122 results about "Fuzzy logic algorithm" patented technology

The invention discloses a safety monitoring system of a liquid cooling over-charging pile, and relates to the technical field of over-charging pile monitoring, the system comprises a data acquisition module, a data processing and analysis module, a dynamic model construction module, a temperature prediction module and a safety early warning module; according to the method, the dynamic model is constructed through the long short-term memory network LSTM, the complex nonlinear relation and the time sequence dependence of the multi-dimensional data are mined by using the gating mechanism of the dynamic model, the accurate characterization of the operation state of the liquid cooling over-charging pile is realized, the actual operation state of the equipment can be accurately described, the temperature data time sequence modeling is performed through the LSTM, and the accuracy of the temperature data time sequence modeling is improved. Parameters such as multi-source temperature and cooling liquid flow are fused, real-time prediction of the temperature change trend is achieved, the defect that a traditional algorithm is insufficient in temperature time sequence dependence capture is overcome, temperature abnormity can be recognized in advance, a safety threshold value is dynamically adjusted through a fuzzy logic algorithm, and self-adaptive threshold value adjustment is achieved in combination with parameters such as charging power. The problem that a traditional fixed threshold value is poor in adaptability is solved, and the early warning accuracy is improved.

The invention relates to the technical field of battery management, and discloses a battery management system with an active monitoring function, which comprises a processor, a battery state monitoring module, an active equalization control module, a performance evaluation module and a fault early warning module. The battery state monitoring module collects various parameters of a battery at different stages, and generates an abnormal signal by using a dynamic threshold algorithm. The active equalization control module adjusts an equalization strategy based on a fuzzy logic algorithm, and a fuzzy rule base can be updated. And the performance evaluation module tracks and evaluates an equalization process, analyzes equalization efficiency and quality, and optimizes an equalization strategy accordingly. The fault early warning module displays abnormal signals in a grading mode and carries out early warning, and the processor is further connected with the aging feature extraction module and the thermal runaway prediction module to achieve battery life prediction and thermal runaway early warning. According to the invention, comprehensive monitoring, efficient equalization control, accurate performance evaluation and reliable fault early warning of the battery are realized, the safety of the battery is improved, and the service life of the battery is prolonged.

The invention discloses a transformer fault detection device based on a fuzzy logic algorithm, and the device comprises a data collection module which obtains the operation original data of a transformer in real time through combining the dissolved gas in oil with the temperature, vibration, current and voltage; the data preprocessing module is used for carrying out missing value processing, noise removal and abnormal value detection and processing on the original data; the feature extraction module is used for realizing dynamic feature selection based on data analysis provided by the data preprocessing module; the fault identification module is used for carrying out abnormal waveform judgment on current, voltage, temperature and vibration parameters through a threshold calculation unit and carrying out threshold adjustment based on an optimization algorithm; and the fault detection module triggers the alarm unit or maintains a normal working state according to an identification result of the fault identification module. According to the invention, through monitoring analysis and timely alarm notification, accurate and efficient monitoring of the transformer fault is realized, and stable operation and long-term reliability of the transformer are ensured.

The invention provides an electric energy quality compensation method and system based on power electronics, and relates to the technical field of power grid dispatching, and the method comprises the steps: collecting the voltage, current, harmonic content, reactive power, three-phase unbalance degree and other real-time multi-mode data of a power grid, and extracting key frequency spectrum features through wavelet transform denoising and windowing interpolation Fourier transform; and constructing a power quality feature vector. A self-adaptive fuzzy logic algorithm and power grid operation parameters are combined, a threshold value of a power quality index is dynamically adjusted, a multi-stage optimization strategy is divided based on the number of standard-exceeding indexes and importance analysis, and a dynamic compensation priority and a compensation strategy are generated. An improved particle swarm optimization algorithm is introduced, an inertia weight is dynamically adjusted, a constraint penalty function is introduced, output parameters of a power electronic compensation device are optimized, and effective compensation of power quality is realized by taking minimization of total active loss as a target.

