1045 results about "Cockpit" patented technology

The invention discloses a cockpit visual work efficiency evaluation method. The method comprises the following steps: establishing a cockpit geometric model according to a real cockpit; establishing a cockpit light environment model according to the cockpit light environment parameters; establishing a cockpit optical material model according to the optical material parameters of the inner and outer surfaces of the cockpit; fusing and establishing a cockpit optical model; a simulation detector is arranged on the cockpit optical model; according to the cockpit optical model and the analog detector, human eye vision optical simulation and dazzle source space tracking and positioning are carried out; according to a glare source space tracking and positioning result, a cockpit structure is optimized; and performing cockpit visual work efficiency evaluation according to a cockpit structure optimization result. According to the cockpit visual work efficiency evaluation method, reasonable evaluation of cockpit visual work efficiency readable visibility in various environments is achieved, simulation is conducted on the cockpit light environment through computer software, and adjustment can be conducted according to the working habit, the vision condition, the age and other factors of each pilot.

The invention relates to the technical field of vehicles, in particular to an intelligent cabin man-machine cooperative control method and device, a vehicle, a medium and a product. Performing fusion processing on the voice signal data, the visual signal data and the tactile signal data of the current driver by using a preset dynamic weight distribution model to obtain a multi-modal fusion control instruction; and under the condition that the multi-mode fusion control instruction meets a preset sight-voice dual-mode condition, synchronously issuing the multi-mode fusion control instruction to a plurality of target execution devices in the intelligent cabin so as to drive each target execution device to perform cooperative control. Therefore, by constructing a cooperative control mechanism of multi-mode sensing and dynamic weight distribution, the problems that the false wake-up rate is high, the voice and graphical interface cooperative efficiency is low and the interaction mode cannot adapt to a complex driving environment due to a single or separated interaction mode in the related technology are effectively solved.

The invention discloses a predictive vehicle control system and method for multi-modal consciousness recognition and electronic equipment, and relates to the field of remote monitoring, and the system comprises a multi-modal sensing module, an intention prediction module, a cross-domain pre-control module and a false trigger suppression module. The multi-mode sensing module is used for collecting three-mode data of sight, gestures and voice and dynamically distributing weights based on environmental parameters; the intention prediction module is used for performing time-space synchronization verification on the three-mode data and calculating comprehensive confidence in a grading manner; the cross-domain pre-control module outputs pre-control instructions to a cabin domain, a vehicle body domain and a power domain based on the effective intention; the false triggering suppression module is used for dynamically adjusting a confidence coefficient threshold value based on a driving scene and has a failure backspacing learning function; wherein each module performs data interaction based on a vehicle-mounted Ethernet / CAN bus.

The embodiment of the invention provides a vehicle intelligent agent control method and device, electronic equipment, a vehicle and a medium, and the method comprises the steps that a central intelligent agent receives task analysis information sent by a cabin intelligent agent, and the task analysis information is obtained by carrying out semantic analysis on an intelligent agent cooperative control task after the cabin intelligent agent receives the intelligent agent cooperative control task; splitting the agent cooperative control task into a plurality of sub-tasks according to the task analysis information, allocating an execution agent to each sub-task, and generating a task execution plan; the execution intelligent agent comprises an intelligent driving intelligent agent and a cabin intelligent agent; and sending a task execution request corresponding to each sub-task to each execution agent according to the task execution plan, so that each execution agent executes the corresponding sub-task according to the task execution request. According to the invention, the cabin intelligent agent, the intelligent driving intelligent agent and the central intelligent agent cooperatively control the user intention of the task around the intelligent agents and cooperatively execute the task, so that the task execution efficiency and effect are improved.

The invention discloses an intelligent cabin healthy light environment adjusting system and method based on biological feedback, and belongs to the technical field of intelligent cabin and vehicle control. The method comprises the following steps: collecting a multi-modal physiological signal of a passenger; physiological signals are processed in parallel through two paths; determining a feed-forward control weight based on the dynamic change degree between the current steady-state emotional state and the future emotional state trajectory; according to the feed-forward control weight, performing weighted fusion on the current steady-state emotional state and the future emotional state track to generate a comprehensive light environment adjustment instruction; according to the comprehensive light environment adjusting instruction, a dynamic light and shadow relieving strategy is generated and executed, and the strategy at least comprises at least one of the color temperature, the brightness or the dynamic rhythm of the cabin light environment. By introducing the feedforward control mechanism, the emotional fluctuation of the passenger can be predicted and intervened in advance, the response delay of the system is reduced, the conversion from passive response to active prediction is realized, and the timeliness and accuracy of emotional relief are improved.

