441 results about "Signal optimization" patented technology

The invention discloses a large model prompt project optimization system and method fusing a domain knowledge graph. The system comprises an analysis module, a template generation engine module, a large model interaction interface module, a feedback analysis module and an optimization strategy module. And the analysis module forms a constraint coding signal containing an entity attribute incidence matrix. The template generation engine module forms an enhanced prompt text stream with a reservoir physical property parameter slot; the large model interaction interface module receives the enhanced prompt text stream and generates a question and answer response data stream containing geological terminologies; the feedback analysis module forms a feedback signal containing semantic deviation measurement through a semantic error vector calculation algorithm; and the optimization strategy module forms a parameter optimization instruction signal and transmits the parameter optimization instruction signal to the analysis module to complete iterative updating of the constraint condition. According to the large model prompt project optimization system fusing the domain knowledge graph, the problem of low answer accuracy of a large model in the oil-gas exploration field due to lack of professional constraints can be solved.

The invention relates to a multi-channel signal integrity optimization method and system for a passive copper cable, and the method comprises the steps: obtaining channel characteristic parameters and signal transmission requirements of the passive copper cable, and carrying out the impedance matching, and obtaining an optimized transmission channel; acquiring real-time transmission data of the passive copper cable, and performing equalization processing on the real-time transmission data through the optimized transmission channel to obtain a stable transmission signal; acquiring crosstalk interference data acquired by the passive copper cable, and carrying out shielding analysis on the crosstalk interference data and the optimized transmission channel to obtain a crosstalk suppression measure; performing frequency characteristic analysis on the crosstalk interference data to obtain frequency compensation information, and performing transmission prediction on the frequency compensation information based on the crosstalk suppression measure to obtain a frequency compensation parameter; and performing multi-channel optimization on the initial signal optimization result according to the frequency compensation parameter to obtain a multi-channel optimization signal. According to the invention, complex and changeable transmission environments can be coped, and stable transmission of signals is ensured.

The invention relates to the technical field of communication control, and discloses an interconnection test method and system for a wireless communication module, and the method comprises the steps: collecting real-time communication data through a multi-source communication interface, extracting topological features through a graph theory model to generate a matrix, and obtaining a signal optimization parameter through a self-adaptive filtering model; and constructing a multi-target test path planning model and outputting optimal path data, and establishing a layered test execution model to complete the test. The system comprises a communication topology analysis module, a path dynamic planning module and a hierarchical test execution module. The method overcomes the defects of a traditional testing method, can comprehensively collect data, accurately analyze topology, effectively process signals, scientifically plan a testing path and perform layered collaborative testing, has remarkable effects in improving testing accuracy, efficiency and comprehensiveness, can adapt to the development requirements of a wireless communication technology, and ensures stable and reliable work of a wireless communication module.

The invention provides a signal optimization transmission system and method based on HPLC and HRF dual-mode communication, a signal acquisition and processing module of the signal optimization transmission system based on HPLC and HRF dual-mode communication is used for acquiring signals of two communication modes of HPLC and HRF in real time, preprocessing the signals and extracting key signal parameters; the dual-mode switching module is used for dynamically switching an HPLC mode and an HRF mode based on channel quality; the access module is used for constructing a longitudinal backbone network and a transverse Mesh network, forming a hybrid topology, optimizing a routing path and coordinating resource allocation and conflict avoidance of HPLC and HRF; the intelligent decision module is used for predicting future interference intensity according to the historical interference data and adjusting channel parameters; the central coordinator module is used for network initialization and resource allocation; according to the system and the method, the HPLC mode and the HRF mode are automatically switched according to the channel quality, the longitudinal HPLC backbone network is combined with the transverse HRF Mesh network to form a four-dimensional communication network, and the robustness in a complex scene is remarkably improved.