The invention relates to the technical field of motor control, and discloses a permanent magnet synchronous motor control system and method for an electric drive hydraulic source, and the system comprises a data collection module which is used for collecting the state parameters of the electric drive hydraulic source and the operation data of a permanent magnet synchronous motor in real time through multiple sensors; the sensor redundancy and fault-tolerant module is used for performing parallel sampling through hardware dual-redundancy configuration and dual sensors; the self-adaptive fuzzy logic control module is used for dynamically adjusting a rotating speed instruction and a torque instruction of the permanent magnet synchronous motor based on the collected state parameters; a data driving optimization module; and a digital twinning prediction maintenance module. A self-adaptive fuzzy logic control module is used for dynamically generating a motor rotating speed and torque instruction based on parameters such as real-time pressure, flow deviation and change rate, a fuzzy logic algorithm is used for processing a nonlinear time-varying load and quick response, a self-adaptive fuzzy logic control rule weight and a power conversion circuit parameter are optimized through a reinforcement learning algorithm, and the control precision is improved. And dynamic self-optimization of permanent magnet synchronous motor control is realized.

The invention relates to the technical field of data processing, in particular to a load awareness and data dynamic layout method and system based on deep learning, and the method comprises the steps: collecting hardware load indexes of all nodes in a distributed cluster in real time, and carrying out the preprocessing of the hardware load indexes; based on the preprocessed hardware load index, calculating a comprehensive load score of each node through a weighted fuzzy logic algorithm; dividing load intervals according to the comprehensive load score and matching a predefined strategy combination; and dynamically adjusting a compression algorithm, a compression ratio and a fragment merging parameter based on the current service flow peak-valley state of the target data block and a matched predefined strategy combination, the service flow peak-valley state being determined through a request processing rate and response time. The objective of the invention is to realize dynamic and fine-grained load sensing and data layout.

The invention relates to the technical field of energy storage battery equalization adjustment, in particular to an energy storage power station intelligent operation scheduling management system which comprises a battery parameter estimation unit, a dynamic equalization threshold generation unit, an equalization circuit control unit, a data fusion prediction unit and a scheduling strategy generation unit. The battery parameter estimation unit calculates key parameters of a single battery, the dynamic equalization threshold generation unit generates a dynamic equalization threshold through a fuzzy logic algorithm in combination with a battery aging stage, multiple parameters and temperature factors, and the equalization circuit control unit determines an energy transfer strategy according to a model prediction control algorithm. A switch tube is controlled through an adaptive phase shift angle adjustment technology, a fault diagnosis and fault-tolerant control mechanism is provided, the data fusion prediction unit predicts key indexes of the energy storage power station, the scheduling strategy generation unit formulates an optimal operation scheduling strategy, and the ability of the energy storage power station to participate in power grid auxiliary service is improved.

The invention discloses a deep concealed ore deposit detection method based on multi-source geological data fusion, and the method comprises the following steps: 1, carrying out a dynamic multi-source data fusion algorithm: introducing a fuzzy logic algorithm, dynamically adjusting the data fusion weight according to the feature significance of different geological units, and developing a cross-scale feature pyramid network, hierarchical feature extraction and fusion are carried out on different detection depth data; 2, optimizing an intelligent model based on prior knowledge; step 3, constructing a real-time interactive three-dimensional modeling system; 4, establishing an efficient multi-means verification system; and step 5, intelligent integration of the exploration process. According to the method, multi-source data are fused through fuzzy logic dynamic weight adjustment, the mineralization mode and the reinforcement learning optimization model are combined, three-dimensional modeling and multi-means combined verification are updated in real time, and the effects of accurate fusion of the multi-source data, dynamic model adjustment and optimization, efficient and accurate modeling, short verification period and low cost are achieved; and the detection efficiency and scale of the deep concealed ore deposit are improved.

