171 results about "Step detection" patented technology

The invention relates to a chip surface defect detection method, system and equipment based on an improved YOLOv10 model and a medium. The method comprises the following steps: constructing a hierarchical detection model; inputting the chip surface image into a feature extraction network to obtain an initial feature map; embedding a coordinate attention module into a predetermined node of the feature fusion network, performing orthogonal direction convolutional coding on the initial feature map to generate a space attention weight, and outputting an enhanced feature map through weighted fusion; constructing a central point prediction branch in a middle layer of the feature extraction network, and outputting a defect thermodynamic diagram, a size parameter and category probability distribution in parallel based on the initial feature map; and inputting the enhanced feature map into a detection head, carrying out space alignment processing in combination with the defect thermodynamic diagram to generate a preliminary detection frame, and carrying out redundancy suppression processing to generate a detection result. According to the invention, the coordinate attention module and the central point prediction branch are introduced to carry out collaborative improvement on the YOLOv10 model, and the problem of pain points in chip surface defect identification is solved.

The invention relates to the technical field of nondestructive testing, and discloses a crack detection device and method for precise automobile parts. The method comprises the following steps: carrying out ultrasonic multi-dimensional pre-scanning and acoustic impedance abnormal region identification on a to-be-detected automobile part, carrying out influence range calculation and sensitive threshold classification based on abnormal region distribution data, and obtaining stress-acoustic response time sequence data through partition grabbing force distribution and stress loading; three-dimensional stress field reconstruction and stress concentration area identification are realized; performing dual-frequency stress modulation excitation and multi-physics field synchronous detection on the stress concentration area to obtain a multi-physics field response signal, performing multi-parameter fusion calculation and multi-scale wavelet decomposition of acoustic components on the multi-physics field response signal, and performing feature dimension splicing to obtain crack feature classification data; and calculating and generating a crack detection result based on feature similarity matching and extension prediction. The precision and reliability of crack detection of the automobile parts are remarkably improved, and the damage risk in the detection process is avoided.

The invention belongs to the technical field of video live broadcast monitoring management, and particularly relates to a live broadcast content real-time compliance detection method and system based on deep learning, and the method breaks through the limitation that traditional text detection only depends on dominant keywords by constructing a text semantic analysis chain matched with original words and mapping sensitive words through constructing metaphor words. The identification of deep semantic violation contents such as metaphor and private obscure expression is realized; the method comprises the following steps: performing linkage verification on text violation clues and image behavior characteristics through behavior characteristic analysis of image frames before and after time positioning association of text violation based on a time positioning cross-modal collaborative detection mechanism, and forming a multi-modal judgment logic for text triggering image confirmation; a progressive detection strategy of hierarchical thresholds is designed, a first threshold is set as violation confirmation and a second threshold is set as suspected violation early warning, and real-time preliminary detection and precise secondary verification are combined, so that stepped processing from suspected violation to violation confirmation is realized, and the real-time performance and accuracy of detection are balanced.

The invention relates to the technical field of equipment energy consumption monitoring and fault diagnosis, and discloses a large-scale equipment energy consumption anomaly detection method and a storage medium. The method comprises the following steps: synchronously acquiring equipment energy consumption, state and environment data, fusing to generate multi-dimensional initial features, and performing multi-scale transformation. And performing preliminary detection on each scale feature subset to generate abnormal confidence and mode description. And screening the feature subsets according to the confidence coefficient, and dynamically selecting a corresponding detection strategy according to the mode description. And performing deep feature extraction on the feature subset based on the selected strategy, inputting the obtained high-dimensional feature vector into a corresponding anomaly evaluation network, calculating a standard state matching degree, comparing with a dynamic threshold to generate a fine-grained judgment result, and finally finishing positioning and attribution analysis in combination with anomaly description to form a detection report. According to the method, efficient and accurate large-scale equipment energy consumption anomaly detection is realized.

