1810 results about "Tracking system" patented technology

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 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.

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 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.

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 relates to the technical field of combustion monitoring, and discloses a combustion instability boundary prediction system based on complex environment condition analysis, and the system comprises a multi-source sensing module, a data fusion module, an anti-interference processing module, an instability feature library, an intelligent decision engine and a digital twinborn body. A dynamic environment interference compensation mechanism is constructed, spectrum correction parameters are configured according to different environment working conditions during real-time prediction of a combustion instability boundary, the problem of spectrum distortion caused by environment disturbance on combustion dynamic parameters is eliminated in real time, spectrum interference deviation of a combustion measurement position can be automatically sensed and corrected, and real-time prediction of the combustion instability boundary is realized. The accuracy of combustion instability sensitive feature extraction is guaranteed, the instability early warning error is reduced, a flame pose real-time tracking system is deployed, fuel control parameter self-adaptive adjustment is triggered immediately when it is detected that the pose is abnormal, autonomous correction of the combustion instability feature recognition position is achieved, and it is guaranteed that the feature capture space is accurately positioned.

The invention discloses an interactive multi-model underwater maneuvering target tracking method and system based on a pure azimuth second-order EKF. The method comprises the following steps: establishing a discrete state space model of a pure azimuth target tracking system; performing second-order linearization on the pure azimuth measurement model; calculating mixed input of a sub-filter corresponding to each model in the model set of the interactive multi-model algorithm; estimating a target state through parallel filtering of the azimuth-pure second-order EKF sub-filter corresponding to each model in the model set; updating the probability of each model in the model set; and combining the estimation results of all the sub-filters to obtain a final estimation result of the target state. According to the method, the target tracking precision can be improved, the problem of highly nonlinear measurement of the UUV on a target detection and tracking system through a passive sonar is effectively solved, good real-time performance is achieved, and real-time tracking of the UUV on the motion state of the underwater maneuvering target is achieved through adaptive matching of the real motion of the target through the interactive multi-model algorithm.

The invention provides a photoelectric pod target identification and tracking system based on a multi-scale attention mechanism, and belongs to the technical field of intelligent vision. Through combination of a multi-scale convolution module and a multi-head self-attention mechanism, accurate detection and tracking of a target in a photoelectric pod video image are realized. The multi-scale convolution module adopts convolution kernels of different sizes, and can extract local features of different scales to adapt to the change of the size of a target; the multi-head self-attention mechanism is used for capturing global features, especially long-distance dependency relationships between targets and backgrounds and between targets. Through fusion of local features and global features, the system improves the precision and robustness of target recognition in a complex scene. Meanwhile, by optimizing the structural design and the feature aggregation method, the calculation complexity of the system is remarkably reduced, and the requirements of the photoelectric pod for real-time performance and high efficiency are met.

The invention discloses an expressway vehicle centimeter-level trajectory tracking system based on 5G-U Beidou fusion, and relates to the technical field of 5G-Beidou traffic trajectory tracking, and the system comprises a multi-source heterogeneous data collection module which collects Beidou positioning, 5G vehicle state and visual data; the multi-frequency multi-constellation fusion positioning module is used for improving the algorithm and improving the ionosphere correction precision; the dynamic environment perception and error correction module is used for constructing a dynamic electronic fence and optimizing an error correction model; the centimeter-level track generation and visualization module is used for improving an interpolation algorithm and smoothing a track; and the cloud collaboration and edge computing module adopts federal learning to realize model collaboration optimization, and also relates to a plurality of innovation modules such as safety guarantee and intelligent service. The system integrates 5G-U and Beidou technologies, realizes centimeter-level high-precision positioning, can dynamically sense the environment and correct errors, improves the data transmission processing efficiency and safety, integrates semantic enhancement, intelligent service and visualization functions, and assists the intelligent development of expressways.

The invention provides an auxiliary method and device for a phased-array antenna to track a low-orbit satellite. The method provided by the invention comprises the steps of predicting a movement track of a satellite based on an initial parameter, and determining an initial direction of a phased-array antenna based on the movement track; analyzing the power density change of a first satellite signal received by the phased-array antenna, predicting the relative position change of the phased-array antenna and the satellite based on the power density change, and adjusting the direction of the phased-array antenna based on the relative position change; when the difference between the direction of the phased-array antenna and the direction of a second satellite signal actually received by the phased-array antenna is larger than a preset value, the direction of the phased-array antenna is corrected based on the actual strength and the receiving direction of the second satellite signal, and the corrected phased-array antenna points to the satellite. The method and the device provided by the invention are used for solving the problems of insufficient precision, strong interference sensitivity, weak recapture capability and the like of a traditional tracking system, and reliable support is provided for efficient and stable communication of low-orbit satellites.

