1789 results about "Real time analysis" patented technology

The invention relates to the technical field of wind power, in particular to a dynamic response test method for a wind power variable pitch system. Comprising the steps of building a high-fidelity digital twinborn model, performing virtual testing and case optimization, performing physical self-adaptive testing execution, performing real-time analysis and decision-making driven by machine learning, dynamically adjusting testing stress, performing data feedback and model calibration and generating a comprehensive health report. Compared with the prior art which mainly depends on a fixed physical test process and artificial experience judgment and has the defects of low test efficiency, large resource consumption and difficulty in comprehensively covering extreme working conditions, the method innovatively introduces a high-fidelity digital twinning technology and a virtual test optimization process; according to the method, massive tests are executed in the virtual environment in a risk-free manner, and the enhanced test cases are intelligently generated, so that test forward movement and accurate optimization are realized, the pertinence and efficiency of physical tests are remarkably improved, and the test cost and period are greatly reduced.

The invention relates to the technical field of food safety intelligent tracing, and discloses a livestock and poultry product epidemic disease and tracing information quality tracing system based on artificial intelligence. The system comprises a raw material file construction module, an abnormal active prediction module, a multi-modal information fusion module, a tracing knowledge graph construction module and an intelligent tracing decision module. The system generates a raw material archive by collecting livestock and poultry raw material information, and analyzes production monitoring data in real time by using a time sequence model to actively predict abnormal nodes of epidemic diseases. After the abnormity is recognized, multi-link heterogeneous traceability information is synchronously called and fused, an abnormal event panorama is generated, and then a traceability knowledge graph is dynamically constructed in combination with an external knowledge base. And finally, automatically generating an optimized traceability query path according to the knowledge graph, and driving a precise traceability process. According to the invention, the conversion from passive recording to active early warning is realized, and the accuracy and efficiency of epidemic disease tracing are improved through deep fusion and intelligent reasoning of multi-source information.

The invention discloses an infrared CO2 high-precision detection system and method based on a self-adaptive balance signal, and belongs to the field of gas detection. The invention discloses an infrared CO2 high-precision detection system and method based on a self-adaptive balance signal. The infrared CO2 high-precision detection system comprises a sensing module, a signal conditioning and collecting module and a self-adaptive filtering processing module. According to the method, the problems of contradiction between response speed and stability, discrete signal tracking lag and sensitivity to environmental interference in the prior art are solved, the weight of a filtering channel is adaptively and dynamically adjusted, and the advantages of a fast filtering channel and a slow filtering channel are combined, so that balance between dynamic response and steady-state precision is realized; the problem of tracking lag of discrete signals is solved by analyzing the change rate of the signals in real time and dynamically adjusting a filtering strategy; and through a hybrid filter and an environment compensation algorithm, the influence of temperature and humidity changes on a detection result is reduced, so that the long-term stability of the sensor is improved.

The invention discloses a dynamic monitoring-based deaerator water supply control method and system for a nuclear power plant, and relates to the field of deaerator water supply control, and the method comprises the steps: firstly, through a hybrid online identification algorithm, analyzing a historical valve position instruction and a water supply flow sequence in real time, and dynamically estimating the local gain and hysteresis width of a valve under the current working condition; then, the parameters identified in real time are utilized to carry out prospective compensation on the expected flow variation calculated by the main controller. In other words, the instruction amplitude is adjusted according to the estimated local gain, and it is ensured that consistent flow response can be obtained under different loads; meanwhile, when the instruction is reverse, the compensation amount is actively applied according to the estimated hysteresis width so as to eliminate the adjustment dead zone and oscillation caused by hysteresis. In this way, the nonlinear object of the valve is equivalent to a linear link with consistent response, and the stability and accuracy of the deaerator water level control system in the full working condition range are remarkably improved.

The invention discloses a wide-frequency-domain weak signal data acquisition method, system, equipment and medium, and relates to the technical field of power system monitoring and fault diagnosis, and the method comprises the steps: collecting an input signal, analyzing the signal characteristics in real time, obtaining the frequency composition and amplitude change information of the signal, and dynamically adjusting the sampling rate according to the signal characteristics. Performing noise reduction processing on the collected signals, eliminating noise interference and retaining effective signal components, performing time alignment processing on the data to form a data set with a unified time reference, extracting multi-category features based on the data set, performing fusion judgment, identifying whether the system is in a fault state, and when it is judged that a fault occurs, judging whether the system is in a fault state or not; if yes, fault analysis and positioning are executed, and an analysis result is generated and output. According to the method, fast Fourier transform analysis is carried out on the signals, the dominant frequency and harmonic components can be accurately recognized, the energy ratio can be calculated, and a reliable data basis is provided for signal feature extraction and fault diagnosis.

