1897results about How to "Short response time" patented technology

The invention discloses an unmanned aerial vehicle-based riverway hydrological sampling inspection method and system. The method comprises the steps of collecting unmanned aerial vehicle multi-parameter sensor data, determining a dynamic sampling rate through an enhanced difference rate adaptive sampling algorithm, performing multi-parameter cross validation and environment compensation, and finally reconstructing a data stream. A hydrological feature extraction transformation algorithm and a hydrological mode recognition algorithm are adopted to recognize a hydrological mode, analyze a state evolution process and predict a development trend; a multi-scale anomaly detection algorithm is adopted to identify anomalies, an anomaly source is positioned through a causal chain tracing algorithm, risk assessment and grading are carried out, an emergency decision scheme is generated, and an inspection strategy is optimized; evaluating system performance, optimizing resource configuration, dynamically adjusting parameters, and updating a prediction model. The intelligent level of river hydrological monitoring is improved, the sampling efficiency and the anomaly detection accuracy are improved, and the decision response time is shortened.

The invention relates to power distribution network fault processing equipment applied to the technical field of power distribution, and aims to improve the fault prediction, diagnosis and processing capabilities of a power system. The equipment comprises a pre-training module, a neural network architecture module, a data acquisition layer, an edge calculation layer, a core processing layer, a context learning module, a reasoning processing module, an output and interaction layer and a continuous learning and improvement layer, and is characterized in that a large-scale pre-trained Transform model is utilized, and professional knowledge of a power system is combined; according to the method, deep understanding and analysis of a complex power grid state are realized, through multi-source data fusion, edge calculation and central cooperative processing, the equipment can monitor the power grid state in real time, accurately predict potential faults and provide intelligent diagnosis and repair suggestions, and model pre-training and optimization are performed by adopting self-supervised learning and federated learning technologies; using reinforcement learning to optimize a fault response strategy; the interpretability of the model is realized, and the decision transparency is improved.

The invention provides an information network security self-defense method and system based on trusted computing, and the method comprises the steps: constructing a trusted computing platform which comprises the steps: deploying a trusted platform module TPM, building a trusted software stack TSS, and implementing a trusted network connection TNC; designing a self-defense architecture, wherein the self-defense architecture comprises deployment of a network layer, an application layer and a data layer defense system, integration of an intelligent sensing system and establishment of a central control and decision center; network traffic and user access behaviors are monitored, and embedded attack behaviors are preprocessed; performing anomaly detection on the network flow and the user access behavior by applying a machine learning algorithm; calculating a real-time risk score based on the anomaly detection result, and generating a security situation awareness report; and based on the security situation awareness report, defining a threat level and a response level, and executing automatic defense.

The invention discloses an intelligent adjusting system of an adaptive multi-environment ship electric control valve actuator, which relates to the technical field of control valve adjustment and comprises an environment judgment module, a real-time monitoring module, an adaptive adjusting module and a feedback optimization module. The environment judgment module collects environment data through various sensors distributed on an electric control valve and around the electric control valve, and the environment data are processed through a signal conditioning and microprocessor application algorithm. According to the intelligent adjusting system of the self-adaptive multi-environment ship electric control valve actuator, the adaptability and the stability of the ship electric control valve actuator in variable environments are remarkably improved; the electric control valve actuator can monitor and respond to environment changes such as temperature, humidity, vibration and electromagnetic interference in real time, it is ensured that the electric control valve actuator can be accurately regulated and controlled under the extreme working condition, and therefore the reliability and safety of a ship system are improved.

The invention belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

The invention discloses an intelligent parking building vehicle dynamic supervision method and system based on an AI intelligent engine, and the method comprises the steps: obtaining original data from a plurality of sensors, carrying out the time alignment, standardization and noise reduction processing, and extracting initial features; performing reliability evaluation on the initial features, generating a dynamic weight matrix, and performing feature fusion; constructing a vehicle trajectory model and extracting a key point sequence, and performing space-time correlation analysis; constructing an exception mode library based on the optimized incidence matrix and a historical exception database, and performing exception matching; and constructing a decision model according to the abnormal event report and the system state matrix, and generating an execution strategy and an instruction sequence. According to the method, the multi-source data fusion and depth feature extraction technologies are adopted, and tensor decomposition and multi-objective optimization methods are combined, so that efficient and accurate supervision of the vehicles in the parking building is realized, and the method has relatively high practicability.

