363 results about "Fuzzy control system" patented technology

The invention relates to a motor energy-saving control method for adaptive parameter adjustment. The method comprises the following steps: constructing an operation parameter sequence and comparing the operation parameter sequence with a preset reference model by collecting the rotating speed, the torque and the current signal of a motor in real time to generate a parameter deviation vector; based on a deviation threshold comparison result, carrying out noise processing on the overrun deviation to obtain a filtered sequence, and calculating a time delay degree in combination with historical parameters; calculating a compensation gain according to the time delay, fusing the compensation gain with the filtered deviation to generate a control instruction sequence, correcting the compensation gain through fuzzy control to obtain a stabilization parameter, and further generating a correction control instruction sequence; and finally, acquiring load data, fusing the load data with the correction instruction to obtain a system operation characteristic value, and generating an optimization state identifier when a judgment standard is met. By adopting the method, adaptive adjustment of the control parameters can be realized, the dynamic working condition is accurately responded, and the energy-saving efficiency of the motor under the variable-load working condition is improved.

The invention provides a pipeline welding all-position welding seam identifying and tracking system and method, and belongs to the technical field of pipeline welding automation. In order to solve the problems of strong arc light interference, low tracking precision, slow response speed and insufficient robustness in the existing pipeline all-position welding tracking technology, the system comprises a sensing layer, a processing layer, a decision-making layer and an execution layer. The welding seam identifying and tracking method comprises the following steps: S1, calibrating the welding seam identifying and tracking system; s2, all-position welding tracking is carried out; and S3, performing adaptive adjustment on different scenes. According to the method, dynamic weighted fusion and fuzzy PID control are adopted, multi-posture welding such as flat welding, vertical position welding, horizontal position welding and overhead position welding can be automatically adapted, parameters do not need to be manually adjusted, and the operation threshold is lowered.

The invention provides an autonomous adjustment method and system for a thermal power plant desulfurization wastewater dosing device, and the method comprises the steps: collecting multiple parameters such as flow, pH value, heavy metal ion and suspended matter concentration in real time through on-line monitoring instruments arranged at a water inlet end, a reaction unit and a water outlet end; constructing and continuously updating a dynamic mathematical model based on historical data and a reaction mechanism; a control system integrating intelligent algorithms such as a neural network and fuzzy control is utilized, and optimal dosing parameters are calculated in combination with the model and real-time data; a dosing pump or a valve is driven to execute precise dosing; and finally, closed-loop optimization is formed through effluent quality feedback. According to the invention, automatic, accurate and self-adaptive adjustment of the dosing device is realized, the defects that a traditional mode depends on manpower and response lags behind are overcome, stable and standard discharge of desulfurization wastewater is effectively guaranteed, meanwhile, medicament consumption and operation cost are remarkably reduced, and the automation level and the capability of coping with working condition fluctuation of the system are improved.

The invention relates to the technical field of beacon light control, in particular to a beacon light anti-interference control method and system based on environmental perception and dynamic modulation. The method comprises the following steps: extracting an ambient light reference based on adaptive weight sliding filtering; in combination with target detection and trajectory prediction, interference pre-judgment features are obtained; on the basis of an evidence theory, analyzing comprehensive interference evaluation characteristics through the ambient light reference, the interference pre-judgment characteristics and the electromagnetic interference characteristics; fusing meteorological environment perception and influence features and self health state features, and evaluating equipment operation risks; and based on modulation signal optimization characteristics, the comprehensive interference evaluation characteristics and the equipment operation risk, fuzzy control and rule reasoning are utilized to realize dynamic output of anti-interference control parameters. In order to solve the problem that the signal recognition rate is reduced due to the fact that a beacon light is easily affected by multiple factors in a complex ocean navigation channel environment, data are obtained through multi-dimensional environment perception, and dynamic modulation control is achieved after feature extraction and fusion.

