1805 results about "Euclidean distance" patented technology

The invention discloses a point cloud differential defect detection system and detection method based on multi-feature fusion, which are mainly used for high-precision detection of surface defects of a side wallboard of a railway passenger car, and the method comprises the following steps: obtaining datum point cloud data through a designed standard side wallboard model of the railway passenger car, and collecting real-time point cloud data through a laser scanner; preprocessing the obtained point cloud data; calculating the difference between the real-time point cloud and the reference point cloud by adopting a difference method for fusing geometric features such as Euclidean distance, curvature and normal vector, namely calculating the Euclidean distance difference, curvature difference and normal vector difference between the reference point cloud and the real-time point cloud; and a comprehensive abnormal score is generated by further combining a dynamic weight fusion mechanism, and different region characteristics (such as a plane, a curved surface and an edge region) are adapted through dynamic threshold adjustment. According to the method, the defect region is segmented through the clustering algorithm, and the defect area and depth are quantified. The method has the advantages of high detection precision, strong adaptability and good real-time performance, and is suitable for complex surface defect detection in industrial production.

The invention discloses a multi-modal large model assisted laparoscope soft tissue registration surgical navigation method and system. The method comprises the following steps: acquiring a preoperative CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) image and an intraoperative laparoscope image, and converting to obtain a multi-modal feature sequence; inputting the obtained multi-modal feature sequence into a large model for fusion to obtain a cross-modal matching matrix; random sampling is carried out by using an RANSAC random sampling consensus algorithm, an error between corresponding point sets of two data is measured by using an Euclidean distance, an analytical solution is obtained through SVD decomposition, and a feature matching error is introduced to obtain an optimal rigid body transformation pose; performing non-rigid deformation local fitting adjustment on the optimal rigid body transformation pose residual error by adopting a parameterized deformation model in combination with learning prediction deformation, and performing loop execution to obtain a soft tissue organ real-time registration image; automatic, high-timeliness and high-precision real-time registration of soft tissues is realized under the multi-mode and multi-variable conditions, and reliable navigation and positioning support is ensured to be provided in a complex operation environment.

The invention relates to the technical field of data processing, in particular to a workpiece machining data processing method and system. The method comprises the following steps: acquiring data points in a historical operation data set of workpiece processing and a clustering center of each clustering cluster; obtaining an abnormal index of the cluster according to the ratio of the number of the data points in the cluster to the number of the data points in the maximum cluster, and obtaining the suspicious degree of the cluster according to the Euclidean distance between each data point in the cluster and the clustering center; taking the range value of the collection moment corresponding to the data points in the clustering cluster as the credibility of the suspicious degree to obtain the feature weight of the clustering cluster; the specific value of the local density of the data point to the feature weight of the clustering cluster where the data point is located is marked as the weighted local density of the data point, the outlier factor of the data point is obtained through calculation, workpiece machining data processing is achieved through the comparison result of the outlier factor and the preset threshold value, and the accuracy of workpiece machining anomaly monitoring is effectively improved.

The invention discloses a stationary pollution source data acquisition and calibration method and system, a medium and a program product, and the method comprises the steps: carrying out the segmentation processing of a plurality of concentration change data, and obtaining a plurality of data segments; determining the concentration change trend of various pollutants, and calculating a correlation coefficient matrix; the Euclidean distance value between the correlation coefficient matrix of the current data segment and each correlation coefficient matrix before sorting is calculated; when the Euclidean distance is greater than a preset threshold value, marking the corresponding data segment as an abnormal data segment; determining the pollutant as a target pollutant, and establishing a material balance equation; based on the material balance equation, generating a theoretical concentration change curve of the target pollutant; calculating a deviation value, and when the deviation value exceeds a preset dynamic error limit value, determining a sensor correction coefficient according to the process parameters; and carrying out calibration processing on the acquired data of the target pollutant according to the sensor correction coefficient. According to the invention, the accuracy of data acquisition and calibration of the stationary pollution source is improved, so that the influence on formulation and execution of pollution prevention and control measures is reduced.