The invention provides a pilot manipulation quality real-time analysis method and system in a wind shear environment, and relates to the technical field of aviation flight, and the method comprises the steps: firstly capturing flight manipulation and environment interaction data including flight state feedback data and pilot manipulation execution data; secondly, establishing a response association link set of the manipulation action and the environment change, taking the wind shear environment change as a trigger node, taking pilot manipulation execution as a response node, and taking flight state feedback as an association node; tracing the matching relation between the time sequence track of the control action and the environment change, and outputting a time sequence matching relation set; analyzing the adaptability dynamic change of the manipulation action based on the time sequence fit relationship set evolution, and outputting an adaptability dynamic change result; and finally, generating a manipulation adjustment guide based on the dynamic change result of the adaptability, assisting a pilot in optimizing manipulation actions in real time, and improving the flight safety and manipulation quality.

The invention relates to a computer technology, discloses a marine monitoring data real-time processing method and system based on edge computing, and solves the problems that an existing marine monitoring system is insufficient in real-time performance, too heavy in communication load, poor in toughness and limited in expansibility. According to the invention, data processing, analysis and decision making are deployed in the edge intelligent unit, original data are collected and preprocessed through the edge monitoring unit, and real-time analysis and local response are carried out; selective communication with a cloud platform is carried out, and global decision and model optimization deployment are carried out by cloud aggregation data. According to the scheme, the real-time response can be improved, the communication load can be reduced, the system toughness can be enhanced, the processing efficiency and energy can be optimized, and the monitoring precision and expandability can be improved.

The invention provides a social robot intention recognition system and method based on heterogeneous graph analysis, and the method comprises the steps: carrying out the unified modeling of multi-type nodes and multiple relation edges based on a heterogeneous graph structure, and systematically capturing the complex multi-dimensional behavior association in a social platform; according to the method, technologies such as a large language model and a knowledge graph are fused, fine recognition and classification of multiple types of behavior modes are supported, and meanwhile, hierarchical evaluation for different organized risks is realized by combining account behavior aggregation and emotional polarity analysis, so that a decision basis is provided for supervision and governance; according to the method, automatic collection and preprocessing of multi-source heterogeneous data and heterogeneous graph construction processes are integrated, manual intervention and repeated work are reduced to the maximum extent, the overall data processing efficiency and response speed are improved, and the real-time analysis requirement in a complex dynamic social environment is met; transparent analysis of robot control sites and behavior strategies is supported, and the interpretability of the system, the user credibility and the executive force of policy supervision are greatly improved.

The invention relates to the technical field of welding automation, and discloses an intelligent welding system for an electric pole steel reinforcement framework. The system comprises a welding execution module, a multi-mode sensing module, an edge calculation module, a real-time regulation and control module and a system control center. Synchronous collection and real-time analysis of images, temperature and sound signals of a welding pool area are achieved, so that defects such as air holes, slag inclusion and incomplete fusion can be recognized online in a key time window when the welding pool is not solidified, and time delay caused by a traditional off-line detection mode is thoroughly eliminated. Through a real-time regulation and control module, on the basis of a multi-target optimization decision and parameter self-adaptive adjustment mechanism, the system can dynamically adjust key process parameters such as welding current, arc voltage and welding speed at the moment of identifying defect germination or process deviation, and real-time intervention and closed-loop control on welding quality are achieved; therefore, the final formation of defect welding seams is avoided fundamentally, and the rework rate and the material waste are remarkably reduced.

The invention belongs to the technical field of network information security, and discloses an information security management system based on network operation and maintenance. The method comprises the following steps: periodically acquiring multi-dimensional traffic data of a network node through a traffic acquisition module, and constructing and labeling a network security situation map by means of an asset labeling module; the authority distribution module automatically adjusts the authority of the abnormal access node by analyzing the node access behavior; the threat detection module analyzes the flow in real time, drives the self-adaptive isolation module to quickly isolate threat nodes according to the generated temporary strategy, and updates the joint defense model for cooperative defense; the attack evolution prediction module performs attack path prediction by extracting attack features and optimizes an isolation strategy accordingly; and information leakage is effectively prevented.

