525results about How to "Suppress interference" patented technology

The invention discloses a PPY-YOLO-based steel surface defect detection method and a PPY-YOLO-based steel surface defect detection system. The PPY-YOLO-based steel surface defect detection method comprises the following steps: step 1, preprocessing a steel surface defect image; 2, a neck structure Faster-Neck, a deep learning module C3K2Faster and a spatial pyramid pooling module SPPFSim are introduced into the YOLOv11 network, and a PPY-YOLO target detection model is obtained; step 3, training a PPY-YOLO target detection model; and 4, acquiring a steel image, and inputting the steel image into the trained PPY-YOLO target detection model to obtain the category and positioning information of the steel surface defects. A new model is constructed, the backbone network and the neck network are improved, the backbone network is formed by connecting the input end of the C2PSA module with the output end of the SPPFSim module, and an up-sampling group and a down-sampling group are added to the neck network, so that the model has a relatively good detection effect.

The invention provides an overhead transmission line defect detection method based on multi-scale feature fusion, and the method comprises the steps: carrying out the correction and registration of a lead image and a laser point cloud image, carrying out the data enhancement, and constructing a detection data set; the method comprises the following steps: respectively inputting two images into a target detection network for training, adding an attention mechanism in a feature extraction network Backbone so as to carry out correlation calculation on a target, and meanwhile, adopting a multi-scale feature fusion detection method in a backbone part of a YOLOV8 operation network, so that the improvement of precision and accuracy is realized on the premise of ensuring the size of a model, and the accuracy of the model is improved. And the requirement of actual overhead transmission conductor defect detection is met.

The invention provides a sewer internal defect detection method and system based on YOLOv11 improvement, and the method achieves the dynamic weight fusion of multi-level features through a self-adaptive feature fusion module, and improves the detection capability of a model for small targets and complex backgrounds. The selectivity of channel characteristics is improved through an SENet attention mechanism, and background interference is reduced; and introducing an MPDIOU loss function to improve bounding box regression loss. The performance of the improved model in detection tasks of sewer defects such as cracks, obstacles, corrosion, deformation and the like is remarkably superior to that of original YOLOv11, and particularly key indexes such as mAP50 (average precision), Box precision (P), recall rate (R) and the like are remarkably improved. According to the invention, efficient and accurate detection of multiple types of defects in the sewer can be realized, and a better solution is provided for maintenance and management of the sewer.

The invention relates to an Internet of Vehicles multi-agent cooperative sensing method and system. The method comprises the following steps: constructing a collaborative sensing network, wherein the collaborative sensing network comprises a self-agent and a plurality of collaborative agents; acquiring and processing sensing data through the collaborative sensing network; performing feature extraction to obtain intermediate features; self-adaptive sparsification is carried out to obtain sparse features, and the sparse features are compressed and transmitted to a self-agent; performing time sequence feature enhancement on the features of all the agents at the self-agent end; fusing the features to obtain fused features; and constructing an Internet of Vehicles perception model, and realizing perception by the detection model according to the fused features. According to the method, the calculation complexity of traditional global attention is reduced from the square level to the linear level through an adaptive sparsification mechanism, the calculation overhead is remarkably reduced while the multi-agent feature interaction precision is kept, and the method is more suitable for real-time operation on the vehicle-mounted edge equipment with limited resources.

The invention relates to the technical field of voltage control, and particularly discloses a voltage control method for a high-reliability high-voltage power supply, and the method comprises the steps: carrying out the input filtering and power conversion boosting processing of a DC voltage signal provided by a power supply, so as to obtain a high-voltage AC signal; a signal processing technology based on deep learning is adopted to perform CEEMDAN decomposition and intrinsic mode feature extraction on a sampled voltage signal, feature saliency modulation dynamic aggregation is performed on each intrinsic mode after decomposition of the sampled voltage signal to suppress noise and interference in the signal, and a pure sampled voltage signal is obtained. And error calculation and MOS tube driving control are carried out in combination with an external control voltage signal, so that accurate adjustment of the output voltage of the high-voltage power supply is realized. Therefore, the precision of voltage control can be effectively improved, and the adaptability to complex and changeable load conditions and interference environments is enhanced, so that the stability and reliability of a high-voltage power supply system are remarkably improved.