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 discloses an electric vehicle charging and discharging method, device and equipment based on fuzzy logic reasoning and price excitation demand response and a medium. The method comprises the steps that modeling is conducted on loss of an energy storage battery of an electric vehicle; in combination with a membership function and a fuzzy rule, based on a Mamdani fuzzy logic algorithm, according to the charging time anxiety degree, the charging cost reduction degree and the residual SOC of the user to which the electric vehicle belongs, determining the intention of the user to participate in charging and discharging scheduling; and in consideration of the intention that the user participates in charge and discharge scheduling, combining a price demand response mode and an excitation demand response mode of the electric vehicle to optimize the operation state of a power grid which is used for charging the electric vehicle. The invention belongs to the field of electric vehicle charging and discharging. According to the method, a corresponding charging and discharging strategy scheme can be accurately formulated according to the requirements of an electric vehicle user, the peak-valley difference of a power grid is reduced while the charging cost and the charging time comprehensive cost of the user are reduced, and the load fluctuation is reduced.

The invention relates to a high-precision self-adaptive temperature regulation and control system for a medical instrument. The system comprises a feature analysis module which collects a multi-dimensional temperature information set with a timestamp and deduces a temperature space-time change trend; and the temperature prediction module generates an optimized temperature distribution diagram by using a Kalman filtering algorithm based on the trend. The temperature deviation value of each area is compared with a preset threshold value, if the deviation exceeds the standard, a dynamic parameter configuration table is generated, and an adaptive power regulation scheme is formulated. And the regulation and control execution module regulates and controls the equipment power according to the regulation scheme to obtain an updated temperature monitoring data set. By analyzing the relevance between the data set and the multi-dimensional temperature information set, a stability optimization parameter is generated, so that a control rule of a fuzzy logic algorithm is adjusted, and a self-adaptive temperature regulation and control scheme is formed. According to the system, accurate prediction, regulation and control of the temperature are realized. The adaptive capacity of the system is improved, and the stability and reliability of temperature regulation and control can be improved by dynamically optimizing the control rule.

The invention discloses a three-dimensional fluorescence dynamic monitoring and automatic processing system for water quality. The system comprises a water sample collection module, a three-dimensional fluorescence spectrum detection module, a pollution fingerprint database, a spectral analysis unit, a diffusion path simulation module based on a distributed sensor network and an automatic control execution module. The diffusion path simulation module is used for acquiring spatio-temporal data through a water quality spectrum sensor, a hydrological sensor and a meteorological sensor, and dynamically predicting a pollutant migration track by combining a water body flow field model constructed by a finite volume method and a first-order dynamic fluorescence attenuation model; the automatic control execution module is based on the pollution recognition result and track prediction, emergency operations such as gate opening and closing and medicament adding are linked through a fuzzy logic algorithm, and full-process automation from monitoring to disposal is achieved. The system completes pollution identification within 10 minutes through a spectral angle mapping algorithm, and the defects that a traditional method lags in response and lacks dynamic prediction and automatic disposal are overcome.

The invention relates to the technical field of automatic driving, in particular to an automatic driving system based on intelligent traffic, which comprises a multi-source heterogeneous sensing fusion unit, a group collaborative decision-making unit and a dynamic control optimization unit, the group collaborative decision-making unit carries out space-time alignment on multi-source heterogeneous data, constructs and updates a global environment perception knowledge graph, carries out modeling on vehicle trajectories based on a Monte Carlo tree search framework in combination with deep reinforcement learning and risk perception, and trains a deep Q network model by adopting federated learning and adversarial training. A traffic flow distribution scheme is generated by applying multi-agent deep reinforcement learning and a game theory, storage data is proved by using homomorphic encryption and zero knowledge, a dynamic control optimization unit predicts, controls and plans an acceleration curve through a nonlinear model, and a transverse path tracking error is controlled by fusing sliding mode control and a fuzzy logic algorithm. And the safety of automatic driving is improved.