The invention relates to the field of intelligent logistics transportation carbon emission real-time tracking, in particular to an intelligent logistics transportation carbon emission real-time tracking system and method. The method comprises the following steps: firstly, dividing a logistics transportation path into path sections, and generating a candidate path section set at the starting point of each path section to obtain candidate path sections; then, on the basis of the obtained traffic data, vehicle parameters and scheduling plans of the candidate path segments, the predicted carbon emission of the candidate path segments is calculated through a path segment carbon emission prediction algorithm; constructing an optimal path set based on the predicted carbon emissions of the candidate path segments; and finally, on the basis of the path segments in the optimal path set, obtaining actual operation data of the vehicle, and on the basis of the actual operation data of the vehicle, calculating the actual carbon emission. The technical problems that carbon emission prediction and optimization lack pertinence, obvious influences of speed change behaviors such as acceleration, deceleration and idling on energy consumption in actual driving are ignored, and comprehensive influences of vehicle load weight, driving speed and traffic conditions cannot be dynamically reflected are solved.

The invention provides a marine target real-time positioning and tracking system and method based on multi-source data fusion, and belongs to the field of target positioning and tracking. The sensor portion collects raw data. The data acquisition and time synchronization module is responsible for receiving and aligning data of each sensor; the multi-stage coordinate system registration and transformation module unifies all observation data to a world coordinate system by using IMU data and preset calibration parameters; the AI visual target detection module processes the video stream to identify a target; the adaptive federated Kalman filtering fusion module receives each path of processed data and outputs optimal target state estimation; and the dynamic task and resource scheduling module performs optimal allocation on system resources according to the current tracking state to form closed-loop control. Through deep fusion of high-precision RTK positioning data, laser ranging data, photoelectric pod attitude information and an AI vision algorithm, the positioning precision, tracking robustness and system real-time performance of a sea target under a complex dynamic sea condition are improved.

The invention relates to the technical field of live broadcast behavior monitoring, in particular to a live broadcast behavior tracking system based on deep learning, which obtains high-quality multi-source information and improves the accuracy of feature analysis by synchronously extracting image and audio data from a live broadcast video stream and combining frame extraction, image enhancement and voice recognition. According to the method, image features are extracted through a pre-trained convolutional neural network, audio features are extracted through a deep learning model, voice transliteration texts are fused, the weight of each modal feature is dynamically adjusted based on an attention mechanism, precise recognition of complex scenes and hidden violation behaviors is achieved, and image camouflage and latent language expression risks are effectively coped with. And the illegal type and confidence are output in real time, once suspected illegal behaviors are detected, alarm, interruption or shielding operation is triggered immediately, and related evidences are uploaded to an auditing database. And efficient, accurate and full-process management and control of the live broadcast violation behaviors are realized.

The invention discloses an indoor UWB-IMU fusion trajectory tracking system and method based on factor graph optimization, and belongs to the technical field of UWB indoor positioning. The system adopts a multi-stage processing framework, and specifically comprises the following steps: firstly, collecting channel pulse response and distance information by virtue of a wireless communication system, constructing a dynamic environment sensing mechanism, and evaluating the current channel state and the availability of each base station in real time; secondly, for the ranging values of the available base stations in the NLOS environment, compensating by using historical information, and for the ranging values of the unavailable base stations, estimating and correcting by using a time step length; and finally, collaborative optimization is carried out on UWB ranging data, IMU inertial information and base station state information through an SW-FGO algorithm, so that positioning stability is ensured, and adverse effects of obstacle shielding and a multipath effect on trajectory tracking precision are reduced. The invention provides a fusion positioning system which combines the advantages of UWB and IMU, can realize the tracking of a target motion track, and is suitable for most LOS and NLOS indoor mixed scenes.

The invention provides a pipeline welding all-position welding seam identifying and tracking system and method, and belongs to the technical field of pipeline welding automation. In order to solve the problems of strong arc light interference, low tracking precision, slow response speed and insufficient robustness in the existing pipeline all-position welding tracking technology, the system comprises a sensing layer, a processing layer, a decision-making layer and an execution layer. The welding seam identifying and tracking method comprises the following steps: S1, calibrating the welding seam identifying and tracking system; s2, all-position welding tracking is carried out; and S3, performing adaptive adjustment on different scenes. According to the method, dynamic weighted fusion and fuzzy PID control are adopted, multi-posture welding such as flat welding, vertical position welding, horizontal position welding and overhead position welding can be automatically adapted, parameters do not need to be manually adjusted, and the operation threshold is lowered.

