451 results about "Fuzzy inference" patented technology

The invention relates to the field of power equipment data processing, in particular to a biomass power generation combustion parameter deep learning method and system, and aims to solve the problem of phase mismatch caused by sampling frequency difference and clock reference offset of multi-source heterogeneous time sequence data. A parallel multi-scale convolution and bidirectional long-short-term memory network hybrid model is constructed, transient fluctuation and long-period trend features are extracted, and combustion stage feature weights are dynamically distributed through a gating attention mechanism. The optimization control module generates a multi-target constraint condition, an operation instruction is output in combination with a fuzzy inference engine, and a digital twin platform simulates an extreme working condition to enhance model robustness. A closed-loop feedback mechanism dynamically adjusts model parameters through combustion efficiency monitoring data and simulation results, and a two-stage fault-tolerant strategy realizes sensor abnormity compensation and historical control strategy backtracking. The problem of asynchronous data stream feature misalignment is effectively solved, and the combustion efficiency prediction precision and the control decision reliability are improved.

The invention discloses a power distribution network line fault positioning detection system, and relates to the technical field of power distribution network fault detection. The system comprises a mixed information acquisition layer, a fault feature extraction layer, an intelligent diagnosis layer and a fault positioning layer. The mixed signal acquisition layer comprises a high-frequency transient wave recording unit, a power frequency measurement unit, a wireless pulse sensor and a distributed optical fiber temperature measurement unit; the fault feature extraction layer comprises a time-frequency analysis module, a preprocessing module and a three-dimensional feature vector module; the intelligent diagnosis layer comprises a convolutional attention network, a space-time diagram neural network and a transfer learning module; the fault positioning layer comprises a particle swarm module and a fuzzy reasoning module. According to the invention, data information of the cable is acquired through the mixed information acquisition layer, a video analysis window function is dynamically matched with signal characteristics, a time domain graph scale, a frequency domain resonance component and a space field intensity gradient are constructed, fault diagnosis and positioning are carried out by using the intelligent diagnosis layer, and the fault positioning detection efficiency of the power distribution network is improved.

The invention discloses a municipal road state sensing method and system based on sensor fusion, and the method comprises the steps: collecting multi-modal road information data from a sensing network along a municipal road, and generating a standardized road feature vector; the method comprises the following steps: extracting frequency domain characteristics of pavement bearing capacity through time-frequency analysis, establishing a coupling model of vehicle load and pavement response, and calculating various road state indexes; outputting a structure health index and marking a thermodynamic diagram of a key damage position; real-time traffic flow data and meteorological environment parameters are fused, and an early warning level adaptive adjustment model based on fuzzy reasoning is established; and when a risk entropy value output by the early warning level adaptive adjustment model exceeds a dynamic threshold value, triggering a multi-level collaborative maintenance decision scheme and generating a visual road state digital twinborn body. According to the invention, the problem of insufficient road state sensing precision in a complex environment is solved, and dynamic evaluation and intelligent early warning of the full life cycle health state are realized.

The invention provides a pressure-bearing equipment damage mode identification and risk assessment method and system, and relates to the technical field of safety engineering, and the method comprises the steps: collecting multi-source sensor data and image data, inputting the data into a deep neural network after preprocessing and feature extraction, extracting spatial features through a convolutional layer, and extracting time sequence features through a recurrent neural network. And using the attention mechanism to fuse the features to identify an injury pattern. And then, constructing a multi-level evaluation index system, performing combined weighting by adopting an analytic hierarchy process and an entropy weight method, inputting weights into an improved Bayesian network model based on a D-S evidence theory, dynamically updating a conditional probability table by the model by utilizing a deep neural network and a fuzzy inference rule, and finally obtaining a risk evaluation result. According to the invention, the damage mode of the pressure-bearing equipment can be effectively identified, risk assessment is carried out, and assessment precision and reliability are improved.

