171 results about "Augmentation system" patented technology

The invention discloses a multi-beam cooperative scheduling method and a multi-beam cooperative scheduling system for low-orbit satellite communication, which mainly solve the problems of poor beam scheduling flexibility and difficulty in adapting to time-varying requirements in a high-dynamic environment in the prior art, and the implementation scheme of the multi-beam cooperative scheduling method comprises the following steps of: collecting online user information by a low-orbit satellite through a wide beam; a hopping beam position is generated by using adaptive clustering under the constraint of channel resources; a hopping beam scheduling algorithm based on deep reinforcement learning outputs an optimal scheduling strategy according to the real-time environment variable; and optimizing a beam hopping time frame structure in each scheduling period, covering accessed users in a beam position through spot beams, and sending service data and control information to complete efficient service transmission. The method can adapt to changes of user distribution and service requirements in low-orbit satellite communication, the utilization rate of spectrum resources is improved, communication delay is reduced, the overall performance and service quality of the system in a high-dynamic environment can be enhanced, and the method can be used for dynamic beam position division, hopping beam scheduling and hopping beam resource allocation in a multi-beam low-orbit satellite communication scene.

The invention discloses intelligent part damage identification and quantitative analysis based on multi-modal fusion, and particularly relates to the technical field of intelligent part damage identification. According to the method, synchronous or asynchronous real-time data acquisition is carried out on a target part, multi-modal features are extracted in combination with a heterogeneous feature extraction network, confidence scores of all modals are calculated, mutual information between the modals is fused, and an attention weighted fusion process is guided; when it is detected that the modality is abnormally suppressed, feature enhancement and dynamic weight adjustment are implemented, key weak signals are prevented from being ignored, fusion features are input into a damage identification model, and a damage identification result, modal weight visualization and early damage risk scoring are output; the technology effectively improves the recognition capability of the model for early and hidden damage, is especially suitable for sensitive capture and fusion judgment of weak modal signals in high-safety scenes such as wind power and aviation, and significantly enhances the early warning accuracy and maintenance foresight of the system.

The invention relates to the technical field of automatic driving, in particular to an automatic driving task processing method and device, equipment and a medium. According to the method, firstly, the navigation guidance information is acquired and directly incorporated into beyond visual range contents (such as remote steering and distance), so that the limitation of the sensing range of a vehicle-mounted sensor is broken through, and the problem that an existing system lacks beyond visual range information is solved. And secondly, semantic analysis is performed based on navigation information and multi-view visual data to obtain text probability distribution, so that association of global navigation context and real-time scenes is realized, and a semantic basis is provided for humanization-like planning. Then, performing cross-modal fusion on the aerial view angle features and the text probability features, so that global navigation logic and real-time environment perception are deeply coupled, and decision is ensured to consider both a short-term environment and long-term planning. And finally, fusing the features and inputting the features into a task network, so that sensing, prediction and planning are more suitable for an actual scene, the utilization capability of beyond-visual-range information in a complex scene is improved, and the adaptability and reasoning capability of the system are enhanced.

The invention discloses an unmanned aerial vehicle autonomous obstacle avoidance and path planning system, which relates to the technical field of unmanned aerial vehicle systems and covers cooperative work of multiple sensors including environment perception, data fusion processing, path planning, flight control, communication, autonomous decision making, power management, a ground control station, system self-inspection and calibration, a load adaptation and optimization module and an environment perception module. The data fusion processing module is used for precisely fusing data; the path planning module generates an optimized path; the flight control module flies stably; the communication module guarantees communication; the autonomous decision-making module makes a decision intelligently; the power management module optimizes a power supply; the ground control station realizes monitoring and operation; a system self-inspection and calibration module ensures reliability; the load adaptation and optimization module adapts to different loads, and all the modules achieve efficient flight of the unmanned aerial vehicle. The flight safety and operation efficiency of the unmanned aerial vehicle are improved, accurate obstacle avoidance and intelligent path planning are achieved, the stability and reliability of the system are enhanced, and wide application and development of the unmanned aerial vehicle technology in multiple fields are promoted.

