28 results about "Spectral matching" patented technology

The invention relates to the technical field of solid waste treatment, in particular to an intelligent waste sorting method based on multi-sensor fusion. Carrying out multi-level fusion decision making: carrying out sewage reflection suppression on the visible light image; carrying out matching identification and rejecting a liquid pollution area based on a near infrared spectrum; fusing the visible light texture, the spectral feature and the electromagnetic signal to generate a space grid feature vector; identifying the material category through a cascade classifier, and dynamically adjusting the weight of each sensor in the decision according to the environmental parameters; self-adaptive sorting execution: triggering a corresponding sorting execution mechanism according to an identification result; based on the transmission belt speed, the execution mechanism response delay and the material physical attribute, the action advance compensation amount is calculated; the action time of an execution mechanism is adaptively prolonged for the stacked materials according to the stacking hierarchy; and online feedback optimization. Through dynamic compensation and time sequence adjustment, the time sequence consistency of multi-sensor data is ensured, and the influence of band speed fluctuation is overcome.

The invention provides a small sample channel state information data expansion method based on a generative adversarial network, and the method comprises the steps: generating a vivid time-frequency domain channel sample through a Wasserstein generative adversarial network based on physical constraints under the condition of rare or missing drive test data; and a time-frequency joint discriminator is combined to effectively distinguish real and forged signals. Statistical authenticity of a generated sample is improved through joint time-frequency domain feature discrimination, a physical constraint module is embedded in a generator, and channel physical characteristics are maintained through forced power normalization and a spectrum matching mechanism. A GAN framework with gradient penalty is adopted in the whole training process, and the training stability is ensured. And data sample generation when the nodes are quickly deployed to a new environment is facilitated.

The invention provides a millimeter wave radar miscellaneous point removing method and device based on frequency spectrum matching, a medium and a program product, and the method comprises the steps: obtaining a multi-channel echo signal of a millimeter wave radar array, and constructing an actual measurement angle spectrum reflecting the energy distribution in each direction through beam forming and angle traversal processing; carrying out peak value search on the actually measured angle spectrum, and determining an effective target angle corresponding to each power peak; according to geometric structure parameters and effective target angles of the millimeter-wave radar array, performing weighted stacking on each target response based on a theoretical echo signal model to obtain a theoretical angle spectrum; performing spectrum matching analysis on the theoretical angle spectrum and the actually measured angle spectrum, and calculating cross correlation coefficient distribution of the theoretical angle spectrum and the actually measured angle spectrum under different angle deviations; and when the cross-correlation obtains a maximum value at the zero offset and is greater than a threshold value, judging that the corresponding target is an effective target, otherwise, regarding the target as a miscellaneous point and removing the miscellaneous point. According to the method and the device, accurate identification of a real target is realized by utilizing the change characteristics of the cross correlation coefficient under different angle deviations.

This invention discloses a robust method, device, and storage medium for laser spot recognition and tracking in complex lighting environments, belonging to the field of computer vision and target detection. Addressing the problems of high false alarm rates and poor robustness in existing methods under interference such as strong ambient light, dynamic projector flicker, and specular reflection, this invention proposes a multi-layered processing framework of "detection-verification-tracking." First, adaptive threshold segmentation is performed on the current frame image to extract candidate regions with brightness higher than the background. Second, multi-dimensional feature verification is introduced. Based on the inherent physical properties of the laser spot (such as spectral matching degree, shape and size, and two-dimensional Gaussian intensity distribution) and temporal motion patterns, candidate regions are jointly verified and filtered to effectively remove various homogeneous and heterogeneous interferences. Finally, through a multi-target trajectory management module, the filtered candidate spots are correlated with historical trajectories (e.g., using the Hungarian algorithm), and combined with Kalman filter prediction and state machine-based trajectory lifecycle management (temporary, confirmed, lost, terminated), stable tracking and output of the laser spot are achieved. This invention does not rely on a large amount of training data, has high computational efficiency, and can significantly improve the accuracy and robustness of laser spot recognition under complex dynamic lighting conditions, providing a reliable front-end perception guarantee for vision-based high-precision laser spatial positioning and interaction systems.