The invention discloses a vacuum cavity pressure maintaining test and leak rate rapid estimation method and system, and the method comprises the steps: achieving the test preparation of multiple sections through a public metering manifold and a multi-way isolation valve, and building a section volume database; the pressure and temperature data are synchronously collected in a fixed sampling period by exhausting air to preset pressure and isolating a pump end; obtaining an equivalent pressure change rate through temperature normalization and deflation baseline deduction in combination with filtering and regression algorithms, and performing conversion to obtain a standardized leak rate; step-by-step detection is carried out on each functional section of the cavity through a segmented bipartite isolation strategy and volume correction, and rapid positioning of a suspicious leakage section is realized; through double-threshold determination of an early warning threshold and an unqualified threshold, a detection report is automatically generated and written into a historical database, and a detection-determination-maintenance closed loop is formed. The method can improve the precision of leak rate detection, and is suitable for vacuum coating equipment, semiconductor process equipment and industrial vacuum treatment devices.

The invention discloses a complex scene target detection method based on a model dynamic joint mechanism, and the method comprises the steps: carrying out the preliminary detection through an RT-DETR model, retaining more potential targets through dynamic threshold adjustment, and projecting a generated detection frame to a feature space of an improved YOLOv12 model through dual-mode feature mapping; the improved YOLOv12 model integrates an SEAM attention module and a rejection loss function so as to enhance feature representation and positioning compactness of an occluded target. Then, a model joint mechanism is adopted to process preliminary results of the two models; through difficult case mining and online learning, missing detection targets are supplemented, and the RT-DETR model is optimized; and intelligently fusing the detection results of the two models through dynamic weight distribution based on scene complexity and hierarchical fusion of a decision tree. And finally, post-processing is carried out by using an improved non-maximum suppression algorithm, and mistaken deletion is reduced. According to the method, the problems of missing detection, false detection and inaccurate positioning of the target in a complex scene are effectively solved, and the recall rate and the accuracy rate of detection are remarkably improved.

The invention discloses an industrial control network flow abnormity real-time detection method based on a P4 programmable switch, and the method comprises the steps: extracting network flow characteristics related to a timestamp in a data plane in real time, carrying out high-line-speed preliminary detection on the traffic by using an industrial control network traffic anomaly detection algorithm based on a threshold rule to discover suspicious traffic, and uploading a preliminary detection result to a control plane; the control plane obtains suspicious traffic characteristic data based on the periodically maintained key traffic characteristic data and uploads the suspicious traffic characteristic data to the detection plane; the detection plane performs fine-grained detection on the suspicious traffic feature data by using a traffic anomaly detection model based on machine learning to obtain a final detection result; and when the difference between the final detection result and the preliminary detection result exceeds a given value, generating a new preliminary detection threshold rule based on an optimization algorithm or deep reinforcement learning, and updating to optimize the detection accuracy. According to the method, the real-time detection delay of the flow abnormity of the industrial control network can be greatly reduced, and the detection accuracy is remarkably improved.

The invention discloses an abnormal data processing method and system for a virtual game, and relates to the technical field of big data processing. Receiving an interaction data frame sequence uploaded by the terminal equipment, and analyzing to obtain an input equipment sampling point set and a displacement coordinate vector; performing feature extraction on the sampling point set of the input equipment to obtain a high-dimensional feature vector; mapping the high-dimensional feature vector to a preset behavior feature space, calculating a multi-dimensional statistical distance of the high-dimensional feature vector in the behavior feature space, and generating a first abnormal confidence coefficient; calling space mapping data corresponding to the virtual scene; performing discretization ray stepping detection in the space mapping data based on the displacement coordinate vector to generate a second abnormal confidence coefficient; performing fusion calculation on the first abnormal confidence coefficient and the second abnormal confidence coefficient based on a dynamic weighting algorithm to obtain a comprehensive abnormal score; and judging abnormal data through the comprehensive abnormal score, and executing a state rollback operation for the virtual object.

The invention discloses a drainage pipe network data cleaning and intelligent repairing method fusing multiple models. The method comprises the following steps that S1, original monitoring data flow is collected; s2, carrying out the preliminary detection and elimination of the abnormity based on IQR; s3, performing upstream and downstream multi-source feature extraction and XGBoost weight analysis; step S4, multivariable depth autoregression prediction based on PatchTST is carried out; s5, generating and publishing continuous high-quality data; according to the method, the key technical problems of various abnormal types, incomplete abnormal detection, low cleaning and repairing precision, poor data continuity and trend consistency, high system integration and real-time processing difficulty and the like commonly existing in the actual collection and transmission process of the online monitoring data of the drainage pipe network are solved.