The invention discloses a fine-grained access control method and system based on risk identification, and belongs to the technical field of information security. According to the method, user subject attributes, behavior attributes and system environment attribute information are collected in real time, a standardized decision matrix is constructed, an interval type-2 fuzzy set (IT2FS) is used for conducting fuzzy modeling on the attributes, and an upper membership matrix and a lower membership matrix are generated. And calculating the dynamic weight of the attribute index in combination with a CRITIC method, introducing a time decay factor to dynamically correct a risk score through an improved TOPSIS method, calculating the Euclidean distance between an access request and a positive / negative ideal solution, and generating a normalized risk closeness degree. And based on the risk score and a preset threshold value, dynamically matching a hierarchical permission strategy, and adopting a static rule and a priority coverage mechanism to eliminate permission conflicts. According to the method, multi-dimensional risk assessment and dynamic weight adjustment are fused, the problems of insufficient real-time performance, subjective weight dependence and weak uncertainty processing capability in a traditional method are solved, the accuracy and security of access control are remarkably improved, and the method is suitable for scenes with high security requirements such as cloud computing and finance.

The invention belongs to the technical field of path planning, particularly relates to a robot dog inspection path planning method and system, solves the problems of poor operation endurance and inspection efficiency of a robot dog in a path planning method in the prior art, and comprises the following steps: S1, obtaining a three-dimensional grid map of an inspection environment; s2, generating an inspection point sequence; s3, sequentially planning each path section based on an improved A * algorithm, wherein the actual cost # imgabs1 # from the starting point of the path section to the current node # imgabs0 # is a scalar value integrating the path length, the traffic energy consumption and the equipment inspection information value; the heuristic cost # imgabs3 # from the current node # imgabs2 # to the end point of the path section is in direct proportion to the Euclidean distance from the current node # imgabs4 # to the end point of the path section; s4, when an obstacle is detected, performing dynamic re-planning of a local path; and S5, setting an optimal marching gait parameter for each terrain road section. And the path length, the passing energy consumption and the information value of the inspection point are comprehensively considered, so that the operation endurance and the inspection efficiency of the robot dog are improved.

The invention relates to the technical field of machine learning, in particular to a grain classification and recognition method and system based on image analysis, and the method comprises the following steps: obtaining grain particle image data, calculating a gray histogram and a gradient change rate, calling a gray co-occurrence matrix to extract grain particle texture density, and scanning a sliding window to obtain local feature parameters of grain particles. According to the method, through combined analysis of a gray level histogram and a gradient change rate, dynamic extraction of particle textures through a sliding window, enhancement of complex surface feature capture, normalization of contrast and direction consistency parameters, construction of weighted feature vectors, dynamic correction of variance contribution degree and suppression of environmental interference, and fusion of near-infrared and short-wave infrared reflectivity changes, the near-infrared and short-wave infrared reflectance change is improved. And quantifying the mean value and peak-to-valley ratio of spectrum difference values, matching visible light characteristics to verify multi-dimensional constraints, constructing a spectrum-texture matrix by local reflectivity rate and gradient change rate, and synchronously evaluating global similarity and spatial distribution difference by adopting Euclidean distance and local offset double-layer matching.

The invention discloses a real-time three-dimensional fusion reconstruction method for a ship comprehensive test scene, and the method comprises the steps: carrying out the preprocessing of a target object image data flow, converting an image coordinate system into a camera coordinate system, obtaining a sparse point cloud model, and constructing a voxel three-dimensional grid; the method comprises the following steps: constructing a scale space by using a Gaussian difference pyramid to detect key points of an image under different scales, calculating a gradient direction histogram around the key points and taking the gradient direction histogram as a descriptor, matching the descriptor by using an Euclidean distance, and estimating relative pose transformation between the images to complete a local matching process. Projecting and mapping the local frame block to a reference frame to form a key frame, and entering a global matching process; and extracting a surface grid of the three-dimensional model from the dense body grid by adopting a Marching Cubes algorithm, traversing each cube unit in the surface grid, and obtaining distance information from a vertex to a nearest surface based on the cube units, thereby generating a final three-dimensional model of the test target object.