The invention discloses an unmanned aerial vehicle low-altitude visual angle target detection method and device based on deep learning, and belongs to the technical field of computer vision. The method comprises the following steps: performing data enhancement on a collected target detection object image data set, and dividing the enhanced target detection object image data set into a training set, a verification set and a test set; an attention mechanism module is added in a backbone network of the YOLOV7-OBB model; the YOLOV7-OBB model is trained based on the training set, the YOLOV7-OBB model in the training process is evaluated based on the verification set, and the trained YOLOV7-OBB model is obtained; and inputting the test set into the trained YOLOV7-OBB model, and carrying out target detection to obtain a detection result. By enhancing a target detection object image data set and adding an attention mechanism module, the YOLOV7-OBB model fully learns key feature information of a target, and the detection precision and robustness are remarkably improved.

A wind turbine generator variable pitch bearing fault diagnosis method based on signal coding specifically comprises the steps that original vibration signal data of a variable pitch bearing are acquired through a wind turbine generator state monitoring system arranged in a wind power plant; bearing vibration signals are respectively coded into a time-frequency diagram and a position matrix diagram by adopting continuous wavelet transformation and position matrix transformation, and the characteristic information of the signals is enhanced by increasing the visual dimension of data; a global feature extraction module and a local feature extraction module are respectively designed based on a global attention mechanism and a window attention mechanism, so that the model can fully learn feature information of different levels; a dual-channel parallel fusion network model is constructed according to global and local feature extraction modules to fuse global and local feature information, and the purpose of suppressing strong random noise interference is achieved. According to the method, the vibration signal characteristics of the variable pitch bearing in different states can be fully captured, and the problems of poor diagnosis performance and low precision of the variable pitch bearing caused by strong random noise interference can be effectively solved.

The invention relates to the technical field of image analysis, in particular to a defect detection method, system and device for a power transmission line and a storage medium. A defect detection neural network composed of a multi-level image feature extraction module, a multi-level image feature fusion analysis module, a first image semantic feature enhancement module, a second image semantic feature enhancement module, an image semantic feature fusion analysis module and an image segmentation module is constructed, so that the calculation burden can be remarkably reduced; the method is suitable for real-time application of the unmanned aerial vehicle, most importantly, the small target can be separated from the background, and the detection precision of the small target in a complex background and a shielding scene is improved; meanwhile, in the process of training the defect detection neural network, the matching degree of a prediction frame and a real frame is improved by controlling the sum of intersection-to-union ratio loss of image bounding boxes, coordinate loss of the bounding boxes and shape loss of the bounding boxes to be smaller than a loss threshold value, and therefore the accuracy of small target detection under the condition that the background is complex or the target shape is irregular is guaranteed.

The invention discloses an ore grinding hard rock target detection method and system based on a HardRockNet network structure, and the method comprises the steps: constructing an ore grinding hard rock target detection model which comprises an input layer, a multi-scale feature extraction layer, a DFPN feature fusion layer and an output layer which are sequentially connected in series; the multi-scale feature extraction layer comprises a plurality of feature extraction sub-layers which are sequentially connected in series and have different feature extraction scales, the output ends of the plurality of feature extraction sub-layers are connected with the input end of the DFPN feature fusion layer, and the plurality of feature extraction sub-layers perform feature extraction by adopting a DPBlock module; and detecting hard rock on the ore grinding picture by using the trained target detection model. According to the invention, the DPBlock module and the DFPN module are combined, and a HardRockNet network structure is constructed, so that the small target detection precision is improved, the robustness of complex backgrounds and target overlapping problems is enhanced, and accurate detection of hard rocks is realized.