The invention provides a method, a system and equipment for improving the computing performance stability of a hardware platform and a medium, and belongs to the technical field of computer system performance optimization. The method comprises the following steps: acquiring architecture characteristic data of a bottom hardware platform through a hardware detection tool, and generating and loading a first-stage system kernel parameter configuration based on the architecture characteristic data; reading the architecture characteristic data, carrying out behavior analysis and type identification on the running process based on the architecture characteristic data, and generating a second-level process scheduling strategy according to the behavior analysis and type identification; reading cache structure information in the architecture characteristic data, and driving a file system to intelligently prefetch and reconstruct a storage layout of memory data based on the cache structure information; and continuously collecting performance index flow data when the system runs, performing real-time analysis on the performance index flow data by using a performance degradation model, generating a feedback control instruction according to an analysis result, and dynamically correcting the kernel parameter configuration of the first-stage system and the scheduling strategy of the second-stage process.

The invention relates to the technical field of power consumption management, in particular to a pump station unit energy consumption real-time analysis system which comprises a load feature extraction module, a frequency stability recognition module, a start-stop window recognition module, a voltage response regulation and control module and a signal filtering module. According to the method, a sliding trend sequence is constructed through load state data and current increment information, slope abrupt change points are identified, trend direction classification is carried out, accurate identification of a frequency operation stability section is realized in combination with frequency variation amplitude, and a regulation and control time window is constructed through trend consistency judgment of energy consumption and flow in the same time domain. A voltage input strategy is set according to the trend change rate, a mapping table is formed, signal filtering and recovery are achieved through trend continuity judgment in confidence evaluation of energy consumption labels, and the behavior segmentation precision, the adaptability of electric energy input regulation and control and the continuity and controllability of overall operation state recognition in the energy consumption monitoring process are effectively enhanced.

The invention relates to the technical field of electric power detection, in particular to an electric power inspection robot detection method and system based on machine vision. According to the method, the three-dimensional model of the target area is constructed by pre-collecting the basic data of the area, the historical defect data is obtained to mark the detection key area, the initial path and the key detection point are optimized by using the genetic algorithm, redundant collection points are removed, and the accuracy of the visual detection path is improved; meanwhile, visual data are analyzed in real time through a path acquisition collaborative analysis module, an optimization instruction is generated to dynamically adjust a path, and the problems of low efficiency and missing detection caused by independent operation of vision and navigation are avoided; the visual perception module collects detection dynamic data of definition, vibration acceleration, environment wind speed and environment light, the environment adaptability module analyzes and outputs a comprehensive fluctuation value, a corresponding fluctuation value interval is obtained, a corresponding environment adaptability regulation and control instruction is generated, and influences caused by motion blur and environment sudden change are avoided.

The invention discloses an AI customer service system dialogue strategy dynamic adjustment method and system, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining user portrait data of a user; describing a user portrait based on the user portrait data; matching and configuring initial customer service personnel from a preset customer service personnel library according to the user portrait; in the multi-round dialogue process of the user and the AI customer service, the emotion value of the user is analyzed in real time, and the emotion value comprises a positive emotion value and a negative emotion value; when the emotion value is a forward emotion value, the dialogue style of the large language model is enhanced in real time through the prompt word engineering module, so that the reply satisfaction degree of the user is improved; when the emotion value is a negative emotion value, adjusting prompt words of the initial customer service personnel and the prompt word engineering module, and continuously monitoring the emotion value in the dialogue; and when the emotion value is converted from the negative direction to the positive direction, strengthening the current adjusted customer service personnel setting and prompt words. According to the method, the user experience can be effectively improved in the interaction process of the user and the AI customer service system.

The invention relates to the technical field of data analysis and control, in particular to a hydraulic climbing frame safety control method based on digital monitoring, which comprises the following steps: acquiring monitoring data in real time through a sensor group arranged on a hydraulic climbing frame, preprocessing and analog-to-digital conversion are carried out on the monitoring data, then the data are formed and sent to a remote monitoring center through a wireless transmission technology, and the preprocessed monitoring data are input into a pre-stored structure safety evaluation model and a hydraulic system performance evaluation model so as to be analyzed in real time; analyzing the hydraulic climbing frame, judging a safety characterization state of the hydraulic climbing frame according to an analysis result, determining a safety risk type based on an abnormal state of the hydraulic climbing frame, sending early warning information through a user interaction platform when a risk is judged to exist, and executing a corresponding safety control instruction; automatic real-time monitoring, intelligent early warning and active control of the safety state of the hydraulic climbing frame are achieved, and construction safety and operation reliability are remarkably improved.