The invention relates to an efficient fused salt heat storage capacity regulation and control method. According to the method, molten salt heat storage system state parameters are monitored in real time, a high-precision temperature sensor is used for obtaining temperature data of a high-temperature molten salt tank and a low-temperature molten salt tank, and a non-intrusive flow sensor is used for collecting the real-time flow value of a molten salt pump. And constructing a user load demand prediction model based on deep learning, and calculating the target heat storage capacity in combination with a power grid dispatching instruction and a real-time electricity price signal. The rotating speed of the molten salt pump is adjusted through a self-adaptive PID controller, and the opening degree of a heat exchanger valve is adjusted through an intelligent valve adjusting system. And when the temperature of the fused salt is lower than a set threshold value, the silicon carbide ceramic heating electric heating device is started, and the finned tube heat exchanger is used for recovering waste heat. And based on the real-time heat storage capacity deviation value, PID control parameters are corrected by applying an improved fuzzy logic algorithm, so that the heat storage capacity of the fused salt is accurately regulated and controlled, and the efficiency and the stability of the system are improved.

The invention discloses a vacuum arc-extinguishing chamber contact surface condition evaluation method and system, and belongs to the technical field of vacuum circuit breakers. According to the method, short-time Fourier transform noise reduction is improved by adopting an adaptive window length, signals are enhanced in combination with dual-tree complex wavelet transform, and data are dynamically normalized by improving K-means clustering; according to the constructed lightweight CRNN model, residual connection and an SE module are introduced into a convolution module, and a bidirectional quasi-recurrent neural network is adopted by a circulation module to capture time sequence features; the mixed loss function is combined with an AdamW optimizer, and the convergence stability is improved through L2 regularization; and dynamically evaluating the ablation degree of the contact based on a fuzzy logic algorithm. According to the method, through time-frequency feature fusion and lightweight network design, high-precision classification of direct-current pre-breakdown waveforms is realized, the problems of degradation, over-fitting and slow convergence of a traditional model are solved, and an intelligent solution is provided for vacuum arc-extinguishing chamber insulation performance evaluation.

The invention provides a clustering-based QoS mapping method and device for a 5G-TSN, and relates to the technical field of 5G time-sensitive network converged communication. The method comprises the following steps: a TSN flow QoS acquisition module sends an acquired TSN flow QoS index to a TSN flow aggregation module; the TSN flow aggregation module performs flow aggregation processing on the TSN flow QoS index through an enhanced density space clustering algorithm to obtain an aggregated flow; the TSN flow aggregation module sends the aggregation flow to a 5QI mapping module; and the 5QI mapping module performs dynamic QoS mapping processing on the aggregation flow through a fuzzy logic algorithm to obtain a mapping result. According to the invention, the TSN service flow can be accurately matched with the 5G QoS parameter, and the problem that the traditional mapping method is rigid and poor in dynamic adaptability can be solved.

The invention relates to the technical field of forklift control, in particular to an efficient and energy-saving off-road forklift electric drive system and a control method. Multi-source data is collected through a multi-source sensor, and data preprocessing is carried out; outputting a control decision instruction according to a priority decision rule based on the preprocessed data in combination with a fuzzy logic algorithm; in combination with the working condition characteristics and the real-time data, dynamic parameter adjustment is performed on the rotating speed, the torque and the current parameters of the motor by utilizing a reinforcement learning framework, and a PWM duty ratio control instruction is generated; on the basis of a PWM duty ratio control instruction and a hydraulic pressure signal fed back by a hydraulic system, the same-frequency anti-phase compensation torque is calculated, a signal is injected to suppress fluctuation, and torque distribution and hydraulic flow are adjusted through a ramp function; and establishing a hierarchical health early warning mechanism according to the historical data, judging the current early warning level, and outputting a maintenance strategy instruction according to the early warning level. According to the scheme, equipment health management and stability are enhanced through reinforcement learning dynamic optimization and graded early warning.

