1064 results about "Line sensor" patented technology

The invention discloses a coastal protection dam settlement monitoring method, and belongs to the technical field of hydraulic engineering safety monitoring. The method comprises the steps that a longitudinal monitoring section is arranged on the slope surface of the back sea side of a dam, a three-measuring-line fiber grating sensor array is arranged, and vertical displacement and horizontal dip angle data are periodically collected through a synchronous triggering unit; establishing a vertical displacement-horizontal dip angle joint analysis model by using a multi-source data fusion module, and eliminating tide level interference through a Kalman filtering algorithm to generate a settlement distribution curve; and triggering third-level to first-level early warning signals based on the grading early warning rule, and transmitting the signals to the terminal equipment. The problems that a traditional monitoring method cannot effectively separate tidal interference, the real-time performance is poor, and multi-dimensional data collaborative analysis is insufficient are solved, high-precision settlement monitoring, complex environment anti-interference and rapid emergency response are achieved through multi-measuring-line sensor deployment, dynamic filtering optimization and a graded early warning mechanism, and the method is suitable for large-scale popularization and application. And the reliability and timeliness of dam safety monitoring are obviously improved.

The invention discloses an electrical fire real-time monitoring system based on a wireless sensor network, and relates to the technical field of electrical fire monitoring. The cloud monitoring platform is in communication connection with an electrical data acquisition module, an electrical fire abnormity analysis module, an abnormity early warning module, a node management configuration module and a visual monitoring module, and all the modules are in electric signal connection; the electrical data acquisition module is used for acquiring monitoring data of electrical nodes by using a deployed wireless sensor and transmitting the monitoring data to the cloud monitoring platform through a wireless communication technology, and the monitoring data comprises current, voltage, temperature, humidity and leakage current. Multi-parameter fusion monitoring is carried out through the wireless sensors deployed at the key nodes of the electrical equipment, compared with a traditional wired sensor, the wireless sensors do not need wiring, installation is flexible, early signals of an electrical fire can be captured more comprehensively, and the accuracy of fire early warning is remarkably improved.

The invention relates to the technical field of risk prediction, in particular to a wireless sensor-based high-altitude operation online monitoring and early warning system and a wireless sensor-based high-altitude operation online monitoring and early warning method. The method comprises the following steps: deploying multiple types of wireless sensor arrays for the aerial work platform to collect structural stress data, environment temperature and humidity and personnel state data, and constructing a multi-source standard data set; performing space-time alignment on the multi-source standard data set to generate a synchronized high-altitude data set; constructing a three-dimensional feature space by using the synchronized high-altitude data set, and generating a high-altitude operation fusion feature matrix; generating a high-altitude operation risk prediction model based on the high-altitude operation fusion feature matrix; therefore, through fusion of multi-source data, alignment of spatial-temporal characteristics, dynamic simulation of risk evolution and construction of a multi-level response mechanism, the risk prediction precision and real-time response capability in high-altitude operation are improved, and a comprehensive, systematic and efficient safety management and control system is finally formed.

The invention relates to a wireless sensor network coverage optimization method based on a star-graffiti optimization algorithm, and belongs to the technical field of wireless sensor network coverage optimization. In order to solve the problems of low convergence precision, easy falling into local optimum and convergence hysteresis of a traditional coverage method, the invention provides an enhanced star-graffiti optimization algorithm (INOA) fusing a hyperbolic sine and cosine optimizer and a nonlinear storage strategy. Firstly, a population is initialized through Bernoulli mapping, and the global exploration capability in a high-dimensional scene is improved; secondly, designing a nonlinear convergence mechanism based on hyperbolic sine and cosine, and dynamically balancing global optimization and local development; and a dynamic nonlinear storage strategy is introduced, so that the convergence speed is remarkably increased. The method effectively improves node deployment optimization in a complex environment, has higher adaptability, improves convergence speed, precision and stability, and realizes node efficient coverage and energy consumption balance.