The invention belongs to the technical field of robot path planning, and provides a path planning method for an electric power inspection mobile robot, which comprises the following steps of: firstly, constructing an environment grid map, establishing a corner directional expansion safety boundary, and performing safety expansion on an outer right angle of an obstacle; secondly, establishing an intelligent neighborhood selection mechanism based on fuzzy control, and performing adaptive adjustment neighborhood search; then, a multi-target heuristic function fusing the local obstacle density, the safety distance and the traffic difficulty is established, path search is carried out, and an initial path is obtained; then, constructing a path optimization strategy, and carrying out redundant point deletion and B spline curve smoothing processing on the initial path until a target executable path is obtained; and finally, optimizing an executable track in the target executable path in a time-space domain through an improved time-domain elastic band algorithm to obtain an optimal path. According to the invention, accurate obstacle avoidance of complex geometric obstacles can be realized.

The invention discloses an unmanned vehicle path planning method based on real-time sampling, and relates to the field of intelligent systems and automatic control. The method comprises the following steps: dividing a regional map into a plurality of concentric sampling regions by taking an unmanned vehicle starting point and an unmanned vehicle target point as symmetry axes, and performing partition search in the plurality of concentric sampling regions through a dynamic density adjustment algorithm to obtain an initial global path; performing path trimming on the initial global path, and performing path smoothing processing on the initial global path after path trimming to obtain a global path; in the improved DWA algorithm, path selection is carried out based on the global path, the weight of the trajectory evaluation function, the unmanned vehicle initial state and unmanned vehicle model parameters, and a lowest-cost path is obtained; the weight of a trajectory evaluation function in the improved DWA algorithm is dynamically generated by adopting a fuzzy controller; and determining the path with the lowest cost as the path of the unmanned vehicle. The method can improve the reliability of unmanned vehicle path planning.

The invention discloses an electric vehicle cluster peak clipping service control method based on charge state fuzzy control. The method comprises the following steps of S1, performing fuzzy theory and peak clipping service demand evaluation; s2, carrying out modeling on charging and discharging behaviors of the electric vehicle; s3, analyzing the charge and discharge degradation cost of the electric vehicle; s4, optimizing a model objective function; according to the control method, an EVs cluster charging and discharging strategy optimization model for maximizing the benefits of EVs users and load aggregators is constructed based on SOC fuzzy control under the condition that the peak clipping demand degree of each time period is considered, the charging and discharging willingness of the EVs users is considered, an adjustable space is reserved for the EVs, the peak clipping service is supported, and the peak clipping demand degree is optimized. And meanwhile, the total expected revenue of the load aggregator and the EVs user is maximized, the optimization model is solved by combining the genetic algorithm and the CPLEX, and a better scheme is provided for the EVs cluster peak clipping service in the source network load storage system.

The invention provides an ozone dissolved air flotation system and a dissolved air release control method thereof. The system comprises a gas dissolving unit, an ozone floatation unit, a detection module, a control module and an adjusting module, the gas dissolving unit is used for transmitting generated data to the detection module; the ozone air floatation unit transmits the generated flow, pressure and water quality parameter data of an air floatation tank to the detection module; the detection module monitors the running states of the air dissolving process and the air floatation process in real time, and transmits the collected real-time data to the control module; the control module performs comprehensive judgment on the data through the core algorithm unit according to the sewage treatment mechanism model, the LSTM neural network and the fuzzy control logic, and generates a corresponding control instruction; and after receiving the instruction of the control module, the adjusting module adjusts the corresponding equipment through an execution element. According to the invention, multi-parameter dynamic coordinated regulation and control of the ozone dosage and the dissolved gas pressure can be realized, and the ozone utilization rate and the system response speed are obviously improved.

The invention relates to the technical field of environment control, in particular to a cold and heat source environment control method and device with an air purification function, and the method comprises the steps: collecting various parameters through a sensor network, and carrying out the data fusion based on a decision rule base to select a dominant working mode; corresponding air treatment operation is executed, and a composite energy-saving strategy including precooling optimization and phase change latent heat assistance is triggered under the refrigeration working condition; fuzzy control and machine learning are applied to optimize system output and predict load, and a decision rule base and algorithm parameters are updated through self-learning; air supply and exhaust power is controlled to maintain the negative pressure gradient of the key area, and an air path distribution mechanism is adjusted to organize a one-way air flow line; the device comprises an intelligent control module, a sensor group, a fresh air module, a purification module, a cold and heat source module, a bypass pipeline and an air path distribution mechanism. The problems that an existing system is poor in function collaboration, low in energy efficiency and unreasonable in airflow organization are solved, and intelligent decision making, deep energy saving and accurate environment control are achieved.

