719 results about "Weighting coefficient" patented technology

The invention discloses an equipment state monitoring method based on multi-source information fusion, and the method comprises the steps: enabling a real-time sensor to collect the operation parameter data, vibration parameter data and environment parameter data of equipment, collecting the historical state data of the equipment with a recording label, and carrying out the preprocessing of the data; time domain features, frequency domain features and working condition features of the data are extracted according to a layering mode, and dynamic weight coefficients of all the features are set; calculating a reference threshold value by adopting a specific model; evaluating the health condition of the current equipment by adopting a depth measurement method, calculating an equipment health factor, and calculating a trend compensation item; and obtaining an equipment state monitoring dynamic threshold based on the reference threshold, the health correction item and the trend compensation item. The invention further discloses an equipment state monitoring device based on multi-source information fusion, corresponding equipment and a storage medium. According to the equipment state monitoring method based on multi-source information fusion provided by the embodiment of the invention, the accuracy, real-time performance and reliability of equipment state monitoring can be effectively improved.

The invention relates to the technical field of bearing fault diagnosis, in particular to a multi-bearing fault positioning and diagnosing method, which comprises the following steps of: acquiring bearing vibration signals of a plurality of sensing points, extracting peak time information to complete synchronous calibration, extracting high-frequency band energy and constructing a feature vector, screening deviation features and clustering and grouping, and identifying an energy abnormal channel, positioning a bearing position, extracting time domain and frequency domain features, comparing fault modes, and outputting a fault type diagnosis result. According to the method, the consistency of multi-channel data synchronization is realized by extracting the peak point of the vibration signal and calibrating the time offset, the high-frequency energy change rate is utilized to match the weight coefficient, the detection sensitivity of tiny fault features is enhanced, and the separability of a composite fault state is improved by combining feature relative deviation rate screening and density trend clustering. A frequency section energy proportion change recognition channel is adopted, accurate positioning of a fault bearing is achieved, and accurate judgment of a fault type is achieved in combination with a multi-feature comparison mode.

The invention relates to the technical field of telecommunication, in particular to a dual-mode communication link transmission method based on dynamic channel perception, which comprises the following steps of: firstly, detecting channel states of two links in parallel in a data transmission gap or according to a preset period to obtain a signal-to-noise ratio and a channel load rate; determining a service quality grade according to a service type of a transmission data packet, selecting an adaptive signal-to-noise ratio and a channel load rate weight coefficient, and further calculating a comprehensive quality score of the two links; dynamically calculating a power line link switching threshold based on a network average time delay requirement or a power line link historical quality stability parameter; comparing the power line link score with a threshold value and a wireless link score, and determining whether to switch or not; and finally, synchronizing the data transmission serial number and the protocol context through signaling to realize seamless switching. According to the invention, the real-time performance of channel sensing and the accuracy of quality evaluation are improved, the reasonable switching decision and the coherent data transmission are ensured, and the reliability of a communication system is enhanced.

The invention provides a data processing priority scheduling method for high-speed satellite communication and related equipment, which can effectively improve the transmission performance of a high-speed satellite communication system so as to adapt to differentiated requirements of various communication scenes. The method comprises the following steps: acquiring cross-layer state information; calling a strategy matrix, wherein the strategy matrix is used for defining weight coefficients of different data types under different network states and channel qualities; calculating a target priority score for the to-be-transmitted data stream according to the cross-layer state information and the strategy matrix; sequencing the target priority scores from high to low to obtain a sequencing result; scheduling transmission queues of the data streams for transmission according to a sorting result, and allocating cache transmission queues for the data streams with high priority scores; when it is monitored that the priority score of the new data stream exceeds a preset emergency threshold value, the new data stream is inserted into a cache transmission queue of the transmission queue, and back pressure control is carried out on data streams of other priorities.

