3798 results about "Tracking system" patented technology

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

A medical system includes an elongate instrument, a tracking system, and one or more processors. The tracking system is disposed along at least a portion of the elongate instrument. The one or more processors are coupled to the tracking system and are configured to: generate a plurality of linked bounded-surface elements corresponding to a set of connected passageways of a patient anatomy; receive shape information from the tracking system, the shape information indicating a shape of at least a portion of the elongate instrument when the portion is disposed in the set of connected passageways; register the shape information to the plurality of linked bounded-surface elements; and based on the registering, display a composite image including an instrument image of the elongate instrument displayed within a model of the set of connected passageways.

The invention relates to the technical field of marine monitoring, in particular to a marine target visual servo tracking method, which comprises the steps of multi-modal sensor fusion, a self-adaptive visual tracking algorithm, a servo control and visual collaboration mechanism and a shielding processing and target re-identification strategy. The problem that tracking is unstable under the conditions of illumination change, ship body shaking, target shielding and the like in a traditional method is solved. The IMU, the GNSS and the visual data are fused through extended Kalman filtering, ship body shaking is compensated, and the target state estimation precision is improved; the improved D-Fi ne target detection model is combined with an online feature updating mechanism to dynamically adapt to the appearance change of the target; the prediction and correction control strategy and the double-closed-loop PI D controller cooperate to adjust the camera holder, and the tracking delay is reduced; the multi-clue shielding detection and space-time joint feature matching technology ensures accurate re-identification of the target after shielding is removed. The real-time performance and robustness of the tracking system on an embedded platform are improved, and an efficient and stable target tracking solution is provided.

The invention relates to the technical field of computer deep learning, in particular to a deep learning task hybrid deployment method and system. The method comprises the steps that S1, a user submits a task through a Kubernetes native interface; s2, a task scheduling optimizer analyzes tasks according to resource requirements and service levels and allocates the tasks to proper nodes; s3, the SLO manager tracks real-time data of various performance indexes of the system and compares the real-time data with a preset service grade target; s4, the Koordlet component executes a specific resource allocation operation on the target node; and S5, the traffic safety monitor monitors the data streams among the nodes in real time. According to the method, a mixed deployment strategy for different types of resources is provided by analyzing the periodical rule of deep learning task resource use, dynamic resource sharing of online and offline tasks is achieved, and meanwhile it is ensured that the system can still run stably and efficiently under the high-load condition.

The invention discloses an intelligent photoelectric theodolite aerial target positioning and tracking system, which relates to the technical field of photoelectric detection, and comprises a multi-mode photoelectric sensor module integrating visible light, infrared thermal imaging, a laser radar and a polarized light sensor and supporting spectrum adaptive switching; the dynamic noise suppression processing module is used for eliminating environmental interference based on a time-space domain hybrid filtering algorithm; the multi-target tracking control module adopts a time-sharing partition scanning strategy and a graph neural network data association algorithm; the anti-interference servo driving module is used for realizing stable tracking under strong disturbance through inertial navigation-visual fusion compensation; and the edge computing platform is used for deploying a lightweight deep learning model to complete target recognition and trajectory prediction. According to the invention, through interdisciplinary collaboration of quantum dot materials, graph neural networks and physical equation constraints, the bottleneck of a single technology is broken through; and through closed-loop optimization of dynamic anti-interference and edge intelligence, full-link enhancement of'perception-decision-execution 'is realized.

The invention relates to the technical field of airport baggage management, in particular to an airport baggage tracking system and method based on a block chain, and the method comprises the steps: baggage information collection and identification: collecting basic baggage information through an intelligent tag, encrypting the basic baggage information to generate a unique identifier, and recording the unique identifier in the block chain; luggage state updating: collecting state change information by using the Internet of Things equipment, and writing the state change information into the block chain after verification of the smart contract; anomaly detection and prediction: simulating and analyzing a luggage state by means of a generative adversarial network, and triggering an alarm and updating the block chain if an anomaly is found; path optimization: adopting a reinforcement learning algorithm to optimize a transportation path according to a real-time state and historical data and recording the transportation path; and performing anomaly analysis and response, analyzing path anomaly based on the graph convolutional network, and processing and recording according to a strategy. The data security and transparency are guaranteed by using the block chain technology, the luggage management efficiency is improved by combining various advanced algorithms, the abnormal risk is reduced, the service experience is optimized, and the method is suitable for the field of airport luggage management.