The invention discloses a multi-mode voice interaction system and method for an intelligent cabin. Firstly, various vehicle working condition parameters in a CAN bus are read in real time, a wavelet domain Wiener filtering and judgment statistic enhancement strategy is dynamically adjusted, and self-adaptive voice purification based on vehicle state prior is achieved. Secondly, a bidirectional long-short-term memory network is adopted as a semantic understanding core, semantic feature vectors are extracted from enhanced signals through segmented maximum pooling operation, and the problem of semantic confusion caused by manifold collapse in a traditional method is effectively solved. And the feature vector and a safety state signal are synchronously input into an improved LLM-CoT engine and a dynamic arbiter, so that context accurate understanding and safety-up priority response are realized. According to the method, vehicle working condition perception and biLSTM depth time sequence modeling are deeply adapted to a vehicle-mounted complex acoustic scene, the error rate of semantic understanding is remarkably reduced in actual measurement under multi-person dialect dialogue, and the accuracy, robustness and safety of intelligent cabin interaction are systematically improved.

The invention discloses an intelligent cabin interaction priority dynamic allocation system and method and electronic equipment, and relates to the field of cabin interaction, and the system comprises a multi-mode signal input layer which is used for continuously receiving original interaction instruction signals of a voice recognition module, a gesture recognition module, an eye movement tracking module and a touch screen module; the context sensing layer is used for collecting vehicle state data, driver state data and environment state data in parallel; the dynamic priority arrangement engine comprises a multi-factor fusion and weight calculation module and an arbitration and scheduling module; the multi-factor fusion and weight calculation module is used for normalizing the context data into a weight influence factor and calculating a real-time dynamic priority score of each interaction mode; the arbitration and scheduling module is used for comparing priority scores and selecting an optimal execution mode, and sending suppression signals to other modes at the same time; and the instruction execution layer is used for receiving the final instruction of the arbitration and scheduling module and issuing the final instruction to a vehicle-mounted application for execution.

The invention belongs to the technical field of amphibious aircraft flight control, and discloses an amphibious aircraft fly-by-wire flight control system and a working method, a cockpit control device is connected with and controls a normal instruction sensor and a backup instruction sensor, the normal instruction sensor transmits a signal to an actuator control electron, and the backup instruction sensor transmits a signal to the actuator control electron; the actuator control electronics and the flight control computer transmit signals mutually, the actuator control electronics and the actuator control module transmit signals mutually, and the actuator control electronics further receive signals of the external signal sensor, the direct angular rate sensor, the flap position sensor and the flight control mode switch; the backup command sensor transmits the signal to the backup flight control computer, and the backup flight control computer transmits the signal to the actuator control module; the actuator control module is connected with and controls the actuator, and the angular displacement sensor is arranged on the actuator and transmits collected signals to the actuator control module. The problem that a current fly-by-wire flight control system is not matched with amphibious aircraft cross-medium application is solved.

A craft is provided with a lever that can control the power / speed of the craft and semi-automatically transition the craft in various modes of operation. This makes control of the craft somewhat similar to control of a boat, thereby bringing a maritime flight control experience to a wing-in-ground effect vehicles, hydrofoiling vessels, seagliders, and seaplanes. Further, various audio, visual, and / or haptic devices in the cockpit can provide alerts / feedback to the operator on the use of the lever and mode transitions.

The invention relates to the technical field of automobile testing, and discloses a method and system for evaluating and testing visual field glare of a driver in a cabin, and the method comprises the following steps: starting from the position of the eyes of the driver, capturing a brightness distribution image of the whole visual field, and dividing the image into a plurality of sub light source regions around a glare source; dividing the visual field into a main sight area and a residual light sight area according to the attention distribution of the driver, and endowing sub light source areas falling into different areas with different weight values; calculating a unified glare index according to the weighted sum of the glare intensity of each sub light source area and the background brightness in the cabin; and finally, comparing the index with a preset threshold value, and outputting an evaluation result. According to the method, the large-area light source is divided into the sub-light sources, weighted calculation is carried out in combination with the attention of the driver, the problem that a traditional glare evaluation model is not suitable for a close-range non-point light source scene of the automobile cabin is solved, a scientific evaluation system matched with the automobile cabin is established, and the glare evaluation result is more accurate.

