616results about "Chaos models" patented technology

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a high-standard farmland intelligent irrigation system based on Internet of Things and data analysis, and the system comprises a soil moisture content sensing module which collects data through a multi-source sensor to construct a three-dimensional soil moisture content distribution model, and generates a soil moisture content characteristic spectrum; the soil moisture content prediction module generates water demand prediction data based on the soil moisture content characteristic spectrum, the meteorological data and the crop growth stage; the irrigation strategy module fuses terrain elevation and pipe network pressure parameters to generate an irrigation control map; the equipment state monitoring module collects water pump current waveform and other data to generate equipment health degree parameters; the pipe network optimization module optimizes pipe network topology and generates an adjusting instruction; the multi-source data fusion module generates fusion evaluation indexes by using an evidence theory, an entropy weight method and the like; and the intelligent execution module generates an execution control instruction accordingly. All the modules cooperate to achieve precise irrigation, and the utilization efficiency of water resources and the intelligent level of farmland management are improved.

The invention relates to the technical field of safety monitoring, and particularly discloses an embankment danger rapid identification method and system, and the method comprises the steps: collecting multi-modal data in real time through arranging a multi-source sensor network; according to the phase space trajectory, extracting a Lyapunov exponent spectrum, correlating the dimension and the Kolmogorov entropy, and forming a structure response chaos degree index; calculating a hydrogeological coupling coefficient in combination with multi-scale decomposition and mutual information analysis; and fusing the two into a three-dimensional dangerous case feature tensor, inputting the three-dimensional dangerous case feature tensor into a pre-training model based on a deep convolutional neural network and a long-short-term memory network, realizing intelligent discrimination of high, medium and low risk levels, generating an adaptive monitoring instruction for a low-risk working condition, outputting a risk evolution trend map, and supporting closed-loop management and control.

The invention relates to the technical field of typhoon prediction, and discloses a typhoon rapid enhancement prediction method based on time-space sequence and multi-modal feature fusion, and the method comprises the steps: constructing a multi-modal time-space sequence data set and an auxiliary data set based on typhoon optimal path data and multi-source satellite observation data; a unified manifold approximation and projection method is adopted to carry out dimension reduction preprocessing on the high-dimensional multi-modal space-time sequence data, and one-dimensional time sequence embedding representation of the typhoon observation sequence is generated; taking the one-dimensional time sequence embedded representation and the auxiliary data as independent input channels, and inputting a trained typhoon observation network model to predict a typhoon rapid enhancement probability; wherein the typhoon observation network model is a multi-mode time-space fusion deep learning architecture, the core of the typhoon observation network model is composed of a variational attention recurrent neural network, and hyper-parameter optimization is carried out through an improved Harris eagle optimization algorithm. According to the invention, accurate and robust identification of the typhoon rapid enhancement process is realized.

The invention belongs to the field of uncertainty quantification and robustness design optimization of aero-engine air-cooled turbines, and particularly discloses a one-dimensional uncertainty design optimization method and system for a reverse cooling turbine based on a one-dimensional aerodynamic analysis method for the reverse cooling turbine. Forming an augmented space by the optimization variables and the uncertainty parameters, and generating a sample set; establishing a Kriging global agent model, and generating an uncertainty parameter sample set; an ASPC model is established, and one-dimensional uncertainty quantification of the reverse cooling turbine is completed; an NSGA-II multi-objective optimization algorithm is coupled, and one-dimensional robustness design optimization of the reverse cooling turbine is completed. The one-dimensional uncertainty design optimization method and system framework of the reverse cooling turbine are provided and established, aerodynamic performance analysis of the reverse cooling turbine can be completed through simple parameters, a geometric entity is not needed, and tasks such as one-dimensional uncertainty quantification and robustness design optimization of the reverse cooling turbine can be achieved.

