571 results about "Fuzzy reasoning" patented technology

The invention provides a machine room energy consumption optimization method and system based on temperature monitoring, and relates to the technical field of energy consumption optimization, and the method comprises the steps: collecting the temperature data of a machine room in real time, continuously monitoring the temperature gradient of each region, carrying out the fusion processing, and constructing a three-dimensional dynamic temperature distribution model; a hot spot area, an airflow dead angle and a cooling capacity waste area are identified through a deep learning algorithm, a flowing track and a mixing process of cold and hot air in a machine room are predicted through a track regression neural network in combination with machine room layout and equipment distribution, and a machine room temperature control strategy set is generated through adaptive neural fuzzy reasoning, correction and updating of a prediction result; and by taking minimum energy consumption and maximum refrigeration efficiency as optimization objectives and taking a safe operation temperature range of equipment in the machine room as a constraint condition, a plurality of parameters are jointly adjusted through a temperature control optimization algorithm to obtain an optimal temperature control curve, and the optimal temperature control curve is converted into a refrigeration system control instruction, so that collaborative optimization of the temperature and the energy consumption of the machine room is realized.

The invention discloses an energy-saving intelligent street lamp automatic emergency response system and a control method thereof, relates to the technical field of industrial Internet of Things control, and solves the problems that an existing intelligent street lamp system is poor in dynamic scene adaptability, single in emergency response strategy and insufficient in communication stability. According to the method, a dynamic priority scheduling matrix is generated through multi-source data fusion and an adaptive weighted decision tree, and an intelligent dimming strategy is trained in combination with improved fuzzy reinforcement learning; a multi-level fuzzy control and fuzzy reasoning system is used for generating emergency parameters driven by accident levels; dynamically selecting an optimal communication link transmission instruction based on a multiple access protocol and multi-scale channel sensing; an IEEE 1588PTP protocol and Bayesian clock drift correction are adopted to guarantee time sequence consistency, and energy consumption and safety balance are optimized through multi-target reinforcement learning; the dynamic adaptive capacity, the emergency response accuracy and the communication reliability of a complex scene are remarkably improved, and collaborative optimization of energy-saving efficiency and road safety is realized.

The invention discloses a distribution network cable dielectric loss withstand voltage partial discharge intelligent detection and evaluation method and system, and relates to the technical field of power equipment state monitoring, and the method comprises the steps: synchronously collecting a dielectric loss spectrum, a withstand voltage leakage current curve and a partial discharge spectrum of a distribution network cable through a phase resolution measurement device; processing the partial discharge spectrum by using a frequency domain adaptive filter to generate a denoised partial discharge characteristic spectrum; constructing a cable multi-dimensional parameter normal domain hyperspherical model, and calculating a deviation distance value; and inputting the deviation distance value into a fuzzy inference engine, performing nonlinear conversion according to a preset fuzzy rule base, generating an insulation state evaluation index and a fault risk probability distribution diagram of the distribution network cable, and realizing accurate evaluation of the cable insulation state. According to the invention, the accurate evaluation of the cable insulation state and the accurate positioning of the fault position are realized, and the operation reliability and maintenance efficiency of the power distribution network are improved.

The invention belongs to the field of data processing, and particularly relates to a dynamic cooperative control system and method for a gas turbine and a micro-grid, and the method comprises the steps: constructing a micro-grid real-time monitoring module, continuously collecting distributed energy real-time output, controllable load demands, bus voltage frequency and equipment state parameters, and carrying out the filtering and noise reduction through a preprocessing unit, thereby guaranteeing the data precision; calculating a real-time power difference value based on the preprocessed data, calling an adaptive neural fuzzy inference system, taking the power difference value, the bus voltage deviation and the frequency deviation as input, and judging whether the power difference value, the bus voltage deviation and the frequency deviation exceed a preset threshold value by means of a fuzzy rule base and a neural network model; if the threshold values are not exceeded, the current states of the gas turbine and the energy storage system are maintained; if any one exceeds the threshold value, a dynamic cooperative control instruction is triggered, precise cooperative control of the gas turbine and the micro-grid is achieved, and the operation stability, the operation efficiency and the reliability of the micro-grid are improved.

