127 results about "Maximum eigenvalue" patented technology

The invention provides an automatic early warning method for sudden weather in a target area, which belongs to the technical field of weather early warning, and comprises the following steps of: establishing a primary dense matrix by adopting adaptive filtering processing and a frequency domain signal separation technology, and generating a secondary dense matrix by applying a marine meteorological recognition model of a spiral progressive network structure; a dynamic statistical equation is used to calculate the physical coupling relationship of each parameter to establish a multi-scale weather process balance matrix, a maximum flow and minimum cut algorithm is used to optimize a weather system coupling relationship network to calculate a coupling degree matrix, and a dynamic threshold adjustment mechanism is established according to coupling strength parameters to adjust the early warning detection frequency. And based on a comparison result of the coupling degree moment order maximum characteristic value and a preset risk threshold value, establishing a grading early warning system and outputting a corresponding early warning signal to control an offshore oil and gas platform emergency response system. The technical problem that a multi-time scale weather process coupling relationship cannot be effectively processed is solved.

The invention relates to the field of reactor data processing, and particularly discloses a numerical calculation method for solving a neutron transport equation. The method comprises the following steps: 1, describing the geometry of a reactor core of a nuclear reactor by utilizing a construction entity geometric modeling method, and subdividing a curved surface grid along a curved surface structure; 2, directly expanding a neutron flux distribution function in each curved surface grid by using a multivariate polynomial; 3, deriving a neutron transport equation and an integral residual equation of a boundary condition under each curved surface grid; 4, solving geometric integral parameters of each curved surface grid; step 5, establishing an adaptive linear system; and step 6, iteratively calculating and solving a maximum characteristic value and an unknown expansion coefficient.

The invention discloses a spaceborne synthetic aperture radar scattering wave mutual interference suppression method and system based on matched filtering, a storage medium and electronic equipment, and the method comprises the following steps: estimating related parameters of mutual interference according to the prior information of an orbit and a time-frequency relation, and carrying out the range pulse compression of an interference signal in echo data; detecting whether the range pulse contains interference or not by adopting a maximum characteristic value sequence detection method; calculating a relative signal to interference ratio containing interference pulses; reconstructing and extracting interference signals under the conditions of high signal to interference plus noise ratio and low signal to interference plus noise ratio respectively by using a feature subspace projection method and a robust principal component analysis method; and filtering the echo data, and carrying out mutual interference inverse distance compression processing on the data so as to recover normal echoes without interference. According to the invention, the compression gain of mutual interference can be effectively utilized, mutual interference signals in synthetic aperture radar echoes can be extracted more effectively, and high-precision interference suppression is completed while useful signals are better protected.

The invention provides a time-varying frequency spectrum map compression surveying and mapping method based on dictionary learning and smooth regularization, and the method comprises the steps: building a sparse base psi through the path loss between two points in a spatial frequency domain, carrying out the sparse representation of a signal in the spatial frequency domain, and building a compression sensing model of a three-dimensional space through combining with a measurement matrix; a measurement matrix in the compressed sensing model is optimized, singular value decomposition (SVD) is used for decomposing psi, sparse bases psi m corresponding to the first m maximum eigenvalues are selected, and an optimized measurement matrix is obtained after m times of iteration; the optimized measurement matrix is substituted into a compressed sensing model, an O-KSVD dictionary is adopted to learn and update a sparse base psi, and spectrum data are recovered; finally, sparse regularization and weighted smoothing regularization are combined to achieve the minimum Euclidean distance between adjacent points in the reconstructed spectrum map, smoothing regularization is used for driving the adjacent points in the reconstructed spectrum map to be similar, sparse representation in the spectrum map is updated through iteration till convergence, and actual spectrum mapping is obtained.

