1468 results about "Weighting coefficient" patented technology

The invention relates to the technical field of devices for adjusting, controlling or stabilizing power or frequency in a power grid, in particular to a rapid frequency modulation method for a wind turbine generator based on disturbance adaptive compensation, which comprises the following steps of: constructing a dynamic disturbance sensing data set by collecting frequency deviation, a frequency deviation change rate and fan state parameters; a window length and a weight coefficient of a short-time window sliding mean algorithm are dynamically optimized by adopting a genetic algorithm, background noise interference of a power grid is suppressed, and high-precision disturbance characteristics are extracted. And reducing frequency interference of inertia identification by using a reverse test signal. And updating boundary layer parameters of the sliding mode control model through an online incremental learning algorithm, dynamically adjusting the output priority based on the disturbance energy entropy, and eliminating power conflicts. A multi-island genetic algorithm is introduced to optimize a mode switching threshold value, and frequency modulation safe exit is realized in combination with adaptive power ramp rate limitation. According to the method, the frequency response speed and the multi-source cooperation efficiency in the dynamic multi-disturbance and inertia time-varying scene are remarkably improved, and the frequency secondary drop risk is reduced.

The invention relates to a remote sensing target detection method and device and a medium, and the method comprises the steps: inputting a feature map to a backbone network for feature extraction, inputting an extracted feature tensor into a multi-branch expansion convolution structure, and extracting multi-scale features through convolution kernels with different expansion rates. Then, multi-scale features are fused through a space and channel double-path attention mechanism, and enhanced features are generated; and the enhanced features are further input into a cascade pooling module to generate multi-level reconstruction features, and weight coefficients are calculated through a gating fusion network to carry out weighted fusion, so that multi-scale fusion features are obtained. Next, these features are input into an asymmetric decomposition convolutional layer for downsampling, and dynamic channel attention calibration is performed to generate channel enhanced features. And finally, inputting the feature map processed by the backbone network and the neck network into a detection head network, and outputting a target bounding box and category prediction. According to the method, high-precision detection of multi-scale rotating targets and high-density small targets is realized in a complex remote sensing scene.

The invention relates to the technical field of pressure sensing, and discloses a pressure mapping method and device for a high-precision pressure sensor array. The method comprises the steps that known standard pressure is applied to a high-precision pressure sensor array, and an original pressure distribution matrix of each sensing unit is collected; executing bicubic spline interpolation operation of tension coefficient compensation to obtain a continuous pressure distribution matrix; inputting a depth separable convolutional network to perform spatial feature extraction and channel feature fusion to obtain a pressure feature description matrix; carrying out confidence coefficient propagation iterative calculation to obtain a pressure confidence coefficient matrix; carrying out reliability grouping and weight coefficient calculation on the sensing units to obtain a pressure data fusion weight matrix; according to the method, the pressure mapping precision is improved, relatively high real-time performance is ensured, and the problems of data distortion, low calculation efficiency and insufficient reliability in a traditional method are solved.

The invention is suitable for the technical field of electrical equipment state monitoring, and provides an electrical equipment state monitoring method based on a non-contact leakage current sensor, and the method comprises the steps: collecting a leakage current signal of the surface of an insulating part of electrical equipment, and obtaining infrared thermal image data and an ultrasonic signal; variational mode decomposition is carried out on the leakage current signal to obtain a plurality of intrinsic mode functions; separating a leakage current effective component and an independent noise source based on a blind source separation algorithm; extracting a time-frequency characteristic of the effective component of the separated leakage current, extracting a local temperature gradient characteristic of the infrared thermal image data and a frequency spectrum energy characteristic of the ultrasonic signal, and generating a multi-modal characteristic vector; analyzing the space-time relevance of the multi-modal feature vector, dynamically distributing each modal weight coefficient for fusion, and generating a comprehensive fault feature; according to the method, the signal-to-noise ratio of the weak leakage current signal is improved, and the misjudgment rate is effectively reduced.