The invention discloses a charging pile output power control method, and particularly relates to the technical field of charging pile management, and the method comprises the following steps: collecting the real-time operation information of a charging station, analyzing the overall load fluctuation condition, and judging whether an early sign that the operation fluctuation intensity is too high exists or not; and if abnormity is identified, further collecting operation information of each charging pile, extracting feature data, and performing stability and adaptability evaluation. And an evaluation result is imported into a fuzzy reasoning model, and the charging pile is divided into a high-quality operation state and a low-quality operation state. In a single abnormal scene, the charging piles in a high-quality operation state maintain a constant-current and constant-voltage strategy, and the charging piles in a low-quality operation state are switched to an adaptive power adjustment strategy; in an overall abnormal scene, all the charging piles are switched to an adaptive power adjustment strategy, so that the operation efficiency and stability are improved; the strategy can be dynamically adjusted according to different operation scenes, and the requirements under different operation conditions are met.

The invention discloses an aircraft cabin environment personalized adjustment method based on sentiment analysis, and the method comprises the steps: collecting the facial expression, voice waveform and environment parameters of a passenger in real time through a cabin multi-source sensor, and generating a standardized physiological signal matrix and anonymized voice features through noise reduction and feature extraction; inputting the physiological signal matrix and the voice features into a pre-trained deep learning model, outputting an emotion index and a classification label, and updating model parameters through a federal learning framework; dynamically generating temperature, humidity and oxygen concentration adjusting instructions and dynamic weights by adopting a fuzzy reasoning system in combination with the emotion indexes and passenger preset preferences; environment adjustment is executed through a closed-loop control system, and environment parameter errors are fed back; synchronously updating a fuzzy inference system rule base and deep learning model parameters by utilizing reinforcement learning in combination with environmental parameter errors and emotion index changes, and completing optimization of a closed loop; according to the invention, real-time dynamic regulation and control and continuous adaptation optimization of the personalized cabin environment can be realized.

The invention relates to the technical field of grease production control, in particular to a grease product production control method and system based on intelligent digitization. The method comprises the following steps: monitoring temperature, viscosity and turbidity data in a grease crystallization process in real time, and generating grease crystallization state snapshot data; performing crystallization state prediction according to the grease crystallization state snapshot data, and performing reaction kettle cooling rate adjustment to obtain a target control cooling rate; performing real-time stirring rotating speed adjustment fuzzy reasoning on the grease crystallization state snapshot data to generate a corrected grease stirring speed; and performing control instruction conversion on the target control cooling rate and the corrected grease stirring speed, executing a production control instruction, and generating a grease crystallization process control report. By accurately controlling the cooling rate and the stirring speed, intelligent control over the grease crystallization process is achieved, and the quality and stability of grease products are remarkably improved.

A genetic algorithm optimization-based fuzzy PID control method and apparatus, and a device. The method comprises: S1, acquiring a preset standard electrode plate quality, a measured electrode plate quality and a system default ray intensity, acquiring an ambient temperature parameter, retrieving a monitoring index, and monitoring the stability of a surface density measurement system according to the monitoring index; S2, on the basis of a preset system parameter of the surface density measurement system, setting a data sampling interval; S3, on the basis of the data sampling interval, continuously sampling a number N of pieces of data using a median average filtering algorithm, removing extreme values, calculating the arithmetic mean value of the N-2 pieces of data, and eliminating sampling value deviation of the arithmetic mean value, to obtain surface density sampling data; S4, performing fuzzification processing on the surface density sampling data, determining a fuzzy rule, and performing optimization in real time according to the fuzzy rule and by means of fuzzy reasoning, to obtain applicable PID parameters; S5, on the basis of the applicable PID parameters, using a genetic algorithm to control a servo motor of the surface density measurement system. Thus, the technical problems of parameter tuning being difficult and the robustness of closed-loop control being low are solved.

