456 results about "Path recognition" patented technology

The invention relates to the technical field of defect detection, in particular to a special equipment defect automatic identification method and system, and the method comprises the following steps: marking a continuous path based on an image gray scale gradient and adjacent differences, identifying the pixel connection intensity to obtain a contour image layer, extracting continuous pixels, analyzing gray scale and gradient features, and expanding a texture direction to generate a defect image block. And marking and connecting the small regions to obtain a closed layer, analyzing texture and edge differences, matching classification tags, and clustering and coding to generate a defect identification tag set. According to the method, boundary structure recognition is enhanced through gray gradient and direction continuity analysis, a texture aggregation area is expanded in combination with gray consistency and edge direction stability, brightness abrupt change points are removed, contour extraction precision is improved, micro crack continuity is recovered, and pseudo defects are eliminated; and multi-dimensional image attributes are fused to identify key region feature differences, so that the classification precision and the spatial mapping consistency are improved, and efficient and accurate defect identification and stable classification are realized.

The invention relates to the technical field of laser cutting, in particular to a laser cutting device and a control method thereof. The method comprises the following steps: collecting data of a laser cutting machine to obtain data of the laser cutting machine; cutting head moving speed data of the laser cutting machine data are extracted, and the cutting head moving speed data are obtained; performing power calculation on the moving speed data of the cutting head to obtain laser power data; spatial modeling is carried out based on the cutting head moving speed data and the laser power data, and a laser cutting machine three-dimensional model is generated; performing cutting path identification on the three-dimensional model of the laser cutting machine to generate cutting path data; and extracting first cutting data of the laser cutting machine data to obtain the first cutting data. Through the Internet of Things technology, the data mapping technology, the data analysis technology and the intelligent control technology, self-adaptive laser cutting control over different materials and thickness conditions is achieved, and therefore the cutting quality and efficiency are improved.

The invention relates to the technical field of image recognition, in particular to an image processing method and system for rehabilitation training action analysis. According to the method, a multi-view image sequence is collected based on a binocular camera device, skeleton key point data of a user in a training process is extracted by utilizing a three-dimensional attitude reconstruction technology, and an action three-dimensional time sequence data set is constructed. The method comprises the following steps: firstly, constructing an individual standard power generation characteristic model of a user, modeling a skeleton driving path of a main muscle group, and forming a personalized power generation reference structure; and then a standard rehabilitation action path is matched through a dynamic time warping algorithm, and the attitude deviation under the key frame is identified. And the system dynamically compares the identified non-standard motion mode with the individual model, judges whether abnormal force generation exists or not and outputs the type and the part of the muscle compensation behavior. And finally, multi-modal feedback information with highlighted graphs, voice prompts and character suggestions is generated in combination with an identification result, so that the identification precision and personalized guidance capability of rehabilitation training are remarkably improved.

The invention discloses an autonomous collision avoidance method and system for an unmanned ship in a mixed traffic mode, and the method comprises the following steps: obtaining the current position coordinate information, course information and surrounding environment information of the unmanned ship in real time through a ship positioning system and a ship recognition system on the unmanned ship; combining the target point, constructing a path plan and calculating the energy consumption of a navigation path, selecting a first navigation path, identifying and classifying static obstacles and dynamic obstacles, monitoring the static obstacles in real time, calculating and evaluating a detour difference, adjusting the path according to a collision avoidance rule, and generating a second navigation path; predicting the movement direction of the surrounding dynamic obstacles, and judging whether a collision risk exists or not; predicting a future position and adjusting the course to generate a third navigation path; and respectively evaluating the electric quantity threshold, the energy consumption threshold, the battery electric quantity and the energy consumption value, and adjusting an energy use strategy in real time. According to the invention, the accuracy of the navigation path is improved, and the safety of the unmanned ship in a complex environment is improved.

