483 results about "Fuzzy logic" patented technology

The invention discloses an industrial robot real-time adaptive control method and system based on digital twinning, and relates to the technical field of industrial robots. The digital twin engine module runs a high-fidelity dynamics simulation model and an environment interaction model, performs real-time state estimation, abnormal working condition recognition and twin parameter dynamic updating, is seamlessly integrated with the control execution module, and provides decision support with high robustness and high adaptability for an industrial scene; the adaptive control module performs online rolling optimization on a control strategy based on a deep reinforcement learning algorithm, generates joint space trajectory correction, tail end precision compensation and dynamic load adaptability optimal instructions, and realizes parameter adaptive setting through fuzzy logic or a neural network; and the fault diagnosis module performs multi-scale time sequence analysis by using an LSTM and convolutional neural network fusion model, detects position offset, moment sudden change or temperature overrun and other abnormalities, and triggers emergency shutdown, sound-light alarm and an adaptive recovery strategy.

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

The invention relates to the technical field of unmanned aerial vehicle flight path planning, in particular to an unmanned aerial vehicle real-time path planning system and method based on dynamic weight distribution and multi-source data fusion. The system comprises a multi-source data fusion module, an integrated laser radar, a millimeter wave radar, a visual sensor and a Beidou positioning unit. The dynamic weight distribution module dynamically adjusts the weight coefficient of each sensor according to the environmental complexity, the threat level and the state of the unmanned aerial vehicle by adopting a mixed decision-making mechanism combining fuzzy logic and reinforcement learning; an improved RRT * algorithm and a Markov decision process are built in the real-time path planning module, and a global optimal path and a local obstacle avoidance track are generated by adopting a layered planning architecture; the unmanned aerial vehicle cooperative control module comprises a dual-redundancy flight control system and a dynamic obstacle avoidance unit; and the communication relay module supports 5G and low-orbit satellite dual-mode communication, updates an environment cognitive model of each unmanned aerial vehicle through federated learning, and realizes multi-source fusion real-time path planning based on dynamic weight distribution and the unmanned aerial vehicles.

The invention discloses a cloud mobile phone distributed task scheduling method and related equipment, and relates to the technical field of cloud mobile phones, and the method comprises the steps: obtaining metadata of a cloud mobile phone task request; performing priority discrimination on the metadata based on a fuzzy logic classifier to obtain a priority score; acquiring a multi-dimensional resource index of each cloud mobile phone node in the cloud mobile phone cluster in real time; determining a task allocation strategy through a multi-objective optimization algorithm based on the dynamic priority score and the multi-dimensional resource index; and in response to the task allocation strategy, performing dynamic migration and resource allocation operation on the cloud mobile phone task request through the containerization execution layer. According to the invention, through combination of fuzzy logic priority evaluation, multi-dimensional resource perception and a multi-target optimization scheduling algorithm, intelligent, refined and global optimal distribution of cloud mobile phone tasks is realized, and the response speed of high-priority tasks, the resource utilization efficiency and the system load balancing capability are effectively improved.

The invention relates to the technical field of temperature control, in particular to an enzyme reaction kettle self-adaptive temperature control system and method, and the system comprises a temperature detection module, a temperature control module, a heat conduction oil flow control module and an automatic regulation and control module; temperature data of a reaction kettle are collected in real time through a temperature detection module, a temperature control module adjusts parameters based on a PID algorithm and fuzzy logic to generate a control instruction, and a heat conduction oil flow control module adjusts flow by combining double-closed-loop cascade control with a feedforward compensation algorithm, so that thermal inertia lag is shortened. And the automatic regulation and control module generates optimized process parameters based on a reaction kinetic model and a genetic algorithm, and dynamically adjusts a temperature set value. The control method comprises the steps of temperature data acquisition, PID control instruction generation, conduction oil flow coordinated regulation, process parameter optimization and the like. The problems that in the prior art, temperature control precision is insufficient, conduction oil flow control is not accurate enough, and technological parameters cannot be automatically regulated and controlled are solved.