The application provides a millimeter wave radar clutter point removal method and device based on spectrum matching, a medium and a program product. The method comprises the following steps: acquiring a multi-channel echo signal, constructing a measured angle spectrum reflecting the energy distribution of each direction through beam forming and angle traversal processing; determining each effective target angle of the measured angle spectrum; based on the geometric structure parameters of the millimeter wave radar array and the effective target angle, weighting and superimposing each target response based on a theoretical echo signal model to obtain a theoretical angle spectrum; performing spectrum matching analysis on the theoretical angle spectrum and the measured angle spectrum, performing point-by-point translation processing on the theoretical angle spectrum along the angle axis, and calculating the cross-correlation coefficient distribution of the two at different angle offsets; when the cross-correlation reaches a maximum value at zero angle offset and is greater than a threshold value, it is determined that the corresponding measured angle spectrum is effective, otherwise it is regarded as a clutter point and removed. The application realizes accurate identification of real targets by using the change characteristics of the cross-correlation coefficient at different angle offsets.

The invention provides an earth surface deformation measurement method and system based on different-satellite different-imaging-mode SAR data. The earth surface deformation measurement method comprises the steps of obtaining different-satellite different-imaging-mode SAR image data; registering the SAR image data to obtain a main and auxiliary image slope distance difference, a resampled main image and a registered auxiliary image; obtaining a main image and an auxiliary image after down-sampling; and performing interference processing to obtain and output earth surface deformation information. According to the method, an end-to-end processing framework of different-satellite different-imaging-mode SAR data is constructed, the risk of spatial baseline decoherence of a different-satellite different-imaging-mode SAR system is effectively reduced, and space-time correlation of different-satellite data is ensured; through full-link innovation of track design, joint registration, spectrum matching and terrain residual suppression, the bottleneck of cross-platform data interference incoherence is broken through, the problem of registration misalignment caused by different imaging mode data spectrum offset and resolution difference is solved, and a systematic solution is provided for high-frequency and high-precision surface deformation monitoring. Through a terrain phase separation technology, a terrain residual error is effectively inhibited.

The invention describes an unattended surveillance node and a method for identifying signal-emitting intruders across multiple physical domains. The system uses a hierarchical triggering architecture where mechanical disturbance sensors (acoustic and seismic) operate as low-power logic triggers. Upon detecting a mechanical signature matching an intrusion profile, the system activates a wide-spectrum radio frequency (RF) analysis. The key innovation lies in a correlational fusion engine that validates the intrusion only if there is a rhythmic or spectral match between the mechanical pressure fluctuations and the detected electromagnetic emissions, allowing the classification of the source (biological, mechanical, or electronic) with high immunity to environmental false alarms.

A concept is presented for adapting a predefined first mesh topology representing an organ to a different second mesh topology of the organ. One such concept includes identifying a correspondence between the first mesh topology and the second mesh topology based on a spectral matching of the first mesh topology and the second mesh topology. The predefined first mesh topology is aligned with the second mesh topology based on the identified correspondence between the first mesh topology and the second mesh topology.

This application provides a method for constructing a mainshock and aftershock sequence and a method for assessing damage to an intake tower. The method includes: obtaining an unfavorable mainshock and aftershock sequence based on ground motion data, and a database of near-field measured mainshocks, near-field measured aftershocks, far-field measured mainshocks, and far-field measured aftershocks; constructing multiple input conditions using the data from the databases and performing unified amplitude modulation processing to obtain the target mainshock and target aftershocks; determining the mainshock spectrum value of the target mainshock and the aftershock spectrum value of the target aftershock; determining the sum of squared residuals (SSE) between the mainshock spectrum value and the aftershock spectrum value, and a scaling factor. k ; Combine the sum of squared residuals (SSE) with the scaling factor k The mainshock spectrum and aftershock spectrum values ​​with a difference less than a preset difference threshold are used as matching results, thereby constructing a mainshock and aftershock combination with good spectral consistency; this application uses an improved spectrum matching method to construct a mainshock and aftershock combination with good comparability.