The invention discloses an unmanned aerial vehicle trajectory tracking system based on multi-sensor fusion, which relates to the technical field of unmanned system autonomous navigation and control, and comprises an unmanned aerial vehicle data acquisition module, an unmanned aerial vehicle feature extraction module, a trajectory prediction deviation scoring module, a trajectory dynamic correction module and a trajectory monitoring module. The unmanned aerial vehicle data acquisition module acquires and preprocesses unmanned aerial vehicle and GPS data, the unmanned aerial vehicle feature extraction module constructs a trajectory tracking feature sequence table, the trajectory prediction deviation scoring module outputs three-dimensional trajectory prediction coordinates and deviation scores, and the trajectory dynamic correction module generates correction parameters. Based on the judgment logic, the judgment logic is converted into an unmanned aerial vehicle correction instruction and attitude feedback quantity, and the track monitoring module constructs a visual interface. According to the method, the trajectory tracking precision of the unmanned aerial vehicle in a complex environment is remarkably improved, real-time detection and dynamic correction of abnormal deviation are realized, the state monitoring capability is enhanced by a three-dimensional visual interface, the trajectory tracking error is reduced, the misjudgment rate is reduced, and the flight safety and the control efficiency are guaranteed.

The invention discloses a full-life-cycle auditing tracking system and method, and belongs to the technical field of data tracking backtracking, and the method specifically comprises the steps: carrying out the global unique identifier distribution of personnel configuration data during the first collection, embedding a timestamp, an operation identifier and a link identifier in each life cycle node of the data, constructing a real-time auditing model, and carrying out the real-time auditing of the data; through data change monitoring, behavior pattern recognition and an anomaly detection algorithm, each data operation is recorded in real time and compared with a preset auditing strategy, risk early warning is triggered, block chain evidence storage is established at a key operation node, a multi-block chain link relation network diagram is constructed based on multiple block chains, and when an abnormal node occurs, a data life chain is generated. Predecessor nodes of the abnormal nodes are backtracked, recursive check is carried out on each predecessor node, and an abnormal source and a responsibility subject are positioned; according to the method, the multi-step association risk can be found, the situation that tracking cannot be achieved during tracking and backtracking is prevented, the missed judgment rate is reduced, and the accuracy and efficiency of tracking and backtracking are improved.

The invention provides a mine abnormal event real-time identification method and system based on time sequence characteristics, and relates to the technical field of mode identification, and the method comprises the steps: carrying out the time-space alignment and semantic annotation of multi-modal monitoring data, and constructing a time sequence knowledge graph; calculating a dynamic association weight between entities, and analyzing a risk propagation path; predicting a risk situation based on a historical evolution rule; and dynamically generating a differential early warning strategy and establishing a closed-loop tracking system. According to the invention, early identification, accurate prediction and efficient disposal of mine safety risks can be realized, and the mine safety management level is improved.

The invention relates to the technical field of intelligent power grids, and provides an intelligent operation centralized control center intelligent scheduling interaction method and system, and the method comprises the steps: generating a heat distribution gradient through environment noise filtering, reflecting the temperature field change of equipment, obtaining a voltage amplitude envelope through fundamental wave and harmonic wave separation, and monitoring the power quality; a current fluctuation coefficient is extracted by combining segmented deviation analysis to evaluate a line load state, and frequency fluctuation parameters are captured by using time domain statistics to track system stability. By constructing a dynamic association map, heterogeneous data such as thermodynamics, electrical quantity, load characteristics and synchronous states are mapped to power grid topological nodes, and a cross-dimension association relationship is established to realize whole-network state coupling analysis. An overload risk, a voltage out-of-limit probability and a system instability threshold are quantified through a hierarchical evaluation mechanism, a multi-target optimization constraint condition is formed, and finally a scheduling strategy considering equipment safety, economy and power supply reliability is generated. When the power system operates, intelligent scheduling is carried out according to the operation state of the power grid.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a generative interface panel having multiple automated assistant services. Each assistant service may access a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