The invention discloses a concrete quality three-level monitoring device and method, and belongs to the technical field of concrete construction. The method aims at solving the cracking problem caused by factors such as temperature stress and shrinkage stress in mass concrete construction and the key technical problems such as data islanding phenomenon and regulation and control lag in an existing monitoring method. According to the technical scheme, a three-level monitoring system of pouring layer-cooling layer-structural layer is constructed, and various key parameters such as temperature, humidity, flow velocity and cooling water flow of concrete in pouring, cooling and stabilizing stages are accurately monitored in real time; the central controller is used for carrying out real-time analysis processing and dynamic regulation and control on the data, so that comprehensive, accurate and continuous monitoring on the concrete construction quality is realized; the method is mainly applied to monitoring scenes of mass concrete construction quality, concrete cracking can be effectively prevented, the engineering quality and safety are remarkably improved, and reliable technical guarantee is provided for the concrete construction industry.

The invention discloses an intelligent driving recording method and system based on voice AI, and relates to the technical field of intelligent driving recording. The method comprises the following steps: acquiring a user voice instruction through voice acquisition equipment, and identifying the user voice instruction through a vehicle-mounted lightweight ASR model; analyzing the instruction to generate a structured control instruction containing an event type and a time reference; determining a time window of the event to be marked in combination with the time reference, the motion data of the IMU sensor and the video data; packaging, encrypting and storing the video clip in the window, the sensor data and the structured control instruction to generate an event record; the video is analyzed in real time through a local lightweight computer vision model, and when a preset dangerous scene is detected, voice early warning is triggered and an associated video is automatically stored. According to the invention, voice interaction control, accurate event marking, real-time danger early warning and efficient data management of the driving record are realized, and the convenience, accuracy and safety of the driving record are improved.

The invention relates to the technical field of fire early warning, in particular to an early fire early warning system based on AI image recognition, and the system comprises an image collection module which is used for obtaining the video stream data of a monitoring area in real time; an AI image analysis module which is in communication connection with the image acquisition module and is used for receiving the video stream data and carrying out real-time analysis on video frames based on a pre-trained fire identification model so as to extract visual features related to the fire; and the early warning judgment module is in communication connection with the AI image analysis module and is used for receiving an analysis result of the visual features. According to the early fire early warning system and method based on AI image recognition, through video image analysis, the system can recognize weak flame or smoke characteristics at the initial stage of a fire and when naked eyes do not obviously see the characteristics, and the delay problem that a traditional smoke-sensing and temperature-sensing detector needs to wait for physical parameters to be diffused to the detector to be triggered is solved.

The invention relates to the technical field of big data processing, and discloses a data extraction method and system for geological mineral exploration, and the method comprises the steps: carrying out the standardization preprocessing of original data containing space coordinates, lithology texts, mineral components, logging curves and mineralization labels; spatial, semantic, concentration and time sequence deep representations are extracted in parallel through a multi-modal geologic feature encoder and fused into high-dimensional vectors; applying structured sparse constraint to the features by using a graph attention mechanism guided by an expert knowledge graph, and strengthening a mineralization association dimension; a dynamic incremental learning engine is combined with an elastic weight solidification mechanism to realize local fine tuning of model parameters and historical knowledge retention; and finally outputting a mineralization potential score, a mineralization factor sequence and an abnormal element combination. According to the method, through triple mechanisms of multi-modal fusion, knowledge embedding and incremental evolution, the accuracy, interpretability and timeliness of data extraction are improved, and the real-time analysis requirement of large-scale mineral exploration is met.

The invention relates to the technical field of machining defect monitoring, and discloses a machining defect real-time positioning and multi-physics field visualization system based on a multi-source track. The system comprises a multi-source trajectory data acquisition module for acquiring a cutter motion trajectory sequence, a temperature distribution sequence and surface topography monitoring data to form a multi-source trajectory data set; the multi-physics field reconstruction module processes the data set to generate defect distribution characteristics containing thermal stress gradient, force distribution cumulant and defect fluctuation coefficient; the defect positioning analysis module calls the pre-training model for real-time analysis and outputs a defect position predicted value and a defect type identifier; the environment compensation correction module corrects a defect position prediction value by combining a temperature distribution sequence and a material thermal characteristic incidence relation; and a visualization strategy generation module generates a multi-physical field visualization strategy set containing thermal field rendering and force field superposition schemes according to the defect type identifier.

