158 results about "Fuzzy logic algorithm" patented technology

The invention discloses a numerical control machine tool operation control system and a double-station sawing and drilling integrated machine tool, and relates to the technical field of numerical control machine tool operation. A multi-axis linkage state, a gas cylinder stress state, vibration frequency, cutting force and deformation data are acquired in real time through a data acquisition module; the dynamic modeling module constructs a digital twinborn model by using finite element analysis and real-time sensing data, and accurately simulates stress dynamic change and deformation trend; the intelligent error compensation module dynamically evaluates the multi-axis linkage precision by analyzing the machining data, generates an error compensation instruction and optimizes the operation parameters of the machine tool; the closed-loop feedback control module adjusts the cutting speed, the feeding depth and the clamping force in real time through a fuzzy logic algorithm, a closed-loop adjusting mechanism is formed, the machining precision and stability are greatly improved, the machining error and the workpiece surface damage risk are reduced, and the machining quality and efficiency of complex workpieces are effectively improved.

The embodiment of the invention provides an intelligent illumination automatic adjustment method and device based on illumination intensity and personnel detection, and reliability evaluation and abnormity correction are carried out on data by synchronously collecting environment illumination intensity and personnel activity data. An illumination activity association model is innovatively constructed, and feature fusion is realized by using a fuzzy logic algorithm. The system establishes a complete evaluation index system including illumination uniformity, a color rendering index and a glare value, and performs constraint optimization based on an illumination scene adaptation model. Through introducing a dynamic compensation mechanism of a personnel detection probability value and a time period priority factor, accurate illumination adjustment control is realized. The method breaks through the limitation of dimming in a traditional fixed mode, and provides a comprehensive technical solution for an intelligent lighting system.

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 discloses a user information management method and system based on application information, and relates to the technical field of user information management, and the method comprises the steps: obtaining user information through an application program, generating a unique distributed identity label, and carrying out the multi-stage verification of the user identity through an edge calculation node and a distributed node network; user behaviors are analyzed in real time through a fuzzy logic algorithm, the priority of user data is adjusted according to an analysis result, and the data access permission is dynamically allocated. According to the method, the unique distributed identity label is generated, multi-level verification is carried out in combination with the edge computing node and the distributed node network, the identity verification efficiency and safety are remarkably improved, user behaviors are analyzed in real time through the fuzzy logic algorithm, the data priority is dynamically adjusted, the access authority is allocated, and the user experience is improved. The efficient management of resources is realized, and the flexibility of authority management and the user experience are improved.

The invention provides a parallel water pump intelligent algorithm optimization combination and control method, which comprises the following steps of: configuring a sensor and a calculation module on an edge node of each water pump, acquiring and preprocessing operation data of each water pump in real time, and generating a unified real-time feature vector and a time sequence feature; dynamically calculating the service life state of the water pump according to the real-time feature vector, and quantifying the residual service life; designing a dynamic start-stop scheme based on a fuzzy logic algorithm according to the service life state, and balancing load requirements and water pump service life distribution; and based on federal learning, the residual life of the water pump is optimized in a global perspective, and an operation strategy based on a start-stop scheme is dynamically optimized and adjusted. A complete solution is formed, and a brand new technical path is provided for efficient and reliable operation of the parallel water pumps.

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 an intelligent auxiliary system and method for regular inspection of a pressure vessel, and aims to improve the intelligence, efficiency and accuracy of inspection of the pressure vessel. The system comprises a data acquisition module, a data processing module, a risk assessment module, an inspection plan generation module, a report generation module, a remote monitoring module and a historical data management module. Operation parameters of the pressure vessel are collected in real time through the sensor, the data processing module carries out elimination and trend analysis on abnormal data, and the risk assessment module assesses the operation state of the pressure vessel through machine learning and a fuzzy logic algorithm. The system dynamically generates an inspection plan according to an evaluation result and adjusts an inspection period through Bayesian updating, and a generated inspection report has a digital signature to ensure safety. The historical data management module stores and analyzes long-term data and supports trend prediction of equipment health conditions, and the remote monitoring module provides real-time state checking and early warning.

The invention discloses a charging pile and system based on power regulation and control. The charging pile comprises a state information acquisition module, a data preprocessing and feature extraction module, a dynamic change mode analysis module, a dynamic change prediction module and a power regulation and control optimization module. The state information acquisition module is used for acquiring each piece of state information of the battery in real time when the charging pile charges the vehicle battery; and the data preprocessing and feature extraction module is used for preprocessing the original data in the data set. By acquiring battery state information in real time, extracting SOC nonlinear change amplitude and thermal management demand change rate, combining data analysis and machine learning prediction, accurately capturing battery charging dynamic change, and dynamically adjusting power regulation frequency by adopting a fuzzy logic algorithm, efficient charging power distribution is realized, the charging efficiency is remarkably improved, and the charging efficiency is improved. Electric energy waste and safety risks are reduced, the service life of the battery is prolonged, the resource utilization rate of the charging pile is optimized, and an innovative technical scheme is provided for intelligent electric vehicle charging.