The present invention relates to the field of real-time data filtering technology, and in particular to a real-time data filtering method and system with multi-dimensional feature fusion. The method comprises the following steps: obtaining data transmission logs and extracting original transmission data features, parsing data frame field structure information; identifying multi-dimensional abnormal coupling of data transmission based on field time gradient change rate and dynamic offset status; further detecting abnormal dilution of transmission data, predicting the data transmission complexity exceeding limit state, and detecting the overload condition of data filtering chip structure; then evaluating the attenuation trend of data real-time filtering efficiency, and optimizing the data real-time filtering path accordingly, completing real-time filtering processing of data transmission complexity exceeding limit condition, and outputting filtering results; the present invention achieves higher credibility of data filtering by filtering data in real time.

The invention relates to the technical field of photoelectric measurement, and discloses a data acquisition and processing method and system for photoelectric measurement. The method comprises the steps of obtaining photoelectric data of a target area, performing preprocessing to realize multi-source data space-time registration, and extracting a space-time fusion photoelectric signal; spectral features are obtained according to the signal, a preliminary detection area is divided, and fine target segmentation and feature optimization are completed; constructing a feature extraction model based on time-frequency analysis to perform target classification, and generating an acquisition result; energy distribution changes are obtained through the multi-period data, and dynamic calibration parameters are generated. The system comprises a memory and a processor, and executes corresponding programs to realize the steps. The method solves the problems of multi-source data fusion, target identification, dynamic calibration and the like in the prior art, improves the precision and reliability of photoelectric measurement, and is suitable for photoelectric measurement scenes in multiple fields.

The invention discloses an image linear target high-precision parameter detection method, and relates to the technical field of image recognition, and the method comprises the steps: carrying out the super-resolution reconstruction of a to-be-detected original image based on a perception optimization SRGAN algorithm, and obtaining a super-resolution reconstruction image; using an adaptive Canny edge detection algorithm to obtain contour information in the ROI region of the super-resolution reconstruction image; based on contour information in the ROI region of the super-resolution reconstruction image, performing linear fitting through an RANSAC algorithm for dynamically adjusting the number of iterations and a tolerant error to obtain a preliminary detection result; and constructing a multi-error-source joint model, and performing error compensation and quantization on the preliminary detection result based on the multi-error-source joint model to obtain a final straight line detection result, thereby completing detection. The method effectively improves the accuracy and stability of straight line detection in a complex environment, and is especially suitable for image processing tasks with low contrast, high noise or significant distortion.

The invention discloses a synchronous detection system for surface density and wall thickness uniformity of a composite bulletproof helmet, which belongs to the technical field of bulletproof helmet detection and comprises a detector cooperative control module, a two-parameter synchronous acquisition module, a data preprocessing module, a two-parameter cooperative constraint module and an intelligent modeling output module. Through the integrated design that the detection unit integrates the 3D scanning detector, the microwave thickness measuring probe and the beta-ray density detector, and in cooperation with the synchronous triggering and double-channel signal acquisition mode of the double-parameter synchronous acquisition module, synchronous acquisition of the surface density and wall thickness data of the helmet is realized; the problems that traditional step-by-step detection is low in efficiency and poor in data relevance are solved, the detection efficiency and the accuracy of quality judgment are greatly improved, meanwhile, a detection report containing process optimization suggestions is output, the problems that traditional detection results are not visual, and connection with the production process is not smooth are effectively solved, and the product quality is improved. And accurate guidance can be provided for mass production quality control and process adjustment.

The invention relates to the technical field of data anomaly identification and bridge safety monitoring, in particular to a burr and step detection method, a data anomaly identification method and a bridge health monitoring method. The burr anomaly detection method provided by the invention comprises the following steps: firstly, dividing a time sequence data set into a plurality of data intervals with equal numerical value widths; taking the mean value of the data interval containing the maximum number of numerical values as a reference value; and calculating a difference value between a numerical value at each time point of the time series data and a reference value as a fluctuation at the time point, and counting a continuous time point of which the fluctuation is greater than a burr threshold and identifying the continuous time point as a burr. The reference value is calculated based on the trend distribution, and then the burr is judged according to the difference value between the sampling value and the reference value. Through the consideration of the trend distribution, the influence of sudden factors is well removed, data analysis is carried out only by considering the influence of long-term factors, a better burr detection effect is obtained, and the burr detection precision is improved.