The invention relates to a winding tension control method and system. The method is applied to a winding tension control device, the device comprises a tension control part and a linear speed control part, and the method comprises the following steps: acquiring a real-time tension detection value and a tension set value of the tension control part, calculating a tension error and a tension error change rate, and performing fuzzy reasoning according to a fuzzy control algorithm to obtain a linear speed control value; pID output parameters are obtained through integral separation PID and used for indicating and controlling a first servo motor of a tension control part; and acquiring a real-time linear speed value and a linear speed set value of the linear speed control part, calculating a linear speed error and a linear speed error change rate, and inputting the linear speed error and the linear speed error change rate into the PID compensation model based on a fuzzy control algorithm to obtain a PID compensation parameter for indicating and controlling a second servo motor of the linear speed control part. According to the method, the tension and the linear speed are subjected to fuzzy control and PID control respectively, the control precision can be improved, and the stability, the response speed and the production efficiency of the winding process are effectively improved.

The invention provides a self-adaptive fuzzy control system for an inverter type arc welding power supply, and belongs to the field of welding process control and equipment automation, and the self-adaptive fuzzy control system specifically comprises the steps that a sensor module collects working numerical values corresponding to key parameters in the welding process in real time; the data processing unit converts the working values into a fuzzy set; the fuzzy controller executes fuzzy reasoning on the fuzzy set based on a preset fuzzy rule base so as to output a fuzzy control signal; the data processing unit performs defuzzification on the fuzzy control signal and converts the fuzzy control signal into an accurate control signal; the inverter power supply module receives the defuzzified accurate control signal to adjust the output power of the welding power supply; the feedback system monitors real-time parameters of welding seam quality in the welding process and transmits the real-time parameters to the self-adaptive module so as to realize closed-loop control; and the adaptive module dynamically adjusts the fuzzy rule base and the membership function according to the welding quality information provided by the feedback system. According to the processing scheme, the control precision and adaptability of the system are improved.

The invention discloses an air conditioner fresh air handling unit energy-saving optimization control method and system based on fuzzy control, and belongs to the technical field of air conditioner system control, and the method specifically comprises the steps that environmental parameters and unit state information are collected in real time through sensors arranged indoors and outdoors, and then the information is subjected to fuzzy processing and converted into fuzzy variables; establishing a fuzzy control rule base by combining actual data according to the characteristics and energy-saving requirements of the air conditioning system; a fuzzy reasoning method is utilized, a fuzzy control quantity is obtained through calculation according to the fuzzy variable and the rule base and then converted into an accurate control value through clearness processing, and the fresh air handling unit is controlled to operate; and the system monitors the environment change and the unit state, and the steps are periodically repeated to form closed-loop feedback control, so that energy-saving optimization is realized.

The invention discloses an intelligent circuit breaker system based on a fuzzy control theory. In the intelligent circuit breaker, current changes are monitored in real time through a fuzzy controller, an input variable is converted into an input fuzzy quantity for expression according to a membership function, fuzzy reasoning is carried out on the converted input fuzzy quantity according to a preset fuzzy rule base to obtain an output fuzzy quantity, and finally the output fuzzy quantity is defuzzified. And a final circuit breaker action instruction is obtained. The method has the beneficial effects that the safety stability and the response flexibility of the system are improved, and the protection strategy is dynamically adjusted, so that various complex power failure scenes can be more flexibly and efficiently handled.

The invention relates to the technical field of water pump fault diagnosis, discloses a condensate pump shafting vibration fault diagnosis method and device based on multi-parameter analysis and a medium, and solves the problems of sensor signal distortion and noise interference of a condensate pump under high-temperature and high-humidity working conditions through multi-source data fusion and environment adaptive compensation. Dynamic time-frequency domain feature extraction and an improved lightweight residual network are adopted, and efficient decoupling and accurate recognition of multi-fault concurrent features are achieved; the fuzzy inference rule base and the shafting fluctuation propagation model are combined, the fault positioning precision and the early fault detection rate are remarkably improved, scientific and reliable technical support is provided for diagnosis and maintenance of the shafting vibration fault of the condensate pump, and safe and stable operation of factory equipment is effectively guaranteed.