The invention relates to the technical field of intelligent spectrum regulation and control, and discloses an intelligent spectrum regulation and control system based on multi-modal data fusion and dynamic optimization, which comprises a multi-modal data acquisition module, a real-time data processing module, a dynamic weight distribution algorithm, a deep reinforcement learning regulation and control algorithm and a distributed collaborative optimization module. According to the system, real-time fusion and dynamic priority adjustment of multi-dimensional data are realized through acquisition of multi-modal data (environment spectrum, temperature and humidity, carbon dioxide concentration and user behavior) and dynamic weight distribution based on LSTM (Long Short Term Memory); dynamic optimization of a spectrum regulation and control strategy is realized by combining a deep reinforcement learning model and utilizing a weighted reward function of a spectrum output error and energy consumption, and efficient light supplement and low energy consumption requirements are balanced; a distributed optimization algorithm with edge computing and cloud collaboration is adopted, computing resources are dynamically allocated, task processing efficiency is greatly improved, and system power consumption is reduced.

The invention discloses a hydraulic engineering operation and maintenance monitoring system based on digital twinning, and the system comprises the steps: S1, a data collection module which is used for obtaining multi-modal actual detection data from a plurality of monitoring points of a hydraulic engineering; s2, a data generation module which simulates and generates multi-dimensional virtual monitoring data and an abnormal sample library related to hydraulic engineering operation and maintenance; s3, a data fusion module which fuses the multi-dimensional virtual monitoring data and the multi-modal actual detection data; s4, a digital twinborn model construction module which dynamically constructs a multi-dimensional digital twinborn model of the water conservancy project based on the fused multi-modal data set; s5, an anomaly detection module which detects potential anomaly and sudden faults through an anomaly sample library generated by the generative adversarial network in combination with a multi-dimensional spatial-temporal trajectory clustering analysis method; and S6, an operation and maintenance monitoring module which is used for providing real-time operation and maintenance monitoring service for the multi-dimensional digital twin model. The method has the advantages of being high in dynamic adaptive capacity, high in monitoring accuracy and high in abnormal response speed.

The invention provides a new energy station operation intelligent management method and system, and relates to the technical field of new energy station operation, and the method comprises the steps: collecting various data in real time through a sensor and monitoring equipment in a new energy station; transmitting the collected data to a central data center, carrying out iterative optimization processing, and determining final data classification, cleaning and compression parameter combination so as to construct a new energy station big data platform; and according to the constructed big data platform of the new energy station, constructing a digital twinborn model of the new energy station so as to reflect the operation state, the equipment health condition and the environment change of the station in real time. Through real-time data acquisition, digital twinborn model construction and simulation analysis, automatic generation and closed-loop management of an operation optimization scheme are realized, and the operation efficiency, prediction accuracy and fault handling capacity of the new energy station are effectively improved.

The invention provides a forest fire prevention unmanned aerial vehicle patrol early warning system and method based on edge calculation, and relates to the technical field of unmanned aerial vehicle patrol early warning. The patrol early warning system comprises an unmanned aerial vehicle platform, a data acquisition module, a data processing and analysis module, a communication and early warning module and a ground control and scheduling platform. According to the invention, calculation and data storage are close to a data source through edge calculation, the data collected by the unmanned aerial vehicle is processed in real time at an edge calculation node without being transmitted to a remote cloud and returning a result, the data processing and response time is greatly shortened, fire hazards can be found in seconds or even shorter time, and early warning can be given out. And the system can analyze data in real time in the flight process of the unmanned aerial vehicle, provides real-time decision support for a ground control center, and can quickly formulate a fire-fighting scheme according to information such as fire behavior and wind direction when a fire occurs, so that the fire-fighting efficiency is improved.

The invention discloses an optical cable routing anti-intrusion method and system, and the method comprises the following steps: laying a sensor network at key nodes along an optical cable, monitoring the data of a surrounding environment in real time, constructing an environment reference model, and integrating the model to a GIS platform, thereby achieving the risk assessment of different regions. Different monitoring frequencies and sensitivities are set for a high-risk area and a low-risk area according to an evaluation result, an anomaly detection module compares real-time signal data with an environment reference model, high-credibility abnormal signals are screened out through self-adaptive threshold adjustment, and when a physical barrier module detects abnormal external force, the physical barrier module sends out the abnormal external force. Triggering the area and the adjacent area to enter a high-frequency monitoring mode to form a dense monitoring area, positioning an intrusion position through a sensor network and a GIS platform, generating a path prediction model, and dynamically adjusting the frequency, sensitivity and alarm threshold value of the monitoring area based on data analysis and GIS geographic information. The adaptability and the resource configuration efficiency of the system in different environments are improved.

