2016 results about "Intelligent equipment" patented technology

The invention belongs to the technical field of intelligent equipment fault diagnosis, and particularly relates to a motor fault real-time diagnosis method and system based on LSTM and random forest. The method comprises the steps that a vibration signal, a current signal and a temperature signal of a motor are collected and preprocessed; performing feature engineering, extracting time domain, frequency domain, time-frequency domain and cross-modal correlation features, and determining an optimal static feature subset through a hybrid screening strategy; constructing a hybrid fault prediction model comprising a random forest model and an LSTM sequential network model; fusing prediction results of the two models by adopting a dynamic credibility weighted fusion mechanism; and performing real-time decision and hierarchical feedback control based on a fusion result. According to the method, advantages of multi-source information and the model are fused, the classification precision of the lag type is remarkably improved, real-time fault diagnosis and active protection are realized, the equipment maintenance cost is reduced, and the method is suitable for motor health management of intelligent agricultural equipment such as mowers.

The invention relates to the technical field of operation and maintenance control, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent equipment configuration changing method and device, equipment and a medium. Generating a preliminary equipment configuration instruction sequence based on the change intention information; rechecking the configuration instruction through the intelligent checking component, and generating a rechecked configuration instruction sequence; sending the rechecked configuration instruction to the target equipment and executing the rechecked configuration instruction; and verifying whether the equipment state meets the expectation, and if not, executing a rollback operation. Configuration accuracy is ensured through knowledge representation, configuration errors are automatically detected and corrected through intelligent verification, configuration efficiency and safety are improved through automatic issuing and state verification, abnormity is quickly recovered through automatic rollback, and reliability and stability of configuration change are improved.

The invention discloses an intelligent equipment management method and system based on digital twinning. The method comprises the following steps: constructing an initial digital twinning model based on a multi-source data fusion result; monitoring the operation state of the initial model in real time and collecting feedback data; based on difference analysis of feedback data and a fusion result, evaluating a model deviation degree; and iteratively correcting the parameters of the digital twinborn model according to the deviation degree to optimize the precision. The method can solve the problem of how to carry out iterative correction on the model precision according to the multi-source data fusion result so as to solve the problem of overlarge deviation of the digital twinborn model.

The invention relates to the technical field of data processing, in particular to a data fusion intelligent equipment linkage management and control system based on a large model, and the system comprises a data collection module which outputs standardized data carrying privacy grading identifiers; the data processing module outputs an optimization algorithm identifier and a collaborative strategy identifier which are synchronously bound; the privacy algorithm fusion scheduling module generates atomization calculation graph data and strategy audit logs fusing privacy operation; the multiple optimization resource module executes calculation graph data and drives a parallel simulation environment rehearsal resource scheme, and the double-closed-loop feedback module is activated when an arbiter continuously detects that deviation exceeds the standard for three times; the safety instruction generation module fuses the output result and the audit log to generate a digital signature equipment control instruction; and the double-closed-loop feedback module updates knowledge base weight parameters through a data feedback loop, and controls the feedback loop to adjust a resource allocation threshold in a grading manner according to simulation confidence, so that dynamic balance between resource efficiency and privacy intensity is realized.

The invention relates to the technical field of body-equipped intelligent robots and intelligent equipment, in particular to a self-adaptive flexible tightening body-equipped intelligent robot which comprises a body-equipped moving platform, the top of the body-equipped moving platform is provided with a body-equipped lifting mechanism, and the top of the body-equipped moving platform is provided with a body-equipped articulated arm module. And a tightening actuator and a clamping jaw actuator are arranged at the tail end of the outer part of the body articulated arm module. According to the self-adaptive flexible tightening intelligent robot and the tightening process method, sensing and self-adaptive adjustment of external environment changes are achieved through multi-module integration, and the tightening process method based on a multi-mode sensing system and an intelligent control system is constructed; vision, force sense and touch sense data are fused, real-time monitoring and dynamic adjustment of the tightening process are achieved through a machine learning algorithm, and the intelligent level and quality stability of tightening operation are effectively improved.

