364 results about "Data fusion algorithms" patented technology

The invention discloses a trajectory optimization method based on a multi-source heterogeneous positioning data fusion algorithm, and relates to the technical field of intelligent navigation and high-precision positioning, multi-modal data are acquired through a multi-modal sensor array, positioning redundancy of scenes such as tunnels and indoor scenes is enhanced, a weight distribution strategy is dynamically adjusted through an Actor-Critic network architecture, and the positioning accuracy is improved. The state space input comprises an environment semantic tag, a historical error sequence and a real-time noise variance, the output action space is continuous weight distribution of each data source, a multi-target reward function optimization strategy is combined, scene adaptability is realized, a local SLAM map, inertial navigation error parameters and a weight distribution strategy are shared in real time based on a V2X protocol, and the real-time performance of the system is improved. According to the method, a single device accumulative error is compensated by using adjacent vehicle data, a terminal locally trains an error compensation model, parameters are uploaded to a cloud end through differential privacy encryption, the cloud end adopts a FedAvg algorithm to aggregate a global model and issue the global model, the error difference between devices is inhibited, and dynamic road network updating and scene differentiation model distribution are supported at the same time.

The invention discloses an unmanned aerial vehicle monitoring and countering integrated system, and relates to the technical field of unmanned aerial vehicle monitoring, and the system comprises a sensing module which is used for carrying out the monitoring of an environment through a plurality of sensors, and obtaining original data; the fusion module is used for processing the original data by adopting a data fusion algorithm to obtain target information; the identification module is used for outputting the type and behavior mode of the unmanned aerial vehicle; the analysis module is used for evaluating the type and behavior mode of the unmanned aerial vehicle by adopting a dynamic threat evaluation method and generating early warning information; the strategy module is used for formulating a dynamic countering strategy; the execution module is used for implementing corresponding countering measures and monitoring the countering effect in real time; and the recording module is used for recording all results in the whole process. Through the technical means of multi-sensor cooperative monitoring, data fusion processing, classification identification, dynamic threat assessment, game decision making, intelligent countering and the like, omnibearing perception, accurate identification, intelligent assessment and efficient disposal of the unmanned aerial vehicle are realized.

The invention discloses a starting control system and method for a main and auxiliary power hybrid ship, and relates to the field of ship power control, and the system comprises a multi-source data collection module which is used for obtaining a multi-source state data set of a ship power system; the fusion processing module is used for performing feature alignment processing on the multi-source state data set based on a multi-modal sensing data fusion algorithm to generate a hybrid power starting parameter set; the dynamic weight distribution module is used for constructing a dynamic weight distribution matrix according to the hybrid power starting parameter set; the cooperative control module is used for generating a main and auxiliary engine combined starting instruction set based on an improved cooperative control algorithm; and the execution and monitoring module is used for executing the main and auxiliary engine combined starting instruction set in a preset time window and monitoring the transient response characteristic of the propulsion system. According to the starting control system and method for the main and auxiliary power hybrid ship, the starting stability and the energy utilization efficiency of the hybrid ship are improved.

The invention discloses an unmanned aerial vehicle flight control system and method with precise positioning and autonomous obstacle avoidance, and particularly relates to the field of control, and the system comprises a positioning module, an obstacle detection module, a data processing and analysis module, an autonomous obstacle avoidance algorithm module, and a flight control module. Static and dynamic obstacles are detected in real time by integrating the laser radar, the millimeter wave radar, the visual sensor and the ultrasonic sensor, and an obstacle information report is generated through a data fusion algorithm based on machine learning; based on the processed environment data, the system uses deep reinforcement learning and a model prediction control algorithm to generate a safe flight path in real time, and an obstacle avoidance decision is optimized; the flight control part adopts a hierarchical control framework, and precise flight of the unmanned aerial vehicle is realized through trajectory tracking, attitude control and actuator control.