The concrete-filled steel tube arch bridge detection system and method based on multi-modal fusion and quantum optimization are used for bridge structure health monitoring. The system comprises a multi-modal data acquisition module, a quantum optimization data fusion module, a defect self-growth simulation damage prediction module, a low-power-consumption intelligent sensor network and a quantum safety remote monitoring module. The multi-modal data acquisition module is fused with an ultrasonic radar, a millimeter wave radar, a hyperspectral image, a LiDAR and a wireless sensor, and bridge surface and internal damage detection is realized; the data weight is dynamically adjusted by adopting a quantum optimization algorithm, and the fusion precision is improved; in combination with a graph neural network and deformable grid calculation, crack propagation and fatigue damage development are predicted, the power consumption of the sensor is reduced through an energy collection technology, and the safety of remote monitoring data is ensured by adopting a quantum safety remote monitoring module. Compared with the prior art, the method improves the detection precision, prediction capability and data safety, and is suitable for health monitoring of various bridges and infrastructures.

The invention relates to the technical field of sleep monitoring, in particular to a sleep monitoring method and system based on combination of electroencephalogram and millimeter wave radar, and aims to solve the problem that OSA judgment accuracy is insufficient due to complication. The method comprises the following steps: acquiring an electroencephalogram signal of a user based on a wireless EEG sensor, and acquiring a physiological signal of the user by using a millimeter wave radar; performing sleep staging analysis on the electroencephalogram signal, and detecting a micro-awakening event in combination with body movement data; the physiological signals are processed through a physiological signal processing model, physiological signal features of the user are extracted, and an apnea event, a hypopnea event, an arrhythmia event, a blood pressure fluctuation abnormal event and a periodic leg movement event of the user are monitored; calculating each OSA judgment index according to the event; and calculating OSA personalized scores according to the OSA judgment indexes, performing OSA severity grading, and generating an OSA severity grading report for assisting medical staff in diagnosis and treatment scheme formulation.

The invention discloses a gradient optimization control method for an aeration system of a sewage plant. The gradient optimization control method comprises the following steps: S1, acquiring key monitoring multi-source data of the aeration system of the sewage plant in real time through an online sensor; s2, setting a security threshold value of each monitoring data to form a security constraint condition; s3, taking unit nitrogen removal energy consumption minimization as an optimization target, and constructing a comprehensive loss function fusing an energy consumption item and a water quality deviation item; s4, iteratively updating the air volume of the aeration system by adopting a self-adaptive gradient algorithm; s5, the optimized and updated air volume is issued to the aeration equipment to be executed, and water quality data and energy consumption data after execution are collected in real time, a control effect is calculated, an award signal is generated, and the award signal serves as feedback to be input into an optimization module; and S6, dynamically adjusting key parameters including the learning rate and the gradient threshold according to a feedback result. According to the invention, the energy consumption can be effectively reduced and the control precision can be improved while ensuring that the effluent stably reaches the standard, and the stable operation of the aeration system of the sewage plant under a complex working condition is ensured.

The invention discloses size detection equipment for casting perforation processing, and relates to the technical field of size measurement. The device comprises a base, a supporting plate and a displacement assembly. The detection part goes deep into a workpiece hole through the contact ball, and the laser emitter and the light sensor are used for detecting the contact position, thereby realizing high-precision size measurement. The displacement assembly adopts the design of a negative pressure rotating cylinder and a resistance sliding rod, adjusts the displacement of a contact ball according to the rotating speed, and flexibly adapts to different hole requirements. Through the control of the planetary gear and the electromagnet, the contact ball can rotate to realize multi-angle detection. The detection table is driven by a central electric cylinder and an edge inclination control electric cylinder to stably adjust the posture of a workpiece. The device is high in automation degree and convenient to operate, and overcomes the defects of low precision, poor efficiency and single-direction detection of traditional manual measurement.