The invention relates to the technical field of intelligent equipment autonomous control, and discloses an AIGA-driven intelligent equipment autonomous control and feedback adjustment method, which comprises the following steps of S1, collecting equipment body state parameters and external environment data in real time through a multi-mode sensor array; s2, combining the collected data with a preset user instruction and a real-time environment semantic analysis result; s3, disassembling the task target into a continuous action instruction set; s4, executing the action instruction set through the layered real-time control architecture; s5, monitoring an action execution effect based on the multi-modal sensor array; and S6, analyzing the relevance between the evaluation result and the action instruction through a causal inference engine. According to the method, an equipment action execution result is fed back to an intention generation layer in real time, a deviation source is positioned through a causal inference engine, a control strategy is dynamically adjusted, closed-loop iteration from one-way instruction execution to perception-decision-execution-verification is achieved, and the effect of self-adaptability in a dynamic scene is achieved.

The invention relates to the technical field of security management, in particular to an AI-based intelligent security remote inspection and control method, which comprises the steps of front-end intelligent equipment deployment, intelligent inspection execution, data processing and intelligent analysis and remote control and linkage response. Compared with the defects of incomplete information and high false alarm rate due to the fact that equipment appearance anomaly detection mainly depends on single-modal data in the prior art, the scheme adopts multi-modal data preprocessing and feature extraction, constructs a cross-modal alignment network architecture, dynamically fuses laser radar, RGB-D images and IMU data by using a self-attention mechanism, and improves the detection accuracy of the equipment appearance anomaly. According to the method, more accurate equipment appearance abnormity identification is realized by combining environment self-adaptive adjustment, and the model is compressed and deployed to an edge end through a knowledge distillation technology, so that real-time reasoning and analysis are realized, the intelligent level of security inspection and the accuracy of abnormity detection are remarkably improved, the false alarm rate is reduced, and the practicability and reliability of the system are enhanced.

The invention relates to the technical field of automatic control, in particular to an intelligent equipment management method and system, and the method comprises the following steps: obtaining a control cabinet log, extracting an equipment temperature load pressure sequence, comparing a set offset to generate a deviation label, collecting an operation time length and load change mapping early warning table, and generating an abnormal record; and extracting task configuration and equipment state re-marking alternating points to generate load configuration, resetting an instruction sequence to generate a scheduling sequence, extracting an instruction time-consuming marking interference section, and switching a standby link to generate an evasion list. According to the invention, by collecting the temperature, the load rate and the pressure sequence in the operation cycle of the equipment, the dynamic state identification capability is improved, the scheduling path is optimized, the resource contention is relieved, the delay path is marked in combination with the original response data, and the standby link is switched to avoid interference, so that the flexibility and stability of linkage scheduling are improved, and the task execution beat accuracy is enhanced; the instruction blocking and interruption risks are effectively reduced, and the operation toughness and response efficiency of a production line are improved.

The invention relates to the technical field of equipment intelligent diagnosis, in particular to a generator digital twinning diagnosis method based on temperature field online simulation, which comprises the following steps: respectively constructing a three-dimensional virtual model, a temperature field simulation model and an intelligent agent diagnosis model; the digital twin is in data connection with the physical entity; data alignment is achieved through a unified space-time coordinate system, a mapping relation is established between a vertex of the three-dimensional virtual model and a grid node in the temperature field simulation model, and a fault probability matrix output by the intelligent agent diagnosis model is matched to a three-dimensional grid of the three-dimensional virtual model through spatial interpolation; according to a fault probability output by the intelligent agent diagnosis model, correcting a boundary condition of the temperature field simulation model, triggering local grid adaptive encryption of the temperature field simulation model, and marking a fault position in real time through a three-dimensional virtual model; and the simulation result of the temperature field simulation model verifies the fault probability output by the intelligent agent diagnosis model. Through the method, the fault identification accuracy can be effectively improved.

