154results about "Fuzzy logic based systems" patented technology

The application relates to the technical field of brain-computer interfaces, in particular to a power multi-person collaborative operation authorization system and method based on a brain-computer interface. The method comprises the following steps: constructing a multi-person collaborative cognitive index system to obtain a multi-modal cognitive factor set, and constructing a value judgment matrix; a least square optimization model is established to obtain a subjective weight and perform consistency checking; objective information entropy weight is extracted in combination with historical data to obtain a comprehensive value weight and construct a fuzzy measure function; a comprehensive authorization priority evaluation value is calculated according to a real-time cognitive factor characteristic vector; disturbance analysis is carried out by using statistical distribution and Monte Carlo sampling to obtain an authorization return success rate and an authorization time delay confidence interval. The application can solve the problems of instruction transmission delay and cognitive ambiguity under strong electromagnetic interference, and through the combination of multi-person physiological cognition and edge computing power, the collaborative quality and authorization decision credibility in a high-risk power operation environment are improved.

The application relates to the technical field of electrical equipment evaluation, in particular to a multi-dimensional electrical equipment stability evaluation method and device for new energy power grid connection, which comprises the following steps: based on a multi-dimensional evaluation index system, collecting multi-dimensional index data of a target electrical equipment, performing feature enhancement processing, and generating processed enhanced features; based on a grid connection power fluctuation coefficient and an equipment operation stage coefficient of the target electrical equipment, adjusting the weight proportion of each dimension and corresponding indexes in the multi-dimensional index data to obtain dynamic weights; inputting the processed enhanced features and the dynamic weights into an evaluation model combining fuzzy comprehensive evaluation and Markov chain prediction, performing stability evaluation on the target electrical equipment for new energy power grid connection, and outputting a stability evaluation result of the target electrical equipment. Therefore, the problems that in the related art, prospective decision support cannot be provided for preventive maintenance and grid connection scheduling, and it is difficult to guarantee the safe and stable operation of the power grid under high-proportion new energy access are solved.

The application relates to a response capability grading evaluation method, device and equipment based on cluster analysis. The method performs secondary clustering on the power consumption terminal according to power consumption information and response information of the power consumption terminal under different dimensions, obtains response capability grade evaluation results of the power consumption terminal under different dimensions, comprehensively determines the target response capability grade of the power consumption terminal according to the response capability grade evaluation results of the power consumption terminal under multiple dimensions, improves the comprehensiveness of information involved in the response capability grade evaluation of the power consumption terminal on the basis of fuzzy evaluation, and further improves the reliability of the response capability grade evaluation of the power consumption terminal, so that the accuracy of the demand response capability evaluation of the power consumption terminal is improved.

This invention discloses a digital identification system and method for high-pressure testing instrument detection data. The system includes: a data acquisition and conversion module for acquiring and converting analog signals; a data preprocessing module for noise removal and anomaly detection; a key parameter extraction module for extracting and optimizing key parameters; a data analysis and identification module for analyzing data and issuing early warnings of anomalies; a feedback adjustment module for optimizing test parameters; and a display and control module for displaying data, analysis results, and alarm prompts. This invention combines sliding window time series analysis, Kalman filtering, temporal convolutional neural networks, and adaptive fuzzy logic control algorithms to intelligently analyze and process the detection data of high-pressure testing instruments, enabling fault early warning, precise optimization, real-time adjustment, and maintenance decision-making. The system features high precision, strong real-time response capabilities, and significant optimization and maintenance effects, significantly improving data processing capabilities and decision-making efficiency during high-pressure testing.