The invention discloses a large-scale unmanned aerial vehicle marshalling method for an emergency fire rescue task, and the method comprises the steps: carrying out the clustering analysis of task points of a rescue region through an FCM algorithm, determining the membership degree of a flight task, and generating a task region; on the basis of real-time environment change of a fire scene, a dynamic membership updating task allocation state is realized through a visual detection algorithm of fusing a convolutional neural network with an FCM; meanwhile, through 3DT route planning optimization based on the laser radar technology, a 3D model recombination map model is established, linear optimization is carried out by introducing time parameters, and optimal path planning is provided for the unmanned aerial vehicle in real time. In the rescue process, data are automatically transmitted in a segmented mode through the slicing technology of the 5G network, an autonomous Mesh centerless network topology structure is implemented when signals are intense, real-time signal optimization is conducted through a repeater of the high-altitude unmanned aerial vehicle, and high efficiency and flexibility of rescue are ensured.

The invention relates to the technical field of medical equipment and intelligent learning, in particular to a critical patient vital sign dynamic monitoring and intelligent early warning system which comprises a multi-modal data acquisition module, a medical equipment linkage control module and an intelligent early warning decision module. A dual-channel Kalman filtering state updating model, a biological impedance monitoring model and an anti-interference and signal optimization model are established in the multi-modal data acquisition module, and multi-dimensional physiological monitoring information of the critical patient is obtained through the models; a physiological information standardization model and a safety control mechanism are arranged in the medical equipment linkage control module, and then multi-dimensional physiological monitoring information is cooperatively controlled to output multi-modal vital sign data; and the intelligent early warning decision module receives and analyzes the multi-modal vital sign data to obtain power spectral density, and predicts the disease development condition of the critical patient in combination with the depth prediction model and the multi-modal vital sign data. All the modules work cooperatively to achieve dynamic monitoring and intelligent early warning of vital signs of critical patients.

The invention relates to multi-mode signal processing, and provides a signal processing method of a computer system. The method comprises the following steps: acquiring different modal signal data by using a plurality of sensors and performing timestamp synchronization; a depth time sequence alignment network and a multi-layer dynamic time warping algorithm are utilized to ensure signal time consistency; multi-modal signal features are extracted and fused through a multi-stream depth feature extraction network and a cross-modal attention mechanism; the system adopts a dynamic convolution kernel generation and meta-learning technology to carry out adaptive signal processing; noise suppression and signal optimization are carried out by using a multi-mode noise reduction auto-encoder and a generative adversarial network, and parameters are optimized and processed through closed-loop feedback control and reinforcement learning strategies. According to the method, synchronization, fusion, adaptive processing, noise suppression and real-time feedback control of multi-mode signals are realized, and the method has the advantages of high precision, high robustness and real-time performance.

The invention relates to the technical field of Beidou navigation, in particular to a big data-based Beidou navigation signal optimization and error correction system, which comprises a multi-source heterogeneous data acquisition module, a data preprocessing module, a multi-dimensional error analysis module, an error collaborative prediction module, a real-time correction parameter generation module and an optimization signal reconstruction module, the multi-source heterogeneous data acquisition module is used for acquiring multi-source heterogeneous data in real time; the data preprocessing module carries out space-time alignment; the multi-dimensional error analysis module constructs a dynamic multi-path feature library; the error collaborative prediction module generates a three-dimensional space error distribution matrix; the real-time correction parameter generation module generates a frequency domain-time domain joint correction parameter; the optimized signal reconstruction module outputs an optimized Beidou navigation signal; according to the invention, high-precision and high-stability Beidou navigation service is continuously provided, and a precise and efficient solution is provided for application of a Beidou system.

The invention belongs to the technical field of artificial intelligence and multi-modal large model reasoning enhancement, and discloses a multi-modal retrieval enhancement generation method based on gradual group relative strategy optimization. A step-by-step reasoning track construction mechanism is introduced, an original problem is disassembled into a plurality of sub-problems, and in each step, a new retrieval query is autonomously generated in combination with reasoning history and current information requirements, and a most appropriate knowledge source is selected for evidence retrieval; and in the reasoning process, each step of decision and answer obtains a fine-grained reward signal. According to the method, a group relative strategy optimization method is adopted, the query quality of each reasoning step, the knowledge base routing accuracy, the answer content format compliance and the final answer accuracy are used as step-by-step rewards for joint modeling, and model parameters are optimized through global and local multiple feedback signals. The method is remarkably superior to the existing similar technology in tasks such as multi-class multi-modal open domain question answering and complex reasoning, and has excellent answer accuracy, retrieval efficiency and multi-modal adaptive capacity.