The invention discloses an intelligent power prediction method considering dynamic load change, and relates to the technical field of power grid load prediction, and the method comprises the steps: collecting original load data, carrying out the preprocessing, constructing a VMD constraint optimization model, carrying out the four-stage improvement of optimization parameters through employing an improved dung beetle optimization algorithm, so as to generate IMF components, reconstructing the IMF component by calculating a sample entropy to obtain a low-frequency component and a high-frequency component; establishing a Kalman filtering state space model based on the low-frequency component, and decomposing the low-frequency component into a residual component and a pseudo trend component through a Kalman filtering recursive algorithm; external features are obtained, the high-frequency component, the residual component and the pseudo trend component are aligned and spliced with the external features, multi-component collaborative prediction is carried out through a local-global interactive attention mechanism, and a final load prediction result is obtained; and generating a power demand visualization chart based on the final load prediction result. And reliable decision support is provided for power dispatching and energy management.

The invention relates to the technical field of power quality monitoring, and discloses a method for identifying power quality disturbance characteristics of a distributed photovoltaic interference power supply, which comprises the following steps: S1, data acquisition and preprocessing: acquiring a power quality signal of a photovoltaic system and carrying out denoising, normalization and time alignment; and S2, carrying out disturbance signal adaptive decomposition based on improved variational mode decomposition, and automatically selecting a mode number by utilizing information geometric optimization. An improved variational mode decomposition method is adopted, information geometric optimization and Bayesian optimization are combined, self-adaptive mode decomposition of disturbance signals is achieved, the optimal mode number and penalty factors can be automatically determined through improved variational mode decomposition, decomposition errors caused by manual parameter setting in traditional variational mode decomposition are avoided, and the method is suitable for large-scale popularization and application. Compared with a fixed parameter variational mode decomposition method in the prior art, the method has the advantages that the analysis capability of complex disturbance signals is improved, and the disturbance mode in the photovoltaic system is accurately decomposed.

The invention discloses an ultrasonic guided wave signal noise reduction method based on HHO-SVMD and singular spectrum analysis, and the method comprises the steps: obtaining a to-be-processed ultrasonic guided wave signal, optimizing a penalty factor of variation mode decomposition (SVMD) through employing an improved Harris eagle algorithm, initializing a population through Circle chaotic mapping, introducing chaotic disturbance and weight, and carrying out the noise reduction of the to-be-processed ultrasonic guided wave signal. Determining an optimal alpha value and decomposing the signal into an optimal modal component; calculating a kurtosis value of each modal component, and screening effective components containing damage information according to a threshold value; singular spectrum analysis denoising is carried out on the effective components, and a self-adaptive window mechanism is introduced to dynamically adjust the window length and the truncation strength; reconstructing the de-noised effective component to obtain a de-noised signal; according to the method, the parameter optimization precision and efficiency are improved, damage characteristics and noise are effectively separated, different dominant frequency signals are adapted, the damage characteristics can still be reserved in a low-signal-to-noise-ratio environment, the noise reduction effect of ultrasonic guided wave signals and the damage detection reliability are improved, and the method is suitable for nondestructive detection of components such as ultra-long small-diameter heat absorption pipes.

The invention discloses a multi-modal large model training data acquisition method and system, and relates to the technical field of data acquisition optimization, and the method comprises the steps: building a causal graph adjacency matrix based on a cleaning alignment data set, carrying out the anti-fact intervention after recognizing a prejudice variable, and generating an anti-fact sample set; combining the anti-fact sample set and the cleaning alignment data set into an enhanced data set; performing cross-modal anti-long-tail compensation on the enhanced data set to obtain a balanced data set; based on the image data and the text data in the balance data set, depth separable convolution feature extraction and BERT semantic coding are carried out respectively, cross-modal fusion is carried out, and fusion features are output; through the steps of depth separable convolution, BERT coding, quantum latent variable evolution and cross-modal semantic verification, the generalization ability, robustness and social adaptability of the multi-modal large model in a complex and real scene are significantly improved.