The invention relates to the technical field of data processing, and discloses an intelligent risk early warning method, device and equipment for a power distribution network, and a medium. Historical risk monitoring data of the power distribution network under multiple dimensions are fused through a graph convolutional network and a variational auto-encoder; a variational recurrent neural network and a long-short term memory network are trained in combination with historical risk fault data of the power distribution network, and an attention mechanism is introduced in the training process to generate a risk assessment model; acquiring real-time risk monitoring data of the power distribution network under multiple dimensions to extract multi-dimensional real-time fusion features and inputting the multi-dimensional real-time fusion features into the risk assessment model for processing to obtain a real-time risk assessment level so as to further process the multi-dimensional real-time fusion features through a multi-criterion decision analysis method and a fuzzy inference system; the target risk assessment level is obtained, the level is compared with the risk early warning threshold value, if the level exceeds the threshold value, early warning is triggered, and the accuracy of power distribution network risk assessment is effectively improved.

The invention discloses a driving behavior risk assessment method and system, and relates to the technical field of intelligent traffic and driving safety, and the method comprises the steps: collecting the operation parameter data of a vehicle, carrying out the feature extraction of the operation parameter data, extracting feature parameters to form a feature vector, and calculating the Mahalanobis distance score of the feature vector; according to the Mahalanobis distance score of the feature vector, carrying out risk grade division by adopting an improved hierarchical fuzzy inference system to obtain a risk grade score; and establishing a driver portrait model according to the risk level score, and when the risk level score exceeds a personalized risk threshold, outputting a driving risk behavior improvement suggestion to complete driving behavior risk assessment. According to the invention, a refined grading basis is provided for individual risk monitoring of the driver, dynamic monitoring and real-time intervention of driver behaviors are realized, traffic accidents can be reduced, and driving safety is improved.

The invention is suitable for the technical field of power transmission line maintenance, and provides a line early warning judgment method and system based on multi-source data fusion, and the method comprises the steps: constructing a multi-dimensional knowledge graph, and building a semantic association network; collecting meteorological and equipment state data in real time, fusing the data into a knowledge graph space through a mapping function, identifying an abnormal meteorological-equipment combination mode by using fuzzy reasoning, and calculating a thickness change rate in combination with an icing growth prediction model; the icing thickness and the galloping probability are calculated, and a composite early warning level is generated; and dynamically generating a risk thermodynamic diagram on the GIS map, and visually displaying a risk propagation path. Through semantic fusion of multi-source data, composite risk modeling and spatialization decision making, the system breaks through single parameter criterion limitation of traditional early warning, realizes intelligent identification and dynamic early warning of meteorological-equipment coupling risks, and significantly improves accuracy of power transmission line disaster early warning and operation and maintenance decision making efficiency.

The invention discloses an e-commerce live broadcast real-time interaction quality evaluation system based on edge calculation, and relates to the technical field of e-commerce live broadcast, and the system comprises a multi-modal interaction data collection module which collects multi-modal interaction data of a live broadcast stream in real time through a distributed edge calculation node cluster, and constructs a multi-dimensional quality feature vector, the multi-modal interaction data comprises a video coding parameter, an audio quality index, user interaction behavior data and network transmission state data; according to the invention, the distributed edge computing node cluster collects the multi-modal interaction data of the live stream in real time, the lightweight space-time attention neural network model carries out data fusion processing, the deep reinforcement learning network generates a quality optimization scheme, and the adaptive fuzzy inference system corrects the optimization scheme in real time. And dynamic parameter adjustment is carried out in combination with network bandwidth fluctuation and a terminal device resource state, so that the effect of accurately and comprehensively evaluating the e-commerce live broadcast interaction quality in real time is achieved.

