1922results about How to "Guaranteed real-time" patented technology

The invention belongs to the technical field of multi-agent collaboration, and provides an electric power cross-modal knowledge fusion multi-agent collaboration processing method and system, and the technical scheme is as follows: analyzing a task execution command instruction input by a user; key elements of the task are extracted, the task is decomposed into a plurality of sub-tasks according to the extracted key elements, and logic association and priorities among the sub-tasks are identified; according to the logical association and priority among the subtasks, allocating the subtasks to the corresponding agents; each agent executes the corresponding subtask according to a set execution path, the executed task result is shared to other agents to cooperatively complete the complex task, and finally a cooperative processing result is obtained through integration. Efficient execution of complex tasks such as document generation, review and management is realized, and smooth completion of the tasks is ensured.

The invention relates to the field of geological disaster monitoring and early warning, in particular to a slope catastrophe early warning method and system. The method comprises the following steps: establishing a space-time database, storing and managing preprocessed slope data from multiple monitoring sources, and sorting to obtain a slope catastrophe data set; performing feature extraction on the slope catastrophe data set to obtain a key information set reflecting slope catastrophe features; analyzing the key information set to obtain a slope catastrophe value; calculating an early warning threshold value and a real-time displacement prediction value of the side slope based on the side slope data in the space-time database, and fusing the real-time displacement prediction value and the side slope catastrophe value to obtain a fusion value; and evaluating the real-time stable state of each monitored section of the slope according to the obtained fusion value and an early warning threshold value, and judging whether to send out an early warning signal and an alarm signal or not according to an evaluation result. According to the invention, accurate identification of the slope catastrophe value and the potential risk area is realized, and the accuracy and reliability of the early warning system are improved.

The invention provides a Python monitoring task resource use method and system, and relates to the technical field of intelligent monitoring and resource optimization, and the method comprises the steps: extracting process call stack information through a multilayer convolutional neural network, constructing a process dependency graph, calculating a process criticality score, and recognizing a core monitoring process. And obtaining resource use data of the core monitoring process through a resource monitoring coroutine, determining an optimal sampling period by adopting a self-adaptive feedback control algorithm, and writing the data into a time sequence database. And performing data aggregation analysis by using a sliding time window algorithm to obtain a performance index, and constructing an anomaly detection model. Dimensionality reduction is carried out based on a principal component analysis method to construct feature vectors, and load types are divided by adopting a clustering algorithm; and generating a differentiated process optimization strategy according to the feature vector and the load type. When performance fluctuation is detected, an optimization strategy is executed, the anomaly detection model and the deep reinforcement learning model are updated according to the performance improvement index, and finally an optimization diagnosis report is generated.

The invention relates to the technical field of banking business marketing data processing, in particular to a multi-channel-based banking business marketing data processing method and system, and aims to guarantee the accuracy and reliability of a marketing strategy by integrating multi-channel data and acquiring and verifying a banking business marketing data source in real time. Then, a machine learning algorithm is used for generating a customer portrait, analyzing a behavior pattern and potential requirements of the customer portrait, generating a personalized marketing strategy, a natural language processing technology is combined, real-time performance and pertinence of marketing content are ensured, pushing is carried out according to customer preferences during pushing, the pushing opportunity is optimized, and the system monitors feedback behaviors in real time after pushing, so that the marketing quality is improved. A strategy effect is evaluated, marketing activities are optimized by using an A / B test, closed-loop feedback is formed, a strategy is continuously improved, and high efficiency and accuracy of a marketing scheme are ensured. According to the method, automatic strategy generation and optimization are realized through the AI technology, the marketing efficiency and customer experience of banking business are greatly improved, and the method is suitable for a multi-channel data processing scene.