The invention discloses a duplex-screen intelligent cabin interaction method and system, and relates to the technical field of intelligent cabin interaction, and the method comprises the steps: collecting user operation behavior data, vehicle state data and environment perception data in real time through a vehicle-mounted sensor group, a man-machine interaction interface and a vehicle bus; establishing a multi-dimensional user data model containing an interaction feature vector, a driving mode matrix and a preference configuration tree; the method comprises the following steps: analyzing user historical interaction data based on an adaptive weighting algorithm, and calculating use frequency weight coefficients of different functional modules; according to the duplex-screen intelligent cockpit interaction method and system provided by the invention, the precision of a user portrait is remarkably improved by constructing a multi-dimensional user data model, personalized interaction experience is realized, and the interface layout and interaction logic can be dynamically adjusted according to a real-time driving scene by adopting a self-adaptive weighting algorithm and a random forest classifier, so that the user experience is improved. The real-time performance and the adaptability of the system are enhanced, and the interaction experience is optimized.

The invention relates to the technical field of information, and discloses a radar dynamic anti-interference method and system based on interference source positioning. The method comprises the following steps: acquiring current signal data and a historical signal sequence, and determining an initial deviation value; extracting a historical deviation sequence according to the initial deviation value to obtain a deviation change trend vector; calculating a trend slope and a fluctuation amplitude, and determining an adjustment trigger signal; extracting feature interaction influence according to the adjustment trigger signal, and determining a weight coefficient update increment; iterative optimization is carried out in combination with the convergence rate parameter, and an optimized convergence rate value is obtained; adjusting deviation compensation model parameters according to the convergence speed value, and outputting a deviation compensation result when the compensation residual error is lower than a preset threshold value; and carrying out positioning calculation according to a deviation compensation result, and determining a corrected positioning coordinate. The model is dynamically optimized through technologies such as a sliding window and gradient descent, the problem of positioning deviation caused by complex environment signal interference is solved, and the positioning precision and the response speed are improved.

The invention provides a novel multi-source information and damage coupled coal mine rock burst prediction method. The method comprises the following steps that rock mass fracture signals, a three-dimensional stress field and acoustic emission parameters are collected in real time through a micro-seismic monitoring array, an optical fiber stress sensor and a rock sound probe; an LSTM-attention fusion module is adopted to carry out spatio-temporal feature extraction on the collected multi-source data, an attention weight coefficient is generated, and a weighted fusion feature vector is output; calculating the rock mass damage degree based on a statistical damage mechanics Weibull distribution theory; based on the law of conservation of energy and the damage mechanics theory, a dynamic energy threshold function is constructed, the mining time effect and the rock mass damage state are incorporated into a quantitative evaluation system, and a dynamic energy accumulation rate threshold function is established by introducing a time accumulation effect and a damage coupling term; when the real-time energy accumulation rate is larger than or equal to the energy accumulation rate threshold value and the time derivative of the energy accumulation rate is larger than the sudden change threshold value, third-level early warning is triggered. According to the invention, advanced accurate early warning of the rock burst can be realized.

The invention relates to the technical field of sensor data fusion, in particular to a real-time dynamic attitude stability control method and system based on multi-sensor fusion, and the method comprises the following steps: collecting multi-source sensor data, and correcting to generate a synchronous sensor data set; calculating a stability coefficient, a signal quality coefficient and a consistency coefficient, weighting the signal quality coefficient and the consistency coefficient to generate a dynamic fusion weight coefficient, executing weighted fusion based on the weight, outputting a fusion attitude state estimation result through adaptive extended Kalman filtering, and generating an optimization control instruction sequence by combining with dynamic constraint model rolling optimization; according to the method, a dynamic weight mechanism based on noise and signal strength is introduced to achieve self-adaptive fusion, the synchronization precision and reliability are improved by combining consistency verification, abnormal signals are corrected through self-adaptive filtering, the attitude estimation stability is enhanced, and the performance of the system is improved. And a control instruction is generated through rolling optimization, so that rapid and accurate adjustment is realized, and attitude keeping and control consistency in a complex environment is improved.