The invention provides an air-ground unmanned cluster system cooperative formation control method driven by reinforcement learning. Establishing a dynamic model for the air-ground unmanned cluster system; a distributed predefined time observer is constructed to estimate the state of a virtual leader, and a state reference is provided for cooperative formation tracking control; and reconstructing a dynamic model of the augmented air-ground unmanned cluster system, and further constructing a control gain matrix. Segmented constant initial excitation is applied to the augmentation system, state data of early-stage operation of the augmentation system is collected and stored, then an initial stability control strategy is obtained by using a data driving method according to the early-stage operation data of the air-ground unmanned system, and finally, the stability of the air-ground unmanned system is improved based on an offline strategy reinforcement learning algorithm and the initial stability control strategy based on the data. And learning an optimal formation tracking controller to realize optimal formation tracking control of the air-ground unmanned system. According to the invention, under the condition that a system model is unknown, the optimal time-varying formation tracking control of the air-ground unmanned cluster system can be realized according to complex task requirements.

The invention discloses a bucket wheel anti-collision system and an anti-collision method, and relates to the technical field of bucket wheel anti-collision. The system comprises a data acquisition module used for acquiring operation data, environment data and historical collision data of the bucket wheel machine, the operation data comprises the position, the speed, the angle and the operation mode of the bucket wheel machine, and the environment data comprises a coal yard three-dimensional map, obstacle coordinates and meteorological data; the historical collision data comprises accident records and false alarm analysis. The state and environment of the bucket wheel machine are mastered in real time through high-precision positioning and multi-mode sensing, collision is effectively prevented in combination with intelligent graded alarm, and safety is remarkably improved. The risk is predicted through big data analysis and machine learning, the operation path is dynamically adjusted, operation is optimized, the maintenance cost is reduced, and the service life of equipment is prolonged. Meanwhile, the cloud monitoring and the mobile terminal APP support remote operation and real-time information pushing, the convenience and applicability of the system are enhanced, and the management efficiency is improved.

The invention discloses a method for aggregating ocean buoy data, which comprises the following steps of: establishing a space-time correlation model according to the spatial distribution density of buoy clusters and the dynamic characteristics of an ocean environment, and adaptively dividing buoy groups with a data compression cooperative relationship; performing a data-related compression operation within the buoy group; constructing a transmission priority dynamic sorting engine, and calculating a dynamic weight coefficient; performing hierarchical packaging processing on the differential coding data, distributing enhanced forward error correction coding resources for high-priority data, and reserving redundant transmission time slots; the method comprises the following steps: receiving a hierarchical data packet through a low-orbit satellite and executing on-satellite preprocessing, reconstructing an original data stream by using a distributed decoder, and meanwhile, fusing multi-dimensional space-time related data to generate an aggregated data product; and finally, the aggregated data product is transmitted to a ground data center through a satellite downlink. According to the invention, uplink data volume is reduced, disaster monitoring timeliness is improved, ground station processing load is reduced, and system environment adaptability is enhanced.

The invention discloses a ship route optimization and safety management method based on real-time environment information, and relates to the technical field of route planning. The ship route optimization method provided by the invention can predict the service life of key parts of an unmanned ship, find potential faults in advance, suggest maintenance measures through an early warning mechanism, and improve the safety of the unmanned ship. Through deep reinforcement learning and a distributed intelligent decision-making system, each unmanned ship can make an intelligent decision based on shared environment information and the state of the unmanned ship, cooperative avoidance of obstacles among fleets is realized, and meanwhile, the route of the unmanned ship is dynamically adjusted by using real-time weather forecast and environment change information, so that the unmanned ship is more intelligent. The method is advantaged in that the method is advantaged in that the method can better adapt to emergencies and environment changes, adaptability and flexibility of ship navigation, real-time environment perception and data sharing are improved, dynamic factors are considered in a planning stage through a distributed intelligent decision-making system, fleet collaborative decision-making is realized, and the method adapts to real-time changes of the marine environment.

