26 results about "Fuzzy set" patented technology

The application discloses a kind of based on measurement and knowledge water surface unmanned system operating state early warning method, belong to unmanned system safety monitoring technical field.To solve the problem of insufficient knowledge utilization, poor dynamic adaptability of existing early warning method, the method of the application includes: collecting position, attitude, ship body state and environment multi-source time series data, and obtaining text form expert knowledge;Adopt sliding window and standardization preprocessing data;Expert knowledge semantic vector is extracted using BERT model;Through Crossformer model coding time series features, knowledge vector is spliced and fused, and system state features are output;Risk membership degree is calculated based on fuzzy set theory, and early warning is triggered according to the maximum membership principle.The application realizes the deep integration of measurement data and expert knowledge, improves the early warning accuracy, robustness and generalization ability.

This invention discloses a method, apparatus, equipment, and medium for the sub-Bruker optimal scheduling of hydropower stations, relating to the field of hydropower station scheduling technology. It includes: determining a fuzzy set of inflow error based on the historical estimated and actual historical inflow of at least one hydropower station; constructing a sub-Bruker optimal scheduling model for the hydropower station based on the historical output, historical head, and historical power generation flow of at least one hydropower station, as well as the fuzzy set of inflow error; reconstructing the sub-Bruker optimal scheduling model based on conditional value-at-risk theory to obtain a reconstructed optimal scheduling model; and determining the target output, target head, and target power generation flow of each hydropower station based on the reconstructed optimal scheduling model and the target flow estimated value of at least one hydropower station. This solution reduces the water wastage of hydropower stations and improves their power generation and operational stability by determining the fuzzy set of inflow error and establishing a sub-Bruker optimal scheduling model.

This invention discloses a packet loss detection method for unmanned surface vessel (USV) formations based on adaptive fuzzy membership filtering. The method includes: establishing state equations describing the motion of both the leader and follower USVs; linearizing these nonlinear equations to simplify the complex curvilinear motion relationships into linear mathematical expressions, thus obtaining simplified state equations for the entire formation system; introducing an adjustable measurement noise figure based on the simplified formation model, dynamically adjusting the filtering intensity according to the current measurement noise level, thereby more accurately estimating the real-time state of the USVs; and simultaneously detecting packet loss during state updates, combining preset communication quality indicators and real-time calculated noise levels to determine the reliability of received data. This invention achieves good detection results for packet loss in noisy channels and can be widely applied in the field of data detection.

The application provides a vehicle fault diagnosis method and device based on multi-source data fusion and a storage medium. The method comprises: collecting real-time state data and real-time alarm data of a target part, obtaining external environment data and map navigation data; after data preprocessing, the multi-source data set target features are obtained by fusion, the target features extracted from the multi-source data set are input into the adaptive neural fuzzy inference system, the target features are mapped to multiple fuzzy sets, and the member function is used for fuzzification; the rule weight of each target feature is calculated to reflect the fuzzy strength; the neural network learning algorithm is used to train the inference system, the member function and the rule weight are adjusted, the fuzzy output is converted into a specific numerical value or a fault level in the defuzzification process by using the output member function, so that the health state of the part is judged. The application provides a fault diagnosis output with high interpretability, improves the accuracy, and can perform fault diagnosis in a wide range of vehicle operating environments.

This invention discloses a spacecraft autonomous mission decision-making method for complex stochastic tasks, belonging to the field of spacecraft mission decision-making technology. The method includes the following steps: S1, establishing a spacecraft mission decision-making model considering disjoint robustness; S2, transforming the model into a two-stage optimization framework problem; S3, introducing fuzzy sets to perform cone optimization transformation on the second-stage decision; S4, transforming the model into a numerically solvable cone optimization problem based on duality theory; S5, solving the cone optimization problem to complete mission scheduling. This invention adapts uncertainty through WKS-format fuzzy sets, combines resource consumption matrices and affine decision rules to avoid resource mismatch, significantly improving the efficiency and robustness of spacecraft mission decision-making and meeting the requirements of real-time dynamic scheduling.