Object detection and tracking systems may use machine-learned transformer models with self-attention for detecting, classifying, and / or tracking objects in an environment. Techniques described herein may include receiving sensor data generated by different sensor modalities of a vehicle, determining different bounding shapes based on the different sensor modalities, and using a machine-learned transformer model to determine associated and / or combined bounding shapes. The machine-learned transformer model may receive a variable number of input bounding shapes representing any number of objects and various sensor modalities. Multiple stages of the transformer may be used to determine associated bounding shapes and to assign attributes for the associated bounding shapes, based on the individual bounding shapes of the different sensor modalities and / or previous bounding shapes for objects detected and tracked in a previous scene in the environment.

The invention provides a hazardous waste storage intelligent management system and method, and the system achieves the cooperative work of a multi-level sensor network, an intelligent data processing module, an automatic storage scheduling module, a grading early warning module, a full-life-cycle information tracking system, and a remote monitoring module. And real-time monitoring, risk early warning, automatic scheduling and whole-process tracking of the hazardous waste storage environment are realized. The sensor network collects data in a warehouse in real time, and the intelligent data processing module carries out analysis based on a machine learning algorithm and dynamically adjusts a risk early warning threshold value. The system triggers an early warning signal according to an analysis result, the storage position and the ex-warehouse sequence of the hazardous waste are optimized through an automatic scheduling module, the full-life-cycle information tracking system records operation data of the hazardous waste through RFID and a block chain technology, the non-tampering performance and traceability of the data are ensured, and the safety of the hazardous waste is improved. And the remote monitoring module allows remote operation of the system through the cloud platform. The system improves the safety, intelligence and automation level of hazardous waste warehouse management.

The invention provides an anti-unmanned aerial vehicle intelligent identification and tracking system based on multi-source data fusion, and relates to the technical field of anti-unmanned aerial vehicle detection, and the system comprises a multi-source data preprocessing module which is used for outputting preprocessed multi-source data; the target detection module is used for carrying out unmanned aerial vehicle target detection on visual data in the preprocessed multi-source data and outputting a detection result containing a bounding box position, confidence and morphological characteristics; the target tracking module is used for performing unmanned aerial vehicle target tracking based on the target detection result and outputting a tracking result; and the fusion decision module is used for confirming the target identity based on the tracking result and the preprocessed multi-source data and outputting a final recognition result. The technical problems of low detection precision of small targets, difficulty in distinguishing similar targets, inaccurate 3D motion prediction, difficulty in re-identification after long-time shielding and the like in the prior art can be solved, and accurate identification, stable tracking and intelligent decision making of the unmanned aerial vehicle target are realized.

A virtual reality (VR)-based system for improving students' reading comprehension and writing skills. The system includes: a VR headset with an integrated eye-tracking system, high-resolution stereoscopic displays and spatial audio output, where the eye-tracking system detects reading patterns, fixation points and saccadic movements to assess reading engagement; a pair of motion tracking gloves with embedded pressure-sensitive capacitive sensors and haptic feedback actuators, allowing students to work with virtual reading material, perform handwriting exercises, and receive tactile reinforcement of text-based interactions; an adaptive learning module based on artificial intelligence (AI) that is configured to dynamically adjust reading complexity and writing exercises, responding to real-time metrics for interaction with students, with the AI ​​module using deep learning-based natural language processing (NLP) models to analyze reading fluency, written coherence, and syntactic complexity; a speech recognition and synthesis subsystem comprising a neural network-based acoustic model and a phoneme segmentation processor, wherein the subsystem assesses pronunciation accuracy, fluency, and articulation deviations by analyzing phonetic waveforms of spoken responses; a multimodal interactive interface that enables students to interact with virtual reading environments via a combination of hand tracking, voice commands and eye-controlled text selection, with the interface allowing students to highlight, comment on and orally summarize text content; A real-time dashboard for teacher monitoring, which is communicatively connected to the VR system. The dashboard offers educators detailed analyses of student comprehension patterns, writing progress, and AI-generated adaptive learning recommendations; and a cloud-integrated content management system (CMS) configured to store AI-curated reading materials, personalized writing assignments, and NLP-processed insights into student performance, with the CMS enabling adaptive curriculum adjustments based on students' learning trajectories.