The invention discloses a voiceprint recognition, identity confirmation and dialogue implementation method applied to sentiment analysis, which can well realize voiceprint recognition and identity confirmation, and can realize mute detection, recording control and recording end judgment and release. Comprising voiceprint recognition and identity confirmation: firstly, determining whether the source of recorded audio is the sound of a specific person by adopting a voiceprint recognition technology; the voiceprint recognition generally performs identity verification by extracting audio features and comparing the extracted audio features with a pre-stored template. The mathematical formula is as follows: assuming that the feature of the input audio is X = {x1, x2,..., xn} and the voiceprint template is T = {t1, t2,..., tm}, the recognition process can be represented as # imgabs0 #, and the # imgabs1 # represents the recognized user tag xj-ti2 represents the Euclidean distance between feature vectors. According to the method, sound cloning and emotion adjustment are realized through deep learning models (GANs and VAEs). In the whole process, through training and optimization of the deep neural network, high-quality emotional voice is generated, and more natural and personalized voice interaction experience is provided.

The invention relates to the technical field of power supply detection, and discloses a fire-fighting equipment power supply detection method and system.The detection method comprises the following steps that voltage fluctuation, current phase and ripple coefficients are collected through a multi-channel sensor group, a feature sequence is generated through sliding window noise reduction, the Euclidean distance is calculated through Z-score standardization, and a calibration instruction is generated when a threshold value is exceeded; kalman filtering adjusts frequency output detection parameters, fast Fourier transform is executed to extract harmonic amplitude to generate a suppression vector, and a least square reconstruction model outputs a state identifier; according to the method, voltage, current and ripples are collected through multiple channels, anti-interference performance and data integrity are enhanced through sliding window noise reduction, a threshold value is adjusted through Z-score and Euclidean distance self-matching, sampling frequency and detection parameters are optimized through Kalman filtering, harmonic characteristics are extracted through FFT, and the state is evaluated through a least square reconstruction model. And a multi-dimensional classification system is constructed by fusing the distortion rate and the phase deviation, so that the detection precision, the sensitivity and the response real-time performance are improved.

The invention relates to the field of navigation, and more particularly discloses a gradient optimization driven unmanned aerial vehicle real-time obstacle avoidance multi-stage trajectory planning method, which comprises the following steps of: in a trajectory initialization stage, firstly, searching a collision-free geometric path considering steering limitation of an unmanned aerial vehicle on an occupied grid map by utilizing an improved algorithm to generate an initial B-spline control point; then, in a trajectory optimization stage, constructing a multi-target trajectory optimization problem, introducing an obstacle avoidance constraint based on an Euclidean distance field map, and optimizing the initial control point set in combination with unmanned aerial vehicle parameters and optimization weights to obtain a trajectory meeting an obstacle avoidance requirement; and finally, for the optimized trajectory, performing dynamic feasibility evaluation based on unmanned aerial vehicle kinematics limitation in a trajectory correction stage, and if the trajectory does not meet the constraint, performing correction based on the minimum curvature constraint on the control point to ensure that the finally generated trajectory is not only obstacle-avoiding but also feasible in dynamics, and finally, determining that the trajectory does not meet the constraint. Therefore, the real-time obstacle avoidance capability of the unmanned aerial vehicle in a complex environment is effectively improved.

The invention provides a path planning method and system for an automatic driving trolley, and relates to the technical field of path planning, and the method comprises the steps: obtaining the data of an acoustic sensor of an underwater environment, and obtaining the contour of the underwater terrain and the position information of an obstacle; carrying out dynamic environment modeling processing; the Euclidean distance is used as a heuristic function to calculate the dynamic model of the underwater environment, and a second optimized path conforming to kinematics and dynamics constraints of the automatic driving trolley is obtained; carrying out smoothing processing on the second optimization path through a dynamic motion primitive technology; constructing a three-dimensional map for path planning and obstacle avoidance; predicting an action track of the robot in a period of time in the future; and through real-time adjustment processing of the self-adaptive control system, path planning of the automatic driving trolley is completed. According to the method, the dynamic characteristics of the underwater environment and the motion constraint of the automatic driving trolley can be comprehensively considered, the three-dimensional map containing the obstacle and topographic information can be constructed in real time, and the future motion trail of the obstacle can be predicted.