The invention discloses a dustproof shed environment balancing method and system based on multivariable fuzzy control, and the method comprises the steps: collecting multivariable environment data, and carrying out the preprocessing of the multivariable environment data through a Kalman filtering algorithm, and obtaining the target multivariable environment data; based on a dynamic weighted summation method, a comprehensive cleanliness risk index and a comprehensive cleanliness risk index change rate of the target multivariable environment data are obtained through calculation; based on a multiple-input multiple-output fuzzy control core algorithm, taking the comprehensive cleanliness risk index and the change rate thereof as input, and outputting to obtain a control value in a percentage form; and adaptive correction is carried out based on a reinforcement learning Q-learning algorithm. Through multivariable data acquisition, comprehensive cleanliness risk index, fuzzy control and self-adaptive correction, control precision and stability jump are realized, manual frequent debugging is not needed, long-term operation control errors are reduced, hidden dangers of dust pollution, humidity condensation and the like are effectively eliminated, and the failure rate after equipment is put into operation is reduced.

The invention discloses an intelligent truck formation transportation control system based on cloud side-end cooperation, and the system achieves the cooperative control through a three-stage architecture of a cloud large model cognition center, an edge-side intelligent control center and an intelligent terminal with a body. Comprising a cloud large model cognition center, an edge side intelligent control center and an emergency response module driven by integrated fuzzy logic of an intelligent terminal system with a body. The invention also discloses an intelligent truck formation transportation control method based on cloud side-end cooperation, and the method comprises the steps: firstly deploying a large model at a cloud end, achieving the domain adaptation through the LoRA fine tuning, analyzing task semantics, and outputting feature parameters; secondly, a vehicle end needs to integrate a multi-mode sensor and a fuzzy controller, a sensing-control local closed loop is established, and it is guaranteed that when communication is interrupted, local safety takeover is achieved through the fuzzy controller; and finally, cloud-edge-end strategy alignment is realized, and autonomous operation in a network disconnection scene is supported. The problems of cognitive decision separation, high communication dependency, insufficient security redundancy and the like in a traditional method are solved.

The invention discloses an energy management method and system for a fuel cell vehicle. The method comprises the steps of obtaining vehicle driving parameters and power battery parameters; identifying a current driving condition according to the vehicle driving parameters; evaluating the working intensity of the power battery based on the power battery parameters to obtain a working intensity index of the power battery; determining target output power of the fuel cell by adopting a fuzzy control rule according to the driving working condition, the SOC of the power cell, the working intensity index and the required power of the whole vehicle; and managing the energy of the fuel cell vehicle based on the target output power of the fuel cell. The method has the advantages of dynamically adapting to complex working conditions, optimizing the energy distribution efficiency and prolonging the service life of the fuel cell and the power cell.

The invention provides a performance simulation test method and system for a pumped storage power station. The method comprises the following steps: acquiring characteristic data of the pumped storage unit, and performing characteristic line method analysis and dynamic impedance modeling to obtain a nonlinear hydraulic system mathematical model; constructing a synchronous generator model and an excitation system model, and constructing an AGC control model and a primary frequency modulation control model; generating a simulation system; generating a coordination control strategy by using a variable gain PID control algorithm based on a fuzzy control theory, and a dynamic weight distribution mechanism and a feed-forward compensation mechanism of an AGC instruction and a primary frequency modulation response; and executing a simulation test of a normal operation condition, a large disturbance condition and a condition switching process, and generating a performance test result. Through accurate modeling and coordinated control strategies, the problem of mutual interference between AGC and primary frequency modulation is solved, the stability and response speed of the pumped storage power station in power grid frequency regulation are improved, and the frequency modulation performance of a unit under different working conditions can be comprehensively evaluated.