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 metering system operation state comprehensive evaluation method and system, and the method comprises the steps: receiving a real-time energy consumption data stream, carrying out the processing through an adaptive threshold anomaly detection algorithm and a Bayesian inference method, and generating a real error range estimation corresponding to each data point; generating a target prediction result by using the time sequence prediction model in combination with historical same-period data, seasonal factors and a reinforcement learning mechanism; comparing the target prediction result with the real-time energy consumption data flow to obtain a comparison result, and generating a comprehensive performance fluctuation evaluation result based on the comparison result and the multi-dimensional state space model; constructing a comprehensive evaluation index system by using an integrated learning algorithm and a fuzzy logic algorithm, and obtaining a comprehensive score corresponding to each ammeter by using the comprehensive evaluation index system; according to the method, the prediction accuracy of the electric energy metering system, the comprehensiveness of health condition evaluation and the scientificity of maintenance decision are improved, and support is provided for efficient management and optimal scheduling of an intelligent power grid.

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 discloses a circuit breaker opening and closing detection method and system for a gas insulated switchgear, and relates to the technical field of circuit breaker opening and closing detection, and the method comprises the following steps: in a circuit breaker opening detection process, firstly carrying out real-time data collection on all circuits in a power grid, and providing initial conditions for subsequent fault simulation and analysis; a target circuit is randomly selected from all circuits, and short circuit faults of the circuit under different conditions are simulated. Through real-time power grid data acquisition, random short-circuit fault simulation and feature extraction in a dynamic monitoring window, in combination with a convolutional neural network and a fuzzy logic algorithm, the short-circuit fault severity degree is intelligently evaluated, the optimal opening speed range is predicted, the actual opening speed is accurately compared, abnormity is timely recognized, the problem of misjudgment in traditional detection is avoided, and the detection accuracy is improved. The high-efficiency response of the circuit breaker under different fault conditions is ensured, and the detection precision and the safety and stability of a power system are effectively improved.

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 discloses an energy-saving thermal management method and system for an energy storage cabinet of a phase-change material, and belongs to the technical field of thermal management of energy storage equipment, and the method specifically comprises the steps: collecting temperature data in the energy storage cabinet through a temperature sensor, and predicting a temperature change trend through a neural network model after the temperature data is subjected to Kalman filtering processing; according to the prediction result, a fuzzy logic algorithm is adopted to judge the temperature state, and a PID control algorithm is combined to adjust the operation parameters of the auxiliary heat dissipation device; and meanwhile, the phase change material automatically absorbs or releases heat according to actual temperature change and works cooperatively with the auxiliary heat dissipation device, and the system also regularly collects real-time operation data and optimizes and updates a neural network model and a fuzzy logic rule so as to improve the temperature management efficiency and realize intelligent and accurate control of the temperature in the energy storage cabinet.

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 discloses an intelligent heat pump control system based on PV / T optical storage direct drive, and particularly relates to the technical field of energy control, which realizes real-time acquisition and comprehensive analysis of PV / T assembly output power, irradiation signals and phase change material temperature change data by adopting a data acquisition module, a control processing module and a heat pump output module; the method comprises the following steps: performing quantitative evaluation on discontinuous fluctuation and thermal response interference in a signal by adopting a fuzzy logic algorithm, Hilbert-Huang transform and a local phase space reconstruction and chaos dynamics model so as to identify a thermal coupling risk of nonlinear abrupt change, and generating an adjustment instruction according to a difference value between a phase change response fluctuation frequency and a preset reference frequency; the heat pump output module adjusts the working medium flow and the compression ratio in real time according to the adjusting instruction, synchronous matching of the phase change heat storage device and the PV / T assembly is achieved, and the energy density and the temperature control precision of the system are effectively 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 provides a hail probability and size identification algorithm based on fuzzy logic and semi-supervised learning, and aims to improve the accuracy of hail early warning and reduce the false alarm rate. According to the algorithm, the probability comprehensive recognition of fuzzy logic and the Self-training semi-supervised learning technology are combined, and the probability and size of hail occurrence are accurately recognized through deep analysis of dual-polarization radar data and meteorological sounding data. The method comprises the specific steps that firstly, the heights of a 0 DEG C layer and a-20 DEG C layer are calculated through polynomial fitting, then a fuzzy logic algorithm is combined with expert experience to determine a membership function and weight of characteristic parameters, and the probability of hail occurrence is calculated; and then, a semi-supervised learning algorithm is adopted to further refine identification of the hail size. A weak classifier is adopted, and a Self-training iteration process is carried out, so that labeled samples are expanded, and the classification accuracy is improved. A final evaluation result shows that the algorithm is superior to a traditional method in the aspects of the hail recognition rate, the false alarm rate, the accuracy rate and the like, has high application value and can provide effective support for forecasting and early warning of hail weather.

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 discloses a volume correction instrument error analysis method and system based on an improved PE equation, and aims to improve the accuracy of fuel gas and biogas metering. According to the equipment, performance testing can be carried out under the conditions of different temperatures (20 DEG C to 40 DEG C) and pressures (101.3 kPa to 104.0 kPa) through an integrated fuzzy logic algorithm, and the average correction error is only 0.22%. A high-precision sensor and a data processing unit are used for monitoring and calibrating the gas volume in real time and compensating the influence of temperature and pressure changes. The fuzzy logic controller adjusts the reading of the gas meter according to real-time data, the accuracy of a metering result is ensured, algorithm optimization and intelligent equipment integration are focused, and the energy management efficiency is improved. The volume correction instrument provides help for a modern energy metering system and optimization of energy efficiency and cost.

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.