The invention discloses a real-time relative pose estimation method in an unmanned aerial vehicle-unmanned vehicle cooperative system, and the method comprises the steps: 1, achieving the real-time pose estimation of each platform through visual feature point tracking and IMU pre-integration through a monocular camera and IMU data, and constructing a sparse feature point cloud map; step 2, combining laser radar point cloud data with an IMU, and applying a Fast-LIO algorithm and an IESKF to complete dense three-dimensional point cloud map construction of each platform; and step 3, fusing the data in the step 1 and the step 2, constructing a factor graph optimization problem through point-surface matching residual error, visual reprojection residual error and IMU pre-integration residual error factors, and solving the relative pose in real time. According to the method, vision and laser radar information are integrated, the pose estimation precision and robustness are improved, the method is suitable for an unstructured environment, and the collaborative operation efficiency is enhanced.

The invention discloses an intelligent security and protection monitoring method and system based on image recognition. The method comprises the following steps: S1, acquiring multi-modal data; s2, performing preprocessing to generate complete target information; s3, performing real-time detection on the generated complete target information, and performing primary processing on a conventional scene; s4, based on an improved visual SLAM technology, obtaining three-dimensional information of multiple cameras; s5, when the edge device detects a potential abnormal condition, performing abnormal behavior analysis on the data by using a Swin Transform model, and optimizing a detection result in combination with the three-dimensional information of the multiple cameras; s6, performing adaptive learning on historical data by using a depth deterministic strategy gradient algorithm, and dynamically adjusting a threshold value of abnormal behavior detection; and S7, after an abnormal condition is detected, processing data through differential privacy protection and an end-to-end encryption technology, automatically controlling security and protection equipment, and giving a real-time alarm. According to the invention, image recognition and deep learning technologies are fully utilized, and accurate real-time monitoring in a complex scene is realized.

The invention discloses a laser SLAM (Simultaneous Localization and Mapping) method in a dynamic environment of a well mine, which relates to data processing and comprises the following steps of: performing mapping by utilizing a Cartograph SLAM algorithm; segmenting the PCD point cloud map by adopting an octree-based segmentation method according to a preset movement or deformation frequency threshold value of an object in each region in the well mining environment; adjusting the map resolution of each area in the PCD point cloud map according to the segmentation result; a change detection algorithm is used to carry out change detection on an area with object movement or deformation frequency exceeding a threshold value in the segmented PCD point cloud map, and new point cloud data of the change area is updated to the original PCD point cloud map in an increment mode; carrying out registration and fusion on the PCD point cloud map after local updating and an original PCD point cloud map; the global consistency is improved by aiming at the poor global consistency existing in the laser SLAM mapping of the well industry and mining in the prior art.

The invention discloses a fatigue driving monitoring and early warning method based on multi-dimensional feature fusion, and relates to the field of intelligent traffic and safe driving, and the method comprises the steps: carrying out the feature extraction of a collected face image of a vehicle driver through a feature extraction model based on a convolutional neural network, and obtaining a face key point; then calculating the eye closing rate, the mouth opening rate, the head posture data and the facial muscle data to finally obtain multi-dimensional features, and fusing the multi-dimensional features by using a multi-layer perceptron to obtain fused facial features; and then, a fatigue state detection model obtained based on recurrent neural network training is utilized to detect the facial feature time sequence at a plurality of continuous sampling moments so as to detect whether a fatigue driving state occurs and trigger alarm feedback. According to the method, the fatigue state of the driver can be identified and judged more accurately through combination of multi-dimensional data fusion and a time sequence analysis algorithm, and the monitoring and early warning accuracy and reliability are improved. And the system can keep high robustness even in an illumination change or complex driving environment.

The invention relates to the technical field of information security, and discloses a data security management system and method in an intelligent power grid, and the system comprises a dynamic encryption and key management module which is used for carrying out the encryption and key management of the data of the intelligent power grid, determining an encryption algorithm according to the sensitivity of the data, and generating a corresponding key, the dynamic encryption and key management module transmits encrypted data and key information to the adaptive access control module so as to ensure the security of data access; and the self-adaptive access control module is used for managing access permissions of the user and the equipment and performing dynamic authorization according to the attribute label and the context information. Through dynamic encryption and adaptive key management, an encryption algorithm and a key length are adjusted in real time according to data sensitivity and a network environment, a quantum key distribution technology is combined, high security and quantum attack resistance of a key are ensured, and then security and real-time performance of data transmission are ensured.