The application discloses a laser radar, which comprises a narrow-line-width multi-longitudinal-mode pulse laser, a photoelectric detector, a perforated mirror beam receiving system, a high-resolution narrow-line-width light splitting device and a plurality of single-photon detectors. The output beam of the narrow-line-width multi-longitudinal-mode pulse laser is emitted through the perforated mirror, and the photoelectric detector serves as a local signal. The echo signal photons reflected by a target are received through the receiving system, and are subjected to angle selection and spectral matching filtering through the light splitting device, and finally subjected to logical AND gate operation through the plurality of single-photon detectors, so that the final signal is simultaneously input into a time-correlation single-photon counter. The working performance of the photon counting laser radar in a strong noise environment is effectively improved, the false alarm probability of the laser radar is greatly reduced under the condition of ensuring the detection probability, the grating combined with the plurality of detectors can greatly reduce the background noise, the signal-to-noise ratio of the echo signal is greatly improved, and the detection capability of the laser radar is improved.

The invention discloses a hyperspectral and multispectral data fusion method of a Transform + KAN network. The hyperspectral and multispectral data fusion method comprises the following steps: S1, constructing a training data set: acquiring high-resolution and hyperspectral reference data HR-HSI-Benchmark, low-resolution and hyperspectral input data LR-HSI-Input to be fused and high-resolution and multispectral input data HR-MS-Input to be fused; after radiation correction, geometric correction, spectrum matching and down-sampling processing are carried out on the high-resolution hyperspectral reference data, low-resolution hyperspectral data LR-HSI-Train for training and high-resolution multispectral data HR-MSI-Train for training are generated; the LR-HSI-Train and the HR-MSI-Train are combined to form a low-resolution hyperspectral-high-resolution multispectral-high-resolution hyperspectral reference data training sample set; s2, constructing a multispectral and hyperspectral fusion network; s3, performing data amplification on the training sample set, and inputting the training sample set into a multispectral hyperspectral fusion network to obtain a trained fusion model; s4, registering the hyperspectral and multispectral data to be fused; and S5, image fusion and output.

The invention provides a morphology parameter measurement method, which comprises the following steps of: constructing a first weight vector set comprising a plurality of weight vectors for a first spectrum matching function, performing spectrum matching on a reference spectrum by using the first spectrum matching function for each weight vector in the first weight vector set, and obtaining a value of a corresponding loss function according to a matching result, and obtaining a first loss function set. Afterwards, a minimum loss function is obtained, because a matching result corresponding to the minimum loss function is more accurate, a weight vector corresponding to the minimum loss function is used as a target weight vector, a second spectrum matching function is constructed by using the target weight vector, the measurement spectrum and the theoretical spectrum, and a morphology parameter measurement value is obtained. The reference sample and the sample to be measured comprise the same periodic structure, so that the target weight vector determined by the reference spectrum of the reference sample can be used for obtaining a more matched theoretical spectrum for the measurement spectrum, and further more accurate morphology parameter measurement values of the sample to be measured can be obtained.

The present application relates to a kind of quantitative methods of spectral imager, specifically, a kind of high spectral remote sensor cross calibration method based on spectral matching factor, existing high spectral remote sensor only relies on single spectral channel in cross calibration process, leading to the technical problem of poor cross calibration precision.This method includes the following steps:1】Obtain spectral channel characteristics;2】Calculate simulated spectral radiance;3】Calculate spectral matching factor;4】Obtain cross calibration test area;5】Calculate the real spectral radiance of each spectral channel of reference remote sensor B;6】Obtain weight coefficient;7】Obtain final spectral matching factor;8】Calculate the real spectral radiance of each spectral channel of to-be-calibrated remote sensor A;9】Calculate the radiation calibration coefficient of each spectral channel of to-be-calibrated remote sensor A, complete the cross calibration of high spectral remote sensor of spectral matching factor.This method reduces error, improves data accuracy and reliability.

The application relates to a hyperspectral remote sensor cross-calibration method based on a dynamic Gaussian distribution, and solves the technical problem that the spectral matching factor obtained by the existing spectral matching factor calculation method is low in accuracy and precision, and it is difficult to improve the cross-calibration precision of the interference type hyperspectral remote sensor. The method comprises the following steps: 1, determining a cross-calibration test area; 2, calculating a simulated spectral radiance; 3, calculating the spectral matching factor of a plurality of spectral channels of a to-be-calibrated remote sensor B which have a greater influence on a to-be-calibrated remote sensor A based on a dynamic Gaussian distribution; 4, obtaining the final spectral matching factor of each spectral channel of the to-be-calibrated remote sensor A; 5, calculating the real spectral radiance of the plurality of spectral channels of the to-be-calibrated remote sensor B which have a greater influence on the to-be-calibrated remote sensor A; 6, calculating the real spectral radiance of the to-be-calibrated remote sensor A; 7, calculating the radiation calibration coefficient of each spectral channel of the to-be-calibrated remote sensor A, and completing the cross-calibration of the hyperspectral remote sensor.