The invention discloses a path optimization device for an earthquake emergency unmanned aerial vehicle, and the device comprises a multi-mode environment perception layer which is responsible for collecting environment data, and finally outputting the data of an obstacle to a predictive obstacle avoidance decision-making layer; the predictive obstacle avoidance decision-making layer is responsible for receiving the data from the upper layer, filtering and calibrating the data, predicting a future trajectory of an obstacle by using an LSTM network, evaluating a collision risk and a safety margin in combination with an unmanned aerial vehicle dynamics model, dynamically planning an optimal path through an improved RRT * algorithm, generating an executable path instruction and sending the executable path instruction to the lower layer; the collaborative task optimization layer is responsible for carrying out self-organizing networking and data sharing among unmanned aerial vehicles through a 5G-AdHoc network according to an executable path instruction, dynamically allocating tasks to be optimal, and coordinating a task execution sequence and resource allocation; the self-adaptive execution control layer is responsible for collecting environment and unmanned aerial vehicle state data, fusing the environment and unmanned aerial vehicle state data and then inputting the environment and unmanned aerial vehicle state data into a PID controller to generate basic control quantity; pID and fuzzy control output are subjected to weighted fusion and converted into a motor instruction.

The invention belongs to the field of multi-odor scene presentation, and particularly relates to a multi-odor-source odor scene dynamic presentation method and system, and the method comprises the steps: obtaining the odor source release point information of a current space, and simulating the odor concentration distribution field of each release point through a distributed simulation model; calculating a mixed odor concentration distribution field through an odor mixing model, and fusing the mixed odor concentration distribution field with the spatial position coordinates to form a mixed odor position concentration distribution field; according to the real-time position and orientation of the advancing individual, the mixed odor concentration distribution of the current position point is matched and secondarily adjusted; inputting the adjusted mixed concentration control vector into a fuzzy control model, generating a smell release instruction, and controlling a smell release device to release mixed smell with corresponding concentration; and finally, dynamically adjusting the released mixed odor and concentration by updating the odor source release point information and the advancing direction of the advancing space in real time. According to the invention, accurate simulation and real-time presentation of mixed odor in a complex space are realized through a multi-level data processing and dynamic adjustment mechanism.

The invention relates to the technical field of control of a hybrid energy storage system of a direct-current optical storage overcharge station, and provides a double-layer fuzzy dynamic power distribution control method of a hybrid energy storage system, which comprises the following steps of: performing combined optimization by taking a direct-current bus reference current Idcref, a super capacitor state SOCsc and a storage battery state SOCbat as input; a first-layer fuzzy controller is constructed to dynamically adjust a filtering time constant T to serve as a parameter of a high-pass filter, and an initial power distribution instruction is generated; constructing a second-layer fuzzy controller to dynamically adjust the super capacitor protection state instruction Isc; and finally, based on the data obtained in the above process, adjusting the output reference current of the hybrid energy storage battery and the output reference current of the super capacitor in real time. According to the method, the charge states of the storage battery and the super capacitor are comprehensively considered at the same time to dynamically distribute the power, and the economical efficiency and the safety of operation of the hybrid energy storage system are improved.

The invention discloses a gas detection precision improving method based on a multi-algorithm fusion framework. A photonic crystal resonant cavity and a tunable band-pass filtering structure are integrated in a Fourier transform spectrometer light path; collecting a wide-spectrum light intensity signal, and constructing a spectral signal database in combination with the enhancement characteristic and the filtering characteristic of the resonant cavity; zero calibration and dynamic baseline deduction operation are carried out, and self-adaptive variational mode decomposition and wavelet transform are adopted to carry out signal denoising on the spectral signals; an environment compensation model is established, and an Arrhenius type correction factor is adopted to suppress water vapor cross interference; constructing an RLS and fuzzy control combined hybrid adaptive filter; separating aliasing spectral signals by adopting a non-negative matrix factorization algorithm; establishing a quantitative relation model; in the online concentration prediction process, the initial concentration value is subjected to recursive optimization by using a hybrid adaptive filter, the deviation is continuously corrected through an environment compensation model, and an accurate concentration value is output. The method can significantly improve the accuracy and stability of multi-gas detection.