The invention relates to the technical field of energy storage battery diagnosis, and discloses an energy storage battery fault diagnosis method and system based on a data fusion algorithm. The method comprises the following steps: acquiring historical operation data and real-time operation data of an energy storage battery in a preset operation period, and generating a historical fault data set according to the historical operation data; performing multi-source feature fusion processing on the historical fault data set to generate a fusion feature parameter set integrating voltage, current, temperature and impedance parameter joint change features; a multi-dimensional fault space is constructed based on the parameter set, coordinate axes of the multi-dimensional fault space correspond to different parameter dimensions, and spatial position coordinates represent parameter change characteristic values; calculating the fault correlation degree of the historical fault event in the multi-dimensional fault space, and determining a fault early warning index set; and extracting real-time characteristic parameters based on the real-time operation data to form a state vector, carrying out space mapping correlation calculation on the state vector and the fault early warning index set in a multi-dimensional fault space, and outputting a real-time fault correlation factor.

The invention discloses a risk assessment method based on soil heavy metal pollution pathway analysis, and particularly relates to the technical field of land risk assessment. Heavy metal pollutant data and geographical location information of a target area are collected to generate a basic data set; constructing a heavy metal migration model based on the basic data set, and predicting a pollutant migration path and concentration change in combination with the comprehensive characteristics of the regional environment to identify a first high-risk region; a second high-risk area is identified by analyzing retention and release behaviors of pollutants in the microfractures in combination with quantitative soil microfracture connectivity characteristics and pollution migration behaviors of a dynamic entropy model; union processing is carried out on the first high-risk area and the second high-risk area to generate a comprehensive high-risk area; heavy metal concentration and land utilization changes in different time periods of the high-risk area are analyzed and synthesized through a multi-dimensional data fusion algorithm, and cumulative risk distribution is generated; and comparing the accumulated risk distribution with enterprise land use planning, evaluating potential environmental responsibility and generating a risk evaluation report.

The invention discloses a shield attitude control method and system based on machine vision, and the method comprises the steps: constructing a motion state model of a shield tunneling machine according to the motion track of a feature point, and carrying out the prediction and correction of a motion state through a Kalman filtering algorithm, and obtaining the real-time attitude data of the shield tunneling machine; vibration data of the shield tunneling machine are obtained, the vibration data are obtained based on a vibration compensation model of an inertial measurement unit, the vibration data and visual sensor data are combined, a data fusion algorithm based on particle filtering is adopted to suppress vibration interference, and stable attitude information is obtained; and constructing a multi-modal data fusion model based on a graph convolutional neural network, and fusing the visual data, the inertial data and the motion state data to obtain a high-precision attitude estimation result.

The invention belongs to the technical field of agricultural sensing data management and control, and particularly relates to a vision-based multi-modal data fusion intelligent monitoring culture method and system, and the method comprises the steps: obtaining the growth image data and environmental parameter data of a strain based on a multi-sensor information source; identifying the growth image data through a deep learning algorithm, and outputting an image identification result; performing fusion processing on the image recognition result and the environment parameter data through a multi-data fusion algorithm to generate a comprehensive environment evaluation index; importing the comprehensive environment evaluation index and the abnormal state information into a growth environment control model, generating an environment regulation and control instruction conforming to a preset strain growth index, and executing the environment regulation and control instruction through an environment regulation and control module; the system can be used for realizing comprehensive environment monitoring, accurately evaluating the strain growth environment condition, and avoiding errors and one-sidedness caused by a single data source, so that culture environment parameters are dynamically adjusted.

The invention relates to the technical field of vehicles, in particular to a multi-sensor data fusion method and device, a vehicle and a medium, and the method comprises the steps: obtaining the sensing data of multiple sensors and the current environment data of the vehicle; environment features in the current environment data are extracted, data fusion weights of multiple sensors are determined based on the environment features, a current driving scene of the vehicle is analyzed according to the environment features, and a data fusion algorithm of the multiple sensors is determined based on the current driving scene; and fusing the sensing data of the multiple sensors according to the data fusion weight and a data fusion algorithm. Therefore, the problems that the sensing data is inaccurate, all driving requirements of the user are difficult to meet, the driving experience of the user is poor and the like due to the fact that sensor fusion sensing cannot cope with various complex environments in related technologies are solved.

