model, dynamically adjusting the route when a triggering condition is met; and collecting images according to the adjusted route to identify diseases. In the application, a differentiated and self-adaptive route planning and execution mechanism based on a building information model is constructed, technical problems that the route mode of the prior art is single, complex components cannot be covered, and the environment cannot be adapted to dynamic changes are solved, and the application has the beneficial effect of being capable of realizing non-blind area and high-precision coverage of all components of a bridge, especially complex nodes, and significantly improving the safety of inspection flight.

The application discloses an unmanned sailboat self-adaptive environment sensing method and a sensing system, and relates to the technical field of unmanned sailboats.The application realizes the leap from passive geometric sensing to active cognition based on physical laws through physical trigger sampling, sea wave same-frequency denoising, true wind field reconstruction and active smoothing interaction, effectively solves the problem of sensing failure of the unmanned sailboat in severe sea conditions, and can guarantee stable sailing of the unmanned sailboat.

This application relates to the field of traffic flow sensing technology, and in particular to a traffic flow sensing method and system. The method involves collecting multi-source heterogeneous traffic sensing data from fixed sensing devices, mobile sensing terminals, and environmental sensors within a target sensing area; sending this data to an edge computing node for preliminary processing and feature extraction to generate structured feature data, which is then uploaded to a cloud platform; aligning the data to a unified spatiotemporal reference on the cloud platform and performing multi-source data fusion processing based on dynamic confidence weighting to obtain fused traffic parameters; dynamically adjusting the fusion weights of each data source using a pre-generated environment-weight mapping model; and finally generating traffic situation information including traffic flow, average vehicle speed, lane occupancy, and traffic event types based on the fused traffic parameters. This method can adapt to environmental changes, improve the accuracy and robustness of traffic flow sensing, and solve the problem of perception bias easily occurring with a single data source in specific environments.

The application discloses a kind of self-adapting environmental energy storage converter test protection regulation systems and methods, comprising: the key parameter in the process of energy storage converter test is collected;According to the stress budget of key device is updated according to key parameter, obtain the stress budget state of key device;According to the abnormal event in the process of test is identified according to key parameter, obtain the identification information of abnormal event;Determine the category of abnormal event;According to the protection regulation strategy is generated according to the category of abnormal event and the stress budget state;When meeting preset trigger condition, for current test step, the alternative step that is equivalent to test target and lower risk is generated, and updated test script is output;According to the protection regulation strategy and updated test script, energy storage converter test process is controlled.It is converted into cumulative stress constraint to reduce the hidden risk caused by repeated impact and improve the stability and controllability of test process.

The application discloses a kind of based on asynchronous SDF's voxel cutting visual tactile collaborative method and system, it is related to virtual reality and force feedback technical field.The method calculates the minimum intersection bounding box of cutting tool in the local space of virtual voxel model in low-frequency main thread, and scheduling GPU only executes rigid boolean subtraction operation to the voxel in the bounding box;In GPU rendering dependency graph, temporary read-back texture is dynamically allocated, and the updated local voxel data is non-blocking read back to CPU end cache;In high-frequency tactile servo thread, the consensus normal is calculated based on probe sampling point weighted calculation, and material characteristics are extracted;According to consensus normal and cutting state, the composite cutting resistance containing viscous damping is calculated;Based on consensus normal, multi-step step sampling is generated to generate adaptive environment light shading and render output.The application solves the delay and force vibration problem in visual tactile collaboration through local data asynchronous read-back and speed penalty mechanism, and improves the realism of virtual cutting.

This invention relates to the field of intelligent building environment control technology, specifically an adaptive environmental regulation system. It utilizes an AI strategy module with an integrated edge computing platform, housed in the main control cabinet. Based on outdoor weather forecasts, local climate information, and group behavior patterns, the module performs multi-parameter environmental control strategy reasoning to generate adaptive environmental regulation commands. Simultaneously, a master-slave hierarchical control architecture is employed. Based on centralized optimization decisions in the main control cabinet, regional autonomous regulation and user interaction are achieved through various user terminals. The AI ​​strategy module further uses predictive models and optimization algorithms to predict future environmental loads based on historical environmental data and real-time conditions, calculating the optimal control parameters for each environmental regulation device. This system not only significantly improves the accuracy and foresight of environmental regulation through AI predictive control and real-time optimization, but also ensures global strategy coordination and energy efficiency optimization through the master-slave hierarchical architecture, guaranteeing rapid response and system reliability for regional control.

