105 results about "Adaptive sensing" patented technology

The present invention relates to an adaptive sensing-based lightweight monitoring method for a fine crack in a complex background region. The method comprises the following steps: step S1, on the basis of region division, performing automatic acquisition of crack information, wherein PTZ camera sensors are used to automatically perform block-wise acquisition on crack regions; step S2, performing an adaptive complex scale calibration process, using a multi-scale template matching algorithm to adaptively correct distortion information of all regions, and performing real-scale conversion from pixel precision; step S3, constructing a lightweight crack segmentation network to process data processed in step S2; and step S4, by means of a quantitative crack-tracking algorithm based on Euclidean distance similarity classification, performing real-time monitoring on each piece of crack dynamic information. Compared with the prior art, the present invention has advantages such as achieving efficient, accurate, and online monitoring and analysis of cracks.

The invention belongs to the technical field of image recognition, and provides a target tracking method and system based on AI vision, and the method comprises the following steps: collecting original video frames; environment adaptive image enhancement; performing multi-target detection and multi-modal feature extraction; estimating local optical flow motion; performing multi-target trajectory association; carrying out shielding processing and re-identification; outputting a track and analyzing a result; according to the method, a physical-deep learning cascade defogging model is set, light / dense fog processing paths are dynamically switched through a dark channel mean value, atmospheric scattering physical prior and U-Net residual error correction are fused, an environment self-adaptive sensing architecture is provided, the failure bottleneck of a traditional single model under sudden change fog concentration is broken through, and the real-time performance of the system is improved. According to the method, an apparent-motion-geometry ternary coupling trajectory cognition system is constructed, a dynamic cost matrix and a feature cache pool are designed, the ID switching problem caused by similar target aggregation and long-time shielding is solved, and the accuracy of target tracking in the shielding environment is improved.

The invention provides a task chain self-healing transmission prediction method and system based on parameter fingerprint holographic perception, and relates to the technical field of parameter transmission, and the method comprises the steps: deploying a probe array to collect a task chain parameter flow, and extracting a feature tensor through a chaotic feature network to generate a feature fingerprint. The feature fingerprints are stored in a dynamic certificate chain, and a high-dimensional transfer matrix is constructed to track performance changes. And constructing an adaptive sensing network according to the dynamic certificate chain and the high-dimensional transfer matrix, and training an improved chaos twinborn model for constructing an anomaly recognition network. And generating a self-adaptive repair strategy according to the abnormal features and the historical data, and storing the self-adaptive repair strategy in a distributed knowledge graph. And training a prediction engine based on the knowledge graph and the chaos twinborn model, predicting the risk of the task chain, and finally evaluating the system performance through deep reinforcement learning to realize continuous optimization. According to the method, the task chain risk can be effectively predicted, self-adaptive repair is realized, and the stability and reliability of the task chain are improved.

The invention relates to the technical field of target tracking, in particular to a photoelectric tracking algorithm and system for self-adaptive target tracking, and the system comprises an image collection module, a cross-spectrum self-adaptive sensing module, an image processing and target recognition module, a self-adaptive tracking algorithm module and a servo control module, and constructs a closed-loop feedback link to achieve the synchronous optimization of parameters in a whole link. The algorithm comprises the steps of image acquisition and adjustment, target detection, parameter optimization, servo driving and feedback. The system can accurately estimate a high-speed turning target, is adaptive to complex environments such as low illumination / haze and the like, completes sheltered target recapture within 0.5 s, has servo compensation precision of + / -0.1 degree, is suitable for airport bird monitoring, port unmanned aerial vehicle tracking and highway vehicle monitoring scenes, and significantly improves tracking stability and practical value.

The invention discloses an intelligent safety monitoring method and system for coal mine excavation, and belongs to the technical field of coal mine safety, and the method specifically comprises the steps: deploying a plurality of adaptive sensing nodes in an excavation area, each node comprising an environment state collection unit and an equipment vibration collection unit, and collecting environment data and mechanical vibration waveforms respectively; then, according to the position of the mining machine and the tunnel structure change, node physical connection is adjusted to form a dynamic topology network, and when mechanical movement exceeds a threshold value, adjacent nodes are subjected to high-density monitoring; then, synchronously generating a coupling data stream according to the collected data timestamps, inputting the coupling data stream into a pre-constructed model, and predicting the overlap ratio of mechanical vibration abnormity and a target gas accumulation area space; when the overlap ratio exceeds a critical value, an emergency response is triggered, the speed is reduced firstly, inert gas is released, and power is cut off if set duration is continued; and finally, reversely correcting surrounding rock stress parameters in the model according to emergency data. According to the invention, the accuracy and response timeliness of early warning of coal mine disasters are improved.

