259 results about "Data fusion algorithms" patented technology

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 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.

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 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 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 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 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.

The invention relates to the technical field of water pump intelligent control, and discloses a water pump controller parameter self-adaptive adjustment control method and system.The water pump controller parameter self-adaptive adjustment control method comprises the steps that meteorological early warning data, pressure pulsation signals of all pump stations and pipe network topology data are obtained; generating a time-space synchronous disaster monitoring data set through a data fusion algorithm; inputting the disaster monitoring data set into the deep space-time diagram neural network, and outputting a pipe network damage probability distribution diagram; generating a pulsation propagation prediction model containing propagation time delay and an attenuation coefficient based on a fluid dynamics equation and the pipe network damage probability distribution diagram; calculating a PID controller parameter coordinated regulation scheme of each pump station by minimizing an objective function including pressure deviation, flow fluctuation and parameter regulation quantity; and system response data is acquired in real time, and adaptive optimization of a control strategy is realized. According to the invention, stable operation of the water supply system under disaster conditions is realized.

The invention relates to the technical field of attitude control, and discloses a method for realizing self-adaptive active leveling compensation, which comprises the following steps: acquiring real-time data of a vehicle, the data being provided by a plurality of sensors; processing the real-time data through a data fusion algorithm; according to the inclination state and the motion parameters of the vehicle, hydraulic supporting leg adjusting parameters are calculated through a multi-objective optimization algorithm; according to the calculated hydraulic supporting leg adjusting parameters, the action of the hydraulic supporting leg is adjusted through a PI D controller for vehicle balance recovery; the stability of the hydraulic supporting leg adjusting system is verified through the Lyapunov stability theory, and the hydraulic supporting leg adjusting system is used for enabling the adjusting process of the hydraulic supporting leg to be completed in all operation states; and the vehicle inclination state is fed back in real time. By fusing multi-sensor data and adopting a Kalman filtering or particle filtering algorithm, high-precision perception of the real-time inclination state and dynamic parameters of the vehicle is realized, and a more accurate and reliable vehicle attitude judgment effect is obtained.

The invention discloses a high-precision electrical calibration method and system for a wedge flowmeter. The method comprises the following steps: step 1, data acquisition and preprocessing; step 2, dynamic working condition identification; 3, updating the self-adaptive model; 4, multi-source data fusion calibration is carried out; and 5, performing closed-loop verification and compensation. According to the invention, multiple types of signals are collected in real time through the multi-dimensional sensor array and de-noised to construct a data set, so that comprehensive and accurate data collection is realized, and a good basis is provided for calibration; carrying out dynamic working condition identification by utilizing a support vector machine, triggering model updating, and endowing calibration with dynamic self-adaptive capability; model parameters are updated by adopting a recursive least square method, compensation items are established, and the characterization capability of the model is enhanced; a calibration coefficient is generated by means of a data fusion algorithm, and the calibration precision is improved; through verification and compensation of a digital twinborn model, traditional defects are effectively overcome, model transplantation errors are reduced, the measurement precision of the wedge flowmeter is improved under extreme working conditions, and production stability and product quality are guaranteed.

The invention discloses a preventive diagnosis and maintenance system and method for offshore wind power equipment, and relates to the technical field of offshore wind turbine generator maintenance, and the system comprises a data input module, a maintenance judgment module, an early warning maintenance module, an intelligent fault diagnosis module and an operation and maintenance management module. The data input module is used for inputting real-time state data of each component of the offshore wind power equipment, and the operation and maintenance management module controls operation and management of the whole preventive diagnosis and maintenance system. According to the invention, information from different monitoring means is fused by adopting a data fusion algorithm, richer equipment state features are extracted through fusion processing, the collected data are preprocessed in the data collection process to improve the accuracy and reliability of the data, and the data collection accuracy and reliability are improved by utilizing complementarity among different monitoring means. The accuracy and reliability of damage identification are improved, and the diagnosis and maintenance system can efficiently and accurately identify the damage position and the damage degree of the fan structure or equipment.