The invention belongs to the technical field of geographic information systems and PS-InSAR, and discloses a stress-strain-considered InSAR building deformation risk assessment method, which comprises the following steps: carrying out PS-InSAR processing on time sequence SAR image data to obtain a time sequence deformation result and carrying out precision verification; adopting a PS point correction method based on a self-adaptive building contour to screen out PS points of the building; selecting five indexes of building strain, building cumulative inclination amount, building cumulative settlement amount, building age and building structure material type; building risk assessment modeling is carried out by applying an analytic hierarchy process, consistency check and weight calculation are carried out, and the maximum characteristic value of a judgment matrix established by the five risk assessment indexes is calculated; grading the indexes of the model, performing risk assessment on each index, and drawing; and verifying a building risk assessment result in combination with actual dangerous house data. According to the invention, accurate evaluation of the deformation risk level of the building is realized.

The invention particularly relates to a method for realizing scenarized wireless network quality evaluation based on an analytic hierarchy process. According to the method for realizing the scene wireless network quality evaluation based on the analytic hierarchy process, a scene health degree evaluation index system is constructed, and a judgment matrix is constructed for each layer of indexes by performing assignment on the relative importance of each layer of indexes; calculating a maximum characteristic value lambda max of the judgment matrix and a corresponding characteristic vector thereof to obtain a weight vector W; after the judgment matrix passes the consistency verification, calculating the comprehensive weight of the final index; scoring each index according to the formulated rule, obtaining the final score of each index in combination with the index weight, adding to obtain the health degree coefficient of the whole scene, and further obtaining the health degree level of the whole scene. According to the method for realizing the scenarized wireless network quality evaluation based on the analytic hierarchy process, comprehensive monitoring on the wireless network and comprehensive evaluation on the scenarized wireless network are realized.

The invention relates to the technical field of weight assignment, in particular to a fixed-point industry development effect evaluation system-based weight assignment calculation method, which comprises hierarchical model establishment, index importance evaluation, hierarchical analysis, entropy analysis and index weight assignment. According to the weight assignment calculation method based on the fixed-point industrial development effect evaluation system, an original judgment matrix is constructed according to relative importance data of indexes of all layers through an analytic hierarchy process, and a maximum feature root and a maximum feature vector are calculated to serve as bases to perform consistency check on the judgment matrix; then, the relative importance data of the indexes are subjected to forward processing through an entropy evaluation method, so that the index proportion and the entropy and information entropy redundancy of each index are calculated, the index weights obtained through the analytic hierarchy process and the entropy evaluation method are subjected to weight combination, weight assignment of each index is obtained through weighted arithmetic average processing, and the weight assignment of each index is obtained; the subjective influence and the influence on weight fluctuation during data change can be effectively reduced.

The invention discloses an image classification method based on multi-manifold joint metric learning, and the method comprises the steps: 1) data preprocessing: constructing Grassmann features and SPD features; 2) constructing a double-flow kernel matrix, including constructing a Grassmann manifold kernel matrix and an SPD manifold kernel matrix, and respectively calculating a Grassmann test kernel matrix and an SPD test kernel matrix; 3) implementing multi-manifold joint metric learning, and extracting kernel feature vectors from the kernel matrix to calculate an intra-class scatter matrix and an inter-class scatter matrix; solving the projection matrix, sorting the feature vectors according to the sizes of the feature values, and selecting the feature vectors corresponding to the first plurality of maximum feature values to form the projection matrix; the method comprises the steps of (1) extracting different ground features, (2) carrying out feature fusion, completing extraction and implementing sample mapping, and (3) constructing a sample-level fusion convolutional neural network and completing classification of different ground features of an original image.The method belongs to the technical field of image processing and machine learning, and the classification accuracy can be remarkably improved.