The invention relates to the technical field of main cable dehumidification, in particular to an automatic circulation dehumidification method and system for a main cable, and the method comprises the steps: obtaining meteorological information, and the internal environment information and external environment information of the main cable; according to the meteorological information and the external environment information of the main cable, generating an external dehumidification scheme by adopting an external dehumidification model constructed by fuzzy control based on rules and external dehumidification cost function optimization; performing external dehumidification on the main cable according to the external dehumidification scheme; according to a preset internal humidity range to which the internal humidity belongs, the internal dehumidification demand state is judged to be a forced dehumidification state, an intelligent decision state or a safe state, and forced dehumidification, intelligent decision dehumidification or no dehumidification are carried out in different internal dehumidification demand states; according to the scheme, the weather and the environment can be integrated, the internal and external dehumidification scheme is generated and optimized, so that active inner ring dehumidification is carried out, the meteorological environment is prevented from influencing the dehumidification effect, and the dehumidification efficiency is improved.

The invention discloses an intensive culture precise feeding control method based on fuzzy-MPC control, and relates to the technical field of aquaculture intelligent control, and the method comprises the steps: employing a sensing system, collecting multi-source sensing data including fish activity images, healthy growth states and water quality environment data in real time, and forming a comprehensive state data set; and based on the acquired multi-source sensing data, constructing an MTL-LSTM-SAT multi-task learning model. According to the method, the multi-task learning model fusing the LSTM and the soft attention mechanism is constructed, the long-term dependency relationship and dynamic association among the water quality, the feeding and the fish state can be captured, the attention mechanism enables the model to be automatically focused on a key time step, the perception ability of the water quality fluctuation and the feeding influence stage is enhanced, and the accuracy of the water quality fluctuation and feeding influence stage is improved. Meanwhile, double-task collaborative prediction of the growth speed and the disease proportion is achieved through the shared feature layer, a prospective state sequence of the future 7 days is output, and a quantitative and reliable prediction basis is provided for feeding decision making.

The invention relates to the technical field of water conservancy irrigation, and discloses an intelligent precise irrigation system and method based on the Internet of Things and fuzzy control, and the system comprises a sensing and execution module which is used for collecting environment data and executing irrigation operation, and a platform application module which is used for receiving the environment data and executing the irrigation operation and comprises an environment data collection unit and an irrigation execution unit. The sensing and execution module is used for storing and analyzing data in the environment data acquisition unit and then generating an irrigation instruction, the platform application module comprises a data storage unit, a data analysis unit and a twin experiment unit, and the network transmission module is used for being in communication connection with the sensing and execution module and the platform application module. The system solves the problems that a traditional irrigation mode is serious in water resource waste, low in automation degree and poor in adaptability, and high efficiency, intelligence and automation of agricultural irrigation are achieved.

The invention discloses an ultra-low temperature refrigerator self-adaptive temperature control method based on fuzzy control, and relates to the technical field of intelligent control and industrial automation. Initializing target temperature, pressure and flow, acquiring data by using a high-precision sensor, and performing Kalman filtering to reduce noise; a fuzzy control rule base is constructed, the temperature deviation and the change rate serve as input, and the refrigerating / heating power adjusting quantity is deduced and output; a compressor, a heating pipe and an expansion valve are adjusted according to the reasoning result, and fine adjustment is switched when the deviation is smaller than 0.5 DEG C; dynamically adjusting the pressure to avoid boiling or cavitation of the secondary refrigerant, monitoring the liquid level of the constant-pressure tank and supplementing liquid; an interference compensation model is established, the system state is monitored in real time, faults are recorded, and pharmacy, military industry and other scenes are adapted. The temperature control precision and stability of the ultra-low temperature refrigerator are improved, pressure-temperature cooperative control is achieved, the interference influence is offset, it is guaranteed that a secondary refrigerant is stable, multi-load and fault switching are adapted, the high-precision requirements of pharmacy and the military industry are met, and the shutdown risk is reduced.