The application discloses a cross-border network anomaly detection method based on intuitionistic fuzzy set and double-path learning, and belongs to the technical field of network security. The application aims at the problems of unstable feature form and poor cross-domain generalization ability caused by ignoring cognitive uncertainty in the existing method, proposes an IRU uncertainty evaluation paradigm, decomposes feature evaluation into three dimensions of evidence strength, mixed degree and form stability, and maps them to the intuitionistic fuzzy set domain; designs a risk-averse robust credibility score RTS to screen credible elite features; and constructs a double-path fidelity training architecture, which constrains the model decision logic to be anchored on the credible features through a fidelity loss function. The application realizes the synchronous improvement of detection performance and feature selection stability on multiple cross-border intrusion data sets, is especially suitable for cross-border network environments with strong resistance and significant distribution offset, and has the ability of rapid cold start deployment.

This invention relates to the field of communication network technology and provides a method for remote communication network handover for a terminal. The method includes: real-time monitoring of signal quality parameters of the current serving network; network optimization evaluation based on triggering conditions; outputting an initial joint score for the current serving cell; acquiring basic measurement data of all cells based on multiple operator frequency points; filtering candidate cells to generate a candidate list; performing refined data acquisition on the candidate cells to obtain multi-dimensional parameters; generating a complete fuzzy feature vector based on fuzzy set theory; calculating a corrected joint score for the candidate cells based on the initial joint score and the complete fuzzy feature vector; further calculating a comprehensive score and ranking to determine the optimal handover target; while maintaining the current serving network connection, establishing a shadow connection and pre-building a context; performing an atomic handover at the optimal handover time; and performing posterior verification based on the complete fuzzy feature vector to complete the network handover. This invention achieves low-latency, high-reliability, and seamless network handover.

The application discloses a kind of hybrid AC-DC system distributed robust chance-constrained optimal power flow calculation methods, it is related to power system operation and control technical field.The method includes: the system data model of fixed efficiency VSC loss model is built;Definition optimization variable and power generation cost minimization objective function;Traditional OPF constraint is built in AC-DC coupling;Wasserstein fuzzy set and minimum volume ellipsoid uncertainty set are constructed based on historical sample;High-precision sensitivity matrix is constructed by full nonlinear power flow disturbance calculation using finite difference method based on AGC participation factor;Two-dimensional wasserstein dimension reduction technique is used to convert distributed robust chance constraint into deterministic robust constraint;Finally, the optimal solution is obtained by using interior point method and output.The application significantly improves the voltage safety level and operation economy of the system under renewable energy fluctuation, and is efficient in calculation, suitable for engineering practice.

This invention relates to the field of integrated defense and early warning technology for clustered targets, and discloses a clustered target threat early warning method based on intuitionistic fuzzy game theory and consensus decision-making. This method collects raw data from multiple sensors and quantifies it into an intuitionistic fuzzy set to generate a clustered integrated decision matrix; calculates the degree of consensus and hesitation potential energy; when consensus is insufficient, it generates resource focusing instructions to drive sensors to adjust physical parameters and perform feedback correction to form a consensus closed loop; it constructs a strategic conflict model to perform game-theoretic linkage analysis to solve for optimal attribute weights and calculates a dynamic early warning threshold; finally, it calculates the final early warning score based on the MABAC algorithm, compares it with the dynamic threshold to trigger an alarm, and outputs a dynamic interpretation vector. This invention solves the problems of decision oscillation and poor adaptability to fixed thresholds in complex electromagnetic environments by using physical information synergy enhancement and strategic game threshold drift mechanisms, thereby improving the accuracy and interpretability of early warnings.

The application discloses an administrative region earthquake risk grade expression and engineering site selection method, system and medium, and the administrative region earthquake risk grade expression is based on fuzzy sets, so that the administrative region grade simultaneously contains a conventional grade and internal deviation (lower deviation and upper deviation), and information loss caused by averaging is reduced; and important engineering site selection based on the fuzzy grade is provided to realize sorting and screening of candidate sites.