The invention provides a production line parameter real-time scheduling method based on reinforcement learning, and belongs to the technical field of production lines, and the method comprises the steps: collecting sensor data to form an original state vector, reducing the dimension of the original state vector into a low-dimensional feature state vector through a state compression encoder, inputting the low-dimensional feature state vector corresponding to a microcosmic scheduling unit into a parameter decision model, and carrying out the real-time scheduling of the microcosmic scheduling unit; the attention weight coefficient of the model is determined by the product of the historical scheduling success rate, the element value of the difference degree matrix and the maximum characteristic value of the inter-stage sensitivity matrix, after a scheduling instruction is output, simulation evaluation is performed in a digital twin platform, and after the scheduling instruction passes, a real production line is issued for execution and multi-time-scale deviation indexes are collected; and calculating a comprehensive reward value according to the indexes, and storing an experience sample to an experience playback buffer area for model online update training, thereby solving the technical problem that production line parameter scheduling is difficult to consider multi-level state feature recognition and dynamic decision weight optimization at the same time.

The invention provides a quantization method and a quantization device, which can perform singular value decomposition on a first matrix formed by word embedding vectors to obtain the maximum characteristic value of each block matrix in M block matrixes, and can determine the quantity of quantization bits of each block matrix according to the maximum characteristic value of each block matrix. The method comprises the following steps of: dividing each block matrix into a plurality of block matrixes, quantifying each block matrix to obtain each quantized block matrix, combining each quantized block matrix to obtain a quantized first matrix, and representing the amount of semantic information carried in each block matrix by a maximum characteristic value of each block matrix to a certain extent, according to the embodiment of the invention, the quantized bit number can be allocated to each block matrix according to the amount of semantic information carried by each block matrix, so that different bit numbers can be allocated to the block matrixes carrying different semantic information, differential bit number allocation can be realized, semantic information loss can be reduced, and user experience can be improved.

The invention discloses a rapid explosion suppression and flame prevention method for a transformer, and belongs to the technical field of power equipment safety, and the method specifically comprises the steps: S1, collecting multi-physical field data in real time through a sensor network disposed in the transformer; s2, based on the multi-physical field data, determining an electrothermal instability index, a fractal dimension of fire nucleus growth, a maximum characteristic value of an irradiance Hessian matrix and a spectral coherence length attenuation rate; s3, combining the electrothermal instability index, the fractal dimension of fire core growth, the maximum characteristic value and the spectral coherence length attenuation rate to construct a characteristic vector; and S4, identifying the initial fault state based on the feature vector, correcting the initial fault state by using a preset physical model to obtain a final fault state, and triggering a grading response strategy according to the final fault state. And a solid data basis is provided for subsequent accurate diagnosis.

The invention provides a financial risk early warning analysis method and system, a storage medium and a device, relates to the field of enterprise risk management, and solves the technical problem that the existing financial risk early warning technology is not timely and inaccurate. The method comprises the following steps: acquiring financial data, operation data and external environment data of an enterprise to obtain a plurality of index data; classifying the plurality of index data into a plurality of risk domains, calculating the risk field intensity of each risk domain based on the index data in the risk domains, and forming a risk vector; obtaining the risk field intensity of each risk domain in a preset period of the enterprise, inputting the risk field intensity of each risk domain in the preset period into a first network based on a graph neural network, and outputting a cross-risk domain infection probability; constructing an adjacent matrix according to the cross-risk domain infection probability; and calculating the maximum eigenvalue of the adjacent matrix and the risk vector, and triggering graded early warning based on the maximum eigenvalue and the derivative of the maximum eigenvalue. The method and the device are used in the whole-process financial risk management process of enterprises.

The invention provides a muscle fiber disorder imaging method, a muscle fiber disorder detection method, a muscle fiber disorder detection system and a storage medium, and belongs to the technical field of biomedical detection software. The method is technically characterized by comprising the following steps: S100, acquiring a boundary voltage difference matrix of muscle to be detected; s200, carrying out normalization processing on the delta V; s300, the contour line of the muscle to be detected is extracted, the area in the contour line is an EIT simulation model area, and then a network node coordinate matrix of the muscle simulation model area to be detected is obtained; s400, normalizing the delta V and inputting the network node coordinate matrix into a pre-trained maximum eigenvalue matrix solving network to solve a maximum eigenvalue matrix delta lambda max; and S500, imaging is carried out. By means of the technical scheme, muscle changes can be visually displayed from the image in the mode of the three-dimensional image and the global muscle fiber disorder coefficient, and the muscle fiber disorder degree is quantified from the numerical value.