Methods and systems for training an end-to-end autonomous driving system using a vision-language planning (VLP) machine learning model in a closed-loop environment. Images associated with an environment about a vehicle are generated, and a BEV model is executed to generate a BEV view based on the images. A planning model predicts navigation trajectories based on the BEV. The VLP model enhances the system by extracting vision-based planning features, generating text prompts, and employing a language encoder to create text-based expectation features. A contrastive learning model identifies similarities between vision and text features, boosting the performance of the BEV and planning models. The system undergoes closed-loop evaluation in a simulated environment, capturing metrics to refine the autonomous driving system.

The invention relates to the technical field of encryption communication, in particular to an encryption method and system for an intelligent digital encryption radio station, and the method comprises the following steps: storing communication node information to an encryption database based on maritime communication environment signal characteristic parameters, extracting communication link quality indexes, recording multi-path interference, and screening a high-frequency band; and detecting the identity and authority change of a communication operator, and analyzing the communication use trend and frequency change to obtain a communication use trend analysis conclusion. Through dynamic monitoring of signal characteristics and self-adaptive optimization of a network structure, real-time reconstruction of a communication path and interference area avoidance are realized, stable transmission in a marine environment is guaranteed, frequency and power are dynamically adjusted in combination with link quality and interference analysis, the coverage rate and reliability are improved, and the method is suitable for large-scale popularization and application. Abnormal identification and risk early warning are realized through comprehensive judgment of communication behaviors and authority changes, the adaptability and protection capability of the system are enhanced, and the stability, security and resource utilization efficiency of a communication network are improved.

The invention discloses an electromagnetic micromirror output feedback tracking control method and device based on adaptive Q learning, and the method comprises the steps: constructing an electromagnetic micromirror model based on a physical structure of an electromagnetic micromirror; combining the electromagnetic micro-mirror model with a reference trajectory to construct an augmentation system so as to convert a trajectory tracking problem into a state adjustment problem; based on the augmented system, designing a Q function Bellman equation to establish a data-driven performance evaluation framework; based on the augmented system, the state of the augmented system is reconstructed through historical input and output data, so that angular velocity measurement is avoided; obtaining an optimal control input and a new Q function Bellman equation based on the reconstructed augmented system state and the Q function Bellman equation; and based on the optimal control input and the new Q function Bellman equation, solving a control problem by using an adaptive Q learning algorithm. The method breaks through the limitation that a traditional model depends on a control method, and achieves optimal tracking control only through historical input and output data.

The invention relates to the technical field of multi-vehicle cooperative control, and discloses a multi-AGV distributed cooperative path planning method and system, and the method comprises the steps: carrying out the task distribution based on the AGV state and position distance after receiving the order picking wave issuing; performing global path planning on the selected AGV, and determining continuous path points to be driven; the AGV dynamically blocks a front path point to form a temporary exclusive channel; detecting conflicts among the AGVs through a double conflict judgment mechanism based on timestamps and direction representation; if no conflict exists, the task is executed, and if the conflict exists, deadlock judgment is carried out firstly; if deadlock occurs, a heuristic avoidance strategy is adopted to search temporary obstacle avoidance points, and a path is re-planned through an A * algorithm in combination with a direction priority principle; and if the deadlock does not exist, directly carrying out path adjustment. According to the scheme, through dynamic blocking and double conflict judgment, waiting and collision are effectively reduced, and the passing efficiency and safety are improved; and in combination with a heuristic avoidance method based on a ternary search tree, the scheduling flexibility and intelligence of the system in a deadlock scene are enhanced.