The embodiment of the invention discloses a dynamic traffic signal control method and device based on data fusion and a medium, belongs to the technical field of intelligent traffic, and solves the problem that the existing traffic signal control is difficult to make effective response when an accident occurs. Performing data fusion on first traffic data acquired by a road monitoring and shooting device and second traffic data acquired by a positioning device of the floating car through a multi-head attention mechanism at the edge node to obtain multi-source traffic characteristic data; the cloud end inputs the multi-source traffic characteristic data into a preset signal optimization model every first preset duration; the preset signal optimization model is updated and optimized through a PPO algorithm and a federated average method, and a global traffic model is generated; and through knowledge distillation, compressing the global traffic model, and migrating an edge traffic model obtained after compression to a corresponding edge node so as to carry out traffic signal strategy output on a corresponding road section through the edge traffic model.

The invention relates to the technical field of photovoltaic power station management, and discloses a photovoltaic power station Internet of Things intelligent management and cloud edge collaborative optimization system. The system comprises an operation data acquisition module which is used for acquiring power station data in multiple dimensions, quantitatively identifying an abnormal operation state and generating a management early warning signal; the equipment state evaluation module is used for extracting equipment characteristics and dynamically evaluating the self-maintenance capability to obtain a state evaluation reference value; the edge processing module is used for monitoring parameters such as data traffic and predicting and analyzing edge processing efficiency to obtain an edge calculation evaluation value; the cloud edge collaboration module is used for jointly analyzing collaborative optimization efficiency and generating a regulation and control signal; the optimization parameter generation module is used for generating equipment maintenance and computing power distribution parameters; the system further comprises an authority control module and a dynamic adjustment module. According to the system, intelligent management and cloud edge collaborative optimization of the photovoltaic power station are realized, the operation efficiency and reliability of the power station are improved, and the safety and dynamic adjustment capability are enhanced.

The invention relates to the technical field of RFID tag signal optimization, and discloses an RFID tag signal optimization method and system based on deep learning. According to the method, feature extraction is carried out on a backscattering signal, core features of a multipath channel are quantized from a frequency domain and a time domain respectively, numerical representation of a channel state is further obtained, then obtained data information is used as input of a deep learning network, training and forward calculation are carried out, original emission parameters are adjusted, and a multi-path channel is obtained. Therefore, the conversion from blind extensive control to precise control of the transmitting end is realized, so that the problem of mismatching of parameter control and channel state caused by information splitting of the transmitting end and the receiving end in the prior art is solved; according to the signal optimization method for realizing joint adjustment of transmitting and receiving parameters through an end-to-end deep learning architecture, the optimization effect of an RFID tag signal in a complex environment is remarkably improved.

The invention discloses a light beam focal length controllable system of a BOSA optical device and an implementation method thereof, relates to the technical field of optical communication, and solves the problems of focal length drift, light beam quality reduction and poor eye pattern quality caused by ambient temperature fluctuation in the prior art. Comprising a first focal length quality analysis module, a second focal length quality analysis module, an intelligent focal length adjustment module and a signal optimization module, the first focal length quality analysis module predicts future environment temperature change through a long-short term memory network, and plans an optimal focal length path in advance; the second focal length quality analysis module identifies light beam characteristic parameters by using a depth image identification algorithm, and performs multi-objective optimization in combination with an environment temperature change trend; the intelligent focal length adjustment module dynamically adjusts the focal length according to the optimization suggestion; the signal optimization module carries out waveform shaping and noise suppression by using a deep learning network, so that the eye pattern quality is remarkably improved; according to the invention, the focal length stability of the system is greatly improved, and the performance and reliability of the optical communication system are enhanced.