The invention discloses a partition detection method for board card quality, and relates to the technical field of detection. The method comprises the following steps: dividing a board card into functional blocks according to a functional structure, and collecting thermal field distribution characteristic data of each block; constructing a standard thermal template, calculating the thermal imaging matching degree of the functional blocks by using an Euclidean distance algorithm, and marking the thermal imaging matching degree exceeding a preset threshold value as an abnormal thermal response region; collecting resistance and resistance gradient change data of an abnormal thermal response area, judging the area as a defect cluster area if conditions are met, and calculating a resistance abnormal amplitude; integrating defect cluster region data to form a composite characteristic spectrum, inputting a defect sample matching model, and outputting a defect classification result and a confidence score; calculating a regional quality score by combining fuzzy logic reasoning and a neural network algorithm and integrating a thermal imaging matching degree, a resistance abnormal amplitude and a confidence score; and fusing the defect classification result and the regional quality score to generate a quality grade label. And based on the quality grade labels, board card quality partition detection is completed.

The invention relates to the technical field of computer vision, in particular to a high-rise facility operation risk monitoring method based on deep learning and point cloud detection, and the method comprises the steps: collecting a three-dimensional point cloud in real time, and extracting a target point cloud through dynamic threshold denoising and template registration; performing joint coding on space geometry and time sequence motion by using a pre-trained space-time diagram network in combination with an attention mechanism; high-reflectivity beacon points are identified, and the change rate of displacement and angular velocity is calculated; constructing a gating fusion model, dynamically weighting and coupling semantic features and measurement data, and generating risk probability distribution through mutual information consistency check; a fuzzy logic classifier with membership degree optimization is used for mapping to four-level early warning, and grading response is triggered; after early warning, a key frame incremental learning fine tuning model is extracted, and preprocessing parameters are reversely optimized to form a closed loop. According to the method, through multi-source heterogeneous data fusion, dynamic adaptive weighting and a self-evolution mechanism, the real-time performance, accuracy and robustness of risk monitoring in a complex construction environment are remarkably improved.

The invention discloses a switch load balancing method based on fuzzy logic control, and the method comprises the following steps: S1, monitoring a network state in real time through quantum sensing equipment, and obtaining related parameters; s2, transmitting the parameters to a fuzzy logic controller for processing; s3, generating a load distribution strategy according to a predefined rule; s4, transmitting the preliminary strategy to each switch node in the block chain network, and recording related data; s5, verifying the data by using a distributed consensus mechanism of the block chain; s6, dynamically evaluating a node trust value based on an enhanced fuzzy trust model, and selecting nodes meeting requirements to participate in a load balancing game decision; s7, carrying out cooperative calculation on the selected nodes by utilizing a game theory cooperation mechanism, and generating a globally optimized load distribution strategy; and S8, optimizing a load distribution strategy through a quantum genetic algorithm, and performing automatic execution through an intelligent contract. According to the method, the advantages of fuzzy logic, the block chain and quantum computing are combined, and the intelligence and safety of load balancing are improved.

The invention discloses a grid-connected and off-grid control system and method for a hybrid energy storage inverter, and relates to the technical field of grid-connected and off-grid control. The grid-connected and off-grid control method for the hybrid energy storage inverter comprises the following steps: S1, system initialization and data acquisition; s2, off-network dynamic load adaptability optimization; s3, power grid fault judgment and mode switching preparation; s4, grid-connected and off-grid mode switching control is carried out; the droop coefficient is adaptively adjusted according to the power change rate, response speed and stability are considered, virtual impedance is introduced to compensate line impedance difference, reactive power equalization is realized in combination with a distributed consistency algorithm, and the problem of multi-machine parallel circulating current suppression is solved. A fuzzy logic rule base is adopted to analyze the data output fault probability, the threshold value is dynamically adjusted, and the misjudgment risk is reduced. Hardware auxiliary synchronization and software pre-synchronization compensation are combined, a phase error feedback loop is introduced, and pre-synchronization robustness is enhanced. The output power is accurately switched and adjusted according to different scenes, and energy storage balance is maintained.