The invention relates to the technical field of computer vision and multi-sensor data fusion, in particular to a multi-sensor image fusion obstacle real-time detection and tracking system, which comprises the following steps of: firstly, fusing data of a camera, a millimeter wave radar and a laser radar, and extracting and fusing multi-modal features; performing multi-target tracking based on a recurrent neural network: generating a target state through space-time modeling, associating a target with a historical track by using an attention mechanism, and maintaining track consistency; and the system performs semantic classification and interaction analysis on the obstacle, predicts the movement track of the obstacle, realizes deep semantic understanding, and finally outputs the identity label and the complete historical track of the obstacle.

Dialogue state tracking for voice assistants involves correctly tracking intent and entities of a task that a user is performing. A dialogue state, having a tracked intent and one or more tracked entities, can then be used to perform the task. Building a dialogue state tracking system within a voice assistant is not trivial. In some embodiments, a dialogue state tracking system involving one or more large language models can be implemented downstream of a natural language understanding system to produce the tracked intent and the one or more tracked entities. In some embodiments, a dialogue state tracking system involving one or more large language models can be implemented upstream of a natural language understanding system to produce rephrased natural language text, which is in turn processed by the natural language understanding system to produce the tracked intent and the one or more tracked entities.

The invention discloses a block chain-based blood full-process traceability tracking and safety management system and method, and relates to the technical field of blood management, and the method comprises the steps: distributing an encrypted digital identifier with a timestamp for each blood unit; collecting physical characteristic data of the blood storage environment in real time; dynamically adjusting the state mark of the blood product based on a preset plasma quality evaluation model; a threshold signature technology is adopted to ensure that multiple medical institutions complete blood information verification and sharing on the premise of protecting privacy; when it is monitored that the blood environment parameters exceed a safety threshold value, graded early warning is generated, and an emergency processing flow is started; and establishing a blood circulation whole process auditing tracking system. The system has the advantages that the blood whole-process traceability system based on the block chain is constructed, transparent credible management of the whole life cycle of blood products from collection to use is achieved, the trust and privacy protection problem in multi-mechanism cooperation is solved, and the blood safety management level and the emergency processing efficiency are comprehensively improved.

The invention relates to the field of navigation signal processing, and discloses a radiation therapy bolus dose feedback and tracking system based on image registration, the system comprises an image acquisition module, an image registration module, a bolus identification module, a dose recalculation module, a dose deviation analysis module and a feedback and adjustment module, space registration is carried out through an FBCT image acquired every day and an original plan CT, and the radiation therapy bolus dose feedback and tracking system based on image registration is obtained. The fitting state between the bolus and the skin is automatically identified, the position of an air gap is detected, and dose recalculation is carried out based on an actual structure. Through comparison with an original planned dose, the system can output a deviation index of a key dose parameter and provide visual feedback, if the deviation exceeds a set threshold value, a prompt is automatically sent out, and a radiotherapy technician and a physician are supported to carry out bolus adjustment or call an adaptive planning engine to reconstruct a treatment plan. Quantitative analysis of bolus fitting quality and closed-loop control of dose delivery consistency are realized, radiotherapy precision and safety are effectively improved, and the method is suitable for superficial radiotherapy scenes containing bolus design.

The invention relates to a target tracking system based on multi-modal deep feature proxy cross attention guidance, and the method combines and uses a visible light image and a thermal infrared image, and improves the performance of a tracking algorithm under a complex illumination condition through an innovative proxy cross attention mechanism. The system comprises a multi-modal detector and a data associator, wherein the detector consists of a double-branch feature extraction network, a proxy cross attention feature enhancement module and a feature pyramid sharing convolution module; the data correlator comprises a neural Kalman filter and a low-confidence-coefficient-based detection box reuse strategy, the challenge of a complex motion scene is solved by dynamically adjusting process noise and observation noise parameters, and the long-term tracking performance is improved. According to the method, multi-modal data fusion is carried out on a deep feature level, so that target detection and tracking are optimized, and the tracking precision and stability in a complex environment are improved.

The invention discloses a high-precision laser double-loop tracking system suitable for a long-distance moving target and a control method, and relates to the field of laser tracking of moving targets. According to the high-precision laser double-loop tracking system suitable for the long-distance and moving target and the control method, the coarse and fine composite axis double-closed-loop tracking control system of the energy charging target and the miss distance time delay compensation system of the energy charging target are effectively combined; the problem of high-precision laser tracking of a long-distance (greater than 100 meters) moving target is effectively solved. The problem of low energy transfer efficiency caused by low tracking precision in an unmanned aerial vehicle laser wireless energy transfer system is solved, and high precision, high real-time performance and high interference resistance of unmanned aerial vehicle laser energy transfer are realized.

