3730 results about "Tracking data" patented technology

The invention provides a robot alarm processing method and system based on a target detection algorithm and a cloud platform, and relates to the technical field of robot alarm processing, and the method comprises the steps: carrying out the detection of an abnormal target based on a convolutional neural network target detection model of the cloud platform in combination with a residual structure, a channel attention mechanism and an improved anchor-frame-free target detection algorithm, performing feature fusion through a feature fusion network guided by depth information to obtain accurate three-dimensional representation data, constructing a dynamic anomaly association graph by using an improved time sequence graph neural network, and generating a multi-level decision sequence in combination with an improved hierarchical reinforcement learning algorithm, and finally, collaborative task planning is carried out through a task decomposition network to obtain a task allocation scheme, and the robot carries out exception handling according to the optimal motion track, obstacle avoidance track data, real-time pose data, key operation sequence data and contact force feedback data.

The invention discloses an intelligent operation and maintenance management and alarm system based on a large model agent, and the system comprises a data collection and fusion module which is used for processing original data collected from an IT system to obtain an operation and maintenance data graph, and the original data at least comprise logs, indexes and link tracking data; the anomaly detection module is used for performing analysis based on the preprocessed data and the time sequence characteristics to determine abnormal data; the alarm optimization module is used for analyzing the semantic information corresponding to the abnormal data so as to determine the priority of the corresponding alarm; the root cause analysis module is used for performing fault reasoning on the abnormal data based on a pre-constructed knowledge graph and generating fault interpretation according to data corresponding to the abnormal data; and the automatic operation and maintenance decision module is used for generating an automatic repair strategy according to the abnormal data and the fault interpretation and executing the automatic repair strategy to repair the abnormal data.

A system for remotely monitoring and managing health statuses of a plurality of users includes software instructions storable on a memory device usable by a computing device, the software instructions causing a hardware processor of the computing device to receive a plurality of sets of health-related information from a plurality of mobile computing devices of a plurality of users. The health-related information includes physiological information derived from wearable devices of the users indicative of an onset of symptoms associated with an infection, contact tracing data, and diagnosis data. The hardware processor determines exposure levels based on at least the contact tracing data, and determines user-specific risk states based on physiological information, diagnosis data, and exposure levels.

The invention relates to the technical field of poultry breeding monitoring, and discloses a chicken flock state inspection monitoring system and method. The method comprises the following steps: firstly, collecting chicken flock visual images, sound signals, environment temperature and humidity and individual activity track data, and constructing a multi-modal data set through labeling and preprocessing; different modal data features are extracted and fused; training a self-supervised contrast learning model to generate a discrimination model, and optimizing hyper-parameters in combination with a genetic algorithm; collecting data in real time, calculating a health state probability, generating an abnormal score, and dynamically updating an early warning threshold value; and if the abnormal score exceeds a threshold value, grading early warning and abnormal positioning are carried out. The system comprises a data acquisition and preprocessing module, a feature extraction module, a feature fusion module and the like. According to the method, the health of the chicken flocks is accurately monitored by using multi-modal data, dynamic early warning and model adaptive optimization are realized, the breeding benefits are improved, and the disease risk is reduced.

The invention relates to the technical field of intelligent control, provides a flow regulating valve servo force control method and system based on a force sensor, and aims to solve the technical problems of response delay, weak overshoot suppression capability and poor long-term operation stability. The method comprises the following steps: acquiring a servo driving current data set and a valve displacement track data set of a target regulating valve; performing pressure feature mapping processing on the servo driving current data set to generate a pressure fluctuation feature set corresponding to the driving current waveform data; the pressure fluctuation characteristic set and the valve displacement track data set are input into a preset force control decision model for dynamic matching processing, and a servo control instruction set is generated; executing multi-stage dynamic adjustment operation on a servo driving unit of the target adjusting valve according to the servo control instruction set, and generating real-time pressure balance state data; and iteratively updating the dynamic matching processing parameters of the force control decision model based on the deviation value of the real-time pressure balance state data and the preset pressure reference value.