The invention relates to a high sea condition unmanned ship dynamic anti-interference control method based on intelligent body.The method comprises the following steps that information is collected, multi-source data are integrated through a data fusion algorithm, and multi-mode sensing data are obtained; constructing a body-equipped intelligent model, taking the multi-modal sensing data as input and the unmanned ship control instruction as output, and performing offline training; constructing an interference prediction model to predict the change trend of interference factors under the high sea condition, and performing adaptive interference compensation according to an interference prediction result; a hierarchical control structure is designed, a task planning layer makes a global navigation plan based on risk assessment, a motion control layer adopts a model prediction control and adaptive sliding mode control combined algorithm to convert instructions, and an execution mechanism layer drives an execution mechanism; meanwhile, online self-adaptive optimization is carried out, and the intelligent model with the body is updated and optimized according to feedback information. Compared with the prior art, the dynamic anti-interference capability, the operation precision and the reliability of the unmanned surface vehicle under the high sea condition are remarkably improved, the environment adaptability and the autonomous operation level of the unmanned surface vehicle are enhanced, and the unmanned surface vehicle is suitable for various high sea condition ocean operation scenes.

According to the tray cycle scheduling system and the application method, historical data and a production plan are fused, an improved algorithm is adopted to predict tray requirements, and an attention mechanism is introduced to improve precision; the method comprises the following steps: acquiring multi-dimensional state information of a tray through a multi-modal sensor, eliminating noise by using a data fusion algorithm, and constructing a digital twin model to realize state synchronization; a double-layer optimization architecture is constructed, an upper layer solves a global scheme by combining an improved particle swarm and a simulated annealing algorithm, and a lower layer dynamically adjusts a path through reinforcement learning; an instruction is generated based on a digital twin model, an event triggering mechanism is adopted to reduce communication load, and virtual-real interaction closed-loop control is realized; a real-time evaluation index system is established, a meta-learning algorithm is utilized to quickly adapt to a new environment, and system parameters are continuously optimized. Multi-module collaborative innovation is achieved, the tray scheduling efficiency and the intelligent level are remarkably improved, production logistics whole-process collaborative optimization is achieved, and core support is provided for cost reduction and efficiency improvement of enterprises.

The invention discloses an intelligent environment monitoring and regulation method for a marine ranch, and belongs to the field of environment monitoring, and the method comprises the steps: collecting water body parameters, employing a data fusion algorithm to generate a comprehensive environment state index, and solving the business problems of data dispersion, response lag and inaccurate adjustment in water body environment monitoring and regulation. A time sequence analysis model and an optimization algorithm are combined, potential anomalies are analyzed, a risk change trend is predicted, environment adjustment requirements are dynamically calculated, a precise regulation and control instruction set is generated and transmitted to equipment to be executed, and meanwhile, through deviation comparison between real-time feedback data and a preset threshold value, comprehensive indexes and regulation and control parameters are iteratively updated, so that environment stability is ensured. Through multi-level data processing and a self-adaptive adjustment mechanism, the timeliness and accuracy of water body environment regulation and control are remarkably improved, closed-loop management from monitoring to optimization is achieved, and efficient support is provided for intelligent decision making in a complex environment.

The embodiment of the invention discloses a double-truck-crane cooperative hoisting monitoring method and device, electronic equipment and a storage medium. The method comprises the steps that component parameters and operation environment parameters of hoisting operation are collected, a reference data model is constructed, and an action expected value and a safety control threshold value are set; state acquisition devices are arranged on the two truck cranes to acquire multi-source data in real time and transmit the multi-source data to a central control system so as to realize synchronous sensing of the two trucks; processing multi-source data based on a data fusion algorithm, and constructing a dynamic attitude model and a collaborative operation state model; a state prediction correction value in the model is compared with the reference data model in real time, deviation is judged, and safety early warning is triggered; and generating a feedback control instruction, dynamically adjusting the hoisting operation, correcting the coordination deviation, and realizing the coordination control of the hoisting posture. In this way, through multi-source data fusion and real-time feedback control, the safety and coordination of cooperative lifting operation of the double truck cranes are remarkably improved.

