1691 results about "Cockpit" patented technology

An attitude display system for use in an aircraft has at least one display unit visible by a pilot within a cockpit. A symbology generator is configured for receiving input from at least one attitude sensor and generating symbology image data representative of a pear-shaped attitude indicator, distorted vertically to represent a pitch of the aircraft and rotated to represent a roll angle of the aircraft. The symbology generator may further be configured for adding a horizontal line that represents a horizon, a regular trapezoid representing a downward gravity vector at a base of the trapezoid, a pitch angle of the aircraft, split wings representing an angle of attack of the aircraft, and a stall warning in a sawtooth crack shape. A tail portion of a T-shape with the curved top edge is itself curved in a direction opposite that of an aircraft spin direction.

The invention discloses a cockpit active recommendation system and method based on a knowledge graph and semantic reasoning, and relates to the technical field of intelligent cockpits. The system receives natural language voice input of a user, executes voice recognition and semantic analysis, extracts user intention, keywords and slot entities, generates structured semantic information, constructs or calls a knowledge graph structure with semantic relation edges in combination with environment context information, and obtains the knowledge graph structure with the semantic relation edges. Semantic path reasoning is carried out based on the path dependence weight and the semantic similarity, a semantic edge label guided graph attention mechanism is introduced to calculate a path consistency score, a candidate recommendation set is generated, the semantic fitting degree and the path score are fused to sort and output recommendation content, and the graph edge weight and the user portrait are updated based on user feedback. According to the method, semantic understanding precision, recommendation path interpretability and system adaptive capacity are improved, and the method is suitable for personalized voice recommendation, man-machine interaction and scene linkage control tasks in an intelligent cockpit.

The invention discloses an intelligent cabin interaction method and system combined with digital twinning, and relates to the technical field of man-machine interaction, and the method comprises the steps: collecting the multi-dimensional perception data of a cabin, and constructing a layered cabin digital twinning model; performing scene analysis on the cockpit digital twin model, extracting driver state features, predicting driver potential interaction demands, generating a multi-modal interaction strategy set in combination with the driver potential interaction demands and a current driving scene, and performing simulation evaluation on the multi-modal interaction strategy set to obtain an interaction evaluation index set; performing multi-objective decision optimization under the driving safety constraint based on the interaction evaluation index set, and determining and adaptively executing an optimal interaction strategy; and acquiring actual execution data and driver feedback data of the optimal interaction strategy, and incrementally updating the hierarchical cabin digital twin model. According to the invention, the interaction adaptability and the safety reliability of the intelligent cabin system in a complex driving scene are obviously improved, and an effective technical support is provided for realizing intelligent man-machine cooperative driving.

The invention relates to the technical field of fire fighting systems, in particular to an intelligent cockpit system for fire fighting, which comprises a perception analysis layer, a decision processing layer, a command execution layer and a reinforcement learning closed-loop architecture for mixed reward shaping, and integrates video streams, audio communication and firefighter physiological data containing heart rate variability through a deep multi-modal fusion module. Generating a global fire scene situation of physical constraint verification; through a risk sensitive type three-dimensional fire scene deduction module, a fire extinguishing strategy considering efficiency and safety is generated; through an immersive augmented reality visual interface, in combination with a synchronous positioning and mapping technology, precise navigation in a complex environment and superposed display of situation information containing risk levels are realized; a Q learning algorithm continuous optimization strategy including domain knowledge intermediate process rewards is adopted, closed-loop optimization of perception-analysis-decision-execution-feedback is achieved, and therefore the overall efficiency of fire rescue operation in modern complex disaster scenes is remarkably improved.