The invention relates to the technical field of engineering, in particular to a geomembrane defect detection system and method under the earth and stone medium coverage condition, and the geomembrane defect detection system comprises a multi-mode adaptive sensing module, an edge calculation module, a data fusion module, a digital twinning module and a decision and application module; compared with the defects that single resistivity or geological radar detection is adopted in the prior art, interference of medium conductivity and metal impurities is likely to happen, and the deep resolution is insufficient, the scheme adopts multi-mode sensor network cooperative work, and a dynamic density layout and time-phased acquisition strategy is combined; through multi-mode complementation of an electrode array, an ultra-wideband radar, a distributed optical fiber and an MEMS vibration sensor, a dominant sensing mode is automatically switched in a conductive clay area, temperature gradient interference is avoided through night acquisition, the penetration depth is increased to 15 meters through multi-band radar fusion, and metal interference is inhibited; the detection precision and the anti-interference capability in a complex medium environment are remarkably improved, and the problems of high false alarm rate and high omission ratio of a traditional method are effectively solved.

The invention discloses a self-sensing monitoring method, system and equipment based on a lightning arrester, and relates to the technical field of power equipment monitoring. The self-sensing monitoring method, system and equipment based on the lightning arrester comprises the following steps: S1, collecting lightning stroke data in real time through multi-sensing fusion, and performing data cleaning and standardization processing on the lightning stroke data; s2, adopting a three-mode sensing and three-source power supply cooperation mechanism to realize state-driven adaptive sensing and energy path switching; s3, automatically scheduling a communication path according to a communication type and link quality, dynamically analyzing and compensating transmitting power, and completing authentication encryption and multi-hop secure transmission; s4, lightning stroke characteristics are extracted, an equipment life prediction model is constructed, residual life prediction is realized, and equipment health grade division and operation strategy adjustment are carried out according to the residual life prediction model; and S5, calculating an electric leakage deviation value by reconstructing a current waveform, triggering graded early warning and linking a communication power and energy supply strategy. The problem that the operation of a power system is affected due to large leakage current monitoring errors is solved.

The invention provides a cross-platform remote power transmission channel forest fire monitoring and early warning system and method, and relates to the technical field of power systems, and the system comprises an adaptive sensing network unit, a data processing unit, a visual display unit, a fire early warning unit and a collaborative decision module. Various environmental parameters including temperature, humidity, wind speed, soil humidity, illumination intensity, air pressure and the like can be collected in real time. And an information fusion algorithm is adopted to carry out weighted integration on the data to generate a comprehensive environmental risk index, so that more comprehensive and accurate environmental monitoring is provided.

The invention discloses an intelligent control method for complex terrain infrastructure line construction site environment risks, and belongs to the field of signal control and processing, and the method comprises the steps: obtaining multi-source environment data through a self-adaptive sensing network; when the data is abnormal or a blind area exists, triggering the unmanned aerial vehicle to obtain high-precision spatial data; fusing the multi-source data and combining the multi-source data with geological information to generate digital risk feature information; inputting the risk feature information into a dynamic risk assessment model to obtain a risk level signal; comparing the risk level signal with a risk threshold in real time, generating a dynamic avoidance control instruction based on a comparison result, and issuing the dynamic avoidance control instruction to the construction equipment in real time; and dynamically adjusting model control parameters by taking a control response result of the construction equipment as feedback data. According to the invention, through a closed loop feedback mechanism, intelligent control of environmental risks is realized, and construction safety and management efficiency are significantly improved.