The present application relates to an output power adjustment method and apparatus, and a computer device and a storage medium. The method comprises: acquiring sampling data corresponding to sampling parameters, and determining a fuzzy set to which the sampling data belongs, wherein the sampling parameters are related to the maximum power point of a photovoltaic power generation system; on the basis of a fuzzy rule corresponding to the fuzzy set, performing fuzzy reasoning on the sampling data, and determining fuzzy control information matching the sampling data, wherein the fuzzy control information comprises a parameter value adjustment direction of working parameters and a step-size adjustment direction of parameter value change amounts; performing defuzzification processing on the step-size adjustment direction to obtain a step-size adjustment value; and outputting a control signal matching the parameter value adjustment direction and the step-size adjustment value, wherein the control signal is used for changing an output power of the photovoltaic power generation system by adjusting parameter values of the working parameters. By using the method, an output power adjustment effect can be improved.

The invention discloses an unhooking robot self-rectification regulation and control method and device combined with follow-up analysis, and relates to the technical field of machine regulation and control, and the method comprises the steps: obtaining the position data, angle data, force feedback data and environment data of an unhooking robot in the operation process, and constructing a robot operation dynamic model; the operation deviation of the unhooking robot is analyzed in real time, the position deviation and the force feedback deviation of the robot in different operation steps are determined, and a deviation data set is generated; performing fuzzification processing on the deviation data set according to a preset deviation correction rule, matching the preset deviation correction rule and performing fuzzy reasoning calculation to generate an initial deviation correction scheme; performing correction strategy optimization based on the initial correction scheme to generate an optimized correction scheme; and controlling the unhooking robot to perform self-correction regulation and control based on the optimized correction scheme. The technical problem that in the prior art, the regulation and control precision is low when unhooking robot self-deviation-correction regulation and control are carried out is solved, and the technical effect of improving the unhooking robot self-deviation-correction regulation and control precision is achieved.

The invention discloses a landscape lighting intelligent control system based on distributed optical sensor data fusion, and relates to the technical field of intelligent control, and the system comprises a data collection module which is used for obtaining environment data in real time; the processing fusion module is used for performing Bayesian fusion on the environmental data to obtain final environmental data estimation; the on-off control module is used for controlling light on-off of the landscape lighting system; the brightness adjusting module is used for adjusting the brightness of light of the landscape lighting system based on a fuzzy reasoning process according to the illumination intensity data and the personnel activity state; and the system evaluation module is used for evaluating the effect of the landscape intelligent adjustment control system according to the light on-off control response analysis of the landscape illumination system and in combination with the energy-saving effect realized through brightness adjustment. According to the method, the problems that the response environment change is not rapid, and system data processing and real-time control are not efficient are solved. Therefore, the purposes of saving energy, improving the lighting effect and enhancing the user experience are achieved.

The invention discloses a visual inspection method for the surface quality of a precise injection mold blank, relates to the field of visual inspection of quality inspection, and is used for solving the problems that the efficiency and quality of weld surface detection of the injection mold blank can be reduced due to image light spots or apertures, and the precision of a final detection result is reduced due to multi-angle image processing. The method comprises the following steps: randomly collecting an initialized image sample, obtaining an image surface light spot area ratio mean value and an image light spot appearance probability, determining an analysis angle of a further detection image, and collecting the similarity of a current angle image welding position and the current angle mold blank reflected light intensity; constructing a support vector machine model to perform angle positioning judgment on the injection mold base, screening out corresponding angle images according to an output result, making a group of fuzzy rules for the reserved angle images according to a surface defect detection coefficient and a surface smoothness coefficient to perform fuzzy reasoning, determining a surface quality result of the injection mold base, ensuring production consistency, and improving the quality of the injection mold base. And the mold blank surface quality detection difference is reduced.