The invention belongs to the technical field of hydroelectric power generation equipment and electromagnetic control, and particularly relates to an active magnetic suspension bearing vibration reduction device and method for vibration reduction of a hydroelectric generating set. The device comprises a displacement sensor, a control system, a power amplifier, a radial magnetic bearing, an axial magnetic bearing and a high-speed motor, the rotor offset and the vibration signal are detected in real time through the sensor, the controller calculates output based on PID, fuzzy control or a self-adaptive algorithm, dynamic adjustable magnetic force is formed by adjusting the current of the excitation winding, rigidity and damping regulation and control are achieved, and therefore vibration of the hydroelectric generating set is restrained.

The invention belongs to the field of machining, and particularly relates to an integrated high-precision nut burying operation machine which comprises a frame body, a displacement mechanism, a nut burying mechanism, a man-machine interaction layer and a data acquisition and processing module. The core of the system is that a multi-source cooperative closed-loop regulation and control system is constructed, a visual detection module integrates a multispectral compound eye bionic system and an improved ICP algorithm, pressure and temperature field adjustment is combined with shape memory alloy, phase change energy storage and fuzzy control, and a process parameter self-optimization and abnormity self-repairing subsystem is matched. The equipment can automatically adapt to workpieces made of various materials, initial optimization parameters are output when the equipment works for the first time, the embedding precision and the pressing-in force stability are effectively guaranteed, the problems of screw loose, deformation and the like are avoided, clamping stagnation, overheating and other abnormalities can be rapidly handled, production interruption is reduced, and high precision, high efficiency and production continuity are considered.

The invention discloses a photovoltaic-photo-thermal power generation system optimization scheduling method and device in the technical field of hybrid energy system integration, and the method comprises the steps: carrying out the optimization scheduling of a photovoltaic-photo-thermal power generation system based on the real-time power generation electric power of a photo-thermal power station, the grid-connected electric power of a photovoltaic power station, and the electric power needed by a power generation system load; establishing a total objective function taking the economic benefit and the reliability level of the photovoltaic-photo-thermal power generation system as indexes; and on the premise of meeting constraint conditions, solving the total objective function through a multi-objective genetic algorithm NSGA-II to obtain a Pareto solution set, obtaining an optimal compromise solution in the Pareto solution set through a maximum satisfaction method in a fuzzy control theory, and realizing optimal scheduling of the photovoltaic-photo-thermal power generation system meeting economical efficiency and reliability based on the optimal compromise solution. The technical problem that an existing photovoltaic-photo-thermal power generation system cannot give consideration to the economic benefit of grid connection of the power generation system and the power utilization satisfaction degree of a user at the same time can be solved.

The invention relates to the technical field of flexible interconnection power distribution networks, and discloses a frequency support control system of a doubly-fed wind turbine generator connected into a flexible interconnection power distribution network and a control method of the frequency support control system. A virtual synchronous frequency support is embedded in the doubly-fed wind turbine generator, and a fuzzy controller based on frequency deviation and change rate is introduced to realize optimization of inertia and a primary frequency modulation coefficient; the system structure can actively support the connected flexible interconnection power distribution network; according to the method, the inertia response coefficient and the primary frequency modulation coefficient can be dynamically adjusted in real time according to the system operation state, and the frequency fluctuation problem caused by the distributed power supply is solved; the inertia response coefficient and the primary frequency modulation coefficient are dynamically adjusted in real time according to the operation state of the flexible interconnection power distribution network, and it is still ensured that the doubly-fed wind turbine generator has good frequency supporting performance under the condition of wind speed fluctuation.

The invention relates to the technical field related to energy-saving illumination control, in particular to an energy-saving illumination method, system and equipment integrating tasks and human body perception, and the method comprises the steps: dividing task scenes, configuring reference illumination parameters, evaluating an illumination comfort index, dynamically adjusting output power and spectral distribution, and determining an adaptive perception interval. And setting an illumination adaptive switching mechanism, when task scene switching is detected, performing illumination parameter smooth transition in combination with the adaptive sensing interval, and generating a non-dominated sorting optimization strategy according to energy consumption feedback and illumination comfort index optimization control. The technical problems that energy-saving control depends on fixed threshold adjustment, excessive low illumination is caused by energy consumption reduction, and visual fatigue is aggravated are solved, task scene division and reference parameter configuration are achieved, fuzzy control is adopted to adjust output power and spectral distribution in real time, and the real-time adjustment of the output power and spectral distribution is achieved in an adaptive sensing interval. Energy consumption optimization is carried out on the premise that the color rendering index and the illumination are guaranteed, and the energy efficiency of the lighting system is improved.