The invention belongs to the technical field of unmanned surface vessel autonomous cruise and marine environment perception, and particularly discloses a marine environment self-adaptive perception method and system based on boundary enhancement. In the method, a semantic-boundary multi-level enhanced shipborne camera image understanding model is provided. In the training stage, an original image and a boundary truth value are coded through a semantic feature coding stream and a boundary feature coding stream respectively, and feature interaction is performed through a boundary-semantic feature fusion stream to enhance the boundary perception ability; the results of the coding stream and the fusion stream are input into a fusion feature decoder, after direction, position and channel three-dimensional feature attention enhancement, an environment category understanding result is output, prediction output of the model generates a prediction boundary through boundary extraction, a boundary truth value is combined in a boundary feature discriminator to construct a quantitative index, and the prediction boundary is subjected to direction, position and channel three-dimensional feature attention enhancement. And performing negative feedback optimization on the model through a multi-task loss function. According to the invention, boundary information in different scenes is covered, and the adaptability of the unmanned ship to the marine environment is improved.

The invention relates to a DRL-based AUV (Autonomous Underwater Vehicle) path planning and obstacle avoidance method in a partially observable environment, which comprises the following steps of: firstly, executing an AUV path planning and obstacle avoidance task under the condition that marine environment information is partially observable; secondly, constructing an underwater environment model and an AUV kinetic model, and constructing a dynamic and static separated dual-channel adaptive sensing model through local environment information acquired by detection equipment; then, path planning is modeled as a Markov decision process, and a multi-objective optimization reward function is designed for collaborative optimization; finally, an improved maximum entropy SAC algorithm is combined with a priority experience playback mechanism to train a strategy network, a gliding mode is dynamically triggered through a physical constraint layer, attitude angle and thrust output is corrected in real time, and the safety boundary condition is monitored in real time. According to the method, the real-time performance of path planning, the dynamic and static obstacle avoidance success rate and the ocean current utilization rate under the complex sea condition can be remarkably improved, and a high-robustness autonomous navigation solution is provided for ocean exploration.

The invention discloses a method and a device for analyzing and processing dynamic data of self-adaptive and active adaptive sensing signals. According to the method, through feature recognition, protocol dynamic matching and data standardization conversion, the problem that multi-source heterogeneous sensor signals cannot be processed in a centralized mode due to format difference is solved. The device comprises a signal interface module, a feature recognition module, a protocol adaptation module and the like, and supports multi-protocol communication and machine learning driven rule generation. The compatibility and the processing efficiency of the data acquisition system are remarkably improved, and the system is suitable for the fields of industrial automation and the Internet of Things.

The invention relates to the field of unmanned aerial vehicles, and discloses a traffic condition monitoring system assisted by a low-altitude inspection unmanned aerial vehicle, which adopts intelligent task scheduling based on non-negative tensor decomposition, path optimization of deep reinforcement learning, a task-path double-closed-loop cooperation mechanism and a multi-modal sensor adaptive sensing technology. The system can adjust the inspection task of the unmanned aerial vehicle in real time, improve the response speed of sudden traffic events, optimize the inspection coverage rate, and enhance the perception ability in a low-light environment. Compared with a traditional inspection mode, the task scheduling precision, the path planning flexibility and the system stability are improved, it is ensured that the unmanned aerial vehicle operates efficiently in the complex dynamic traffic environment, and accurate and reliable technical support is provided for intelligent traffic management.

The invention discloses a self-adaptive sensing anti-shake regulation and control method and system for a spinal surgery auxiliary mechanical arm, and relates to the technical field of spinal surgery, and the method comprises the steps: collecting multi-dimensional data of a mechanical arm tail end actuator through a multi-dimensional sensor group, and transmitting the multi-dimensional data to an AeroStead space-level stability calculation platform; the platform carries out filtering processing on the data, and vibration of the mechanical arm and disturbance components caused by external interference are separated; constructing a spine mechanical arm rigid-flexible coupling dynamic disturbance observation and compensation model by using the disturbance component to observe disturbance and generate a compensation amount; calling a spinal operation area three-dimensional reconstruction and dynamic obstacle avoidance mixed density network model, and fusing different images to generate an operation area three-dimensional dynamic model and an obstacle avoidance adjustment signal; a mechanical arm motion control instruction is generated through a force sense position cooperation self-adaptive control algorithm; and the mechanical arm is driven to move for closed-loop regulation and control. According to the method, through fusion of multiple models and algorithms, the disturbance compensation precision, the operation area modeling accuracy and the control collaboration are improved, and the operation safety is guaranteed.