The invention discloses a super capacitor energy storage life evaluation method under a fire storage frequency modulation working condition, relates to the technical field of power system energy storage, and can at least partially solve the problems that an existing super capacitor life evaluation method is poor in adaptability to the frequency modulation working condition, insufficient in multi-factor coupling quantization and lack of engineering criteria. The method comprises the steps of simulating a fire storage frequency modulation working condition and collecting multi-dimensional operation data of a super capacitor; preprocessing the collected data and extracting life characteristic parameters such as capacity attenuation rate, equivalent C rate, average temperature rise, frequency modulation load factor and the like; key influence factors are screened through correlation analysis, weight coefficients are trained through a machine learning algorithm, and a coupling influence coefficient model is constructed; establishing a capacity attenuation equation, an equivalent series resistance growth equation and a response delay growth equation, and fusing three criteria to determine a life end point; and dynamically correcting model parameters based on the real-time frequency modulation data and calculating the residual life. Accurate evaluation and dynamic prediction of the service life of the super capacitor under the fire storage frequency modulation working condition are achieved.

The invention discloses a power system frequency risk quantification and coordination control method and system, and belongs to the technical field of power systems and automation thereof. The method comprises the following steps: collecting power grid online operation data and system working conditions; frequency response curves of the system after different faults are obtained through fault simulation; dividing the frequency into different frequency bands according to a binary criterion formed by a plurality of frequency threshold values and corresponding duration time, and distributing an initial weight coefficient for each frequency band; obtaining a dynamic weight coefficient of each frequency band by combining the frequency change rate in the frequency band and a preset weight gain coefficient; based on the dynamic weight coefficient of each frequency band, performing time integration on the frequency deviation value in each frequency band to obtain a transient frequency risk assessment value; and judging the system stability and the risk level, and giving a corresponding frequency control strategy according to the risk level so as to coordinate and control resources. According to the method, the short-time power disturbance and the permanent power disturbance can be effectively distinguished, and a basis is provided for on-line and off-line safe and stable control.

The invention provides a radar signal depth feature extraction method and system based on adversarial sample defense, electronic equipment and a storage medium, and relates to the technical field of radar signal processing.The radar signal depth feature extraction method comprises the steps that a spacecraft synthetic aperture radar original echo signal is collected and converted into a time-frequency feature map, and intra-pulse and inter-pulse features are extracted in parallel to generate a combined matrix; receiving a deception jamming signal by using a polarization radar group, generating a signal data body without polarization influence through complex coherent superposition, inputting the deception jamming signal and the signal data body into a feature decoupling adversarial network, and separating bullet micro-motion target feature data by means of mutual information maximization constraint; and finally, inputting into a multi-scale local attention module to extract multi-band characteristic components, performing adaptive weight coefficient fusion according to power entropy, performing polarization channel energy correction, and outputting anti-interference fingerprint characteristics with micro-Doppler characteristic enhancement, so that anti-interference processing of the spacecraft synthetic aperture radar signals can be realized. And warhead target anti-interference fingerprint features with micro-Doppler feature enhancement characteristics are effectively extracted.

The invention discloses a distribution network power line anti-interference communication method based on adaptive filtering, and belongs to the technical field of distribution network power line communication. According to the method, a communication signal adopting a frequency shift keying modulation mode is acquired from a power line coupler of a distribution network through a signal acquisition module, the communication signal is preprocessed by using a digital signal processing unit, spectral analysis is performed on the preprocessed signal, and characteristic parameters of interference noise are extracted; a coefficient vector of a finite impulse response adaptive filter is initialized based on the characteristic parameters, the order of the filter is in direct proportion to the reciprocal of the dominant interference frequency, fine adjustment is carried out based on noise energy distribution through a table look-up method or a linear mapping relation, and the weight coefficient of the filter is adjusted in real time in the communication process. The adjustment step length is dynamically adjusted according to the real-time estimated signal-to-noise ratio, and finally, the processed signal is output through the adaptive filter and data demodulation is carried out. The method is mainly used for improving the anti-interference capability and the communication reliability of the power private line communication of the distribution network.

The invention discloses an SAR image despeckle method and device, and relates to the technical field of image processing. The method comprises the following steps: constructing an SAR image despeckle model based on an encoder-decoder structure; acquiring an SAR observation image polluted by noise; extracting shallow layer features of the SAR observation image; separating the shallow features into high-frequency features and low-frequency features; aggregating the spatial context information of the high-frequency features and encoding the spatial context information into a query matrix, a key matrix and a value matrix; calculating high-frequency output characteristics according to cross covariance attention among the matrixes; extracting noise distribution features in the low-frequency features and splicing the noise distribution features with the low-frequency features to obtain low-frequency output features; learning complementarity weight coefficients of the high-frequency output features and the low-frequency output features, and fusing the high-frequency output features and the low-frequency output features to obtain deep features; reconstructing the deep features output by the last layer of the decoder, and outputting a residual image; and connecting the residual image with the SAR observation image residual to obtain an SAR freckle-removed image.