The invention discloses a side slope stability intelligent monitoring and supporting optimization method based on digital twinning, which comprises the following steps: extracting gradient, curvature and geological layering characteristics from a holographic terrain model, calculating a preliminary change trend of stress distribution of a rock-soil body in combination with a finite element analysis method, and determining spatial position distribution of a potential sliding surface of a side slope; according to the space coordinate set of the real-time monitoring point position, the data acquisition frequency of the sensor is adjusted through a time sequence analysis method, and a high-frequency monitoring data set reflecting dynamic changes of stress and underground water is obtained; extracting stress peak values and underground water level fluctuation characteristics from the high-frequency monitoring data set, and generating a multi-dimensional information matrix containing terrain, stress and water dynamics by combining spatial characteristics of the holographic terrain model and adopting a data fusion algorithm; according to the state parameters of the digital twinborn body, if it is judged that the stress distribution exceeds a preset threshold value, the stress distribution of the supporting structure is recalculated through finite element analysis, and an adjusted slope supporting design scheme is obtained.

The invention relates to the technical field of automobile brake control, in particular to a self-adaptive brake kinetic energy recovery control method and system based on multi-sensor fusion. Tire wear and road surface feature data are collected through a multi-source sensing fusion unit, and a tire wear coefficient and a road surface friction coefficient are generated through an intelligent decision calculation unit; the method comprises the following steps of: integrating the nonlinear correlation of the multi-source sensing fusion unit and the self-adaptive control execution unit through a rule and data fusion algorithm, outputting a comprehensive friction coefficient, and dynamically adjusting the braking kinetic energy recovery force and response time by the self-adaptive control execution unit according to the comprehensive friction coefficient. The system comprises a multi-source sensing fusion unit, an intelligent decision calculation unit, the self-adaptive control execution unit and a closed-loop feedback calibration unit. The closed-loop unit corrects data deviation through cross validation of the laser radar and the motor torque inversion model, the problems of poor working condition adaptation and unreliable data are solved, and the kinetic energy recovery efficiency, the braking safety and the driving smoothness are improved.

The invention provides a detection method for evaluating the sintering quality of an artificial heat-conducting film. The method comprises the following steps: constructing a personalized database containing electrical conductivity data corresponding to the artificial heat-conducting film under different materials and different process conditions; identifying performance fluctuation parameters of different sintering production devices and the same sintering production device in different production periods by analyzing the conductivity data; the method comprises the following steps: arranging multiple types of sensors on artificial heat-conducting film sintering production equipment, and collecting key parameters in the sintering production equipment; fusing the multiple types of key parameters through a data fusion algorithm to obtain a fused data set; the sintering production equipment is adjusted according to the performance fluctuation parameters; and inputting the key parameters in the fusion data set into the sintering quality evaluation model, and comparing the key parameters with the standard parameters of the personalized database to obtain a comparison result. Through the quality evaluation model and the automatic feedback mechanism, intelligent adjustment of the sintering process is achieved, and the product quality and the production efficiency are improved.

The invention discloses a negative pressure pipeline leakage point detection and targeted repair system and method, and the method achieves the confirmation of a pipeline leakage point through a detection end, and achieves the repair of the pipeline leakage point through a repair end. The detection end comprises a distributed optical fiber, an unmanned aerial vehicle and a control platform; through distributed optical fibers laid along a pipeline, pipeline vibration and temperature abnormity are monitored in real time, a leakage area is preliminarily positioned, then infrared scanning is performed on a suspicious area through an unmanned aerial vehicle carrying a high-precision thermal infrared imager, a temperature field distribution diagram is generated and uploaded to a control platform, and the control platform outputs leakage point coordinates through a data fusion algorithm; the repairing end comprises self-repairing gel and a magnetic control robot; the small cracks with the width smaller than 1 mm are externally sprayed with self-repairing gel, and laser curing is conducted after the cracks are sucked in through negative pressure; for large cracks with the width larger than or equal to 1 mm, the magnetic control robot is put into the pipeline, resin is sprayed to the inner wall of the pipeline, and ultraviolet curing is conducted. The system and the method can remarkably improve the maintenance efficiency of the negative pressure pipeline.

