21results about How to "Realize continuous tracking" patented technology

The invention discloses a cross-temperature health state assessment method and system for an underwater robot battery, and relates to the technical field of battery health state assessment, and the method comprises the steps: S1, collecting data in the operation process of an underwater robot; s2, an environment temperature profile is constructed, and working condition types are identified; s3, constructing a multi-dimensional health feature vector; s4, constructing an LSSVM and SVR regression model, and optimizing model hyper-parameters; s5, calculating characteristic stability and comprehensive complexity indexes, and adaptively selecting a model; s6, identifying the working condition category of the charge-discharge cycle, extracting and standardizing the features, calling the adaptive model, iteratively outputting SOH estimated values, and splicing to form an SOH evolution curve; according to the cross-temperature health state evaluation method and system for the underwater robot battery provided by the invention, the problems of unstable cross-temperature performance and insufficient generalization ability in the prior art are solved.

The invention provides a security situation awareness method and system based on a three-dimensional GIS and a panoramic video, and the method comprises the steps: constructing a three-dimensional scene model of a monitoring region on a three-dimensional GIS platform, accessing a plurality of cameras and an environment sensor, and achieving the space-time alignment and coordinate unification of a video stream and multi-source sensing data; projecting a video frame to a three-dimensional scene based on internal and external parameters of a camera to generate a panoramic video picture, and performing detection and cross-camera tracking on pedestrians and vehicles by adopting a target detection model to obtain a target three-dimensional track; predicting a target position and local density change by using a time sequence prediction model, judging anomalies including congestion, retention and retrograde driving, and sending out an early warning; camera deployment and cooperative tracking are planned through a point location optimization algorithm, and monitoring blind areas are reduced; the system comprises a data acquisition module, a space-time alignment module, a three-dimensional mapping module, a target detection and tracking module, a situation analysis module, a point location planning module and a situation display module, and can improve the automation and precision of security situation awareness in a complex scene.

This invention discloses a high-precision clock synchronization method between two wireless devices, comprising: Initially, a first wireless device sends a first message containing a transmission timestamp to a second wireless device. Upon receiving the message, the second wireless device overwrites its local reception timestamp with this first message, completing initial clock alignment. Subsequently, after a turnaround time, the second wireless device returns a second message to the first wireless device and calculates the one-way time of flight based on the time interval and turnaround time. At the next predefined superframe moment, the first wireless device sends a third message. The second wireless device calculates and corrects its local clock frequency offset based on this message and the timestamp of the previous message, while simultaneously correcting the local reception timestamp of the third message using the one-way time of flight. Thereafter, the above message exchange and calculation process is repeated at each superframe moment to continuously maintain high-precision synchronization. This invention fundamentally improves the clock synchronization accuracy between wireless devices.

The application discloses a method and system for searching for a lithologic trap in a sand-rich formation based on a marine flooding surface, and comprises the following steps: single-well INPEFA marine flooding surface identification; marine flooding surface well-to-well lateral comparison; well-to-seismic combination to identify the seismic response of the marine flooding surface; lateral and planar tracking of the seismic response of the marine flooding surface; thickness recovery of the marine flooding surface mudstone layer; and superposition of the thick marine flooding surface mudstone and sand body to define the lithologic trap. The application realizes fine identification and division of the marine flooding surface, successfully establishes a multi-period sequence framework in a sand-rich layer section with very slight logging characteristics, and significantly improves the reliability. The mudstone caprock identified by the application can be continuously tracked in the whole area on a seismic profile, and further, the sand-mud combination characteristics can be accurately identified in the established fine sequence framework, so that the lithologic trap is effectively circled, and more accurate technical support is provided for oil and gas exploration.

The invention provides a multi-mode imaging and three-dimensional laser scanning fused radioactive distribution modeling method, and relates to the technical field of three-dimensional image processing, and the method comprises the steps: collecting point cloud data and radiation imaging data of a to-be-detected region, and carrying out the time-space synchronization and fusion, and obtaining initial fusion data; obtaining scene semantic virtual radiation imaging data based on the point cloud data; performing dynamic fusion modeling based on physical inversion and data assimilation to obtain an optimal three-dimensional source item distribution vector; and continuously updating the optimal three-dimensional source item distribution vector through time series data assimilation and dynamic updating. According to the method and the device, the technical problem that in the prior art, due to the lack of deep fusion of a radiation transmission physical process, physical distortion of a reconstructed radioactivity distribution model is caused, and accurate source item positioning and real-time risk assessment are further influenced can be solved, and the purposes of improving the accuracy of source item space positioning and activity quantification and improving the accuracy of source item space positioning and activity quantification are achieved. And the technical effects of continuous tracking and simulation prediction of the dynamic evolution of the radiation field are realized.