The application provides a mounting method and terminal based on a virtual data peripheral, comprising: obtaining a main thread message loop of a target application, and creating a custom message type in the main thread message loop; receiving a mounting request, and parsing virtual data and an execution mode in the mounting request; based on a determination result of the execution mode, calling corresponding execution logic to mount the virtual data; and outputting a mounting result of the virtual data. The application realizes a lightweight and extensible test data mounting scheme based on message queue intervention, solves the problems of poor stability and limited extensibility of a traditional injection tool due to an asynchronous remote injection mode being unable to reliably intervene in a synchronous execution process, and provides a universal mounting mechanism that is adaptive to an environment, thereby improving the depth, flexibility and execution reliability of testing.

The application provides a data center energy-saving air conditioning system and a regulating method, which has the characteristics of significantly reducing energy consumption and improving operation efficiency by integrating historical data collection, preprocessing, prediction model construction and intelligent control, wherein the system accurately predicts air conditioning energy consumption by using a multivariate linear regression model, optimizes the air conditioning set temperature by combining a gradient descent method, realizes precise adaptive regulation, updates the model online by using a recursive least square method, ensures long-term adaptation to environmental changes, reduces invalid operation of the air conditioning system, fully utilizes external natural conditions to reduce energy consumption, and provides an innovative and practical solution for data center energy saving.

The application relates to the field of data processing and provides an encoder compression rate adjusting method and a data feedback method and device. The method comprises the following steps: inputting target data into a first neural network model constructed in advance to obtain the optimal compression rate of the target data; the first neural network model is obtained by training a neural network based on target data samples marked with optimal compression rate labels; and the compression rate of an encoder is adjusted to the optimal compression rate. By inputting the target data into the first neural network model, the optimal compression rate of the target data can be obtained, and then the compression rate of the encoder is adjusted to the optimal compression rate, so that the adaptive adjustment of the compression rate is realized, the data compression process is more flexible and reliable, and the problems that the existing data compression mode cannot adapt to dynamic changes of the environment and the compression process is not flexible and reliable are solved.

This invention provides a fully automated laser scanning detection system and method for tunnels and chambers, belonging to the field of underground space exploration technology. The system includes: a drone, a multi-sensor fusion positioning module, a 3D laser scanner, an adaptive environmental perception and obstacle avoidance module, and a ground control station. The multi-sensor fusion positioning module provides high-precision pose information in environments without GPS signals; the 3D laser scanner collects laser point cloud data of the tunnels and chambers; and the adaptive environmental perception and obstacle avoidance module senses changes in dust and illumination in real time and plans a safe flight path. This invention effectively overcomes the challenges of complex environments such as GPS rejection, uneven illumination, and dust interference within tunnels through multi-source data fusion and adaptive algorithms. It achieves fully automated, high-precision, and high-efficiency 3D topographic detection in areas inaccessible to personnel, providing a reliable technical means for monitoring surrounding rock deformation and assessing risks in goaf areas.

This application relates to a device and method for identifying distribution network defects and external damage hazards, including a multimodal data acquisition module, an edge computing processing module, an adaptive environment adjustment module, a multi-machine collaborative scheduling module, a secure communication module, an intelligent early warning and grading module, and a cross-platform compatible interface module. The multimodal data acquisition module is used to acquire visible light images, infrared thermal imaging, and environmental sound signals; the edge computing processing module uses an AI model for defect and hazard identification; the adaptive environment adjustment module performs defogging and noise reduction preprocessing on the data; the multi-machine collaborative scheduling module realizes spatiotemporal isolation and intelligent planning of drones; the secure communication module uses dual-link communication and national cryptographic encryption to ensure data transmission; and the cross-platform compatible interface module enables data interaction between multiple systems. This application improves the identification accuracy and environmental adaptability of distribution network inspections, ensures data transmission security, and achieves seamless integration of multi-machine collaborative operations and cross-platform data.

The application relates to an indoor positioning method and system which is adaptive to an environment. The method comprises the following steps: establishing an indoor positioning measurement model to calculate a preliminary coordinate of a device; establishing an indoor environment error model to correct a preliminary observation distance; establishing a wireless base station optimization model to select a target wireless communication base station; determining a target device preliminary coordinate of a handheld terminal device to the target wireless communication base station, correcting a target preliminary observation distance through the indoor environment error model, and calculating a target coordinate of the handheld terminal device based on the indoor positioning measurement model according to the corrected target observation distance. Since the indoor environment error model is arranged, the preliminary observation distance can be corrected, and the positioning precision of the handheld terminal device can be improved. Through the establishment of the wireless base station optimization model, the base station which is affected by factors such as multipath interference and has poor signal quality can be eliminated, so that the real-time positioning is ensured, and the indoor positioning precision can be improved.