The invention discloses a cloud platform data monitoring and management system of an integrated immersion liquid cooling cabinet, and particularly relates to the technical field of cloud platform data management, and the cloud platform data monitoring and management system comprises the following steps: configuring a cooling liquid: configuring the type and quantity of the cooling liquid according to the heat dissipation demand of the cabinet; temperature, flow and cooling liquid consumption data in the cabinet are collected in real time, the flow and quantity of the cooling liquid are dynamically adjusted based on the data to ensure that the system maintains the optimal heat dissipation effect under different load conditions, historical data are analyzed through machine learning, the system can establish a cooling liquid aging state prediction model, and the cooling liquid aging state prediction accuracy is improved. A cooling liquid aging evaluation index is generated, the cooling liquid aging problem is early warned in advance, regular calibration of the sensor is based on dynamic adjustment of the cooling liquid aging state and data deviation, the data acquisition precision is ensured, and a data fusion algorithm is used for optimizing sensor data and eliminating the influence of failure or error of a single sensor.

The invention discloses a fire-fighting non-destructive detection method based on multi-modal detection, and relates to the technical field of fire-fighting engineering, and the method comprises the following steps: carrying out the rapid scanning of fire-fighting equipment through an ultrasonic array, analyzing and positioning a suspected defect region based on a time domain reflection signal, and generating a three-dimensional coordinate mapping model; and performing multi-angle X-ray projection acquisition on the positioning area, and generating an internal structure cross-sectional image through a three-dimensional reconstruction algorithm. According to the fire-fighting non-destructive detection method provided by the invention, by integrating various technical means such as ultrasonic array scanning, X-ray tomography, thermal imaging analysis and microwave sealing detection, comprehensive and accurate identification of potential defects of fire-fighting equipment is realized, and the dimensionality and precision of defect detection are improved; the structural integrity of the equipment is kept to the maximum extent, extra risks caused by destructive detection are avoided, and the reliability and accuracy of detection are improved through a data fusion algorithm.

The invention discloses an accurate analysis method for Internet user behaviors based on artificial intelligence, and the method belongs to the technical field of Internet, and comprises the steps: collecting user behaviors and basic attribute information through a multi-source data collection technology, and integrating data through a data fusion algorithm after data cleaning and preprocessing; designing and extracting a plurality of behavior features, screening key features by using a feature selection algorithm, and performing feature transformation processing at the same time; constructing a deep learning model fusing a recurrent neural network (such as LSTM and GRU), a convolutional neural network and the like, training the model by using a large-scale data set, and optimizing parameters; establishing a real-time analysis system, inputting new data into the model in real time, providing personalized service and recommendation according to an analysis result, and collecting feedback data to update the model online; and finally, evaluating the model by adopting multiple evaluation indexes, verifying the generalization ability through methods such as cross validation and the like, and further optimizing and adjusting the model.

The invention discloses an outdoor workstation air quality monitoring and intelligent ventilation system and method, and belongs to the technical field of environment monitoring and intelligent ventilation control. The system is composed of an environment sensing module, an intelligent control unit and a ventilation execution device. And the environment sensing module realizes pollutant concentration field reconstruction through a multi-dimensional data fusion algorithm. The intelligent control unit constructs a dynamic ventilation model based on an improved fuzzy PID algorithm, and generates an optimal ventilation strategy in combination with meteorological prediction data and workstation structure parameters. And the ventilation execution device adopts a modular design to realize directional air supply and pollutant blocking. The method comprises four steps of real-time environment data acquisition, dynamic risk assessment, ventilation parameter optimization and execution mechanism linkage control, and remote intelligent regulation and control are realized through cooperation of edge computing and a cloud platform. According to the scheme, a passive response mode of a traditional ventilation system is broken through, and the technical advantages of accurate pollutant tracing, dynamic optimization of ventilation efficiency and intelligent adjustment of equipment energy consumption are achieved.