The invention discloses an intelligent ship situation awareness prediction method and system, and relates to the technical field of intelligent ship situation awareness and trajectory prediction, and the method comprises the steps: carrying out the time-space synchronization of collected operation environment data of a ship through a timestamp alignment method, and generating a ship situation data set; based on the ship situation data set, the course angle change rate and the Euclidean distance of the obstacle relative to the ship are extracted, and weighted fusion is carried out on the dynamic collision probability and the approaching time of the obstacle to obtain an obstacle threat degree index; inputting the obstacle threat degree index into a ship trajectory prediction model, and outputting the minimum meeting distance between the ship and each obstacle; and carrying out risk grade division on the minimum encounter distance according to an international maritime collision avoidance rule, outputting a graded early warning signal, and formulating a navigation strategy to execute obstacle avoidance and route adjustment. According to the method, the technical problems of risk perception lag, single and rough avoidance action and insufficient trajectory prediction accuracy in the prior art are effectively solved.

The invention belongs to the technical field of face recognition, and particularly relates to a face recognition algorithm adaptive to illumination variation, which uses Gaussian filtering to reduce noise caused by illumination variation, convert a color image into a grey-scale image, reduce calculation complexity, perform adaptive histogram equalization, estimate global illumination conditions in the image and improve the face recognition accuracy. Calculating the main direction and intensity of illumination in the image, predicting the current illumination condition, and enabling the image brightness to adapt to different environment illumination; identifying a face region in the image, using a deep learning method to position face related points, using an LBP to extract local features insensitive to illumination variation, and using a deep convolutional neural network to extract global features; introducing a multi-illumination data set; the Euclidean distance is used for comparing the extracted feature vectors, the similarity between the extracted feature vectors and known faces in a database is recognized, according to the similarity score, threshold judgment is adopted for AQW to obtain a final recognition decision, and the method has the effect of being capable of accurately adapting to facial features under different illumination conditions in real time.

The invention discloses an efficient personalized federal learning method, device and product based on knowledge distillation. The method comprises the following steps: firstly, clustering clients with similar data distribution by using a client clustering algorithm based on Euclidean distance; each client completes local model training on a local private data set, completes model prediction on a public data set based on dynamic temperature adjustment and uploads a soft prediction result to the server; the server completes global aggregation; and finally, the client downloads a global aggregation result, and completes local model updating and starts a new round of local model training by using a triple dynamic distillation strategy. According to the invention, more accurate and efficient personalized federal learning is realized.

According to the transformer substation construction safety distance monitoring method, device and equipment and the storage medium provided by the embodiment of the invention, the three-dimensional target detection model is established through the obtained point cloud data of the transformer substation, and the first three-dimensional target detection is carried out based on the three-dimensional target detection model so as to detect whether the electrified equipment exists in the to-be-monitored area; and under the condition of determining that the electrified equipment exists in the to-be-monitored area, obtaining three-dimensional position information corresponding to the electrified equipment, dividing a risk area based on the three-dimensional position information, and then performing secondary three-dimensional target detection based on the three-dimensional target detection model so as to detect whether an invader exists in the risk area. When the condition that an invader exists in the risk area is determined, the type of the invader is further judged, namely whether the invader is an invader or a construction machine is judged, and finally the Euclidean distance between the electrified equipment and the invader is calculated based on the boundary information; and if the Euclidean distance exceeds a preset distance threshold, outputting alarm information based on the category information.

The invention provides a multi-layer soft soil foundation settlement prediction method, and relates to the field of settlement prediction, and the method comprises the steps: obtaining soft soil parameters through arranging a multi-source monitoring device, carrying out inversion to generate multi-dimensional feature vectors containing particle size distribution, void ratio, compression modulus gradient and effective stress, and dividing structure piece elements to construct a feature set; the Euclidean distance between the fragments is calculated and clustered to generate a structural similar region, and a judgment threshold is dynamically adjusted and combined based on the change rate of the structural information entropy to construct a dynamic structural map; newly added or cracked similar areas are identified, a structure sudden change strength index is generated, and when a threshold value is exceeded, a jump prediction model is constructed according to entropy change, pore pressure gradient and deformation rate to output discontinuous settlement increment; and generating an error field by using the difference value of the actually measured settlement and the predicted value, and correcting the structural information entropy and merging the threshold value after weighted averaging according to the similar region, thereby realizing the self-adaptive correction of the structural map and iterative optimization prediction.