The invention relates to the technical field of equipment management, in particular to an intelligent equipment linkage configuration system and method, and the system comprises an equipment management module, a linkage rule configuration module, a rule engine module, an energy-saving strategy binding module and an authority safety module. The equipment management module uniformly registers and manages the Internet of Things equipment; the linkage rule configuration module provides a visual interface and analyzes a natural language condition generation rule in combination with a fuzzy logic algorithm; the rule engine module monitors equipment state triggering linkage in real time; the energy-saving strategy binding module uses a dynamic algorithm to optimize a power utilization threshold value; the authority security module realizes dynamic authorization through hierarchical authority and a two-dimensional code; the diagnosis self-healing module locates and repairs a fault based on a multi-modal algorithm; and the energy consumption analysis module adopts a normalized model to quantify the energy-saving effect. According to the invention, the problems of complex configuration and poor compatibility of a traditional system are solved, intelligent and secure linkage of equipment is realized through multi-module cooperation and algorithm innovation, and the reliability and resource utilization efficiency of the system are improved.

The invention discloses an intelligent control method for power distribution and efficiency optimization of an energy storage power station, and the method comprises the steps: obtaining real-time operation data and historical load records of all PCS units in the energy storage power station, and obtaining a current load change trend through data fusion processing; according to the current load change trend, a fuzzy logic algorithm is adopted to analyze the performance state and the output proportion of the PCS unit, and the initial load distribution weight of each unit is determined; aiming at the optimized distribution scheme, generating a self-adaptive priority ranking rule by adopting a genetic algorithm, judging whether the rule covers real-time state change or not, and obtaining a dynamic ranking list; extracting a high-priority PCS unit from the dynamic sorting list, obtaining an equipment loss index of the high-priority PCS unit, and determining an output increase proportion of a low-loss unit through index comparison processing; and according to the output increase proportion, fusing the resource utilization rate data, and if the utilization rate is lower than a preset threshold value, redistributing idle unit power to obtain overall efficiency improvement configuration.

The present application belongs to the technical field of vehicle mass online estimation, in particular to a vehicle mass online estimation method based on fuzzy logic and weighted least square method, comprising the following steps: 1) obtaining longitudinal acceleration sensor measurement value signal, longitudinal speed signal, engine output torque signal, gear signal, throttle opening signal, brake pedal signal and steering wheel angle signal from vehicle CAN bus; 2) correcting the measurement value of longitudinal acceleration sensor; 3) calculating equivalent driving force and equivalent acceleration; 4) calculating working condition fitness degree by using fuzzy logic algorithm; 5) estimating vehicle mass by using weighted least square method; and 6) suspending and starting vehicle mass estimation. The structure is reasonable, and the accuracy and working condition adaptability of vehicle mass online estimation can be improved during use.

The invention relates to the technical field of load segmented transfer, in particular to a 10kV distribution line load segmented transfer method and device based on an electric energy meter and a storage medium. Comprising the steps that S1, electrical parameters of key nodes of a 10kV distribution line are monitored in real time based on an intelligent electric energy meter, and the electrical parameters at least comprise voltage, current and a power factor; s2, mapping the monitored electrical parameters to a predefined fuzzy set by using a fuzzy logic algorithm, and reasoning and quantitatively evaluating the overload degree of the equipment based on a fuzzy rule; selecting a to-be-transferred load path based on a Dijkstra algorithm; and after it is confirmed that the load verification of the to-be-transferred path is passed, carrying out load transfer. According to the method, the electrical parameters on the key nodes are monitored in real time, overload evaluation is carried out in combination with the fuzzy logic algorithm and the optimized defuzzification algorithm, and the overload condition can be recognized more accurately even under the condition that current and power factors fluctuate sharply due to frequent starting and stopping of large industrial equipment.

The invention relates to the technical field of grain drying equipment, in particular to an electric rapid grain discharging device of a grain dryer and an intelligent control method, and the electric rapid grain discharging device comprises a grain storage bin, a grain distributing assembly, a conveying assembly and an intelligent regulation and control unit. The grain distributing assembly achieves flexible grain discharging through a rotating disc and an adjusting ring, the conveying assembly adopts a conveying belt with protruding strips to prevent grain from sliding, and the intelligent regulation and control unit combines multi-sensor data and a fuzzy logic algorithm to dynamically adjust the grain discharging speed and flow. Through structure optimization and intelligent control, the grain discharging efficiency, stability and adaptability are remarkably improved, the problems of unstable flow and low intelligent degree in the prior art are solved, and reliable technical support is provided for modernized agricultural production.