The invention relates to the technical field of industrial equipment intelligent simulation, and discloses a simulation model construction method and system, and the method comprises the following steps: 1, analyzing the electrical control logic of industrial equipment, and converting the electrical control logic into a mixed integer nonlinear programming problem containing a discrete Boolean variable and a continuous variable; 2, modeling the physical field based on a tensor network decomposition algorithm, and generating a compressed low-rank tensor network model; 3, dynamically adjusting parameters of the tensor network model by adopting a hierarchical optimization strategy, wherein the hierarchical optimization strategy switches a global search algorithm and a local optimization algorithm according to an error convergence state; 4, allocating and executing symbol calculation, tensor network operation and parameter optimization tasks through a heterogeneous calculation system; and 5, collecting production line sensor data in real time. According to the method, different physical field sub-models are dynamically activated through Boolean variables, and the accuracy of joint simulation in a complex industrial scene is remarkably improved.

The invention discloses a temperature dynamic control system for optical fiber optical wand processing. The temperature dynamic control system comprises an acquisition unit, a control unit, a learning unit and an integration unit. The acquisition unit captures production line material imaging data through a multi-macro camera array, acquires temperature, tension, humidity and speed data in real time by using a production line sensor network, and forms production state comprehensive data after edge computing node fusion preprocessing. The control unit establishes a dynamic mathematical prediction model based on the comprehensive data, predicts future temperature states of a plurality of temperature zones and generates an optimization control input instruction; the learning unit receives an optimization control input instruction and historical temperature data, and generates a self-adaptive control strategy by adopting a reinforcement learning algorithm; the integration unit integrates the control input instruction and the self-adaptive control strategy to form a final control instruction, the temperature of each temperature zone is adjusted, and the temperature control precision and the system stability are ensured.

The invention relates to the technical field of concrete transportation monitoring, and discloses a concrete transportation monitoring method and system, and the method comprises the following steps: S1, data collection: obtaining concrete state data and environment data in real time through a data collection module, the data acquisition module comprises a temperature sensor, a humidity sensor, a vibration sensor, a light sensor and a high-definition camera device; s2, data fusion and preprocessing; s3, data transmission and cloud storage; s4, cloud data analysis; and S5, performing anomaly detection and early warning response. By means of various sensors and high-definition camera devices in the data acquisition module, state data and environment data of concrete are obtained in real time, multi-dimensional information monitoring is achieved, the edge calculation module conducts data preprocessing, data transmission delay is remarkably reduced, the real-time performance of the system is improved, whole-course non-blind-area monitoring of the concrete transportation process is achieved, and real-time monitoring of the concrete transportation process is achieved. And data accuracy and reliability are ensured.

The invention provides a wireless sensing platform with an edge computing function, and relates to the technical field of wireless sensors. In order to solve the problems that in the prior art, a wireless sensor is large in data redundancy, high in transmission delay and weak in emergency recognition and response capacity in a complex scene, a joint processing mechanism combining a causal event recognition assembly, a causal data fusion assembly, an event separation assembly and a data distribution assembly is adopted. By detecting a causal change event in an environment and dynamically extracting and fusing sensor data with a strong causal relationship, redundant data transmission caused by emergencies is reduced, transmission delay is reduced, the robustness of causal data fusion in a concurrent event scene is improved, and the reliability of data fusion is improved. According to the invention, efficient and accurate sensing and response to emergencies in a complex environment are realized.