The invention discloses a minimum inverse eigenvalue radar signal detection method adopting stochastic resonance preprocessing, and the method comprises the steps: carrying out the secondary sampling of a received signal, compressing the frequency of the signal to match a stochastic resonance system, and expanding the adaptability of the stochastic resonance system to a higher-frequency signal. Then performing stochastic resonance preprocessing on the signals, enhancing signal energy under a low signal-to-noise ratio to obtain processed signal information, sending the processed signals to a CFAR signal detector, constructing a covariance matrix of the signals of the reference unit and the detection unit, performing eigenvalue decomposition on the covariance matrix to obtain a minimum inverse eigenvalue, and finally obtaining a minimum inverse eigenvalue; the minimum inverse feature value of the detection unit is compared with the average minimum inverse feature value of the reference unit to determine the existence problem of the signal. According to the method, the problems that a matrix constant false alarm detector based on the maximum characteristic value is easily influenced by noise uncertainty and a constant false alarm detector based on information geometry has relatively high calculation complexity are solved.

The present invention belongs to the technical field of new power systems, and specifically relates to a grid-type control transformation method, system, equipment and medium. In view of the deficiencies in the calculation of the transformation capacity by the existing grid-type control transformation method, the present invention adopts the following technical scheme: the grid-type control transformation method for realizing subsynchronous oscillation suppression includes: obtaining the critical short-circuit ratio of the following grid type and the critical short-circuit ratio of the grid type; calculating the initial generalized short-circuit ratio; calculating the influence factor of the initial generalized short-circuit ratio on the rated capacity of each converter and sorting it; selecting the station with the largest influence factor for equal-capacity grid-type transformation, calculating the generalized short-circuit ratio after the system transformation and deciding whether to add the transformation station; after determining the transformation position, the maximum eigenvalue of the extended admittance matrix is ​​less than the critical short-circuit ratio of the grid type as a constraint, and solving to obtain the minimum transformation capacity. The beneficial effects of the present invention are: based on the influence factors and each short-circuit ratio, the minimum transformation capacity is obtained, and the economic cost is minimized.

The invention discloses a neural network prediction-based LMS (Least Mean Square) power inversion adaptive step length adjustment method, which relates to the technical field of satellite navigation and signal processing, and comprises the following steps of: constructing and training a neural network model, inputting a current frame input signal power and a maximum characteristic value, and outputting a current frame prediction step length; compared with an LMS power inversion algorithm with a fixed step length, the method introduces a neural network to dynamically predict the optimal step length, solves the problem that traditional empirical step length adjustment is not adaptive, reflects interference intensity in combination with a covariance matrix and a maximum characteristic value of an input signal, realizes interference sensing adjustment, can increase the step length under the condition of relatively strong interference, and improves the accuracy of interference sensing adjustment. Convergence is accelerated and the tracking capability is enhanced; and under the condition of weak interference, the step length is reduced, so that steady state imbalance is reduced, and the anti-interference performance is improved. The method can solve the problem that in an existing LMS power inversion algorithm, step length parameters are difficult to adjust in a self-adaptive mode, and especially the problem that convergence is slow or system divergence is caused by improper step length setting in a multi-interference channel or dynamic environment.

The invention provides a wheat disease identification method and device, and is applied to the technical field of crop disease identification. The method comprises the steps of obtaining a training sample set; determining mass-to-charge ratio signal intensity data of volatile organic compounds of the wheat leaf samples in the training sample set, determining a maximum characteristic value of each mass-to-charge ratio channel in the mass-to-charge ratio signal intensity data, mapping the maximum characteristic values of all the mass-to-charge ratio channels into pixel points, and obtaining a two-dimensional pixel matrix of the wheat leaf samples; extracting parent ions and pixel points where fragment ions corresponding to the parent ions are located from the two-dimensional pixel matrix, and rearranging the positions of the extracted pixel points in the two-dimensional pixel matrix to obtain a recombined feature map; and inputting the recombinant feature map into a wheat disease recognition model for network training, wherein the wheat disease recognition model is used for recognizing the disease type and infection degree of wheat according to the mass-to-charge ratio signal features in the recombinant feature map.