The invention relates to the technical field of vehicle bodies, in particular to a collision force transmission inclined strut structure. The hinge column inner plates are arranged at the two ends of the front-end large casting, one end of the inclined strut structure body is installed on the front-end large casting, the other end of the inclined strut structure body is installed on the hinge column inner plates, and the inclined strut structure body, the front-end large casting and the hinge column inner plates form a triangular stable structure. The section of the inclined strut structure body is of an M-shaped structure. The inclined strut structure has the beneficial effects that according to the technical scheme, the inclined strut structure body with the special structure is designed, one end of the inclined strut structure body is installed on the front-end large casting, and the other end of the inclined strut structure body is installed on the hinge column inner plate, so that the inclined strut structure body, the front-end large casting and the hinge column inner plate form a triangular stable structure; the inclined strut structure can effectively reduce the impact force of head-on collision on a passenger compartment, so that the safety performance of the cockpit is improved.

The invention relates to the technical field of automobile cabin environment control, and discloses an intelligent cabin environment adaptive adjustment system based on an AI large model, which comprises a sensing module, an AI large model decision module, an execution module and a feedback module. According to the intelligent cabin environment self-adaptive adjustment technology based on the AI large model, a mixed decision sub-model of an AI large model decision-making module adopts an MPC-TD3 fusion algorithm, and a user behavior learning sub-model of the AI large model decision-making module learns user physiological parameters and operation records collected by a sensing module; the multi-physics field prediction sub-model predicts a cabin environment and a power battery state, and corrects a decision result in combination with comfort parameters fed back by a PMV-PPD evaluation model and an SET evaluation model of the feedback module in real time, so that the problems that a single MPC model is insufficient in robustness under complex working conditions and a single reinforcement learning model is difficult to quantify the comfort feeling of a user are solved, and the user experience is improved. And the dynamic response speed and the adjustment precision are considered.

The invention provides a communication interconnection multi-seat cabin system which comprises seats with a partition seat position detection function and at least one mobile terminal independent of seat cabins, the seats are in wireless communication connection with the mobile terminals, and body position sensing assemblies used for detecting the body states of passengers are arranged on the seats; the multi-seat cockpit system further comprises a game console with a wireless communication interconnection function and a display screen, the seats, the game console, the mobile terminal and the display screen form a multimedia cockpit interaction system, and a passenger sends a posture operation instruction on the seats through the body position sensing assembly to relieve fatigue. According to the invention, not only can a passenger on one automobile seat operate the game, but also passengers on a plurality of automobile seats can interact with each other, so that the communication among a plurality of passengers is facilitated. The posture of a user can be adjusted through the body position sensing assembly, and muscles can be stretched and physical fatigue can be relieved by adjusting the body posture while communication interaction is carried out.

The invention relates to the cross technical field of avionics and edge computing, in particular to a comprehensive cockpit intelligent edge computing method and system, which comprises the following steps: receiving a computing instruction from ground equipment or an airborne module, scheduling corresponding edge computing application store software from a control module based on the computing instruction, and sending the edge computing application store software to the control module; the method comprises the steps of obtaining an edge calculation application demand, collecting target data from a sensor module and / or an airborne module according to the edge calculation application demand, preprocessing the target data to obtain target processing data, distributing the target processing data to an AI calculation module for AI reasoning calculation, and obtaining structured result data; structured result data is analyzed through the control module, an operation scheme is generated and displayed through the display control device, and the technical problems that due to framework rigidity, an existing cabin computing system cannot flexibly schedule computing resources, the real-time processing requirement under complex tasks is difficult to meet, and the overall processing efficiency is low are solved.

The invention provides a substation intelligent operation and maintenance method and cockpit system based on Gaussian model and Internet of Things data fusion, and the method comprises the steps: carrying out Gaussian point cloud modeling based on substation information, and obtaining an initial three-dimensional Gaussian point cloud model, associating the equipment attribute information with the initial three-dimensional Gaussian point cloud model based on equipment monomer identification to obtain a target three-dimensional Gaussian point cloud model; based on the parking area and the vehicle parameters of the transformer substation, simulating initial operation and maintenance parameters of the operation vehicle in the transformer substation, and based on the target three-dimensional Gaussian point cloud model and the initial operation and maintenance parameters, carrying out operation risk detection to obtain a risk detection result; optimizing the initial operation and maintenance parameters based on the risk detection result to obtain optimized operation and maintenance parameters, and generating an electronic operation and maintenance fence based on the optimized operation and maintenance parameters; and loading the optimized operation and maintenance parameters and the electronic operation and maintenance fence to the target three-dimensional Gaussian point cloud model for operation and maintenance safety early warning. The safety and efficiency of the maintenance operation of the transformer substation are improved.