A laser-based target damage evaluation method, device and equipment, the laser-based target damage evaluation method comprising: obtaining a target damage evaluation system; calculating the target power density of laser through the target power density model of laser; predicting the response of each evaluation index of target through the damage effect proxy model based on the target power density of laser; calculating the fuzzy evaluation result vector of each evaluation index according to the response of each evaluation index and the corresponding membership function; according to the fuzzy evaluation result vector of each evaluation index and the corresponding weight, weighted fusion is carried out layer by layer to obtain the fuzzy evaluation result vector of the whole target; the comprehensive vector is obtained by normalizing the fuzzy evaluation result vector of the whole target, and the damage level corresponding to the maximum membership degree vector in the comprehensive vector is evaluated as the damage level of the target. Through the present application, the final target damage evaluation result can be more accurate.

The present application relates to the technical field of intelligent decision-making assistance for financial marketing, and particularly relates to a decision-making assistance method and system for personalized scenarios. The method comprises the following steps: constructing a fuzzy complementary judgment matrix, calculating an initial weight vector based on the fuzzy complementary judgment matrix, obtaining a candidate weight vector through row coupling degree and column coupling degree constraint optimization, generating a personalized weight vector according to the vector similarity between a borrower feature vector and the cluster centers of each customer cluster, synthesizing the borrower feature vector and the personalized weight vector to obtain a decision result, performing credit compliance verification on the decision result, and outputting the decision result if the verification is passed. The present application obtains a comprehensive scenario adjustment vector by using the vector similarity between a borrower feature vector and the cluster centers of each customer cluster, and optimizes the candidate weight vector using the comprehensive scenario adjustment vector to obtain a personalized weight vector, so that personalized credit decision-making can be realized.

This invention proposes a method and system for recommending English annotation methods with low forgetting rate based on the Analytic Hierarchy Process (AHP), belonging to the fields of optimization and intelligent decision theory. First, from the perspective of English learners, this invention analyzes and identifies five key factors affecting the rate of forgetting English vocabulary. Then, using the Analytic Hierarchy Process (AHP), a weight matrix that passes consistency detection is constructed for each influencing factor, and the actual influencing factor values ​​are normalized based on fuzzy logic. Based on the weight matrix that passes consistency detection and the normalized influencing factor scores under different text annotation methods, the estimated forgetting rate of English vocabulary for English learners under the corresponding annotation methods is obtained. The annotation method with the lowest estimated forgetting rate is selected as the optimal recommended annotation method to assist English learners in vocabulary learning. Finally, a one-month experimental verification of the proposed recommendation algorithm is conducted, and the experimental results demonstrate the effectiveness of this invention.

This invention relates to a knowledge and data fusion-driven, interpretable primary headache auxiliary identification method. The method includes: collecting patient information and preprocessing it to obtain a multi-dimensional feature vector; inputting the multi-dimensional feature vector into an interpretable primary headache auxiliary identification model to obtain a probability distribution of headache identification results; outputting the headache type with the highest probability and its identification confidence, and generating a risk warning; wherein, the hierarchical fuzzy logic layer of the interpretable primary headache auxiliary identification model, based on the multi-dimensional feature vector, calculates the fuzzy membership degree of each feature when it belongs to different headache types according to the differentiated membership function of different headache types, obtaining fuzzy membership degree features; the KAN feature fusion network fuses the fuzzy membership degree features with the original vector to obtain classification features; the classifier outputs the probability distribution of headache types based on the classification features. Compared with existing technologies, this invention provides a solution for intelligent headache diagnosis that combines accuracy, robustness, and interpretability.

The present application belongs to the technical field of federated learning, and particularly relates to a space information network bidirectional individualized federated learning method, system and device, which comprises the following steps: firstly, aggregating pre-training models of each ground station to obtain a global initial model and broadcasting the global initial model to clients; in subsequent rounds, a satellite distributes ground station models to clients in a targeted manner; the clients aggregate multiple ground station models by weighting with historical similarity as the weight, generate an individualized initial model for training, calculate the similarity metric and membership of the client model and the ground station model, fuse the historical similarity to update the similarity metric and membership, assign a set of collaborative ground stations to the clients and forward the model, and the ground stations aggregate the client models by weighting with historical similarity as the weight to obtain updated specialized models. Through the cooperative bidirectional individualized mechanism, the present application solves the model adaptation problem caused by data heterogeneity in the space information network, ensures the stability of training, and improves the performance of the individualized models of the clients and the ground stations.