The invention provides a multi-screen cabin entertainment system, and relates to the technical field of embedded systems and intelligent cabins, and the multi-screen cabin entertainment system is characterized in that an application layer is deployed with a plurality of cabin entertainment applications, and when the application layer is started, content rendering requests are sent out respectively; the hardware layer is provided with a development board, is provided with a CPU core cluster and a coprocessor, and is connected with different cabin display screens through different video output interfaces. The system layer respectively distributes an independent thread and an independent frame buffer area for each content rendering request on the CPU core cluster, and establishes a mapping relation between each frame buffer area and each video output interface; and sending operation result data of each independent thread to a coprocessor for rendering, correspondingly storing a rendering result in a frame buffer area, and then sending the rendering result to a cabin display screen for display through a video output interface. The method has the advantages that on the premise that the number of hardware coprocessors is not increased, complete separation of multi-screen rendering threads and frame buffer is achieved, and multiple cockpit entertainment applications can run at the same time and do not interfere with one another.

The invention relates to the technical field of TEM models, provides a pilot dynamic evaluation method, system and device based on a TEM model and a storage medium, and solves the problems of low accuracy of flight training evaluation and poor adaptability of a training scheme. The method comprises the steps that flight control, physiological monitoring and cockpit voice data are acquired, and a multi-modal data stream is formed through time synchronization; performing threat perception, error management and non-technical skill three-level evaluation on the feature vector by using a TEM model, generating corresponding indexes, and fusing the indexes into a comprehensive feature vector; respectively processing the time sequence and cognitive features by means of a dual-channel long-short-term memory network, and extracting deep features; performing decision tracing by adopting an SHAP value so as to locate a capability defect node; finally, defect types are matched through a personalized improvement scheme recommendation mechanism, and self-adaptive training content is generated. According to the invention, the accuracy of flight training evaluation and the adaptability of the training scheme are improved.

The invention provides a vehicle-mounted oxygen production control method and device, a vehicle-mounted oxygen production system and electronic equipment, and relates to the technical field of vehicle control. The method comprises the steps that the oxygen partial pressure in a cockpit, the altitude of the environment where the cockpit is located and the oxyhemoglobin saturation of passengers in the cockpit are obtained; determining an oxygen supply demand value based on the oxygen partial pressure, the altitude and the oxyhemoglobin saturation; and based on the oxygen supply demand value, an oxygen generation module is controlled to start oxygen generation or stop oxygen generation. The starting and stopping of the oxygen generation module are controlled based on the oxygen supply demand value, waste caused by starting of the oxygen generation module when oxygen supply is not needed can be avoided, and therefore the energy consumption of vehicle-mounted oxygen generation can be greatly reduced.

An aircraft includes a flight deck including a flight deck door configured to be moved between an open position and a closed position, a first monument, second monument, and a secondary barrier door moveably secured between the first monument and the second monument. The secondary barrier door is configured to fold into a stowed position in which a path to the flight deck is open, and unfold into a deployed position in which the path to the flight deck is closed.

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 discloses an intelligent cockpit driver intention prediction method and system fusing scene semantics, and the method comprises the steps: collecting cockpit interaction data and driving dynamic data, extracting a data sequence of a specified time length through a sliding window method, and constructing a driver interaction intention data set; constructing an instruction fine tuning data set of scene semantic understanding based on logic rules and manual inspection, and converting the driving dynamic data into a scene semantic text through a fine tuning large language model; training a deep learning model by using the constructed driver intention data set, and outputting the most probable driver interaction intention; according to the method, the limitation of independent modeling of a driver action sequence and spatio-temporal information in a traditional method is effectively solved, collaborative modeling of driver interaction actions and scene spatio-temporal characteristics is formed by fusing context information of a scene spatio-temporal causal relationship, and accurate prediction of interaction intentions of various services such as air conditioners, music and seats in an intelligent cabin is achieved.