The invention provides a rolling bearing fault diagnosis algorithm based on an improved VMD optimized CNN-GRU neural network. The rolling bearing fault diagnosis algorithm aims at solving the problem that early faults of a rolling bearing are difficult to recognize under complex working conditions. The method comprises the steps of providing an OCSSA algorithm fusing an eagle algorithm and a Cauchy variation strategy, realizing adaptive optimization of VMD parameters, remarkably relieving modal aliasing, and improving signal decomposition precision; according to the method, the CNN-GRU end-to-end deep diagnosis framework is constructed, time-frequency fusion features are automatically extracted, time sequence dependence is modeled, and dependence on artificial features is reduced; the model performance is verified on a CWRU bearing data set, the average recognition accuracy rate reaches 98.67%, compared with a mainstream model, the precision and operation efficiency are obviously improved, and the good generalization ability and engineering application potential are achieved.

The invention relates to the field of state monitoring and fault diagnosis of power equipment, in particular to a transformer fault diagnosis method based on a chaos evolutionary optimization algorithm, which comprises the following steps of: 1, acquiring a magnetic flux leakage signal during operation of a transformer; 2, optimizing a parameter modal number K and a penalty factor alpha of variational modal decomposition by using a chaos evolutionary optimization algorithm; 3, performing variational mode decomposition on the magnetic flux leakage signal to obtain an intrinsic mode function component; 4, calculating the envelope entropy of the intrinsic mode function component, and obtaining an effective intrinsic mode function component through screening; 5, extracting the energy entropy and the sample entropy of the effective intrinsic mode function component to form a feature vector; and 6, inputting the feature vector into a pre-trained support vector machine classifier, and outputting a fault type diagnosis result of the transformer. According to the method, the CEO algorithm is combined with the ergodicity of chaotic mapping and the global search capability of the evolutionary algorithm, and the problems that VMD parameters K and alpha are sensitive and depend on experience, and a traditional optimization algorithm is prone to local optimum are effectively solved.

The invention discloses a dynamic correction method for digital twinborn model parameters of line equipment of MPC, which belongs to the technical field of power system automation, and comprises the following steps: acquiring original working condition data through distributed edge nodes, extracting load fluctuation, environmental disturbance and mechanical spectrum key features, forming chaotic feature vectors, and carrying out dynamic correction on the chaotic feature vectors; according to the method, the complex interaction is quantified through the multi-physics field coupling entropy source formula, the equipment state evolution process can be described more accurately, the sensitivity of the system to the initial disturbance is further evaluated through introduction of the Lyapunov index, the potential instability risk is warned in advance, and in the power transformer overheating fault prediction process, the method has the advantages of being high in reliability and high in reliability. According to the method, thermal-electric coupling abnormity can be detected several hours ahead of time, sudden shutdown is avoided, the safety and reliability of equipment operation are remarkably improved, real-time self-adaptive correction of parameters is achieved through double-channel parallel calculation, and the adaptability to complex working conditions is remarkably improved.

The invention discloses a cable-stayed bridge cross brace optimization system based on system reliability and an intelligent algorithm, which relates to the technical field of bridge engineering, and comprises a multi-source parameter input module used for collecting and processing data such as a main beam section, stay cable parameters, a strain sequence, a vibration sequence, a satellite cloud picture, a traffic monitoring flow, laser point cloud and the like; and outputting the structured design data, the monitoring data flow, the dynamic load spectrum, the probability distribution model and the construction error correction parameters. According to the invention, all-dimensional data such as design, monitoring, load, materials and construction errors are integrated through the multi-source parameter input module, and multi-level and dynamic reliability evaluation is carried out on the component, the subsystem and the system level by using the system reliability analysis module, so that the component failure probability, the system reliability index time sequence and the like can be output; therefore, the real reliability level of the cross brace of the cable-stayed bridge in the whole life cycle is reflected more accurately, and the one-sidedness of a traditional method is avoided.