In a method of predicting an optimal process condition model for a semiconductor fabrication process, process parameter information of a unit process in the semiconductor fabrication process may be collected. First characteristics information of objects to be processed before the unit process and second characteristic information of processed objects after the unit process may be extracted. Process global uniformity (PGU) may be calculated using the first characteristic information and the second characteristic information. A data set of the unit process may be created using the process parameter information and the PGU. A virtual process environment function of the unit process may be created using the data set. The optimal process condition model of the unit process may be created using the virtual process environment function.

This invention proposes an innovative model predictive control-based current control method and system for permanent magnet synchronous motors (PMSMs), aiming to address the problems of slow dynamic response and low control accuracy in traditional control methods when facing parameter variations and external disturbances. By constructing an accurate mathematical model, this invention not only improves the dynamic response speed and control accuracy of the motor but also significantly reduces torque ripple, thereby enhancing the stability and reliability of motor operation. Furthermore, the introduction of intelligent algorithms such as neural networks or fuzzy logic, as well as adaptive control algorithms, further enhances the system's adaptability and robustness. The application of space vector modulation technology optimizes the current waveform, further reducing torque ripple and improving the smoothness of motor operation. The control method of this invention is suitable for industrial applications with extremely high requirements for dynamic performance and control accuracy, demonstrating its broad application potential in the field of modern industrial motor control.

A method, system and apparatus for configuring a computing resource of a service are provided, which relate to the field of resource configuration. The method includes that: an online learning algorithm that is used for recommending, to each service on a platform, a computing resource quota adapted to a service type of the service is preset; when a new service is launched on the platform, a target computing resource quota adapted to the service type of the new service is recommended according to the online learning algorithm; and initial configuration is performed for a computing resource of the new service according to the target computing resource quota.

The present invention belongs to the field of intelligent computing, and specifically relates to a multi-task collaborative attention TSK fuzzy system modeling method, which is used to provide an interpretable multi-task intelligent evaluation model for fermentation food safety assessment. The method includes two main parts: a new multi-task TSK fuzzy system model structure and a multi-task collaborative optimization process. In the intelligent evaluation model, the proposed multi-task collaborative processing unit is used to perform collaboration among multiple evaluation tasks. By using the multi-task feature selection layer and task attention structure respectively to extract the unique information of each task and the relevant information between tasks, the evaluation performance of each evaluation task can be better improved. In the collaborative optimization process, the present invention uses multi-task collaborative regularization to achieve more efficient mining and utilization of the unique information of each task.

The application belongs to the technical field of data processing, and provides a kind of engineering structure flexibility level calculation method based on fuzzy evaluation method, comprising: establishing the finite element model of target engineering structure, according to finite element model and historical earthquake record, nonlinear time history analysis is carried out on target engineering structure, and seismic vulnerability curve is established;According to the seismic vulnerability curve, determine a plurality of loss variables, assign each loss variable to its own triangular fuzzy number, and obtain the standardized fuzzy decision matrix after standardization processing;Based on the standardized fuzzy decision matrix, combined with importance judgment and fuzzy solution, the weight coefficient of each loss variable is determined;Based on the standardized fuzzy decision matrix and weight coefficient, combined with the approximation of ideal solution sorting algorithm, the structure flexibility level index is obtained.The scheme provided by the application can not only comprehensively integrate multidimensional loss variables, but also process the fuzziness and uncertainty of the whole evaluation process.