This application provides a blockchain-based intelligent processing method, system, electronic device, and storage medium for cross-border e-commerce orders, relating to the field of blockchain technology. The method includes: receiving order information, material spectral data, and genuine product feature data to construct a multi-dimensional knowledge graph; triggering hyperspectral scanning based on product identification to generate material composition data, and performing spectral matching with the knowledge graph to obtain a matching degree; simultaneously extracting key region feature sets from the hyperspectral image using the U-Net image segmentation algorithm, and comparing them with an anti-counterfeiting feature library to obtain spatial similarity; when both indicators meet customs thresholds, activating the target smart contract based on an emergency identifier and generating an execution record; finally, encapsulating the verification data into a verification package, generating a hash value, and writing it to the blockchain, establishing a bidirectional index with the order through the smart contract to achieve state synchronization. This application improves the automation level and data reliability of cross-border e-commerce product customs clearance verification.

A metasurface inverse design method based on a generative adversarial network (GAN) controlled by an anchor network is proposed. The method involves collecting metasurface design parameters and spectral response data, preprocessing them, and encoding them into a format that can be handled by a deep learning model. K-means clustering is used to analyze the training data to generate structured control vectors. An AcGAN framework is constructed, integrating the spectral similarity metric SOC into the loss functions of the generator and discriminator to enhance spectral matching accuracy. Through adversarial training, the generator is trained to produce designs that conform to the target spectral characteristics, while the discriminator and the AnchorNet are optimized for rapid prediction and evaluation of the spectral response. The trained generator, combined with the control vectors and random noise, generates candidate designs. The candidate designs are then screened to select the optimal design. This invention effectively improves the spectral control accuracy and design efficiency of metasurface design, solving the challenges faced by traditional methods in handling complex spectral requirements.

This application relates to a seismic motion simulation method that matches long-period response spectra. By extending the response spectrum analysis period to 20 seconds and constructing a low-frequency characteristic seismic motion database, high-precision characterization of seismic motion characteristics across the entire frequency band from 0.03 to 20 seconds is achieved. This method overcomes the limitation of traditional models that decay too quickly in the amplitude descent segment, providing a seismic motion input solution for the seismic design of long-period structures such as super high-rise buildings that combines broadband spectral matching accuracy with fidelity to the physical characteristics of the geological environment.

The invention discloses a mineral anomaly identification method and system based on spectral remote sensing, and relates to the technical field of mineral anomaly identification, and the method comprises the following steps: obtaining hyperspectral remote sensing image data of a target area; performing atmospheric correction and geometric correction on the hyperspectral remote sensing image data to obtain surface reflectance data, and extracting a spectral reflectance curve; performing weak signal enhancement processing on the spectral reflectivity curve to obtain an enhanced spectral curve, and calculating a spectral matching degree between the enhanced spectral curve and each mineral end member in a standard mineral end member spectrum library; the spectrum matching degree is analyzed, and the mineral abnormal condition is judged according to the analysis result. According to the method, a systematic weak signal enhancement technology system is constructed, so that the technical problem of low recognition accuracy of a traditional method in a deep ore body and a low-grade mineralized area is solved.

The invention provides a mineral spectrum global matching method and device based on multi-band feature vectors. The method comprises the following steps: carrying out continuum removal processing on an input spectrum to obtain a target spectrum; determining an effective spectrum band and a central wavelength thereof of the target spectrum based on a preset threshold strategy; determining an absorption characteristic parameter corresponding to the effective spectrum band; constructing and determining a first target comprehensive absorption feature vector according to the absorption feature parameter of the effective spectrum band; determining a second target comprehensive absorption feature vector according to the absorption feature parameter of the effective band of the preset reference spectrum; determining a basic Euclidean distance and an augmented Euclidean distance of the first target comprehensive absorption feature vector and the second target comprehensive absorption feature vector; and determining the mineral corresponding to the reference spectrum with the minimum basic Euclidean distance or augmented Euclidean distance as a target mineral. Through the above method, the multi-band feature vectors are fused and mineral identification is performed based on the basic Euclidean distance or the augmented Euclidean distance, so that the accuracy of mineral identification can be improved.