The invention discloses a peritoneal dialysis anti-blocking high-circulation nursing device for pediatric nephropathy patients, and belongs to the technical field of medical nursing. Comprising the following modules: a multi-mode sensing module for collecting pressure, flow velocity, turbidity, vibration spectrum and temperature data in a catheter in real time; the signal optimization processing module highlights weak features of potential blockage and improves data quality; the feature fusion analysis module is used for comprehensively evaluating the state of the catheter; the AI prediction analysis module is used for predicting the occurrence probability, the position and the possible time window of blockage; the risk early warning and intervention module is used for sending out graded early warning signals according to the prediction result and generating personalized anti-blocking intervention suggestions; the micro-fluidic execution module inhibits formation of initial sediments through a micro-pulse technology and dynamic fluid mechanics adjustment, and effectively reduces the risk of blockage; and the remote medical support module provides visual data visualization and intervention suggestions, supports remote monitoring and emergency intervention, and is seamlessly integrated with a hospital system while ensuring data security.

The invention relates to the technical field of prediction algorithms, and discloses a high-precision active power filtering and prediction algorithm for a three-phase ammeter, which comprises the following steps: acquiring original signals of three-phase voltage and current for baseline correction, performing time-frequency analysis on non-stationary harmonic waves and noise of the original signals, and dynamically adjusting filtering parameters; voltage and current phase alignment is carried out through FIR phase shift and zero crossing point detection, a phase-locked loop is constructed by using GRU to track the frequency of a power grid, the sampling frequency is dynamically adjusted, and frequency mutation is detected; iapFFT transformation is optimized to suppress spectrum leakage, a phase error is corrected through a deep learning network, and weak harmonic detection is enhanced by using an attention mechanism; classifying harmonic waves, and adaptively calculating total active power; dimensionality reduction is carried out on historical power and environmental parameters through an auto-encoder, and future active power is predicted; low-power-consumption hardware is adapted, and training efficiency and data security are optimized; three-phase signals are processed in parallel, and FPGA storage and FFT / FIR cores are optimized for filtering processing; and dynamically adjusting parameters of the phase-locked loop.

The invention discloses a roadbed pavement test and data acquisition system under the action of moving load, which comprises the following steps of: acquiring high-precision pavement unevenness data, decomposing features through a space-time convolution variational self-encoding network and inhibiting noise; based on the normal stiffness and the morphological gradient, the density of the sensor is adjusted by adopting dynamic grid division, and multi-modal data is fused by utilizing Bayesian weight. And adaptively extracting signal details by applying multi-scale transformation, and designing a filter to enhance the damage signal and optimize the structure entropy. And constructing a multi-field coupling grid control equation to realize dynamic topological optimization reconstruction. And establishing a damage evolution state space, and training the prediction model through a multi-stage reinforcement learning framework in combination with experience playback. And constructing a digital twin mapping model, integrating Bayesian optimization and reinforcement learning, and establishing a virtual-real feedback channel to realize dynamic decision optimization. According to the invention, innovative improvement on a traditional test method and a data acquisition system is provided, and meanwhile, complex nonlinear dynamic behaviors can be accurately simulated and analyzed.

The present invention provides a method for optimization of active and passive beamforming and signal reception configuration of a dual IRSs-assisted ISAC system. By jointly optimizing active beamforming at a base station, the reception of a sensing signal by the base station, and passive beamforming at an IRS, the achievable rate of communication users is maximized while ensuring that the signal-to-noise ratio of the sensing signal meets the minimum requirement. In the present invention, to solve the complex nonconvex problem generated, first, fractional programming is used to decouple an optimization problem, then, a successive convex approximation algorithm and an alternating direction method of multipliers are used to transform an intractable nonconvex problem into multiple tractable subproblems, and finally, an alternative optimization method is used to efficiently solve for a high-quality sub-optimal solution. Simulation results show that the provided solution has good convergence and effectiveness, and the solution can effectively improve the performance of IRS-assisted ISAC systems.