The invention provides a noise reduction method and device, equipment and a medium, weighting processing is performed on a first time domain reference noise signal vector to obtain a second time domain reference noise signal vector, a sampling point closer to the current moment is endowed with a higher weight, the noise reduction performance is optimized, and the noise reduction performance is improved by updating a weight coefficient and the second time domain reference noise signal vector. Therefore, the noise cancellation signal is determined on the time domain, filtering on the time domain is realized, the noise cancellation signal is generated to carry out noise reduction processing on the primary noise in the target noise reduction area, extra time delay caused by frequency domain filtering is avoided, noise reduction time delay is reduced, and the filtering effect is improved. The updating process of the weight coefficient is completed in the frequency domain, updating is achieved in the frequency domain with smaller blocks, the calculation amount is reduced, the interval time of noise reduction processing is shortened, the calculation efficiency of the algorithm is improved, the processing delay is reduced, and the noise reduction effect is improved.

The invention provides an array image demosaicing method based on dynamic convolution and adaptive coding, which relates to the technical field of image processing, and comprises the following steps: acquiring single-channel original image data and a corresponding color filtering array arrangement type identifier; converting the arrangement type identifier into a multi-dimensional physical feature vector, and inputting the multi-dimensional physical feature vector into a feature processor of a neural network model to generate a weighting coefficient vector; carrying out weighted combination on the plurality of special arrangement transformation matrixes through a weighting coefficient vector to obtain a transformation component, adding the transformation component and a basic convolution kernel parameter to obtain a dynamic convolution kernel parameter, and carrying out directional modulation on a specific spatial position of the dynamic convolution kernel parameter based on a direction weight component in a multi-dimensional physical feature vector; and performing convolution operation on the original image data by using the dynamic convolution kernel parameters, extracting multi-scale features, reconstructing image features, and outputting multi-channel color image data, so that the method can be adaptive to different color filter array types, and the demosaicing precision and generalization capability are improved.

The invention discloses an intelligent spectrum temperature measurement method based on a gated hybrid expert model, and the method comprises the steps: carrying out the registration of a multi-channel image, extracting an average gray value in a region of interest, and calculating the gray value ratio between different channel pairs; each group of channel pair corresponds to one expert sub-model, the gray value ratio of the channel pair is input of the expert sub-model, and each expert sub-model gives a preliminary prediction temperature according to the gray value ratio of the corresponding channel; the plurality of expert sub-models are constructed into a gated hybrid expert framework based on a gated network BiGRU, and the gated network distributes a corresponding weight coefficient to each expert sub-model; and carrying out weighted summation on the temperature prediction value output by each expert sub-model and the corresponding weight coefficient to obtain a final prediction temperature value. According to the method, the gray value ratio is serialized, intelligent decision making is carried out by using the time sequence modeling capability of the BiGRU network, adaptive fusion of all expert models is realized, and errors caused by emissivity changes are effectively inhibited.

The invention relates to the technical field of image reconstruction, and provides an ultrasonic sound velocity image reconstruction method based on deep learning, which comprises the following steps: fixing an array probe and coupling the array probe with a detected workpiece, intercepting and collecting samples and forming a snapshot set, constructing estimation of a covariance matrix, and calculating a channel weighting coefficient. Weighting to generate a preliminary imaging graph and a pixel-level confidence graph; splicing the preliminary imaging image, the pixel-level confidence map and the corresponding multi-channel features to form an input feature map, and processing and outputting a reconstructed ultrasonic imaging image and a corresponding confidence correction image by using a deep neural network; generating candidate reconstructed images in a plurality of spatial scales, and weighting and fusing components of the candidate images according to pixels on each scale according to scale reliability and pixel-level confidence to obtain a fused reconstructed image; candidate extraction, feature quantization, probabilistic discrimination and severity grading are implemented, and a detection report is generated in a standardized format and recorded for subsequent backflow training.