The invention discloses a supply chain intelligent tracking system oriented to process monitoring, relates to the technical field of supply chain intelligent management, and is used for solving the problems of flexibility and anti-risk capability of supply chain breakpoint management. Comprising a supply chain data processing module, an energy transmission analysis module, a breakpoint prediction module and a supply chain adjustment module. According to the method, original data are collected from supply chain nodes, effective features are generated in combination with a time sequence, classification features and a high-dimensional data processing technology, a breakpoint prediction model is constructed through a gradient lifting decision tree, breakpoint risks are identified and evaluated, a supply chain network topology structure is constructed, and propagation paths and influence ranges of the breakpoint risks are calculated recursively. Determining an affected node set, formulating an adjustment and recovery strategy in combination with a prediction result, optimizing resource allocation and logistics routes, quickly recovering key nodes, ensuring continuity of a supply chain, and upgrading an early warning mechanism to dynamically monitor risks, thereby improving management flexibility and anti-risk capability of the supply chain.

The invention discloses a self-adaptive photovoltaic tracking control method and system, and the method comprises the steps: collecting and fusing multi-source data, detecting the operation environment of a photovoltaic system in real time, and carrying out the real-time processing of the collected environment data; constructing a self-adaptive photovoltaic tracking control model, training the self-adaptive photovoltaic tracking control model, and dynamically adjusting a tracking strategy of the photovoltaic panel according to historical data and real-time environment change; a dynamic error compensation mechanism is introduced, errors of a tracking system are corrected in real time, a multi-objective optimization strategy is designed, and tracking modes are automatically switched according to different environmental conditions. The system comprises a multi-source data acquisition module, a photovoltaic tracking control module and a tracking mode switching module. According to the invention, the precision and efficiency of operation monitoring of the photovoltaic system are improved; high-quality data input is provided for a photovoltaic tracking control model, and the decision-making ability of the model is improved; the intelligent and automatic photovoltaic panel tracking strategy is realized, and the power generation efficiency of a photovoltaic system is improved.

A covariance accumulation-based direction of arrival estimation method for an S-mode signal at a low signal-to-noise ratio, relating to the technical field of information processing. According to the method, a delayed covariance matrix of array output data is comprehensively utilized, a weight is determined by means of a correlation feature of the S-mode signal, and the delayed covariance matrix of array output data is accumulated, so that the direction of arrival estimation precision of a MUSIC method for the S-mode signal in a low signal-to-noise ratio environment is improved, establishing more accurate angle tracking for an S-mode target in the low signal-to-noise ratio environment, effectively reducing the false alarm probability, and expanding the effective working ranges of angle measurement and a tracking system.

A system for performing interactions within a physical environment including: a robot base that undergoes movement relative to the environment; a robot arm mounted to the robot base, the robot arm including an end effector mounted thereon; a first tracking system that measures a robot base position; a second tracking system that measures movement of the robot base; and, a control system that uses a robot base position to at least partially control the robot arm to move the end effector along an end effector path, wherein the control system: determines the robot base position at least in part using signals from the first tracking system; and, in the event of failure of the first tracking system: determines a robot base position using signals from the second tracking system; and, controls the robot arm to move the end effector along the end effector path at a reduced end effector speed.

The invention discloses a commerce and trade circulation supply chain optimization method based on multi-modal data fusion, and the method comprises the following steps: 1, multi-modal data collection: collecting the multi-modal data of the whole supply chain in real time through an Internet of Things device, an enterprise ERP system, a logistics tracking system and a third-party platform, the multi-modal data comprises text data, image data, voice data, time sequence data and spatial data. When the scheme is implemented, multi-source heterogeneous data of the Internet of Things, ERP and the like are integrated, so that the problem of an information island in a traditional supply chain is effectively solved; position coding and a cross-modal attention mechanism are introduced into the multi-modal fusion model, so that the data alignment precision is remarkably improved, for example, when'emergency order adding 'in a voice communication record is associated with a corresponding order image, the weight distribution accuracy is improved; the application of the block chain technology ensures the transparency and traceability of the supply chain decision.