The invention provides a PCB multi-type defect detection method and device based on frequency domain perception enhancement, a storage medium and a program product, and relates to the technical field of computer vision target detection.The method comprises the steps that a to-be-detected PCB image is input into a backbone network for shallow feature extraction, and shallow feature maps of different processing stages are obtained; respectively inputting the shallow feature maps into a multi-domain collaborative feature enhancement network, performing decomposition enhancement on the shallow feature map in each stage through wavelet transform to obtain an enhanced low-frequency component and an enhanced high-frequency component, and performing first reconstruction on the enhanced low-frequency component and the enhanced high-frequency component through first inverse wavelet transform to obtain an enhanced low-frequency component and an enhanced high-frequency component; obtaining a frequency domain enhanced feature map of each stage; inputting the frequency domain enhanced feature map into a neck network, and enabling the neck network to perform multi-scale feature fusion on the frequency domain enhanced feature map to obtain a fused feature map; and inputting the fusion feature map into a prediction output end for target prediction to obtain a PCB defect detection result.

The invention discloses an anti-interference filter based on FFT (Fast Fourier Transform), and relates to the field of GNSS (Global Navigation Satellite System) receivers. A radio frequency front end is used for receiving GNSS signals and down-converting the GNSS signals into IF (Intermediate Frequency); the analog-to-digital converter ADC is used for converting the GNSS signal which is received by the radio frequency front end and moved to the intermediate frequency into a digital signal; and the anti-interference filter is used for converting the digital signal from time domain representation to frequency domain representation, eliminating an interference signal in a preset frequency range in a frequency domain according to a preset demand index, and inversely transforming the frequency domain signal subjected to interference elimination back to the time domain. The invention further discloses an interference suppression method of the anti-interference filter based on the FFT, the anti-interference filter based on the FFT is suitable for various GNSS receivers, the anti-interference performance of the receivers can be effectively improved, and the accuracy of navigation and positioning is ensured.

The invention discloses a data fusion method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: carrying out the classification of various types of sensor data in an unmanned system according to a set classification rule, and obtaining the classification data; performing preprocessing based on the basic attributes of the classified data to obtain preprocessed classified data; performing data-level fusion on the preprocessed classification data to obtain a plurality of classification fusion data; based on a deep recurrent neural network DRNN, in combination with a bidirectional long-short term memory unit Bi-LSTM and a sparse attention mechanism, constructing a data fusion model, and optimizing the data fusion model through model pruning to obtain an optimized data fusion model; and inputting the plurality of classified fusion data into the optimized data fusion model for secondary fusion to obtain a final fusion feature. The potential of multi-source information is fully mined, and the accuracy and effectiveness of decision making in a complex environment are improved.

The invention relates to the technical field of radio frequency quadrupole accelerators, in particular to a method for predicting electromagnetic field distribution of a radio frequency quadrupole accelerator by adopting a support vector machine, which comprises the following steps of: acquiring an electric field signal, a magnetic field signal and electromagnetic field distribution in the radio frequency quadrupole accelerator by presetting an electric field probe and a Hall sensor; denoising, de-trending, normalizing and smoothing the collected electric field signal and magnetic field signal to improve the signal quality and the accuracy of subsequent analysis; performing wavelet transform analysis on the collected electric field signals, extracting electric field characteristic parameters, performing wavelet transform analysis on the collected magnetic field signals, and extracting magnetic field characteristic parameters. According to the method, the wavelet transform and the support vector machine are combined, the advantages of the wavelet transform in the aspects of multi-scale signal analysis and noise suppression can be fully played, and meanwhile, the precision, efficiency, robustness and adaptability of radio frequency quadrupole accelerator electromagnetic field distribution prediction are remarkably improved by utilizing the strong classification and regression capability of the SVM.