The application provides a kind of radiation calibration method and device for sub-meter optical remote sensing satellite data, it is related to remote sensing technical field, including: based on global atmospheric parameter observation site, extract and establish pseudo stable ground library;For each kind of pseudo stable ground, establish BRDF model and spectral curve reconstruction model, calculate the spectral matching factor of reference sensor and to be calibrated sensor;Based on the to-be-calibrated image of pseudo stable ground, the theoretical ground reflectivity of to-be-calibrated image is calculated using spectral matching factor and BRDF model;Based on the theoretical ground reflectivity, the observation angle and the measured atmospheric parameters of site, the theoretical apparent radiance of to-be-calibrated image is calculated;The theoretical apparent radiance of to-be-calibrated image on time series is fitted with DN value, the variation trend curve of cross calibration coefficient is determined, to carry out radiation calibration for sub-meter optical remote sensing satellite data.The application can realize high frequency, high precision cross calibration of sub-meter optical remote sensing satellite data.

The invention provides a radiation calibration method and device for sub-meter optical remote sensing satellite data, and relates to the technical field of remote sensing, and the method comprises the steps: extracting and building a pseudo-stable earth surface library based on a global atmospheric parameter observation station; aiming at various pseudo-stable earth surfaces, establishing a BRDF model and a spectrum curve reconstruction model, and calculating spectrum matching factors of the reference sensor and the to-be-calibrated sensor; based on the to-be-calibrated image of the pseudo-stable earth surface, calculating the theoretical earth surface reflectance of the to-be-calibrated image by using the spectrum matching factor and the BRDF model; calculating the theoretical apparent radiance of the to-be-calibrated image based on the theoretical surface reflectance, the observation angle and the site actually measured atmospheric parameters; and fitting the theoretical apparent radiance of the to-be-calibrated image on the time sequence with the DN value, and determining a change trend curve of the cross calibration coefficient so as to perform radiation calibration on the sub-meter optical remote sensing satellite data. According to the invention, high-frequency and high-precision cross calibration of sub-meter optical remote sensing satellite data can be realized.

The application is particularly a kind of intelligent extraction method and system for urban road collapse hidden danger sensitive elements, relating to the technical field of urban road underground hidden danger detection, comprising: obtaining the template signal corresponding to each sensitive element category in the sensitive element feature library, respectively calculating the waveform similarity and the frequency spectrum matching degree of each independent signal source component and each category template signal.In the application, the blind source separation processing mode of wavelet packet decomposition combined with FastICA algorithm is adopted to decompose the multi-source aliasing echo signal into statistically independent signal source components, solving the technical problem that the feature extraction on the aliasing signal directly leads to feature ambiguity due to the mutual interference of signal sources, making the feature extraction process of sensitive elements have traceability and physical interpretability.

This invention discloses a detection method for a marine magnetic field sensor based on diamond NV centers, comprising: S1 baseline calibration; S2 alternating fluorescence measurement between two single-quantum resonance frequencies and taking the difference to achieve DQ readout; S3 adjusting the triaxial compensation current based on closed-loop control to maintain the NV operating point at zero magnetization bias; S4 obtaining background magnetic field changes through digital demodulation and adaptive filtering; S5 calculating the gradient based on the spatial difference of multi-point light spots, and combining threshold and spectral matching methods to determine the presence and trajectory of the submersible. The threshold is calibrated based on existing submersible magnetic field characteristics, and the short-time Fourier transform signal spectrum is compared with known submarine characteristic spectrum data. If the similarity exceeds the set threshold, an alert is issued. The detection method of this invention can improve measurement accuracy and anti-interference capability, and is suitable for real-time monitoring of weak magnetic anomalies (such as submersible magnetic moment disturbances).

The application discloses a kind of electromagnetic interference source diagnosis positioning methods based on IC level EMC data assets, by constructing read-only interference source positioning database, extracts IC eigenfrequency spectrum characteristics;Obtain measured EMC data for spectrum matching, identify dominant interference source and record the whole diagnosis process, generate structured, traceable diagnostic process data assets;Verification data is stored to user local application knowledge base and generates local correction coefficient.The application realizes accurate diagnosis of interference source, data and assetization of diagnosis process, solves the problem of accurate quantization of multiple interference sources in complex systems, and the diagnosis process is traceable, and the diagnosis idea can be used for reference, to improve EMC diagnosis efficiency.