The invention provides an inertial sensing range and sensitivity adjusting method, which belongs to the technical field of parameter adjustment, and comprises the following steps: after an item opening instruction is obtained, obtaining basic information of a moving body, and calling an adaptive sensing range according to the basic information; different detection nodes are set in the whole process before a project is started, the feedback degree of a moving body is recorded in sequence when execution is carried out to each node, values of corresponding positions of an internal curve are increased or decreased according to the feedback degree, and corresponding sensitivity is dynamically set according to the internal curve. The feedback speed and external performance of the moving body are judged in the project execution process, and the follow-up content and links of the project are adjusted in combination with the action model library till the project is completed or closed. The inertial sensing range and sensitivity adjusting method provided by the invention can carry out specific dynamic adjustment according to the state of the moving body and the actual situation, has strong pertinence and ensures smooth completion of a project.

The invention relates to the technical field of intelligent control of engineering machinery, and provides a self-adaptive perception control method and system for dual-radar cooperative scheduling. According to the method, the first rotary radar and the second rotary radar are respectively installed on the rotatable support; obstacle distribution data of a tower crane hoisting operation area are obtained in real time through the first rotating radar and the second rotating radar, and real-time stage information of hoisting operation is obtained in real time through the operation state sensing module. Calculating a sensing dead angle position according to the real-time stage information; dynamically generating a first task instruction and a second task instruction according to the priority level, the sensing dead angle position and the obstacle distribution data of the target monitoring area, and controlling the first rotary radar and the second rotary radar to adjust the rotation angle and the sensing parameter, so that the first rotary radar performs emphasized sensing on the target monitoring area with high priority, and the second rotary radar performs emphasized sensing on the target monitoring area with high priority. And the second rotary radar performs compensation sensing on areas corresponding to the sensing blind area and the sensing dead angle of the first rotary radar.

The invention relates to the technical field of artificial intelligence, in particular to a self-adaptive sensing strategy switching method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining multi-dimensional sensing data collected by a plurality of sensing modes on a robot; calculating the reliability score of each sensing mode at the current moment according to the multi-dimensional sensing data and the historical performance of each sensing mode; and determining a role label of each sensing mode according to the reliability score and the current task of the robot, determining a target sensing strategy of the robot under the current task according to the role label, and switching the sensing strategy of the robot into the target sensing strategy. Therefore, the problems that dynamic adjustment cannot be performed according to the current environment and the sensing effect is poor due to the adoption of a fixed mode combination in related technologies are solved.

The invention provides a weak measurement assisted echo wall mode microcavity time-varying signal sensing method and system. The method comprises the following steps: constructing a weak measurement assisted echo wall mode microcavity adaptive sensing platform for time-varying signal sensing; a time-varying signal is sensed through the echo wall mode microcavity, and a time-varying phase is introduced into the sensing platform; the light intensity of the echo wall mode microcavity sensing platform assisted by weak measurement is received through a photoelectric detector and a balance detector, the size of a current time-varying phase is calculated, and then the size of a current time-varying signal is solved; according to the size of the current time-varying phase, the bias phase, the first reference phase and the second reference phase are adjusted, so that the system works in a working interval of a preset condition; and estimating the time-varying signal in real time until the measurement is finished. On the basis of ensuring high-sensitivity sensing, the sensing interval of the echo wall mode microcavity is further expanded, and self-adaptive real-time measurement of the time-varying signal which is not limited by the sensitivity of the microcavity is realized.

The invention discloses an energy-saving adaptive three-dimensional sensing system. An adaptive 3D sensing system includes one or more camera devices and one or more projectors. An adaptive 3D sensing system captures an image of a real-world scene using one or more camera devices and computes a depth estimate and a depth estimate confidence value for pixels of the image. The adaptive 3D sensing system calculates an attention mask based on the one or more depth estimation confidence values, and commands the one or more projectors to send a distributed laser beam into one or more regions of the real-world scene based on the attention mask. An adaptive 3D sensing system captures 3D sensed image data of one or more regions of a real-world scene and generates 3D sensed data of the real-world scene based on the 3D sensed image data.