The invention provides a construction method and an inversion method of a mourhue surface depth gravity inversion model, and relates to the technical field of artificial intelligence, and the construction method of the mourhue surface depth gravity inversion model comprises the steps: obtaining a mourhue surface data set of a to-be-detected region; a corresponding gravity anomaly data set is obtained according to the mourhue surface data set; training and optimizing an initial model according to the gravity anomaly data set to obtain a final mourhos surface depth gravity inversion model; wherein the mourhue surface depth gravity inversion model is used for obtaining corresponding mourhue surface fluctuation data, and the initial model is obtained by adding an attention network in a U-Net basic network; according to the method, the accuracy and efficiency of inversion are improved, and a powerful tool and method are provided for research of a complex geological environment.

The invention discloses a wafer defect detection method and system based on an attention guidance network, and belongs to the technical field of wafer defect detection, and the method comprises the following steps: 1, adjusting light supplement according to a detection demand, and then collecting a wafer image; 2, performing spectrum enhancement preprocessing on the acquired wafer image; and 3, extracting the features of the enhanced image through a multi-scale distributed feature extraction backbone network, and fusing the extracted features through an attention-guided feature pyramid network. Complex background interference of the wafer is effectively suppressed, and defect characteristics are remarkably enhanced; constructing a feature extraction mechanism capable of capturing local details and long-range context information at the same time; balanced and accurate detection of multi-scale defects is realized; the physical priori of the defect is embedded into the network in a learnable manner, so that the learning efficiency and generalization are improved; while ultrahigh precision is ensured, low model complexity is maintained, and the real-time requirement of a production line is met.

The utility model discloses an optical film thickness monitoring system based on an FPGA (Field Programmable Gate Array), which belongs to the field of optical film coating and comprises an optical path and a circuit, the light path comprises a light source, a light chopper, a beam splitter, a monitoring piece and a monochromator, the circuit comprises a photoelectric conversion board and an FPGA development board, the light path part is responsible for generating and modulating light signals, the circuit part is responsible for signal conversion, amplification, demodulation, noise filtering and extreme point interpretation, on the FPGA development board, a digital signal processing technology is utilized, a reference signal is used as a benchmark, and the optical path part and the circuit part are integrated. The main signal is demodulated, in the demodulation process, noise and interference caused by factors such as environment and equipment can be eliminated, some noise and interference still exist in the demodulated main signal, the FPGA development board can further filter out the noise and improve the signal-to-noise ratio of the signal, and the extreme point of the main signal subjected to noise filtering processing corresponds to the change of the thickness of the film. The algorithm on the FPGA development board can accurately interpret the extreme points, so that the change information of the film thickness is obtained, the change of the film thickness can be more accurately identified, and the monitoring precision is improved.

The invention discloses a deep sea cable fault accurate positioning method based on electromagnetic induction and transient characteristic analysis, and relates to deep sea electric power engineering. According to the technical scheme, the method comprises the steps of 1, signal excitation used for transmitting a signal source; 2, signal filtering is carried out to counteract geomagnetic background noise; 3, signal correction is carried out to solve the problems of modal aliasing and endpoint effect existing when a traditional EMD is used for processing complex signals; 4, signal feature extraction, wherein transient pulse features in the signals can be effectively enhanced; and step 5, fault positioning. The method is mainly used for non-intrusive fault detection of the power cable in the kilometer-level deep sea high-pressure environment, and high-precision fault point positioning is achieved.