The invention relates to the technical field of multimedia interaction, in particular to an intelligent interaction control method and system for digital exhibition hall multimedia equipment, and the method comprises the following steps: S1, carrying out the arrangement of a sensor, and carrying out the intelligent sensing and data collection; s2, generating an intelligent interaction strategy; s3, equipment cooperative control and execution; and S4, performing real-time feedback and system optimization. According to the method, user behaviors and environment data are collected in real time through the multi-modal sensor, user requirements are accurately speculated in combination with a deep learning algorithm, the optimal interaction strategy and task priority are generated, individuation and interaction efficiency of user experience are remarkably improved, multi-device cooperation is optimized through distributed task scheduling, and user experience is improved. According to the method, display content and environment parameters are dynamically adjusted, self-optimization of the system is realized by utilizing user feedback, real-time performance, smoothness and reliability of interactive experience of the exhibition hall are ensured, rapid fault response and accurate maintenance demand prediction are realized through anomaly detection and equipment log analysis, and the stability and sustainability of operation of the exhibition hall system are greatly improved.

The invention discloses a vehicle-mounted sign retroreflection brightness coefficient detection method and system, and the method comprises the steps: obtaining the image data and point cloud data of a to-be-detected road region shot by an observation module at the top of a detection vehicle, and carrying out the detection of the to-be-detected road region according to a pre-trained first deep learning detection model; obtaining a relative distance between the to-be-detected signboard in the to-be-detected road area and the detection vehicle; when the relative distance meets a preset shooting distance, triggering the detection module to continuously shoot the signboard to be detected with light source illumination and without light source illumination, and respectively inputting two continuous frame images obtained by continuous shooting into a deep learning classification recognition model to obtain a target signboard image, the difference between the average gray values of each target signboard image is calculated to obtain an interference-free gray value, the retroreflection brightness coefficient of the signboard to be detected is obtained according to a preset retroreflection conversion formula, the continuous frames of images are used, interference brought by the external environment during measurement of the retroreflection brightness coefficient of the signboard is eliminated, and the measurement accuracy is improved.

The invention provides a water quality and feeding linkage management method and system based on the Internet of Things, and relates to the technical field of feeding management, and the method comprises the steps: deploying a sensor, and collecting water quality data; a water quality and feeding linkage model is constructed based on the water quality data, and the feeding amount is dynamically adjusted; feeding the feed based on the feeding amount, and optimizing the feeding amount according to real-time feedback; establishing a water quality abnormity monitoring model, and monitoring water quality data; if the water quality is abnormal, starting an adjusting mechanism; water quality data and feeding amount are remotely monitored, trend analysis is performed based on historical data, and automatic decision making is realized through an intelligent algorithm.

The invention relates to a mechanical arm safe obstacle avoidance control method and system based on a high-order control obstacle function, aiming at a multi-degree-of-freedom mechanical arm system with an obstacle in a task trajectory space, the high-order control obstacle function suitable for a high-relative-order system is used, and the method comprises the following steps: establishing a dynamic model of a multi-degree-of-freedom mechanical arm; designing a stable tracking controller based on the task trajectory in combination with a backstepping control method; an obstacle collision detection model is established, and safety constraints are provided for a control system through a high-order control obstacle function; and in combination with the control constraint and the optimal control problem, solving the optimal control input through quadratic programming. According to the method, the optimal task trajectory tracking can be completed on the premise that the tail end of the mechanical arm meets the safety constraint, the calculation complexity is effectively reduced, and the real-time performance is guaranteed.