The invention relates to the technical field of signal noise reduction and reconstruction, in particular to a microseismic signal noise reduction and reconstruction method and system based on multi-scale decomposition, and the method comprises the steps: S1, carrying out the multi-scale Shaplet decomposition of a noise-containing microseismic signal based on different sequence lengths, measuring the matching degree between each signal block and all candidate Shaplets through Euclidean distance, and obtaining the matching degree between each signal block and each candidate Shaplet; constructing a multi-scale feature matrix M; s2, based on the multi-scale feature matrix M, multi-scale weighted importance measurement is carried out through a time convolution network and an attention mechanism so as to evaluate the importance of each signal block in the noise reduction process; and S3, constructing a U-Net microseismic signal noise reduction model, designing a loss function in combination with the reconstruction error and dynamic time warping, and carrying out multiple iterative training on the U-Net microseismic signal noise reduction model until the error meets a preset requirement. According to the method, noise can be effectively identified and removed, and especially in data containing different noise, the method is beneficial to high-quality noise reduction of micro-seismic monitoring data.

The invention discloses a network pruning method and system based on multi-scale weight analysis. The method comprises the following steps: S1, loading a pre-training network model which needs to be lightened; s2, calculating the normalized sensitivity value of each convolution layer in the model in the step S1 based on a gradient analysis method; s3, according to the sensitivity value obtained in the step S2, calculating the pruning proportion of each convolutional layer, and according to the pruning proportion, calculating the number of channels needing to be pruned in each layer; s4, constructing a multi-scale weight analysis model based on an L2 norm, an Euclidean distance and KL divergence, calculating importance scores of channels in each convolution layer of the pre-trained network model, obtaining the number of channels needing to be cut out of the layer according to the step S3, and performing pruning operation on the channels of which the scores are lower than a preset pruning proportion; and S5, performing fine tuning and re-training on the pruned model. According to the method, the channel redundancy and importance are accurately evaluated, so that the accuracy of image recognition can be further improved under the same pruning proportion.

The invention discloses an autonomous cognitive algorithm of a patrol robot hidden area based on a 3D laser radar, and relates to the technical field of artificial intelligence. The invention provides an autonomous cognitive algorithm for a patrol robot hidden area based on a 3D laser radar. The autonomous cognitive algorithm comprises the following steps: acquiring a non-ground point cloud by adopting a ground point cloud filtering algorithm; for the obtained non-ground point clouds, dividing concentric circle regions according to Euclidean distances, and performing Euclidean clustering on the point clouds in the concentric circle regions to obtain clustered point cloud clusters; according to the point cloud cluster, calculating an axis alignment bounding box, and obtaining a hidden point coordinate; and according to the point cloud cluster, calculating a directional bounding box, and calculating coordinates of a shielding point in a sight line shielding mode, thereby automatically calculating and obtaining a shielding angle. Therefore, by the adoption of the method, the hidden area is reasonably selected according to the angle information in the patrol process through the 3D laser radar data, the hiding effect is achieved, the patrol task is better completed, and high robustness and practicability are achieved.

The invention discloses a cue word compression method and system based on TF-IDF and cross entropy, belongs to the technical field of large model cue word compression, and aims to solve the problems that redundant information is introduced into long cue words, the model efficiency is reduced and the cost is increased. To-be-compressed content is divided into sentences at the sentence level and then converted into embedded vectors, and the Euclidean distance is calculated in combination with problem vectors so as to screen related sentences; calculating a TF-IDF value at the word level through a word frequency and an inverse document frequency to extract keywords and recombine sentences; and selecting a reference model and a basic model at the Token level, identifying the key Token based on a cross entropy loss difference value, and splicing the key Token in sequence to generate a compressed cue word. According to the method, a complex calculation structure is avoided, the inference efficiency is improved while the semantic integrity is maintained, and the resource consumption is reduced.