The invention relates to the technical field of power system stability control, in particular to a broadband oscillation suppression method and system for an offshore wind power flexible direct current grid-connected system and a medium, and the method comprises the steps: sequentially injecting positive and negative sequence perturbation sine harmonic signals at a grid-connected point of a wind turbine generator, constructing a multi-input multi-output impedance matrix, reducing the order of the multi-input and multi-output impedance matrix into single-input and single-output equivalent positive sequence impedance; the stability margin of the offshore wind power flexible direct current grid-connected system is obtained based on equivalent positive sequence impedance quantification; when the stability margin is lower than a preset margin threshold value, reconstructing the fan grid-side converter into a matrix control structure by cascading a weighting coefficient matrix between the inner and outer ring control loops of the fan grid-side converter; and iteratively optimizing the matrix type control structure based on impedance sensitivity, and synchronously connecting a low-pass filter in series in a voltage and current feedback channel to carry out impedance remodeling so as to finish broadband oscillation risk suppression. According to the invention, broadband oscillation suppression of the offshore wind power flexible direct current grid-connected system is realized through impedance modeling and a matrix type control strategy.

The invention discloses a coal and rock mass fracture instability multivariate signal fusion analysis method, and belongs to coal and rock mass analysis. The method comprises the following steps: carrying out time-frequency domain processing on an acoustic emission signal to obtain an accumulated ringing characteristic value # imgabs0 # and an accumulated b characteristic value # imgabs1 #; a main control frequency characteristic value # imgabs2 # and an amplitude characteristic value # imgabs3 # are accumulated; a spatial characteristic value # imgabs4 # of acoustic emission; fitting a fitting function # imgabs7 # from the characteristic value # imgabs5 # to the characteristic value # imgabs6 # respectively; according to the fitting function # imgabs8 #, calculating a correlation coefficient matrix R between the characteristic values # imgabs9 # to # imgabs10 #; according to the matrix R, determining a weight coefficient # imgabs11 # of each characteristic value; a first-order derivative function # imgabs13 # and a second-order derivative function # imgabs14 # of the fitting function # imgabs12 # are obtained, and according to the first-order derivative function # imgabs15 # and the second-order derivative function # imgabs16 #, a demarcation time point # imgabs17 # in the coal and rock mass damage process is determined; and the boundary time point # imgabs19 # is corrected by using the weight coefficient # imgabs18 #, and a corrected boundary time point # imgabs20 # is obtained. In order to solve the problems that an existing coal and rock mass fracture instability judgment index is single, and coal and rock mass fracture instability early warning is low in single index precision, the early warning precision is improved through multi-element signal fusion.

The invention discloses a transformer partial discharge detection method and system based on multi-modal feature combination, and relates to the technical field of transformer detection, and the key points of the technical scheme are that the method comprises the steps: collecting an electromagnetic wave signal, a pulse current signal and a sound wave signal of a target transformer; respectively extracting an electromagnetic wave time-frequency matrix, a pulse current waveform parameter and a sound wave space coordinate; performing multi-modal feature fusion on the electromagnetic wave time-frequency matrix, the pulse current waveform parameters and the sound wave space coordinates based on a dynamic weighting strategy to generate a joint feature tensor; and inputting the joint feature tensor into a pre-constructed neural network model, identifying a discharge type and optimizing a positioning coordinate. According to the method, multi-modal features are subjected to tensor fusion, cross-modal correlation learning is realized through the neural network model, information islands can be eliminated, and the misjudgment rate is effectively reduced; and electromagnetic wave-current-sound wave signal adaptive fusion is realized through a dynamic weight coefficient, and the signal-to-noise ratio is obviously improved through characteristic complementation in an oil paper insulation discharge scene.