The invention discloses an intelligent glasses image adjusting system based on eye movement tracking and gesture fusion, and relates to the technical field of intelligent equipment. A multi-modal sensing module is arranged to construct a multi-modal sensing layer to capture eyeball movement tracks and gesture actions; a fixation point prediction module is set to process a dynamic scene through a space-time attention mechanism to obtain a fixation point prediction area, a gesture semantic understanding module is set to process gesture actions based on a Transform architecture, and the gesture actions of a user are converted into image adjustment instructions. An image enhancement strategy setting module designs a multi-stage image enhancement strategy according to the fixation point prediction area and the image adjustment instruction, and sets a dynamic adjustment intensity control module to perform adaptive adjustment to obtain a dynamic adjustment intensity control result; an eye movement-gesture cooperative control module is arranged to provide an eye movement-gesture cooperative control mechanism to realize image area selection and parameter adjustment, and accurate image area selection and parameter adjustment are realized.

The invention discloses a complex water area garbage intelligent detection and collection method based on multi-modal perception, and the method comprises the steps: obtaining image perception data and semantic environment data of a complex water area; the method comprises the following steps: performing preprocessing, performing feature extraction and semantic coding, and then performing multi-modal feature alignment fusion, feature extraction and multi-task detection to obtain a garbage detection result, thereby effectively realizing deep fusion of semantic priori and visual features, and improving the accuracy and robustness of garbage recognition in a complex water area environment; then task importance evaluation is carried out to construct a garbage disposal priority queue, a path dynamic planning strategy oriented to energy consumption constraint and task priority collaborative optimization is constructed to carry out path generation, navigation energy consumption is reduced on the premise that task benefits are considered, and garbage detection and collection tasks in a complex water area are achieved; the system adapts to various intelligent devices such as unmanned ships, unmanned aerial vehicles and shore-based platforms, and has high autonomy, environmental adaptability and engineering deployability.

The invention discloses a digital twinning-based heterogeneous body-equipped intelligent equipment collaborative management system and method, and belongs to the technical field of digital twinning and body-equipped intelligent systems. The system comprises a digital twin platform, a communication module, a task scheduling module, a cooperative control module and an edge computing node. The digital twinborn platform constructs a high-fidelity three-dimensional model of a physical environment and digital twinborn bodies of the intelligent devices with the bodies; the communication module is used for realizing bidirectional data interaction between the platform and the intelligent equipment; the task scheduling module performs task allocation and path planning by adopting a reinforcement learning algorithm based on the global dynamic situation map; the cooperative control module models an equipment space-time relationship through a graph neural network, predicts conflicts in real time and generates a dynamic avoidance strategy; and the edge computing node carries out real-time preprocessing on the equipment sensing data. According to the method, global optimization scheduling and multi-device intelligent cooperation are realized, and the overall efficiency, robustness and adaptability of the system are remarkably improved.

The invention provides a target perception and avoidance decision-making method in visual navigation of intelligent equipment, and belongs to the technical field of intelligent decision-making. The method comprises the following steps: constructing a space-time diagram neural network, introducing semantic tag codes to define nodes and edges of the space-time diagram neural network, and predicting the trajectory of each obstacle through a regression network; an environment semantic label is defined, the environment semantic label and the predicted obstacle trajectory are coupled, and a semantic enhanced risk field is generated; and constructing a semantic road network map based on the risk field of semantic enhancement, generating a global path point sequence, and calling a dynamic step RRT * algorithm to avoid obstacles by taking the current global path point as a temporary target. According to the method, the risks of different areas can be evaluated more accurately, the risk value is adjusted according to the type and the position of the obstacle, the safety and the reliability in a complex environment are improved, and decision errors caused by environment changes are reduced.