The invention discloses a CO2 well flooding oil casing pressure intelligent control and anti-freezing and anti-blocking system, and relates to the technical field of oil and gas field development engineering.The CO2 well flooding oil casing pressure intelligent control and anti-freezing and anti-blocking system comprises the steps that a controller starts a self-checking program, loads a preset target pressure value, a preset temperature early warning threshold value and a preset ice blocking critical temperature parameter, completes initial configuration, and sends the initial configuration to the controller; zero calibration and precision verification are carried out on the front-end pressure sensor, the rear-end pressure sensor and the temperature sensor, front-end pressure data, rear-end pressure data and temperature data are collected, data filtering and abnormal value removing processing are carried out, and the real-time temperature data and CO2 physical property parameters are obtained. According to the invention, hardware self-inspection and sensor precision calibration are realized through the controller initialization module, the data acquisition module completes multi-source data synchronous acquisition and filtering processing, and the dynamic risk assessment module based on an improved Span-Wagner equation and a fuzzy logic algorithm can accurately calculate a CO2 phase change critical point and output a quantitative risk index. The problems of energy waste and untimely freezing and blocking prevention caused by a traditional fixed threshold value method are solved.

The invention discloses an intelligent monitoring method and system for the microenvironment of a power distribution cabinet, and the method comprises the steps: collecting the multi-parameter data of the microenvironment of the power distribution cabinet in real time through a plurality of sensors disposed in the power distribution cabinet, and enabling the multi-parameter data to at least comprise temperature, humidity, voltage and current; transmitting the multi-parameter data to a processing platform through an internet of things network; on the processing platform, performing data fusion processing on the multi-parameter data to obtain comprehensive environment parameters; based on the comprehensive environmental parameters, performing environmental state evaluation by using a fuzzy logic algorithm, and outputting a security level; based on the time sequence data of the comprehensive environmental parameters, using a time sequence prediction model to carry out fault prediction, and outputting a parameter change trend; and performing anomaly detection based on at least one of the comprehensive environmental parameters, the security level and the parameter change trend, and sending out an alarm signal when an anomaly is detected. The invention aims to realize real-time accurate monitoring of the microenvironment of the power distribution cabinet, early warn various potential risks in time, avoid the situations of terminal strip corrosion, poor contact of a secondary line, short circuit, lightning breakdown, false alarm of a switching signal, false operation of a primary switch and the like caused by the microenvironment problem, improve the power supply reliability and guarantee the safety of a power grid.

The invention provides a grounding nuclear explosion damage range calculation method. The method comprises the following steps: decomposing nuclear explosion energy into four main components: shock wave energy flow, thermal radiation energy flow, seismic wave energy flow and electromagnetic pulse energy flow; a heterogeneous medium model containing an atmosphere model and a geologic model is constructed, terrain and geologic data are fused to generate a joint map, and spatial correlation of the terrain and the stratum is described. Shock wave and thermal radiation propagation is calculated through a nonlinear wave equation and a dynamic atmospheric transmittance model, and the influence of seismic waves and electromagnetic pulses is evaluated. And establishing a damage probability mapping relation by adopting a fuzzy logic algorithm, drawing a nuclear explosion damage range graph, and performing dynamic updating through a Bayesian learning module. According to the invention, through multi-physics field coupling analysis and a dynamic learning mechanism, the accuracy of nuclear explosion damage assessment is improved.