The invention belongs to the technical field of data analysis, and relates to a port ship berthing dynamic risk prediction and pilot intelligent matching method. According to the invention, by quantitatively calculating the comprehensive qualification score and dividing the grade, dynamic matching of the pilot and the task risk grade is realized, an evaluation mode dominated by artificial experience is broken, the accuracy of pilot qualification evaluation is improved, and the operation error rate caused by insufficient personnel qualification is reduced; by dynamically simulating the berthing process and calculating risk indexes such as the offset rate and the collision energy, the berthing risk is pre-judged and graded, the berthing risk identification accuracy is improved, equipment operation parameters, dangerous goods states and meteorological data are analyzed in real time, risks such as faults, leakage and extreme weather are accurately identified, corresponding measures are triggered, and the safety of berthing is improved. Targeted management of berthing risks is facilitated, and accident loss and port operation interruption risks are effectively reduced.

The invention relates to a remote sensing image data processing method and system based on a Spark architecture, and belongs to the technical field of remote sensing image data processing. The method comprises the following steps: converting a collected original file into a COG format, constructing a hybrid index through a spatial dimension reduction technology, extracting metadata and establishing a metadatabase; based on the hybrid index, quickly positioning a target tile through spatial query, reading a data block, and constructing a TileLayerRDD of a spatial partition in a Spark cluster; and dynamically assembling and calculating the directed acyclic graph according to the algorithm warehouse template, and submitting the directed acyclic graph to a Spark cluster for distributed memory calculation. According to the method, rapid retrieval is achieved through the COG format and the mixed index, pre-slicing is replaced with Spark distributed computing, the storage pressure is greatly reduced, and on-demand real-time analysis and high-concurrency online service of mass remote sensing data are finally achieved through micro-service and elastic resource scheduling.

The invention belongs to the technical field of vehicle safety control and man-machine interaction, and particularly relates to a man-machine co-driving takeover decision-making, stability control and optimization method, which comprises the following steps of: A, constructing a driving safety field model, reflecting the influence of a dynamic environment and road elements on a vehicle by constructing the safety field model, and providing a real-time basis for takeover decision-making; b, constructing a driver human factor capability field, and evaluating the takeover capability of the driver through multi-modal data acquisition and real-time analysis; c, dynamically coupling a safety field and a human factor capability field in the takeover decision; d, a man-machine takeover mode judgment and control power distribution method; e, the takeover scheme is screened and optimized, and further screening and adjustment are carried out through the comprehensive cost function of the optimization algorithm and used for being executed in actual operation. The method has the advantages that the handover between the automatic driving system and the driver is intelligently controlled by evaluating the environmental risk, the driver ability and the take-over time in real time, and it is ensured that driving right transfer is safely, timely and efficiently completed.

A system is provided.SOLUTION: A system, comprising: means for collecting applicant paperwork; means for analyzing the collected paperwork and utilizing a generative AI model to evaluate the applicant's skills, experience, and educational background; means for generating and displaying evaluation results in the form of a visual report; means for utilizing the generated evaluation results to generate a set of interview questions; and means for integrating the paperwork examination and interview results, performing a final evaluation, and recommending informal candidates.SELECTED DRAWING: Figure 1

The invention provides a production equipment state real-time analysis method and system based on edge calculation, and relates to the technical field of information, and the method comprises the steps: analyzing the dynamic change trend of a light beam central axis according to the offset trajectory of the light beam central axis, and determining the spatial distribution characteristics of the light beam axis through the dynamic change trend of the light beam central axis in combination with an LED array arrangement SIC mode; if the adjustment angle in the lens calibration instruction set exceeds a preset angle threshold, dynamically adjusting the position of the optical axis of the lens according to the offset amplitude and direction of the optical axis, compensating the offset of the light beam axis in real time, and obtaining a calibrated optical axis parameter; according to the color temperature distribution uniformity, determining a target lens position through a lens position adjustment attribute; according to the calibrated optical axis parameters and color temperature distribution uniformity, stable light beam center axis distribution is determined, spectrum consistency is determined, and a performance evaluation data set is obtained through comprehensive evaluation.