The invention provides a resource dynamic monitoring and control method and system based on quota tracking. According to the pipe gallery deformation monitoring and resource optimization system based on optical fiber and sound wave sensing, underground pipe gallery structure stress field propagation data are obtained through an optical fiber sensor array, energy attenuation characteristics of stress waves at reinforcing rib intersection points are analyzed, and a dynamic mapping model of structure deformation risks and monitoring node resource requirements is established. Meanwhile, the frequency domain propagation characteristics of abnormal vibration signals are captured through acoustic sensors arranged at the expansion joints, and a resource allocation strategy matrix is formed; and constructing a quota tracking database, comparing the actual consumption rate of the frequency spectrum quota with a predicted value, generating a resource use efficiency report, and performing frequency spectrum redistribution of frequency hopping interval optimization and transmitting power adjustment on the nodes in the high stress concentration region. According to the technical scheme provided by the invention, the resource dynamic optimization of the underground pipe gallery structure is realized, and the risk early warning precision and the communication efficiency are improved.

The invention discloses a course recommendation method based on a learning track and a knowledge graph, and particularly relates to the technical field of education course recommendation, and the method comprises the following steps: collecting and integrating learning track data of students, mapping the learning track data to the knowledge graph to generate a learning track-knowledge joint graph, and carrying out knowledge point mastering and learning rate analysis; obtaining a knowledge mastering value and a learning rate of each knowledge point; based on the knowledge mastering value, the system divides the knowledge points into strong items and weak items; for weak knowledge points, weak recommendation degrees are calculated in combination with knowledge mastering values and learning rates, and courses are preferentially recommended according to the recommendation degrees; for the strong knowledge points, the strong recommendation degree is calculated based on the learning rate, advanced courses are recommended, and the learning effect and personalized learning experience of students are improved; according to the method, strong and weak knowledge points of students can be accurately identified, so that learning recommendation with extremely high pertinence is provided, the students can more efficiently learn on a personalized learning path, and the learning result is improved.

Disclosed herein are a system and a method for controlling multi-UAV cooperative tracking of a ground-moving target. The system includes UAVs, a processor, and a storage device. The UAVs are wirelessly communicatively connected to the processor. The processor is configured to generate a UAV command transmitted to the UAVs. The UAVs are configured to transmit tracking data to the processor. The processor includes a terrain building module, a flight simulation module, a UAV parameter generation module, a flight trajectory prediction module, a flight trajectory determination module, and a flight trajectory control module. The system provided in the embodiments of the present disclosure integrates Model Predictive Control (MPC) with an Improved Whale Optimization Algorithm (IWOA) to enhance the algorithm's convergence speed and precision, thereby improving the accuracy and stability of target tracking.

The invention discloses a network security verification method and system for a security system, and belongs to the technical field of network security, and the method comprises the steps: obtaining a biological feature recognition request triggered by a user on terminal equipment, synchronously collecting dynamic behavior track data, and generating a behavior feature code; inputting physiological feature data and behavior feature codes in the biological feature recognition request into a feature verification model, when an output biological behavior correlation index is lower than a first preset threshold value, triggering a dynamic verification strategy adjustment instruction, generating a multi-dimensional verification superposition strategy, and executing at least two verification operations in the multi-dimensional verification superposition strategy, the verification result is input into the distributed time synchronization node to generate an encryption token; and when the synchronous matching degree of the encryption token among different nodes exceeds a second preset threshold value, outputting a security verification passing instruction. According to the method, multi-modal feature fusion, dynamic hierarchical verification and time-space encryption technologies are adopted, replay attacks are prevented, an adaptive defense system is constructed, and reliable authentication of a high-security network scene is ensured.

The invention provides an aluminum alloy auxiliary frame welding accurate positioning and quality control method and system. The method comprises the steps that S4, welding gun track control parameters are updated through a dynamic adjustment instruction, laser tracking data are fused to optimize the welding path angle in real time, and the accurate movement track of each section of welding seam is determined; s5, acquiring real-time thermal expansion data during welding execution, monitoring temperature field distribution of a welding seam area by adopting an optical measurement technology, and judging whether thermal deformation adjustment exceeds a preset threshold value or not; s7, micron-sized welding spot positioning operation is executed according to the stable positioning state, the dynamic path optimization result is fused to generate a final welding instruction, and consistent welding seam quality data is output; and S8, after weld joint quality data are obtained, weld joint deviation and geometric consistency are detected through a laser tracking technology, whether welding quality meets design requirements or not is judged, and a quality verification report is generated. According to the method, the precision and consistency of intelligent welding of the aluminum alloy auxiliary frame are remarkably improved.