The invention relates to the technical field of visual navigation, in particular to a robot navigation method based on vision, which comprises the following steps: acquiring a grayscale image to extract dynamic point locations, screening boundary features to reconstruct a feature set, calculating gradient change to generate candidate guide points, converting coordinates to construct a path track set, and outputting a navigation planning path. According to the method, rapid elimination of static backgrounds is realized through difference threshold selection, dynamic region extraction accuracy is effectively improved, combined screening of boundary line feature point frequency and average feature density is combined, key feature stability is enhanced, and the identification capability of image local structure mutation is enhanced by using a variance change rate of a gradient amplitude sequence. Robustness of path guide point extraction is improved through judgment of an abnormal section, dynamic construction of a path track is realized by means of continuous time integration of image coordinates, path identification precision and spatial positioning continuity are optimized, and continuity, precision and real-time performance of navigation path planning are improved.

The invention relates to the technical field of welding automation and robot visual perception, and discloses a robot intelligent welding method and system.The method comprises the steps that a three-dimensional point cloud image is constructed through a laser triangulation method; filtering and enhancing the point cloud image, and extracting a weld point cloud set by adopting a U-Net semantic segmentation model; carrying out space path identification and attitude regression based on the set to obtain a track element of a position-attitude pair; fitting a path by adopting a cubic B-spline curve and interpolating to generate a continuous welding track; and in the welding process, visual feedback is combined, and proportional differential control logic is introduced for closed-loop track error compensation. Compared with the technical problem that in the prior art, weld joint path recognition and accurate tracking cannot be stably achieved under complex structures such as curved surfaces and variable cross sections, a perception control closed loop based on visual servo is constructed, high-precision automatic welding of the complex weld joint structures is achieved, and the track fitting precision is improved.

The invention relates to the technical field of unmanned aerial vehicle remote identification, in particular to an unmanned aerial vehicle flight path detection method and system, and the method comprises the following steps: obtaining an airspace broadcast signal, detecting a newly added signal, correcting a terminal time difference, recognizing an information source position, extracting a position sequence, reconstructing an unmanned aerial vehicle flight path, comparing a declaration path, and recognizing the identity of an unmanned aerial vehicle. And detecting an abnormal unmanned aerial vehicle and an abnormal flight path, extracting a broadcast time sequence, identifying a time overlapping interval, detecting a remote control end, and obtaining a control identification result. According to the invention, by monitoring the airspace signal in real time and dynamically detecting the newly added signal source, the monitoring precision of the signal source is improved, the time difference correction and space cross reconstruction of a plurality of monitoring terminals are combined, the positioning precision of the unmanned aerial vehicle position is optimized, and a track reconstruction method is adopted, so that the real-time monitoring capability of abnormal behaviors is enhanced; by identifying the information source of the control end and extracting the position information, the airspace safety management and illegal flight prevention capability is improved.

The invention discloses a method and a system for processing route planning data for electric power line inspection. The method comprises the following steps: acquiring line distribution and topographic feature data; gridding the power network region, determining the patrol priority, and generating a region priority sequence; the overall patrol task is decomposed, a plurality of task subsets are generated, one task is allocated to each unmanned aerial vehicle, and each task subset comprises an initial position and a target line section; generating an initial route path based on the topographic features; comprehensively analyzing all initial paths, identifying potential airspace intersections and performing conflict prediction, and generating a conflict-free route path set by performing height hierarchical adjustment on related paths; and calculating predicted flight time and energy consumption of each unmanned aerial vehicle, redistributing boundary tasks according to a load balancing principle, and finally generating a balanced multi-unmanned aerial vehicle cooperative route scheme. According to the method, the safe and efficient multi-machine collaborative inspection route can be automatically generated through systematic task decomposition, conflict prediction and load balancing optimization.