The invention discloses a GIL state integrated monitoring system based on multi-source information collaborative research and judgment, and belongs to the technical field of electrical equipment monitoring. A GIL state integrated monitoring system based on multi-source information collaborative study and judgment comprises a multi-dimensional sensing unit and an intelligent analysis unit. According to the method, the problem that the real state of the GI L equipment is difficult to comprehensively and accurately reflect in the prior art is solved, the accuracy and efficiency of data monitoring are improved through optimized deployment of the sensors, the integrity and reliability of data are ensured through real-time monitoring of the data transmission state, and a data fusion algorithm and an artificial intelligence algorithm are adopted, so that the real-time monitoring of the GI L equipment is realized. According to the method, the multi-source data is deeply mined and analyzed, the abnormal mode and the fault trend of the G-IL equipment are effectively recognized, early warning is achieved, and workers can rapidly understand and respond by visually displaying the collected multi-source data, the analysis result and the fault warning.

The invention discloses a visual sand storage tank reserve accurate monitoring method and system based on multi-dimensional sensing fusion, and the method comprises the following steps: installing a sensor at a key position of a sand storage tank, collecting the multi-dimensional data of the sand storage tank in real time, and carrying out the preprocessing of the collected multi-dimensional data; according to the method, pressure, height, form and humidity data of a sand body are acquired through cooperation of multiple sensors such as pressure, ultrasonic, laser radar and humidity sensors, a dynamic data fusion algorithm and partition mechanical modeling are combined, and reserves trend prediction is realized by using LSTM (Long Short Term Memory); a three-dimensional digital twinborn model is constructed, sand body distribution and flow states are dynamically displayed by color / transparency, a multi-level early warning mechanism is established in combination with multi-dimensional parameters, and meanwhile, a block chain evidence storage technology is introduced to perform uplink evidence storage on key data to ensure that the data cannot be tampered and operation is traceable.

The invention relates to the technical field of automatic sorting of agricultural products, in particular to an egg quality intelligent sorting system and method based on multi-modal sensing. The system comprises a multi-mode sensing module, a data processing module and a sorting execution module. The multi-mode sensing module integrates a hyperspectral imaging unit, a voiceprint vibration detection unit and a weight sensing unit, and comprehensively collects egg surface images, vibration frequency and weight data; the data processing module analyzes the image data through deep learning and a data fusion algorithm to detect cracks and blood spots, and fuses multi-modal data to generate features to adapt to characteristic changes of different batches of eggs; the sorting execution module determines the sorting priority according to the fusion features and executes accurate sorting; the system utilizes the synergistic effect of the multi-mode sensing technology to comprehensively evaluate the internal and external quality of eggs, the detection precision is remarkably improved, efficient and intelligent egg sorting is realized, and the automatic sorting technology level of agricultural products is improved.

The invention provides a highway bridge joint vibration and deformation condition monitoring method, and particularly relates to the technical field of bridge structure health monitoring, and the method comprises the steps: laying a fiber bragg grating sensor array at a bridge joint, and the array comprises a plurality of fiber bragg grating sensors which are distributed at intervals along the length direction of the joint; and each sensor is connected with the joint filling body contact surface through a pre-embedded fixing structure. The sensor array collects strain signals and vibration signals in the joint in real time, analog-to-digital conversion and caching are completed through the data collection unit, digital sensing signals are generated and stored, and a historical monitoring data set is constructed. A noise suppression algorithm is adopted to filter the signal, and ambient temperature and humidity and electromagnetic noise interference are eliminated. Features are extracted based on a data fusion algorithm, and crack feature parameters reflecting the seam fine crack expansion trend are generated. A historical monitoring data set and the parameters are combined, a supervised learning model is used for calculating a dynamic adjustment threshold value, a real-time monitoring early warning signal is generated through comparison, and early-stage high-sensitivity monitoring of the seam is achieved.