The invention relates to the technical field of large models and target detection, and discloses an intrusion target detection method based on a large model, and the method comprises the steps: obtaining a to-be-detected image, and inputting the to-be-detected image to a first large model, so as to obtain a preliminary analysis result used for representing the content of the to-be-detected image; determining a target detection algorithm from a plurality of preset target detection algorithms according to the preliminary analysis result; running a target detection algorithm, and processing the to-be-detected image to obtain a preliminary detection result; and inputting the preliminary detection result into the second large model to verify the preliminary detection result so as to generate a final detection result. According to the method, the flexibility and the automation level of the detection process can be remarkably improved, and a single system can cope with more diversified detection tasks, so that the universality of the system is enhanced. And the cognitive and inference capabilities of the artificial intelligence large model are utilized to identify and correct possible misjudgments, so that the accuracy and reliability of the final output result are improved, and the probability of false alarm or missing alarm is reduced.

The invention discloses a radar weak maneuvering target tracking-before-detection method based on dynamic programming and Hough circle detection. The method comprises the following steps: constructing an accurate motion model and an echo measurement model in a target adjacent situation for detection and tracking in a complex multi-target scene; according to the DP-TBD method based on sequential deletion, preliminary detection and target point extraction of adjacent targets are realized, and false detection and missing detection caused by clutter interference are avoided. And the target measurement point is corrected by combining a Hough circle detection technology, so that the accuracy and the reliability of a detection result are greatly improved. The Hungary algorithm is adopted to solve the matching and distribution problems among multiple targets, detection and tracking under the condition of high target proximity are achieved, and accurate association of the multiple targets is achieved. The method not only improves the precision and robustness of target detection and tracking, but also provides an efficient solution for multi-target tracking in a complex radar scene.

The invention discloses a CC attack gateway layer detection method and system based on an intelligent sliding time window, and belongs to the technical field of network security. The method comprises the following steps: analyzing the HTTP traffic of a gateway layer in real time, and extracting multi-dimensional features of the traffic and user behaviors; performing real-time aggregation and multi-dimensional analysis on the features by using a sliding time window mechanism; based on a risk score accumulation mechanism, matching the aggregation result with a pre-configured rule base to obtain a preliminary detection result and a risk score; when the initial detection is negative, further performing verification detection by using a pre-trained machine learning model; and finally, according to the risk score and a machine learning result, judging an attack and executing a progressive disposal measure. Through streaming processing, rule and model cooperation, intelligent degradation and other mechanisms, the problem that detection precision and processing performance cannot be achieved at the same time in a gateway scene in the prior art is effectively solved, and high-precision CC attack defense under the requirements of high concurrency and low delay is achieved.

The invention relates to the technical field of information, and provides a real-time target detection and automatic operation integrated system and method, and the method comprises the steps: capturing a target image in a dynamic environment through a visual sensor, processing the image through a YOLO algorithm to recognize the position and posture of an object, and obtaining a preliminary detection result; obtaining the corrected position information, generating an operation parameter adjustment instruction in combination with the current equipment state, judging whether the adjustment instruction meets the real-time requirement or not, and obtaining an optimized parameter set; matching the verification-passed adjustment permission with the optimization parameter set, if the verification-passed adjustment permission is consistent with the optimization parameter set, transmitting the parameter set to an automatic equipment controller, and determining a final execution instruction; a subsequent target image is re-processed through a refined filtering model, and the operation history is recorded in combination with an authority management log to determine a system security state; and extracting an abnormal index from the safety state of the system, and if the abnormal index is lower than a threshold value, maintaining the current parameter configuration to obtain stable operation configuration.

The invention relates to the technical field of industrial machine vision detection, in particular to a positioning detection method integrating deep learning and morphological operation. Comprising the following steps: S1, constructing a target positioning detection data set; s2, constructing an improved deep learning detection network; s3, training the improved deep learning detection network; s4, preliminary detection: inputting a to-be-detected image into the trained network, outputting a preliminary target detection frame and a corresponding confidence coefficient, and synchronously extracting a target candidate region image corresponding to the detection frame; s5, performing adaptive morphological optimization; and S6, fusion decision making: calculating the intersection-to-union ratio IOU of the preliminary detection frame and the edge after morphological optimization, and performing hierarchical fusion in combination with the confidence coefficient of the preliminary detection frame. According to the invention, through improving the collaborative design of the deep learning network, the adaptive morphological operation and the hierarchical fusion decision, the core problems of poor scene adaptability, small bubble missing detection of single deep learning and inaccurate edge positioning of traditional morphological detection are effectively solved.