The invention provides a low-delay multi-channel video data processing method and system for remote driving, and is applied to the field of data processing. According to the invention, a low-delay multi-channel video data full-process processing system is constructed for remote driving of a target unmanned vehicle. And four types of core data including hardware, video, network and control are obtained. Aiming at multiple cameras, images are screened according to driving scene priorities, full-coverage dead-corner-free, core view-angle-first and vehicle-type adaptation constraints are applied, and preprocessed multiple video streams are generated. Encoding, slicing and decoding the preprocessed video stream to construct a low-delay processing mechanism; a customized WebRTC protocol is adopted, routing is optimized, direct connection between a vehicle end and a cockpit is achieved, meanwhile, the network state is monitored, and transmission stability and safety are guaranteed through multiple constraints when the network state is abnormal. Fusing multiple paths of decoded pictures, arranging and superposing multiple types of key information according to driving habits, and combining constraints to generate an offering cabin display result; and finally, cooperatively processing the control instruction and the video feedback to form an interactive monitoring visual interface.

The utility model relates to the technical field of road rollers, and particularly discloses a road roller which comprises a cab, a base fixedly mounted on the lower portion of the cab, rear wheels rotatably mounted on the lower portion of the base, a support fixedly mounted on the front side of the base and fixedly connected with the lower portion of the cab, and sliding grooves formed in the two sides of the support. The outermost side of the sliding groove is arc-shaped, the middle of the sliding groove is rectangular, the inner side of the sliding groove is round, a rolling wheel is rotationally installed on the support, the bottommost portion of the rolling wheel and the bottommost portion of the rear wheel are located on the same horizontal line, a mud scraping device is further arranged on the support, and mud on the rolling wheel can be conveniently and rapidly cleaned through the mud scraping device. After cleaning is finished, the control rod can be rotated to the original position, the pull rod is pulled back under the action of the spring, the rotary knob can be rotated to switch to the scraper on the lower side, next use is facilitated, and the abraded scraper can be replaced in time.

The invention relates to the technical field of aviation safety monitoring, in particular to an aircraft cargo hold fire alarm detection system based on AI anomaly recognition and thermal imaging linkage, and the system comprises a visible light image collection module which is used for obtaining a real-time visible light image in an aircraft cargo hold; the infrared thermal imaging acquisition module is used for acquiring multi-region and multi-channel thermal imaging images in the cargo hold and constructing a thermal distribution diagram; the smoke and gas sensing device is used for monitoring the concentration change of hazardous gas in the air and outputting continuous detection data; the multi-modal AI identification processing module is used for carrying out fusion analysis on the multi-modal data; the pod linkage verification module is used for performing external verification sampling on the target area when the multi-modal analysis result is low in confidence but has risks, and forming a final judgment result in an auxiliary manner; and the alarm output module is used for converting the final judgment result into an alarm signal, sending the alarm signal to a cockpit HUD or a main control system through a flight control system, and automatically switching a prompt mode according to the fire behavior grade.

The invention discloses an automatic test analysis method and system for an intelligent cockpit large model, and relates to the technical field of test analysis, when a thread pool adjustment mechanism is triggered, resource data of a thread pool in a test process of a test case is obtained, and the state uncertainty of the thread pool is calculated; obtaining all test task relationship dependency data corresponding to the test case to calculate a task dependency degree; according to the state uncertainty of the thread pool and the task dependency degree, calculating a non-regulation degree; and judging whether to adjust the thread pool according to the non-regulation degree to adjust the state of the thread pool in the automatic test of the intelligent cockpit large model. According to actual judgment, in the automatic testing process of the intelligent cockpit large model, when a thread pool adjusting mechanism is triggered, whether the state of the thread pool needs to be adjusted truly or not can be judged, unnecessary frequent adjustment is avoided, the phenomena of waste of system resources and reduction of execution efficiency are reduced, the stability of the system is ensured, testing tasks are scheduled in order, and the testing efficiency is improved. The stability of the test process is ensured.