The invention discloses a sand and dust pollution health risk early warning terminal and early warning method, and the method comprises the steps: obtaining multi-source input data, such as meteorological parameters, sand and dust pollutant concentration and geographical environment, in real time, and carrying out the preprocessing through a data fusion algorithm, thereby obtaining a standardized data set; based on the data set, utilizing a trained time sequence prediction model to output a sand and dust pollution level prediction value of a future preset time length; meanwhile, an exposed crowd sensitivity index and regional health baseline data containing the disease incidence rate and the meteorological sensitive crowd proportion are constructed; calculating a comprehensive health risk index through a dynamic health risk assessment model in combination with the predicted value, the sensitivity index and the baseline data; and when the index exceeds a preset threshold dynamically adjusted based on the incidence peak range in the baseline data, generating a graded early warning signal and issuing the graded early warning signal in real time, thereby realizing accurate early warning of the dust pollution health risk.

The invention discloses a self-repairing method and system for multi-modal sensor fusion, electronic equipment and a medium, and relates to the technical field of automatic driving. The method comprises the following steps: generating a physical layer health score and a logic layer health score about a heterogeneous sensor based on original data; judging whether the heterogeneous sensor fails or not by combining the physical layer health score and the logic layer health score; inputting the effective original data into a generative adversarial network under the condition that the existence of the invalid heterogeneous sensor is determined, performing cross-modal generation, and outputting first virtual data of the invalid heterogeneous sensor; according to the comprehensive health score of the heterogeneous sensor, dynamically distributing a fusion weight coefficient of the heterogeneous sensor; and based on the fusion weight coefficient, fusing the effective original data and the first virtual data by using a data fusion algorithm, and outputting a fusion perception result. By adopting the method, the compensation precision can be improved, and a reliable sensing result is output.

The invention discloses a UPA pressure automatic compensation system and method for mask substrate polishing. The UPA pressure automatic compensation system comprises a pressure execution module, a distributed sensing feedback network and an intelligent control unit. Wherein the pressure execution module is used for directly polishing a mask base plate and comprises a force application shaft, a polishing head, an air source, a UPA air bag and an electric proportional valve, the distributed sensing feedback network comprises an air path pressure sensor and a tail end contact pressure sensor, and the intelligent control unit receives a collection signal of the distributed sensing feedback network and sends the collection signal to the polishing head. On the basis of a current process formula and process parameter coupling model, a data fusion algorithm, a feed-forward compensation algorithm and a fuzzy PID algorithm are operated to obtain a feed-forward compensation amount and a fuzzy PID output amount, the feed-forward compensation amount and the fuzzy PID output amount are superposed, a total control voltage is generated and output to an electric proportional valve, and then the input air pressure of a UPA air bag is adjusted. Therefore, closed-loop control over the polishing pressure of the mask substrate is achieved. Through multi-sensor data fusion, fuzzy PID self-adaptive control and intelligent feed-forward compensation based on a process parameter coupling model, real-time, accurate and constant control of the polishing pressure is realized, so that the surface precision and yield of the mask substrate are improved.

The invention provides a macrobrachium rosenbergii quality grading system based on machine vision. The macrobrachium rosenbergii quality grading system based on machine vision comprises a prawn body placing platform used for placing macrobrachium rosenbergii to be tested; the three-dimensional acquisition module comprises a structured light projection device, a binocular stereo camera and a flight time depth sensor; and the three-dimensional reconstruction module is used for constructing the data of the three-dimensional acquisition module into a complete three-dimensional point cloud model through a data fusion algorithm and a point cloud registration algorithm. According to the macrobrachium rosenbergii quality grading system based on machine vision, the overall scheme has robustness and expandability, the system is suitable for various large-scale farms and grading scenes, and the intelligent level of macrobrachium rosenbergii quality control is greatly improved.