The invention belongs to the technical field of voice processing, and discloses an intelligent cockpit multi-mode voice interaction system and method, and the system comprises a voice triggering unit which collects the environment audio and video information in a cockpit, judges whether to enter a voice interaction mode or not through combining with the environment perception parameters in a vehicle, and sends the voice interaction mode to the vehicle; when the voice interaction triggering condition is satisfied, generating a voice interaction input signal matched with the current environment; the mouth shape analysis unit is used for carrying out acoustic feature extraction on the voice interaction input signal, synchronously analyzing the lip motion trail of the driver in the video information, establishing a corresponding relation between voice phonemes and mouth shape motion, and forming a joint analysis feature; the candidate generation unit is used for carrying out segmented alignment on the joint analysis features and constructing a continuous multi-modal fragment sequence; performing time synchronization on the multi-modal fragment sequence, and projecting the multi-modal fragment sequence to a predefined intention space to obtain a candidate intention set containing different candidate intentions; and the man-machine interaction experience of the intelligent cabin is improved.

The invention discloses a duplex-screen intelligent cabin interaction method and system, and relates to the technical field of intelligent cabin interaction, and the method comprises the steps: collecting user operation behavior data, vehicle state data and environment perception data in real time through a vehicle-mounted sensor group, a man-machine interaction interface and a vehicle bus; establishing a multi-dimensional user data model containing an interaction feature vector, a driving mode matrix and a preference configuration tree; the method comprises the following steps: analyzing user historical interaction data based on an adaptive weighting algorithm, and calculating use frequency weight coefficients of different functional modules; according to the duplex-screen intelligent cockpit interaction method and system provided by the invention, the precision of a user portrait is remarkably improved by constructing a multi-dimensional user data model, personalized interaction experience is realized, and the interface layout and interaction logic can be dynamically adjusted according to a real-time driving scene by adopting a self-adaptive weighting algorithm and a random forest classifier, so that the user experience is improved. The real-time performance and the adaptability of the system are enhanced, and the interaction experience is optimized.

The invention discloses a real-time emotion perception and voice interaction system for an intelligent cabin. The system comprises a multi-modal data acquisition module used for synchronously acquiring a facial image, a voice signal and text input of a driver; the visual feature enhancement unit is used for carrying out restoration and emotion distribution extraction on the low-quality image; the audio noise reduction and feature extraction unit is used for extracting voice emotion features; the text emotion coding unit is used for fusing relative position coding and context semantic information; the cross-modal fusion module is used for outputting an emotion classification result by integrating visual, audio and text features; the personalized emotion database is used for storing historical emotion data of the user and performing emotion trend prediction and early warning judgment; the large language model feedback module is used for generating structured cue words according to the emotion recognition result and the driving situation and generating natural language feedback; the voice synthesis and output module is used for adjusting voice parameters and performing feedback output through a vehicle-mounted multi-channel; according to the invention, the emotion expression of human-vehicle interaction is enhanced.

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 an intelligent cockpit implementation method and system based on single-model multi-task reasoning, and belongs to the field of automotive electronics, and the method comprises the steps: inputting shared depth features into a detection head and a first classification head after original image data in an intelligent cockpit is preprocessed; the output of the detection head is subjected to detection post-processing to obtain a target detection anchor frame and position information, and the output of the first classification head is subjected to classification post-processing to obtain distraction identification classification; after the target detection anchor frame and the position information are preprocessed, human body area sub-images are input into a safety belt recognition and classification model; inputting the face region sub-images into a fatigue recognition classification model, a face ID matching model, a fixation point estimation model, a sight line estimation model and a head posture estimation model in parallel; and a face image sequence and a voice sequence in the continuous video stream are collected, the face image sequence and the voice sequence are respectively input into the lip language recognition model and the voice recognition model, a recognition result is input into the large language model to be processed to generate an interaction instruction, and a response is output through the voice synthesis module.