The invention discloses a shared power bank battery safety early warning method and system based on big data, and relates to the technical field of battery safety management, and the method comprises the steps: constructing an information flow network, extracting the information propagation intensity of a directed edge, calculating the abnormal propagation rate of equipment, and generating a cumulative propagation effect in a time window. And calculating a fractional derivative of the cumulative propagation effect, constructing an adjacent matrix and calculating a maximum characteristic value, and calculating the spectral diffusion contribution and a comprehensive abnormal value of the equipment in combination with the cumulative propagation effect and the maximum characteristic value. According to the method, the dynamic strain field is combined with the flow field velocity analysis, the spatial distribution description capability of the internal structural risk of the battery is enhanced, the comprehensive coupling factor is combined with the information flow network propagation model, and the abnormal propagation mechanism modeling capability of the shared power bank in the large-scale mass use process is improved.

The invention relates to an information source number estimation method, in particular to an information source number estimation method based on orthogonal matching pursuit and signal subspace matching. The invention aims to solve the problems that the signal-to-noise ratio of array snapshot data in an underwater acoustic environment is generally not high, the array snapshot data is easily interfered by colored noise and coherent signal sources, and for a high-speed moving target, the number of effective snapshots is small, so that the accuracy of traditional signal source number estimation is low. According to the method, an orthogonal matching pursuit method is utilized to iteratively solve a support vector and a feature vector corresponding to a residual maximum feature value of the support vector to construct two subspaces, and signal source number estimation is realized through a signal subspace matching criterion. Simulation results show that compared with an existing signal source number estimation method, the method has the advantages that the requirement for the signal-to-noise ratio is lower, the performance is better under the condition of small snapshots, and meanwhile the method is not sensitive to coherent signal sources and colored noise. Lake test data processing results show that the method can effectively estimate the number of fixed and moving underwater acoustic targets.

The invention relates to the technical field of tunnel boring machine construction, in particular to a TBM tunneling suitability fuzzy comprehensive evaluation method in an exploration design stage, and the method comprises the steps: obtaining influence factors used for evaluating TBM tunneling suitability D; constructing an index system for evaluating the tunneling suitability of the TBM, and obtaining a judgment matrix of each parameter; calculating a feature vector corresponding to the maximum feature value of each parameter judgment matrix and the maximum feature value of the judgment matrix; the consistency proportion of the judgment matrix is calculated, consistency judgment is conducted, and the membership degree of each parameter and the sequence of the membership degree of each parameter are obtained; calculating to obtain the membership degree of the TBM tunneling suitability; and a suitable tunneling threshold value d is preset, D is compared with d, and evaluation on the tunneling suitability of the TBM in the target area is completed. The system adopts the TBM tunneling suitability evaluation method. The device can execute the TBM tunneling suitability evaluation method. By means of the method, the accuracy of the TBM tunneling evaluation result is improved, and the guiding effect on engineering construction is improved.

The invention relates to the technical field of energy storage system parallel control, and particularly discloses an optimization control method and system based on an energy storage inverter, and the method comprises the steps: building a phase cooperation matrix; constructing a weighted parallel network operator, analyzing the dynamic change of the maximum characteristic value based on a percolation phase change theory, and generating a percolation early warning signal in combination with an inversion probability coefficient; supercells are divided through a reformed group algorithm; solving a virtual impedance scale transformation coefficient fusing the maximum characteristic value of the super-cell and a power margin correction term, dynamically configuring the virtual impedance scale transformation coefficient, and injecting a chaotic disintegration signal to cut off long-range correlation; monitoring the percolation long-range correlation disintegration degree and the circulation entropy, and judging whether the system is stable or not; the system comprises a collaborative acquisition module, a phase change analysis module, an anchor point analysis module, a signal injection module and a stability analysis module. The method is beneficial for suppressing circulating current resonance and synchronous instability of multi-inverter parallel connection, and is suitable for a large-scale energy storage cluster grid-connected or off-grid operation scene.