The invention relates to a continuous punch forming method of a high-hardness alloy thin-wall lock case in the field of intelligent manufacturing, which comprises the following steps: according to a stress distribution diagram, adjusting a die gap by adopting a self-adaptive algorithm, dynamically optimizing gap parameters for a local stress concentration area, and determining real-time gap configuration; if the temperature deviation of any area in the temperature field exceeds 10 DEG C, recalculating heat source distribution through a heat flow simulation algorithm, and determining optimized heating parameters; acquiring surface topography data of the annealed thin-wall lock case through an optical scanner, and analyzing fracture and wrinkling defects by adopting an image processing algorithm to obtain defect distribution characteristics; if the proportion of the fracture area in the defect distribution characteristics exceeds 2%, the forming failure probability is predicted through a neural network model, and whether the stamping speed is adjusted or not is determined; and according to the defect distribution characteristics and the softening consistency data, a data fusion algorithm is adopted to comprehensively evaluate the forming stability, and optimized operation parameters of the continuous stamping production line are obtained.

The invention relates to the field of wind power systems, in particular to an energy efficiency dynamic optimization method and system of a wind power hydrogen production coupling system. The method comprises the following steps: acquiring real-time operation data of a wind power plant, including wind speed, generated power and equipment state information; based on the real-time operation data, a dynamic energy efficiency model of the wind power hydrogen production system is constructed through a data fusion algorithm; predicting energy efficiency performance of the wind power hydrogen production system under different operation conditions according to the dynamic energy efficiency model; based on the prediction result, a multi-objective optimization algorithm is adopted to generate an optimal operation strategy, and the optimal operation strategy comprises start-stop control of hydrogen production equipment, a power distribution proportion and charge-discharge scheduling of an energy storage system; and issuing the optimal operation strategy to a control unit of the wind power hydrogen production system to realize dynamic optimization of energy efficiency. The problems of high data fusion difficulty and insufficient energy efficiency model dynamics in the prior art are solved.

The invention discloses a multi-dimensional analysis house building health monitoring and early warning system and method, and relates to the technical field of monitoring and early warning. During operation of the system, various sensors are deployed to monitor the physical states of different materials in a building, collect surrounding environment data and transmit the data to a central processing unit in real time, and through a data fusion algorithm, real-time monitoring and early warning are achieved; the method comprises the following steps: integrating sensor data from different materials into a unified data set, preprocessing and normalizing the data, constructing a multi-material interaction mathematical model, calculating an effective stress sigma eff of a building structure, constructing a building overall health assessment model, and calculating a building health comprehensive index HI; according to the building health comprehensive index HI and the comparison result of the health index of each material and the safety threshold value, the health change of the building is detected in real time, an intelligent decision support system is provided, and building management personnel are helped to carry out long-term maintenance planning and resource optimization distribution.

The invention provides a method for monitoring cracks in a high-pressure vibration state of a wind tunnel in real time, and belongs to the technical field of wind tunnels. A piezoelectric ceramic sensor array is arranged on the wall surface of the wind tunnel, a vibration fatigue matrix and a structure resonance matrix are established, and a self-adaptive filtering algorithm is adopted to preprocess sensor signals; establishing a vibration coupling equation set to extract crack characteristic parameters, constructing a dynamic threshold discrimination matrix and a risk assessment matrix, utilizing a multi-sensor data fusion algorithm to accurately position crack positions and sizes, calculating risk levels according to a crack risk assessment index equation, and performing three-level safety discrimination. A complete crack real-time monitoring and risk assessment system in the high-pressure vibration environment is formed, and the technical problems that in the prior art, crack monitoring precision is low in the high-pressure vibration environment, and real-time risk assessment cannot be achieved are solved.