The invention provides a nuclear power safety valve remote monitoring and fault diagnosis method and system, and the method comprises the steps: obtaining multi-modal data collected through a sensor array; performing preprocessing and feature extraction on the multi-modal data to obtain various features of the multi-modal data; the fault type of the nuclear power safety valve is predicted based on the multiple features in combination with a CNN-LSTM hybrid model, and a preliminary fault type prediction result is obtained; and performing fuzzy logic optimization on the preliminary fault type prediction result to obtain a final fault type prediction result, and performing real-time alarm according to the final fault type prediction result. Relevant parameters of the parameterized three-dimensional digital twinborn model of the nuclear power safety valve are obtained in real time, and data of the parameterized three-dimensional digital twinborn model of the nuclear power safety valve are updated through the relevant parameters; through the parameterized three-dimensional digital twinborn model of the nuclear power safety valve, the operation and maintenance information of the fault part corresponding to the final fault type prediction result is visualized, and the accuracy, comprehensiveness and operation and maintenance assistance of fault prediction are improved.

The invention discloses a continuous charging production line intelligent management and control strategy and framework, and the framework further comprises a multi-physics field coupling modeling module which carries out the physical mechanism modeling and state prediction of a production process through a heat-fluid-solid three-field coupling model on the basis of a conventional data collection and control module; the intelligent twin module adopts a mode of dynamically fusing a mechanism model and a data-driven residual model to improve the precision and robustness of system state estimation; the scheduling optimization module is used for realizing dynamic adaptive scheduling of AGV paths and production tasks based on a multi-target deep reinforcement learning strategy; and the multi-layer safety causal reasoning module realizes advanced prediction and active prevention and control of safety risks through a causal map, fuzzy logic and a linkage response mechanism. According to the method, the problem that safety, quality and efficiency are difficult to collaboratively optimize in the prior art is solved, and the predictability, safety, scheduling efficiency and adaptive capacity of the production line are remarkably improved.

The invention discloses a hypergraph feature representation method, system and device based on fuzzy logic and a medium, and the method comprises the steps: obtaining original data, and constructing a hypergraph structure containing vertexes and hyperedges according to the original data; the hypergraph structure is initialized, and an initialized hypergraph structure is obtained; and inputting the initialized hypergraph structure into a preset hypergraph convolutional fuzzy network model for feature representation processing to obtain a hypergraph structure after feature representation processing. According to the hypergraph convolutional fuzzy network model, fuzzy representation and the hypergraph technology are deeply fused, the membership core concept of fuzzy logic is introduced, the hard coding association limitation of an existing hypergraph representation learning method that no black is white is broken through, the technical problems that a traditional hypergraph model cannot capture association gradients and fuzzy semantics are lost are effectively solved, and the learning efficiency is improved. Therefore, the flexibility and accuracy of the model are effectively improved, and stable prediction is provided in an uncertain data environment.

The invention relates to the technical field of software-defined wide area network data transmission, and discloses a software-defined wide area network data transmission method and system based on edge computing. According to the method, network state data are collected through distributed probes deployed at edge nodes, and a dynamic transmission path between the nodes is calculated after data cleaning and feature extraction. A collaborative filtering algorithm is utilized to analyze historical and real-time data to generate a transmission optimization strategy, and a fuzzy logic decision maker is combined to generate a dynamic routing strategy. And predicting a future congestion state based on network characteristics and a routing strategy, and adjusting flow scheduling parameters accordingly. And finally realizing visual display and time sequence storage of the network state data, the routing strategy and the scheduling parameters. The method can effectively reduce transmission delay and improve the utilization rate of network resources.