According to the large power grid reactive power optimization method and device, the storage medium and the computer equipment provided by the invention, the advantages of the two algorithms are fully exerted through the hybrid chaos quantum particle swarm optimization algorithm and the dimension-by-dimension convex space search algorithm. According to the chaotic quantum particle swarm algorithm, the global search capability and the capability of jumping out of local optimum of a particle swarm are enhanced by utilizing the characteristics of quantum behaviors and chaotic mapping, and the problem of premature convergence of a traditional heuristic intelligent algorithm is avoided. And according to the dimension-by-dimension convex space search algorithm, fine search is carried out on each excellent particle in different dimensions, a local optimal solution is determined, and the search precision and efficiency are further improved. According to the design of the hybrid algorithm, special optimization is carried out aiming at the characteristics of a reactive power optimization problem model, such as variable property difference, constraint complexity and the like, and the technical defects of poor optimization effect and optimization efficiency of an optimization solution algorithm in the prior art are effectively overcome.

The invention discloses an automatic debugging and fault diagnosis system, which relates to the field of automatic equipment maintenance and comprises a data acquisition and preprocessing module, a chaotic feature analysis module, a fault mode identification and prediction module, a debugging and diagnosis execution module and a system management and interaction module. The chaos phenomenon in equipment operation data is deeply analyzed through the chaos feature analysis module, whether the equipment operation state is in a chaos state or not and the chaos degree are accurately judged by using chaos feature parameters such as a Lyapunov index, fractal dimension and correlation dimension, and the fault mode identification and prediction module is combined, so that the fault detection accuracy is improved. The system can recognize a potential intermittent fault mode in advance, predict the fault occurrence time and probability and send out an early warning signal, the fault diagnosis method based on the chaos theory remarkably improves the accuracy and timeliness of fault diagnosis, workers are helped to take preventive maintenance measures in time, the non-planned downtime is shortened, and the fault diagnosis efficiency is improved. The production efficiency is improved.

The invention provides an intelligent operation and maintenance method and system for a data medium table based on a multi-modal large model, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-modal data to construct a knowledge graph, expanding the knowledge graph based on node features and connection weights, matching similar scenes to recognize historical decision information, and generating and evaluating a decision scheme set. An optimal scheme is selected, a new decision scheme is generated through reinforcement learning optimization, and finally operation and maintenance operation is executed and a result is recorded. According to the method, intelligent decision-making and self-optimization of operation and maintenance of the data medium station are realized, and the operation and maintenance efficiency and accuracy are improved.

The invention relates to the technical field of circuit board detection, and provides a circuit board damage detection optimization method and system, and the method comprises the following steps: S1, synchronously collecting a contact resistance signal, a probe pressure signal and environment data of a circuit board test point through a probe array, and carrying out the optimization processing of the collected data parameters; s2, establishing a contact impedance dynamic model, depicting the dynamic change of the contact impedance of the circuit board, extracting a characteristic signal for analysis, and mining the mode characteristics of an abnormal signal; and S3, fusing the abnormal multi-source evidences of the circuit board, processing the uncertainty and relevance among the evidences, and reversely deducing the positions and reasons of the potential defects on the circuit board. Through a multi-dimensional verification and error self-correction mechanism, the error between a defect solution and a real defect is quantified and reversely deduced by using posterior error estimation, so that the reliability and consistency of a detection result are improved, a reliable result support is provided for engineering application of circuit board damage detection, and meanwhile, the dependence on detection experience of detection personnel is avoided.

The invention relates to the technical field of medical health monitoring, in particular to an epileptic seizure cycle HRV feature mining and early warning system and method.Multi-channel electrocardiosignals are collected through wearable equipment, RR intervals are extracted in a layered mode, a high-dimensional manifold and a dynamic graph are constructed, quantum state attention and chaos pooling are combined, and key nodes and attractor modes are recognized; the method comprises the following steps: extracting epileptic risk dynamic characteristics, generating multi-dimensional risk scores and dynamically calibrating, finally outputting graded early warning and intervention suggestions, realizing intelligent prediction and management of epileptic seizure, revealing inherent geometric characteristics of HRV data through a manifold mapping technology, and compared with a traditional Euclidean space analysis method, the method provided by the invention has the advantages that the efficiency is high; the real distance between different physiological states can be measured more accurately, and the accuracy of feature characterization is improved.