The invention relates to an unmanned aerial vehicle high-precision positioning method based on a satellite-based enhancement system and adaptive Kalman filtering. The method comprises the following steps: acquiring multimode satellite data; analyzing the satellite-based enhanced telegraph text data, and performing error correction on the original observation data; based on the corrected observation data, dynamically adjusting a process noise covariance matrix by using the motion state of the unmanned aerial vehicle, and constructing an adaptive Kalman filtering model; and based on an adaptive Kalman filtering model, fusing the corrected observation data and inertial measurement unit data to obtain unmanned aerial vehicle positioning. The method aims to solve the problems that unmanned aerial vehicle positioning in complex environments (such as urban canyons, electromagnetic interference areas and mountainous areas without ground base stations) is remarkable in multipath effect and has dynamic noise interference, and traditional differential positioning depends on ground base stations. The centimeter-level continuous positioning of the unmanned aerial vehicle in a complex scene is realized through triple technical breakthroughs of satellite-based differential correction, dynamic optimization of environmental perception filtering parameters and multi-source data tight coupling solution.

The invention discloses a monitoring multi-beam phased array control system based on FPGA (Field Programmable Gate Array) control, which belongs to the technical field of radars and comprises a control module, an antenna array, a memory, an upper computer and a serial port-to-parallel port communication module. According to the invention, dynamic loading and online updating of beam parameters are realized, rapid switching of multi-beam modes is supported, amplitude-phase compensation parameters stored in a memory and data of a temperature sensor are linked in real time, the duty ratio can be dynamically adjusted, the environmental adaptability of the system is improved, and the storage structure is optimized. The logic resource consumption of large-scale array control is reduced, state code real-time feedback enhances the system maintainability, and quick response and safe turn-off in a high-temperature scene are ensured in combination with over-temperature protection interruption.

The invention relates to a visual inertial fusion positioning method and system based on feature optimization, and belongs to the technical field of visual inertial navigation of autonomous robots. According to the method and the system, on the basis of a Geometric Feature Track Selection Visual Native Navigation System (GFT-VINS), through a feature trajectory selection strategy based on normal epipolar geometry, high-quality feature trajectories are screened out, low-quality trajectory interference is avoided, the positioning precision is improved, error accumulation caused by environmental changes and feature anomalies is reduced, and the system stability is enhanced. Meanwhile, in combination with a multi-state Constraint Kalman filter (MSCKF) framework, the efficient calculation capability is kept, the requirements of the autonomous robot for high precision, high stability and efficient calculation in a complex environment are met, and wide application of the autonomous robot is promoted.

The invention discloses a hierarchical visual language navigation memory enhancement system in a cross-floor scene, relates to the technical field of intelligent navigation, and adopts the technical scheme that the hierarchical visual language navigation memory enhancement system comprises three core parts, namely a hierarchical visual semantic model construction module, a memory enhancement algorithm module and a navigation system integration module. The hierarchical visual semantic model construction module is used for constructing a basic visual feature layer, a floor semantic layer and a cross-floor semantic association layer; the memory enhancement algorithm module comprises a short-term memory module, a long-term memory module and a memory fusion and update strategy module; and the navigation system integration module is used for integrating the hierarchical visual semantic model and a memory enhancement algorithm into the system. Three-dimensional space characterization misalignment and path planning error accumulation can be effectively avoided, the accuracy, environment adaptability and decision-making efficiency of cross-floor navigation of the intelligent agent are remarkably improved, and the method has important technical innovation value and application prospects.

The invention provides an event-based flight simulator simulation method and system, a terminal and a medium, and relates to the technical field of flight simulators, the method comprises the following steps: setting components of a flight simulator, and setting public attributes and equipment attributes; describing a simulation object file; configuring an initial scene; configuring a development environment; configuring and developing a visual plug-in; configuring a virtual instrument, and defining input and output of the virtual instrument; compiling a logic processing plug-in, and executing a logic processing process based on the logic processing plug-in; the logic processing plug-in is used for setting pointers and parameters so as to process events sent by the event processor and access attributes. The design level and development efficiency of the system are improved, the reusability of the system is enhanced, the stability of the system is improved, and the user experience is also improved.