The invention discloses a full-closed-loop automatic spraying method, system and device based on geometrical characteristic driving and multi-physical quantity feedback and a storage medium. The method comprises the following steps: performing three-dimensional scanning on a workpiece and extracting geometric features; generating a spraying track and a spraying posture for keeping a topological structure based on the geometrical characteristics; motion, process and coating quality parameters are collected in real time through a sensor, and are processed and fused to form a state vector; inputting the state vector into an intelligent control module, performing optimization by combining model predictive control, fuzzy logic and a neural network, and outputting trajectory and process parameters; a control strategy is updated according to the quality deviation of online detection, and closed-loop correction is carried out; spraying is automatically executed in the closed spraying cabin, and unmanned operation of the whole process is achieved. According to the method, complex curved surface spraying can be completed without manual programming, and the method has the advantages of high flexibility, multi-parameter closed loop, self-adaptive optimization and the like.

The invention discloses an oil storage tank oil-water interface measurement system and method and a storage medium, and relates to the technical field of oil-water interface measurement, and the system comprises a data acquisition module, a data processing module, a decision support module and a data security module. According to the oil-water interface measuring system and method for the oil storage tank and the storage medium, a three-dimensional sensing network (distributed sensor network) is constructed by adopting a pressure sensor array and phased array ultrasonic scanning technology and combining dielectric property detection of a microwave dielectric constant sensor; the pressure, the dielectric constant, the liquid level and the vibration data in the tank body are obtained, basic data are provided for subsequent processing, the comprehensiveness of data obtaining can be improved through the sensing network, the pressure data are dynamically calibrated through Takagi-Sugeno fuzzy logic, the influence of temperature drift is effectively eliminated, and the accuracy of data obtaining is improved. The ultrasonic signals are subjected to feature enhancement through the generative adversarial network, and the emulsion layer boundary is accurately recognized.

The invention provides an AGC hydropower station intelligent control method based on multi-source data fusion. Constructing a control feature vector of the multi-dimensional feature; performing spatial-temporal feature modeling on the control feature vector, and extracting a time sequence dependency relationship between power grid load change and hydraulic dynamic response and a spatial coupling effect between units; establishing a multi-objective optimization function, and dynamically adjusting the weight coefficient of each objective through fuzzy logic according to the current working condition; a self-adaptive differential evolution algorithm is adopted to carry out on-line optimization on an active power distribution coefficient of a unit and PID parameters of a speed regulator, and the requirements of guide vane opening change rate constraint and water hammer effect avoidance are met. According to the method, multi-source heterogeneous data such as power grid, hydraulic engineering and equipment states can be effectively fused, multi-target dynamic optimization control is realized through the space-time attention model and the adaptive differential evolution algorithm, and the control precision, the response speed and the equipment operation safety of the hydropower station AGC system are remarkably improved.

The invention provides a method for rapidly detecting the content of nitrogen oxides in air. The method comprises the following steps: acquiring original signal data of nitrogen oxides in air in a target detection area in real time through a plurality of nitrogen oxide sensors; performing noise reduction filtering processing on the original signal data to generate target signal data; performing signal drift compensation correction on the target signal data based on a preset sensor calibration model to generate standardized concentration data; performing space-time correlation analysis on the standardized concentration data through a nitrogen oxide concentration prediction algorithm model in combination with environment parameter data collected in real time to generate a target prediction concentration value; and comparing the target predicted concentration value with a preset concentration standard threshold value based on a fuzzy logic judgment algorithm, and constructing a detection report. The whole process from data acquisition to report generation is intelligent, and the detection flexibility is remarkably improved.

The invention relates to the technical field of intelligent scheduling, and particularly discloses an intelligent scheduling decision control method and system for a distributed car washing robot, and the method comprises the steps: constructing a weighted graph model which comprises a vehicle position, obstacle distribution and path reachability, and dynamically adjusting the weight of an edge according to the path length, energy consumption and time; optimizing the graph structure by adopting an improved minimum spanning tree algorithm, and calculating a path efficiency index in combination with fuzzy logic and a neural network technology; on the basis of the optimized subgraph, applying a shortest path algorithm to obtain a local optimal path, and introducing a dynamic adaptation coefficient to evaluate the response capability of the path to environment change; fusing the path efficiency index and the dynamic adaptation coefficient into a comprehensive scheduling feature vector, inputting the comprehensive scheduling feature vector into a trained deep learning scheduling model, and predicting an optimal scheduling strategy in combination with a task priority and a robot resource state; and dynamically adjusting the moving path and the operation density of the robot according to a scheduling result, and realizing self-adaptive evolution and cooperative control of the system.