This invention relates to the field of solid waste treatment technology, and particularly to a smart waste sorting method based on multi-sensor fusion, comprising: dynamic registration of multiple sensors; multi-level fusion decision-making: suppressing wastewater reflection in visible light images; identifying and eliminating liquid contamination areas based on near-infrared spectral matching; generating spatial grid feature vectors by fusing visible light texture, spectral features, and electromagnetic signals; identifying material categories through a cascaded classifier and dynamically adjusting the weights of each sensor in the decision-making process based on environmental parameters; adaptive sorting execution: triggering the corresponding sorting execution mechanism according to the identification results; calculating the advance compensation amount based on conveyor belt speed, execution mechanism response delay, and material physical properties; adaptively extending the execution mechanism's action time according to the stacking level for stacked materials; and online feedback optimization. Through dynamic compensation and timing adjustment, the temporal consistency of multi-sensor data is ensured, overcoming the influence of belt speed fluctuations.

The invention belongs to remote sensing image processing and information extraction, and particularly relates to a hyperspectral image comprehensive processing method suitable for mineral information extraction, which comprises the following steps: acquiring a hyperspectral image; selecting a hyperspectral image A of the target area, and determining an extracted target mineral B; selecting a standard spectrum of the target mineral B from the standard mineral indoor spectrum library, and determining a characteristic wave band absorption peak wave band C of the standard spectrum of the target mineral B; selecting discriminating wavebands E and F for determining a characteristic waveband absorption peak waveband C; reading pixel values H1, H2 and H3 of spectral curves in hyperspectral image pixels in wave bands E, C and F, and calculating relative abundance B1 of the target mineral B; carrying out matching operation on the standard spectrum of the target mineral B and the hyperspectral image pixel by utilizing a spectrum matching algorithm to obtain a result value B2; and calculating the abundance B3 of the target mineral B in the hyperspectral image pixel by using a band algorithm. According to the invention, the influence of other ground features or noise can be reduced, and the data processing speed and precision are improved.

The invention provides a method and a system for identifying purity of hazelnut peptide health-care products based on a spectrum technology, and relates to the technical field of spectrum detection.The method comprises the following steps: acquiring biological activity values and initial spectrum data of samples with different purities, and establishing a corresponding relation database of the biological activity values and the initial spectrum data; acquiring a spectral interference signal of a to-be-detected product through an infrared spectrometer, carrying out encryption sampling and phase compensation in a phase key interval, and generating intermediate spectral data; identifying a characteristic absorption peak matched with the database, expanding a spectral range by using a central wave band of the characteristic absorption peak, and non-linearly enhancing the signal to form target spectral data; and quantifying the spectral contribution degree of the active component to calculate an active purity index, and obtaining a confidence index in combination with a signal-to-noise ratio and a standard spectral matching degree. And finally, dynamically determining an activity threshold according to the database, and outputting a purity identification report of the health care product to be detected through index comparison and confidence verification, so that accurate identification of the purity of the hazelnut peptide health care product can be realized.

The invention discloses a hyperspectral mineralization alteration information extraction method and system based on semantic segmentation, and relates to the technical field of mineralization alteration information extraction. The method comprises the following steps: investigating a research area according to geological data, and analyzing geological information, related to mineralization, of the research area; the method comprises the following steps: preprocessing hyperspectral data of a mining area of a research area by utilizing ENVI software, and making a data set suitable for a semantic segmentation model; constructing a semantic segmentation model, and extracting alteration mineral information of the research area; remote sensing geological interpretation is carried out on the research area, and linear structure factors beneficial to mineralization are extracted in combination with principal component analysis; and in combination with remote sensing altered mineral information and linear construction factors, constructing an evidence weight model, and delineating a metallogenic prediction prospective area. The deep learning model is utilized to learn the spectral continuity and the spatial texture mode of the hyperspectral data at the same time, the defects that traditional spectral matching is sensitive to mixed pixels and poor in local noise robustness are effectively overcome, and technical guarantee is provided for efficient development and utilization of mineral resources.