The invention relates to a mechanical arm compliance tracking system and a control method. The system comprises a mechanical arm module, a mechanical arm pose control module, a force acquisition module and a dynamic self-adaptive hybrid impedance controller. In the dynamic self-adaptive hybrid impedance controller, through a dynamic self-adaptive hybrid impedance control algorithm, dynamic contact force tracking in an uncertain environment is completed, force overshoot in a contact stage is avoided, and meanwhile, a force tracking error in a dynamic tracking stage is kept. The controller is capable of adjusting its update rate parameters online in order to track the reference force in an uncertain environment.

The invention relates to a distributed outbound system optimization method based on cloud computing. The method comprises the following steps: disassembling an outbound task into fine-grained subtasks including dialing, recording, interaction and recording; generating a unique identifier for each subtask; based on a distributed task scheduler; according to node real-time performance and network delay, dynamically distributing tasks, and introducing a task priority rule; monitoring a task execution state in real time through a distributed tracking system; recovering and redistributing abnormal and failed tasks; fragmenting the call-out data according to regions or customer groups, and distributing the call-out data to different nodes; processing the call record and the client feedback by using a stream processing framework comprising Apache Flink; adding a call task including Kafka into a distributed task queue; setting a dynamic priority queue, and adjusting the priority according to the task timeliness; setting an overtime threshold value for the call task, and monitoring a task state in real time; and when the task is overtime or failed, triggering a retry strategy including adjusting the call period and switching the node.

The invention discloses an automatic data compliance check tracking system based on a smart contract, and relates to the technical field of data check, and the system comprises the steps: building a hierarchical mapping database of domain classification samples and compliance check depth; deploying a field identification engine, and analyzing the data packet information according to the field identification engine to determine a field type; identifying a compliance check depth corresponding to the domain type based on the hierarchical mapping database, and loading a corresponding check rule cluster from an intelligent contract strategy library according to the compliance check depth; and performing compliance check according to the check rule cluster, and recording a compliance check state to perform blockchain upchain storage. The technical problems that in the prior art, the execution efficiency of a data compliance check process is low, and traceability cannot be achieved are solved, and the technical effects that the compliance check efficiency and accuracy are improved, and the check process is credible and traceable are achieved.

The invention discloses a visible light and infrared image fusion target tracking method based on visual cue learning, and relates to the field of extended target tracking. Comprising the steps of inputting video image data, converting an image through a block embedding module, extracting multi-scale information through scale perception attention, extracting each modal feature through a double-branch backbone network, performing specific modal visual prompt learning and inter-modal visual prompt interaction, and performing cross fusion of an adapter and a prediction head. According to the method, a more reliable and efficient target tracking system is realized by introducing a multi-scale information extraction mechanism, enhancing visual prompt learning and interaction capability and developing a more advanced multi-modal fusion strategy, and through the improvements, the performance of target tracking in a complex environment can be remarkably improved, and the requirements of practical application are met.

The invention relates to the technical field of target detection and tracking, and discloses a multi-modal real-time target detection and tracking system, which is characterized in that a multi-modal data acquisition module integrates a high-definition camera, a millimeter-wave radar, a laser radar and an infrared sensor and is used for acquiring target information from multiple dimensions such as visual images, distance, speed and angle, three-dimensional point cloud and thermal radiation; and the data preprocessing module is used for carrying out denoising, enhancement, normalization, filtering, coordinate conversion and temperature correction processing on the original data acquired by the multi-modal data acquisition module. Multiple sensors are integrated to collect multi-dimensional data, after preprocessing, efficient fusion is achieved through hierarchical fusion and an attention mechanism, the detection module is combined with an improved algorithm and a dynamic threshold value, the tracking module fuses multiple features and has the online learning ability, and the system control module achieves intelligent management. The system greatly improves the accuracy, the real-time performance and the stability of detection and tracking, has remarkable advantages of an innovative technology, and provides a new scheme for related fields.