The application relates to a dynamic vision-based puncture deviation correction method, device, equipment and storage medium, which comprises collecting image data and spatial position data of a to-be-punctured object, then extracting target positioning information from the image data and constructing a corresponding three-dimensional deformation model; determining corresponding offset data according to the three-dimensional deformation model, fusing the target positioning information and the offset data to generate a deviation vector; based on the deviation vector, performing fuzzy control rule calculation and combining historical control data to generate a control output; by analyzing the change trend of the deviation vector at multiple time points, predicting the offset direction and amplitude of a preset time period to generate a feedforward compensation amount, and then generating a compensation control instruction; generating three-order trajectory information of a puncture path according to the compensation control instruction to complete dynamic deviation correction control according to the three-order trajectory information. The application has the effect of improving the precision of an automatic puncture system.

The application discloses a control method and equipment for active power smoothing and reactive power voltage regulation of a wind storage system, and the method comprises the following steps: S1, performing adaptive noise complete set empirical mode decomposition on a wind power signal to obtain a plurality of mode components; S2, according to a wind power grid fluctuation standard, reconstructing the mode components into high-frequency components and low-frequency components, and taking the high-frequency components and the low-frequency components as a primary active power instruction of a storage system and a wind power plant grid-connected power respectively; S3, based on a real-time state of charge of the storage system and the primary active power instruction, correcting the primary active power instruction by using a fuzzy control method, and simultaneously optimizing and adjusting the state of charge of the storage system; and S4, according to the grid-connected power of the wind storage system and a grid-connected point voltage, generating a reactive power instruction of the storage system by using a fuzzy control method, so as to realize the regulation of the grid-connected point voltage.

The invention discloses an adaptive adjustment method for capacity balance of an SSD storage system. The adaptive adjustment method comprises the steps of system state real-time sensing and data collection, multi-dimensional capacity balance urgency degree intelligent evaluation, adaptive migration decision based on fuzzy control, efficient data selection and controlled migration execution, effect feedback and model parameter self-optimization. According to the method, the balancing strategy and the migration rate are dynamically adjusted by sensing the system load and the SSD state in real time, so that the system can make an optimal decision under different service pressures, and the method has high adaptability; the migration permission rate confirmed based on fuzzy control serves as a rate limiting mechanism, it is ensured that capacity balancing operation is stably carried out in the background all the time, severe jitter of service performance caused by balancing I / O is effectively avoided, a feedback learning mechanism is introduced, the system can automatically optimize internal decision model parameters according to the effect of historical operation, and the system performance is improved. And the automatic management level and the resource utilization efficiency of the SSD storage system are remarkably improved.

The invention discloses an unmanned ship garbage collection method and system based on multi-source fusion and causal reasoning, and the method comprises the steps: carrying out the physical consistency fusion of multi-source observation data, and completing the detection, category recognition and material parameter estimation of floating garbage through a multi-mode deep learning network; constructing a two-dimensional local flow field around the unmanned ship, and giving short-time drift trajectory prediction and drift risk indexes on a target scale; constructing and training a parameterized causal prediction model, and evaluating the expected decline amount of the total load of the garbage in the whole water area after each sub-area is cleaned, so as to obtain a task priority sequence; the probability that a target enters an effective area of a collection port and the risk that the target is pushed away by a flow field under different approaching directions and speed strategies are given through a behavior prediction network before contact, finally fuzzy control input is constructed, and approaching and collection behaviors are adjusted in a self-adaptive mode according to different garbage materials and flow field disturbance. And high recovery stability and robustness are maintained in a complex hydrodynamic environment.