The invention provides a visitor identification and multi-dimensional user portrait routing distribution method based on private domain traffic, and relates to the technical field of data analysis, and the method comprises the steps: collecting multi-source data information of a visitor from a private domain traffic pool, constructing a multi-dimensional feature vector, generating a visitor identity fingerprint feature, and building a visitor identity identification model based on the visitor identity fingerprint feature; constructing a multi-level label system based on a recognition result of the visitor identity recognition model; performing feature extraction on historical behavior track data and historical content interaction data of visitors by adopting a transfer learning algorithm, classifying the extracted features based on the multi-level label system to generate user portrait features, dynamically updating the user portrait features in an incremental learning mode, constructing a multi-dimensional user portrait model, and displaying the multi-dimensional user portrait model. And distributing different visitor requests to the corresponding service nodes by adopting a routing distribution strategy in combination with the real-time load states of the service nodes.

The invention discloses a seawall overtopping amount and overtopping risk calculation method considering a mangrove forest wave dissipation effect, and relates to the technical field of coast engineering. The method comprises the following steps: acquiring typhoon path data, inputting the typhoon path data into a Holland typhoon empirical model, and accurately generating a typhoon wind field; calculating coastal site tide level data in combination with a coastal storm surge numerical model; obtaining wave data during a typhoon by using a storm surge typhoon wave forecasting model based on the SWAN; historical typhoon data and relevant information of the seawall and the mangrove forest are collected, a water level-wave-vegetation-seawall coupling model is constructed, the wave dissipation effect of the mangrove forest is accurately simulated, and wave characteristic calculation is optimized; constructing a database, training the database through a CNN neural network model, and improving the prediction precision of seawall front wave height and wave force parameters; the wave overtopping amount is calculated based on the prediction parameters, a scientific basis is provided for wave overtopping risk assessment, it is ensured that real-time assessment of the wave overtopping risk is more accurate and efficient, and the disaster prevention and reduction capacity is effectively improved.

The invention relates to the technical field of safety monitoring, in particular to a smart park safety management method and system based on AI visual identification, and the method comprises the following steps: obtaining a video stream captured by a monitoring camera of a smart park, extracting the movement track data of a person through AI, and carrying out the statistics of the stay duration and access times of a target object in each grid; and constructing retention heat distribution of the grid region to obtain a personnel trajectory retention heat value. According to the invention, through the data processing of the monitoring video stream and the combination of the gridding analysis of the personnel motion trail, the accurate capture of the abnormal cruise behavior is enhanced, and through the calculation of the change rate of the access times between the adjacent grids, the statistics of the round-trip condition of the target among the plurality of areas and the combination of the passing authority data, the abnormal screening is carried out. The method improves the recognition capability of abnormal movement modes, achieves the dynamic adjustment of regional safety risk levels, improves the response capability of park security, enables the recognition capability of abnormal behaviors to be enhanced, and reduces the false alarm rate.

The invention relates to the technical field of knitted fabric defect detection, in particular to a knitted fabric defect detection method and system based on machine vision, and the method comprises the steps: firstly obtaining a first image set through collecting the production process data of a scarf; constructing a scarf multi-scale edge detection model to identify the first image set, dynamically adjusting a threshold according to the actual brightness and gradient distribution of the image, and generating a first edge feature set; gradient information fusion is carried out on a background dynamic image and the first edge feature set, a scarf region segmentation model is constructed, a scarf production real-time image is segmented, and a time sequence segmentation image set and scarf flaw edge data are obtained; performing real-time analysis on the scarf flaw edge data and the time sequence segmentation image set to obtain scarf time sequence edge flaw track data; and constructing a scarf flaw edge complexity evaluation model to analyze the scarf time sequence edge flaw trajectory data, obtaining the fractal dimension of the scarf flaw edge, and generating the complexity of the scarf flaw edge.

The invention relates to the technical field of intelligent traffic management, and discloses a road traffic signal lamp management system based on big data, which comprises a data acquisition and fusion module for acquiring traffic sensor data, visual data, GPS track data and external environment data, generating unified traffic state data through space-time alignment, data filtering and multi-modal data fusion; and the flow prediction module is used for predicting the traffic flow of a single intersection and a region by using a time sequence model and a graph neural network based on the traffic state data generated by the data acquisition and fusion module. Through multi-modal data acquisition and space-time alignment, Kalman filtering, Bayesian inference and other technologies, deep fusion of different data sources is realized, the comprehensiveness and accuracy of data are significantly improved, dynamic changes of a complex traffic scene can be truly reflected, high-quality basic data are provided for subsequent flow prediction and optimization control, and a core technical support is formed.