The invention belongs to the technical field of microsphere preparation, and particularly relates to a magnetic microsphere and a preparation method thereof. Through cooperation of the cross-linking agent and the monomer, the polystyrene microsphere with good swelling and shrinking performance is provided; in the swelling process, introduced divalent and trivalent iron ions can be coordinated with carboxyl groups of the microspheres and are uniformly dispersed in the microsphere structure; then, by adjusting the pH value, the iron ions react to generate ferroferric oxide distributed in the microspheres and on the surfaces of the microspheres, and in the shrinkage process of the microspheres, the density of aromatic rings is reduced by the ferroferric oxide based on space occupation of the ferroferric oxide, so that background fluorescence is reduced, and the signal-to-noise ratio and sensitivity of fluorescence detection are improved. In addition, ferroferric oxide has an optical shielding effect and can be mutually matched with a coating layer arranged outside the microsphere, so that the fluorescence intensity of the polystyrene material magnetic microsphere is jointly reduced.

The invention relates to a target detection method and system for knitwear defect detection, and the method comprises the steps: obtaining a knitwear defect picture, and processing the knitwear defect picture to obtain a knitwear defect data set; constructing an initial defect detection model, wherein the initial defect detection model is composed of a trunk feature extraction network, a channel priority convolution attention module, a deep convolution module and multiple detection heads; constructing a target loss function to train the initial defect detection model to obtain an optimized defect detection model; and inputting the processed knitwear defect data set into the optimized defect detection model to obtain a defect detection result. The method can effectively improve the knitwear defect detection precision and efficiency, and has the advantages of being rapid in detection, high in universality, high in intelligent degree, low in background false detection rate and the like.

The utility model discloses a thermocouple measuring device, comprising a TC input module used for collecting thermocouple voltage and successively carrying out bias processing and signal filtering conditioning; the cold end compensation module is used for acquiring the RTD voltage of the cold end of the thermocouple on the field side; the ADC module is used for sending the RTD voltage and the thermocouple voltage to the MCU control unit; the MCU control unit is used for calculating the real voltage of the thermocouple and obtaining the actual temperature value of the thermocouple according to the real voltage; the isolation module is used for isolating signals of the field side and the control side; and the power supply module is used for supplying power to each module. According to the utility model, the isolation module and the TC input module are added, signals of a field end and a control end can be effectively isolated, the influence of ESD, surge and pulse on the device is effectively reduced, the design of an analog filter circuit, independent cold end measurement and the like is added, the external interference of the system is reduced, and the design of bias voltage is added to realize thermocouple short-line self-diagnosis. Through the design, the reliability and the measurement precision are improved.

The invention discloses an airborne multi-mode cooperative target detection method based on depth measurement, and relates to the technical field of signal processing, and the method comprises the steps: firstly, carrying out the data collection of a target region synchronously through employing an optical camera, a radar sensor and a radiation source sensor which are carried on an airplane, obtaining an optical image, radar data and radiation source data, and carrying out the detection of a target region; carrying out pretreatment on the raw materials; then target detection of respective platforms is carried out on the preprocessed optical image, radar data and radiation source data; and finally, performing alignment and fusion processing on the obtained target detection results of the optical image, the radar data and the radiation source data based on a method of combining depth metric learning with a weighted attention fusion mechanism, and finally obtaining a comprehensive target detection result. The airborne multi-mode cooperative target detection method based on depth measurement provided by the invention not only can improve the accuracy of target detection, but also can effectively improve the robustness and adaptive capacity of the system.

The invention relates to the technical field of instrument and process measurement, in particular to a multi-range pressure gauge self-adaptive switching system and method based on time sequence feature perception, and the system comprises a data collection module which generates a smooth pressure time sequence signal in real time; the time sequence feature sensing module receives the smooth pressure time sequence signal and generates a pressure prediction trend signal of a future short-time window based on the pressure change trend feature; the self-adaptive decision-making module compares the pressure prediction trend signal with a preset current measuring range threshold value range; the switching execution module responds to the pre-switching trigger signal when the switching locking signal is in an unlocking state, and outputs the pre-switching trigger signal to the multi-range sensor group to complete switching of working channels; and the data fusion module performs weighted fusion processing on the two paths of output signals according to the switching process state signal provided by the switching execution module. According to the invention, the problem of switching lag of an existing pressure measurement system in the face of a pressure transient working condition can be solved.