The invention provides a novel low-altitude traffic management method based on a satellite-based technology, and the method comprises the steps: obtaining the initial position data of a low-altitude aircraft through a satellite-based navigation system, carrying out the multi-source fusion positioning through Beidou or GPS satellite signals, and obtaining the high-precision initial positioning data; according to the initial positioning data, correcting the positioning data in real time by adopting a satellite-based enhancement system to obtain high-precision positioning data; the method comprises the following steps: acquiring flight states and route information of aircrafts through real-time communication data, and judging potential conflict risks among the aircrafts for a high-density airspace or a cross-regional flight scene to obtain conflict early warning data; and according to the conflict early warning data, extracting a target route and a speed constraint of the aircraft from the real-time communication data to obtain optimized route data. The problems of accurate positioning, stable communication, conflict avoidance, whole-process management and the like of the low-altitude aircraft in a complex environment are effectively solved, and the safety and efficiency of low-altitude flight are improved.

The invention relates to the technical field of industrial automation, in particular to an inspection path generation method and device, a storage medium and computer equipment, when an inspection path is generated, time-space synchronization can be carried out on data collected by different sensors, geometric and physical consistency of a motion track is ensured, the dynamic positioning precision is further improved, and the accuracy of the inspection path is improved. Moreover, when part of the sensors fail, the robustness of the system can be effectively enhanced through a synchronized redundant data maintenance function, and the data processing delay can be reduced through time-space synchronization, so that the real-time control requirement of a dynamic scene is met, and a map can be updated in real time according to the current environment to improve the accuracy of navigation and positioning. The method adapts to complex environments; in addition, the current positioning error can be corrected according to the target point cloud data and the target image data so as to further improve the positioning accuracy, and the relatively accurate inspection path and control instruction can be generated based on the corrected positioning result and the target map.

The invention relates to the field of field groundwater magnetic resonance detection methods, in particular to an augmentation system and method for field groundwater magnetic resonance detection small sample data. Comprising a generation network configured to receive random noise and generate augmented data based on the random noise; the discrimination network comprises a time domain discrimination network and a frequency domain discrimination network; the generative network and the time domain discrimination network form a first discrimination channel, the generative network and the frequency domain discrimination network form a second discrimination channel, and in the second discrimination channel, augmented data generated by the generative network and the noisy data are jointly input into the frequency domain discrimination network; the frequency domain discrimination network is used for discriminating whether the source of input data is a generation network or noisy data in a frequency domain. According to the method, a magnetic resonance detection small sample noisy data set is established, and multi-scale features in an amplitude spectrum and a phase spectrum are captured through the frequency domain discrimination network; the collaborative driving generation network generates a high-quality sample which is close to small sample noisy data in waveform form and spectral characteristics.

The invention relates to the technical field of unmanned aerial vehicle early warning, in particular to a low-altitude unmanned aerial vehicle intelligent early warning system and method based on multi-source data fusion, and the system comprises a collection module, an extraction module, a judgment module, a type determination module, a grade determination module, an adjustment module and an early warning module. According to the invention, accurate identification and intelligent early warning of ice layer adhesion disturbance of the low-altitude unmanned aerial vehicle flying in foggy days are realized by fusing multi-source data, the flight environment and the state of the unmanned aerial vehicle can be comprehensively reflected by collecting parameters such as water vapor concentration, image information and wake flow speed, and the sensitivity of the system to the interference state is enhanced; according to the method, feature parameters such as particle radius, distribution density, ice layer area and thickness extracted from images are utilized to construct a multi-dimensional judgment basis of interference identification and grading judgment, and the problem of low early warning accuracy when complex flight interference is dealt with due to a single data source or a static prediction model is effectively solved.