The invention provides a cross-domain collaborative information tracking and positioning auxiliary method and system, and the method comprises the steps: firstly collecting an original data set generated by a target event in a plurality of associated domains, carrying out the cross-domain identifier matching processing to obtain a standardized event data set, and then carrying out the information association processing of the standardized event data set, according to the method, context semantic features and cross-domain time sequence behavior features of a target event are generated, then collaborative analysis is performed on the features based on a preset collaborative analysis model, a cross-domain collaborative tracking index set is generated, and a dynamic tracking path topology is constructed according to the cross-domain collaborative tracking index set. According to the method and the device, the target event is tracked and positioned, the positioning auxiliary strategy matched with the target event is generated, and finally the positioning auxiliary strategy is synchronized to the cross-domain tracking system to activate the cooperative positioning operation, so that the cooperative processing of multi-domain information is realized, and the target event tracking and positioning accuracy and efficiency are improved.

The invention relates to the technical field of artificial intelligence, in particular to a news viewpoint evolution trend tracking system based on artificial intelligence, which comprises a collector, an extractor, an aggregator, a modeler, a detector and a presenter. A disturbance sample generation and consistency detection mechanism is combined, so that the clustering process is more robust, and the recognition precision of the viewpoint cluster semantic center is remarkably improved; according to the method, a black hole detection mechanism based on density estimation and multi-point similarity joint modeling, an anti-fact intervention mechanism and a dynamic graph density-structure joint estimation model are adopted, a node disturbance situation can be constructed, the influence of the node disturbance situation on a semantic structure can be inferred, and through fusion of semantic consistency, density offset and energy anti-fact scoring, the dynamic graph density-structure joint estimation model is obtained. High-confidence screening of abnormal nodes is realized, and the purity of a viewpoint atlas structure and the reliability of semantic evolution modeling are effectively improved.

Systems and methods for decentralized data storage and retrieval within a distributed computing system include partitioning datasets into encrypted data shards that are each associated with a unique identifier. A processing system operating within the network may distribute these shards across storage nodes selected based on performance metrics such as latency, storage utilization, and reliability. Metadata for each shard, including its identifier, unique cryptographic key, and node location, may be recorded in a tamper-evident distributed ledger. Upon receiving a retrieval request, the system may query the ledger to identify shard locations, retrieve the shards in parallel, validate their integrity by comparing ledger metadata with metadata from storage nodes, decrypt the shards using their respective cryptographic keys, and reconstruct the dataset. These systems and methods enhance data security, integrity, and fault tolerance while allowing efficient and verifiable decentralized storage and retrieval in dynamic, distributed environments.

The invention provides a patient rehabilitation tracking system, a rehabilitation training regulation and control method based on active perception and environment self-adaption, equipment and a medium, and the method comprises the steps: determining a cooperative motion state of a nerve-muscle-bone system according to a multi-modal physiological data flow of rehabilitation training tracking of a target patient; performing intention-muscle force cooperative identification based on the cooperative motion state to obtain motion intention and muscle force contribution distribution of the target patient; determining a dynamic risk map of the target patient during rehabilitation training; variable universe fuzzy reasoning is carried out on the motion intention, the muscle force contribution distribution and the dynamic risk map, and then a rehabilitation adjustment instruction matched with the rehabilitation state of the target patient is obtained; the rehabilitation progress of the target patient is continuously tracked, and the rehabilitation training robot is controlled to execute self-adaptive power-assisted compensation according to the rehabilitation adjustment instruction. By means of the scheme, motion intention decoding and muscle force contribution analysis can be conducted on rehabilitation training of the patient, and rehabilitation training regulation and control are conducted in combination with the dynamic environment risk.

The invention relates to an acquisition equipment parameter determination method and equipment for improving welding seam image quality, and the method comprises the steps: determining the current parameters of acquisition equipment, and obtaining a welding seam region image under the current parameter condition; high-brightness pixel points and low-brightness pixel points in the welding seam area image are extracted to determine the high-brightness pixel proportion, the low-brightness pixel proportion and the high-brightness pixel distribution divergence; according to the high-brightness pixel proportion, the low-brightness pixel proportion and the high-brightness pixel distribution divergence, whether the welding seam area image is qualified or not is judged; if yes, taking the current parameter as a determined parameter of the acquisition equipment; and if not, correcting the current parameter to obtain the updated current parameter, and returning to determine the current parameter until the determined parameter of the acquisition equipment is obtained. The problems that in the prior art, under the complex illumination working condition, proper collection equipment parameters cannot be determined to ensure clear presentation and stable extraction of a welding seam feature area, then the robustness and positioning precision of a welding seam tracking system are affected, and the technical adaptability is reduced are solved.