The application discloses a layered sampling monitoring device and method for freeze-thaw cycle test, which comprises a freeze-thaw cycle box, a layered soil sample culture unit, an in-situ monitoring unit and a non-destructive sampling unit. The application provides a layered sampling monitoring device and test method for freeze-thaw cycle test, which has a reasonable structure design and is convenient to operate. The device can realize non-destructive or slightly destructive extraction of target soil samples after different freeze-thaw cycles without damaging the continuity of the whole test, and can realize real-time monitoring of the moisture content and temperature changes of the soil samples in the freezing and thawing process, thereby providing accurate and continuous test data for in-depth study of the evolution law of the freeze-thaw cycle on the soil properties. The device and method of the application significantly improve the continuity and accuracy of the data, and provide a powerful tool for revealing the effect of freeze-thaw on the soil erosion mechanism.

The invention discloses a three-phase circuit breaker mechanical state intelligent diagnosis and fault prediction method and system, and relates to the technical field of power equipment state monitoring and intelligent operation and maintenance. The method comprises the following steps: synchronously acquiring three-phase vibration and acoustic signals in a same opening / closing window, performing stage segmentation after preprocessing, constructing stage fusion features, and obtaining three-phase characterization vectors of all stages through a shared characterization encoder; inter-phase comparison self-supervised learning is carried out only by using health data, and inter-phase consistency constraints are introduced. Inter-phase mismatch is calculated on line, a stage-phase anomaly score and a total anomaly score are obtained based on a health statistical model, an abnormal phase and an abnormal stage are output when a threshold value is exceeded, and key time slice / frequency band evidence is generated; and constructing a health index sequence according to the total abnormal score and predicting the residual life. According to the invention, fault label dependence can be reduced, abnormal positioning interpretability and cross-working-condition stability are improved, and predictive maintenance is supported.

This invention relates to a health monitoring method for carbon fiber composite structures, comprising: acquiring electrical conductivity data of the composite structure under stress; generating an initial resistance distribution map and identifying abnormal regions based on the electrical conductivity data to obtain a preliminary signal set containing potential damage locations; extracting spatial feature vectors from the preliminary signal set; processing the spatial feature vectors using a feature extraction model to filter out noise interference to obtain an enhanced signal sequence; processing monitoring data at multiple time points using a time-series fusion model to generate a damage evolution trend map reflecting the damage propagation trend; performing hotspot region analysis based on the spatial distribution of signal peak points in the damage evolution trend map; and adjusting the distribution density of sensing channels to generate optimized monitoring system configuration parameters if the spatial dispersion of the peak points exceeds a preset range.

The utility model discloses a real-time monitoring system for vibration isolation efficiency, attenuation change and service life of a pipeline shock absorber, which comprises a load sensor used for monitoring load change borne by the shock absorber in real time; the displacement sensor is used for monitoring the deformation quantity of the shock absorber in real time; the data processing unit is connected with the load sensor and the displacement sensor and used for processing data of the load sensor and the displacement sensor; the data processing unit is further connected with an early warning unit, a display unit and a power supply unit, the early warning unit is used for warning, the display unit is used for displaying monitoring data, and the power supply unit is used for providing a working power supply. According to the utility model, the vibration isolation efficiency, the attenuation trend and the residual life of the pipeline damper are monitored in real time by collecting load and displacement data, so that an early warning function is realized.

The application relates to a traffic flow detection method based on improved YOLOv8 and DeepSORT, which comprises the following steps: S1, acquiring a training data set; S2, obtaining initial values of hyperparameters of a target detection model; S3, after the target detection model is trained based on the training data set for multiple times, whether a first exit condition is met is judged, if yes, step S6 is executed, otherwise, step S4 is executed; S4, a Pareto Bayesian algorithm is used to update the hyperparameters to obtain candidate values of multiple groups of hyperparameters, and a genetic algorithm is further used to process the obtained candidate values of the multiple groups of hyperparameters to obtain a candidate value of a group of hyperparameters to replace the hyperparameters in the target detection model; S5, whether a second exit condition is met is judged, if yes, step S6 is executed; S6, each video frame of a to-be-detected video is detected by using the target detection model to obtain traffic flow data. Compared with the prior art, the application has the advantages of improving the detection accuracy in an unmanned aerial vehicle scene.

The invention discloses an automatic nipple opening positioning system and guiding system for duodenal nipple intubation guidance, solves the problem of how to realize high-precision and high-robustness positioning of a nipple opening under multiple constraints, and belongs to the technical field of medical image processing and computer-aided surgical navigation. According to the invention, a generative model is utilized to actively deduce anatomical features of a shielded area, and continuous tracking in an instrument shielding scene is realized; establishing an absolute mapping relation between a pixel position and a target coordinate through injection explicit space geometry prior, and obtaining a nipple opening pixel-level coordinate; visual uncertainty estimation and instrument-tissue kinematics coupling are fused, a physically constrained adaptive filtering mechanism is constructed, and smooth coordinates are obtained; the positioning system is used for constructing a guiding system, closed-loop interaction of instrument control through a handle and inlet coordinate positioning feedback is achieved in an operation environment, and dual-scene application of preoperative training and intraoperative navigation is supported.