The invention provides a greenhouse intelligent temperature control automatic adjustment optimization method based on multi-parameter dynamic coordination, and relates to the technical field of agricultural facility environment control. The method comprises the following steps: acquiring temperature, air humidity, wind speed and airflow distribution environment data inside and outside a greenhouse in real time; the environment state is quantified through a dynamic evaporation efficiency coefficient DEEC and an airflow distribution uniformity index ADUI; the flow of a water pump, the rotating speed of a fan and the opening degree of a ventilation system are dynamically adjusted in combination with an improved PID control algorithm, and the weight of an integral term is dynamically adjusted along with the ADUI to enhance the response efficiency; a time sequence prediction model based on LSTM is adopted to fuse historical and real-time data, a parameter correction amount is predicted, and the adaptation capability of the system to a new environment is improved in combination with a transfer learning algorithm; system hardware comprises a low-power-consumption wireless sensor, a frequency conversion actuator and an embedded control terminal. Through combination of multi-parameter perception and cooperative control, accurate temperature and humidity control and energy efficiency optimization are realized, resource consumption is reduced, and automation and intelligence levels of environment adjustment are improved.

The invention relates to an underwater wireless sensor network path sensing routing method based on deep reinforcement learning, which comprises the following steps that: firstly, a node constructs and periodically updates a transmission preference model based on local and neighbor node interaction information; secondly, deploying a deep reinforcement learning model at each underwater sensor node to perform distributed routing strategy learning; and finally, generating a global guide vector by the sink node according to the routing path information of the received data packet, reversely spreading the global guide vector to the source node, fusing the global guide vector with a local transmission preference vector of the node to generate a guide reward, optimizing the deep reinforcement learning model, and updating a routing strategy. According to the method, the problems of difference and complexity of underwater transmission tasks can be solved, and the network data transmission efficiency and the overall service quality are improved in combination with local preference and global guidance while the node online learning is kept to adapt to the dynamic underwater environment.

The invention discloses a regulation and control method for stably improving the water quality of water produced by a reverse osmosis system of a power plant, and relates to the field of water quality regulation and control, which comprises the following steps: configuring a water inlet flow regulation device, performing initial setting, configuring the position of an online sensor, performing data acquisition, setting judgment rules of turbidity, differential pressure and transmembrane pressure difference, and determining the water quality. And configuring a flocculant adding device and adding control logic, configuring a backwashing and adding linkage control state machine, and carrying out data smoothing, trend analysis and self-adaptive threshold adjustment. When the water quality of the raw water fluctuates for a short time or the pollution load is increased, dynamically adjusting the water inlet flow, the backwashing intensity and the flocculant adding concentration based on parameters such as turbidity, pollution index, conductivity and pressure difference which are acquired by an online sensor in real time; and meanwhile, the concentrated water reflux proportion and the high-pressure pump frequency are automatically corrected by continuously calculating the desalting rates of the first-stage and second-stage reverse osmosis devices, so that the running state of the membrane component is kept in an optimal interval.

The invention relates to the technical field of environment detection, and discloses an extensible underground water and soil in-situ online observation system, which comprises an underground water and soil automatic online monitoring management platform, an underground water control module, a soil control module and an auxiliary module, the underground water and soil automatic on-line monitoring management platform can check the state of the detection module and the detected data. The problems that when an existing underground water and soil on-line monitoring device monitors underground water and soil through an on-line sensor, the sensor may have drifting or errors in a complex environment, and the data accuracy is affected are solved; in remote areas and complex terrains, data transmission interruption can be caused by network instability; the problems that underground water and soil equipment are managed separately, data output is not synchronous, the data analysis difficulty is increased, and complex data analysis and modeling are difficult to achieve are solved.

The embodiment of the invention provides a full-life-cycle slope protection net system which comprises an active protection net system (1), the active protection net system (1) comprises a plurality of fiber grating sensors (14), and the fiber grating sensors (14) are arranged on a first net body (11). The strain information acquisition module is used for acquiring strain information of a first anchor rod (13) and / or strain information of an anchor buckle according to deformation of the first net body (11) and converting the strain information into a first electric signal or a first optical signal; the fiber grating sensor (14) is electrically connected to an edge computing node near the active protective net system (1); and the edge computing node is electrically connected to the cloud platform through the wireless sensor (3). The local stress and deformation information of the protective net can be accurately obtained in real time, and accurate data support is provided for subsequent early warning and regulation.