The application provides a power distribution network power supply service recovery planning method and device for extreme weather and related products, and relates to the technical field of power distribution networks. The method pre-establishes a flexible microgrid topology based on branch interruption scenarios, optimizes the allocation of mobile repair power supply vehicles, and configures node black start capability; when an extended extreme event occurs, the microgrid topology is reconstructed, the network topology and node power-on state are adjusted; the reinforcement scenario intelligent fuzzy set and the wind power output fuzzy set are used to represent two types of uncertainties, namely branch random interruption and wind power output fluctuation; a distributed robust optimization model is established by combining distributed system operation constraints and long-time energy storage operation constraints; the column and constraint generation algorithm with parallel alternating optimization method is used to solve the model; finally, the power distribution network recovery service performance evaluation coefficient is calculated to quantitatively evaluate the recovery effect. The application can effectively cope with multiple uncertainties under extreme weather, optimize the recovery resource scheduling, and improve the robustness and economy of the power distribution network power supply recovery.

This invention discloses a source-grid-load-storage coordinated scheduling method based on divergence regularization and bibliometric optimization, belonging to the field of power system optimization. Addressing the problems of random output fluctuations, distribution shifts, insufficient robustness of traditional methods, and inefficiency in solving problems under high-proportion wind and solar grid integration, this invention first constructs a continuously differentiable comprehensive loss function containing multiple uncertainties and soft and hard constraints. Then, it uses a generalized Sinkhorn distance with χ²-divergence regularization and Gaussian reference measure to construct a fuzzy set, equivalently transforming the original Min-Max problem into a nested two-layer continuous dual model. Finally, it employs a nested stochastic gradient descent algorithm, with the inner layer estimating the dual multipliers and the outer layer updating the scheduling strategy and projecting it onto the physical feasible region, achieving a rapid solution. This invention overcomes the limitations of historical data support sets, can prevent risks from unknown extreme weather events, avoids the dimensionality curse, is compatible with first-order AI algorithms, and balances grid operation safety and economy, making it suitable for robust scheduling of large-scale, high-proportion renewable energy power systems.

The present application relates to a kind of method for judging landslide disaster risk using bipolar fuzzy set, comprising the following steps: step 1), data collection includes two parts;First part: collect landslide point data;Second part: collect Sentinel-1A radar image data, precise orbit ephemeris data, Google satellite image data, DEM data, slope, slope and curvature data, fault data, river system data, geomorphic type data, road network data, seismic intensity data;Step 2) determination of landslide disaster risk evaluation factor;Step 3) establish landslide disaster risk evaluation model based on bipolar fuzzy set.The result of the present application using bipolar fuzzy set evaluation method is basically consistent with the result of BP neural network evaluation.By taking Zhaotong City, Yunnan Province, Luding County as an example, it is finally concluded that the evaluation results of 10 of the 12 townships are relatively close, reaching 83%, which shows that the method has strong applicability;Thus it can be popularized in other areas.

This invention relates to the field of automatic control technology for rail transit, specifically to a method for optimizing train speed curves considering wind resistance uncertainty. The method includes: 1) allocating redundant time based on a train dynamics model and particle swarm optimization algorithm to generate an offline feasible trajectory dataset that satisfies operational constraints; 2) using an offline reinforcement learning framework with behavioral cloning and discriminator regularization terms to train a baseline control strategy using the offline feasible trajectory dataset; 3) constructing an empirical distribution based on historical wind resistance data and using a Wasserstein distance metric to construct a fuzzy set containing deviations from the actual wind resistance distribution; 4) using the fuzzy set as an adversarial training environment to perform secondary reinforcement training on the baseline control strategy under the worst-case wind resistance distribution, generating a final robust optimal control strategy. This method enables high-speed trains to achieve a balanced optimal performance across all basic requirements within extreme wind resistance constraints.