The invention relates to the technical field of digital twinning, in particular to a digital twinning-based wind turbine generator full life cycle management system, which comprises a deformation parameter resolving module, a skeleton driving reconstruction module, a load damage calculation module, a health difference evaluation module and a balance control decision module. According to the method, the instantaneous curvature and the torsion rate are calculated by collecting cross section wavelength drift data, the dynamic bending posture of the flexible part in the space is reconstructed in real time, continuous tracking of stress circulation and a closed lag loop is achieved by combining pneumatic simulation and the residual peak-valley sequence stack technology, the accumulated damage degree of the part in the whole service cycle is accurately quantified, and the reliability of the part is improved. By analyzing the discrete deviation of individual damage relative to a group mean value, an asymmetric load distribution mechanism based on health state difference is established, the fatigue extension trend of high-damage parts is effectively relieved, premature failure caused by uneven load distribution is avoided, and life balance and collaborative life extension of key parts of a unit in the whole life cycle are achieved.

The invention relates to the technical field of neural network system structures, in particular to a fuzzy AI model training method for a data processing accelerator, which comprises the following steps: acquiring fuzzy data, sending the fuzzy data into the accelerator, tracking a first dragging point of a signal to merge an interference position set, identifying offset nodes along a path, and serially connecting to construct a sequence; and observing round-trip conflict tracks in the channels to recombine signal segments, stripping steering positions, extending non-offset linear paths, connecting the channels to the end of the process in sequence, positioning feature mapping segments, and identifying sample relationships to obtain a fuzzy training result. According to the method, the initial disturbance position is recognized by capturing signal response changes, path offset nodes are continuously tracked, dynamic connection is achieved, a clear track sequence of a conflict area is constructed to improve the connection stability, guide path fragments are recombined to define the channel propulsion sequence, and propulsion disorder caused by multi-path staggering is avoided; logic smooth conduction is achieved, mapping association is enhanced, and it is ensured that a fuzzy data guiding result has integrity and path consistency.

The present application relates to the technical field of wharf structure safety evaluation, and provides a wharf resilience evaluation method based on 4R four-dimensional coupling, comprising the following steps: responding to a wharf resilience evaluation request, constructing a 4R four-dimensional resilience evaluation system; mapping into a Bayesian network, wherein the root node of the Bayesian network is a basic index, the intermediate node is a 4R-dimensional index and corresponding sub-index, and the leaf node is a wharf comprehensive resilience grade; generating a root node probability according to historical data, and generating a conditional probability table of the intermediate node and the leaf node; converting the root node prior probability and the conditional probability of the intermediate node and the leaf node into high / low probability, inputting the Bayesian network, calculating important factors of each root node on the comprehensive resilience grade, identifying high sensitivity values as weak links through reverse diagnosis, and determining a priority repair or investment order based on the evidence high sensitivity value. The index system and the Bayesian network based on 4R four-dimensional coupling are constructed, and the probabilistic evaluation of the wharf resilience, the identification of key short boards and the dynamic and continuous tracking are realized.

The invention relates to the technical field of WiFi probes, in particular to a mobile equipment positioning method and equipment based on a WiFi probe and a medium, and the method comprises the steps: screening out target WiFi of which the corresponding connection times exceed a preset times threshold value based on historical WiFi connection records of target users, and forming a target WiFi list corresponding to the target users, sending the target WiFi to a plurality of pre-deployed WiFi probe devices, so that each WiFi probe device simulates each target WiFi in sequence, obtaining scanning information of a mobile device of a target user received by each WiFi probe device, and positioning the mobile device corresponding to the target user based on the scanning information to obtain track information of the target user; according to the invention, accurate positioning and continuous tracking of the acquired mobile device can be realized without knowing the WiFi password and without depending on the mac address of the fixed terminal.

The application discloses a dialysis patient hypotension intelligent monitoring system and method based on a multi-modal signal, relates to the technical field of medical physiological monitoring, and comprises a multi-modal data acquisition module, a data preprocessing module, a multi-modal feature fusion module and a hypotension identification and early warning module. The system synchronously acquires physiological signals and dialysis equipment operation parameters of a patient, determines the operation parameters as excitation modal data and the physiological signals as response modal data, extracts excitation-response coupling features representing excitation driving response, and fuses the features with single-modal features to obtain cross-modal fusion features, according to which, a hypotension risk grade is identified and graded early warning is performed. The application utilizes the time sequence coupling structure of ultrafiltration excitation and physiological response as features, identifies circulation compensation reserve failure in advance from the dynamic system identification level, solves the problem that the prior art symmetrically processes each modal and the monitoring lags behind blood pressure drop, and improves the safety of hemodialysis treatment.