The invention discloses a defect detection method, device and equipment and a medium, and relates to the technical field of power grids, artificial intelligence and the like, and the method comprises the steps: carrying out the multi-view collection of a target object based on an unmanned plane, and obtaining an original image; performing defect detection on the target object based on the original image to obtain a preliminary detection result; under the condition that the preliminary detection result shows that the confidence coefficient of the defect of the target object is smaller than a preset threshold value, triggering a meta-cognitive processing mode, and performing three-dimensional reconstruction based on the original image to obtain a three-dimensional model of the target object and acquisition parameter information of the original image; obtaining a sampling view angle sequence of the target object based on the three-dimensional model, the original image and the acquisition parameter information; based on the sampling view angle sequence, rendering a three-dimensional image corresponding to the original image in the three-dimensional model to obtain an image rendering result; and performing defect detection on the target object based on the image rendering result to obtain a defect detection result.

The invention discloses a multi-thread-based real-time communication audio equipment detection method, which realizes comprehensive coverage of various system environments and driver versions by parallelly starting a plurality of threads corresponding to bottom audio acquisition interfaces APIs of different operating systems, and introduces a mechanism for automatically adjusting the sampling rate, the sound channel number and the sampling format. The problem of silence or abnormal noise caused by mismatching of sampling parameters is effectively solved, and the effectiveness of audio data acquisition is improved. Meanwhile, a neural network voice activity detection model and a comprehensive scoring strategy of time domain statistical indexes are combined, so that the system can accurately recognize effective voice in a complex noise environment, and misjudgment and missed judgment of traditional time domain threshold detection are avoided. The dynamic incremental detection duration strategy balances the detection speed and accuracy, ensures that a user can quickly obtain a preliminary detection result, performs full analysis for abnormal conditions, and improves the detection robustness.

The invention provides a micro-service anomaly detection method, system and equipment, and the method comprises the steps: obtaining the observable data of a micro-service, the observable data comprising real-time monitoring data and service call chain data: inputting the observable data of the micro-service to an anomaly preliminary detection module to obtain preliminary anomaly detection data; the preliminary anomaly detection module is an anomaly detection agent based on a neural network; calling latest log data based on the preliminary anomaly detection data, and inputting the log data into a clustering model to obtain a clustering result; determining micro-service abnormal data based on the clustering result; and outputting the micro-service abnormal data to a Kafka message queue. According to the micro-service anomaly detection method provided by the invention, the efficient neural network and the machine learning algorithm are introduced, and through two rounds of anomaly determination processes, the problems that an existing micro-service anomaly detection method is prone to false alarm and low in anomaly detection accuracy are solved.

The invention relates to the technical field of UWB range finding, and discloses a UWB range finding method, a base station, a UWB positioning method and a UWB system.The UWB range finding method comprises the steps that CIR information of a target label is acquired, first-path preliminary detection is carried out on the CIR information, and an initial first-path index is obtained; obtaining an energy characteristic, a slope characteristic and a peak value characteristic according to the CIR information and the initial first path index; according to the energy characteristic, the slope characteristic and the peak value characteristic, carrying out first diameter secondary detection to obtain a first diameter detection result; and performing correction or calculation according to the first diameter detection result to obtain a target distance measurement value. According to the invention, the ranging and positioning precision of the UWB technology in a complex indoor environment can be improved, the error is reduced, and the system stability is improved.

The invention discloses a high-voltage cable buffer layer ablation degree evaluation method and device, and belongs to the technical field of high-voltage cable detection. Aiming at the problems of high false detection rate and low positioning precision of the existing detection technology, the method provides three steps of detection processes: firstly, drilling holes along preset intervals of a cable to take gas, analyzing gas components by using a gas chromatograph, and screening sections containing ablative characteristic gas; secondly, an infrared thermal imager is adopted to accurately position temperature abnormal points, and gas is remeasured; and finally, determining a gas weight in combination with an optimal and worst method, constructing a quantitative model through a multi-criterion compromise solution sorting method, calculating an abnormal point comprehensive score and dividing risk grades. According to the method, through a cooperative detection mechanism of gas primary screening, infrared positioning and retest evaluation, the detection efficiency and accuracy are remarkably improved, early ablation symptoms can be recognized, and the omission ratio is reduced.