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand / supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data / distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. Cardiovascular condition is tracked using histogram, trend, and summary information related to heart rate and / or cardiac features such as S-T segment measures of heartbeats. Heartbeats with elevated rates, and below a “high” range, provide medically relevant detections including medication non-compliance. Novel methods of power management and patient monitoring are disclosed.

The invention relates to the technical field of intelligent electric control systems, and discloses an intelligent electric control system and a control circuit board with the intelligent electric control system. The system comprises a signal acquisition module used for acquiring an input signal and judging a signal partition; the mode mapping module is used for constructing a mapping relation between the signal partition and the control partition; the parameter combination module is used for extracting key partitions and judging a parameter combination matching degree to obtain a bias combination; the state updating module is used for updating the mapping relation based on the deviation combination; and the control management module generates a control management library. The control circuit board integrates each module hardware circuit unit to realize signal transmission and data interaction. Accurate processing, dynamic mapping, optimal parameter combination, state real-time updating and scientific control management of the input signals are achieved, the intelligence, adaptability and reliability of the electronic control system are improved, and the system is suitable for electronic control scenes of various intelligent devices.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as personal intelligence, financial science and technology, medical health and the like, and discloses a task execution strategy generation and adjustment method, device, equipment and medium. Encoding the visual information, the audio information and the language instruction information to obtain a visual feature, an audio feature and a language feature, fusing the visual feature, the audio feature and the language feature to generate a comprehensive feature, generating an initial task execution strategy based on the comprehensive feature and executing a corresponding action, and in the execution process, according to real-time feedback information of the environment, executing the corresponding action according to the initial task execution strategy. And dynamically adjusting the initial task execution strategy by adopting a reinforcement learning model to obtain an updated task execution strategy. Through multi-modal information fusion and reinforcement learning dynamic adjustment, optimization and flexible updating of a task execution strategy in a complex environment are realized, and the autonomous decision-making capability of the intelligent equipment is improved.

The invention belongs to the technical field of intelligent voice processing, and particularly relates to a spoken language pronunciation training and correcting system based on intelligent equipment, which acquires a user rhythm feature set including pitch change rate, accent intensity, pause duration and intonation contour by acquiring spoken language audio data sent by a user for a target text, and corrects the spoken language pronunciation training and correcting system. The method comprises the following steps: acquiring spoken language audio data, converting the spoken language audio data into a phoneme sequence aligned with target text time, generating a rhythm deviation degree report according to comparison evaluation of a user rhythm feature set and the phoneme sequence, an execution intonation mode, accent distribution, speech stream sound change and speech speed rhythm, and determining the rhythm deviation degree according to a rhythm problem type in the report in combination with user historical learning data. And forming and outputting a correction scheme including a text prompt, a targeted minimum contrast training unit and a listen-and-read simulation task, solving the problem of weak capability of correcting hyper-phoneme in a second language spoken language of an adult, and improving the authentic and fluency of the spoken language, thereby realizing efficient personalized learning.

The invention, which relates to the technical field of optical storage and charging, discloses an optical storage and charging cooperative control method for a transformer area, and the method comprises the steps: carrying out the intelligent and multivariate collection and prediction through intelligent equipment, constructing a function through collected data, solving an optimal solution, selecting an optimal optical storage and charging cooperative control strategy from the optimal solution, generating a corresponding control instruction, and carrying out the transmission of the corresponding control instruction. Cooperative regulation and control of a photovoltaic power generation system, an energy storage system and a charging pile system are achieved, a feedback and correction mechanism is further established, and it is ensured that the photovoltaic storage and charging system stably operates according to a preset strategy. According to the transformer area light storage and charging cooperative control method, a light storage and charging cooperative control mechanism is established by comprehensively considering photovoltaic power generation, an energy storage system and a charging pile system, power resource distribution in a transformer area is optimized, maximum consumption of photovoltaic power generation is achieved, the light abandoning phenomenon is reduced, meanwhile, unnecessary charging and discharging loss of the energy storage system is reduced, and the power utilization rate of the transformer area is improved. And the impact of disordered charging of the charging piles on a power grid system is relieved, so that the overall operation performance of a transformer area power system is improved.