The invention relates to the technical field of organic display data processing, in particular to an organic display interface multichannel signal modulation method, which comprises the following steps of: generating a standardized data set through gamma correction and gamut mapping, and inputting the standardized data set into time sequence, amplitude and frequency sub-models deployed in a federal learning framework; and respectively analyzing the pulse width-refresh rate relevance, the driving voltage-brightness nonlinear relationship and the conduction period-mobility dynamic response, generating an optimized weight coefficient, and fusing the optimized weight coefficient into a boundary constraint condition of a cross-sub-model. A migration rate parameter index, amplitude gradient calculation and duty ratio correction service of chain calling is constructed based on a service grid, a reinforcement learning dynamic routing strategy is combined to avoid resource competition nodes, and brightness error characteristics are periodically fed back to reversely optimize gradient updating. According to the method, the conflict between time sequence deviation accumulation and local optimum is effectively suppressed, the color gradation continuity, the dynamic range and the edge sharpness are improved, and efficient cooperative modulation of multi-channel signals is realized.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice feature processing method, device, equipment and medium. Performing time resolution analysis based on the fused Mel band energy to generate a multi-scale Mel spectrum amplitude value, and performing nonlinear transformation on the multi-scale Mel spectrum amplitude value according to the noise intensity parameter to generate a noise suppression Mel component; and generating a perception weighting coefficient according to an auditory perception model, and executing frequency domain energy adjustment on the noise suppression Mel component to generate Mel spectrum representation. On the basis of frequency resolution self-adaption, time resolution dynamic adjustment and auditory perception modeling, nonlinear transformation and perception weighting processing are applied to the multi-scale Mel spectrum amplitude value, the influence of noise interference on voice features can be effectively reduced, and the key information retention capacity of voice signals is enhanced.

The invention relates to the technical field of communication, in particular to an airborne Tiantong phased-array antenna and an application method thereof. According to the method, phased-array antenna beams are driven to perform electronic scanning according to a geometric azimuth angle, beacon signal blind acquisition is performed in a preset airspace in a wide beam mode, and when an initial signal is detected, the mode is immediately switched to a narrow beam scanning mode; dynamically adjusting a phase weighting coefficient to realize pointing adjustment, so that a signal quality parameter is continuously converged in an optimal threshold interval; carrying aircraft maneuvering acceleration feedback is introduced, beam pointing offset is dynamically compensated based on a pre-constructed interference feature library, and signal fluctuation caused by a multipath effect is suppressed; and continuously checking a data validity flag bit of the main inertial navigation system, seamlessly switching to an antenna built-in inertial navigation data stream when a failure threshold is triggered, and activating a rapid recapture program to maintain zero interruption of a communication link. Through dual-source inertial navigation data fusion and intelligent beam control, rapid and accurate tracking of Tiantong satellites in an airborne environment is realized.

The invention discloses a rotary machine fault diagnosis method based on residual contraction convolution and an attention mechanism, and belongs to the field of rotary machine fault diagnosis. According to the method, firstly, a soft threshold filtering activation function is designed based on Meta-ACON, a soft threshold filtering module is developed around the activation function, and the module can adaptively learn a filtering threshold and a forward dynamic weighting coefficient, so that irrelevant features are effectively filtered, and the expression ability of effective features is enhanced. And then, in combination with multi-channel and multi-scale residual convolution, a multi-channel residual shrinkage convolution unit is constructed to realize multi-scale and multi-channel effective feature extraction. Besides, according to the method, a high-order statistical modeling idea is utilized, a GSoP attention mechanism is introduced behind a multi-channel residual shrinkage convolution unit, and the ability of extracting discriminative features is enhanced through second-order statistical information of a high-level channel feature map. Test results show that the method has good fault identification capability and generalization capability.

The invention relates to the technical field of data processing, in particular to a backtracking analysis model construction method based on an attack chain, which comprises the following steps that: a kernel layer security agent acquires process, file and network behavior characteristics in a hardware isolation environment, and generates an event tuple; the tensor network pipeline performs three-dimensional decoupling mapping on the tuple into a behavior fingerprint vector, an orthogonalization noise feature and an asymmetric adjacent tensor, and compresses the behavior fingerprint vector, the orthogonalization noise feature and the asymmetric adjacent tensor into a space-time topology tensor block; the reinforcement learning controller constructs a directed acyclic graph based on the tensor blocks, calculates connectivity loss and outputs an event risk score; the dynamic routing engine constructs a decision tree model according to the risk mark, the burst frequency and the correlation entropy, and implements three-level shunting and a multiple simulation system to generate an anti-interference index; and when the deviation between the physical trajectory and the digital model exceeds the tolerance, the closed-loop feedback weight coefficient updates the loss function parameter and adjusts the channel resource weight. And the problem of threat discovery delay caused by attack chain breakage under massive events is solved.

