543 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.

A system is provided for managing Internet of Things (IoT) networks. The system includes a learning module configured to employ machine learning models with hyperparameters optimized through a hyperparameter optimization process; wherein the process includes evaluating a set of hyperparameters against a performance metric to select optimal hyperparameters that enhance the adaptability and efficiency of dynamic membership functions within an adaptive fuzzy logic engine (AFLE).

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 relates to the technical field of communication engineering, and discloses a power line carrier and wireless dual-mode fusion optimized communication method, which comprises the following steps of: 1, acquiring signal quality, noise and delay data of a power line and a wireless link in real time, and constructing a prediction model by utilizing historical data; step 2, dynamically adjusting a judgment threshold value of link switching in real time based on the prediction model, and ensuring that switching is triggered only when the signal change exceeds a preset range; step 3, based on the dynamic threshold, combining multiple indexes of signal-to-noise ratio, delay, packet loss rate and network load; according to the method, the necessity of link switching can be more intelligently judged by dynamically adjusting the judgment threshold of link switching and adopting a fuzzy logic decision mechanism, frequent switching during tiny fluctuation of signals is avoided by dynamic threshold adjustment, and a fuzzy logic system integrates multiple factors, so that the decision is finer, and the reliability of link switching is improved. And unnecessary delay and resource waste are reduced, so that the stability of a communication system is improved.

The invention relates to the technical field of power monitoring, in particular to a moisture-proof environment-friendly ring main unit online monitoring system with continuous fault indication and a ring main unit, and the system comprises an electric parameter dynamic analysis module, an environment collaborative verification module, a closed-loop control execution module and a behavior path optimization module. According to the method, the current fluctuation and the power difference value are matched through dynamic time warping, the temperature rise rate and the condensation index are calculated through linear regression, multi-dimensional verification is formed through fuzzy logic judgment, operation parameters are adjusted in real time through PID control, strategy weight is optimized through a genetic algorithm, feature extraction is reversely corrected, and the fault recognition precision and the response speed are improved; closed-loop feedback is established to continuously optimize the system state, environment and electrical parameter coupling analysis is enhanced, the misjudgment probability of a single threshold value is reduced, parameters are dynamically corrected, the adjustment real-time performance and accuracy are improved, the adaptive capacity under the complex working condition is enhanced, a complete closed loop of collection, analysis and feedback is established, and the insulation degradation judgment reliability is improved.

The invention provides a sewage treatment plant terminal effluent carbon source quality detection and evaluation method, which realizes full-flow dynamic evaluation and regulation of carbon source quality through on-line monitoring and intelligent algorithm fusion. According to the method, an online water quality full-spectrum detector is used for collecting original spectrum data flow, and after preprocessing such as variational mode decomposition denoising and mutual information feature selection, organic matter content quantification, variation trend analysis and anomaly detection are completed in combination with algorithms such as a support vector machine and an autoregressive moving average model. An entropy weight method is introduced to dynamically adjust the weight of the evaluation model, model parameters are optimized based on a gradient descent algorithm, a process adjustment instruction is generated through reinforcement learning and fuzzy logic, and an automatic system is linked to execute regulation and control. According to the method, the problems of hysteresis and singleness of traditional offline analysis are solved, multi-dimensional real-time evaluation, abnormal quick response and process dynamic optimization of the quality of the carbon source are realized, the sewage treatment efficiency and the effluent quality stability are improved, and a technical support is provided for continuous standard reaching of the quality of the carbon source.

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 comprehensive monitoring and early warning method and device for roadway surrounding rock, and relates to the technical field of underground engineering geotechnical engineering. Collecting physical parameter data of the surrounding rock in real time through a sensor network; transmitting the collected physical parameter data to a monitoring center through a network, and carrying out filtering, denoising and abnormal value elimination to obtain standard multi-source data; comprehensively analyzing the multi-source data by using a method of combining Bayesian estimation and fuzzy logic, and extracting a stability score of the surrounding rock stability; and training the relationship between the physical parameter data and the stability score, establishing a prediction model of the surrounding rock stability, and performing early warning when the predicted real-time stability score exceeds an early warning threshold. Therefore, through the constructed surrounding rock stability prediction model, comprehensive and real-time monitoring and high-precision early warning of the surrounding rock of the roadway are realized, the unstable condition of the surrounding rock is found and processed in time, and safety accidents are avoided.