The invention belongs to the technical field of industrial corrosion monitoring, and particularly discloses a monitoring and detection data fusion-based pipeline corrosion state prediction method and system. Corrosion probe monitoring and ultrasonic thickness measurement detection technologies are jointly adopted, ultrasonic thickness measurement point positions can be automatically switched on a circle in the radial direction of the pipeline, the corrosion probe can collect the average corrosion rate, the lost wall thickness can be obtained through continuous integration of the average corrosion rate and time, and the thickness of the pipeline can be measured. The residual wall thickness can be obtained by continuously subtracting the lost wall thickness from the initial wall thickness, and the residual wall thickness value can also be directly obtained through the ultrasonic corrosion detection technology. The residual wall thickness obtained through corrosion monitoring and corrosion detection is subjected to a data fusion algorithm to obtain a fused residual wall thickness value, a residual wall thickness curve after data fusion is continuously learned through an LSTM intelligent prediction algorithm, trend prediction of the curve can be carried out, and then a wall thickness prediction result in a period of time in the future is obtained. The overall corrosion condition of the pipeline can be judged according to the prediction result.

The invention discloses a security upgrading method for an intelligent terminal, and the method comprises the steps: obtaining current terminal data and historical terminal data corresponding to the intelligent terminal when the intelligent terminal receives an upgrading instruction; fusing the current terminal data and the historical data by using a preset data fusion algorithm to obtain comprehensive terminal data corresponding to the intelligent terminal; analyzing and processing the integrated terminal data by using a preset analysis algorithm to determine a prediction failure rate corresponding to the intelligent terminal; formulating an upgrading strategy corresponding to the intelligent terminal according to a preset upgrading mechanism and the prediction failure rate; and upgrading the intelligent terminal according to the upgrading strategy. According to the method, intelligent decision making and risk prevention in the upgrading process of the intelligent terminal are achieved, through automatic failure prediction and risk assessment, the requirement for manual intervention is reduced, the operation and maintenance cost is reduced, and safe upgrading of the intelligent terminal in a complex environment is guaranteed.

The invention discloses a VOCs emission dynamic prediction and energy-saving control system for catalytic combustion equipment in a casting workshop and a medium, the system comprises a high-frequency VOCs concentration monitoring unit which adopts TDLAS and FTIR spectrum analyzers and obtains high-precision concentration data through a data fusion algorithm, and a secondary differential signal of the TDLAS analyzer is independently extracted as a key feature; the data preprocessing and feature engineering module is used for processing multi-source data; the dynamic emission prediction module is internally provided with an attention mechanism time sequence convolutional network ATCN model, and realizes the ultra-early prediction of the emission concentration by fusing the secondary differential signal and historical data; the multi-objective optimization control module adopts a model predictive control MPC framework, the lowest comprehensive operation cost is taken as an objective, a dynamic virtual carbon penalty coefficient beta can be dynamically adjusted according to a workshop production plan, an advanced control instruction is generated through rolling optimization, and the equipment power and the fan frequency are adjusted; according to the method, ultra-early prediction, prospective control, energy conservation and consumption reduction of VOCs treatment are realized.

The invention discloses an adaptive tower crane anti-collision early warning method based on data fusion. The method comprises the following steps: step 1, acquiring attitude data of equipment, surrounding environment data and interaction data between tower cranes through data acquisition equipment; performing time synchronization and coordinate unification; 2, according to the relative space relation between the tower cranes and the relative space relation between the tower cranes and the environment, multiple collision risk scenes are divided, key risk features in all the scenes are extracted, and fusion processing is conducted on the features through a data fusion algorithm; 3, according to the real-time working condition parameters, dynamically adjusting the weight of the key risk characteristics in each scene; carrying out the normalization processing of the fused risk features, and carrying out the weighted fusion according to the weight, and obtaining a global risk value; 4, setting a third-level threshold value, and triggering a corresponding early warning action according to an interval to which the global risk value belongs; and collecting risk characteristic data feedback in real time, and if the risk is not reduced to a safety range and meets a time interval condition, repeatedly executing early warning evaluation and instruction sending.