The invention belongs to the technical field of distributed optical fiber sensing and low-altitude security and protection, and particularly relates to an unmanned aerial vehicle long-distance tracking method based on distributed optical fiber vibration sensing. The invention relates to an unmanned aerial vehicle vibration / electromagnetic fusion multi-mode identification method, a special installation formation design, a dispersion compensation and amplitude phase error compensation design, an unmanned aerial vehicle target vibration / electromagnetic fusion multi-mode identification design, a detection capability calculation method of an unmanned aerial vehicle vibration sound pressure-space attenuation law-distributed optical fiber processing equipment detection capability coupling model, a long-distance sensing optical cable and an electromagnetic monitoring node. According to the tracking method, a special optical fiber installation mode, dispersion compensation and phase error compensation of an original phase signal, a vibration / electromagnetic fusion multi-mode identification method, an unmanned aerial vehicle vibration sound pressure-space attenuation model-distributed optical fiber processing equipment detection capability corresponding method and other innovative work are adopted; a mature distributed optical fiber vibration sensing system is utilized to realize long-distance passive detection and target tracking of the low-slow-small unmanned aerial vehicle.

The invention provides an RFID trajectory tracking system based on least square speed adaptation, and the method comprises the steps: firstly building a half-wavelength observation distance expansion model, and effectively solving the problem of error divergence caused by a conventional phase unwrapping scheme; then, generating an initial position candidate set by using a triangular centroid weighting algorithm, performing instantaneous speed estimation by using a least square algorithm, and introducing a dynamic threshold-weight constraint mechanism to obtain a high-quality initial state set; thirdly, speed estimation based on least square is fused in the EKF, speed data are updated through the least square method before each round of antenna scheduling is started, target tracking trajectories estimated based on different initial positions are obtained, and the problem that in the target tracking process, the speed suddenly changes or the tracking error is large during steering is solved; and finally, dynamically deleting the tracking trajectory with the zero weight, and selecting the trajectory with the maximum weight value as the final tracking trajectory of the target, thereby effectively reducing the calculation complexity of the algorithm. The invention provides an RFID trajectory tracking system which is convenient and simple to deploy, can realize trajectory tracking of a target tag, and is suitable for most indoor positioning scenes.

The invention discloses an optical communication network operation and maintenance fault reporting, positioning and tracking system, which relates to the technical field of optical communication network operation and maintenance, and comprises a coarse positioning module for performing phase demodulation and interference signal matching on optical fiber echo data, identifying a sudden change point and an abnormal interval, and generating a potential fault interval and a coarse positioning coordinate; the fine positioning module is used for transmitting a coded pulse signal to a link where the optical fiber echo data is located according to the potential fault interval and the coarse positioning coordinate, and performing matched filtering and deconvolution processing to generate a fine positioning echo characteristic curve; the fault identification module is used for carrying out deep learning classification on the fine positioning echo characteristic curve, identifying a fault type and generating a diagnosis result; and the visualization module is used for mapping the diagnosis result to the topology and geographic position information of the link section where the optical fiber echo data are located, and generating a visual interface. According to the invention, the manual troubleshooting time is obviously reduced, and the fault processing efficiency and reliability are improved.

The invention provides an acoustic coalescence elimination method for hydrogen bubbles in molten aluminum, and belongs to the technical field of molten aluminum bubble elimination. According to the method, sound field collaborative focusing is achieved by arranging a phase control array transducer set in a molten aluminum smelting furnace, primary gathered sound waves and efficient coalescence sound waves are applied in stages according to bubble size evolution in a double-frequency scanning mode, and the hydrogen bubbles in the molten aluminum are eliminated. A target frequency is dynamically adjusted in combination with a bubble resonance frequency optimization model to match the inherent frequency of a bubble, a sound-bubble coupling dynamic tracking system is used for monitoring bubble evolution triggering stage switching in real time, and a sound field focus point dynamically scans and covers a three-dimensional space area through phase delay control. A delay detection method is adopted to eliminate cavitation bubble interference and accurately measure the hydrogen content, sound intensity and frequency parameters are optimized according to a sound field parameter self-adaptive regulation and control model, and the technical problem that hydrogen bubbles in a molten aluminum deep area are difficult to be effectively driven by sound waves and cannot be fully coalesced and eliminated is solved.