251 results about "Signal optimization" patented technology

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 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 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.

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.

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.

The invention relates to the technical field of the Internet of Things, in particular to a sewage detection and analysis system based on the Internet of Things, which comprises a data acquisition unit, a data transmission unit, a data processing and analysis unit and a monitoring and management unit. According to the invention, the self-adaptive bionic sensor array integrating self-cleaning and fault self-healing functions and the mobile patrol robot with autonomous navigation, obstacle avoidance and refined retest capabilities are combined with signal optimization and equipment management of the front-end processing and self-maintenance module; continuous and accurate acquisition and stable operation of multi-dimensional parameters in a complex sewage environment are realized, and a high-quality data source is provided for subsequent data processing, so that the coverage range, data quality and pollution abatement decision efficiency of sewage monitoring are remarkably improved, and fine management and rapid emergency response of the complex sewage environment are effectively supported.

The invention discloses a customer service telephone traffic platform real-time scheduling data optimization method based on edge computing, and relates to the technical field of communication. The customer service telephone traffic platform real-time scheduling data optimization method based on edge computing comprises the following steps: S1, collecting resource data and state monitoring data in an edge computing environment for preprocessing, and constructing a telephone traffic scheduling database after storing the resource data and the state monitoring data; s2, carrying out stability analysis through resource load, response time and preheating progress; s3, fusing task execution, data synchronization, business adaptation and node stability, and evaluating the preheating state of the cold seat; s4, generating a traffic adaptation index through the resource load, the preheating state and the task queue data; and S5, monitoring the resource load, the task queue and the response time of the node in real time to form a load feedback signal, optimizing a task scheduling strategy and generating a load report. The problems that resource scheduling of a customer service telephone traffic platform is insufficient and the preheating state of a cold seat is indefinite under the conditions of high load and high concurrency are solved.

The invention discloses a portable satellite communication station signal optimization method and system, relates to the field of wireless communication, and is used for synchronously realizing dynamic adaptive management of multiple scattering paths. The method comprises the following steps: generating an earth surface image through multi-modal detection data, and carrying out layered calibration on scattering intensity; identifying a high-reflection sub-region according to the surface image and the layered calibration information thereof, and performing unified management on high-reflection and weak echo paths; based on the gain, interference and angle information of the multiple scattering paths, performing sub-array division on the antenna array according to priorities; phase stepping correction is carried out on the antenna units of the sub-arrays, and emission excitation is combined in adjacent path coverage areas; through a dynamic power scheduling and beam forming algorithm, the direction gain of each sub-array is optimized and sidelobe leakage is controlled. According to the invention, synchronous management of high reflection and weak echo paths can be realized, and the dynamic adaptability to scattering scenes is improved.

The invention provides a multi-scene-oriented intelligent rail signal timing optimization method and device, and relates to the technical field of intelligent public transportation, and the method comprises the steps: building a basic signal model according to the total number of current arriving vehicles and a signal constraint condition, and carrying out the calculation of each lane operation scene, and obtaining the optimal period duration; calculating the priority buffer time according to the parameters of the lane operation scene and the geometric parameters of the intelligent rail, and constructing the unified period duration through the priority buffer time and the optimal period duration; inputting the unified period duration into the basic signal optimization model, and respectively solving each lane operation scene to generate a signal timing scheme; optimizing signal timing in the remaining time of the current period based on the signal timing scheme to obtain a transition scheme; and performing signal timing switching on the signal timing scheme of the current scene according to the transition scheme to obtain a signal timing result. According to the invention, the problem that the signal timing scheme cannot be smoothly switched in a complex traffic scene is solved.

The invention relates to a multi-channel air pressure monitoring method, an adaptive monitoring circuit and computer equipment, and the method comprises the steps: synchronously collecting an original air pressure signal set in a target region through a multi-channel air pressure sensor, and carrying out the signal optimization processing, thereby obtaining an air pressure signal set; performing data analysis on a signal cluster divided by the air pressure signal set according to a channel grouping relationship to obtain an air pressure monitoring value of each channel, and calculating a differential pressure value between the channels in the cluster; and inputting the air pressure monitoring value and the differential pressure value into a two-dimensional data anomaly monitoring model of a dual-threshold system established based on historical data and environment auxiliary parameters, and outputting an anomaly judgment result and an anomaly weight ratio, so as to solve the problems of asynchronous monitoring data and high anomaly misjudgment rate in the traditional single-channel monitoring method, and improve the accuracy of the monitoring result. And the environmental stability of the target area is ensured.