The invention discloses a large-stroke aircraft airfoil static force loading system for aircraft strength testing, and particularly relates to the technical field of aviation structure testing, and the large-stroke aircraft airfoil static force loading system comprises a multi-region loading execution module for realizing distributed autonomous loading and state sensing; the boundary coordination control module realizes force field continuity between loading areas through flexible fusion; the asynchronous self-synchronization adjustment module adjusts a loading step pitch based on local feedback and a loading offset prediction function; the redundant closed-loop feedback control module adopts a force value and displacement dual-channel path; the loading path modeling and reproduction module realizes historical path playback and loading track reproduction by constructing a track data set; according to the invention, continuous coordination of multi-region loading force fields is realized, the adaptive adjustment capability of loading step pitch and frequency is improved, and the loading behavior is dynamically optimized through a path difference feedback mechanism; a redundancy feedback and path reproduction mechanism is configured, and the fault tolerance and loading consistency of the system are enhanced; and intelligent scheduling is realized based on the shared table, so that the loading precision, stability and efficiency are remarkably improved.

The invention discloses a positioning method and device and electronic equipment. The method comprises the following steps: receiving original satellite-based service data accessed by terminal equipment, the original satellite-based service data comprising original correction data which is acquired by the terminal equipment from a satellite-based enhancement system and is used for correcting a positioning error; an analysis strategy corresponding to the terminal equipment is determined, and the analysis strategy is used for extracting error correction parameters of a preset type in the original correction data; an analysis strategy is adopted to analyze the original satellite-based service data, target correction data are obtained, and the target correction data comprise error correction parameters of a preset type; and determining a positioning result of the terminal equipment according to the target correction data and navigation satellite data of the terminal equipment. The technical problem that a terminal user cannot autonomously adjust an analysis strategy or a positioning algorithm due to the fact that a satellite-based enhanced positioning algorithm adopted in the related technology integrates a data analysis algorithm and a positioning algorithm is solved.

The invention relates to a coal mine underground accurate positioning card reader direction identification method, and belongs to the technical field of wireless communication and accurate positioning. In order to solve the problems of low direction identification precision and direction jump caused by dense metal supports, complex electromagnetic environment and serious multipath reflection in a coal mine in the prior art, a multi-source information fusion solution is provided. The method comprises the following steps: initiating a time of flight (TOF) ranging request through an identification card; based on the TOF distance difference interval gradient distribution weight point number, RSSI intensity difference interval differential weighting and CIR characteristic value peak-to-average ratio delay spread multipath component number, analyzing signal quality weighting, and combining historical direction information to dynamically compensate the weight point number; and finally determining the direction of the identification card by comparing the total weight points of the left and right antennas. According to the method, the direction identification accuracy in a complex mine environment is remarkably improved, the multi-path interference and signal fluctuation influence are effectively inhibited, the system anti-attenuation capability is enhanced, and the continuous stability of moving target direction tracking is guaranteed.

The invention discloses a GNSS / INS (Global Navigation Satellite System / Inertial Navigation System) vehicle-mounted fault-tolerant integrated navigation method and system based on isolated forest fault detection, a medium and a product, belonging to the field of vehicle-mounted integrated navigation and aiming at improving the navigation positioning precision and reliability of an automatic driving vehicle in an urban environment. The purpose of the invention is realized through the following technical method, and the method comprises the following steps: collecting navigation data and carrying out inertial navigation calculation; an isolated forest model is constructed based on historical navigation incremental data, and anomaly detection of GNSS measurement is realized; calculating an abnormal score of the isolated forest to recognize a GNSS measurement fault; a switchable factor of GNSS measurement is adjusted in a self-adaptive mode; and solving an optimal navigation state based on the optimized factor graph to realize fault-tolerant processing of GNSS abnormal measurement. The method effectively reduces the influence of signal interference such as multi-path effect and non-line-of-sight receiving on the navigation system, enhances the stability and robustness of the system, and is suitable for complex urban application scenes such as automatic driving and intelligent transportation.