The invention relates to the technical field of mechanical arm control, in particular to a mechanical arm and a control method thereof. The method comprises the following steps that real-time load information of all joints of the mechanical arm in the operation process is obtained, the acceleration and rotational inertia of the mechanical arm are monitored in real time, and real-time inertia force data are generated; the torque compensation amount needed by the mechanical arm is calculated according to the real-time inertia force data, and torque compensation control data are generated; performing dynamic analysis on the torque compensation control data, and performing real-time adjustment on the output torque of the servo motor based on the current load and inertia change condition to generate inertia compensation data; and the motion path and the motion speed of the mechanical arm are dynamically optimized according to the inertia compensation data, and path optimization data are obtained. Through real-time monitoring, dynamic adjustment and intelligent optimization, the operation precision, the movement efficiency, the stability and the self-adaptive capacity of the mechanical arm in the complex working environment are remarkably improved.

The invention is suitable for the technical field of anomaly detection, and particularly relates to a cloud edge multi-mode abnormal event detection system based on a dynamic decision-making mechanism, and the system comprises a data collection module which is used for collecting videos and audios, and obtaining multi-mode data; the data preprocessing module is used for preprocessing to obtain preprocessed data; the key feature extraction module is used for carrying out key frame feature extraction and key frequency band feature extraction; the multi-mode anomaly detection module is used for carrying out anomaly judgment; and the cloud large-model intelligent processing module is used for analyzing the detected abnormal fragments and outputting a generative text result. According to the cloud edge multi-mode abnormal event detection system based on the dynamic decision-making mechanism provided by the invention, more comprehensive event understanding and anomaly detection are provided through combination of video and audio data. And deep features are extracted at the edge end by using the convolutional neural network, so that the detection accuracy is improved, and the false alarm rate is effectively reduced.

The invention discloses a power distribution network topology identification and state estimation method based on a message passing neural network and online deep learning, and the method comprises the steps: training a neural network through historical measurement data, building a power distribution network topology identification model, combining the obtained topology information with corresponding power grid measurement data, and constructing a graph data set; taking the obtained graph data set as a training set to train a message passing neural network to obtain a power distribution network state estimation model; and finally, inputting the real-time measurement data into a state estimation model of the topology identification model to obtain a real-time state estimation result under the section. According to the method, the power distribution network topology can be accurately identified under limited measurement data, accurate state estimation is provided for a power distribution system, and technical support is provided for subsequent safety assessment and optimized operation.

The invention discloses a real-time track obstacle detection method and system and a medium. The method comprises the following steps: acquiring a visual image of a track area; segmenting the visual image by using a lightweight target segmentation model to obtain a single-frame track line, an obstacle and the category of the obstacle; according to the single-frame track line, a final track area is obtained based on dynamic multi-frame track area fitting; judging whether the obstacle is in the final track area or not, and if yes, obtaining a detection frame of the obstacle in the visual image; and according to the obtained detection frame of the obstacle in the visual image, projecting the obstacle detection frame in the visual image into the radar point cloud data to obtain an obstacle point cloud range frame, and according to the obstacle point cloud range frame, calculating the distance from the obstacle to the train head. According to the invention, real-time detection and distance measurement can be carried out on the track obstacle, and the track area can be accurately determined in the image, so that whether the obstacle is in the track area or not can be judged, and a more accurate obstacle distance measurement result can be obtained.

The invention discloses an unmanned aerial vehicle positioning navigation and path planning optimization method based on machine learning, and the method comprises the steps: carrying out the three-dimensional grid division of an unmanned aerial vehicle activity region based on a sensor coverage range, obtaining the obstacle layout condition of each grid, calculating the environment complexity of each grid based on the obstacle layout condition, and carrying out the calculation of the environment complexity. Setting response time for the grid according to the environmental complexity, constructing an obstacle avoidance model, obtaining an obstacle avoidance strategy according to the obstacle avoidance model, constructing a way-finding algorithm based on the obstacle avoidance strategy, taking the response time as interval search time of the way-finding algorithm, and obtaining a feasible path set based on the way-finding algorithm in an unmanned aerial vehicle activity area, and constructing a multi-objective function to optimize the feasible path set to obtain an optimal path. The method adopts a machine learning method, can adapt to the requirements of different environments and tasks, effectively enhances the obstacle avoidance capability and optimizes the path, improves the autonomy and intelligence level of the unmanned aerial vehicle, and has good interpretability.