Some aspects of the present disclosure provide an adaptive sensing method that enables separate power control parameter configuration for uplink (UL) communication transmission or sidelink (SL) communication transmission and sensing transmission. When a signal or a channel is used for both communication and sensing, two sets of parameters may be configured, a first set of parameters for UL or SL communication and a second set of parameters for sensing. Some aspects of the present disclosure provide a distributed power control method that involves slow power ramping of a transmitted signal used for sensing. The distributed power control method may also include interference avoidance. Some aspects of the present disclosure provide a centralized power control method that involves, a centralized network side device, such as a base station, sending a signaling to adapt UE power to mitigate interference between UEs when interference is detected.

The invention discloses an expansion joint dynamic sensing system based on GIL multi-dimensional sensing, and relates to the technical field of expansion joint sensing, the system comprises a data acquisition module, a multi-modal fusion module, a self-adaptive sensing module, a self-adaptive optimization module, a state evaluation module and an application interface module; the data acquisition module is used for acquiring multi-modal data of the expansion joint; the multi-modal fusion module is used for fusing different modal data and outputting integrated features after fusion; the self-adaptive sensing module is used for sensing an environment state and constructing a sensing model; the adaptive optimization module optimizes parameters of the perception model in real time in combination with historical data and real-time input; the state evaluation module is used for evaluating and predicting the state of the expansion joint; and the application interface module provides real-time monitoring, fault diagnosis and prediction maintenance. According to the invention, environment state sensing is carried out, real-time optimization is carried out according to actual conditions, and the capability of judging the state of the expansion joint is improved.

The present disclosure relates to a multidimensional adaptive sensing system and method for event detection with improved accuracy. The system dynamically adjusts, using baseline and compound models, a triggering conditional threshold of each event based on real-time variations in environmental parameters and sensor health. The adaptive system allows for fine-tuning of sensitivity to environmental changes, seasonal variations, and sensor decay, thereby reducing false positives and negatives. Installation flexibility is achieved by selecting and deploying a subset of models tailored to available data at the sensor's location, ensuring optimal operation regardless of specific environmental conditions or sensor configurations. The adaptive sensing system is used in environmental control systems for dynamic routine management, sensor health management, a triage system for complex event analysis, and multifaceted compliance assurance solution. Further, the adaptive sensing system is used for automated configuration, model deployment, and maintenance by leveraging edge computing units and a central control unit.

The invention discloses a multi-parameter shallow gas detection method which comprises the following steps: S1, self-adaptive sensing network layout and multi-parameter synchronous acquisition: monitoring nodes synchronously acquire underground electrical parameters, soil gas composition and concentration, soil mechanics and pore pressure parameters and an environmental noise background value; accessing a hydro-meteorological data stream of the target area in real time; s2, multi-source interference signal identification and data purification: obtaining a purified multi-parameter data sequence reflecting real geological anomalies; s3, water-gas-force coupling dynamic modeling and migration simulation: outputting a shallow gas migration risk thermodynamic diagram; s4, intelligent diagnosis based on transfer learning and causal discovery: performing time sequence analysis on the purified multi-parameter data sequence, and identifying a gas reservoir core area, a dominant migration channel and dominant control factors influencing gas aggregation and migration; and S5, dynamic risk assessment and graded early warning: quantitatively calculating the risk probability.

Aspects of the present application provide an adaptive perception method that enables separate power control parameter configuration for uplink (UL) communication transmission or sidelink (SL) communication transmission and perception transmission. When a signal or channel is simultaneously used for communication and perception, two sets of parameters may be configured, a first set of parameters being used for UL or SL communication and a second set of parameters being used for perception. Some aspects of the present disclosure provide a distributed power control method that includes a slow power ramp-up for a perceived transmission signal. The distributed power control method may also include interference avoidance. Aspects of the present application provide a centralized power control method that includes when interference is detected, a centralized network side device (e.g., a base station) sends signaling to adjust UE power to reduce interference between UEs.