The invention relates to the technical field of smart power grids, in particular to a detection signal design method and device for multi-injection-point and multi-load-node identification, and the method comprises the steps: calculating the static sensitivity of a plurality of to-be-identified load nodes corresponding to each target equipment injection node in a plurality of target equipment injection nodes based on steady-state operation data, establishing a detection signal optimization model taking a reactive detection signal as a decision variable by taking the total minimum load model parameter identification error of a plurality of to-be-identified load nodes as a target and combining static sensitivity, and introducing reactive energy constraints and signal structure constraints of a plurality of target equipment injection nodes and solving; generating an optimal reactive power detection signal waveform of each target equipment injection node, performing evaluation by using a target evaluation index, when an evaluation result meets a preset constraint condition, injecting the optimal reactive power detection signal waveform into the system, and after a response is generated, performing identification on a load model parameter by using the response, and obtaining an identification result of each load node to be identified.

The invention relates to the technical field of pipeline repairing equipment, in particular to a constant tension control system and method of a buried pipeline repairing spiral winding machine. The system comprises a data acquisition module, a tension control module and an execution adjustment module. The safety tension range is calculated through the data acquisition module, the target tension is determined, the tension control module establishes a non-linear relation model fusing the coiling diameter time-varying characteristic and the angle geometric path influence, and a linear decoupling strategy of feed-forward compensation and PID feedback is combined. Core interference caused by rolling diameter reduction and angle change can be actively and speculatively offset, the execution adjustment module optimizes distribution of control resources by accurately mapping a control quantity into a driving signal, model parameters are dynamically and finely adjusted according to the deviation between model prediction and actual output, and feed-forward compensation quantity is reversely corrected. The invention relates to a self-adaptive intelligent system with continuous self-calibration and self-evolution.

The invention relates to the technical field of intelligent traffic, and discloses a traffic flow prediction method and system based on traffic large model area signal optimization, and the method comprises the steps: obtaining road network structure data and traffic flow density distribution data; performing correlation analysis and feature fusion according to the road network structure data and the traffic flow density distribution data to obtain a regional traffic feature vector, and predicting vehicle flow based on the regional traffic feature vector to obtain a vehicle flow predicted value; according to the vehicle flow prediction value, evaluating the intersection congestion trend, and combining the regional traffic feature vector to carry out spatial dependence and network topology feature fusion processing to obtain an initial parameter set constrained by space; and carrying out optimization solution calculation on the initial parameter set subjected to spatial constraint to obtain a personalized signal control strategy. According to the method, the optimization range can be narrowed by introducing the physical topology constraint, and the response speed of signal control is remarkably improved while congestion overflow is prevented.

The invention provides a personalized semantic understanding learning method, medium and system under an AI platform, and belongs to the technical field of semantic understanding. According to the technical scheme, the personalized semantic understanding learning method comprises the steps that a personalized semantic file recording user question and answer habits and personal terms is constructed, and dual time decay values are set; a semantic understanding optimization model is established based on an attention mechanism, personalized feature vectors and input semantic vectors are fused, a concept drift detection algorithm adopting bipartite graph maximum matching and a Hungary algorithm is designed to monitor semantic habit changes in real time, and an incremental learning mechanism based on a variable sliding window is established to calculate a semantic difference matrix. A forgetting function based on a Gaussian kernel is applied to adjust the historical semantic feature weight according to the time distance and the use frequency, and a reinforcement learning feedback module is constructed to collect user satisfaction evaluation and generate reward signal optimization model parameters; and executing a self-iterative optimization process to periodically update semantic archives and models so as to continuously adapt to personalized semantic requirements of users.