The application discloses a kind of multi-dimension effective research and judge transformation power station in fire alarm system false alarm method, through acquisition and fusion smoke concentration, temperature, humidity, electromagnetic field intensity, grounding state and so on multi-channel sensor data, combine environmental label with operation and maintenance log to construct scene data set;Through normalization, wavelet denoising, dimensionality reduction and so on multi-level feature processing, improve data quality and distinguishability;Using space-time graph neural network realizes the division of multi-time period environment subarea and typical signal mode, and based on fuzzy set theory and bayesian inference, alarm signal is fused and judged in multiple time periods, realize the dynamic discrimination of false alarm and real alarm, the accuracy and robustness of the present application are effectively improved under complex environment alarm discrimination.

The application relates to a double-layer optimization method and system based on an energy concentrator, which firstly constructs a double-layer optimization model of a master-slave game architecture, wherein an upper-layer model of the double-layer optimization model takes minimization of total operation cost as an optimization target and comprises first-stage day-ahead scheduling decision and second-stage real-time adjustment decision; the double-layer optimization model is reconstructed into a single-layer mixed integer linear programming model through KKT conditions and strong duality theory; experience distribution is generated based on historical data and Huber fuzzy set is constructed to depict RG uncertainty, so as to form a min-max-min distribution robust optimization model under the first-stage day-ahead scheduling decision-worst distribution selection-second-stage real-time adjustment decision architecture; and finally, a customized C&CG algorithm is adopted for iterative solution. The method balances economy and robustness, improves multi-subject coordination and source-load interaction efficiency, and provides a stable and efficient scheduling scheme for the energy concentrator.

The application discloses a parameter tuning method, which comprises the following steps: acquiring a performance result of a business system running under partial parameters, wherein the partial parameters are part of all parameters of the business system; performing fuzzy processing on the performance result according to multiple fuzzy sets respectively, to obtain multiple membership degrees, wherein the multiple membership degrees and the multiple fuzzy sets correspond to each other; determining multiple groups of fuzzy recommended parameters according to the multiple membership degrees, wherein the multiple membership degrees and the multiple groups of fuzzy recommended parameters correspond to each other; and performing defuzzy processing on the multiple groups of fuzzy recommended parameters, to obtain target recommended parameters, wherein the target recommended parameters are used to update the partial parameters. The application can perform parameter tuning under the condition that the parameters are incomplete, solves the problem that the system performance is not reliable due to the incomplete parameters, and finally recommends a parameter combination meeting the performance requirements of the business system.

This invention provides a method and system for dynamic reconfiguration and load restoration of balanced subnets. It constructs a two-stage optimization model: the first stage determines mobile resource deployment and dynamic topology connections; the second stage determines equipment power output under uncertainty. After system modeling and defining multiple types of decision variables, an objective function is constructed to maximize the difference between the total weighted restored load and the total active power loss. Three types of constraints are established: topology, equipment operation, and system safety, and the schedulable load is linearized. To address the uncertainty of renewable energy output, Wasserstein fuzzy sets are introduced to construct bibru bar chance constraints. These constraints are transformed using conditional value at risk theory, and the model is then reconstructed using strong duality theory and the stochastic saddle point theorem, transforming it into a mixed integer convex optimization problem with second-order cone constraints. This invention can efficiently achieve dynamic networking and load restoration optimization of balanced subnets while ensuring system safety.

The application discloses a kind of considering source load uncertainty electric carbon coupling system safety boundary construction method, it is related to power system operation and control technical field, including the following steps: S1. establish electric carbon coupling system linearization model, S2. based on data distribution robust fuzzy set is constructed, S3. optimization problem is established with distribution robust chance constraint, S4. CVaR approximation is used with dual theory is transformed into deterministic programming, S5. the explicit expression of dynamic safety boundary is obtained by iterative algorithm.The application generates the intersection of dynamic safety boundary which is physical safety domain and low-carbon feasible region by deeply embedding carbon emission flow dynamic tracking mechanism into the whole process of safety boundary construction, and the out-of-limit risk of line power, node voltage and node carbon potential can be directly monitored by dispatcher, so that the unified, quantitative and visualized representation of electric-carbon dual safety state of system is realized, and the operation risk caused by ignoring carbon constraint is fundamentally avoided.