The invention relates to the field of optical cable early warning, in particular to a DVS-based optical cable early warning management and control method and an early warning system. Analyzing the brightness change data, performing path identification on the brightness change data, and determining a target position of the brightness change data; acquiring environment data of the target position, and verifying the brightness change data according to the environment data; based on a verification result, when the target position meets an abnormal condition, analyzing an abnormal parameter of the target position; predicting an abnormal change parameter of the target position according to the abnormal parameter of the target position; and determining a management and control strategy according to the abnormal parameters and the predicted abnormal change parameters, generating early warning information from the abnormal data of the target position based on the management and control strategy, and sending the early warning information to a management end. By considering environmental factors, the abnormity of the optical cable can be accurately detected, possible faults can be predicted, efficient management and control are provided, and accurate monitoring of the optical cable is realized.

The invention relates to the technical field of structure health monitoring, in particular to an immovable cultural relic structure health detection system based on multi-source sensor fusion, which comprises a multi-source texture response acquisition module, a curved surface drift path identification module, a crack region extraction module, a stress response trend offset module and a health level region output module. According to the method, the continuous coding of the structure texture is combined with the micro-strain response characteristics, so that the accurate judgment of the synchronous drift of the surface texture of the cultural relic structure is realized, the identification sensitivity of potential micro deformation is improved, a multi-period track set is constructed according to the center deviation trend in a time sequence, and the evolution tracking capability of space behaviors is enhanced; the crack linkage area is subjected to consistency screening through image gray abrupt change and strain direction, the accuracy of crack boundary identification is improved, the safety of an immovable cultural relic structure is guaranteed, and meanwhile collaborative improvement of real-time monitoring, the response speed and the automation level is considered.

The invention discloses a dominant path identification method and system in a multi-source scene based on a perturbation response model, and relates to the technical field of computers. According to the method, the influence of the link state change on the steady state of the system is dynamically analyzed based on a perturbation response mode instead of depending on a static index, the method is closer to a real interaction mechanism of the network, and the mechanism modeling precision is improved. By traversing the link state, the actual contribution of each link to the response of the target node is quantified, and the complex interaction of weak connection and multi-link coordination is prevented from being ignored. Through the process that the system is evolved to the steady state again after disturbance, flow distribution and competition at the position of a shared node are naturally modeled instead of depending on local optimal addressing, the global reasonability of dominant path recognition is improved, and the problem of multi-source target resource competition is solved. The method is suitable for various networks, and has a core support effect on construction of a propagation risk early warning model based on network science, implementation of a key node intervention strategy and optimization of an intelligent governance mechanism of a social information system.

The invention, which relates to the technical field of engineering automation, discloses a BIM-based cable bridge routing autonomous optimization visualization system comprising a heterogeneous professional semantic modeling module, a path identification module, a path generation module, a path conflict evolution module, a path reconstruction module and an output module. The system identifies a cable thermosensitive area by constructing a cross-professional semantic map, constructs a multi-target path evaluation function and generates a high-quality candidate path by adopting a variable weight genetic algorithm; introducing a construction stage dynamic compression prediction mechanism to improve the path robustness; visual expression of the whole process of path design is realized through versioned path reconstruction and a three-dimensional animation evolution function; and finally outputting structured path data and an obstacle avoidance strategy which can be used for construction guidance. According to the invention, the intelligent level of cable bridge path planning, the engineering implementability and the system integration efficiency are significantly improved.

The invention relates to the technical field of liquid cooling energy storage control, in particular to an efficient liquid cooling energy storage system and a control method thereof, and the system comprises a path monitoring module, a path recognition module, an abnormality marking module, a switching execution module and a path reinspection module. According to the method, through combined calculation of the main channel pressure change rate and the outlet pressure deviation value, early recognition of the cooling efficiency attenuation trend is achieved, the channel abnormity sensing precision is improved, the temperature recovery rate is introduced to serve as a heat release accumulation criterion, the dynamic judgment capacity for heat retention is enhanced, and the cooling efficiency is improved through a double abnormity parallel judgment mode. Misjudgment is avoided, early warning accuracy is improved, main and standby channel switching is implemented, an independent cooling path structure is formed, pressure and temperature fluctuation is dynamically rechecked after a standby path is started, stable switching is ensured, the continuous monitoring capacity is achieved, and a parameter trend recognition, path reconstruction and state verification closed-loop mechanism is integrally constructed. And the operation stability and the thermal control reliability of the energy storage system are improved.