The invention relates to the technical field of pipe network leakage risk assessment, in particular to a pipe network leakage risk assessment method and system based on a hydraulic model. The method comprises the following steps: acquiring urban pipe network structure data and urban pipe network sensing data, and constructing a pipe network hydraulic-voiceprint fusion model; performing multi-scale abnormal fluctuation space-time decoupling based on the pipe network hydraulic-voiceprint fusion model to obtain potential leakage structure data of the pipe network; predicting a pipe network leakage point influence range according to the pipe network potential leakage structure data, and performing fuzzy logic leakage probability judgment to obtain a leakage deduction report; acquiring real-time pipe network sensing data, and quantifying a risk level to obtain actual leakage risk level data of a pipe network node; screening leakage suppression strategies according to the actual leakage risk level data of the pipe network nodes; and real-time pipe network repair feedback data is obtained, health state evolution is carried out, and a leakage pipe network structure repair evaluation report is obtained. According to the invention, the accuracy and reliability of pipe network leakage detection can be improved.

The invention discloses a liquid cooling energy storage power station intelligent temperature control system based on DNN and fuzzy logic, and relates to the technical field of energy storage. Comprising a data acquisition and multi-dimensional preprocessing module used for acquiring liquid cooling energy storage multi-source working condition data in real time, performing feature extraction and performing data preprocessing; the DNN depth temperature prediction module is used for constructing a DNN depth temperature prediction model, predicting the temperature of the battery pack, identifying an overtemperature risk and taking safety guarantee measures; the fuzzy logic intelligent decision module is used for predicting the flow of the cooling liquid and implementing a temperature control regulation decision; and the execution and feedback self-optimization closed loop module is used for correcting the flow of the cooling liquid and realizing a self-learning evolution closed loop of temperature control regulation. The problems that an existing liquid cooling energy storage power station temperature control system is poor in non-linear and dynamic change adaptability, insufficient in temperature control precision and response speed and poor in energy consumption optimization, complex heat management requirements cannot be met, and then comprehensive operation performance is reduced are solved.

The invention provides a hybrid model fault early warning method and system based on time sequence prediction and fuzzy logic, and the method comprises the steps: collecting operation and maintenance data of equipment in a continuous operation period, constructing an equipment state feature set, carrying out the feature correlation analysis of the equipment state feature set, generating an equipment state potential vector which reflects a dynamic coupling relation between parameters, and carrying out the fault early warning of a hybrid model. Calling a pre-trained fuzzy logic reasoning model to carry out fuzzy rule matching, and generating a fuzzy state set containing a multi-dimensional fuzzy subset; performing space-time correlation processing on the fuzzy state set to generate a multi-dimensional decision cloud picture, and performing spatial reconstruction on the multi-dimensional decision cloud picture through a quantization weight distribution mechanism to obtain an optimized decision matrix; and generating a fault early warning signal containing a fault early warning level and fault position positioning information based on the confidence distribution characteristics corresponding to the fault types. According to the invention, the accuracy and timeliness of fault early warning can be improved, and reliable decision support is provided for equipment operation and maintenance management.

The invention discloses a rainfall type landslide early warning method based on multi-source environment threshold and nonlinear fusion. The method comprises the following steps: constructing a landslide disaster-inducing factor set; introducing soil humidity, vegetation indexes and evapotranspiration, and calculating a threshold parameter corresponding to the rainfall type landslide event to obtain an environment threshold point set; according to the landslide disaster-inducing factor set, constructing and training a landslide susceptibility model based on progressive learning; adopting a double-layer nonlinear fusion model based on multi-source data fusion and quality perception gating, and combining a landslide susceptibility model to obtain comprehensive risk probability distribution; and performing graded early warning based on the comprehensive risk probability distribution to complete rainfall type landslide early warning. According to the method, on the basis of traditional rainfall parameters, early-stage disaster-pregnant environment factors are introduced, and a multi-source environment threshold value is formed; fuzzy logic reasoning is adopted to realize nonlinear fusion of the threshold information and the landslide susceptibility base map; and dynamically reflecting a regional environmental condition evolution process in combination with an annual iterative updated susceptibility layer, and realizing finer and more adaptive space grading early warning.