The invention discloses a method for detecting illegal behaviors of pre-heater unblocking operating personnel. The method comprises the following steps that 1, a large model receives remote multi-video stream information; 2, the large model outputs a preliminary detection result and related information; 3, the obtained results are distributed to different task threads for corresponding post-processing and alarm rule judgment; and step 4, carrying out unified image processing and alarm information transmission work on all task data meeting the alarm condition. According to the invention, the detection output of the large model is post-processed to realize a single-model multi-task alarm function, so that the function of detecting and alarming the illegal behaviors of the pre-heater unblocking operation personnel is achieved, and the situation that the life safety of the operation personnel is endangered is avoided.

The invention discloses a design method of a three-order active filtering automatic test system, and the method comprises the steps: controlling a PXI platform through LabVIEW programming to achieve the generation of a sweep frequency signal and the collection of filtering response, and completing the signal processing, characteristic analysis and result visualization. The system integrates six core technologies, namely a modularized hardware architecture, automatic process control, frequency domain parameter precision measurement, multi-order characteristic synchronous detection, software and hardware collaborative linkage and simulation data comparison. On the basis of retaining traditional functions such as setting waveform type, amplitude, sweep frequency range, sampling rate, trigger mode, recording cut-off frequency, gain and other parameters, exponential sweep frequency accurate excitation and adaptive signal acquisition are innovatively realized, link interference is reduced through matrix switch multi-channel scheduling, a LabVIEW parallel thread and a feedback node mechanism are combined, and the adaptive signal acquisition is realized. And closed-loop analysis of amplitude-frequency / phase-frequency characteristics and real-time drawing of a Bode diagram are completed.

The invention discloses a motor stator and rotor magnetostatic multi-dimensional shape and position analysis and evaluation method and device, and the method comprises the steps: fixing a motor stator in a magnetostatic state, employing a four-quadrant orthogonal loading mechanism, manually applying an axial / radial combined load to a rotor, simulating the load of the motor in actual work, fixedly connecting a laser to the output shaft of the rotor, and carrying out the measurement of the output shaft of the rotor. And the laser is used for imaging on the photosensitive imaging plate and drawing the rotating track of the rotor. For the load in each direction, the inclination angle theta i of the stator and the rotor of the motor is calculated according to the laser imaging track; and according to the time difference and the position difference of the laser imaging points before and after the load is applied, the axial offset delta Xi and the radial eccentric distance delta Yi are calculated. The system performs statistical processing on four-direction load data through an adaptive weighting algorithm, and outputs a form and position deviation mean value # imgabs0 # # imgabs1 # to establish a method for evaluating the form and position of the stator and the rotor of the motor. According to the invention, high-precision synchronous detection of the multi-dimensional form and position deviation of the stator and the rotor is creatively realized in a static magnetic state.

The invention relates to the technical field of communication, in particular to a communication method for inhibiting an overhigh peak-to-average ratio in an orthogonal time-frequency-space system, which comprises the following steps of: 1, converting a signal from a delay-Doppler domain convenient for channel characterization to a time-frequency domain convenient for modulation implementation; step 2, performing preliminary suppression on the peak-to-average ratio by adopting a T-SLM method, generating a plurality of groups of candidate signals, and selecting one group with the peak-to-average ratio lower than a preset threshold or with the minimum peak-to-average ratio; step 3, carrying out Hisenberg transformation on the selected signal to obtain a time domain signal; carrying out iterative amplitude limiting filtering processing, adding a cyclic prefix, and then sending; step 4, removing the cyclic prefix by a receiving end, and recovering to a time delay-Doppler domain through Wigner transform and Sextile Fourier transform; performing preliminary detection by adopting a message passing algorithm to obtain a hard decision value and a log-likelihood ratio; and selecting a high-reliability observation value based on a log-likelihood ratio, and constructing a reliability selection matrix.