The invention discloses an automatic alarm system of an air cushion type belt conveyor. The automatic alarm system comprises a sensing layer, a control layer and an execution layer. The sensing layer constructs a multi-dimensional sensing network through an air film thickness sensor, an air pressure sensor, a belt state sensor, an air chamber airflow audio analysis module, a motor power fluctuation detection module and a distributed optical fiber sensing module. The control layer adopts an edge-cloud computing collaborative architecture, an edge computing node is responsible for real-time data preprocessing, the fog computing layer runs a digital twin model to complete data fusion and state simulation, and a cloud computing center stores historical data and optimizes an algorithm and is provided with a three-level early warning and adaptive sensing mechanism. The execution layer executes parameter adjustment, sound-light alarm or emergency shutdown according to instructions of the control layer. Comprehensive monitoring, intelligent diagnosis and automatic regulation and control of the operation state of the air cushion type belt conveyor are achieved, and the operation reliability and the maintenance efficiency of equipment are remarkably improved.

The invention provides an integrated efficient reaction clarification system based on multi-sensor data fusion, and relates to the technical field of clarification systems. The self-adaptive sensing module is used for preprocessing the real-time operation parameters; the deep fusion processing module is used for generating trend pre-judgment data through the prediction model; the self-optimization control module is used for calculating and generating an initial equipment regulation and control instruction and optimizing the initial equipment regulation and control instruction; and the intelligent cooperative processing module is used for secondary optimization of the water quality regulation and control parameters. By means of the regression prediction model, risks are pre-judged in advance, and problem pre-prevention and control are achieved; through parameter adaptation, secondary optimization and accurate instruction transmission according to functional areas, all devices are pushed to operate cooperatively, the operation stability of the devices and the rationality of medicament consumption are effectively improved, manual intervention is greatly reduced, and the intelligence, high efficiency and reliability of industrial wastewater treatment are comprehensively enhanced.

The invention discloses a TPU film for robot electronic skin and a preparation method of the TPU film, and relates to the technical field of flexible electronic materials, the film takes TPU introduced with Diels-Alder dynamic reversible bonds as a matrix, a conductive filler is formed by dopamine-modified silver nanowires and MXene, the concentration of the conductive filler is in continuous or quasi-continuous gradient decrease, and the conductive filler is a conductive filler formed by silver nanowires and MXene. An integrated network with self-repairing and environmental stability is formed and is used as an electrode; phase change microcapsules and triboelectric nanofibers are compounded in a low-conductivity contact area to form an intelligent response layer which is self-adaptive in sensing threshold and capable of generating power by friction. Through one-step multi-nozzle electrostatic spinning forming, the problem that self-repairing, signal stability, environmental adaptability and energy independence in electronic skin are difficult to consider at the same time is solved.

The present disclosure relates to a multidimensional adaptive sensing system and method for event detection with improved accuracy. The system dynamically adjusts, using baseline and compound models, a triggering conditional threshold of each event based on real-time variations in environmental parameters and sensor health. The adaptive system allows for fine-tuning of sensitivity to environmental changes, seasonal variations, and sensor decay, thereby reducing false positives and negatives. Installation flexibility is achieved by selecting and deploying a subset of models tailored to available data at the sensor's location, ensuring optimal operation regardless of specific environmental conditions or sensor configurations. The adaptive sensing system is used in environmental control systems for dynamic routine management, sensor health management, a triage system for complex event analysis, and multifaceted compliance assurance solution. Further, the adaptive sensing system is used for automated configuration, model deployment, and maintenance by leveraging edge computing units and a central control unit.

The invention relates to the technical field of coal mine safety monitoring and intelligent early warning, and discloses an edge computing driven coal mine underground multi-parameter self-adaptive sensing and anomaly recognition sensor network, which comprises a plurality of mining intrinsic safety type intelligent sensor nodes for forming a distributed monitoring network, a multi-parameter acquisition module, a data processing module and a data processing module, various monitoring parameters of the underground coal mine are collected in real time; the edge calculation module is deployed with a lightweight long and short term relation-improved gating cycle unit time sequence prediction model, performs adaptive drift correction on a monitoring parameter background value to dynamically generate a threshold range, and generates an abnormal initial judgment result; the cooperative communication module is used for performing data interaction with adjacent nodes after the abnormal initial judgment result is generated, and generating a space-time cooperative verification result; and the exception decision module is used for generating a multi-parameter fusion judgment result according to the exception initial judgment result and the time-space cooperation verification result and outputting graded early warning. According to the method, the accuracy, the real-time performance and the engineering landing performance of coal mine underground dynamic disaster precursor identification are remarkably improved.