The invention discloses a self-adaptive detection device for the size of an inner cavity of a part machined by a special-shaped machine, and relates to the technical field of part detection. According to the invention, a multi-stage reflection light path formed by a first reflection lens, a second reflection lens, a third reflection lens and a fourth reflection lens is utilized to sequentially reflect and conduct laser according to a fixed 45-degree angle, so that the light is irradiated on the light sensor. The design can effectively detect whether the detection tube has mechanical micro-deformation, and greatly improves the precision of inner cavity size detection. The L-shaped extension rod convenient to disassemble is combined with the detection contact ball, so that self-adaptive extension of the detection tool to different inner cavity sizes and shapes is realized. By flexibly changing and combining the structure, the extension depth of the detection contact ball can be adjusted according to the specific requirements of the inner cavity of the part, and the full contact with the inner wall of the part is ensured.

A pump can include a pump head, a body housing, a motor disposed within the body housing, and a motor shaft extending from the motor. The pump head can include a sensor, at least a first interface portion, and at least a first elongate conductor electrically connecting the sensor to the at least a first interface portion. The body housing can include at least a second interface portion and at least a second elongate conductor electrically connecting the at least a second interface portion to pump electronics. The sensor can be configured to sense when the at least a first interface portion does not contact the at least a second interface portion. When the at least a first interface portion does not contact the at least a second interface portion and the pump head is removed from the body housing, the pump head can be pivotable relative the motor shaft such that an orientation of the pump head can be changed with respect to the body housing.

The invention belongs to the technical field of rail safety real-time monitoring, and particularly relates to a rail safety real-time monitoring system based on rail excitation and big data analysis. The rail safety real-time monitoring system comprises sensor monitoring nodes, a wireless sensor network and a cloud intelligent processing center. The sensor monitoring node is used for recording rail vibration data when a vibration monitoring device is arranged on a rail and an extrusion excitation effect between wheels and the rail is monitored; the wireless sensor network constructs a wireless sensor network based on a narrowband Internet of Things node and a plurality of sensor monitoring nodes, then forms a rail monitoring network, and uploads all rail vibration data to the cloud intelligent processing center; and the cloud intelligent processing center analyzes the rail vibration data based on a big data analysis algorithm, and determines a rail coping strategy according to an analysis result. The rail monitoring system which is low in cost, high in efficiency, capable of responding in real time, free of line occupation and easy to maintain is developed, and the safety and efficiency of railway operation are improved.

A fog lamp induction warning control system comprises a data acquisition module, the data acquisition module comprises a vibration sensor, an acoustic sensor and a light sensor, and the vibration sensor is used for capturing vibration waveforms generated during vehicle collision and acquiring vibration data in real time; the signal processing module is used for processing signals of the data acquisition module, and the data processing module performs fusion by using weighted average; setting a threshold value to judge whether the fused features belong to abnormal conditions or not; the threshold value is determined through statistical analysis or experiment of historical data, and the output result exceeding the threshold value is output; the warning module comprises a fog lamp and a sound alarm arranged on the edge of the fog lamp; the invention aims to solve the problem that a secondary collision accident is easily caused because a traffic accident warning cannot be sent to a driver behind in time after a vehicle is collided.

The invention discloses an elastic wave device, a filter or a multiplexer, and belongs to the technical field of elastic waves, the elastic wave device comprises a piezoelectric layer, a nitride, a sapphire substrate and a conductive material film pattern; in the third direction, the nitride is arranged on the sapphire substrate, the piezoelectric layer is arranged on the nitride, and the conductive material film pattern is arranged on the piezoelectric layer; the conductive material film pattern comprises interdigital transducer electrodes and reflector electrodes, and the reflector electrodes are located on the two sides of the interdigital transducer electrodes in the first direction. The elastic wave device is good in impedance characteristic and free of any obvious clutter response, can be widely applied to the application fields with high requirements such as communication, radars and wireless sensors, and meets the requirements of a modern communication system for high-frequency and high-performance filters and multiplexers.