The invention provides a non-circular signal sensing method based on a weighted coherence matrix, which belongs to the technical field of wireless communication, and comprises the following steps: acquiring a complex value receiving signal, and calculating a real value augmented sample covariance matrix; based on the real value augmented sample covariance matrix, constructing a real value sample coherence matrix, and calculating a corresponding weight coefficient; constructing test statistics based on the weighted sum of squares of the real value sample coherence matrix and the weight coefficient; and calculating a sensing threshold based on the false alarm probability, comparing the sensing threshold with the test statistics, and determining a spectrum sensing result. According to the method, the second-order statistical property of the non-circular signal is described by using the real-value augmented sample covariance matrix, and weighted square fusion is performed on the real-value sample coherence matrix in combination with the weight coefficient driven by the sample data, so that better sensing performance can be obtained.

The invention discloses a wind power blade structure health monitoring method based on multi-field decoupling optical fiber sensing, particularly relates to the technical field of wind power equipment state monitoring, and is used for solving the problem of health state misdiagnosis caused by coherent interference of environmental noise and damage signals. The method comprises the following steps: collecting vibration, strain and temperature signals and generating a background noise vector; calculating a frequency domain coherence function of the vibration signal and the noise vector to identify an interference danger frequency band; analyzing the polarization angle distribution of the vibration signal in the frequency band, and inverting the physical path topology of the noise transmitted into the blade; when the path passes through a predefined weak area, improving a damage weight coefficient of a strain signal of a corresponding frequency band and carrying out Hilbert phase shift reconstruction to generate an anti-interference strain feature vector; calculating the energy dissipation rate of the vector in a dangerous frequency band, and judging an energy leakage event caused by damage by combining a viscoelastic constitutive threshold value of the composite material; the damage type and the spatial position are judged according to the time domain correlation between the energy leakage event and the temperature signal, and the hidden damage detection reliability is improved.

This invention discloses a servo-elastic stabilization method based on multi-sensor information fusion, belonging to the field of flight control. Specifically, it involves: extracting and modeling the main load-bearing structure of the aircraft; calculating the modal frequencies, mode shapes, and mode shape slopes of the aircraft under unconstrained conditions; then combining the aircraft, a first sensor, servo motors, and the control system to form a closed-loop servo-elastic system; calculating the open-loop system transfer function from the control input to the feedback input of the control system, drawing an open-loop Bode plot, and determining the dangerous frequencies and corresponding dangerous natural modes; based on this, placing a second angular velocity sensor on the aircraft; then calculating and correcting the weighting coefficients of the two angular velocity sensor signals, and introducing the weighted signal into the control system instead of the original signal; comparing the system characteristics to ensure the servo-elastic stability of the aircraft without affecting the rigid body phase and amplitude.

The invention discloses a psychological state recognition method and system based on physiological signals, and relates to the technical field of intelligent preoperative evaluation and planning of thoracic surgery, and the method comprises the steps: S1, collecting multi-modal physiological signals, and forming an original signal set; s2, performing parallel preprocessing on the original signal set to generate a denoised time sequence signal; s3, dynamically generating a modal weight coefficient based on the quality parameters, and performing weighted fusion on the multi-modal time sequence signals by using the weight coefficient; and S4, inputting the fused feature vector into a personalized recognition model, and outputting psychological state probability distribution through an adaptive feature mapping layer in the model. And S5, according to the user feedback signal or the distribution offset of the continuous monitoring data, triggering online parameter updating of the personalized recognition model, and generating a recognition model after incremental optimization. According to the psychological state recognition method and system based on the physiological signals, the problem that psychological state recognition is inaccurate due to large individual difference, strong environmental interference and weak dynamic adaptability can be solved.