The invention relates to the field of e-commerce recommendation, and discloses a user behavior data analysis system and method based on a time series, and the method comprises the steps: continuously capturing user operation events, extracting a short-term interest fluctuation factor, a category offset index and an interaction strength parameter, and forming dynamic weight mapping for constructing a transient preference map; implicit association detection of a cross-category event chain is executed on the transient preference map, and a multi-dimensional influence gradient vector reflecting the potential interest evolution trend is generated; tracking an offset path of a key interest node through a hierarchical interest-driven algorithm by utilizing a multi-dimensional influence gradient vector, and identifying candidate behavior fragments causing recommendation delay; performing priority label reconstruction on the candidate behavior fragments to form a local behavior network with a weight gradient; based on a local behavior network, key nodes with unbalanced interest distribution in a specific time window are detected, and an evolution path for real-time adjustment of a recommendation engine is generated. The method has the advantage of improving the relevance and instantaneity of the recommended content.

The invention discloses an expressway non-inductive toll collection system and method based on Beidou positioning. The system comprises a cloud charging cloud platform which is used for receiving positioning data of a vehicle, identifying a vehicle path and a driving mileage, and carrying out split settlement of highway toll; the side end computing device is used for lifting the vehicle with the confirmed identity in advance; the mobile terminal is used for providing an appointment passing service and an automatic payment service; the Beidou vehicle-mounted terminal is used for collecting vehicle position information, vehicle speed information and azimuth angle information in real time, automatically marking timestamps and then sending the timestamps; automatically recording and sending the location and the passing time of the passed RSU equipment; the checkpoint camera is used for identifying a license plate and automatically recording and sending vehicle passing time. According to the scheme, vehicle passage online reservation can be realized, vehicle path identification is realized based on Beidou high-precision positioning, vehicle passage fee splitting calculation is realized based on a cloud platform, and the vehicle can quickly pass through a toll station at a free-flow vehicle speed.

The invention relates to an industrial internet multilayer causal motif abnormal propagation path identification method and system, and the method comprises the steps: firstly carrying out the construction and extraction of a multilayer high-order motif, extracting a motif unit which expresses the local high-order structure features through the construction of a semantic hierarchical graph structure in combination with a frequent sub-graph mining and cross-layer motif alignment mechanism, and carrying out the recognition of the abnormal propagation path of the multilayer causal motif. Stable and uniform multi-layer motif representation is formed; then, on the basis of the structural equation model, motif variables are regarded as endogenous variables of a causal model, a causal path between motifs is mined by introducing conditional mutual information and a Bayesian structure learning algorithm, an average causal effect is calculated to construct a causal consistency matrix, and causal community division is realized in combination with a weighted modularity optimization method; and finally, quantifying the dynamic change of a community causal structure by constructing a causal deviation graph between an expected causal graph and an observed causal graph, and assisting in identifying a causal-driven abnormal propagation path. According to the method and the system, accurate detection and causal traceability of equipment-level and subsystem-level abnormal modes in an industrial system can be realized.

The invention relates to the technical field of transaction verification, in particular to a block chain financial transaction tracing method and system, and the method comprises the following steps: calculating a node interaction proportion, recognizing illegal chain segments and marking nodes, analyzing a field combination sequence matching path structure, judging numbering jump and behavior path pairing, and extracting a field corresponding relation and a matching difference. And identifying a node state offset and a structure jump trend, and generating a state chain segment backtracking section record. According to the method, the interaction frequency and the behavior type proportion are introduced to serve as the node state recognition basis, dynamic credible judgment of boundary nodes is achieved, the recognition precision of chain segment structure interruption is enhanced through the path field combination sequence and the serial number continuity, the serial number jumping behavior and node action offset are combined, the chain jumping path recognition capacity is optimized, and the reliability of the system is improved. The field consistency verification effect is improved by adopting field logic pairing and value domain offset comparison, and the backtracking positioning capability of the broken link node is enhanced by using a path state offset and backtracking pointer association strategy.