The invention relates to an intelligent equipment function reliability verification method and device, equipment and a storage medium, and the method comprises the steps: obtaining operation environment parameters and equipment state data of intelligent equipment, and carrying out the construction evaluation of a redundancy verification architecture, and obtaining an initial verification reference; acquiring real-time operation data of the intelligent equipment, and performing function association analysis on the real-time operation data and the initial verification reference to obtain function abnormality features; acquiring state information of associated equipment of the intelligent equipment, performing cross validation on the state information of the associated equipment according to the function abnormality characteristics, and performing fault analysis on the state information of the associated equipment and the function abnormality characteristics to obtain an evolution fault injection scheme; and carrying out abnormal behavior identification on the real-time operation data to obtain an equipment repair requirement, and carrying out feasibility evaluation on the equipment repair requirement based on the function abnormal characteristics to obtain a dynamic repair strategy. According to the invention, a comprehensive guarantee mechanism can be provided for reliable operation of the intelligent equipment.

The invention discloses a new energy power generation equipment intelligent regulation and control method and system based on big data. The method comprises the steps of obtaining operation data of new energy power generation equipment, performing data dimension reduction processing to obtain structured data, and performing feature extraction and clustering to obtain an influence factor candidate set; according to the influence factor candidate set, performing spatial-temporal feature fusion analysis to obtain a multi-dimensional feature vector, and performing continuous sampling and quantization to obtain a quantization sequence; on the basis of the quantization sequence, potential risks are predicted through a long-short term memory network, risk marking is carried out, risk distribution map dynamic adjustment control parameters are obtained, and an optimization strategy set is obtained; based on the optimization strategy set, equipment operation parameters are selected and adjusted through a real-time feedback mechanism, a system stability index is obtained, multi-dimensional evaluation verification is carried out, and if an expected threshold value is not reached, regulation and control parameters are optimized through a genetic algorithm, and a final control instruction is output. According to the invention, real-time prediction of risks and intelligent regulation and control of equipment can be realized.

The invention discloses a filter intelligent backwashing control system based on a pressure sensor, and relates to the technical field of intelligent equipment, and the filter intelligent backwashing control system comprises the following steps: S1, the system firstly executes pressure difference reference initialization, and determines a pressure difference reference value; s2, constructing a corrected differential pressure offset model, and determining the current differential pressure offset of the system; s3, constructing a blockage risk assessment model, and pre-judging whether the system needs to start backwashing or not; s4, carrying out segmented pulse backwashing control; and S5, constructing a closed-loop judgment model, and evaluating the actual backwashing effect of each stage. According to the method, a high-credibility differential pressure reference is obtained through a reference calculation model; the blocking trend is tracked in real time through the moving average pressure difference offset; constructing a blockage risk coefficient fusing the offset, the offset rate and the duration, and judging the flushing opportunity in multiple dimensions; a three-section flow is adopted to distribute flushing strength and duration according to needs; and after each section of pulse is finished, the backwashing effect is evaluated in a closed-loop manner through the closed-loop judgment model.

The invention relates to the technical field of data processing and analysis, in particular to an intelligent large health system and a data processing method thereof, and the method comprises the steps: collecting the data of a multi-source heterogeneous data source in real time, and comprehensively capturing the multi-dimensional health data of a user; a multi-modal health knowledge graph is dynamically constructed and optimized through an efficient data preprocessing and feature alignment technology, and the integration value and the utilization efficiency of data are improved; a graph neural network and a time sequence analysis model are utilized to realize accurate evaluation of the health state of the user and timely prediction of future risks, and predictability and pertinence of health management are effectively improved; based on a reinforcement learning algorithm, a highly personalized intervention sequence can be generated according to the individual condition of a user, the accuracy of health intervention is enhanced, and the positive change of the health behavior of the user is promoted; an intervention instruction is executed through intelligent equipment, user feedback is monitored in real time, an intervention strategy is dynamically adjusted, and the flexibility and adaptability of intervention measures are ensured.