The invention discloses a knowledge graph-based method for accurately positioning and breaking through discipline weak points, which comprises the following steps of: predicting knowledge points possibly accessed by students in the next step by using a deep learning model based on an updated knowledge graph, capturing a multi-dimensional dependency relationship among the knowledge points through a graph neural network, and combining historical learning track data of the students to obtain a knowledge point database; generating a personalized learning resource recommendation list; dynamically adjusting the priority of the recommendation list according to the comprehensive association degree of the subject weak point and the key path, and improving the priority of the knowledge point of the key path highly associated with the subject weak point; and according to the priority of the recommendation list, adopting a multi-level caching strategy to pre-cache learning resources, maintaining a hotspot knowledge point index in a memory, storing secondary hotspot resources in a solid state disk, storing cold data in a mechanical hard disk, and dynamically adjusting cache content of each level through a cache replacement algorithm.

The invention discloses a flight control method and system of an unmanned aerial vehicle tracking technology, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the following steps: before an unmanned aerial vehicle takes off, starting an airborne sensor in advance, executing parameter initialization and calibration operation, and installing a magnetic marker on a tracking target; acquiring the body position of the unmanned aerial vehicle in the flight process, and sensing the target position of the tracking target in real time by adopting a multi-sensor data fusion algorithm; obstacles existing in the flight path are detected in real time, a three-dimensional environment model is integrated, and the motion state of a tracking target is captured in combination with a follow-up visual tracking algorithm; target tracking data are stored and fed back in real time, and the battery state and the energy consumption speed of the unmanned aerial vehicle are monitored. By combining the magnetic guidance technology, multi-sensor data fusion and a follow-up visual tracking algorithm, high-precision target tracking can be realized in a dynamic complex environment, meanwhile, obstacles are effectively avoided, and the flight stability and tracking precision of the unmanned aerial vehicle are ensured.

The invention discloses an acousto-optic multi-view-field detection device and method for underwater defects of a hydraulic structure, and the method comprises the steps: obtaining original data, carrying out the filtering processing of the original data, and obtaining the filtered original data; based on the filtered original data, constructing a preprocessed multi-modal data tensor; performing spatial registration processing on the multi-modal data to obtain multi-modal data under a unified coordinate system; a self-adaptive tensor decomposition method is adopted for processing, and real-time position coordinates and movement track data of the robot are obtained; processing by adopting a multi-scale feature analysis method to obtain a target position coordinate and a target feature description; and processing by adopting a space-time optimization method and an uncertainty analysis method to obtain a final target positioning result and a confidence evaluation result. According to the method, the detection precision and the system robustness are improved, the detection accuracy is improved, the system positioning precision reaches the centimeter level, and the method is particularly suitable for intelligent defect detection in a complex hydraulic environment.

The invention provides a room obstacle detection optimization method and system based on a cross-modal attention mechanism. Wherein a dynamic compensation coefficient is calculated by acquiring motion track data of the mobile equipment, and a stable visual coordinate system is constructed. Visible light and near-infrared wave band acquisition of the multispectral sensor is synchronously triggered, the visible light captures geometric deformation characteristics of a floor projection surface, and the near-infrared detects a local heat radiation abnormal area. And extracting a thermal feature connected domain conforming to periodic biological motion as a living obstacle, and marking a collision risk boundary of a static obstacle. And based on a living body motion law and a collision boundary deviation trend, synchronously optimizing the dual-band exposure parameters and updating the trajectory prediction model, and forming a closed-loop feedback link of sensing and planning. According to the technical scheme provided by the invention, the living body and the static obstacle are accurately distinguished in a dynamic environment, and the sensor parameters are adaptively adjusted to improve the real-time obstacle avoidance response capability.

A dog training method for intelligently correcting pet behaviors based on multi-modality comprises the steps that multi-modality perception data of a pet is obtained, the multi-modality perception data comprises acoustic signals, motion signals and track data, an acoustic feature tensor, a motion feature tensor and a position feature tensor are constructed, and a unified coupling tensor is formed; inputting the coupling tensor into a pre-trained behavior recognition model, and outputting a behavior tag; the emotion recognition module outputs an emotion label, and updates a character label in a sliding window mode; inputting the behavior label, the emotion label and the character label into an intervention strategy generation model and outputting an adaptive intervention strategy, wherein the intervention strategy comprises a pacifying type strategy and a stimulation type strategy; according to a historical feedback effect record, adjusting a feedback intensity parameter of the intervention strategy, calculating a feedback convergence index and an emotion recovery index, and dynamically correcting a feedback level and a delay strategy of the intervention strategy.