The invention relates to the technical field of Bluetooth earphones, and discloses a Bluetooth earphone automatic pairing method and device for multi-terminal switchover, and the method comprises the steps: carrying out the equipment information scanning of a preset region range of a Bluetooth earphone, obtaining an equipment identifier, a signal intensity value and an equipment state parameter, and storing the equipment identifier, the signal intensity value and the equipment state parameter in a priority database; inputting a double-layer deep belief network to perform feature mapping calculation to obtain an equipment connection score; establishing a first equipment priority queue and performing dynamic updating calculation to obtain a second equipment priority queue; distributing independent signal processing channels for parallel processing to obtain effective connection equipment; performing periodic acquisition, and generating a switching instruction when a difference value exceeds a preset threshold value; according to the method and the device, the probability of switching failure is effectively reduced, and the stability of automatic pairing of the multi-terminal switching Bluetooth headset is improved.

The invention relates to a real-time monitoring method and related equipment for an intelligent manufacturing digital twinning system, and the method comprises the following steps: constructing a three-dimensional digital twinning model based on a real-time image sequence; based on the three-dimensional digital twinborn model, analyzing whether an abnormal phenomenon exists in the production field, and if yes, performing abnormal region identification in the three-dimensional digital twinborn model through a preset abnormal identification algorithm to obtain abnormal state data; performing abnormity deduction on the production field based on the abnormal state data to obtain an abnormal evolution trend; and performing risk assessment on the production field based on the abnormal evolution trend to obtain a risk assessment result, and selecting a corresponding risk control strategy based on the risk assessment result, thereby solving the problem that a traditional method is difficult to provide enough details to comprehensively know reasons behind an abnormal phenomenon, and improving the risk control accuracy. And subsequent risk assessment and selection of control strategies are influenced.

The invention discloses a rendering method and system for generating a monitoring event queue of an air conditioner host. The method comprises the steps of obtaining operation data and monitoring data of air conditioner equipment, constructing an equipment connection diagram, extracting spatio-temporal characteristic data and constructing a comprehensive data model. Constructing a multi-level event processing queue, and generating an event scheduling strategy scheme; constructing a dependency graph of the rendering process, and generating a resource allocation scheme; constructing a rendering strategy scheme in combination with pre-stored historical rendering data, and generating a time series data set; generating a version chain table in combination with the historical data version information; performing data consistency check, and outputting a synchronous rendering data set; executing the rendering task sequence to obtain a rendering result; meanwhile, the execution state of the rendering process is monitored, and a rendering execution report and a rendering state report are generated. According to the method, the average response time of the system is prolonged, the accuracy of data processing is guaranteed, the stability of the system is improved, and powerful technical support is provided for stable operation of the air conditioner host monitoring system.

The invention relates to an Internet of Things equipment intelligent management platform based on edge computing, and aims to realize efficient and intelligent management of Internet of Things equipment through an edge computing technology. According to the platform, the edge nodes are deployed near the data source, so that local processing and storage of the data are realized, the data transmission delay is remarkably reduced, and the response speed of the system is improved. And the platform is provided with an intelligent management module, so that the equipment state can be monitored in real time, faults can be predicted, repair operation can be automatically executed, and the stability and reliability of the system are enhanced. And a self-adaptive data stream processing algorithm is adopted, and the data stream is dynamically adjusted according to a real-time network state and equipment requirements, so that the high efficiency of data processing is ensured. The secure transmission module protects the data transmission process through an end-to-end encryption technology, and prevents data leakage and tampering. The cloud-side collaborative resource scheduling system realizes optimal allocation of resources, and a user-defined rule engine provides high flexibility and personalized management strategies to meet different user requirements.