The invention discloses a multi-mode perception and reinforcement learning engineering machinery intelligent regulation and control method and system, and belongs to the technical field of intelligent control and industrial automation. According to the system, stratum data within the range of 20-50 meters in front of a shield tunneling machine are collected in real time through multi-mode sensing equipment such as a distributed optical fiber sensor, a cutterhead vibration sensor and an electromagnetic wave radar, and original data are processed by adopting a wavelet-Fourier combined noise reduction algorithm; and inputting the processed multi-modal data into a reinforcement learning intelligent decision-making model based on a CNN-LSTM hybrid network architecture. The model is trained through a dynamic reward function, and weight coefficient combinations can be automatically switched according to different construction scenes. According to the system, parameters such as the rotating speed, the thrust and the grouting amount of a shield cutter head are regulated and controlled in real time through the self-adaptive control module, and the slurry utilization rate is increased through the gradient pulse grouting technology. The precision, efficiency and safety of shield construction are remarkably improved, and meanwhile energy consumption and construction cost are reduced.

The invention relates to the field of data signal processing, in particular to a network algorithm cable terminal discharge early warning method, device and system based on dual-channel waveform identification. The method comprises the following steps of: firstly, acquiring a periodic ultrasonic signal and an ultrahigh frequency electromagnetic field signal generated by discharging of a cable terminal, converting the two signals into a corresponding time-frequency diagram by utilizing short-time Fourier transform, and performing normalization processing to serve as input of a subsequent network; a dual-channel high-order feature extraction network formed by cascading a ConvNeXt backbone network and a convolution block attention module is constructed, and is used for extracting time-frequency diagram detail fault features of ultrasonic signals and high-frequency electromagnetic field signals. And finally, by constructing an adaptive feature fusion network, fusing the fault features of the two channels, and obtaining cross-channel fault joint features by using an adaptive weight coefficient vector, thereby outputting a diagnosis result, and realizing accurate evaluation of a defect state of an adjacent cable terminal.

The invention relates to integrated airborne communication antenna equipment and a communication method. The method comprises the following steps: carrying out CSI data acquisition and space-time focus modulation on a communication channel to obtain CSI space-time characteristic data; performing residual vector quantization operation to generate a quantized data stream, and performing recursive coding processing to obtain a target data stream; mapping the target data stream to a three-dimensional tensor space, constructing a real value tensor model, and performing iterative operation to obtain a CSI feature vector; performing fractional order wavelet transform decomposition on the CSI feature vector to obtain a radio frequency feature set, and inputting the radio frequency feature set into a federal fractional scattering network for feature processing to obtain an identity authentication result; and calculating an amplitude-phase weight coefficient of the antenna array, generating a beam parameter, and performing compensation matrix operation on the beam parameter according to the CSI feature vector to obtain a pre-coded signal. According to the invention, the dynamic adjustment of the antenna beam parameters is realized, and the accuracy and anti-interference capability of beam forming are improved.

The invention discloses a wireless network high-reliability transmission method based on double-link redundancy, which relates to the technical field of wireless communication, and comprises the following steps: establishing double communication links, and collecting link quality data of the double communication links; performing weighting operation on the link quality data according to a preset weighting coefficient to obtain a first link score and a second link score, and judging the first link score and the second link score; issuing an original data packet and a backup data packet of the original data packet to the dual communication link according to a judgment result, and collecting a data transmission state in a receiving time window; and when it is monitored that the original data packet is lost in the receiving time window, extracting a backup data packet of the original data packet for data recovery, and recalculating the link score of the original data packet. Through organic combination of multi-dimensional link quality evaluation, link selection of weighted calculation, double-link parallel transmission and rapid data recovery, the network resource utilization efficiency is improved while the data transmission reliability is guaranteed.