The invention relates to the technical field of industrial automation, in particular to an automatic inspection method and device in a complex environment, a storage medium and related equipment, data acquired by different sensors can be subjected to space-time synchronization to improve dynamic positioning precision, and when part of the sensors fail, a function can still be maintained through synchronized redundant data, so that the reliability of the system is improved. The robustness of the system is effectively enhanced, and the data processing delay can be reduced through space-time synchronization, so that the real-time control requirement of a dynamic scene is met; locally updating the newly acquired global map according to the static characteristics and the target attitude data, thereby reducing the calculation amount, improving the accuracy of navigation positioning, and adapting to a complex environment; the current positioning error is corrected according to the target point cloud data and the target image data, so that the positioning accuracy is further improved, and the inspection path and the control instruction are generated based on the corrected positioning result and the target map, so that the operation risk is effectively reduced while automatic inspection is realized.

The invention provides a multi-source heterogeneous sensor adaptive fusion positioning and mapping method and device, and relates to the technical field of automatic driving and mobile robots. The method comprises the following steps: for a mobile robot in an indoor environment, acquiring sensor data through a sensor data acquisition module, inputting the sensor data into a front-end data processing module to obtain processed sensor data, and inputting the processed sensor data into an environment scene recognition module to obtain an environment scene type recognition result; and inputting the result into a back-end state estimation and optimization module to obtain a map construction and pose estimation result. Through innovative front-end feature extraction, a rear-end optimization mechanism and a multi-sensor fusion strategy, the problems in the prior art are solved. By introducing a low-cost magnetometer, the robustness of the system in a GPS signal missing or complex electromagnetic environment is enhanced, meanwhile, the calculation efficiency and precision in different environments are improved through a self-adaptive optimization mechanism, and finally, high-precision and high-robustness autonomous positioning and mapping functions are achieved.

The embodiment of the invention provides a satellite navigation ranging link integrity monitoring method and device, a computer readable storage medium, an electronic device and a computer program product, and the method comprises the steps: screening out at least one first satellite meeting a preset monitoring condition from a preset satellite set according to satellite data broadcasted by a satellite-based augmentation system; calculating a first deviation of a satellite signal of the first satellite at the first site according to the satellite data; calculating a second deviation of the satellite signal of the first satellite at the first station according to the actual observation data of the first station; and determining a consistency index of the first satellite according to the first deviation and the second deviation, and determining whether the integrity of the navigation ranging link of the first satellite reaches the standard based on the consistency index. Therefore, signal anomalies are captured on different scales, limitation of a single monitoring method is avoided, monitoring precision is improved, and accurate monitoring of integrity of a satellite navigation ranging link is realized.

The invention relates to the technical field of prediction and matching, in particular to a ship diesel / optical / storage hybrid power system load prediction and dynamic power matching method, which comprises the steps of multi-source data acquisition and preprocessing: acquiring data in real time, and establishing a time sequence data set with a unified timestamp; multi-modal data fusion processing: carrying out abnormal value detection and missing data reconstruction to generate a feature fusion data set; dynamic load prediction modeling: constructing a hybrid prediction model combining deep learning and time sequence analysis; generating a dynamic power distribution scheme: generating the dynamic power distribution scheme according to the optimal power distribution proportion; real-time dynamic adjustment and feedback optimization: triggering a dynamic adjustment mechanism to update power distribution parameters based on the deviation value between the real-time operation data of the power system and the power distribution scheme; and control instruction generation and execution: converting the updated power distribution parameters into a control instruction set of each subsystem. According to the invention, the operation stability, economy and intelligence level of the system are obviously enhanced.

Methods and systems for training an end-to-end autonomous driving system in a closed-loop environment using a Visual Language Planning (VLP) machine learning model. An image associated with a vehicle surroundings is generated, and a BEV model is executed to generate a BEV view based on the image. The planning model predicts a navigation trajectory based on the BEV. The VLP model enhances the system by extracting vision-based planning features, generating text cues, and employing a language encoder to create text-based expected features. And comparing a learning model to identify the similarity between visual and text features, and enhancing the performance of the BEV and the planning model. The system experiences closed-loop evaluation in a simulated environment, obtaining metrics to improve the autonomous driving system.