The invention discloses a mixed regular variational mode decomposition method, device and equipment suitable for an unsteady flow field of an aircraft and a storage medium, and relates to the technical field of flow analysis, and the method comprises the steps: determining a bandwidth estimation value and a frequency separation weight coefficient based on the unsteady flow field, an expected mode number, a spatial mode and a time evolution coefficient, constructing a first regular term and a second regular term, weighting to obtain a mixed regular term, introducing a flow field reconstruction term to establish a flow field modal decomposition variational optimization problem, converting the problem into a frequency domain optimization problem through Fourier transform, and simplifying the problem into a sub-convex optimization problem by adopting an alternating direction multiplier method; an analytic solution is obtained through a variational method and iteratively updated to obtain a flow field space mode, a time evolution coefficient frequency domain representation, a center frequency and a Lagrange multiplier, the time evolution coefficient is obtained through inverse Fourier transform, a mode decomposition result is obtained, and therefore high-precision mode separation of the unsteady flow field is achieved. And the calculation efficiency and the iteration convergence speed of the variational optimization problem are improved.

The invention discloses a ship water gauge scale line fitting algorithm, and relates to the technical field of ship water gauge scale calculation, and the method comprises the steps: obtaining a ship water gauge region image through an image collection device to extract a measurement point coordinate, and building a mapping relation between a pixel position and a physical draft; a weighted least square method is adopted to perform nonlinear curve fitting, a weight coefficient is calculated based on a local neighborhood standard deviation, a polynomial order p is dynamically adjusted according to an adjusted decision coefficient, and the decision coefficient is iteratively optimized to improve the balance between amplitude and model complexity. And the system repeatedly corrects model parameters through a dynamic verification mechanism of the residual sum of squares and a preset threshold value, and finally an accurate water gauge scale fitting curve is generated. The method effectively solves the problems that traditional manual observation is prone to environmental interference and large in measurement error, high robustness is kept in a complex water area environment, and reliable data support can be provided for the fields of ship load evaluation, channel safety management and the like.

The application discloses a dual-channel parallel detection airborne phased array radar ground moving target extraction method, comprising the following steps: acquiring coherent pulse train data of an airborne phased array radar; forming sum-difference beam data according to preset sum-difference beam weighting coefficients; constructing a pulse compression matching function to perform pulse compression on the sum-difference beam data; performing platform motion compensation and pulse Doppler processing on the pulse compressed sum-difference beam data; performing high-speed channel determination according to amplitude statistical results of each Doppler unit of the sum beam, and performing CFAR detection; performing low-speed channel determination according to the amplitude statistical results of each Doppler unit of the sum beam, processing low-speed channel data by using sum-difference 3DT-STAP algorithm, and performing CFAR detection and amplitude normalization processing on the processed data; merging the CFAR detection results of the high-speed channel data and the CFAR detection and amplitude normalization processing results of the low-speed channel data, and extracting a point track.

The invention discloses a color consistency control method in a bamboo kitchen ware carbonization process, and belongs to the technical field of bamboo processing. The method comprises the following steps: acquiring a multi-dimensional spectral data vector of the surface of the bamboo kitchen ware; determining a multi-dimensional change rate vector of the spectral data; respectively generating a first candidate control signal and a second candidate control signal based on the spectral data and the change rate vector; determining a system dynamic state index based on the change rate vector; generating a dynamic weighting coefficient according to the dynamic state index of the system; and performing weighted fusion on the first candidate control signal and the second candidate control signal by using a dynamic weighting coefficient to generate a final heat regulation control signal. According to the invention, the method achieves the self-adaptive optimal control of all working conditions through the construction of a dual-mode decision fusion composite control system, guarantees the quick response in a dynamic stage, guarantees the smooth convergence in a steady-state stage, and fundamentally improves the thermal inertia problem of the system.

The invention relates to a fractional order PID (Proportion Integration Differentiation) control method and system based on an FPGA (Field Programmable Gate Array). The method comprises the following steps of: 1, setting fractional order PID related parameters according to the requirements of a controlled object; 2, calculating a fractional order calculus weight coefficient according to the fractional order PID related parameters, and storing the fractional order calculus weight coefficient in the FPGA; 3, collecting a state feedback signal of the controlled object, and calculating an integral term operator and a differential term operator; step 4, calculating control output and performing amplitude limiting processing, and updating the state of the controlled object by using the control output after the amplitude limiting processing; and 5, repeating the steps 1-4 to realize fractional order PID closed-loop control. By optimizing a discrete convolution algorithm, adopting a parallel pipeline architecture and configurable parameter design and combining fixed-point number operation and security boundary constraint, the numerical stability and hardware security of the system under high-speed operation are ensured, the calculation speed is remarkably increased while the operation precision is ensured, and the method is suitable for large-scale popularization and application. And the method is suitable for control occasions with extremely high real-time requirements.