The invention relates to the technical field of ball valves, provides an intelligent sensing modular ball valve and a control method, and designs a valve body module and a control module. The valve body module is integrated with a multi-mode sensor, comprises a spirally-distributed acoustic sensor array, a physical quantity sensor group and a valve element state detection sensor, and monitors fluid acoustic characteristics, pressure, temperature, flow, liquid level and valve element displacement and torque in real time. The control module analyzes sensor data through a data fusion algorithm, and early identification of cavitation faults and detection of micro-crack faults are achieved. The system adopts an 8-bit dynamic coding mechanism to accurately position the fault type, and adjusts the opening or torque of the actuator in a linkage manner. The method has multi-parameter cooperative sensing, early fault early warning and self-adaptive control capabilities, and the reliability and safety of the ball valve under complex working conditions are remarkably improved.

The invention discloses a Fourier transform and S transform combined battery impedance spectroscopy measurement method and device, and aims to improve the precision, speed and reliability of impedance measurement of a battery energy storage system. According to the method, a multi-frequency component excitation signal is generated through a controllable current source, and current and voltage response signals of a battery are synchronously acquired and are respectively input into a Fourier transform module and an S transform module for frequency domain analysis. The Fourier transform module extracts impedance spectrums of low and medium frequency bands, and the S transform module extracts impedance spectrums of medium and high frequency bands through Gaussian window time-frequency analysis. And a data fusion algorithm is adopted to perform composite processing on results of the two modules, including a direct selection method, an arithmetic average method, an amplitude and phase calibration method and a weighted average method, so that smooth transition and optimization between frequency bands are realized, and finally the electrochemical impedance spectrum is generated. Through collaborative analysis of Fourier transform and S transform, parameter optimization and a fusion algorithm, the efficiency and accuracy of impedance spectroscopy measurement are remarkably improved, and the method is suitable for rapid monitoring and diagnosis of the battery state.

The present invention discloses a multi-modal system for real-time plant hydration monitoring and irrigation management. The system comprises at least one acoustic sensor configured to emit controlled sound waves through plant tissues and detect corresponding vibrations and resonance frequencies using sensitive microphones or piezoelectric sensors to assess plant hydration. The system includes at least one bioelectrical sensor for monitoring bioelectrical signals. At least one nanotechnology-based sensor is configured to detect molecular changes in water content within plant cells through embedded or externally applied nanosensors. An aerial imaging device mounted on an unmanned aerial vehicle captures data related to canopy temperature, leaf color, and hydration indicators using infrared, multi-spectral, and thermal cameras. A central processing unit receives and analyzes data using predictive models, integrates the data via data fusion algorithm to generate a plant hydration profile, and controls water delivery through an irrigation management unit based on the hydration profile.

The invention relates to the field of coal mine intelligent management and control, and particularly discloses a coal conveying system dust management and control integrated platform based on multi-sensor collaboration, which comprises the following components: a multi-sensor collaboration monitoring module, a central controller, a dynamic control module, a cloud server and a graded alarm and emergency module. The system has the beneficial effects that through multi-sensor collaborative layout and a data fusion algorithm, global coverage monitoring of the dust concentration of the coal conveying system is realized, a dust removal strategy is dynamically optimized, redundant operation of equipment is avoided, and the overall energy-saving efficiency is improved. The platform integrates dust, gas and fire monitoring functions, adopts a fuzzy logic algorithm to optimize a dust removal strategy, supports graded alarm and emergency linkage, remarkably improves dust removal efficiency and safety, and is suitable for industrial scenes such as thermal power generation and coal transfer.

The invention belongs to the field of advanced inorganic non-metallic materials, and relates to a 3D special-shaped glass bending tempering forming method and system, which comprises the following steps: according to initial stress distribution data, partitioning a glass curved surface by adopting a k-means clustering algorithm, determining an independent temperature control parameter required by each partition, generating a multi-mode temperature field partitioning scheme, partition temperature control data is obtained; according to the stable temperature distribution data, a non-contact electromagnetic suspension technology is adopted to control the distance between the glass and the mold, electromagnetic field intensity distribution data is generated, the stress state of the glass is calculated through finite element analysis, and contact stress reduction data is obtained; and aiming at the optimized mechanical property data, integrating the temperature field, the deformation rate and the stress distribution data by adopting a data fusion algorithm, generating a process parameter database for bending and tempering forming of the 3D special-shaped glass, and determining final production control parameters.