The invention provides a method for identifying a power grid frequency signal assessment standard, and the method comprises the steps: constructing a system architecture composed of a master station, a substation and a communication network, and achieving the initialization of equipment through a finite-state machine; the method comprises the following steps: collecting power grid frequency, power generation side frequency difference and communication state multi-dimensional data, processing signals by applying median filtering, a sliding window and an EWMA algorithm, introducing fuzzy logic to evaluate a system state, and dynamically adjusting a response strategy; an improved PID algorithm is adopted to track frequency change, feed-forward compensation and PSO parameter optimization are combined, a hidden Markov model is utilized to predict a communication fault, and local mode switching is triggered in advance; main station, mixing and local three-state switching is carried out according to communication states and load changes, and a frequency modulation dead zone is automatically adjusted according to power grid loads through a dynamic threshold value self-adaptive algorithm; the control effect is verified by using a digital twinning technology; and the safety and reliability of the system are guaranteed by optional quantum encryption and active-active redundancy design. The whole scheme effectively reduces the power loss of power generation enterprises, and maintains the safe and stable operation of a power grid.

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 discloses a fuzzy logic self-adaptive adjusting system of box-type temperature control equipment, and relates to the field of fuzzy logic self-adaptive adjustment, and the fuzzy logic self-adaptive adjusting system comprises a feature extraction module, a mapping module, an activation module, a control module and an adjusting module. The method comprises the steps of obtaining a temperature field, energy consumption and airflow velocity, establishing a thermodynamic vortex field model and performing feature extraction analysis, obtaining a curvature phase energy third-order feature tensor, performing high-order singular value decomposition, constructing a five-dimensional manifold space, and obtaining a final geometric invariant curvature membership degree and a learnable parameter tensor integral solution through a reaction diffusion equation and minimum projection. The method comprises the following steps: constructing a high-dimensional rule network, obtaining a rotation invariant rule activation tensor through quantum entropy screening and nonlinear activation, constructing a fractional order model, obtaining a final control signal through potential energy well resonance and coherence optimization analysis, constructing a parameter update vector through a key coefficient, and adjusting the final control signal in real time.

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 discloses a power system frequency modulation method and system based on flywheel energy storage auxiliary wind power, a flywheel energy storage system is equivalent to a first-order inertia link, an inertia time constant of the flywheel energy storage system is dynamically adjusted, a virtual damping coefficient is optimized in real time in combination with a fuzzy logic controller, and the stability of power grid frequency is remarkably improved. When the frequency of the power grid fluctuates, the flywheel energy storage system can respond quickly, the frequency modulation power close to the rated frequency can be output in an extremely short time, and the steady-state frequency deviation is strictly controlled in an extremely small range. Meanwhile, the fuzzy logic is used for dynamically correcting the power instruction, and the problem of frequency modulation instruction tracking errors caused by wind power output randomness is effectively solved. Besides, after frequency modulation is finished, the damping coefficient is dynamically adjusted through a fuzzy decision mechanism, the flywheel rotating speed recovery time is greatly shortened, secondary frequency disturbance caused by rotating speed lag is avoided, and the recycling efficiency of flywheel energy storage in a continuous frequency modulation scene is ensured.

According to the incineration pollution cooperative control method based on multi-target reinforcement learning, a multi-modal sensor array is deployed in a hearth, a flue and a fly ash processing unit, multiple parameters such as combustion temperature and flue gas retention time are collected in real time to construct a high-dimensional dynamic characteristic matrix, and data collection and processing are carried out to meet subsequent model requirements. Based on the dynamic characteristic matrix, defining three types of objective functions of pollutant emission minimization, fly ash metal recovery rate maximization and resource cost minimization, adopting a depth deterministic strategy gradient algorithm to construct a multi-objective reinforcement learning joint optimization model, and taking a combustion air ratio and the like as an action space; and strategy exploration and value evaluation are realized through a dual-network structure. Meanwhile, a priority evaluation module based on fuzzy logic is constructed, the weight coefficient of an objective function is dynamically adjusted according to real-time working conditions, and dynamic weight distribution and multi-objective collaborative optimization control are achieved. According to the method, multiple targets in the waste incineration process can be effectively balanced, the incineration efficiency and the resource utilization level are improved, and pollutant emission is reduced.

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 cloth bag processing, and discloses a non-woven fabric bag processing parameter regulation and control method and system based on deep learning, and the method comprises the steps: obtaining the real-time rotation speed and tension data of equipment, carrying out the signal filtering and state calculation, obtaining a cloth traction state, carrying out the fuzzy logic processing, and determining the adjustment amount; obtaining a target rotation speed adjustment amount; according to the target rotation speed adjustment amount, threshold judgment and parameter dynamic updating are carried out, a new tension fluctuation index is obtained, fitness evaluation is carried out, and the product specification fitness is obtained; according to the product specification fitness, a dynamic adjustment decision is made, a parameter regulation and control scheme is obtained, risk quantification is carried out, and a production interruption risk level is obtained; and according to the production interruption risk level, risk threshold verification and optimization algorithm correction are carried out, a defective percentage inhibition value is obtained, tension stable signal analysis and global fusion output are carried out, and final processing equipment parameter configuration is obtained. The method can improve the qualified rate of products.

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.