The invention discloses a fluid antenna position and radar communication precoding matrix optimization method in a URLLC scene. The method comprises an integrated architecture supporting communication and perception of multiple user terminals and a single perception target; constructing a communication signal model between the base station and the user terminal in the URLLC scene and a sensing signal model between the base station and the detection target; establishing a mathematical model of a radar communication signal optimization problem by taking maximization of a radar sensing signal-to-noise ratio as a target and taking base station transmitting power, URLLC time delay requirements and fluid antenna position limitation as constraints; the optimization problem is decomposed into three sub-problems, an alternating iteration optimization algorithm is adopted, the three sub-problems are alternately optimized, and a convergence solution is obtained through iteration. According to the invention, the URLLC technology and the multiple-input-multiple-output antenna array and fluid antenna technology are utilized, communication and radar sensing are executed on the same frequency spectrum at the same time, and the requirements of communication and radar sensing in a future network are met.

The invention discloses a traffic control and guidance system and method based on an ant colony algorithm, belongs to the technical field of road traffic control, and solves the problems that a neural network model in an existing method mainly focuses on respective route conditions of a plurality of bifurcation routes of a bifurcation on a one-way driving road, and the information fusion degree between traffic elements is low. The method comprises the following steps: identifying dynamic characteristic information of vehicles in a road network, pre-constructing a road network optimization model based on an ant colony algorithm combined with deep learning, and analyzing a pheromone spread function cluster solution; according to the invention, the road network optimization model based on the ant colony algorithm and the deep learning is pre-constructed, the collaborative optimization of traffic signal control and vehicle induction is realized, the traffic signal optimization control strategy and the vehicle optimization path strategy are output, so that the signal timing and the induction path can be matched with each other, and the control accuracy is improved. Therefore, the operation efficiency of the traffic system is improved and the accuracy and effectiveness of the guidance strategy are ensured.

The invention discloses an AI-based urban traffic flow prediction and dynamic signal optimization method and system, and relates to the technical field of traffic management, and the method comprises the steps: obtaining real-time traffic data, historical flow data and external environment data of a target region, obtaining multi-source data, and constructing a space-time matrix corresponding to the target region according to the multi-source data; learning the space-time matrix by using a preset space-time fusion model, and outputting a prediction result of the traffic flow in a future preset time period through the space-time fusion model obtained by learning; wherein the space-time fusion model is a combined architecture of a graph convolutional network and a Transform network; and dynamically adjusting traffic signal control parameters in the target area through a multi-agent learning algorithm based on a prediction result. According to the method, closed-loop regulation and control of'data perception-prediction modeling-autonomous decision 'are formed, so that the limitation of spatial feature modeling staticization and time correlation analysis fragmentation of a traditional method is broken through, and the problem of regional imbalance caused by single-point optimization is effectively solved.

The invention discloses a sensing intelligent driving multi-mode traffic signal optimization method and system, and relates to the technical field of traffic signal optimization, and the method comprises the steps: collecting traffic flow data; constructing a multi-mode traffic flow model; connecting a plurality of traffic lights in the target area to obtain a street lamp control signal; based on the traffic flow data and the street lamp control signal, using the multi-mode traffic flow model to predict a traffic flow change trend; and configuring a timing optimization scheme of a plurality of traffic lights in the target area according to the traffic flow change trend prediction result. According to the method and the device, the technical problem of low traffic intersection passing efficiency caused by the fact that a traffic signal lamp timing scheme cannot be dynamically adjusted accurately according to real-time traffic conditions and multi-mode traffic behavior interaction relations in the prior art is solved, and the purposes of optimizing the traffic signal lamp timing of the target area according to the prediction result and improving the traffic signal lamp timing efficiency are achieved. And the traffic efficiency of the traffic intersection is effectively improved.