The invention discloses an intersection signal control method based on minimum blocking probability and highest traffic capacity, and relates to the technical field of intelligent traffic systems. A bilevel planning optimization model is constructed, an upper layer model takes minimization of intersection blocking probability as a core target, a blocking probability function is established through dynamic quantification of traffic flow random volatility, and a sequential quadratic programming algorithm is fused to optimize a signal period and effective green light time parameters of each phase; the lower layer model is based on a Webster delay model, and feeds back the traffic state in real time by taking the minimization of the total delay of the vehicle as a target. The upper layer model and the lower layer model realize collaborative optimization through a closed loop mechanism of parameter adjustment-delay feedback-iterative optimization, and meanwhile, a dynamic weight coefficient is introduced to flexibly coordinate multi-target conflicts of the blocking probability, delay and traffic capacity. According to the method, the dynamic response capability of signal timing to the real-time traffic demand is remarkably improved, the peak delay of the intersection is effectively reduced by 20%, the average peak delay is effectively reduced by 15%, and the road network toughness is enhanced.

The invention provides a regional electromagnetic intelligent evaluation method and system based on spectral entropy weighting, and the method comprises the following steps: collecting electromagnetic spectrum data and the motion state of a patrol vehicle, and constructing a virtual aperture data matrix through the motion information; space covariance analysis and subspace projection are carried out on the matrix, multipath scattering interference is separated and eliminated, and a pure field intensity sequence is obtained through reconstruction. By calculating the normalized spectrum entropy of the Doppler power spectrum of the pure field intensity sequence, an inverse proportion weight coefficient representing the signal confidence is generated; and weighting the weight to a pure field intensity sequence, and mapping the pure field intensity sequence to a geographic space. And dynamically adjusting the density of the evaluation grid according to the field intensity gradient, and finally carrying out weighted centroid calculation based on inverse proportion weight to obtain a regional electromagnetic environment evaluation result. According to the method, the technical problem that a regional evaluation result is not accurate due to the fact that multipath components cannot be separated and single-point data quality cannot be evaluated in traditional mobile patrol is solved.

The invention is suitable for the technical field of acoustics, array signal processing and sonar, and provides a broadband high-gain beam pattern optimization method suitable for any array. S1, calculating a weight vector matrix of each order of eigen beams; s2, solving a broadband weighting matrix through CVX; s3, synthesizing a broadband high-gain beam pattern; according to the method, the optimal beam pattern is decomposed into characteristic beams with different robustness and directivity based on generalized characteristic decomposition, under the constraint of expected beam pattern performance, the joint weighting coefficient of each order of characteristic beams is optimized, the broadband weighting matrix of the characteristic beams is calculated, and the final broadband high-gain beam pattern is synthesized according to the weighting coefficient. According to the method, the broadband high-gain beam pattern can be obtained under the constraint condition, meanwhile, the effects of constant beam width, robustness and high gain are achieved, compared with a method disclosed in the prior art, the directivity of a beam former is higher, and the method is suitable for any array.

The invention discloses a signal fusion processing method for a motor imagery training brain-computer interface, and particularly relates to the technical field of neural signal processing. Multi-channel scalp electroencephalogram data of a user are collected in real time, a time-varying space covariance matrix sequence is constructed through Riemannian geometric space mapping analysis, and a phase amplitude coupling index sequence between different brain rhythms is analyzed and calculated through cross-band coupling dynamics; performing feature hierarchy joint coding on the spatial covariance matrix sequence and the phase amplitude coupling index sequence to generate a real-time neural representation tensor; calculating the distribution divergence of the real-time neural representation tensor relative to the static reference feature manifold in the initial training stage so as to quantitatively represent the mismatch degree; and finally, dynamically adjusting a weighting coefficient and a fusion structure of the multi-modal signal fusion device according to the characterization mismatching degree, and outputting a motion control instruction matched with the current brain state of the user in real time. According to the method, the motion intention decoding precision and stability in the motion imagination training process are improved.