The invention discloses a distribution network line fault detection method, system and device based on pulse reflection waves and a medium, and belongs to the technical field of fault detection, and the method comprises the steps: deploying pulse sources, generating pulse signals, recording a reference waveform of each pulse source, receiving reflection wave signals, extracting features, and comparing reflection wave features with the reference waveforms to recognize abnormal signals. Filtering the abnormal signal, calculating an error index and a harmonic index of the filtered signal to preliminarily judge a fault point, calculating a path difference of the fault point, constructing a path matrix, and optimizing the path matrix according to a three-phase signal of the distribution network; and extracting a reflected wave signal corresponding to the position of the fault point in the path matrix, converting the reflected wave signal into a two-dimensional image, and classifying the fault point through a neural network. According to the method, the accurate preliminary judgment of the fault point is realized, the anti-noise capability and the analysis precision of the signal are effectively improved, the accuracy of the path matrix is remarkably enhanced, and the efficient detection, positioning and classification of the complex distribution network fault are realized.

The invention relates to the technical field of new energy automobile electric drive NVH optimization, in particular to a new energy automobile electric drive NVH optimization method, which comprises the following steps of: extracting an actual measurement order, and judging whether an electric drive system is wrapped by an acoustic packet or not; sensing a plurality of amplitudes, and carrying out weighted summation and fusion on the plurality of amplitudes; when the acoustic packet is wrapped by the electric drive system, the average value of the multiple amplitudes is calculated as a comparison order, and when the acoustic packet is not wrapped by the electric drive system, the maximum amplitude in the multiple amplitudes obtained through weighted summation fusion is called out as the comparison order; respectively calculating a motor core characteristic order and each reduction ratio characteristic order of the speed reducer; judging the feature order to which the pair belongs, if the comparison order belongs to the motor core feature order, judging the stator order and the rotor order again, and outputting an optimization signal according to the judgment result; if the comparison order belongs to the speed reducer characteristic order, outputting a corresponding optimization signal; and after the reminding signal and the optimization signal are output, subjective evaluation is carried out.

The invention discloses a travel traffic road condition evaluation planning system based on fusion processing. The travel traffic road condition evaluation planning system comprises a multi-source data acquisition engine, a data fusion preprocessing unit, a road condition state fusion evaluation unit, a short-time prediction evolution unit, an intelligent path planning unit and a coordination signal optimization unit, according to the method, historical and real-time dual-mode prediction verification channel and regional coordination signal control feedback is established, a road network state evolution trend decoupling stability quantitative model is used for driving a planning decision, road average delay in peak periods in typical urban road network tests is decreased, the traffic capacity of main roads is improved, the response time of extreme meteorological events is shortened, and the reliability of the urban road network is improved. The road condition evaluation precision is improved through heterogeneous data fusion and dynamic rule engine coupling, a multi-source data acquisition engine is coupled with environmental data in real time, and signal noise interference is inhibited through a credibility weighting mechanism in a data fusion preprocessing unit.

The invention relates to the technical field of sensors, and discloses a signal optimization method based on a diagnosability evaluation result, and the method comprises the following steps: S1, obtaining monitoring signal data collected by a plurality of candidate sensors in a normal state and a plurality of fault states of equipment, and S2, carrying out the optimization of the signals based on the monitoring signal data, according to the method, a fault detectability model is constructed, the fault detectability model is used for quantifying the difference between a fault state and a normal state on sensor signals, and when sensor signal optimization is carried out, the fault detectability model and the fault isolability model are constructed in a unified manner; and a detectability evaluation index and an isolability evaluation index are calculated based on a joint mean-variance difference value, so that comprehensive quantitative evaluation of the signal fault detection capability and the fault isolation capability is realized, and it is ensured that performance requirements of different dimensions of fault diagnosis in a signal optimization process are balanced. And the comprehensiveness and accuracy of fault diagnosis are improved.

The invention discloses a task-oriented dialogue system reward function optimization method and system, and belongs to the technical field of natural language processing. The method comprises the following steps: acquiring an expert dialogue track from a dialogue system data set, extracting a state-action-reward triple, training an initial reward function by using a maximum entropy inverse reinforcement learning framework, and initializing a strategy network; an Actor-Critic reinforcement learning algorithm is adopted to train a strategy network, and a suboptimal trajectory is collected to dynamically update a reward function; and taking the dynamic reward function as a unified evaluation signal, and optimizing the strategy network to form a dialogue strategy model. Through dynamic reward function optimization, manual rule dependence is reduced, the generalization ability, the task completion rate and the stability of a dialogue system are improved, and the method is suitable for complex dialogue scenes in multiple fields.