The invention relates to the technical field of tunnel and underground space robot autonomous navigation, and provides a tunnel path planning method and system for clearance corridor and elastic geodesic refinement, and the method comprises the steps: calculating the Euclidean distance from a structural terrain to a nearest obstacle or wall, taking the Euclidean distance as clearance, generating a direction-independent cost, and constructing a dual metric; running Riemannian geometry fast travel and backtracking geodesic under the condition that starting and ending point pixel coordinates are given to obtain a coarse path, after rasterization, calculating the distance from pixels to the coarse path, obtaining a widening radius by using clearance along the coarse path, performing nearest neighbor propagation to a global domain to obtain a radius graph, defining a corridor, and performing corner propagation by the coarse path to obtain an approximate curvature field; calculating to obtain a score, partitioning through thresholding, performing piecewise linear penalty, updating the total cost related to the direction, re-marking according to partitions, taking the reciprocal as a velocity field, and obtaining a final path through a solver. The problems of safe obstacle avoidance, curvature control and efficient solution need to be met at the same time in engineering are solved.

The invention discloses a wearable thermal power plant personnel route planning device based on UWB, and relates to the technical field of route planning, an inspection task is generated based on an inspection plan, and the Euclidean distance between an inspector and an inspection task point is calculated through a linear programming method by combining real-time position data of the inspector and adopting the minimum distance optimization principle, so that the route planning efficiency is improved. Distributing an inspection path; equipment state scores, historical fault scores and safety risk scores are introduced, a multi-factor weighted optimization target is constructed, high-fault-frequency and high-risk equipment is subjected to priority inspection, safety accidents possibly caused by equipment abnormity are reduced, meanwhile, the advancing route of inspectors is optimized, and the inspection coverage rate is increased; the UWB electronic fence technology is adopted to monitor an inspector in real time, the Euclidean distance is adopted to calculate the distance between the inspector and a high-risk area, through a two-stage early warning mechanism, visual, vibration or voice reminding is provided when the inspector approaches or enters the dangerous area, and a safety early warning signal is synchronously sent to a background.

The invention discloses a coal mine earthquake time sequence feature prediction method based on a multi-source data space-time diagram convolutional network, and belongs to the field of mine dynamic disaster prevention and control. The method comprises the following steps: firstly, fusing micro-seismic data and charge data, and extracting and integrating features of two data sources by using a feature fusion technology so as to more comprehensively capture dynamic features of an underground environment; then, constructing a graph structure, taking the sensors as nodes, and defining edges among the nodes by using Euclidean distances among the sensors so as to reflect spatial correlation of geological activities; and dynamically capturing the correlation between the time dimension and the space dimension through the space-time diagram convolutional network. And then, optimizing hyper-parameters in the model by using WOA to obtain an optimal parameter combination, and improving the performance and accuracy of the model. And finally, introducing a space-time attention mechanism, and dynamically adjusting data input by calculating a time attention matrix and a space attention matrix so as to better capture space-time characteristics and improve the accuracy of model prediction.

The invention relates to the technical field of computer vision, in particular to a reloading pedestrian re-identification method based on a visual language pre-training model. The method comprises the following steps: a training stage: inputting an image to obtain a clothing mask image, and generating clothing irrelevant / relevant prompts; text encoder parameters are fixed, prompt weights are optimized, text prompts are input into an encoder to obtain features, and a classifier is constructed to achieve image-text alignment through cross entropy loss; using a visual encoder to extract mask pattern features, and constraining a class center Euclidean distance to realize image-image alignment; stripping clothes characteristics: extracting clothes area characteristics and corresponding text characteristics, optimizing by a classifier, and introducing orthogonal loss to decouple clothes correlation; in the reasoning stage, a query image is input into a trained image encoder to extract features, and cosine similarity ranking and result returning are calculated according to the features of the image library. According to the technical scheme, the recognition accuracy of the pedestrian re-recognition method under the condition of pedestrian clothing change can be improved.