The invention discloses an online self-calibration filtering method for inertial navigation, and relates to the technical field of inertial navigation, and the method comprises the following steps: obtaining multi-modal data, and establishing an inertial device error mathematical model and an inertial navigation system error mathematical model; carrying out recursive estimation on the state variables by the extended Kalman filtering model to obtain error parameters of the inertial device and the navigation system; constructing a fuzzy adaptive model based on the error parameters, generating an observation noise correction factor through a fuzzy logic rule, and correcting an observation noise covariance matrix in the extended Kalman filtering model in real time; carrying out online estimation and compensation on error parameters of the inertial navigation system by utilizing an extended Kalman filtering model, and outputting compensation parameters containing zero offset and scale factors in real time; correcting the original data based on the compensation parameters, injecting the original data into the navigation solution, and updating the fuzzy adaptive model according to the compensated error data.

The invention discloses a robot dexterous arm teleoperation sharing control method and a related device, and relates to the technical field of robotics.The method comprises the steps that smooth expected pose information and expected angle state information are obtained, and pose information of a mechanical arm of a slave end at the current stage and angle state information and contact force information of a dexterous hand are obtained; based on the expected pose information and the joint angle information of the mechanical arm, the inverse kinematics of the mechanical arm is solved through a nonlinear optimization method, and a joint angle instruction of the mechanical arm is obtained; based on the expected angle state information, the angle state information of the dexterous hand and the contact force information, utilizing a fuzzy logic theory and a dynamic compliance primitive model to carry out adaptive variable impedance control on the dexterous hand to obtain a joint torque instruction of the dexterous hand; and feeding back the contact force information of the dexterous hand in the next stage to a main end operator. The success rate and the working efficiency of the teleoperation robot can be improved.

The invention discloses an electromechanical industry digital process evaluation method based on data analysis, and particularly relates to the technical field of digital evaluation, a behavior path map, a time sequence causal dependence model and an operation frequency density model are constructed based on information provided by the electromechanical industry, whether structural deviation or behavior abnormity exists is analyzed, and the evaluation result is obtained. If the initial deviation label is generated, calculating a behavior consistency score and a process integrity score, inputting the scores into a fuzzy logic device for risk reasoning, and outputting a credible level; according to the credibility level, judging whether the data is suitable for digital process evaluation, and if the data does not meet the preset requirement, feeding back a corresponding optimization strategy; according to the method, digital behavior structured analysis is realized by constructing a behavior path map, a time sequence causal dependence model and an operation frequency density model; a deviation triggering mechanism is set, and abnormal operation behaviors are accurately recognized; the credibility level is output based on the scoring result, the information applicability is judged according to the credibility level, an optimization strategy is fed back, and the evaluation intelligence level is improved.

The invention discloses a transformer internal multi-parameter information fusion discharge diagnosis and evaluation method, and relates to the technical field of power equipment state monitoring and fault diagnosis. According to the method, eight types of sensors are deployed in a transformer, and electrical, mechanical, thermotechnical and insulating multi-physical-field parameters are cooperatively collected; thirdly, performing three-stage preprocessing of abnormal value elimination, standardization and differential noise suppression on the data; then 12 types of fault features are extracted; carrying out feature layer fusion by adopting a dynamic weight distribution method based on AHP-working condition correction, and constructing a fusion feature vector; performing decision-making layer fusion through a five-layer fuzzy logic-neural network hybrid model, and outputting a fault probability and a fault type; and finally, dividing five fault levels according to the fault probability and outputting specific processing suggestions. According to the method, multi-parameter adaptive fusion and high-precision diagnosis are realized, the accuracy is high, the average time delay is low, and the problems of incomplete fault coverage and poor working condition adaptability of a traditional method are effectively solved.