The invention relates to the technical field of intelligent construction site safety management and Internet of Things, and discloses a construction personnel safety protection system and method for intelligent construction site safety management, and the system comprises a data collection module, a four-dimensional space-time tensor construction module, an incremental tensor decomposition module, a dynamic risk field modeling module, and a federated learning parameter updating module. A multistage safety response module; the method comprises the steps of collecting personnel data through intelligent equipment and constructing a four-dimensional tensor, extracting core features through dynamic decomposition to establish a risk propagation model, optimizing parameters in combination with federal learning, and finally triggering three-level response according to a risk field gradient to realize full-process closed-loop management and control of construction safety. According to the method, multi-modal data are fused to construct a four-dimensional tensor, incremental decomposition is carried out to extract dynamic features, a physical field model is coupled to predict risks, federated learning is used for collaborative privacy optimization, hierarchical response is carried out for closed-loop linkage control, and construction safety and precise protection and efficient emergency are realized.

The invention discloses an agricultural heterogeneous multi-core SoC (System on Chip) architecture and equipment integrating vision and environmental perception. According to the architecture, a heterogeneous multi-core design is adopted, a universal CPU and an AI acceleration and preprocessing module are integrated on an on-chip system, an efficient task scheduling mechanism is combined, real-time management and control of multi-source heterogeneous tasks such as image processing, radar resolving and path planning are achieved, and the system response speed and the task execution efficiency are improved. In the aspects of communication and storage, NoC and AXI buses are adopted to cooperatively construct a high-speed data path, DDR4 and Flash mixed storage is supported, and the transmission stability of large-bandwidth data is guaranteed. The power supply and clock management module realizes modularized start and stop and energy consumption optimization through a multi-voltage domain and dynamic power consumption control technology, and is suitable for outdoor long-time operation. The IO and peripheral interface module provides abundant interfaces, and system expansibility and deployment flexibility are improved. The overall framework integrates the advantages of high integration level, high adaptability and low power consumption, and a solid calculation basis is provided for agricultural intelligent equipment.

The invention relates to the technical field of intelligent glasses production equipment, and discloses intelligent glasses leg metal shielding cover frame automatic rubber ring pasting equipment which comprises a feeding sliding table, a glasses positioning table, an outer supporting attaching assembly used for pressing the inner side face of a rubber ring, and an opposite extrusion attaching assembly used for pressing the upper face and the lower face of the rubber ring. And the release paper adsorption positioning assembly is used for accurately positioning release paper to which the rubber rings to be assembled are adhered on the periphery of the frame of the metal shielding cover to which the rubber rings are to be adhered on the inner sides of the left glasses leg and the right glasses leg, and the equipment utilizes the glasses positioning table to position and limit the glasses frame and the glasses legs to which the rubber rings are to be adhered. Then the release paper adsorbing and positioning assembly is used for manually positioning and adsorbing the release paper pasted with the rubber ring firstly and then automatically positioning and adsorbing the release paper pasted with the rubber ring, then the outer supporting and attaching assembly and the opposite extruding and attaching assembly are used for automatically assisting in pasting the inner side face, the upper face and the lower face of the rubber ring respectively, the precise automatic pasting action is completed in a narrow space, the pasting position precision is high, and the production efficiency is high.