The invention discloses an optical cable fault diagnosis system and method based on a vibration filtering algorithm, relates to the technical field of optical cable fault diagnosis, and solves the problems of low efficiency, long response time and the like in the prior art and the technical problem that the health condition of an optical cable is difficult to comprehensively assess through single optical power data. The quality data of the optical cable are acquired; training an artificial intelligence model based on the historical vibration signal data to obtain an optical cable fault identification model; recognizing the vibration data based on an optical cable fault recognition model to obtain a recognition result; calculating an optical cable aging evaluation coefficient based on the vibration signal data and the optical power loss data; constructing an optical cable aging evaluation coefficient correction model based on the environment data to correct the optical cable aging evaluation coefficient obtained through calculation, and obtaining a target optical cable aging evaluation coefficient; and judging whether to carry out fault early warning based on the identification result and the target optical cable aging evaluation coefficient. The technical problem is solved.

The invention relates to the technical field of multi-source information integration, and discloses an intelligent fire-fighting command method and system based on a multi-modal AI large model.According to the intelligent fire-fighting command method and system, fire scene data are obtained in real time through multi-terminal information collection, scene reconnaissance feedback is conducted through flame and smoke recognition and dangerous goods recognition through an image recognition algorithm, and the fire-fighting commanding efficiency is improved. A pre-trained multi-mode live restoration model is utilized to carry out restoration simulation on a fire scene, a multi-source live simulation model is generated to analyze and formulate a personalized rescue plan, positioning and state information of a rescue unit is collected in real time, a rescue unit simulation model is constructed, and specific rescue execution steps are analyzed according to the personalized plan. The steps help commanders to realize accurate commanding and efficient rescue, the rescue decision accuracy is improved, the response time is shortened, the resource allocation is optimized, the emergency response capability and the successful rescue rate of a fire scene are remarkably improved, and the problem that in the prior art, the efficiency is low when multi-source information is manually integrated to restore the fire situation is solved.

The embodiment of the invention discloses a multi-unmanned aerial vehicle cooperative traffic state monitoring method, device and equipment and a medium, and relates to the technical field of multi-unmanned aerial vehicle cooperative control, and the method comprises the steps: carrying out the routing inspection in a preset routing inspection region through a plurality of unmanned aerial vehicles; identifying whether the traffic state is abnormal or not through the trained deep learning model; when it is recognized that the traffic state is abnormal, cooperative unmanned aerial vehicles are selected, the cooperative unmanned aerial vehicles are distributed to the traffic state abnormal areas, and traffic state data are cooperatively collected through the cooperative unmanned aerial vehicles; each cooperative unmanned aerial vehicle fuses the traffic state data of other cooperative unmanned aerial vehicles and then outputs fused traffic state data; and obtaining a real-time traffic state monitoring result based on the fused traffic state data of the cooperative unmanned aerial vehicle. According to the method, the same scene is perceived from different angles through the multiple unmanned aerial vehicles, data of multiple view angles can be fused, the accuracy of accident detection is improved, and data support can be provided for related departments to deal with traffic anomalous events.

The invention discloses a long-distance communication trunk optical cable fault positioning method and system, and the method comprises the steps: determining a monitoring demand, configuring an optical power meter, an optical spectrum analyzer and an OTDR, installing the optical power meter, the optical spectrum analyzer and the OTDR at a node, connecting monitoring equipment and a data collection system, configuring collection frequency, a threshold value, a storage and transmission strategy, configuring a data analysis algorithm, and starting the monitoring system to collect data. The optical cable performance is monitored in real time, data are automatically analyzed to identify abnormity, a fault alarm is generated and notified to maintenance personnel, and the data are integrated to automatically calculate the fault position. The fault detection and positioning efficiency is improved, the network reliability and maintenance efficiency are enhanced, and guarantee is provided for stable operation of the optical cable network.

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 discloses an instruction execution acceleration method based on a GPGPU and a GPGPU architecture, and relates to the field of high-performance computation.The method comprises the steps that a thread execution task output by a host compiler is received, and the thread execution task is scheduled according to the current idle state of a computing unit; capturing instruction data output by the host compiler, and executing concurrent instruction decoding according to the corresponding instruction type to obtain a machine code corresponding to the instruction data; obtaining an operand corresponding to the instruction data, and generating a to-be-transmitted instruction based on the machine code corresponding to the instruction data and the operand after secondary verification; according to the calculation complexity, determining an optimal calculation unit, and sending the to-be-launched instruction to the optimal calculation unit; and receiving an execution result of the to-be-transmitted instruction, and returning the execution result to the terminal. The thread execution task is dynamically scheduled according to the idle state of the computing unit, so that computing resources can be fully utilized, and the operation efficiency is improved.