The invention provides a wireless sensor dynamic voltage regulation method and system based on load current prediction, and relates to the technical field of voltage regulation. According to a load current prediction sequence output by the load current prediction model and response characteristics of output voltage of a direct current-direct current converter in the wireless sensor under different working conditions to the load current, feed-forward compensation amounts of the power supply voltage of the wireless sensor under different working conditions are determined; suppressing the output voltage ripples of the DC-DC converter to obtain the disturbance suppression degree of the output voltage ripples of the DC-DC converter; and performing closed-loop adjustment on the power supply voltage of the wireless sensor through the disturbance suppression degree and all the feedforward compensation amounts to obtain a closed-loop gain coefficient of the power supply voltage, and further generating a voltage adjustment instruction of the wireless sensor according to the closed-loop gain coefficient. According to the invention, the ripple interference of the output voltage of the DC-DC converter can be suppressed in the voltage regulation process of the wireless sensor.

The invention provides a scroll compressor cross ring dynamic strain wireless measuring device, which relates to the technical field of compressor state monitoring and comprises a high-temperature-resistant strain gauge, a wire fixing system and a wireless sensor. Wherein the high-temperature-resistant strain gauges are respectively attached to stress sensitive areas of a static key, a ring beam and a movable key of the cross ring every time; the wire fixing system comprises a three-stage fixing structure formed by an adhesive, an internal wire, an external wire and an internal binding post; the three stages of fixing structures are a strain gauge end fixing structure, a lead transition section fixing structure and a binding post end fixing structure in sequence; the high-temperature-resistant strain gauge is connected with the wireless sensor through an internal wire, a binding post and an external wire, and continuous signal collection is achieved. By implementing operation according to the technical route, the impact load borne by the cross ring at the moment of starting and stopping of the compressor can be collected in a high-quality mode, data collection in a closed cavity is achieved, and an experimental basis is provided for subsequent strength design of the cross ring.

The invention provides a multi-line collaborative direct-current ice melting method, system and device, and relates to the technical field of power transmission and distribution of power systems, and the method comprises the steps: obtaining real-time environment data and line icing thickness data corresponding to an ice melting line based on a line sensor network; constructing an ice melting index calculation model, calculating a line ice melting index and judging a real-time icing state; obtaining the moving distance and cost of ice melting equipment corresponding to the ice melting line, and calculating a comprehensive evaluation index and determining a line ice melting sequence in combination with the line ice melting index; constructing an energy consumption distribution optimization function, obtaining real-time electrical parameters of the ice-melting lines, and determining line ice-melting energy consumption of each ice-melting line in combination with the line icing thickness data; based on the line ice-melting sequence and the line ice-melting energy consumption of each ice-melting line, a multi-line collaborative ice-melting strategy is determined, so that the overall ice-melting efficiency can be improved, the total ice-melting time and energy consumption are reduced, and the energy utilization efficiency is improved.

The invention belongs to the technical field of steel smelting, and particularly relates to an RH refining water model flow field monitoring device and method based on a trackball. An RH refining water model flow field monitoring device based on trackballs comprises an RH refining water model, a trackball unit, an image acquisition unit and a data processing and display unit, the RH refining water model comprises a vacuum tank and a simulation steel ladle, simulation molten steel and a circulating water pump are arranged in the simulation steel ladle, the trackball unit comprises a plurality of trackballs, and the trackball unit is connected with the vacuum tank and the simulation steel ladle. The tracking balls are dispersed in simulated molten steel, the tracking balls are hollow balls, micro wireless sensors, storages and wireless transmission signal communication modules are arranged in the tracking balls, the image acquisition unit comprises a plurality of cameras, and the data processing and display unit is connected with the image acquisition unit. The liquid flow field change in the vacuum tank is collected and analyzed by adopting the motion trail of the tracking ball, the real-time visual monitoring of the flow field characteristics is realized, and the device has the advantages of low cost, high precision, strong adaptability and the like.