The invention relates to a power grid business intelligent auditing method based on AI enhancement, and the method comprises the following steps: S1, collecting data from a power grid SCADA system and a financial system, integrating historical and real-time data, and constructing an auditing knowledge graph; s2, constructing an instruction recognition model by using RoBERTa to analyze a natural language instruction of an auditor, and combining knowledge graph information with a language model to improve the understanding and reasoning ability of an instruction background; s3, constructing a multi-task deep learning framework to realize processing and fusion of multiple auditing tasks; s4, on the basis of a natural language instruction input by an auditor, extracting query targets, conditions and parameters involved in the instruction according to the instruction recognition model, obtaining corresponding auditing data in the database, and obtaining intention classification; and S5, calling a corresponding auditing model based on the intention classification result and the auditing data, and obtaining an analysis result. According to the invention, efficiency and precision in power grid business auditing work are effectively improved.

The invention relates to the technical field of industrial equipment fault diagnosis, and provides an industrial equipment real-time fault diagnosis method and system based on a generative model, and the method comprises the following steps: collecting the original operation data of industrial equipment in real time through a plurality of sensors, carrying out the preprocessing of the original operation data, obtaining the real-time operation data, and carrying out the calculation of the real-time operation data; the preprocessing comprises abnormal value detection and standardization; constructing a generative model based on the real-time operation data, generating a normal data sample of the industrial equipment in a normal state through the generative model, and integrating the real-time operation data and the normal data sample to obtain a multi-dimensional data sample; performing feature extraction on the multi-dimensional data sample by using a deep learning model to obtain a fault diagnosis vector; performing fault mode identification on the fault diagnosis vector through a classification model to obtain a fault diagnosis result, and performing state judgment on the fault diagnosis result according to a preset threshold to obtain an industrial equipment state; and the state and the fault diagnosis result of the industrial equipment are displayed in real time through a dynamic monitoring interface. According to the method, the real-time performance and the overall efficiency of fault diagnosis are improved, and the loss caused by equipment faults can be effectively reduced.

The invention belongs to the technical field of fault prediction, and discloses a fault prediction method and system for an intelligent accessory monitoring platform. The method comprises the following steps: an intelligent accessory monitoring platform constructs a knowledge graph, a fault prediction model and a fault maintenance strategy generation model; performing knowledge mapping on the real-time monitoring log by using a knowledge graph to obtain a semantic enhanced real-time monitoring log; performing fault prediction by using a fault prediction model according to the real-time operation monitoring data and the semantic enhanced real-time monitoring log to obtain a real-time fault prediction result; generating a fault maintenance strategy by using a fault maintenance strategy generation model according to the real-time fault prediction result to obtain a real-time fault maintenance strategy; and visually displaying the real-time fault prediction result and the real-time fault maintenance strategy of the attachment. According to the invention, the problems of insufficient fault prediction accuracy, insufficient data processing capability and low intelligent degree in the prior art are solved.

The invention discloses a map navigation method and system based on integrated navigation, and particularly relates to the field of integrated navigation, comprising compass data acquisition, magnetic declination correction, map integration, navigation system data acquisition, navigation system data analysis and integrated navigation. According to the method, the precision and robustness of the navigation system are remarkably improved, different environments can be dynamically adapted, and the error influence is reduced; rich geographic information support is provided by presetting an electronic map and POI marks; the real-time navigation service provides visual road condition information and path planning, the user experience is enhanced, and on the whole, more accurate, reliable and convenient navigation service is provided for the user.