The invention discloses a permanganate index multipoint inversion method based on hyperspectral remote sensing and a water quality sequence. The method comprises the following steps: acquiring a hyperspectral remote sensing image and water quality time sequence data of a point location to be inverted; a remote sensing regression inversion model is constructed, and a spectral information training set is used for training; constructing a Gaussian hidden Markov model, and training by using water quality time sequence grouped data; inputting the spectral information in the test set into the remote sensing regression inversion model to obtain an inversion result of the point location to be inverted; and inputting the water quality time sequence grouped data in the test set into the Gaussian hidden Markov model, and inferring the current hidden state of the Gaussian hidden Markov model by using the inversion result of the point location to be inverted to obtain the final permanganate index prediction result of each point location. According to the method, the Gaussian hidden Markov model and the hyperspectral remote sensing regression model are combined, multi-dimensional data are utilized, the influence of sparse sampling points on precision is reduced, interference of the same-spectrum foreign matter phenomenon is inhibited, and the precision of permanganate index multi-point inversion is improved.

The invention discloses an automatic two-way transmission and level conversion buffer circuit applied to a Flash chip. The automatic two-way transmission and level conversion buffer circuit comprises an LDO (Low Dropout Regulator) circuit, a direction control buffer circuit and an automatic direction buffer circuit, the input end of the LDO circuit inputs a VIN signal and an EN enable signal respectively, and the LDO circuit provides voltage for other chips or internal circuits according to the VIN signal and the EN enable signal; an EN enable signal and a DIR selection signal are input into the control end of the buffer circuit for controlling the direction, and switching and switching of forward transmission and reverse transmission of signals input into the buffer circuit for controlling the direction are controlled through the EN enable signal and the DIR selection signal. An EN enable signal is input into the control end of the automatic direction buffer circuit, and level conversion of two input ports of the automatic direction buffer circuit is controlled through the EN enable signal; compatibility of the Flash chip to different SPI protocols and different voltage domains are met, interference of power supply noise to an internal chip is effectively restrained, and control over the transmission direction of transmission signals is achieved.

The invention discloses a distributed photovoltaic panel anomaly detection method under a high-altitude view angle based on a YOLOv8n model, and belongs to the technical field of target detection. The model comprises the following steps: acquiring a distributed photovoltaic panel image data set under a high-altitude view angle, and performing data enhancement and labeling; an improved YOLOv8n model is constructed, and the improvement comprises the steps that a C2fAT module is introduced into a backbone network, and the small target feature extraction capacity is enhanced; an SPPF module is replaced by an SPPF-LSKA module, and complex background interference is suppressed; an EMA attention mechanism is introduced into the neck network, and the multi-scale adaptive capacity is improved; optimizing a training process by adopting a WIoU v3 loss function; and training and optimizing the model by using the training set, and finally outputting a detection result through the test set. According to the invention, the problems of false detection and missing detection of the distributed photovoltaic panel in a high-altitude view angle are effectively solved, the detection precision of the distributed photovoltaic panel is further improved, and inspection personnel are helped to troubleshoot the photovoltaic panel in an abnormal state in the high-altitude view angle.

A narrow-pulse-width multi-wavelength laser based on LED pumping comprises a reflecting mirror, a first LED pumping module, an optical rotation crystal, a second LED pumping module, an acousto-optic Q-switched crystal and an output mirror which are sequentially connected in series. The LED pumping module comprises a laser rod located in the center, a glass sleeve is arranged on the periphery of the laser rod in the length direction, a supporting frame is arranged on the periphery of the glass sleeve, a plurality of sets of water-cooling copper blocks are evenly arranged on the supporting frame, and an LED stacking array is arranged on the side, close to the glass sleeve, of each set of water-cooling copper blocks. Each LED stacking array comprises an LED light bar and a PCB (Printed Circuit Board) for fixing the LED light bar, and the PCB is connected with the water-cooling copper block; cooling water circulation modules are arranged at the two ends of the laser rod in the length direction respectively, and water through holes are formed in the cooling water circulation modules and the water cooling copper blocks respectively. A laser resonant cavity system of the laser takes an LED as a pumping source, combines an acousto-optic Q-switching technology, and simultaneously realizes compression of laser pulse width and multi-wavelength output.