The invention provides a terahertz inversion method and system based on fuzzy reasoning and a teaching optimization algorithm, and belongs to the field of terahertz inversion. The method comprises the following steps: acquiring a time domain signal by using a terahertz time domain spectrometer, obtaining a frequency domain signal through Fourier transform, and preprocessing the frequency domain signal; constructing a transmission propagation model of terahertz waves in the to-be-detected sample, and obtaining a model terahertz frequency domain signal based on the model; establishing a composite objective function based on the actually measured terahertz frequency domain signal and the model terahertz frequency domain signal; performing parameter inversion on the composite objective function based on fuzzy reasoning and a teaching optimization algorithm to obtain an optimal individual; according to the invention, the precise matching of the key features of the terahertz signal is realized by constructing the composite objective function fusing the frequency domain overall error, the peak frequency error and the peak amplitude error. By introducing a fuzzy reasoning mechanism, the problem of premature convergence or falling into local optimum of the algorithm is effectively avoided, the convergence speed is improved, and the inversion process is more efficient and stable.

The invention discloses an automatic temperature control heating method and system. The method comprises the steps that heating state data are obtained in real time and preprocessed; a deep neural network is adopted to predict the heating state data to obtain heating control parameters; predicting the historical heating state sequence data by adopting a long short-term memory network to obtain a future parameter change value; a reinforcement learning algorithm is adopted for reinforcement feedback, and an optimization control signal for the heating control parameters is obtained; performing fuzzy reasoning by adopting an adaptive neural fuzzy reasoning system, and adaptively adjusting rules and weights to obtain a fuzzy control signal; and integrating the parameters and the signals, and generating a heating control signal for the heating equipment through weighted fusion. According to the technical scheme, intelligent automatic control over the heating equipment is achieved, the monitoring capacity and the process management and control capacity of the heating equipment are greatly improved, the heating quality of products is improved, the rejection rate of the products is reduced, and the automation and intelligence level is improved.

The invention discloses a power grid voltage fluctuation suppression method and system based on dynamic voltage regulation, and the method comprises the steps: synchronously collecting a three-phase voltage signal through a sensor array, generating a multi-dimensional feature vector through Park transformation, and extracting a time-frequency joint feature through a mixed multi-scale decomposition algorithm, and inputting a model fusing fuzzy reasoning and a space-time diagram neural network, generating a cooperative adjustment strategy in combination with power grid topology and equipment constraints, and optimizing model parameters through a closed-loop correction mechanism driven by a dynamic forgetting factor. The system comprises a signal acquisition module, a characteristic decomposition module, an intelligent decision module and a closed loop correction module. According to the method, high-precision suppression of power grid voltage fluctuation is realized, the multi-node cooperative adjustment capability is improved, the method can be adaptive to power grid operation state changes, technical feasibility and economic rationality are considered, and an effective scheme is provided for modern power grid voltage stable control.

The invention discloses a method and a system for adjusting the internal environment of a mobile laboratory cabin, particularly relates to the technical field of environment control, and aims to solve the problem that the existing environment adjusting method adopts a fixed control target and cannot adapt to differentiated environment requirements in a multi-task scene of a mobile laboratory. The method comprises the following steps: acquiring a current task identifier of a mobile laboratory, identifying a corresponding experiment task execution stage, and dynamically generating an inter-parameter collaborative constraint relation matrix based on a fuzzy inference rule base; a regulation and control demand vector is generated by collecting a multi-dimensional environmental parameter measurement value and combining a pre-stored environmental parameter expected value; calculating a regulation and control conflict risk entropy value based on the matrix and the vector, and performing risk judgment; when the risk tolerance is exceeded, a roundabout regulation and control strategy is generated as an optimization guide parameter; finally, an environment control instruction is generated in a coordinated mode according to the optimized guiding parameters and sent to an executing mechanism; the dynamic adaptation of the environmental parameter regulation and control strategy and the experiment task is realized, and the precision and reliability of multi-parameter cooperative control are improved.

The invention discloses an industrial control network data monitoring system based on a cloud platform, and particularly relates to the technical field of industrial control network communication, comprising a data acquisition module for acquiring communication protocol data between an industrial control network and the cloud platform in real time to form a data set; the data analysis module analyzes the data set, calculates indexes such as network timeliness, throughput capacity and transmission efficiency, and generates a performance evaluation data set; the fuzzy reasoning module is used for deducing communication protocol performance based on the data set and dividing the communication protocol performance into high-class, medium-class and low-class; the feedback module sends corresponding signals according to protocol performance classification, and the adjusting module adjusts the size of a data monitoring window according to feedback signals; the method can be suitable for various industrial communication protocols, provides sufficient basis for whether the communication protocols need to be improved or not through a unified analysis mechanism, adapts to dynamic changes of an industrial network, achieves self-adaptive adjustment, and improves the stability and efficiency of the industrial control network.