The invention discloses an accident early warning analysis method and system based on driving data, and relates to the technical field of accident early warning analysis, by extracting a road condition complexity coefficient Cen, a sight distance chaos coefficient Cvs and a dynamic interference coefficient Cdn, the system not only considers the current state, but also analyzes the dynamic interference characteristics of the current state when modeling the external environment, so that the accident early warning analysis accuracy is improved. And the system still has high reliability in the scenes of rain and fog, night, congestion and the like. The comprehensive score value Srisk is not single binary judgment any more, multi-factor fusion interpretation is carried out in combination with information such as operation disturbance, posture deviation and fatigue indexes, a response result WIN with grading characteristics is output, and multi-level early warning from voice reminding to active intervention can be achieved. And the dynamic feedback optimization module constructs a risk response parameter psi stab according to a score fluctuation trend, dynamically adjusts a weight coefficient omega j of the multi-modal model, generates a new coefficient n omega, and realizes self-parameter adjustment and adaptive reinforcement learning.

The invention relates to the technical field of anti-interference, in particular to a communication signal interference elimination method and system, and the method comprises the following steps: obtaining communication signal time domain data, calculating a signal energy change rate, a signal abrupt change rate and a phase drift rate, marking and distinguishing a signal region according to a calculation result, and obtaining a communication signal non-stationary interval identification result. According to the method, the time domain data of the communication signal is subjected to fine-grained calculation, and the signal energy change rate, the signal abrupt change rate and the phase drift rate are extracted, so that accurate signal region distinguishing is realized, the regression modeling parameter range is adjusted, the influence of instantaneous data of an abrupt change region is reduced, and the stability of signal reconstruction is improved; according to the method, signal-to-noise ratio calculation and interference power fluctuation amplitude evaluation are combined, channel state parameters are dynamically adjusted, interference intensity evaluation is more accurate, the filtering step length, the weight coefficient and the gain coefficient are optimized based on error information, signal processing adapts to different types of interference, and integrity and stability of communication signals are enhanced.

The invention relates to the technical field of communication, and discloses a communication data transmission and temporary storage method and device and a storage medium, and the method comprises the following steps: S1, receiving and partitioning initial data, and storing the initial data in a cache region; s2, calculating weighting coefficients of the data blocks and performing priority ranking; s3, encrypting the data blocks according to the sequence; s4, transmitting the encrypted data to a target end through a security protocol; s5, the target end decrypts the data and verifies the integrity; s6, temporarily storing the verified data blocks in a temporary storage area until all the data blocks are transmitted; and S7, after the transmission is completed, recombining and storing the data block in the target equipment. According to the method, the data is subjected to blocking, priority ranking, encryption transmission and decryption recombination, so that the security, integrity and transmission efficiency of the data in the network transmission process are ensured, and the method is suitable for a communication system needing high security and high data integrity.

The invention belongs to the field of micro-grids, and particularly relates to a micro-grid hybrid energy storage scheduling method, which comprises the following steps of: acquiring a historical load curve, renewable energy source prediction data and energy storage unit parameters, and determining a charging and discharging strategy of an energy storage unit by utilizing the historical load curve and the renewable energy source prediction data; an SOC safety interval is initially set, a multi-target weight coefficient is continuously optimized, and a filtering frequency band threshold value is obtained; mPC scheduling optimization is periodically carried out, a power instruction is issued in real time, and closed-loop control of an SOC safety interval, a multi-target weight coefficient and a filtering frequency band threshold value is formed; on the premise of hybrid energy storage of the micro-grid, the service life of an energy storage unit is prolonged and the optimal charging and discharging frequency is kept in the scheduling process, an optimal power instruction sequence is continuously output in a rolling optimization mode to assist scheduling, and scheduling control is carried out on sub-devices of the energy storage unit under assistance of SOH estimation. The problem that a single energy storage sub-device is overused is effectively solved.