The invention relates to the technical field of artificial intelligence, in particular to a big data information analysis method and system based on artificial intelligence, and the method comprises the following steps: obtaining a jump segment direction vector, classifying label nodes with consistent directions, splitting a path lacking a mapping relation to form an interruption segment, recognizing a node with continuous directions as a bridge segment entrance, and obtaining a bridge segment direction vector; according to the method, by means of classification according to the node sequence and the channel number in the segment hopping direction, direction aggregation boundary determination and semantic field combination path position interruption recognition, the node sequence structure positioning precision and the paragraph division reasonability are improved, and the focus analysis set is generated by combining the path segments. Bridge segment connection is screened based on direction consistency, path continuation logic, behavior tracks and aggregation numbers are kept in sequence and hierarchy regularization, a behavior aggregation sequence of a semantic chain is constructed, and the continuity of path recognition in a channel aggregation area and the stability of node attribution are enhanced.

The invention provides a voice keyword detection method, a storage medium and electronic equipment, and relates to the technical field of voice signal processing, and the method comprises the steps: determining a phoneme scoring path and a background sound path corresponding to an acoustic feature sequence of a to-be-detected target voice; identifying a noise type corresponding to each noise background sound; constructing an initial first linear grammar finite state converter according to the phoneme scoring path, and configuring a corresponding wildcard arc for the first linear grammar finite state converter by using each extracted noise type to obtain a second linear grammar finite state converter based on noise perception; creating a composite weighted finite state converter through a composite operation; and decoding the composite weighted finite state converter to predict whether the target keyword exists in the target voice. Therefore, the keyword detection system can sense different noise environments, the sensitive degree of the system to noise is dynamically adjusted, and the keyword detection accuracy in the high-noise environment is guaranteed.

The invention provides an intelligent mine management and control method and system based on the Internet of Things technology, and relates to the technical field of mine management and control, and the method comprises the steps: obtaining the roadway feature data and mining progress data of a mine in a mine; the influence degree on sensor signal transmission is calculated according to geological stress change data and equipment electromagnetic radiation intensity, and the distribution positions of the sensors are planned by combining roadway distribution data. And generating a sensor migration path plan, adjusting a point distribution position to obtain an optimized point distribution position, simulating a signal propagation path by adopting a Monte Carlo algorithm, and identifying a key area of which signal attenuation exceeds a preset threshold value. And adjusting the optimized point distribution position of the key area to obtain key area distribution points, and formulating a sensor distribution and control scheme in combination with the optimized point distribution position. According to the invention, the distribution positions of the sensors are optimized, the monitoring efficiency and the stability of signal transmission are improved, and the real-time monitoring and dynamic adjustment of the mine environment are realized.

The invention relates to the technical field of reflected waves, in particular to a coal mine disaster comprehensive evaluation system based on three-component micro-motion detection, which comprises a frequency direction extraction module, a propagation included angle calculation module, a sensitive path identification module, a critical turning identification module and a structural disturbance mapping module. According to the method, the continuous sequence is formed in the space through the direction vector constructed through the main frequency value of the measuring point, an included angle screening strategy is used for eliminating incoherent direction sections, it is ensured that the path has trend consistency, the frequency amplitude difference value and symbol inversion are used for detecting a mutation point, and the response recognition capacity for directional changes is enhanced; the normalized frequency ratio is combined with a standard deviation and a difference value to carry out joint calculation so as to quantify a structure jump degree and improve the accuracy of reflex position expression, disaster boundary points form an image set, and a structure identification channel is established by taking a frequency direction as a principal line; and the spatial expression definition and precision are improved in the aspects of path aggregation, boundary precision, disaster recognition and partition and continuous structure drawing.