This invention discloses a method, system, storage medium, and electronic device for suppressing mutual interference in spaceborne synthetic aperture radar (SAR) scattered waves based on matched filtering. The method includes the following steps: estimating relevant parameters of mutual interference based on prior orbit information and time-frequency relationships; performing range pulse compression on the interference signal in the echo data; detecting whether the range pulse contains interference using the maximum eigenvalue sequence detection method; calculating the relative signal-to-interference ratio (SIR) of the interference pulse; reconstructing and extracting the interference signal under high SIR and low SIR conditions using the eigenspace projection method and robust principal component analysis method, respectively; filtering it from the echo data; and then performing inverse range compression on the data to restore the interference-free normal echo. This invention effectively utilizes the compression gain of mutual interference, thereby more effectively extracting mutual interference signals from SAR echoes and achieving high-precision interference suppression while better protecting useful signals.

The invention relates to a full-blind multi-master-user spectrum sensing method based on extreme value characteristic values, which is characterized in that a sensing judgment quantity is constructed by using a difference value of the extreme value characteristic values and harmonic average: firstly, a multi-antenna sensing node calculates a sampling covariance matrix # imgabs0 # by using a received signal vector so as to calculate a maximum characteristic value and a minimum characteristic value of the sampling covariance matrix # imgabs0 #; thirdly, constructing a perception decision amount # imgabs1 # by using the ratio of the difference value of the extreme value characteristic values to the harmonic average value, and calculating a decision threshold t based on the analysis result of the extreme value characteristic values; and finally, carrying out perception judgment: when # imgabs2 is greater than t, judging that a multi-master user signal exists; otherwise, determining that the multi-master user signal does not exist. The method provided by the invention has reliable false alarm performance, and the detection probability higher than that of classical maximum and minimum characteristic value ratio and covariance absolute value detection can be obtained under the condition of low signal-to-noise ratio; and the method can realize total blind detection, the judgment threshold can be directly calculated based on closed expression, and the method has the advantages of simplicity and convenience in calculation and low complexity.

This invention provides a radar target detection method and apparatus based on dual-polarization maximum eigenvalues. The method includes: receiving first polarization echo data and second polarization echo data, and determining a detection unit and at least two reference units based on the first polarization echo data and the second polarization echo data; for any unit, determining a cross-covariance matrix based on the first polarization echo data and the second polarization echo data, and determining the maximum eigenvalue corresponding to the unit based on the cross-covariance matrix; determining an average maximum eigenvalue based on the maximum eigenvalues ​​corresponding to each reference unit; and determining a target detection result based on the maximum eigenvalue corresponding to the detection unit, the average maximum eigenvalue, and a preset threshold factor, wherein the target detection result includes whether the target exists or does not exist. This reduces the computational complexity of radar target detection, fully leverages the distinguishability of target echo signals and sea clutter, and improves the detection accuracy of radar target detection.

The invention discloses a multivariable intelligent monitoring method and system for aviation obstruction beacons. The method comprises the following steps: acquiring primary monitoring variables of a plurality of aviation obstruction beacons and calculating to obtain corresponding secondary monitoring variables; constructing a monitoring matrix by taking the same secondary monitoring variables of all the aviation obstruction lights as sub-columns, and calculating a covariance matrix and a maximum characteristic value of each sub-column; when all the maximum characteristic values are larger than or smaller than the characteristic threshold value, unified fault judgment is carried out on all the aviation obstruction lights based on whether any real-time lampshade temperature is normal or not, and if not, the next step is executed; calculating a fault coefficient of each data in the sub-column corresponding to the maximum characteristic value greater than the characteristic threshold value, and performing single fault judgment on each aviation obstruction beacon based on whether the corresponding real-time lampshade temperature is normal or not; and for the aviation obstruction beacon marked normally, the light brightness of the aviation obstruction beacon is regularly and adaptively adjusted based on the current time period and the real-time environment brightness. According to the invention, the fault identification efficiency and accuracy are improved through a multi-dimensional fault judgment mechanism.