The application is suitable for the technical field of data processing, and particularly relates to a data security risk early warning method and system based on artificial intelligence. The method comprises the following steps: obtaining metadata list and sensitive data list; matching data stream event set with sensitive data in the sensitive data list and data dyeing to obtain data stream event list; constructing data stream graph; calculating final risk value of each node; adding nodes with final risk value greater than initial threshold value into infection source set; calculating predicted infection probability of each node in future time window based on final risk value of each node and infection source set; determining dynamic early warning threshold value based on final risk value of each node and predicted infection probability; if final risk value is greater than dynamic early warning threshold value, triggering early warning and generating early warning report of nodes with final risk value greater than dynamic early warning threshold value. The method can quickly locate the affected nodes and predict the diffusion path after the data security problem occurs.

The application discloses a kind of measurement equipment independent quantum key distribution methods based on AI's frequency bin coding, including computer system executes following steps: input quantum signal sent by communication both sides, relative radial velocity between communication node and infeasible relay node and historical quantum interference statistical characteristic data;Quantum channel observation and frequency bin quantum state construction, the dynamic evolution of frequency bin caused by relative motion, frequency compensation decision based on reinforcement learning, the influence of residual frequency error on quantum interference performance, frequency bin utilization rate and error propagation caused by interference degradation and security key rate generation.The application can effectively improve the adaptability and operation reliability of MDI-QKD system based on frequency bin in dynamic, multi-noise environment, and provides an intelligent solution for security key distribution in high-capacity quantum communication network.

The invention provides a sea patrol and reconnaissance device based on cooperation of a wave glider and an amphibious unmanned aerial vehicle, and the device comprises the wave glider integrated with an unmanned aerial vehicle take-off, landing and charging platform, the wave glider is used for carrying out long-endurance patrol on the sea surface, and under the condition that an abnormal target is detected or an instruction of a shore-based command center is received, the unmanned aerial vehicle takes off, lands and charges the unmanned aerial vehicle; sending a control instruction; when the amphibious unmanned aerial vehicle is parked on the unmanned aerial vehicle take-off, landing and charging platform, the amphibious unmanned aerial vehicle is electrically connected with the unmanned aerial vehicle take-off, landing and charging platform for charging; when the amphibious unmanned aerial vehicle receives the control instruction, the amphibious unmanned aerial vehicle takes off from the wave glider to get close to the abnormal target, reconnaissance is conducted on the abnormal target, and reconnaissance data are transmitted back to the shore-based command center through the wave glider; and after reconnaissance is finished, the amphibious unmanned aerial vehicle returns to the unmanned aerial vehicle taking-off, landing and charging platform. According to the invention, the maritime detection capability is improved.

The invention relates to the technical field of data analysis, can be applied to business scenes such as financial science and technology, and discloses a multi-source data space-time fusion analysis method, device and equipment and a medium, and the method comprises the steps: obtaining multi-source data containing time sequence image data and description text, and carrying out the space-time fusion to generate standard fusion data, extracting corresponding features by using a visual processing module and a text processing module, performing consistency comparison to generate a first analysis report, constructing a dynamic state file based on time sequence features, writing the dynamic state file into the report, monitoring the file to identify an abnormal event, and after the abnormal event is identified, obtaining time sequence image data before and after the abnormal event is identified, and performing change detection to generate a second analysis report. Through multi-source data fusion and cross-modal feature analysis, a dynamic state file is constructed in combination with time sequence features, and change detection is executed, so that automatic tracking and anomaly recognition of the state of the target object are realized, and the intelligent level of agricultural insurance business analysis and risk prevention and control is improved.

The invention relates to a lightweight convolutional neural network construction method and system for glue pudding defect detection, and the method comprises the steps: constructing a color intensity distribution interval for a brightness contrast value and a color saturation change rate in each region according to a preliminarily labeled gradient region distribution diagram, analyzing and calculating the intensity change trend in the region through a histogram, and obtaining a color intensity distribution interval; determining a quantitative description value of the color difference; adjusting local focusing parameters during image acquisition according to classification labels of defect severity in combination with crack depth estimation values and color intensity distribution intervals, optimizing the detail capture capability of high-risk areas, and determining a monitoring enhancement scheme for specific defects on the surfaces of the rice dumplings; and by monitoring a strengthening scheme, updating crack distribution density ratio and color distribution uniformity degree data processed by a production line image, generating a dynamic detection template aiming at the stuffing seepage defect on the surface of the sweet soup ball, and finishing continuous tracking and identification of a specific defect type.