The present application relates to the technical field of context adaptation, and specifically discloses a multi-dimensional perception adaptive counter-question triggering and context adaptation method and system, comprising the following steps: step S1: performing semantic fuzzification processing on an initial creative text input by a user, extracting keywords and assigning them to a preset fuzzy set to generate an intent fuzziness vector; step S2: combining the correlation degree matrix between each fuzzy set to calculate an intent entropy value for quantifying the uncertainty of the user's intent; step S3: when the intent entropy value exceeds a threshold value, automatically triggering a challenging counter-question to guide the user to supplement key information, and splicing the feedback and the original text to form an enriched creative context; and step S4: inputting the creative context into a plurality of pre-built expert agents, each agent outputting professional challenge opinions based on a domain knowledge base; screening out effective opinions by analyzing the support degree between the challenge opinions, and performing semantic fusion with the creative context to generate an optimized creative scheme text.

This invention relates to short-term wind speed prediction using an interval-type 2 T-S fuzzy model based on phase space reconstruction, belonging to the field of intelligent prediction and advanced control technology for new energy power systems. It solves the problems of large errors, long prediction times, and unanalyzable logic in existing wind speed prediction models. The method includes: collecting and preprocessing wind speed data up to the current moment to obtain a wind speed time series; reconstructing the phase space of the wind speed time series to obtain a phase space trajectory matrix; inputting the phase space trajectory matrix into a trained interval-type 2 T-S fuzzy model to output short-term wind speed prediction values; wherein, in the interval-type 2 T-S fuzzy model, the antecedent uses an irregular Gaussian membership function to describe the fuzzy characteristics of the input variables, and the left and right standard deviations of the irregular Gaussian membership function are adjustable. This achieves effective extraction of chaotic features from the wind speed sequence and real-time prediction of short-term wind speed.

The application develops a generalized limit residual thrust calculation method for three-dimensional stability evaluation of slope, and belongs to the field of geotechnical engineering safety analysis. The application effectively solves the inherent limitation problem that the traditional two-dimensional limit equilibrium method cannot truly reflect the three-dimensional space effect and lateral constraint effect of slope instability, and also solves the problem that the existing three-dimensional method leads to non-unique calculation results and insufficient precision due to too many assumptions of inter-strip forces. By establishing a micro-unit body sliding surface control differential equation under a symmetric mode and fusing a fuzzy programming search strategy, the most dangerous three-dimensional sliding surface form is automatically identified, and a unique minimum safety factor solution is given. The method significantly improves the objectivity, accuracy and reliability of the slope stability evaluation, and provides a scientific basis and evaluation theoretical method for the safety analysis and disaster prevention decision of slope engineering under complex load.

The application discloses a method and system for selecting injection wells for profile control in large-well-spacing reservoirs, and belongs to the field of oilfield enhanced oil recovery. An index evaluation system and a comprehensive evaluation mathematical model are established, the evaluation weights of indexes in the index evaluation system are determined, the index values of each well group to be evaluated in the comprehensive evaluation mathematical model are normalized, the index parameter values of each well group to be evaluated are taken as input values, the comprehensive evaluation values of the profile control potential of each well group to be evaluated are obtained, the profile control potential comprehensive evaluation values of each well group to be evaluated are compared with the preset profile control well selection threshold value, and it is determined whether the well group to be evaluated is selectable or not. The potential index of each injection well group in a reservoir for deep profile control at a certain time point is quantitatively calculated and evaluated, the profile control feasibility of the well group is comprehensively evaluated, and the profile control potential of each well group in the same reservoir is further compared horizontally. The application has wide data sources, is convenient to implement, and has reliable evaluation results.

Graphical element search technique selection, fuzzy logic selection for anchors and targets, and hierarchical graphical element identification for robotic process automation (RPA) are disclosed. The fuzzy logic selection of anchors and targets may be part of a larger, tiered, or hierarchical process for identifying graphical elements in the UI. When a selector for a UI element is not found with at least a confidence threshold, similar elements potentially corresponding to the selector for a UI element target may be searched based on fuzzy matching of the target and corresponding anchor(s). Geometric matching may also be employed between the target UI element and its respective anchor(s). The combination of fuzzy matching and geometric matching may allow for more flexible and accurate identification of the exact selector with which an RPA robot is attempting to interact.