The application relates to the technical field of differential protection, in particular to a power distribution network distributed differential protection method and system suitable for a 1.4GIGS communication network, which comprises the following steps: based on the current and voltage parameters of each node of the power distribution network, real-time data is extracted and analyzed, the difference between the nodes is calculated, the nodes meeting the conditions are screened out by analyzing the difference values, signal receiving monitoring is carried out, and a list of key monitoring nodes is generated. In the application, the node signal transmission path is optimized through a power distribution algorithm, the time-frequency resource distribution of the signal is dynamically adjusted, the efficiency and stability of signal transmission are realized, the bit error rate in the signal transmission process is effectively reduced, the response speed and accuracy of the power distribution network differential protection system are improved, the interference information in the signal is processed through a particle swarm optimization algorithm, the frequency spectrum structure of the signal is optimized by rearranging and rotating the interference signal, the transmission parameters of the signal are adjusted, and the stability and anti-interference ability of signal receiving are greatly improved.

The application provides a traffic signal optimization control method and system, the method comprises the following steps: carrying out data preprocessing on original perception data to obtain real-time traffic state data; converting the real-time traffic state data into road qualitative running state based on a qualitative conversion grading table; constructing a traffic signal control knowledge graph, embedding and updating the real-time traffic state data to obtain updated embedding features, and encoding the knowledge units in the traffic signal control knowledge graph to obtain a target traffic signal control knowledge graph; determining a reasoning context based on a natural language query; determining a natural language enhanced prompt, inputting the natural language enhanced prompt into a pre-trained large language model to output a traffic signal control optimization strategy, and the application can realize accurate perception of complex traffic scenes, dynamic fusion of knowledge, and generation of an interpretable and highly reliable signal timing strategy.

The invention discloses a complex urban environment-oriented Beidou high-precision positioning optimization method and system, and the method comprises the steps: obtaining original observation data containing a multipath effect, constructing an airspace attention mechanism graph neural network multipath signal recognition model, and distinguishing multipath and sight distance signals; and satellite orbit, ionosphere and troposphere errors are corrected for the screened sight distance signal data, a GNSS RTK robust positioning model based on factor graph optimization is constructed, a gradient descent iterative optimization algorithm is adopted to solve and adjust the weight, and a positioning result is output. The system comprises a multi-source signal acquisition preprocessing unit, a multipath signal identification unit, an error correction unit, a model construction configuration unit, an iterative optimization solution unit and a result output unit. According to the method and the system, the positioning model is optimized by accurately identifying the multipath signals, so that the accuracy and the stability of Beidou positioning in a complex urban environment are improved.

The invention relates to an intersection group collaborative optimization method and device in a man-machine mixed driving scene, and a storage medium. The method comprises the following steps: dividing a to-be-optimized urban area into a plurality of traffic zones; predicting the ownership of automatic driving vehicles and human driving vehicles in the traffic zone according to the vehicle type purchase intention data and a pre-constructed multiple linear regression model; according to a binomial Logit model, calculating a human driving travel ratio and an automatic driving travel ratio of the traffic zone, according to the travel ratio and the inventory, calculating the travel occurrence amount of human driving vehicles and automatic driving vehicles of the traffic zone, and obtaining a preset attraction amount of the traffic zone; calculating traffic demands of human-driven vehicles and automatic-driven vehicles between the starting point and the ending point of the traffic zone according to the travel occurrence amount and the attraction amount; constructing a flow distribution model according to the road network of the traffic zone; and solving the intersection group collaborative optimization model according to the flow distribution model to obtain a final signal optimization scheme.

A method is provided that includes receiving a configuration associated with monitoring of a physical control channel for a paging downlink control information (DCI). The configuration indicates at least one first monitoring occasion for each paging occasion of a number of paging occasions for a paging frame. And the at least one first monitoring occasion for at least one of the number of paging occasions is indicated by a time location relative to a first monitoring occasion for a reference paging occasion. The method also includes monitoring the at least one first monitoring occasion for one of the number of paging occasions associated with a user equipment (UE) based on the configuration, where the at least one first monitoring occasion is monitored for the paging DCI.

A method includes receiving a coded video bitstream comprising coded information of one or more pictures, and determining a signaled value of a syntax element from the coded video bitstream. The syntax element is associated with a use state of an optimization technique for encoding the one or more pictures into the coded video bitstream by an encoder, the syntax element is structured to have at least three possible values corresponding to three use states of the optimization technique, a first value of the syntax element indicates an unknown state of the optimization technique for encoding the one or more pictures, a second value of the syntax element indicates a used state of the optimization technique for encoding the one or more pictures, and a third value of the syntax element indicates a no-use state of the optimization technique for encoding the one or more pictures.