The invention provides a farmland wireless sensor anomaly detection method and system based on an auto-encoder, and relates to the field of sensor anomaly detection, and the method comprises the steps: S1, each intermediate module carries out the transmission pre-processing of the received monitoring data of a plurality of collection modules; s2, regarding the acquisition module, aiming at each monitoring time point, the detection module calculates crop trust data, weather trust data and task trust data corresponding to the monitoring time point; and S3, the detection module generates a trust feature matrix of the acquisition module, inputs the trust feature matrix subjected to normalization processing into the auto-encoder, and quantifies a trust evaluation result of the acquisition module according to a reconstruction error output by the auto-encoder. According to the invention, the abnormal farmland wireless sensor can be accurately detected.

The invention relates to a partial discharge on-line early warning device and method for a 35kV high-voltage switch cabinet, and belongs to the technical field of early warning devices.The partial discharge on-line early warning device comprises an acquisition module, a receiving module and a master station module, and data acquired by the acquisition module is wirelessly transmitted to the receiving module; the receiving module transmits data to the master station module through the RS485 module, the acquisition module comprises a plurality of wireless temperature sensors, a wireless IMD sensor unit and a high-frequency current sensor, and the high-frequency current sensor is electrically connected with the wireless IMD sensor unit. The wireless IMD sensor unit and the high-frequency current sensor are detachably installed in a high-voltage cable bin of the high-voltage switch cabinet. According to the partial discharge on-line early warning device for the 35kV high-voltage switch cabinet, the temperature sensor is arranged to monitor the temperature of the switch cabinet, find out thermal fault hidden dangers in advance, give an alarm and monitor discharge signals of the switch cabinet, so that the reliability of safe operation of the high-voltage switch cabinet is improved.

The invention, which relates to the technical field of the unmanned aerial vehicle, discloses a direction-adjustable unmanned aerial vehicle accessory comprising an environment sensing module, a target tracking module, a control module, a direction adjustment execution wireless communication module and a data storage module. The environment sensing module is composed of a light sensor, a wind direction sensor and a temperature sensor, the image recognition module sub-module and the signal tracking module sub-module are combined, accurate tracking of a target is achieved, the image recognition sub-module can capture and analyze image features of a target object in real time, and the image recognition sub-module can recognize the image features of the target object in real time. Even if target features are changed due to environmental changes, the tracking accuracy can be ensured by dynamically adjusting the weight coefficient of a tracking algorithm and re-determining a feature region and a feature value, meanwhile, the signal tracking sub-module receives a signal sent by a target object, fusion verification is carried out on the signal and an image recognition result, and the tracking accuracy is improved. And the reliability and the stability of target tracking are further improved.

The invention provides a ground tiny obstacle detection method and device for an indoor mobile robot, and the method comprises the steps: carrying out the external parameter calibration, time synchronization and interpolation fusion of multi-line sensor data, so as to obtain ground point cloud data; removing the unreasonable point cloud data, and carrying out modeling to obtain a ground background; screening ground suspicious obstacle points, performing neighborhood curvature clustering, and obtaining a plurality of suspicious obstacle clusters; all the subareas of the ground and the suspicious obstacle clusters are incorporated into a graph optimization framework, and global fitting of the ground curved surface is obtained; performing connectivity and layering processing on the suspicious obstacle cluster through a multi-scale morphological operation method and a region growth correction method; judging the stability of the suspicious obstacle cluster by adopting a time sliding window, and judging the state of the suspicious obstacle cluster by utilizing speed so as to filter dynamic interference; performing attitude or texture verification on the suspicious obstacle cluster to determine an obstacle cluster; and inputting the determined obstacle cluster into a navigation map of the indoor mobile robot.