The invention relates to the field of block chains, in particular to an intelligent cabin digital twin recommendation system, method and device based on a block chain, and the method comprises the steps: obtaining data collected by an OBD sensor, a camera, an environment sensor and a biosensor; carrying out fuzzy processing on the collected data through edge calculation; performing feature extraction on the data after fuzzy processing, and generating a six-dimensional feature vector based on a context awareness recommendation algorithm; according to the scene label distribution weight of the six-dimensional feature vector, determining a recommendation priority; obtaining corresponding twinborn decision data according to the priority score; calculating a feature hash value of the twinborn decision data by adopting an SHA-3 algorithm, and generating a data fingerprint; dividing the data fingerprints into vehicle condition data and user data according to data types, and performing cross-chain storage; and performing credibility evaluation and screening on the multi-source data stored in a cross-chain manner, and feeding back the data to a user side. According to the scheme, a data island is broken, the security protection of privacy is enhanced, and the credibility and transparency of information recommendation are improved.

Optical sensors (e.g., cameras) and (e.g., IR) illumination sources may be distributed in an environment (e.g., an interior space such as a cabin or cockpit of an ego-machine) and synchronized to generate frames of sensor data, which may be used to reconstruct 3D geometry and / or 3D pose of an occupant, operator, or other object in the environment. For example, stereo vision may be used to generate one or more depth maps from image data generated using different cameras, the depth map(s) may be transformed into a 3D point cloud, and surface reconstruction may be applied to reconstruct the 3D geometry of surface(s) in the environment. A 3D pose, one or more keypoints (e.g., facial landmarks), or some other representation of the shape of the reconstructed surface(s) may be extracted from the reconstructed surface and used in one or more downstream tasks, such as driver and / or occupant monitoring tasks.

Systems and methods are disclosed relating to the detection and correction of sensor misalignment for monitoring systems and applications in a cabin. For example, a misalignment between a current sensor state and a calibrated sensor state can be detected and quantified using appropriate test and reference images of sensor data. If a misalignment above a threshold is detected, the test image of sensor data can be used to generate and apply a corresponding calibration adjustment. The presented methods can be used to detect and correct sensor misalignment for use by autonomous machines, semi-autonomous machines, other types of ego machines, and / or other sensing applications.

The invention belongs to the technical field of computers, and particularly relates to a dynamically configured cockpit host video test multiplexing method. The method aims at solving the problems that a traditional testing means is poor in universality, incomplete in coverage and low in automation degree. A structural equipment description file is constructed by analyzing a host technical document, an adaptive dynamic test configuration template is automatically generated in combination with a test case library, and flexible configuration of multi-channel video stream parameters, abnormal injection modes and time sequence logic is supported. A containerized virtual signal source is deployed on a universal computing platform, and an analog video stream conforming to an input specification is generated in real time. Through the equipment description driving and virtualization signal generation technology, the multiplexing capability and coverage integrity of the testing system are improved, efficient and automatic testing of multiple types of hosts is achieved, and the hardware cost and the deployment period are remarkably reduced.

The invention discloses a multi-modal data fusion anomaly detection method and system for an intelligent cockpit, and the method comprises the steps: deploying a plurality of types of sensors in the intelligent cockpit, collecting the multi-modal data of a driver, and carrying out the timestamp alignment; based on the multi-modal data, a multi-modal anomaly detection model based on Transform is trained, and the multi-modal anomaly detection model comprises a single-modal feature extraction sub-network and a cross-modal attention fusion mechanism; and deploying the trained multi-mode anomaly detection model to an intelligent cabin vehicle-mounted platform, performing anomaly detection in real time, and triggering a corresponding driving safety response according to a detection result. According to the invention, the precision, the real-time performance and the robustness of anomaly detection are improved.

The invention discloses a power transmission and transformation equipment maintenance and overhaul decision generation system, method, product and equipment, and relates to the technical field of power equipment state monitoring and operation and maintenance management, the system comprises a sensing layer, an intelligent brain layer and an application layer, the sensing layer collects multi-source heterogeneous monitoring data; the intelligent brain layer extracts feature indexes through data fusion, constructs a digital twin model fused with a multi-physical field mechanism, adopts an AI algorithm to realize fault prediction and health assessment, and generates a maintenance strategy based on multi-objective optimization; and the application layer provides a panoramic cockpit for visual interaction. Through a technical chain of multi-source data fusion, digital twinning, AI prediction and multi-objective optimization, the limitation of data islands, early warning lag and decision isolation in traditional operation and maintenance is broken through, normal form transformation from passive response to active prediction and from local optimization to global optimization is realized, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. The accuracy, the safety and the economical efficiency of operation and maintenance of the power transmission and transformation equipment are remarkably improved, and the method has wide engineering application value.