The invention discloses a hearing aid auxiliary fitting system, and relates to the technical field of hearing aids, and the system comprises a hearing detection module which is responsible for detecting the hearing of a patient from multiple dimensions; the data acquisition module is used for accurately acquiring various hearing data of a patient; the analysis processing module is used for processing and analyzing the collected data; the weight distribution module is responsible for dynamically distributing weight coefficients to the processed data; the auxiliary debugging module is used for carrying out auxiliary debugging on the hearing aid fitted by the patient; the signal optimization module adopts a mathematical algorithm to suppress environmental noise and improve the stability of the signal; the follow-up guide module dynamically adjusts the configuration of the hearing aid according to the follow-up data, and provides long-term support after fitting. Data acquisition and analysis processing are performed synchronously, efficient acquisition of data can be ensured, and parameters of the hearing aid can be adjusted according to needs of a patient through cooperation of the weight distribution module and the auxiliary debugging module, so that the hearing aid can adapt to hearing conditions of the patient in a personalized manner.

The invention relates to a silicon substrate signal optimization wiring structure and electronic equipment. The silicon substrate signal optimization wiring structure comprises a plurality of metal wiring layers which are mutually stacked along a direction vertical to a substrate; the multiple metal wiring layers comprise a first metal wiring layer and a second metal wiring layer which are adjacent to each other in the direction perpendicular to the substrate; the first metal wiring layer comprises a first ground wire, and the second metal wiring layer comprises a second ground wire; an overlapping area exists between the orthographic projection of the first ground wire in the direction perpendicular to the substrate and the orthographic projection of the second ground wire in the direction perpendicular to the substrate. The first ground wire and the second ground wire are electrically connected through a first conductive structure; the first conductive structure is embedded in a ground wire interconnection hole of the multi-layer metal wiring layer; the orthographic projections of the ground wire interconnection holes in the direction perpendicular to the substrate are all located outside the overlapping area. According to the invention, the coupling crosstalk effect in the metal wiring layer in the substrate is improved, so that the signal transmission integrity of the substrate is improved.

The invention discloses a distributed regional real-time signal control optimization method based on electronic police data, and the method comprises the following steps: 1, vehicle arrival prediction: employing vehicle departure information provided by an upstream intersection to predict the arrival rate of vehicles in each flow direction of an entrance lane of a current intersection in the future, and enabling the predicted arrival rate to serve as an input variable of signal control optimization; step 2, optimizing signal control; firstly, a multi-target dynamic planning algorithm is designed to update signal timing by taking minimization of the total delay of the intersection and the maximum queuing length of the intersection as targets, and then a signal optimization result is executed and provided for adjacent intersections. According to the method, historical decisions and real-time sensing data of adjacent intersections are fully utilized, arrival of complete vehicles is predicted, meanwhile, the long queuing phenomenon caused by prediction error accumulation is effectively avoided, and reasonable balance of multiple targets is achieved.

The application relates to the technical field of electromagnetic measurement, and discloses a signal optimization processing method and system for electromagnetic frequency sensitive material, the method comprising the following steps: performing scanning measurement on an original response signal of the electromagnetic frequency sensitive material under frequency domain excitation to obtain amplitude-frequency characteristic parameters and phase-frequency characteristic parameters; performing frequency domain conversion analysis on the amplitude-frequency characteristic parameters and the phase-frequency characteristic parameters to obtain a complex spectrum change gradient; identifying the sensitivity response of dynamic frequency of the electromagnetic frequency sensitive material, and merging the identified frequency point groups into characteristic frequency subbands; mapping the characteristic frequency subbands into a frequency optimization strategy library to obtain an optimization strategy; performing compensation correction on the original response signal to obtain an optimized signal; performing signal-to-noise ratio evaluation on the optimized signal, and iteratively optimizing the frequency optimization strategy library according to the evaluation result, so that the signal optimization processing effect of the electromagnetic frequency sensitive material is realized; and the application can improve the signal optimization processing efficiency of the electromagnetic frequency sensitive material.