The invention relates to a frequency mixing method and device for eddy current detection, electronic equipment and a storage medium, which are used for improving the detection efficiency of eddy current detection. The method comprises the following steps: acquiring manual calibration pipe data, wherein the manual calibration pipe data comprises eddy current signal data of a plurality of different channels; screening out a plurality of pieces of pipeline area signal data from all the eddy current signal data; performing signal analysis on the signal data of each pipeline region one by one, and extracting signal data of a candidate region of the support plate; identifying target support plate signal data from the support plate candidate region signal data by using a sliding window detection method; calculating a mixing factor according to the target support plate signal data; and performing frequency mixing processing on target test tube data by using the frequency mixing factor to obtain target frequency mixing signal data.

The invention belongs to the field of multi-sensor fusion systems, and discloses a multi-sensor fusion system simultaneous positioning and mapping method and a related device, and the method comprises the steps: obtaining an environment image, a laser point cloud and IMU data; scene semantic information is extracted through a multi-modal large model; dynamically generating a weight matrix of vision and laser radar relative to an IMU (Inertial Measurement Unit) by utilizing a vision and point cloud condition deep neural network in combination with scene information and IMU observation; and performing local and global optimization by combining each sensor speedometer factor, an IMU pre-integration factor and the weight matrix, and finally outputting robot state estimation and map point coordinates. Through semantic understanding and a self-adaptive weight mechanism driven by a conditional deep neural network model, a fusion strategy can be intelligently adjusted before environment change or sensor degradation, the robustness, precision and consistency of a system in an unstructured scene are improved, and the problems of positioning drift and map failure caused by fixed weight in a traditional method are solved.

The invention discloses a financial credit data verification system with zero knowledge proof, and relates to the technical field of financial science and technology. According to the system, a ZKP multi-source credit data acquisition module acquires multi-dimensional credit data of a borrower, and a data available and invisible mechanism is adopted to protect privacy; the credit data entropy analysis module quantifies data uncertainty based on a clustering algorithm, dynamically generates a threshold value in combination with a historical entropy value, and quickly screens abnormal data; a ZKP enhanced feature extraction module captures a dynamic change rate index and zero knowledge proof features; the deep verification model evaluation module utilizes a deep belief network and a federated learning framework to realize high-precision anomaly recognition on the premise of protecting privacy; the verification strategy regulation and control module generates a parameter regulation and control strategy through a swarm intelligence optimization algorithm; the feedback optimization module dynamically adjusts parameters through a closed-loop feedback mechanism, and improves the efficiency, precision and safety of financial credit data verification.

The utility model discloses a kind of impedance adjustable high-speed terminal structures, it is related to high-speed terminal technical field.The impedance adjustable high-speed terminal structure, the outer shell is rectangular, the outer shell both sides are fixedly connected with mounting lug, the mounting lug is L shape, and mounting bolt is threadedly connected on the mounting lug.The impedance adjustable high-speed terminal structure, outer shell is fixed with mounting bolt through L-shaped mounting lug, adapts multiple equipment installation scene, and installation is firm and flexible;Insulating base is fixed by positioning pin and positioning hole, and assembly precision is high, effectively prevent base displacement, guarantee the relative position stability of each component, reduce the influence of vibration on terminal performance, each component is modularized assembly through mounting hole, lower mounting slot and other structure module, installation step is clear, convenient for production and assembly and later maintenance replacement, reduce assembly difficulty and cost.