The invention relates to the technical field of vehicle-mounted intelligent computing, in particular to a hybrid key task scheduling method, system and equipment based on a virtualization technology. The method comprises the following steps: firstly, carrying out hybrid criticality grading on vehicle-mounted intelligent cabin tasks, and establishing a priority table; then, creating a dynamic resource partition based on a QNX Hypervisor platform, monitoring a system load state in real time through a hardware performance counter, and predicting a computing power demand of a large model; dynamically calculating an allocation scheme in combination with the task criticality level and the system energy efficiency state; then determining an optimal calculation unit selection strategy and a model precision grade by adopting an energy consumption-time delay joint optimization model; and finally, executing encrypted storage and integrity verification of large model parameters in a trusted environment, and completing end-side deployment and security verification. According to the method, the dynamic resource scheduling of the mixed key task is realized, the resource utilization rate is improved, the real-time requirement of the safety key task is ensured, and the dynamic balance between the performance and the energy efficiency is realized.

The invention relates to the technical field of automobile intelligent cabin control, and provides a self-adaptive control method and system based on an intelligent cabin, which considers the driving state of a vehicle, integrates a self-adaptive adjustment mechanism in the vehicle to control the intelligent cabin, updates personalized adjustment parameters of cabin equipment in real time based on real-time sign data and environmental data, and improves the control accuracy of the cabin equipment. The seat adjusting requirements of different users in different environments can be met; the scene type of the current scene is recognized according to the real-time physical sign data, scene self-adaptive adjustment is carried out based on personalized adjustment parameters, target adjustment parameters are output to drive cabin equipment, the rocking chair function is combined with an automobile seat, scene requirements are perceived through the user state, and the user experience is improved. Further, advanced dynamic adjustment is carried out on personalized adjustment parameters, intelligent control of converting passive response into demand pre-judgment is achieved, personal pertinence is high, adaptability is good, and the use experience of a user is effectively improved.

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 invention relates to the technical field of flight training, and particularly discloses an intelligent auxiliary training method and system for the driving skill of a helicopter pilot, and the method comprises the steps: deploying a plurality of sensors in a simulated helicopter cockpit for collecting operation, physiology, eye movement and other original data; after data standardization preprocessing, constructing a trainee training state comprehensive data model by using a multi-modal data fusion algorithm; dynamically generating a simulated flight scene by using VR and AR; carrying out comprehensive evaluation by adopting a multi-modal evaluation model based on deep learning in combination with multi-source data and student scene performance, and generating a multi-dimensional report; an evaluation result is fed back in real time through multiple channels such as voice and touch, and customized training resources are pushed; and formulating an exclusive training plan by using a personalized learning path planning algorithm based on multiple training data. Through multi-source data fusion and an intelligent algorithm, the training state of the trainee is comprehensively and accurately evaluated, a personalized training scheme is provided, and the driving skill training efficiency and quality of the helicopter pilot are effectively improved.

The invention provides a data processing method and device of a camera control system, a vehicle and a storage medium. The method comprises the following steps: in response to the fact that an automatic driving domain controller is identified to be powered on, performing state detection on a cabin domain controller to obtain a first state detection result; in response to the first state detection result, determining that the cockpit domain controller is a current master control domain controller, and sending permission switching request information to the cockpit domain controller; permission switching response information corresponding to the permission switching request information is received; switching the current master control domain controller from the cockpit domain controller to an automatic driving domain controller in response to determining that the permission switching request information meets a preset switching condition based on the permission switching response information; and receiving environment image data of the camera module by using the automatic driving domain controller. According to the invention, the technical problems of high energy consumption and low response speed during data processing of a camera control system in the prior art are solved.

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 provides an awakening method and system, and relates to the technical field of intelligent cabins, and the awakening method is applied to an instrument system, and comprises the steps: receiving an awakening event and business data corresponding to the awakening event; in response to the wake-up event, waking up the TBOX system, and not waking up the operating system of the vehicle-mounted terminal; and sending the wake-up event and the service data to the TBOX system, so that the TBOX system maintains a wake-up state or a sleep state based on the wake-up event and the service data. The power consumption caused by wakeup can be reduced.