The invention belongs to the technical field of tunnel safety monitoring, and particularly relates to a rock stratum stress-deformation coupled tunnel safety real-time dynamic modeling method, which comprises the following steps of: embedding a rock mass mechanical relationship into a neural network model as a hard constraint, and establishing explicit mapping of monitoring data and a physical field, a model parameter field is dynamically optimized by adopting ensemble Kalman filtering real-time general field detection data, and a data driving and physical mechanism collaborative deduction mechanism is designed, so that long-term prediction error accumulation is effectively inhibited; a stress field, a deformation field and other physical quantities deduced and output by the model are directly utilized to calculate disaster risk indexes with clear mechanical significance, and a physically interpretable early warning decision is realized by fusing fuzzy reasoning and multi-source risks; the technical problems of physical misalignment, weak long-term generalization and poor early warning interpretability of a data-driven model are solved.

The invention relates to the technical field of simulation, and particularly discloses a real-time simulation method for detecting photoelectric tracking equipment, which comprises the following steps of: performing feature decoupling processing on photoelectric signals through a dual-channel adaptive neural network architecture, and constructing an equipment mathematical model by adopting a dynamic gating fusion mechanism based on a processing result; the method comprises the following steps: establishing a multi-physics field coupling simulation environment based on a heterogeneous computing architecture, introducing an equipment mathematical model, performing three-field co-evolution through light transmission modeling, electromagnetic field distribution calculation and target motion prediction, and generating a dynamic test scene containing space-time relevance; according to the method, a mode of combining mixed feature analysis and fuzzy reasoning is adopted, multi-dimensional performance indexes are extracted from simulation data, and multi-target dynamic optimization is carried out through a strategy of combining a quantum evolution algorithm and swarm intelligence optimization; by means of a digital twin platform and a hardware-in-loop interface, real-time verification and closed-loop optimization are achieved, and the response speed and tracking precision of photoelectric tracking equipment in a complex environment are greatly improved.

The invention discloses a wind turbine generator monitoring and early warning system based on the Internet of Things sensing technology, and relates to the technical field of the Internet of Things, and the system comprises a data collection module which collects the operation state information of key parts of a wind turbine generator in real time, and transmits the operation state data to an edge calculation unit; the prediction module is used for identifying an abnormal mode, analyzing time domain and frequency domain characteristics of the vibration signal in combination with fast Fourier transform, detecting a mechanical fault, and obtaining a residual life prediction value according to a residual life prediction model; the preliminary early warning module is used for determining an equipment health state score based on fuzzy reasoning, and performing preliminary early warning locally according to the equipment health state score; and the grading early warning module is used for carrying out grading alarm on the wind turbine generator equipment according to the mechanical fault condition in combination with the equipment health state score, and providing maintenance decision suggestions. By integrating the Internet of Things technology, edge computing and artificial intelligence, intelligent monitoring and early warning of the wind turbine generator are achieved, and the reliability and safety of equipment operation are improved.

The invention discloses a fabricated beam bridge finished product quality evaluation method fusing fuzzy reasoning and a neural network, and belongs to the field of comprehensive evaluation, and the method comprises the steps: constructing a fabricated beam bridge finished product quality intelligent evaluation system which comprises a first-level index and a second-level index; adopting a fuzzy number to describe quantitative and qualitative indexes, and constructing a fuzzy judgment matrix; calculating an initial weight vector by using an analytic hierarchy process and a defuzzification technology; calculating an improved regularization item balancing data driving and expert priori; and performing weight optimization and weighted summation on the parameters of each component by using a weighted neural network, and outputting a finished product quality score of the fabricated beam bridge. The invention aims to construct an intelligent evaluation system for the quality of the finished product of the fabricated beam bridge, realizes collaborative evaluation of expert knowledge and a data driving method by fusing a deep learning technology, improves the accuracy and robustness of the quality evaluation of the finished product of the fabricated beam bridge, and provides an efficient and reliable technical means for the intelligent evaluation of the quality of the fabricated beam bridge.