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a business process visualization early warning and strategy response method, which comprises the following steps: setting a physical interrupt node to collect interrupt operation track data, generating multilevel logic interrupt type structured data based on the operation track data, the method comprises the following steps: acquiring a preset business strategy, mapping the preset business strategy into a visual topological graph, identifying a preset business strategy verification failure type, counting interruption frequency, triggering a grading early warning instruction, and generating a page element weight adjustment parameter and a fault-tolerant threshold set. According to the method, the visual topological graph is constructed and the high-frequency interruption behavior is identified, so that real-time identification and graded early warning of business strategy verification failure are realized, and adaptive adjustment of page elements and strategy thresholds is further driven, so that the user insurance conversion rate is effectively improved and the front-end operation loss risk is reduced.

The invention provides an accurate flight path tracking control method, device and equipment for an unmanned aerial vehicle, and the method comprises the steps: generating fusion state data through the fusion of GPS flight path data and flight state information, and constructing a flight path-state phase space model through employing a phase space reconstruction technology; phase trajectory feature parameters are extracted based on dynamic trajectory analysis, and dynamic attractor features are determined through attractor recognition processing; analyzing and identifying the center position of an attractor and the position of a topological singular point by using phase space topology, and constructing composite potential field intensity distribution formed by overlapping a conventional gravitational potential field and a repulsion-attraction bipolar potential field; gradient calculation is carried out on the composite potential field, and adaptive modulation is carried out based on the relative relation between the current position and the attractor center; in a local coordinate system taking a singular point as an original point, decomposing a modulation gradient vector into radial and circumferential components, and forming a spiral potential field through selective rotation enhancement; and finally, generating a tracking control instruction and performing signal modulation output, thereby realizing accurate tracking control of the unmanned aerial vehicle on the preset route.

The invention provides a track prediction method and device based on a pre-trained large language model, and relates to the field of artificial intelligence. The method comprises the following steps: acquiring historical flight path data of a target aircraft and carrying out data preprocessing to obtain a flight path sequence; constructing a cue word, and inputting the cue word into a pre-trained large language model to obtain a word embedding representation; the cue word comprises track prediction task description and track data description; encoding the track sequence to obtain an encoded track sequence and a track position sequence, and performing word segmentation on the track position sequence; constructing a space-time diagram of the track sequence based on the encoded track sequence, and extracting space-time dependent feature representation of the track sequence; fusing the word embedding representation and the space-time dependent feature representation by using a pre-trained large language model to generate a track information feature representation; and inputting the track information feature representation into a trained prediction module to obtain predicted values of the longitude, the latitude and the height of the target aircraft at the future moment.

The invention provides a highway event intelligent association analysis and prediction method, which comprises the following steps: constructing an event time-space association network according to time-space distribution characteristics of events, in which nodes represent events and edges represent association between the events; training the event space-time association network by adopting a graph neural network model to obtain an association evolution rule of the event in time and space; if an event possibly occurring in the future is predicted, other associated events possibly caused by the event are reasoned according to the event space-time association network, and an event causal logic chain is formed; according to the event causal logic chain, evaluating the effects of different coping strategies by adopting a reinforcement learning algorithm to obtain an event handling scheme; in the event occurrence and evolution process, vehicle track data and road condition monitoring data are continuously obtained, and the event time-space correlation network and the prediction model are dynamically updated.

The invention discloses a track representation enhancement method based on a dynamic subgraph. A historical sign-in track of the user is given, semantic track representation aims at learning low-dimensional vectors capable of revealing user behavior patterns and semantic intentions from user track data, and convenience is provided for subsequent prediction tasks. The method comprises the following steps: firstly, constructing a city knowledge graph based on a space-time semantic tetrad, and explicitly modeling the association between a user track and a space-time scene; secondly, extracting a semantic sub-graph sequence taking a user sign-in track as a core from the user sign-in track by using a relation perception dynamic sub-graph extraction mechanism; and then, learning representation of a sub-graph sequence based on an attention aggregation network of a cross-sub-graph, and further obtaining knowledge-enhanced user track representation. And finally, verifying the characterization quality based on a prediction module, and outputting K sign-in points which the user is most likely to be interested in as results. Through the modeling method of the dynamic subgraph sequence, time-space sensitive trajectory semantics are effectively modeled, and the accuracy of a prediction task is improved.