The invention discloses a distribution network line fault early warning and accurate positioning system, and relates to the technical field of distribution network line monitoring. In order to solve the problems that in actual operation, due to the complexity of a power grid, the situation of false alarm or missing alarm may exist, and in actual application, accurate fault positioning is crucial for quickly recovering power supply and reducing power failure time, and the accuracy of fault position positioning is low. Distribution network line data and traveling wave signals are accurately acquired in real time through the data acquisition unit, abnormal changes in a distribution network can be found in time through the fault early warning unit, the fault type can be rapidly identified, the fault occurrence position can be accurately calculated through the fault positioning unit, the rod fixing data of a fault point can be determined, and the fault positioning accuracy can be improved. According to the invention, the time of troubleshooting by operation and maintenance personnel is reduced, the fault processing efficiency is improved, the operation and maintenance personnel can remotely monitor the operation state of the power distribution network, the convenience and efficiency of operation and maintenance are improved, intelligent monitoring and management of the power distribution network are realized, and the operation and maintenance cost is reduced.

The invention discloses a tunnel crack detection method and a tunnel crack detection system, and belongs to the field of tunnel safety detection.The tunnel crack detection method comprises the steps that a tunnel inspection robot is utilized to conduct inspection in a tunnel to obtain complete three-dimensional data in the tunnel so as to construct a three-dimensional map in the tunnel, and the position of each crack area in the three-dimensional map is determined; determining an inspection tour area corresponding to each crack area and executing path planning, and generating an optimal inspection tour path of the unmanned aerial vehicle; the optimal inspection path enables the unmanned aerial vehicle to sequentially pass through each inspection area and then leave the tunnel after entering the tunnel under the condition that obstacle avoidance conditions are met, and the total time is shortest; enabling the unmanned aerial vehicle to move according to the optimal inspection path, and scanning the crack region by using the depth camera when the unmanned aerial vehicle moves to one inspection region so as to obtain fine three-dimensional data of the crack region; and performing analysis processing on the fine three-dimensional data of the crack area to complete tunnel crack detection. According to the invention, the efficiency and precision of long tunnel crack detection can be improved.

The invention discloses an integrated navigation method based on deep learning and improved Kalman filtering, belongs to the technical field of artificial intelligence and integrated navigation, and is used for processing integrated navigation data, including using GPS / INS integrated navigation when a GPS signal is available, and using angular velocity and acceleration output by an inertial measurement unit IMU to determine whether the GPS signal is available or not. Calculating and estimating the attitude, position and speed of the object, and fusing the attitude, position and speed with GPS data to obtain a final combined navigation result; and when the GPS signal is unavailable, predicting a pseudo position and a pseudo speed when the GPS signal is unavailable through the neural network model, and inputting the pseudo position and the pseudo speed into the adaptive square root volume Kalman filter for data fusion to obtain a predicted position and a predicted speed when the GPS signal is unavailable. According to the invention, the prediction precision of the neural network model is improved, the robustness of the filtering algorithm is optimized, and the method has real-time performance; the influence of noise on the system is reduced, and data fusion of the GPS and the INS and a high-precision solution are realized.

The invention discloses a blockchain and homomorphic encryption-based power data privacy protection method and system, and belongs to the technical field of power data privacy protection, and the method comprises the steps that a data provider encrypts power data by using Paillier homomorphic encryption, uploads a ciphertext hash value to a blockchain, carries out the digital signature of a ciphertext, divides a key, and carries out the encryption of the key; enabling each data demander to receive the ciphertext, the signature and the key fragment; and the data demander performs cooperative decryption after performing data source and integrity verification to obtain a plaintext. Through distributed storage of a key sharing scheme, the security of the key is remarkably improved, single-point faults are avoided, the fault tolerance of the whole system is improved, and the flexibility of data transaction is optimized; for a mass data transaction process, the encryption and decryption efficiency is improved, the data processing speed and performance are improved, the real-time performance of data transaction is ensured, the resource consumption is reduced, and the user experience is improved.