The invention provides a smart city construction system based on big data, and relates to the technical field of smart cities, and the system comprises a traffic data collection module, a transmission and preprocessing module, a flow prediction and analysis module, an intelligent signal optimization module, a parking lot scheduling and induction module, a decision support module and a user service module. The traffic flow prediction and analysis module predicts a traffic state in a set time length in the future through a space-time diagram convolutional network in combination with a seasonal autoregression model; and the intelligent traffic signal optimization module is used for dynamically adjusting signal lamp timing based on a deep reinforcement learning algorithm and realizing intersection linkage through a regional cooperative control algorithm. According to the invention, the space-time diagram convolutional network, the deep reinforcement learning intelligent algorithm, the block chain and the distributed computing technology are fused to construct a multi-source data-driven traffic whole-flow intelligent management system, so that the whole-chain intelligence from real-time sensing and dynamic optimization to cross-platform service is realized; and the collaboration, the prediction accuracy and the service ecological openness of the urban traffic system are obviously improved.

The invention relates to the technical field of communication signal optimization, in particular to a distribution automation communication signal optimization method and system for an underground environment. The method comprises the following steps: carrying out a multi-band data transmission test based on a perturbation frequency test packet, and collecting a multi-band power distribution communication signal; dynamic space-time attenuation analysis is carried out, and a multi-band electromagnetic space-time attenuation matrix is constructed; performing node-by-node response delay calculation based on the multi-band power distribution communication signal to generate a full-band delay sensitivity curve; safe and effective frequency band evaluation is carried out based on the multi-frequency-band power distribution communication signals, high-delay frequency band elimination processing is carried out based on a full-frequency-band delay sensitivity curve, and a frequency band hopping optimization pool is constructed; and performing real-time environment interference attenuation analysis on the frequency band hopping optimization pool according to the multi-frequency-band electromagnetic space-time attenuation matrix, and constructing communication frequency band hopping driving logic. According to the invention, through dynamic intelligent communication frequency band hopping, the communication stability and quality of power distribution automation are improved.

The invention discloses a memory signal integrity enhancing system based on multi-dimensional parameter self-adaption, and belongs to the technical field of signal transmission. The system comprises a multi-dimensional signal acquisition module, a multi-dimensional parameter analysis module, a self-adaptive adjustment module, a memory signal optimization module and a feedback monitoring module, the multi-dimensional signal acquisition module is combined with multi-dimensional parameter analysis, signals of a data bus, an address bus and a control bus are synchronously acquired, a dynamic adjustment strategy is generated according to a model, and the self-adaptive adjustment module and the memory signal optimization module are optimized according to the dynamic adjustment strategy. Different optimization means are adopted for different parameter types, a customized optimization system with one parameter and one scheme is formed, it is ensured that the optimized target signal is optimal in amplitude stability, time sequence accuracy, anti-noise capacity and impedance matching degree, the feedback monitoring module is combined with the multi-dimensional parameter analysis module, and the multi-dimensional parameter analysis module is combined with the feedback monitoring module. And through full-dimension index monitoring and dynamic calibration, the continuous adaptability of the system to the running state of the memory and the long-term stability of signal integrity are guaranteed.

The invention relates to the technical field of sensor detection, in particular to a comprehensive detection system and method for a petroleum logging sensor. According to the method, the temperature and pressure data in the well logging shaft are obtained, and the temperature change rate and the pressure change rate are calculated, so that the influence of the environment on the measurement data can be accurately evaluated, the compensation of the temperature-pressure coupling influence is further realized, and the measurement precision is remarkably improved. On the basis, the amplitude change and the phase deviation of the measurement signal are further combined, the interference characteristics of the electromagnetic environment on the measurement signal are extracted, and signal optimization is carried out by calculating interference influence parameters, so that the reliability and the anti-interference capability of the measurement data are enhanced. Meanwhile, the influence of load change on the sensor in the logging process is analyzed, the deviation condition of a pressure measurement value is identified, and the measurement error is corrected by using an influence coefficient, so that the measurement data is more stable. Multi-cycle measurement data are compared and analyzed, and the long-term drifting condition of the data can be identified.