An adaptive variable step-size LMS filter based on a hyperbolic tangent function and its filtering method and computer equipment belong to the field of filter technology, and solve the problem that the LMS algorithm improved based on a nonlinear function requires too many manual adjustments of parameters and that the convergence speed and convergence accuracy of the traditional LMS algorithm cannot be satisfied simultaneously. The method of the present invention includes: determining the filter tap length and the number of iterations; initializing the filter parameters; obtaining the filter input signal vector; determining the instantaneous expected signal of the filter; obtaining the instantaneous output signal; obtaining the instantaneous error signal of the filter and the accumulated amount of the error signal based on the instantaneous output signal; obtaining the instantaneous step size using a step-size update formula based on a hyperbolic tangent function; and continuously iterating the filter tap weight coefficient vector to construct an LMS filter using the filter tap weight coefficient vector obtained after the number of iterations. The present invention is suitable for improving the performance of the LMS filter, and the filter of the present invention can be widely used in various signal processing fields.

The invention provides an electric energy quality device power smooth flexible capacity expansion method for a new energy access scene. The method comprises the following steps: acquiring three-phase voltage, current and power signals, obtaining standardized three-dimensional tensor data through multi-dimensional tensor spectrum projection decomposition, calculating a characteristic value and a characteristic tensor base, and generating a dynamic characteristic vector and a weight coefficient set; a multi-layer probability graph matrix is constructed, a feature mapping matrix and a hierarchical contribution coefficient are obtained through iterative calculation, a fluctuation feature tensor is constructed, and a multi-time scale prediction model is established; constructing a control manifold space and a measurement tensor, calculating a topological connection matrix, designing a multi-dimensional control law and performing stability optimization; and constructing an embedded feature matrix and an inter-module distance matrix, calculating a capacity expansion weight and a power distribution scheme, and performing spectral domain equalization control and grid-connected coordination control. According to the invention, the power smoothing effect is obviously improved, and the system capacity expansion flexibility is enhanced.

The invention relates to a spaceborne laser radar wind field data correction method based on a ground-based laser wind finding radar, and belongs to the field of radio, and the method comprises the steps: calculating horizontal wind speed components U1 and V1, and horizontal wind speed components U2 and V2 of different height layers according to collected data; determining an optimal nonlinear regression correction model; generating U and V wind fields with high spatial resolution; constructing a power spectrum correction model, and generating power spectrum signals of the corrected high-spatial-resolution grids U and V; generating a satellite-borne laser wind finding radar antenna direction weighting function, and obtaining a weight coefficient corresponding to each grid point in the high-spatial-resolution grid; and constructing a satellite-borne laser wind-finding radar wind field data correction model, and finally obtaining corrected satellite-borne laser wind-finding radar wind field data. According to the invention, accurate simulation of the power spectrums of the U and V components of the satellite-borne laser wind-finding radar with low spatial resolution is realized, and accurate and efficient correction of the U and V components of the satellite-borne laser wind-finding radar is realized.

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 provides a live broadcast room recommendation method and system, and the method comprises the steps: collecting the historical browsing data of a user, carrying out the analysis of the historical browsing data, and determining the multi-dimensional key interest features of the user and the weight coefficient of the key interest feature of each dimension; extracting live broadcast labels of the live broadcast rooms, and traversing the live broadcast labels based on the multi-dimensional key interest features and the corresponding weight coefficients to obtain preference popularity values of the live broadcast rooms; and obtaining a to-be-recommended live broadcast room based on a sorting result of the preference popularity values, recommending the to-be-recommended live broadcast room to the user, dynamically updating browsing data of the user, and synchronously and dynamically updating the to-be-recommended live broadcast room based on a dynamic updating result. According to the invention, highly personalized live broadcast room recommendation can be realized, diversified and unique interest requirements of users are met, the probability of finding interested live broadcast rooms by the users is improved, the experience and satisfaction of the users on a live broadcast platform are effectively improved, and the viscidity and activeness of the users to the platform are enhanced.