This invention discloses a hierarchical virtual power plant scheduling method based on EWMA adaptive dynamic carbon potential coordination. The method involves modeling the virtual power plant and constructing a hierarchical scheduling architecture. Historical operating samples of the virtual power plant are processed hierarchically using a distributed sieve to construct a joint fuzzy set. An objective function is constructed using the day-ahead scheduling layer and the joint fuzzy set to obtain a baseline scheduling plan. The baseline scheduling plan is corrected using a real-time correction layer to obtain corrected scheduling amounts for each energy device. The joint fuzzy set is updated according to the EWMA adaptive mechanism. Based on the updated joint fuzzy set, an uncertainty propagation model and a carbon propagation model for the virtual power plant are constructed. Carbon potential analysis of the virtual power plant is performed based on the uncertainty propagation model, the carbon propagation model, and the corrected scheduling amounts, thereby optimizing the cross-energy scheduling strategy of the virtual power plant. Through closed-loop optimization of uncertainty modeling, carbon-energy coordination, and scheduling decisions, the robustness of the virtual power plant under multi-source disturbances and uncertainties is improved.

This invention relates to a human-machine universal cognitive semantic decoding method for autonomous driving in mixed traffic. It aims to dynamically adjust the temporal boundary of data sampling based on the urgency of the driving task and the required cognitive load, thereby reducing the cognitive and decision-making differences between the driver and the autonomous driving system, and achieving human-machine cognitive semantic decoding. First, a dynamic time window model is constructed based on task analysis. Second, discrete questionnaire data is encoded into interval type-2 fuzzy sets with uncertain coverage. Then, a hierarchical and cascaded cognitive fuzzy inference architecture is constructed to achieve situation identification and risk assessment. Finally, semantic decoding is achieved through fuzzy inference and type reduction algorithms, transforming quantized cognitive vectors into vehicle control parameters. This method builds a human-machine universal cognitive semantic decoding framework based on interval type-2 fuzzy logic, establishing a universal cognitive interaction mechanism that allows mutual understanding between humans and machines, and achieving autonomous driving decision-making and control with both high interpretability and human-like characteristics.

Embodiments of the present application provide a scheduling method of an integrated energy network, a computer device and a storage medium. The method comprises: scheduling energy equipment in the integrated energy network to obtain candidate scheduling data, taking the minimum cost of the integrated energy network as the target; determining an error fuzzy set according to the candidate scheduling data and corresponding prediction data of the integrated energy network; performing variational inference on the error fuzzy set to obtain target error data with distribution determined; adjusting the candidate scheduling data according to the target error data to obtain target scheduling data of the integrated energy network, and scheduling the integrated energy network according to the target scheduling data. The accuracy of scheduling the integrated energy network is improved.

The application discloses an arch dam digital twin vibration sensor optimization arrangement method considering uncertainty, comprising the following steps: 1) establishment of an arch dam finite element model and sampling of material parameters; 2) arch dam modal analysis and noise simulation based on the finite element model; 3) modeling of the arch dam vibration sensor optimization arrangement considering modeling uncertainty and noise influence into a multi-objective optimization problem, and optimization and solution by using an improved multi-objective and sound optimization algorithm based on integer coding; and 4) selection and output of an optimal arch dam sensor arrangement scheme by using a compromise optimization method based on fuzzy set theory. According to the method, the material parameters are modeled as random variables to represent the modeling uncertainty of the arch dam, and random noise is introduced into the corresponding mode to simulate the influence of noise on the modal shape in actual testing, so that the obtained optimal sensor arrangement scheme has stronger anti-interference capability and can guarantee that the digital twin can still obtain reliable sensing data in an uncertain environment.