The invention relates to the technical field of data processing, in particular to a video stream real-time target detection and tracking system based on deep learning, and the system comprises a data collection module which captures an original video stream and outputs a bimodal image sequence; the adversarial generation module synthesizes low-frequency samples; the feature alignment module dynamically adjusts the channel attention weight; the adaptive detection module switches a main detection path and an auxiliary detection path according to the scene complexity index, the main path generates a target detection frame by adopting lightweight convolution, and the auxiliary path activates the re-identification sub-network to generate a supplementary detection frame; the space-time diagram tracking module uses the channel attention weight to carry out cross-modal matching to correct conflict nodes; the trusted decision module generates a probabilistic behavior decision. The system continuously compresses the distribution difference between training data and an actual scene through a closed-loop optimization mechanism of coordinate feedback driving sample synthesis, channel weight adjustment distribution calibration and trajectory continuity constraint decision reliability, and improves the robustness of target detection and tracking in a dynamic environment.

Methods, systems and apparatuses for infant and child monitoring, for changing pads, and for tracking heath. One system is an intelligent changing pad system for infants and children that includes a main body with a changing surface, a scale integrated within the changing surface for measuring weight, a plurality of sensors configured to monitor health-related metrics of a human including temperature and hydration levels, a graphical user interface for displaying the human's health metrics and alerts, and a wireless communication system for transmitting the monitored data to a remote computing device.

The invention discloses a photovoltaic tracking method and system based on deep learning optimization scheduling. The method comprises the following steps: a photovoltaic tracking system and an environment data acquisition device acquire initial information; after the initial information is obtained, a tracking camera of the environmental data acquisition device obtains the relative position of the sun by using a sun-oriented movement trajectory tracking algorithm model, then calculates a tracking angle according to the position information of the sun and drives the camera to rotate to a theoretical angle so as to realize tracking of the sun; while tracking the sun, the tracking camera captures and captures an image of weather near the sun rich in weather characteristic information, and the captured image information, data acquired by the environmental data acquisition device and historical photovoltaic power generation power data recorded by the MPPT solar controller are sent to the photovoltaic tracking controller; and the photovoltaic tracking controller makes different control strategies according to the contents. The probability of invalid tracking of the system in a complex environment is reduced, so that the photovoltaic tracking system is based on the optimal state, and the power generation efficiency is improved.

The invention relates to the technical field of carbon footprint dynamics, in particular to a railway traffic carbon footprint dynamic tracking system and method based on a block chain and edge calculation. The data acquisition unit is used for acquiring train operation state, environment parameters and traction energy consumption data; the edge calculation unit fuses the multi-source data acquired by the data acquisition unit through adaptive Kalman filtering to obtain standardized data, converts a train operation state into carbon emission based on an instantaneous power model, and introduces a dynamic emission factor to calculate and obtain accumulated carbon emission; and the block chain network unit is used for compressing the accumulated carbon emission data and then generating a Merkle tree root hash upper chain, and storing the carbon emission data to a block chain. Incremental uplink and rapid verification of the data are realized, the on-chain data processing efficiency and the query traceability are improved, and the problems of high storage pressure and low verification efficiency of a traditional on-chain evidence storage mode in a high-frequency track data writing scene are solved.

The invention belongs to the field of eye movement tracking, and particularly relates to an eye movement tracking system, method and equipment integrating electroencephalogram signals and a non-contact eye tracker. The objective of the invention is to solve the problems of limited acquisition range of a non-contact eye tracker and low spatial resolution of eye movement behavior deduction by EEG signals in the prior art. The method comprises the steps that eye movement data and electroencephalogram signals are synchronously collected, after calibration mapping and time sequence alignment preprocessing are conducted, an electroencephalogram modal behavior recognition model is supervised and trained through eye movement fixation point labels, and the incidence relation of the two modals is established. A dynamic fusion strategy is adopted in the online stage, when eye movement data are valid, a model prediction result and the eye movement data are fused to output a fixation point, when eye movement fails, the fixation point is calculated in combination with model prediction and historical data, enhanced compensation of eye movement tracking by electroencephalogram signals is achieved, the defect that the spatial resolution of a single EEG signal is low is effectively overcome, and the accuracy of eye movement tracking is improved. And the robustness and the tracking precision of the system in a complex scene are improved.