An aircraft section, between a cockpit and a cabin, comprises an aisle to access to the cockpit from the cabin, and a monument extending in a longitudinal direction and a transverse direction. The monument includes a first lavatory, a second lavatory adjacent thereto in the transverse direction and comprising the aisle, a wall separating the first lavatory from the second lavatory, a boundary wall delimiting the first lavatory and a portion of a rear wall of the cockpit, and a second boundary wall delimiting the second lavatory and a second portion of the rear wall of the cockpit. The boundary walls are parallel or continuous. The monument further includes an outer door movable between an open position, where the cockpit aisle is accessible from the cabin area, and a closed position, where the cockpit aisle is blocked with respect to the cabin area.

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 relates to the technical field of intelligent automobiles, in particular to an intelligent cabin multi-AI Agent cross-domain collaborative super-brain system and an interaction method, and the system comprises a sensor cluster, a central decision AI Agent, a five-domain AI Agent and a cross-domain synchronization mechanism. The sensor cluster is used for collecting multi-dimensional data and transmitting the multi-dimensional data to the central decision AI Agent; the central decision AI Agent is used for processing the data, including analyzing fused data to establish a mapping relation corresponding to scenes and services, performing cross-domain priority arbitration and realizing inter-domain data synchronous scheduling; the five-domain AI Agent comprises a cabin domain AI Agent, a power domain AI Agent, a vehicle body domain AI Agent, a chassis domain AI Agent and an automatic driving domain AI Agent and is used for executing specific functions. According to the invention, the problem of function islands of a traditional intelligent cabin is effectively solved, deep coupling and dynamic linkage of cross-domain functions are realized, the function utilization rate is increased from less than 20% in the prior art to more than 78%, and the utilization rate of system resources is remarkably improved.

Embodiments of this application provide a control method and apparatus, and a transportation means. The method may be applied to the field of intelligent cockpits. The method includes: obtaining sensor data when an input from a user in a cockpit for indicating to generate an image is detected, where the sensor data includes a first image acquired by a camera of the transportation means or a speech signal acquired by a microphone of the transportation means; and controlling, based on the sensor data, a display area in the transportation means to display a second image. Embodiments of this application may be applied to an intelligent vehicle or an electric vehicle, to help improve a degree of personalization of content displayed in the cockpit, so as to help improve use experience of the user.

The invention belongs to the technical field of flight test, and discloses a cockpit vision field evaluation test flight method suitable for a troposphere manned airship, which comprises the following steps: selecting an airship test condition; the airship climbs to a preset field height and keeps level flight; a preset flight test action is completed; and commenting by a flight tester, and filling in a cockpit vision field commenting card. According to the method, the blank of the flight test method for evaluating the visual field of the cockpit of the troposphere manned airship with the vector propulsion system is filled, and the flight test efficiency and the flight test quality of the manned airship are improved.

The invention belongs to the technical field of flight simulation training equipment, particularly relates to an intelligent temperature control system for a flight simulator cabin, and solves the problem that the requirements of flight simulator temperature control on multi-dimensional physiological perception fusion, multi-target collaborative optimization and rapid personalized adaptation and active safety protection of new pilots cannot be met in the prior art. According to the method, a multi-modal sensing layer processes multi-channel skin physiological data, physical environment data and pilot electrocardiosignals, a human body heat balance equation for blood flow correction is constructed, and physiological feature vectors including predicted core body temperature, predicted average votes and predicted dissatisfaction percentage are generated; the decision-making layer generates a temperature control instruction and a confidence evaluation value through a meta-learning model, a reward function is in a four-dimensional dynamic weighting form, and the weight is adjusted through an IF-THEN rule according to the state; and the dynamic execution layer adopts a PID closed loop to adjust the partition temperature and triggers response in a grading manner. Accurate and personalized temperature control is achieved, and the training quality and safety are guaranteed.

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.