The invention discloses an intelligent fault diagnosis system for a cold and heat source center, and belongs to the technical field of fault judgment of heating, ventilation and air conditioning equipment. The system collects an operation feature vector X (t) in real time, and a state signal database stores data; a signal processing module of the upper computer subsystem adopts empirical mode decomposition X (t) to generate an IMF sequence and a residual sequence rn (t), a characteristic analysis module judges chaotic characteristics by calculating the maximum Lyapunov index of each IMF, and a neural network modeling module selects a modeling path according to chaotic / non-chaotic marks to generate a predicted value. The result integration module fuses the multi-path prediction results, compares the multi-path prediction results with a preset threshold interval, and outputs normal, early warning or fault state marks; and the result display module dynamically visualizes the state curve and the maintenance suggestion. According to the system, real-time judgment and early warning of abnormal operation of the cold and heat source equipment are realized, and decision support is provided for preventive maintenance.

The invention discloses an expressway hazardous chemical substance transportation emergency resource scheduling method and system. The method comprises the steps that expressway hazardous chemical substance transportation accident information is acquired through an expressway monitoring system and vehicle-mounted sensor data; according to the accident information, constructing an emergency scheduling bilevel planning model based on a rescue path and rescue cost; solving the rescue cost objective function by adopting an improved enzyme action EAO search optimization algorithm to enable the rescue cost to be the lowest; an improved mirage MSO optimization algorithm is adopted to solve the rescue path objective function, and an optimal rescue path is obtained; according to the solving results of the rescue cost objective function and the rescue path objective function, obtaining the optimal scheduling of the highway hazardous chemical substance transportation emergency resources; according to the method, dynamic collaborative optimization of the distribution scheme and the optimal rescue path is realized, and the rescue cost is reduced while the rescue efficiency is improved.

The invention discloses a task preemption scheduling method, which relates to the field of task preemption scheduling, and comprises the following steps of: performing dynamic task priority analysis and queue topology reconstruction by acquiring task basic data to obtain a coupling task group set, performing resource satisfaction degree decision by acquiring node resource data to obtain a global resource satisfaction degree mark, and performing task preemption scheduling. Performing resource matching degree space-time prediction on the basis of the global resource satisfaction degree mark to obtain optimal preemption time, performing preemption decision on the optimal preemption time and the coupling task group set to obtain a total preemption loss value of the node, and performing resource fragment integration scheduling on the basis of the total preemption loss value of the node to obtain a final scheduling scheme. The node direct scheduling model is created based on the global resource satisfaction degree mark, the optimal node identifier is obtained, the resource fragments are integrated to construct the comprehensive fragment index of the multi-dimensional resources, the overall utilization rate of the resources is improved, the overall income of the system is improved, and the recovery cost and the time overhead of the system can be reduced.

A multi-policy improved mayfly algorithm based on a memory mechanism, which algorithm belongs to the field of swarm intelligence algorithms. The technical solution involves: determining basic parameters of an unmanned aerial vehicle task allocation problem and influence parameter values of a mayfly algorithm; using chaotic initialization to randomly generate mayfly positions, obtaining, by means of decoding, a task allocation scheme corresponding to each mayfly position, and obtaining, according to the scheme, a corresponding fitness value; constructing a memory population on the basis of a memory mechanism, calculating the probability of each mayfly joining a positive memory population, generating the positive memory population, and generating a negative memory population by means of the positive memory population; and fusing an ε-greedy policy into a mayfly position update formula, and updating the mayfly positions. Therefore, the early-stage global search capability is improved; a greedy policy is introduced, such that the early-stage global search capability and the later-stage local search capability are adaptively enhanced; and in the present invention, a memory mechanism is added, such that the synergistic effect between populations is increased, thereby increasing the convergence speed and avoiding unnecessary searches.