The application discloses a self-adaptive environment intelligent parachute opening method and an intelligent parachute, and relates to the technical field of parachute opening, and particularly relates to a self-adaptive environment intelligent parachute opening method and an intelligent parachute.The application comprises the following steps: collecting environment parameters, pre-processing data and extracting features, calculating and selecting an opening window, executing an anti-overload opening action, searching and locking a target after opening, and dynamically adjusting a main parachute posture in real time, so as to realize the purpose of automatically lowering the parachute towards a target direction and area.The beneficial effects of the application are as follows: through multi-dimensional environment parameter collection and an intelligent decision-making model, the opening time and strategy can be dynamically adjusted according to different height, speed, airflow and terrain conditions, the parachute posture after opening can be dynamically adjusted, the problem of poor environmental adaptability of a traditional opening method is solved, and the purpose of automatically locking a target and lowering the parachute towards a target area is realized.

This invention discloses a multi-condition adaptive control method, system, and storage medium for fans, relating to the field of automation control. It solves the technical problem of difficulty in adaptively controlling fan speeds to adapt to environmental changes during fan regulation. The invention obtains the effective control range of each fan in a factory using a digital twin model; acquires target data within the effective control range; extracts feature values ​​from the target data to obtain characteristic target data; issues alarm signals based on the characteristic target data; determines fan control factors based on the characteristic target data; and adaptively adjusts the fans based on the fan control factors. This invention improves the adaptability and controllability of fans to the environment, and reduces ineffective energy consumption among multiple fans.

This application relates to the fields of artificial intelligence and stage technology, and provides an audio control method and system for stage performances. The method includes: collecting real-time environmental parameters of an open-air stage, including at least two of rainfall, wind speed, ambient noise, temperature and humidity, and audience noise; inputting the real-time environmental parameters into a preset dynamic interaction model, and outputting the disturbance amount of each frequency band; the dynamic interaction model is a nonlinear model trained through multi-factor combination samples, and the multi-factor combination includes the actual disturbance amount of each frequency band under different combinations of environmental parameters; calculating the compensation gain of each frequency band based on the disturbance amount of each frequency band; and compensating each frequency band of the stage performance audio signal to be output based on the compensation gain to obtain a compensated audio signal. This invention enables the system to adapt to environmental changes through learning and feedback, and to more accurately model complex interaction relationships, ensuring that key audio content remains clearly identifiable regardless of how environmental factors are superimposed.

This invention belongs to the field of video processing technology and is used to solve the problem that existing technologies cannot extract the features that truly represent the essence of interference from luminance time-series data and establish a discrimination mechanism that can adapt to environmental changes. Specifically, it is a multi-dimensional intelligent detection method for video quality, including the following steps: acquiring luminance information of multiple consecutive frames in a video stream, constructing a luminance time-series sequence, extracting the main frequency component and its phase information from the luminance time-series sequence, calculating the phase entropy value based on the phase information of M consecutive time windows; dynamically adjusting the judgment threshold for luminance anomaly detection based on the deviation of the phase entropy value from a preset benchmark, and performing out-of-bounds detection on the average luminance of the current frame based on the judgment threshold; and verifying the preliminary anomaly detection results with the motion information of the current frame. This invention, by introducing phase entropy as a quantitative indicator of the periodic intensity of luminance fluctuations, can effectively identify periodic interference disguised within the normal luminance average range.

This disclosure provides an intelligent autonomous remotely operated underwater vehicle (ARV) and its control system, relating to the field of machine learning technology. The system includes: an embodied environment perception and risk assessment system that collects environmental risk information and current operation status information and constructs a current risk vector; an adaptive boundary generation and optimization system that stores at least two risk boundary vectors and calculates a similarity index between them and the current risk vector; a control mode decision and switching system that determines and executes a target control mode based on a comparison of the similarity index with a preset threshold; and an intelligent performance evaluation and feedback optimization system that collects key performance indicators to determine a task score and decides whether to update the risk boundary vector based on the task score. During updating, the adaptive boundary generation and optimization system generates candidate boundary vectors based on the task score and replaces the original risk boundary vector after verification. This solution solves the problems of existing technologies where ARV control mode switching relies on manual intervention and is difficult to adapt to environmental changes.

The present disclosure relates to a dynamic integration time controller for time-of-flight ranging systems. According to embodiments, a system can dynamically control the integration time in a time-of-flight (ToF) device based on the current operating state. A signal scaler processes calibration data and real-time measurements to determine the desired signal and ambient rate. Parallel calculations determine the desired integration time, one based on achieving maximum ranging distance and the other based on the measurement accuracy requirements. The system selects the longer integration time and applies user-defined bounds to optimize power consumption while maintaining specified performance. By adapting the integration time to ambient light conditions and target reflectivity characteristics, the system is able to shorten the integration time in favorable conditions while automatically increasing the duration when needed for long distances or low reflectivity targets.