The invention relates to a method and system for adjusting the rotating speed of a brushless electronic fan, and relates to the field of new energy automobile heat management. Collecting vehicle condition interference parameters; forming target temperatures corresponding to the three-electricity system based on the three-electricity system dynamic heat production parameters, the vehicle condition interference parameters and the scene rules; current temperatures corresponding to the three electric systems are obtained in real time; determining a temperature deviation duration based on the current temperature and the target temperature, and forming a deviation temperature value and a deviation change rate; determining an adjustment coefficient according to the deviation temperature value, the deviation change rate, the temperature deviation duration and a fuzzy logic algorithm; obtaining the current fan speed, and determining a target fan speed based on the adjustment coefficient and the current fan speed; forming a fan adjusting scheme; and executing fan parameter adjustment operation according to the fan adjustment scheme. The method has the effects of improving the accuracy and adaptability of adjusting the rotating speed of the fan, optimizing the heat dissipation capability of the three-electric system and prolonging the service life of equipment.

The invention discloses a safe power utilization decision generation method and system, and the method comprises the steps: collecting operation parameters and environment parameters of power utilization equipment in real time through a sensor disposed in a power utilization network, integrating power grid dispatching data, user historical power utilization behavior data and meteorological data, and constructing a multi-source heterogeneous data pool; performing edge calculation processing on the data, including data cleaning, normalization and data fusion based on a dynamic space-time weight distribution algorithm, generating a dynamic power utilization state matrix, inputting the dynamic power utilization state matrix to a deep learning model, identifying an abnormal mode in equipment operation in real time, and quantifying a risk level based on a fuzzy logic algorithm, and based on the risk assessment result, constructing a multi-objective optimization model with safety, energy consumption cost and power supply continuity as optimization objectives, executing the optimized decision scheme through an Internet of Things controller, and monitoring the execution effect in real time.

The invention relates to the technical field of logistics data management and industrial cloud platforms, and discloses a logistics data management method and system based on a cloud platform. The method comprises the following steps: acquiring multi-source data and preprocessing the multi-source data to obtain a cleaned data set; processing the data by adopting a fuzzy logic algorithm to obtain an uncertainty quantitative index; when the index exceeds a threshold value, integrating the road condition and demand data based on a Bayesian network, and generating a prediction deviation correction value; a transportation plan is updated in real time, and an optimized route is obtained; a dynamic visualization report is generated after the decision accuracy is further verified; and finally, an adjustment instruction is output through closed-loop control. The method is especially suitable for transportation optimization of cold-chain logistics in complex scenes such as haze, and can effectively improve the accuracy and real-time performance of a transportation decision and the cargo fresh-keeping quality.

The invention discloses an agricultural frequency conversion monitoring method and system fusing LSTM-GRU time sequence prediction and fuzzy logic, relates to the technical field of agricultural intelligent monitoring, and solves the problems of energy waste and easy missing of data at a critical moment caused by adoption of a fixed data acquisition frequency during agricultural precise monitoring in the prior art. The method comprises the following steps: collecting farmland environment data, predicting future farmland environment data by using an LSTM-GRU model, evaluating an environment change state based on a prediction result sampling fuzzy logic algorithm, if an evaluation result shows that the environment is stable, reducing the data collection frequency, otherwise, improving the data collection frequency, and realizing frequency conversion control of data collection. Therefore, the monitoring precision and the energy consumption overhead can be dynamically balanced under different environmental conditions.

The invention belongs to the technical field of airport boundary security and protection monitoring, and discloses an airport boundary grading early warning linkage method and system based on fuzzy logic reasoning. According to the method, an airport boundary is subjected to panoramic scanning to generate an infrared and visible light panoramic image, intrusion target detection and identification are performed on a boundary defense area in the infrared and visible light panoramic image through a multi-mode target detection algorithm based on deep learning, intrusion target early warning grade division is performed through a fuzzy logic algorithm, and an intrusion target early warning result is obtained. And carrying out early warning level priority judgment on the intrusion target, carrying out track labeling on a plurality of coordinate points closest to the intrusion target in the surrounding panoramic base map by using an OPENCV algorithm library, displaying the motion track of the current intrusion target, and judging the intention of the intrusion target. According to the invention, different airport environments are adapted through the rule base self-optimization capability, the manual patrol load is reduced while the high reliability is ensured, and the intelligent level and the safety prevention and control efficiency of the enclosure security and protection system are enhanced.