The invention relates to the technical field of financial leasing systems, in particular to a financial leasing system and method based on a cloud platform, and the system comprises a leasing personnel input unit which is used for obtaining leasing demand information; the rental equipment data acquisition unit is used for acquiring equipment state information; the lease service management unit is used for processing lease matching and risk control; the demand unit is used for analyzing the lease demand; the leasing personnel registration unit is used for managing leasing personnel information; the rental platform resource library is used for storing system data; wherein the lease service management unit realizes lease matching and risk management through a dynamic multi-dimensional lease matching and risk control algorithm, and a state time sequence matching model greatly improves the accuracy of lease matching by combining historical data and real-time market demands; the multi-dimensional lease scoring system further considers a plurality of factors such as lessee credit and equipment conditions, and provides a comprehensive evaluation basis for subsequent decision making.

The invention provides an electric control box remote data acquisition system based on the Internet of Things, and relates to the technical field of data processing, and the method comprises the steps: obtaining the operation state data of an electric control box through a sensor, transmitting the operation state data through a wireless signal network, obtaining a data frame set, analyzing the data identifier of each data frame, verifying the integrity of the data, and obtaining the data frame set; determining performance parameters of the wireless signal network, detecting whether an abnormal change occurs in the transmission process, generating a solution when the abnormal change occurs, generating a retransmission signal when packet loss occurs in the transmission process, determining a retransmission queue according to the data identifier, and transmitting the retransmission signal according to the retransmission queue when delay occurs in the transmission process. The priority of a retransmission queue and the distribution of network bandwidth are dynamically adjusted, and when signal attenuation occurs in the transmission process, the execution sequence is adjusted according to the operation record and result of a solution by switching a transmission channel and adjusting transmission power. The transmission process of the operation data of the electric control box can be detected.

The invention discloses a light-operated reconfigurable metasurface for full-space terahertz wave beam regulation and control, solves the problem of how to provide ubiquitous full-space wave beam coverage for multi-user wireless communication application, and belongs to the field of artificial electromagnetic metamaterials. The super subunit comprises N super subunits which are arranged periodically, each super subunit comprises 4 * 4 identical coding units, corresponding codes are '0' and '1' respectively when illumination exists or not, and the phase response difference of the two coding units at 0.7 THz is 180 degrees. Each super subunit comprises five layers, the third layer is used for switching between reflection and transmission, when vanadium dioxide is in a medium state, the super surface is in a transmission mode, and when vanadium dioxide is in a metal state, the super surface is in a reflection mode. The first layer, the second layer, the fourth layer and the fifth layer are used for controlling the amplitude and the phase of the total-space terahertz wave, the conductivity of the photosensitive silicon pattern is changed under excitation of light with different intensities, the phase of the total-space terahertz wave beam is effectively modulated, and one-bit terahertz information coding is achieved.

The invention relates to the technical field of automatic inspection, in particular to an automatic road surface inspection robot control method and equipment, and the method comprises the steps: collecting environment data and robot motion state data through integrating a plurality of sensors, carrying out the preprocessing, carrying out the data fusion through employing a Kalman filtering algorithm, and generating a comprehensive road surface state map; a disease area is accurately identified and positioned through a convolutional neural network, dynamic path planning and polling strategy optimization are performed through a reinforcement learning algorithm, adaptive control of a complex road surface environment is realized through a fuzzy control algorithm, and meanwhile, a targeted road surface maintenance suggestion and an emergency processing scheme are generated according to real-time polling data, so that the real-time polling of a road surface is realized. According to the method, the scientificity and timeliness of road surface maintenance and traffic management are effectively improved, the road surface diseases can be accurately recognized and the inspection path can be optimized in a complex environment, and the road surface inspection efficiency and the intelligent level of road maintenance are remarkably improved.

The invention is suitable for the field of electric power safety, and provides a fault prediction and maintenance method and system for safe operation of electric power equipment, and the system comprises a data collection module, a data analysis and abnormity identification module, a dynamic alarm and response module, a geographic information system integration module, and a maintenance path optimization module. The system adopts a non-contact dynamic electric field monitoring system to monitor power equipment, can identify potential insulation failures or electrical faults in real time, and significantly reduces the risk of contact between operation and maintenance personnel and high-voltage equipment. And in combination with an intelligent alarm system, real-time data are analyzed through machine learning, an alarm is automatically triggered, and the accuracy and response speed of fault identification are improved. In addition, the monitoring system is integrated with a geographic information system, automatic optimization of an optimal maintenance response path is achieved, and it is ensured that faults are rapidly processed. According to the comprehensive method, the safety of the power equipment is improved, the fault risk is reduced, the maintenance efficiency is optimized, and the comprehensive method has important practical value.