The invention belongs to the field of tailing pond safety risk identification and accident analysis, and discloses a tailing dam accident path identification method based on a mixed causal knowledge graph and a related device, and the method comprises the steps: building a mixed causal logic model through employing an obtained tailing dam accident initial hazard source; solving a set of accident cause links of the tailing dam by utilizing the mixed causal logic model; in the set of the tailing dam accident cause links, the logic relation of the tailing dam accident and the accident cause links are used as nodes and edges of a knowledge graph respectively, and a Python + Neo4j method is used for constructing a tailing dam accident knowledge graph; and coding and classifying the tailing dam accident knowledge graph by using a BERT-GCN-SVM model to realize tailing dam accident path identification. The mine tailing dam accident path identification method is improved and optimized based on the thought of the knowledge graph, a set of mine tailing dam accident chain identification method is formed, and the mine tailing dam accident path identification method can be used as a new tool for mine tailing dam accident directional control and disaster situation deduction.

The invention relates to a dynamic path identification method and device based on multi-sensor fusion and an ETC system, and the method comprises the steps: carrying out the data collection through multiple sensors, and obtaining multi-mode original data; processing the multi-modal original data to obtain multi-modal input data; performing quality evaluation and calculation to obtain a quality evaluation value and a quality evaluation result of each modal; based on the quality evaluation value and the quality evaluation result of each modal, carrying out dynamic weight distribution on the multi-modal input data by adopting a dynamic weight distribution algorithm to obtain weighted multi-source observation data; and performing fusion and prediction according to the multi-source observation data, the spatial-temporal characteristic data and the environmental parameters to obtain a target motion track of the target vehicle. Through a multi-sensor dynamic fusion mechanism, the weight of each sensor is adjusted, and the problems of poor environmental sensitivity, insufficient spatial resolution, data processing bottleneck and multi-source data missing are solved.

The invention relates to the technical field of image recognition, in particular to a blind sidewalk recognition method based on image processing. The method comprises the following steps: collecting a ground scene image, and carrying out noise suppression and illumination normalization processing to obtain a ground scene image to be processed; inputting the to-be-processed ground scene image into a pre-constructed convolutional neural network model to identify feature textures of the blind sidewalk bricks; dividing a blind sidewalk area in the ground scene image to be processed by using the blind sidewalk brick feature texture, and determining a direction gradient feature of the blind sidewalk area; executing blind sidewalk direction consistency constraint based on the direction gradient features to collect blind sidewalk structured path segments; recognizing a blind sidewalk fracture area based on directional gradient features; and performing cross-frame target tracking according to the structured path segment of the blind sidewalk, and outputting a continuous blind sidewalk trajectory. The automatic recognition rate of the blind sidewalk area is improved based on the image recognition technology, and the continuous recognition capacity of the blind sidewalk path in the complex illumination and shielding environment is enhanced.

The invention discloses a clock domain crossing circuit timing sequence closed-loop optimization method and system, and relates to the technical field of integrated circuit design, and the method comprises the steps: reading a circuit netlist and timing sequence constraints, recognizing a converged path, and carrying out the clustering analysis to form a plurality of path groups; establishing a time sequence analysis model, and dynamically calculating the maximum delay constraint of the data signal in the path group; matching and executing at least one time sequence optimization strategy for each path group; and carrying out closed-loop time sequence verification, retaining a legacy path set which does not meet the constraint, and repeatedly executing the time sequence optimization strategy until a convergence condition is reached or the maximum preset number of iterations is reached. According to the method disclosed by the invention, the cross-domain path needing to be optimized in the DMUX structure can be accurately positioned without manual intervention, so that the path identification efficiency is improved, constraint omission or grouping deviation caused by manual operation is avoided, design defects are reduced from the source, and the design reliability of a chip is improved.