The invention relates to the field of artificial intelligence, and discloses a multi-base cooperative detection method based on an improved marine predator algorithm. The method comprises the following steps: adopting a Tent chaotic sequence to guide initialization so as to improve the global exploration capability; dynamically dividing a three-stage search process according to the effective dimension, and adaptively prolonging the global exploration time; constructing Levy disturbance vector enhanced directivity search of chaotic modulation; designing a composite fitness function containing key area weighting and spacing penalty; and an FADs effect and an elite solution library linkage mechanism are introduced to break through convergence stagnation. The system comprises a parameter input module, an environment loading module, a probability calculation module, a chaos generation module, a stage control module, a disturbance modulation module, a fitness evaluation module and the like. The convergence speed and the key sea area detection efficiency of the multi-base sonar layout scheme are improved, and experiments show that the convergence speed is improved, and the key area detection probability is improved.

The invention discloses a lithium iron phosphate battery equivalent model parameter identification method based on improved SCSO. The method comprises the following steps: step 1, constructing a second-order equivalent circuit model, and determining to-be-identified parameters; step 2, collecting current, voltage and time data of the lithium battery under a dynamic working condition, and constructing a fitness function based on a deviation between a model prediction voltage and an actually measured voltage; 3, adopting a chaos initialization strategy to generate an initial population of the improved sodat swarm optimization algorithm, and setting a search boundary in combination with parameter physical constraints; 4, introducing a dynamic weight disturbance mechanism, a self-adaptive variation strategy and a triangular walking strategy, carrying out iterative updating on the position of the salmons population, and reserving a high-quality solution through greedy selection; and step 5, repeatedly executing the step 3 and the step 4 until the fitness function reaches the preset precision or the number of iterations reaches the maximum value, and outputting the optimal identification parameter. According to the method, global exploration and local development capabilities are balanced by dynamically adjusting the search weight, parameter boundary crossing is avoided in combination with enhanced boundary behaviors so as to guarantee physical rationality, population diversity is improved by means of a triangular walking strategy, and inherent defects of a conventional SCSO algorithm in complex parameter optimization are effectively overcome.

The invention belongs to the technical field of electric power, and discloses a load prediction and energy dynamic scheduling method and system for a multi-area micro-grid. Comprising the following steps: acquiring the total load power, the energy storage state, the charging and discharging power and the load power of each transformer area in a forward first time period; inputting the total load power and the load power of each transformer area into a cloud LSTM model to obtain a load prediction baseline of each transformer area in a second time period in the future; constructing a multi-target scheduling model taking the minimization of the total cost and the minimization of the load change rate as targets, and solving the multi-target scheduling model based on a quantum chaos collaborative algorithm to obtain a prediction charging and discharging strategy; inputting the load prediction base line, the prediction charging and discharging strategy, the real-time energy storage state, the real-time charging and discharging power and the real-time load power deviation value into an edge end LSTM model to obtain the charging and discharging power adjustment amount in a third time period in the future; and obtaining a target charging and discharging strategy to carry out energy storage adjustment. According to the invention, energy scheduling can be carried out more accurately to meet actual requirements.

The invention relates to the field of intelligent aided navigation, in particular to a heterogeneous multi-agent collaborative full-scene continuous service system based on semantic comprehension, which realizes seamless navigation experience of visually impaired people among different scenes such as outdoor road sections, public facility internal channels and narrow spaces through a task division and handover mechanism driven by semantic comprehension. The semantic understanding module is used for constructing a semantic topological space and identifying a scene type and a user intention; the task division decision module optimizes a multi-agent cooperation structure based on a spectrogram theory; the task handover control module predicts scene switching opportunity and achieves smooth transition between intelligent agents, the system integrates a seeing-eye dog service intelligent agent, an unmanned aerial vehicle service intelligent agent, an intelligent walking stick service intelligent agent, a wearable equipment service intelligent agent and an environment fixing facility intelligent agent, and the most suitable intelligent agent combination is automatically selected according to different scenes. And an omnibearing, continuous and intelligent navigation service is provided.