The invention discloses an automatic control system based on full-automatic intelligent surface treatment equipment, which relates to the field of industrial production surface treatment and comprises a data acquisition module, a data analysis module, a process decision module, a control execution module, a fault monitoring module, a man-machine interaction module, a data storage and management module, a self-adaptive learning module, a remote monitoring and maintenance module and a green energy-saving module. An advanced sensor is combined with a micro-nano sensing technology, a deep reinforcement learning and transfer learning combined algorithm is applied, genetic optimization fuzzy reasoning is adopted, through magnetic suspension and compound control, faults are monitored based on deep learning, a VR technology is introduced to realize human-computer interaction, and a block chain technology is adopted to store data. And a meta-reinforcement learning algorithm is used to automatically adjust decision rules and parameters, and the remote collaborative robot carries out maintenance operation on the equipment. The system is accurate in data acquisition, scientific in analysis and decision, accurate in control, timely in fault diagnosis, convenient in man-machine interaction, efficient in data management, energy-saving and capable of comprehensively improving the production efficiency.

The invention discloses an electronic plant air conditioner control method for setting PID parameters based on a fuzzy algorithm. The method comprises the steps that a design scheme is preliminarily determined according to plant design indexes, and a schematic diagram of a control system is planned; establishing a mathematical model; carrying out PLC control program design and configuration programming; designing a fuzzy controller; fuzzy design of input and output variables; defining an input and output membership function; establishing a fuzzy control table and rules; designing a system fuzzy reasoning algorithm; setting defuzzification output; designing a Simulink system control block diagram, and establishing a real-time control working space; and the communication between the MATLAB and the PLC is realized. According to the method, the control experience of a person is summarized into a group of conditional statements described qualitatively by utilizing a fuzzy logic function of Matlab software, and the conditional statements are quantified by utilizing a fuzzy set theory, so that the controller can accept the experience of the person, simulate an operation strategy of the person and overcome uncertain factors of the person, and the system has higher reliability.

The invention discloses an anti-interference microwave electrically tunable filter regulation and control system and method, and particularly relates to the technical field of communication anti-interference, the interference suppression of microwave signals is realized through the following steps: firstly, based on a preset frequency range, monitoring the frequency spectrum change of the microwave signals in real time, and identifying the type and source of interference signals; when the microwave electrically tunable filter is initially tuned, a single-channel strategy is adopted, a preset suppression measure is executed according to interference frequency characteristics, and interference signals are effectively filtered out; finally, a feedback control mechanism is used, the performance of the filter is monitored in the adjusting process, the strategy is dynamically corrected, and the stability and high efficiency of the anti-interference performance are ensured; according to the method, the performance of the microwave filter is evaluated in real time through the environment change characteristic index and the environment complex characteristic index, the positive feedback or negative feedback signal is generated through the fuzzy reasoning model to serve as the basis for adjusting the work of the filter, and the response capacity to the interference environment is improved by adopting a feedback control mechanism; and the high-efficiency anti-interference performance is ensured.

The invention discloses a new energy battery load optimization system and method based on an adaptive algorithm, and relates to the technical field of battery control, and the system comprises a multi-source collection module, an analysis decision module and an optimization adjustment module. The multi-source acquisition module acquires driving state data, battery state data and road surface state data; the analysis decision-making module obtains the remaining capacity of the battery through a Kalman filtering algorithm in combination with the battery state data, reconstructs the road surface state data into a road surface three-dimensional model through a fusion three-dimensional reconstruction algorithm, and obtains the slope of the road surface through a sampling analysis method; the optimization adjustment module obtains the driving state data, the battery remaining capacity and the road slope, performs fuzzy reasoning and defuzzification according to a membership function and a fuzzy rule to determine the optimal battery output power, comprehensively evaluates the fuzzy reasoning benefit based on a reward function, optimizes the membership function parameter through reinforcement learning, and obtains the optimal battery output power; and intelligent optimization matching of the battery output power, the driving state and the driving road condition is realized.