The invention proposes a semiconductor device test optimization method, and the method comprises the steps: initializing a test environment, obtaining the test data of a semiconductor, and automatically configuring the test environment; constructing a signal adjustment model to dynamically generate a preliminary test signal; designing a sensor network to monitor a preliminary test signal and response of the semiconductor device in real time, dynamically adjusting sensor data, and designing a data optimization acquisition strategy; dividing the parameters into a high priority and a low priority, dynamically compressing the parameters, implementing fast Fourier transform, extracting spectrum components as features, and analyzing the features; decompressing and preprocessing the compressed data, and performing deep fault analysis and positioning; and optimizing test data of the conductor, carrying out secondary test, and verifying a parameter adjustment result. According to the invention, the test quality and efficiency of the semiconductor device can be obviously improved, the product failure rate caused by insufficient test can be reduced, and the method plays an important role in semiconductor manufacturing industry, especially high-end chip production.

The invention provides a distributed photovoltaic output power estimation method, and the method comprises the steps: installing a load sensor and a photovoltaic output sensor at an access point, and collecting original data including load data and photovoltaic output data in real time; processing the collected original data; carrying out time synchronization on the load data and the photovoltaic output data; key features are extracted from the cleaned original data, and photovoltaic output is predicted through a time sequence analysis model; inputting load data and photovoltaic output data which are acquired in real time into the trained time sequence analysis model; the time sequence analysis model performs real-time operation according to the load data and the photovoltaic output data, and estimates the photovoltaic output power at the current moment; and based on the estimated photovoltaic output power and the real-time load data, the intelligent coordination controller performs dynamic adjustment, so that the photovoltaic output is matched with the load power. According to the invention, efficient estimation and optimal control of photovoltaic output are realized.

The invention relates to the technical field of image processing, in particular to a real-time exposure control and image synthesis method for a multi-lens array, which comprises the following steps: an acquisition step: acquiring images shot by a plurality of lenses and corresponding sensor read-out rate information; a processing step comprising: determining exposure parameters through a multi-level hardware level exposure control unit based on the image and the sensor readout rate information; adjusting exposure settings of the plurality of lenses according to the exposure parameters; generating a mask based on the adjusted images shot by the plurality of lenses; performing image synthesis according to the mask and the images shot by the plurality of lenses; the output step comprises the step of outputting the synthesized image, the method can complete exposure parameter adjustment of a plurality of lenses within the millisecond level, and the real-time performance of the system is greatly improved. Meanwhile, an exposure prediction mechanism based on reinforcement learning enables the system to quickly adapt to scene changes, and the problem of response lag of a traditional method is effectively solved.

The invention provides a multi-core AMP architecture inter-core communication method and system based on a high-security embedded real-time operating system, and the method comprises the steps: respectively operating an authentication version of a Quhua high-security embedded real-time operating system on an AMP heterogeneous multi-core platform, and guaranteeing the real-time performance of a task while providing functional security. And two driving interface mechanisms of IPC Not ify and IPC RPMsg are provided, so that the real-time performance of inter-core communication of the multi-core AMP architecture is improved. The IPC Not ify transmits messages based on the HW mailbox of the HW fifo and interrupts the receiving of core messages, and has low time delay; and a heterogeneous multi-core synchronization function is supported, so that a user does not need to concern the starting sequence of the AMP heterogeneous multi-core system and mainly focuses on development of inter-core communication application programs of the user. The IPC RPMsg provides a two-way communication link based on a channel between the cores, and supports large data volume communication interaction between the cores. A universal shared memory area is configured, and a spinlock technology is adopted to synchronize multi-core operation and prevent multi-core access conflicts. A method for sharing the same physical device by multiple cores is provided, and the problem of insufficient device hardware resources is solved.