The invention relates to a network intrusion detection method based on improved WGAN sampling and ensemble learning, and solves the defects that for high-dimensional and class-unbalanced network flow data, a base learner of an integrated model is insufficient in adaptive capacity, noise interference is difficult to restrain, and key attack modes are difficult to mine in the prior art. The method comprises the following steps: acquiring network flow data; performing data enhancement based on a DDWGLO framework; constructing a network intrusion detection model based on Stacking; training a network intrusion detection model; and detecting network intrusion in real time. According to the method, the DDWGLO is adopted for data enhancement, the weight is adaptively allocated based on the Newton-Raphson optimization algorithm improved on the basis of Circle chaotic mapping, and then the accuracy of network intrusion detection is improved.

The invention discloses a geological data classification method and device based on deep learning, and relates to the technical field of data classification. The method comprises the following steps: constructing a geological data classification model by using a deep learning algorithm based on a double-layer coevolution framework; the method comprises the following steps: collecting multi-source geological data, and preprocessing the multi-source geological data to obtain preprocessed standard geological data; and inputting the preprocessed standard geological data into the geological data classification model to generate a classification result. The problems that in the prior art, multi-source heterogeneous data fusion is difficult, the model optimization efficiency is low, and the model generalization ability is insufficient are solved.

The invention discloses a power distribution network traveling wave fault positioning method and a related device, and relates to the technical field of power distribution networks, and the method comprises the steps: collecting a transient signal after a fault at a preset key monitoring point of a power distribution network, extracting an initial traveling wave head from the transient signal, and measuring the moment when the initial traveling wave head reaches other preset monitoring points in the power distribution network, obtaining traveling wave arrival time between the key monitoring point and each monitoring point; the time difference between the traveling wave arrival time is used as the actually measured traveling wave arrival time difference to construct a fault positioning optimization model with the purpose of minimizing the error sum of squares of the theoretical traveling wave arrival time difference and the actually measured traveling wave arrival time difference; and solving the fault positioning optimization model through a pre-constructed chaos particle swarm optimization algorithm to obtain a traveling wave fault positioning result of the power distribution network. The method solves the problems that the positioning effect is not ideal due to the fact that the prior art is prone to falling into local optimum and is poor in adaptability to a complex topological structure, and the convergence speed cannot meet the requirement for rapid power supply recovery.

The present invention discloses a microgrid multi-objective optimization method and system based on an improved SABO algorithm, relating to the field of energy management technology for microgrids. The method comprises the following steps: establishing a microgrid multi-objective optimization scheduling model based on distributed power sources in the microgrid; introducing a chaotic mapping mechanism, an elite-guided development strategy, and a golden sine algorithm into the subtraction average optimization algorithm to obtain an improved subtraction average optimization algorithm; constructing a multi-objective improved subtraction average optimizer based on the improved subtraction average optimization, using the multi-objective improved subtraction average optimizer to solve the multi-objective optimization scheduling model, and performing energy scheduling on the microgrid based on the solution results. The present invention uses the improved multi-objective SABO to solve the microgrid multi-objective optimization scheduling model, thereby ensuring a uniform distribution of the Pareto solution set and improving the calculation speed, thereby achieving efficient multi-objective optimization scheduling of the microgrid.

A method for detecting wave vortex coupling and vortex-induced resonance phenomena in a stirring reactor comprises the following steps: 1) simulating the stirring reactor, and collecting velocity field data in the stirring reactor, torque data of a stirring paddle and average stress data; 2) decomposing the torque data and the average stress data; 3) obtaining a Q criterion trailing vortex structure of a rotation domain based on the velocity field data; 4) determining a mapping relation between the torque data and the Q criterion trailing vortex structure and a mapping relation between the Q criterion trailing vortex structure and the average stress data; 5) stirring by using the stirring reactor, and collecting actual torque data in the stirring process; 6) determining an actual Q criterion trailing vortex structure through the actual torque data; determining actual average stress data through an actual Q criterion trailing vortex structure; and 7) predicting the torque data and the service life of the stirring paddle at the next moment by using the artificial intelligence chaos controller. According to the invention, detection of wave vortex coupling and vortex-induced resonance phenomena in the stirring reactor is realized.