The invention belongs to the technical field of automobile active safety, and provides a liquid tank type vehicle rollover risk early warning method and device, a medium and equipment. According to the technical scheme, the roll angle and the vehicle speed of a liquid tank type vehicle, position information of the vehicle and the liquid level height in a liquid tank are obtained; determining the total weight of the liquid tank vehicle based on the roll angle of the liquid tank vehicle and the liquid level height in the liquid tank; determining the current turning radius of the vehicle based on the position information of the liquid tank type vehicle; the total weight, the vehicle speed, the roll angle and the current turning radius of the vehicle serve as input variables, the current rollover risk serves as an output variable, the input variables and the output variable are fuzzified, and the current rollover risk of the vehicle is predicted after fuzzy reasoning; determining the current rollover risk level of the vehicle according to the current rollover risk value of the vehicle; and formulating a corresponding rollover early warning scheme based on the rollover risk level of the vehicle, and performing graded early warning. And through a refined grading early warning method, the rollover risk is effectively avoided.

The invention discloses an unmanned aerial vehicle adaptive path planning system and method based on information fusion and collaboration, and the system comprises an environment sensing module which is used for collecting the landform, obstacle and threat information of a mining area in real time, and constructing a three-dimensional digital elevation model; the global path planning module is used for generating an initial global path by adopting an improved Theta * algorithm, and optimizing a path turning point through a father node relink; the dynamic obstacle avoidance module is used for generating a local obstacle avoidance path by combining real-time obstacle information with a speed sampling range constrained by an unmanned aerial vehicle kinetic model based on an improved DWA algorithm; the autonomous decision module is used for dynamically adjusting the man-machine authority level based on fuzzy reasoning and switching a planning mode; the execution module is used for realizing trajectory tracking through motor rotating speed control and outputting a rotating speed change curve of each motor and unmanned aerial vehicle state parameters; and the data fusion module is used for synchronizing the remote sensing data and the path planning result to the cloud and supporting multi-machine collaboration and historical data backtracking.

The invention discloses a low-altitude economic unmanned aerial vehicle data processing method and system based on a large model, and relates to the technical field of low-altitude economic data processing, and the method comprises the steps: extracting low-altitude multi-target decision features of a low-altitude semantic decision map, carrying out the fuzzy reasoning of the low-altitude multi-target decision features, and generating an anti-interference control instruction set; based on the anti-interference control instruction set, fault logs and task execution data during operation of the unmanned aerial vehicle are collected, and an evolution strategy library is generated through association rule mining; and performing causal analysis according to the evolution strategy library, generating a cluster coordination rule upgrade package, performing dynamic verification on the cluster coordination rule upgrade package, and outputting a self-healing strategy. According to the method, the reliability and the safety of task execution of the unmanned aerial vehicle are improved by constructing the semantic association large model and generating the anti-interference control instruction set.

The invention relates to the technical field of robot health physical examination, and discloses a robot noninvasive health physical examination and monitoring platform. A health data acquisition module of the system integrates physiological parameters acquired by a multi-modal sensor to generate a standardized health data set; the physical sign load analysis module calculates parameter fluctuation difference to generate a physical sign load distribution state value; the physical examination parameter optimization module screens optimal physical examination parameters to generate an operation parameter set; the environment adaptability adjusting module generates an environment response physical examination scheme in combination with the environment and body position data; a health threshold value dynamic setting module adjusts an abnormal judgment threshold value to generate a grading early warning threshold value; the health trend prediction module generates a health risk prediction value through fuzzy reasoning; and the physical examination feedback control module adjusts operation parameters and scanning frequency to generate a self-adaptive regulation and control strategy. According to the platform, the accuracy and the intelligent level of health physical examination are improved, and the non-invasive health monitoring requirement of the user can be better met.