The embodiment of the invention provides a shear wave velocity inversion method and device, electronic equipment and a storage medium, and relates to the technical field of seismic wave inversion. The method comprises the following steps: acquiring a plurality of frequency dispersion energy diagrams from different seismological observation systems in the same observation area, preprocessing the plurality of frequency dispersion energy diagrams, and constructing an image matrix; constructing a target kernel matrix by using the image matrix and a sub-sample set obtained from the image matrix; performing eigenvalue decomposition on the target kernel matrix, and performing image reconstruction according to the obtained maximum eigenvalue of the target kernel matrix to obtain a fusion frequency dispersion energy diagram with higher resolution and higher quality; and performing shear wave velocity inversion by using the fused frequency dispersion energy diagram to obtain a shear wave velocity profile of the observation area, thereby remarkably improving the accuracy and reliability of the shear wave velocity inversion.

The invention discloses a potential safety hazard investigation and judgment management system and method, and relates to the technical field of potential safety hazard investigation. The system comprises a hidden danger checking module, a hidden danger identification and classification module, a hidden danger grade determination module, a hidden danger regulation and acceptance module and a data statistics and ledger management module which are operated in sequence. According to the technical scheme, an AHP analytic hierarchy process is introduced, a hidden danger grade judgment value G is calculated, a default weight and a user-defined mode can be set, and construction of a judgment matrix is supported, so that the maximum characteristic value, the consistency index CI and the proportion CR are calculated, and only CRlt is calculated; and the judgment result is valid when the judgment result is 0.1, so that quick response in a conventional scene is ensured, and decision support is provided for major hidden dangers, thereby effectively determining the allocation priority of the renovation resources, ensuring that subsequent high-risk hidden dangers can be preferentially handled, and realizing accurate grading of the potential safety hazards.

The present invention provides an active estimation method and system for a broadband VHF array channel delay error, belonging to the technical field of direction finding. The method comprises: obtaining the direction vectors of each frequency point of an auxiliary signal source relative to each array element in a broadband VHF array and performing normalization processing; determining the eigenvector corresponding to the maximum eigenvalue of the covariance matrix of each frequency point of the auxiliary signal source and performing normalization processing; calculating the channel delay error corresponding to each frequency point in an observation frequency band based on the eigenvector and the corresponding direction vector corresponding to the maximum eigenvalue of the covariance matrix of each frequency point after normalization processing; and calculating the broadband VHF array channel delay error based on the channel delay error corresponding to each frequency point in the observation frequency band. The present invention can achieve efficient and accurate estimation of the broadband VHF array channel delay error, ensure broadband lightning VHF direction finding performance, and is suitable for correcting the broadband VHF array channel delay error.

The invention relates to the technical field of new energy grid-connected stability control, in particular to a small-signal steady-state discrimination method and system for a double-fed wind power plant controlled by a virtual synchronous machine and a medium, and the method comprises the steps: building a linear state space model for a double-fed fan controlled by a single virtual synchronous machine; listing a characteristic polynomial, and deducing a single-machine stability criterion inequality by using a Routh criterion; the method comprises the following steps: constructing a full-order state space matrix for a doubly-fed wind power plant comprising M units; decomposing the matrix into M independent subsystems, and performing eigenvalue decomposition on the current collection network impedance matrix to obtain a maximum eigenvalue; the M independent subsystems are equivalent to M-1 isolated subsystems without network interaction and an equivalent single-machine subsystem; and carrying out Routh criterion derivation on the equivalent single machine subsystem to obtain an explicit stability criterion inequality of the double-fed wind power plant grid-connected system and carrying out steady state discrimination. According to the invention, the problem that the stability boundary cannot be explicitly displayed under the complex power grid topology in the prior art is effectively solved.