The invention provides an intelligent cabin control method and system, and the method comprises the steps: obtaining the original data of a sensor through a TOF module, and extracting the point cloud data of a target region of a target user based on the original data of the sensor; acquiring hand point cloud data and face point cloud data of the target user according to the target area point cloud data, acquiring hand joint coordinates based on the hand point cloud data, and acquiring facial expressions and fixation points based on the face point cloud data; and further controlling corresponding cabin functions based on the hand joint coordinates, the facial expressions and the fixation points. According to the method and the device, the user can control the functions of the intelligent cabin by waving the fingers or sliding the corresponding amplitude by gestures, a touch screen or a physical key is not needed, and the driving safety is guaranteed; meanwhile, related information is obtained based on the point cloud data, the recognition accuracy can be guaranteed, the computing power consumption is small, and the driving safety is further guaranteed.

The invention relates to the technical field of automobile driver data monitoring, and provides a test method and system based on the response capability of a driver to identify an intelligent driving cabin icon, and the method comprises the steps: collecting the feature attributes of the intelligent driving cabin icon of a tested vehicle, and combining a plurality of intelligent driving cabin icons into a target template according to each display position and change time; a road side test is carried out, a target image of the display area of the intelligent driving cabin icon is collected in real time through a first visual sensor, whether the target image is matched with the intelligent driving cabin icon in the target template or not is judged, and if yes, the response capacity of a driver to the intelligent driving cabin icon is collected; otherwise, continuing to collect the target image. According to the invention, the reaction capability of the driver to the intelligent driving cabin icon can be accurately and rapidly obtained under the road test condition.

The invention discloses an intelligent driving recognition system and method based on deep learning, and relates to the technical field of automatic driving and intelligent cockpit display, the system comprises a data acquisition and providing module, a neural network model module, a data post-processing and fusion module, a graphic rendering and control module and a display module, and RGB images and three-dimensional point clouds are acquired, so that the three-dimensional point clouds can be obtained; the method comprises the following steps: executing pixel-level semantic segmentation by using a hybrid neural network combined with a residual block, generating a drivable area and an obstacle mask, performing coordinate registration on point cloud by a system, screening and aggregating obstacle point cloud clusters by using a segmentation result, executing time sequence filtering processing on a road boundary based on an Euclidean distance, and generating stable boundary information. A graphics rendering module generates a geometric model accordingly. According to the invention, the problems of boundary jump identification and non-standard obstacle rendering distortion are solved, the display of driving, parking and navigation functions is realized, and the precision and interactive experience of intelligent driving perception are improved.

The invention discloses a multi-modal data fusion cabin safety monitoring method and system, and relates to the technical field of automobile safety driving. The method comprises the following steps: obtaining personnel, vehicle and environment data in a cabin; the data of personnel in the cabin comprises a driver and passengers, the vehicle data comprises a vehicle speed, an acceleration, a steering angle, a brake pressure and a light state, and the data of environment in the cabin comprises an audio signal in the cabin; performing depth feature extraction and cross-modal feature fusion on the acquired personnel, vehicle and environment data in the cockpit to obtain a cockpit scene feature vector; performing deep semantic analysis and risk assessment on the cockpit scene feature vector to obtain a security situation portrait; a corresponding intervention strategy is generated through a hierarchical decision-making mechanism according to the security situation portrait, the strategy is converted into a specific control instruction, and then the control instruction is converted into actual cabin and vehicle behaviors; a complete safety protection system is formed, and the safety of personnel in the cabin is guaranteed.

The invention discloses an airborne flight safety enhancement system and method based on multi-modal interaction, and relates to the field of crossing of avionics systems and artificial intelligence, and the method comprises the steps that an environment sensing module generates structured environment information; the airborne equipment monitoring module performs real-time health state analysis; the large language model module comprises a computing platform sub-module, a language model sub-module, a knowledge base sub-module, a voice recognition sub-module and a voice synthesis sub-module; and the output module is used for displaying the decision support information generated by the large language model module on a cockpit display device. The method has the advantages that through deep cooperation of multi-mode perception fusion, distillation large language model intelligent decision, natural voice interaction and airborne health monitoring, on the premise of ensuring airworthiness safety, the flight safety is remarkably improved, the pilot load is reduced, and the decision efficiency is optimized.