According to the efficient water treatment system based on the reverse osmosis membrane and the control method provided by the invention, gradient anti-pollution membrane arrangement and reconfigurable intelligent waterway design are creatively combined, so that a foundation is laid for high-flux and low-pollution operation from a physical structure; meanwhile, a full-closed-loop intelligent control system is constructed, and by fusing fuzzy logic, an optimization algorithm and a neural network prediction model, the system can integrate multi-dimensional information such as inlet water quality, membrane pressure difference and water yield, judge pollution risks in real time and adaptively and seamlessly switch among a high recovery mode, a segmented flushing mode and a prediction cleaning mode, so that the system is high in recovery efficiency and high in reliability. Meanwhile, parameters such as pump frequency and valves are dynamically optimized to find an optimal balance point of recovery rate and energy consumption; according to the invention, the recovery rate can be stably improved, the membrane pollution rate is greatly reduced, the chemical cleaning period and the service life of the membrane element are prolonged, and finally, the comprehensive operation and maintenance cost of the system is remarkably reduced while the water treatment efficiency and the water quality safety are improved.

The invention discloses an internal combustion engine in-cylinder transient self-adaptive exposure control method based on FPGA acceleration, and belongs to the technical field of internal combustion engine endoscopic visualization. In order to solve the technical problem of image overexposure or underexposure caused by violent illumination change in the combustion process in an internal combustion engine cylinder, a real-time exposure detection unit is fused with a camera built-in sensor and an endoscope added sensor to monitor light intensity data; the adaptive exposure control unit accelerated by the FPGA adopts adaptive PID control and fuzzy logic control to generate a parameter adjustment direction, the adaptive PID dynamically adjusts gain based on a machine learning model, and the fuzzy logic outputs a decision according to a fuzzy rule base of brightness, light intensity and errors; the parameter adjusting unit adjusts the aperture size, the shutter speed and the light sensitivity in a linkage mode according to the target and actual gray scale difference value, and millisecond-level multi-parameter exposure control is achieved. Overexposure of a highlight area and loss of details of a low-light area are effectively avoided, and stable and clear images are obtained in the whole combustion process.

The invention discloses an operation risk analysis method and system for a virtual power plant, relates to the field of risk analysis, and solves the problem of insufficient operation stability of an existing virtual power plant, and the method comprises the steps: S1, collecting the operation data of various devices of the virtual power plant; s2, acquiring historical fault data, and constructing a fault rule base; performing fuzzy logic reasoning based on historical fault data to obtain membership values of the abnormal parameters; real-time equipment parameters are collected, calculation is carried out, and a fault judgment value is obtained; fault equipment is judged; s3, calculating the fluctuation condition of the equipment parameters, constructing a fluctuation interval, analyzing and calculating a fault risk value according to the fluctuation interval, judging pre-fault equipment, counting the fault equipment and the pre-fault equipment, and constructing a maintenance equipment list; s4, adjusting the energy supply of the equipment according to the energy supply plan in combination with the maintenance equipment list; the stability of the virtual power plant can be effectively improved.

The invention belongs to the technical field of irony detection, and discloses an inconsistency perception quantum fuzzy irony detection system and method, and the system comprises a multi-mode feature coding module which is responsible for coding originally inputted text and image data, and converting the data into a high-dimensional feature matrix; the cross-modal inconsistency embedding module is responsible for respectively extracting inconsistency features of a fact level and inconsistency features of an emotion level from the encoded text and image features; the inconsistency fuzzification module is responsible for utilizing fuzzy logic processing and representing inherent uncertainty in fact and emotion information to prepare for subsequent quantum calculation; and the quantum modal fusion and detection module is responsible for performing deep fusion and interaction on the fuzzified features in a quantum calculation space, and capturing a complex cross-modal relationship by using the characteristics such as quantum entanglement and the like. The invention designs a multi-mode irony detection framework of a hybrid quantum fuzzy neural network based on inconsistency perception.