The invention relates to the technical field of dispensing equipment, and discloses double-platform five-axis dispensing equipment which comprises a peripheral dispensing movement adjusting mechanism and a filling dispensing movement adjusting mechanism which are arranged in parallel in the X-axis direction, a USC cleaning mechanism, a peripheral dispensing mechanism, a curing lamp, a 2D detection mechanism, a 3D detection mechanism and a carrying mechanism are sequentially arranged above the peripheral dispensing movement adjusting mechanism in parallel in the Y-axis direction, and a filling dispensing mechanism is arranged above the filling dispensing movement adjusting mechanism. The filling and dispensing mechanism and the peripheral dispensing mechanism are arranged in parallel in the X-axis direction, and a peripheral visual alignment mechanism and a filling visual alignment mechanism are correspondingly mounted on the peripheral dispensing mechanism and the filling and dispensing mechanism respectively; the dispensing working efficiency of the equipment is effectively improved, the technical problems that a gluing path is difficult to recognize and dispensing is difficult to complete are effectively solved, the dispensing accuracy is improved, and the accuracy of the dispensing position and the dispensing uniformity are guaranteed.

The invention relates to the technical field of software security, in particular to a vulnerability assessment method and device, electronic equipment and a storage medium, which are used for improving the accuracy of vulnerability assessment. The method comprises the following steps: positioning a target vulnerability, extracting a full path of the target vulnerability from a trigger entrance to an outbreak point, and identifying a trigger condition and a trigger step of the target vulnerability according to the full path; based on the triggering condition and the triggering step, the triggering condition complexity and the utilization step number of the target vulnerability are determined, and the step number is used for representing the step number needed by utilization of the target vulnerability; based on the utilization step number, the triggering condition complexity, the risk degree of the target vulnerability, the utilization concealment degree and the utilization success rate, the availability score of the target vulnerability is determined, and the discovering difficulty of the target vulnerability is represented through the utilization concealment degree.

According to the passive underwater cooperative positioning system and method provided by the invention, an intelligent signal processing and analysis module in the system receives a target underwater sound backscattering signal and a target environment parameter output by an environment adaptive feedback module, and then a path identification result and a positioning result are obtained by combining an identification model; obtaining a sound velocity correction value and a propagation time correction value based on the target environment parameters and a sound velocity correction model so as to obtain the moving speed of the target object; the cooperative positioning calculation module predicts the positioning information of the next moment based on the historical positioning information and the positioning information of the current moment; the environment self-adaptive feedback module receives the underwater sound backscattering signals generated by the multi-mode signal acquisition module and the acquired environment parameters, and transmits the underwater sound backscattering signals and the acquired environment parameters to the intelligent signal processing and analysis module as target underwater sound backscattering signals and target environment parameters; and the dynamic adjustment module is also used for dynamically adjusting the state parameters of the underwater acoustic backscattering signals and the environmental parameters so as to obtain new target underwater acoustic backscattering signals and target environmental parameters.

The embodiment of the invention provides a social contact propagation path identification method and device based on interactive behaviors and electronic equipment, and belongs to the technical field of data processing.The method comprises the steps that interactive behavior data of a user is collected through multiple social contact media platform API interfaces, and data cleaning and standardization processing are conducted on the interactive behaviors; constructing an information propagation network based on the user interaction behavior data, and calculating the weight of each contact surface in the network through a contact surface weight calculation function; constructing a propagation feature vector based on the contact surface and the weight of the contact surface, and identifying and classifying an information propagation path Ts based on the propagation feature vector and a path calculation function Fl; and analyzing the propagation speed and the key node of each social media platform through the cross-platform propagation path feature vector G, and evaluating contribution values of different social platforms in an information diffusion process. The method can improve the accuracy and real-time performance of propagation path identification, and has the capability of cross-platform propagation path identification.

The invention provides a power grid fault propagation path identification method and system based on a graph neural network, and relates to the technical field of power grid fault identification, and the method comprises the steps: obtaining a power grid topological structure and fault observation data, constructing a direction perception graph neural network, achieving the one-way message passing based on potential energy difference, and obtaining a node potential energy value and embedded representation; edges are screened to generate a directed graph, and candidate paths are generated in the potential energy gradient direction; and verifying the satisfaction degree of the graph neural network evaluation path to the physical constraint through an invariant, and finally selecting the path with the minimum topological entropy. According to the invention, the accuracy and reliability of fault propagation path identification are improved.