The invention discloses a multi-dimensional cutting precision control system applied to a laser pipe cutting machine, and relates to the technical field of laser cutting control, and the multi-dimensional cutting precision control system comprises a data acquisition module, a prediction module, an actual verification module, a judgment module and a response module; the data acquisition module is used for acquiring cutting track data of the initial laser pipe cutting machine, including but not limited to cutting speed, cutting depth, cutting angle and cutting path; a sensor is installed at the corresponding position of the laser pipe cutting machine; according to the laser pipe cutting machine, through the integrated data acquisition module, key parameters such as the cutting speed, depth, angle and path of the laser pipe cutting machine in the cutting process can be monitored in real time, the control precision of the cutting process is remarkably improved, and therefore the consistency and reliability of the cutting quality are ensured; the system can more accurately predict and compensate deviation possibly occurring in the cutting process, and the rejection rate caused by parameter fluctuation is reduced.

The invention relates to the technical field of risk assessment, and discloses an intelligent compliance risk assessment and monitoring system, which comprises a threat detection module, a management module, a data protection module and an automatic risk assessment module, the management module is used for identifying potential network attacks, malicious behaviors or abnormal traffic and providing automatic early warning, and the management module is used for docking global and local data protection and privacy laws and regulations, automatically analyzing compliance states of enterprises, generating compliance reports and providing correction suggestions; the data protection module is responsible for encrypting sensitive data, managing data access authority and tracking data flow, and the automatic risk assessment module performs automatic assessment on risk conditions of enterprises by utilizing artificial intelligence and machine learning technologies, predicts future compliance risks and generates a coping scheme. The method has the advantage of monitoring and analyzing potential threats and compliance risks.

The invention provides a geographic information surveying and mapping method, a geographic information surveying and mapping device and a geographic information surveying and mapping system. The method comprises the following steps: acquiring geographic data; inputting the track data of the unmanned aerial vehicle into the flight strip adjustment model to obtain flight strip parameter values; performing error correction on the laser data according to the air strip parameter value to obtain laser point cloud data; performing point cloud filtering on the laser point cloud data to obtain ground point cloud data and non-ground point cloud data; generating digital elevation model data according to the ground point cloud data, and extracting ground feature features according to the digital orthophoto map and the non-ground point cloud data to obtain ground feature data; and superposing the surface feature data and the contour line data under the same spatial reference to generate a three-dimensional real scene model of the target geographic area. The method solves the problem that in the prior art, geographic information surveying and mapping only depend on space coordinate points to distinguish different objects or scenes, so that surveying and mapping become more difficult.

The invention provides an identity recognition method and system based on unmanned aerial vehicle management and control. The method comprises the following steps: acquiring encrypted identity data based on unmanned aerial vehicle state monitoring, and generating an initial code containing a unique identifier, a position and a state parameter; dynamically adjusting a coding rule according to the acceleration variation, and generating a differential sequence matched with the motion state; generating a composite identifier containing a timestamp and a geo-fence through irreversible conversion; fusing the positioning data to dynamically bind the identifier and the track to generate synchronous track data; when the geofence is triggered, redundancy calibration is activated to generate anti-interference data; the composite identification and the anti-interference data are subjected to fragmentation packaging, and a topology dependence encryption block is constructed through a Hash abstract chain; and recombining the encrypted blocks in sequence based on the Hash chain, and checking the space-time consistency of the encrypted blocks and the geofence during recombining. According to the invention, the identity anti-counterfeiting and cross-region transmission security in the dynamic flight of the unmanned aerial vehicle is improved.

The invention discloses an intelligent auxiliary fracture monitoring system for orthopedic nursing, which comprises the following steps that: the system acquires mechanical parameters, movement track data and biological signals of a fracture patient through a multi-mode sensor, and intelligently analyzes the data by using a deep learning algorithm; generating a fracture state diagnosis result and recovery trend prediction data; the low-power-consumption wireless communication module realizes real-time transmission of diagnosis data and supports remote system parameter adjustment; the personalized nursing scheme generation module generates a rehabilitation plan, a load adjustment strategy and an auxiliary device configuration scheme through an optimization algorithm based on the patient biomechanical model and the diagnosis result; and the adaptive feedback adjustment module dynamically adjusts a data acquisition strategy and processing parameters to ensure the real-time response capability of the system to the state of the patient. The system is suitable for the technical field of intelligent auxiliary monitoring, and can realize real-time monitoring, intelligent diagnosis and personalized rehabilitation guidance of fracture patients.