The invention discloses a partition detection method for board card quality, and relates to the technical field of detection. The method comprises the following steps: dividing a board card into functional blocks according to a functional structure, and collecting thermal field distribution characteristic data of each block; constructing a standard thermal template, calculating the thermal imaging matching degree of the functional blocks by using an Euclidean distance algorithm, and marking the thermal imaging matching degree exceeding a preset threshold value as an abnormal thermal response region; collecting resistance and resistance gradient change data of an abnormal thermal response area, judging the area as a defect cluster area if conditions are met, and calculating a resistance abnormal amplitude; integrating defect cluster region data to form a composite characteristic spectrum, inputting a defect sample matching model, and outputting a defect classification result and a confidence score; calculating a regional quality score by combining fuzzy logic reasoning and a neural network algorithm and integrating a thermal imaging matching degree, a resistance abnormal amplitude and a confidence score; and fusing the defect classification result and the regional quality score to generate a quality grade label. And based on the quality grade labels, board card quality partition detection is completed.

The invention discloses a power grid fault positioning system and method based on traveling wave distance measurement, relates to the technical field of fault analysis, and solves the technical problems that traveling wave feature analysis, temperature compensation and high-precision synchronization technologies are not effectively integrated, and rapid and accurate fault positioning of a smart power grid is difficult to meet. Through the technical combination of dynamic temperature compensation, high-precision time synchronization and intelligent fault classification, the bottlenecks of traditional traveling wave distance measurement in the aspects of precision, reliability and intelligence are systematically solved, temperature sensors are deployed at key nodes of a cable, the temperature is monitored in real time, the wave speed is dynamically corrected through a formula, the overall error is reduced, and the fault location accuracy is improved. Meanwhile, an optical fiber two-way time transmission method is combined, transmission delay errors are reduced, synchronization reliability is improved, finally, a mapping library is constructed through laboratory simulation and field data, a Euclidean distance matching algorithm is combined, fault reasons are automatically recognized, and rapid positioning of the faults and accurate recognition of the fault reasons are achieved.

A system is configured to: (a) provide a deep learning model; (b) receive a concept, the concept including text; (c) determine a word embedding representing the concept using the deep learning model; (d) determine a centroid for the concept, the centroid representing an average position of concept characteristics in a high-dimensional word embeddings space; (e) determine Euclidean distances for each of the concept characteristics; (f) determine cosine similarities for each of the concept characteristics; (g) determine a prominence index for each of the concept characteristics based on the determined Euclidean distances and the determined cosine similarities; and (h) provide, to a client device, a ranking of the concept characteristics based on the prominence index.

The invention discloses an intelligent cavity operation navigation method and system based on multi-mode perception fusion, and relates to the technical field of surgical medical treatment. Firstly, bionic instrument posture data and cavity environment data are obtained; then, based on the bionic instrument posture data and the orifice environment data, the target curvature of each section is calculated through a bionic kinematics model, and the magnetorheological fluid pressure is dynamically adjusted to control the local rigidity; meanwhile, the cavity point cloud topological graph is converted into an obstacle density graph, and path weight matrixes of different paths are quantified based on a swarm intelligence algorithm; and finally, according to the target curvature, the local rigidity and the path weight matrix, driving a bionic instrument to execute compliant motion and cooperative obstacle avoidance operation. According to the method, multi-modal data (curvature, rigidity and contact force) are fused through a bionic kinematics model, navigation deviation correction is achieved in combination with a curvature-force nonlinear mapping model and three-dimensional Euclidean distance monitoring, and the navigation precision is improved in combination with a path re-planning mechanism.

The invention discloses a distributed denial of service attack defense method and system, a medium and a program product, and the method comprises the steps: calculating feature parameters of network flow data; generating a multi-dimensional feature vector; constructing a time sequence state transition matrix; extracting a feature root sequence, and calculating a modulus length change rate; constructing a dynamic phase diagram based on the mode length change rate; the Euclidean distance between adjacent orbit points in the dynamic phase diagram is calculated, when it is determined that an abnormal attractor appears, corresponding flow data are extracted, and a self-similarity index is calculated; when the self-similarity index is greater than a third preset threshold value, determining a high danger level; when the value is greater than a fourth preset threshold and less than or equal to a third preset threshold, determining that the danger level is a middle danger level; when less than or equal to a fourth preset threshold, determining that the danger level is low; executing a discarding strategy on the high danger level; executing a bandwidth limiting strategy for the middle danger level; a delayed forwarding policy is performed for a low hazard level. The accuracy of formulating and implementing the protection strategy is improved, and the security of the network is further improved.