Provided in the embodiments of the present disclosure are a management system, method and apparatus for a distributed system, and a computing device. The management system comprises task processing nodes and an assessment node; the task processing nodes are configured to acquire corresponding target decision-making data in response to a target management task of a distributed system, and generate node decision-making results of the target management task on the basis of decision-making logic of the task processing nodes themselves and the target decision-making data; the assessment node is configured to acquire the node decision-making results generated by the task processing nodes, and, on the basis of global constraints and the node decision-making results, determine a global decision-making result of the target management task. The complete decision-making process is divided into two stages for processing, where each task processing node independently makes decisions on the basis of its own decision-making logic, and then the assessment node assesses the node decision-making results on the basis of the global constraints; there is no awareness between the task processing nodes, thus avoiding complex inter-node interaction logic and reducing development and maintenance complexity.

The application discloses a new energy station equivalent inertia identification, characterization and evaluation method considering all working conditions, relates to the new energy power generation technical field, and aims to solve the problems of insufficient working condition adaptability, single inertia cognition dimension and poor application of evaluation results in the prior art. The application defines typical operation working conditions of the new energy station and constructs a working condition perceiver; inertia parameter identification is performed in a dual mode of environmental excitation and event driving, and the identification results are fused and calibrated; a multi-dimensional inertia characterization vector is constructed; a multi-level evaluation system is built based on the characterization vector, adaptive graded early warning and risk root diagnosis are realized through fuzzy comprehensive judgment; and finally, decision support and visualization are completed. The application improves the accuracy and robustness of working condition adaptability and inertia identification, and provides core support for power grid dispatching operation and safety evaluation.

This application discloses a dynamic load power balancing control method for microgrids, relating to the field of power system automation and intelligent control technology. It includes: acquiring the power fluctuation amplitude, interruption duration, and priority dynamic decay rate of the load to form an initial feature set; quantifying the coupling degree between power fluctuation and interruption duration using fuzzy logic to determine a time-period fluctuation index; when the time-period fluctuation index exceeds the limit, calculating a weight adjustment coefficient by fusing multiple features using a neural network; searching for a target power supply sequence for high-weight loads using a genetic algorithm; updating load priorities through secondary fuzzy inference based on real-time power fluctuations on the supply side; calculating a response delay correction factor using a neural network; and adjusting the target power supply sequence to obtain the final power allocation scheme. This invention integrates multiple algorithms to achieve closed-loop control, improving the power balance and dynamic adaptability of the microgrid, ensuring stable power supply, and is applicable to microgrid load control scenarios.

This invention discloses a method and system for assessing tunnel smoke conditions, belonging to the field of traffic management technology. The method includes: simulating predicted diffusion data of air pollutants in the current tunnel using the lattice Boltzmann method, which can accurately simulate the diffusion of air pollutants and facilitate subsequent analysis; acquiring environmental parameters of the current tunnel to correct the predicted diffusion data, which can improve the accuracy of the predicted diffusion; simulating the discrete migration behavior of moving units in the tunnel based on cellular automata and two-dimensional grids, which can simulate the behavior of moving units in the tunnel and facilitate the determination of the development of the situation; and establishing a tunnel smoke accident situation assessment model based on fuzzy hierarchical analysis, which combines the predicted diffusion data, the environmental parameters, and the discrete migration behavior to output an assessment value, which can numerically describe the changes in the accident situation.

This invention provides a method, system, device, and medium for tracing the source of electric vehicle fires, including: acquiring multiple fire characteristics and causes of fires in electric vehicles; classifying the fire characteristics to obtain multiple state categories; forming a state space from the fire characteristics and an action space from the fire causes; determining the membership degree of each fire characteristic on a fuzzy linguistic variable value threshold using a trapezoidal membership function; setting state transition constraints according to the state categories; and then, based on the state transition constraints and membership degrees, using a Q-learning algorithm to iteratively learn the Q-value of any action in the action space for any state in the state space, obtaining multiple relevant Q-values; and selecting the fire cause corresponding to the largest relevant Q-value as the current fire cause of the electric vehicle. This invention solves the problems of missing fire source tracing data and reliance on expert experience judgment in existing technologies, which makes it difficult to guarantee objectivity and rapid location.