The invention discloses a fire safety detection system based on edge-cloud collaboration, and belongs to the technical field of fire safety, the fire safety detection system comprises an edge end and a cloud end, the edge end comprises a sensor module and a fire response module, the cloud end comprises a fire analysis module and an auxiliary traceability module, and the sensor module is connected with the auxiliary traceability module. Comprising a smoke sensor, a light scattering sensor and an infrared sensor and is used for receiving field data in real time, transmitting the field data to the edge end for judgment and transmitting a control signal into the fire-fighting response module based on a judgment result of the edge end. According to the fire safety detection system based on side cloud cooperation, on the basis of the side cloud cooperation technology, the edge end is responsible for field monitoring and emergency alarm, the cloud end is responsible for data integration, analysis, information transmission and auxiliary alarm, data support is provided for subsequent fire decision making, meanwhile, the fire spreading trend and the fire grade are analyzed and predicted in real time, and the fire safety detection efficiency is improved. And the fire treatment efficiency can be improved.

The invention discloses a multi-agent cooperative control system based on multi-mode brain-computer interface-visual tracking, and the system comprises the following modules: a signal collection and processing module which employs an 8-lead electroencephalogram cap to collect electroencephalogram original data, selects occipital region parts (PO7, PO8, P3, P4, Pz, O1, O2 and Oz) as an electroencephalogram data collection region, and carries out the EEG data collection; the feature extraction and classification module is used for extracting SSVEP signal features by using a space-time convolutional network, optimizing classification precision in combination with an integrated learning strategy, extracting EOG signals based on a Transform model, integrating an LMS adaptive filter and denoising the EEG signals in combination with wavelet transform, expanding the frequency tracking range of the adaptive filter, and reducing signal loss to the greatest extent; the cooperative control strategy design module is used for mapping the electroencephalogram signals into control instructions of different intelligent agents, and cooperative control over the unmanned aerial vehicle and the trolley is achieved; and the target detection and tracking module is used for optimizing an unmanned aerial vehicle visual tracking system based on a PP-YOLOE model and realizing accurate tracking and automatic adjustment of the trolley.

The invention provides a fixed-time optimal control method and system of a single-connecting-rod mechanical arm, and relates to the technical field of optimal control of the single-connecting-rod mechanical arm. The fixed-time optimal control method comprises the steps that a kinetic model of the single-connecting-rod mechanical arm to be controlled is obtained, and the model is converted into a state model by introducing a state variable; according to the state model, an active-disturbance-rejection control strategy is adopted, and an expansion state observer is established and used for observing disturbance state information of the single-connecting-rod mechanical arm; based on the state model and the extended state observer, a backstepping method technology and a reinforcement learning algorithm are combined, an optimal controller for the single-connecting-rod robot mechanical arm is designed, and self-adaptive optimal backstepping control is conducted on the single-connecting-rod robot mechanical arm; the active disturbance rejection control technology and the fixed time optimal control are combined, a new control strategy and an optimization path are developed, and the control performance and the stability of the mechanical arm system are further improved.

The invention discloses an intelligent self-adaptive real-time task scheduling method and system based on multi-dimensional optimization. The method comprises the following steps: S1, inputting a task; s2, a DAG task is decomposed into independent sequential sub-tasks; s3, dynamically calculating the priorities of the tasks by adopting a multi-factor dynamic priority scheduling module, and updating the priorities of the tasks; s4, dynamically adjusting an acceleration factor by adopting a self-adaptive acceleration factor management module; s5, vertex correlation analysis is carried out, and a variance of processor load imbalance is calculated by adopting a load balanced distribution algorithm and is minimized; s6, in combination with static analysis and dynamic inspection, a hybrid schedulability judgment module is adopted to judge the tasks, and judgment results are fused; and S7, fusing decisions by adopting a decision fusion algorithm, obtaining a scheduling strategy through a scheduling strategy coordinator, executing the task, and monitoring the task in real time. Real-time monitoring and real-time optimization scheduling are realized, and the utilization rate of resources is improved.