The technical scheme of the invention relates to the technical field of control and adjustment, in particular to a crawler-type articulated vehicle braking system applied to a low-temperature environment. Through multi-module collaborative optimization and a closed-loop control mechanism, the problems of unbalanced dynamic distribution of braking force, insufficient redundancy fault tolerance and signal distortion under the low-temperature working condition are solved. The data acquisition module is used for acquiring a hinging angle, an inertial measurement unit attitude, a braking temperature and a vehicle state signal in real time; the optimization control module performs multi-objective optimization on the dynamic weight coefficient, the redundancy switching threshold value and the fuzzy rule parameter based on a genetic algorithm to generate a weight distribution scheme of a hinge angle compensation mode and an inertial measurement unit dominant mode; the adjusting module generates a brake pressure reference value through temperature self-adaptive analysis and synchronizes hydraulic control units of a front vehicle and a rear vehicle, and all the modules remarkably improve the dynamic stability and the low-temperature working condition fault-tolerant capability of a brake system through full-link cooperation of environment perception, parameter iteration and redundancy execution.

A method for the joint active and passive beamforming and received signal optimization in an ISAC system assisted by dual IRSs is provided. The method jointly optimizes active beamforming at Base Station (BS), reception of sensing signals at the Base Station (BS), and passive beamforming at IRSs, so as to maximize communication sum-rate of users while ensuring that SNR of sensing signals meets a minimum requirement. To address the complex non-convex optimization problem, the method first applies fractional programming to decouple problem, then adopts successive convex approximation algorithm and alternating direction method of multipliers to transform intractable non-convex problem into multiple tractable subproblems, and finally employs an alternating optimization method to efficiently acquire the high-quality suboptimal solutions. The simulation results demonstrate that disclosed scheme exhibits satisfactory convergence and effectiveness, and can significantly improve the performance of IRS-assisted ISAC systems.

The invention relates to the technical field of beacon light control, in particular to a beacon light anti-interference control method and system based on environmental perception and dynamic modulation. The method comprises the following steps: extracting an ambient light reference based on adaptive weight sliding filtering; in combination with target detection and trajectory prediction, interference pre-judgment features are obtained; on the basis of an evidence theory, analyzing comprehensive interference evaluation characteristics through the ambient light reference, the interference pre-judgment characteristics and the electromagnetic interference characteristics; fusing meteorological environment perception and influence features and self health state features, and evaluating equipment operation risks; and based on modulation signal optimization characteristics, the comprehensive interference evaluation characteristics and the equipment operation risk, fuzzy control and rule reasoning are utilized to realize dynamic output of anti-interference control parameters. In order to solve the problem that the signal recognition rate is reduced due to the fact that a beacon light is easily affected by multiple factors in a complex ocean navigation channel environment, data are obtained through multi-dimensional environment perception, and dynamic modulation control is achieved after feature extraction and fusion.

Disclosed are methods and apparatuses for signaling overhead optimization for aerial user equipment (e.g., an unmanned aerial vehicle). An embodiment of the subject application provides a UE including: a processor; and a wireless transceiver coupled to the processor, wherein the processor is configured to, with the wireless transceiver: transmit a measurement report in response to that a cell number of a local maintained cell list becomes equal to or less than a report on leave threshold due to removal of at least one neighbor cell fulfilling a leave condition from the local maintained cell list, wherein the processor is enabled with report on leave for multi-cell trigger; and / or transmit flight path available information in at least one message in response to flight path information being available and a preconfigured or configured condition being met.

The invention discloses a water pump flow and lift online detection device and method using an acoustic emission technology, and relates to the technical field of water pump performance detection. The water pump flow and lift online detection device using the acoustic emission technology comprises an acoustic emission sensor acquisition module, a transient event processing module and a water pump flow and lift detection module. Whether acoustic emission signal optimization is carried out or not is judged according to the quality parameters of the emission signals, the transient events are marked at the same time, then the transient events are classified when the transient events are detected, the transient events are fed back and prompted to the preset personnel, and if the transient events are not detected, the preset personnel are prompted to the preset personnel. If yes, mapping is conducted through acoustic features extracted from acoustic emission signals of the non-transient events to obtain the water pump flow and lift, finally the water pump flow and lift are used for reflecting evaluation of the mapping accuracy of the water pump flow and lift, whether mapping optimization is conducted or not is judged, and the online detection accuracy of the water pump flow and lift is improved. The problem that in the prior art, water pump flow and lift online detection accuracy is low is solved.