The invention discloses an audio signal processing method and device based on MOE, a medium and a product, and relates to the field of audio signal processing. According to the technical scheme, firstly, the audio signal processing device decomposes the audio signal into the sub-band signals in different frequency intervals, and the complexity parameter of each sub-band signal is calculated; on the basis of the sub-band signals and complexity parameters corresponding to the sub-band signals, the gating network in the hybrid expert model can intelligently distribute the optimal expert network combination for each sub-band signal, the speciality of each expert network is fully utilized, the processing efficiency is improved, the optimal expert network combination is screened by adopting a processing weight coefficient threshold, and the processing efficiency is improved. And redundant processing is avoided. And finally, the audio signal processing device obtains a structured audio data stream through quantization coding and data packaging. According to the dividing and treating method, the quality and efficiency of audio signal processing are improved, the processing strategy can be flexibly adjusted according to the signal characteristics, and the adaptability is high.

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 discloses a flexible control method for new energy grid connection, and relates to the technical field of new energy grid connection control. The method comprises the steps of obtaining grid-connected point electrical parameters through a self-measurement mode or a communication interface mode, performing harmonic feature extraction and compensation based on a deep learning network, and adopting a state estimation method to fuse multi-source data to estimate power grid parameters; and constructing a multi-objective optimization function including voltage quality, a power factor and harmonic suppression, dynamically adjusting a weight coefficient of the optimization function by using a swarm intelligence algorithm, and realizing inverter switching sequence optimization through multi-objective model prediction control. Through a dual-mode communication redundancy mechanism and local intelligent control, while a communication function of a traditional main link is reserved, a standby wireless communication link is introduced to realize real-time backup transmission of a control instruction, and through a local autonomous decision-making capability, the problem of control failure caused by a communication problem in a traditional scheme is fundamentally solved.

The invention relates to the technical field of numerical control machine tool precision detection and compensation, in particular to a coordinated motion planning control method and related products in the five-axis machining process, and the method comprises the steps: building a machine tool digital twin model, and combining constraint conditions; calculating a dynamic load rate and adjusting an error weight according to the load rate; an optimal cooperative motion instruction is obtained through multi-objective optimization; obtaining a feed-forward compensation amount and a feedback compensation amount; fusing the feedforward compensation amount and the feedback compensation amount to obtain a total compensation amount, and correcting the optimal cooperative motion instruction in real time to generate a final control signal; according to the method, dynamic constraints of different dimensions of a shaft are unified, the real-time dynamic load rate of the shaft is calculated, and weight coefficients of a translation shaft error and a rotation shaft error in an objective function are dynamically adjusted and optimized according to the difference; and constructing a multi-objective optimization function, solving an optimal cooperative motion instruction according to the dynamic weight, and integrating feed-forward compensation and feedback compensation to generate a final control signal so as to realize accurate control.

The invention discloses a centrifugal pump cavitation state recognition method based on multi-source sensor signals, which comprises the following steps: collecting the multi-source sensor signals of a centrifugal pump at different water temperatures and power frequencies to obtain multi-modal data with cavitation state labels; resampling the multi-modal signal data by using a linear interpolation method, and synchronizing and aligning the signals in a time domain; performing time-frequency analysis on multi-sensor signals by using short-time Fourier transform, performing dimensionality reduction on features through principal component analysis, and extracting one-dimensional features and two-dimensional features; a one-dimensional convolutional neural network and a two-dimensional convolutional neural network are used to carry out deep learning on signal features of different modes, and feature layer fusion is carried out on multi-dimensional features through a feature fusion strategy; classifying the fused feature vectors by using time sequence characteristics in a mode of gating a circulation unit; and adopting a weighted fusion strategy, setting a weight coefficient of each signal source according to the classification accuracy of each signal source in fault diagnosis, and inputting weighted data into a classification identification model support vector machine to obtain a final cavitation state identification result. According to the method, through the synergistic effect of the signals of the multi-source sensor, the recognition precision and robustness of the cavitation state are comprehensively improved, and the method has important economical efficiency and technical popularization value.