The invention provides a real-time classroom analysis method based on containerization and the Internet of Things technology. The method comprises the following steps: firstly, collecting classroom data to identify a current teaching link; the acquisition parameters are dynamically adjusted; then loading a lightweight AI model corresponding to the current teaching link to extract core data features of the link; and finally, accessing the edge computing unit to a cloud analysis platform through a containerized cloud edge collaborative architecture. The cloud analysis platform dynamically schedules computing resources of the edge end and the cloud end according to a preset teaching cycle rule; and meanwhile, core data characteristics uploaded by the edge end are received, and a teaching analysis result is generated in combination with the current teaching content. According to the embodiment of the invention, through teaching link dynamic identification and perception parameter adaptation, link exclusive lightweight AI model feature extraction, containerized cloud edge collaborative resource scheduling and full-link collaboration of cloud deep analysis, the breakthrough of classroom analysis from general adaptation to scene deep coupling is realized, and the core behavior identification accuracy is significantly improved.

The invention discloses a ground wire full life cycle management method, relates to the technical field of power equipment asset management, and aims to solve the problems that manual recording is easy to tamper, cloud diagnosis delay is high and an alarm mode is single and easy to neglect in traditional ground wire management. According to the method, operation fingerprints and spatio-temporal information during grounding wire hooking are recorded through the block chain technology, and it is ensured that data cannot be tampered; a built-in lightweight AI model of the edge computing unit is used for analyzing sensor data in real time to carry out localized fault diagnosis; and triggering a multi-mode sound-light alarm controlled by the PWM signal according to the diagnosis result grade. The system realizes full-life-cycle credible traceability, sub-second fault response and high-recognition-rate alarm of the state of the grounding wire, and is suitable for intelligent operation and maintenance of the grounding wire in the field of a transformer substation and the like.

The invention discloses an arc-shaped curved surface ribbed beam spherical roof construction optimization method based on spatial data, and relates to the technical field of building construction. According to the method, a multi-dimensional data acquisition and preprocessing system covering coordinates, loads and temperatures is constructed, a support, template and steel bar three-dimensional model is constructed based on a standardized data set, and dynamic regulation and control of template curved surface precision are realized in combination with curved surface modeling and a CPDI deviation index algorithm, so that the method is adaptive to the hyperboloid form of the cross arc ribbed beam, and the construction efficiency is improved. The load distribution uniformity of a supporting system is analyzed in real time, real-time linkage of data and a three-dimensional model is achieved by means of a digital twin platform, the construction parameter adjustment response time is effectively shortened, the formwork reworking and frame body reinforcing cost is reduced, and the node load standard deviation is greatly reduced through dynamic optimization of the vertical rod density and the inclined strut angle; in cooperation with temperature data closed-loop regulation and control, concrete inside and outside temperature difference over-limit cracking is avoided, early warning and management and control of risks in the whole construction process are achieved, and it is guaranteed that the geometric shape of the roof meets the design requirement.

The present invention describes an artificial intelligence (AI) enabled electroencephalography (EEG) system that integrates Pathway Hierarchical Adaptive Referencing (PHAR) for localized signal detection, large language models (LLMs) for automated EEG reporting, and Internet of Medical Things (IoMT) connectivity for adaptive neuromodulation control. The system can also deliver transcranial electrical stimulation (tES) pulses and function as an electrical impedance tomography (EIT) system. PHAR employs a multi-layered multiplexer hierarchy and adaptive referencing topologies to optimize EEG signal acquisition and spatial resolution. LLM integration enables automated generation of human-readable EEG reports. IoMT connectivity allows closed-loop neuromodulation, where real-time EEG analysis guides the adjustment of stimulation parameters. The system can deliver tES pulses and perform EIT expands its functionality, allowing for targeted neuromodulation and impedance-based brain imaging. This integrated system revolutionizes EEG-based diagnostics, treatment, and research in neurology and neuroscience, offering a comprehensive and versatile tool for understanding and modulating brain function.

The invention relates to the technical field of unmanned aerial vehicle autonomous reconnaissance, in particular to an unmanned aerial vehicle reconnaissance decision-making method, device and equipment based on edge calculation and a medium, and the method comprises the steps: obtaining the multi-dimensional data of an environment and a target through an optical camera, an infrared thermal imager and a radar carried by an unmanned aerial vehicle; target identification and strategy reasoning are carried out on the multi-dimensional data, a target type and attributes are identified from the multi-dimensional data through a lightweight convolutional neural network, and a battlefield situation is obtained in advance to generate a strike strategy; obtaining a threat level and a task priority, and dynamically adjusting the flight path of the unmanned aerial vehicle and the strike strategy according to the threat level and the task priority; and obtaining a strike result corresponding to the strike strategy, analyzing the strike result in real time, judging a target destroy state through an airborne sensor, and updating a subsequent task strategy. According to the invention, the autonomy of the unmanned aerial vehicle system is improved.