The application discloses a kind of dynamic 3DGS driven backhoe dredger cross-media cooperative positioning method and system, the method includes: carrying out multimodal data acquisition;Based on dynamic 3DGS technology respectively constructs underwater scene dynamic 3DGS adaptive model and water scene dynamic 3DGS adaptive model, relies on IMU pre-integral constraint to build multimodal tight coupling positioning framework, complete heterogeneous data depth fusion and conflict resolution, optimize model parameter adaptive rule;Construct IMU error residual function, through IMU drift online correction algorithm to compensate benchmark drift error, combined with graph optimization framework and Gauss-Newton iteration method complete multi-source error joint correction;Based on Transformer network constructs underwater operation scene autonomous prediction model and water operation scene autonomous prediction model, combined with fuzzy logic decision algorithm exports underwater positioning mode and the adaptive selection instruction of water positioning mode;Through cooperative positioning module realizes positioning data coordinate unification and time series smoothing processing, exports global operation space cooperative positioning result.

This application discloses a method, apparatus, device, and medium for ambiguity fixing optimization considering the ionosphere, relating to the field of satellite positioning technology. The method includes: acquiring observation data of a target satellite from each base station within a detection area; repeatedly dividing all base stations into multiple triangulation units based on triangulation; constructing and solving an ionospheric delay correction model for each triangulation unit based on the observation data of each base station; determining the ionospheric delay spatial smoothness corresponding to each triangulation unit; performing ambiguity optimization on several triangulation units with an ionospheric delay spatial smoothness not less than a preset anomaly threshold based on the initial ambiguity of the target satellite, obtaining several optimized ambiguities, and fixing these optimized ambiguities as the true ambiguity values ​​of each base station for the target satellite within the corresponding triangulation unit. By implementing this application, the accuracy of base station ambiguity fixing can be improved.

The application discloses a control method of a pulverizing system considering multi-parameter cooperation and relates to the technical field of thermal power generation. Operation parameters of a thermal power generating unit at a current time point are acquired; the operation parameters include parameters of a boiler combustion process, parameters of a pulverizing system, storage state information of an external powder bin and parameter information of a boiler variable load; the operation parameters are input into a data correlation model to obtain initial control instructions of the pulverizing system; the data correlation model is obtained based on fusion training of fuzzy reasoning and neural network algorithms; the initial control instructions are taken as initial positions of a population, a maximum comprehensive evaluation function value is taken as a target, and the initial control instructions are iteratively optimized in a combination mode of a particle swarm algorithm and a genetic algorithm, so that a global optimal solution of the last iteration is taken as the control instruction of the pulverizing system; the comprehensive evaluation function is constructed based on the performance of the pulverizing system; and the pulverizing system is controlled to operate through the control instruction. The method can realize efficient operation of the pulverizing system.

This invention provides a method for assessing the confidence level of field test indicators and theoretical analysis errors, relating to the field of advanced energy technology. The method includes: identifying key meteorological parameters by analyzing the sources of uncertainty in measurement data; establishing a comprehensive comparison relationship and mapping function for measurement data by analyzing the probability distribution of measurement data under key meteorological parameters; calculating the standard uncertainty under different evaluation results based on electrical quantity measurement data, combined with the comprehensive comparison relationship and mapping function; calculating the effective degrees of freedom of the combined standard uncertainty based on the standard uncertainty; calculating the environmental correction coefficient of the measurement data using the effective degrees of freedom of the combined standard uncertainty and a table lookup based on the confidence level; and applying the calculated environmental correction coefficient to the measurement data to adjust the measurement data. This invention ensures dynamic compatibility between the uncertainty and accuracy of field measurement results.