The invention relates to the technical field of agricultural intelligent equipment, in particular to a field sowing grain distance detection system and method based on computer vision, and the method comprises the steps that an image collection module collects 4KRGB and near-infrared data of a sowing area; the image processing module is used for processing and correcting the acquired image in real time; the grain distance feature deep learning analyzer strengthens seed feature extraction through an attention mechanism based on an improved YOLOv5 algorithm, accurately distinguishes weeds from seeds, and calculates a center distance deviation value of adjacent seeds based on a preset target grain distance; the positioning fusion module fuses Beidou positioning data and a visual odometer track, generates seed longitude and latitude and burial depth coordinates by using a high-precision farmland surface elevation model, and maps the seed longitude and latitude and burial depth coordinates to a farmland electronic map; and the dynamic regulation and control execution mechanism adjusts the rotating speed of the seeder or the seeding gap according to the grain distance deviation value and a priority strategy, and dynamically corrects the grain distance. Therefore, the problems of high detection error rate, single detection algorithm, poor data relevance and the like in the prior art are solved.

The invention belongs to the technical field of image processing, and particularly relates to a graphite ore grade detection method based on an improved YOLO11 model, C3k2-CAS and Detect-SEAM modules are introduced, the feature extraction capability is enhanced, the expression of different scales of ore textures is optimized, and the perception capability of the model for grade difference is improved, so that the detection precision is remarkably improved; the attention mechanism of the CAS module is adopted to replace the traditional multiplication operation, the calculation complexity and the model parameter quantity are greatly reduced while the detection performance is maintained, and the improved model is more efficient than a baseline model and is more suitable for edge device deployment; a plurality of data enhancement strategies are combined, so that the model can maintain high robustness and stable detection capability in a complex industrial environment; the trained optimization model can be efficiently deployed to graphite ore grade detection intelligent equipment, real-time and accurate industrial field detection is achieved, the mineral separation efficiency is greatly improved, and an efficient and reliable computer vision solution is provided for the intelligent mining industry.

The invention belongs to the technical field of intelligent wireless sensing and radar signal processing, and particularly relates to a millimeter wave radar gesture recognition feature processing method, which is particularly suitable for scenes with environment interference (such as walking of others and static clutter), and specifically comprises the following steps: S1, preliminary filtering by a self-adaptive filter; s2, carrying out improved I CEEMDAN decomposition; s3, I MF component classification and processing; s4, signal reconstruction; the experimental result shows that the method provides a robust and efficient solution for gesture recognition, and can be widely applied to the fields of man-machine interaction, virtual reality, intelligent equipment control and the like.

The invention discloses an acoustic system, an ear wearable device and a head-mounted intelligent equipment, the acoustic system comprises an audio device, a sound guide part and a sound output part, the sound guide part and the audio device are separately arranged, the input end of the sound guide part is arranged at the output end of the audio device, and the output end of the sound output part is arranged at the output end of the audio device. The sound output part is arranged at the output end of the sound guide part, the sound guide part is communicated with the audio device through a first sound hole, the cross section area of one end, close to the first sound hole, of the sound guide part is larger than that of the first sound hole, the sound output part is formed by at least one second sound hole, and the second sound hole is communicated with the first sound hole. The area of the cross section of one end, close to the sound output component, of the sound guide component is larger than that of the cross section of each second sound hole. The method can effectively improve the audio quality, and is applied to the technical field of head-mounted intelligent equipment such as intelligent glasses.

The invention discloses an intelligent equipment management and monitoring method based on the Internet of Things. The method comprises the following steps: S1, collecting and preprocessing equipment state data through a quantum sensor; s2, constructing a quantum sensing network, and dynamically optimizing a network topology structure by using a quantum entanglement technology; s3, mapping the equipment state data into a quantum state, and optimizing time delay and spatial distribution in a data transmission process through quantum superposition and quantum interference technologies; s4, correcting error data in the transmission process by adopting a quantum error correction algorithm, and encrypting the corrected data through a quantum key distribution technology; s5, predicting the state of the equipment, and fusing a prediction result with the encrypted data to form a management instruction; s6, executing the management instruction and adjusting the equipment management strategy; and S7, monitoring the running state of the equipment in real time and updating the management instruction. The method has the advantages of high efficiency, accuracy, safety and high adaptability, and the equipment operation and maintenance efficiency is improved.