The invention relates to a cargo transportation track real-time tracking method based on the Internet of Things, and the method comprises the steps: carrying out the node collection and multi-source fusion of sensor data (including GPS, RFID, timestamps and the like) of all links of logistics, and employing data standardization and time sequence verification, thereby achieving the consistency processing of different devices and node data. The method is characterized in that standardized trajectory data and breakpoint detection data are stored in a distributed manner by using a block chain non-tampering and multi-party consensus mechanism, third-party multi-source data and a fuzzy reasoning model are fused, and confidence completion is performed on trajectory breakpoints. The system can output a complete and traceable trajectory path, anomaly recognition and responsibility attribution result, and provides a visual and third-party notarization interface. According to the method, the authenticity, the integrity, the space-time consistency and the responsibility traceability of the logistics track data flow are remarkably improved, and the logistics full-link monitoring and risk management capabilities are enhanced.

The invention provides a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system, and belongs to the technical field of artificial intelligence risk prediction. The vehicle is provided with an upper-layer torque decision module, a lower-layer torque cooperation module and a vehicle-mounted sensor; selecting a pivot steering auxiliary strategy according to an actual driving scene, and pressing a function key to enter a working condition; vehicle dynamic information is obtained through a vehicle-mounted sensor, whether a vehicle is in an in-situ steering state or a small-radius steering state is judged by combining vehicle body static data, and when a selected function key is in an in-situ steering state or a small-radius steering state, the vehicle dynamic information is obtained in real time, and fuzzy reasoning and a neural network algorithm are combined. The ground attachment coefficient and the target yaw angle speed are intelligently judged, and therefore accurate control over the steering torque is achieved; or a double-controller structure is adopted, the torque of each wheel can be effectively distributed and adjusted under complex working conditions, and flexible control and stable running of the vehicle in a narrow space are ensured.

The invention relates to a fault early warning method, device and equipment for an energy storage system. The method comprises the following steps: acquiring target data of a target parameter; the target data is generated by preprocessing real-time operation data and real-time environment data of the energy storage system; extracting a target feature corresponding to each target parameter based on the target data; calculating a feature confidence interval of each target parameter based on the historical data of the target parameters, and marking suspected abnormal features based on the feature confidence intervals; identifying at least two fault types based on the suspected abnormal features; based on the current environment data, the load power of the energy storage system and the historical operation and maintenance data of the energy storage system, carrying out fuzzy reasoning on the risk membership degree corresponding to each fault type and dynamically adjusting the basic weight coefficient corresponding to each fault type; and determining a comprehensive risk index based on the risk membership degree corresponding to each fault type and the dynamically adjusted dynamic weight coefficient, and performing fault early warning analysis processing based on the comprehensive risk index. The method can improve the accuracy of fault early warning.

The invention is suitable for the technical field of data processing, and provides a distributed data consistency control method and system, and the method comprises the steps: constructing an enhanced vector clock containing a fuzzy membership parameter and a time window deviation value; a dynamic fuzzy inference engine is built in combination with factors such as network delay jitter; calculating conflict event logic precedence relation probability distribution by utilizing an engine; generating an execution sequence hypothesis by integrating the multi-dimensional information; selecting an optimal scheme based on a dynamic confidence threshold; and resources and consistency are balanced through a three-level conflict processing mechanism. According to the method, the problem that a traditional vector clock cannot process conflict event sorting is solved, the accuracy and adaptability of distributed data consistency control are improved, and efficient collaboration of the system is guaranteed.

The invention discloses a parking space recommendation and reservation method, which comprises the steps of normalizing acquired multi-source heterogeneous data to generate parking lot static attribute data, real-time parking space state data and dynamic user request data; constructing an objective basic potential field by using fuzzy reasoning according to a fuzzy rule set capable of performing adaptive evolution based on performance feedback; modeling long and short term preferences of the user by adopting methods such as hierarchical Bayesian and an attention mechanism so as to generate a user subjective preference modulator; an objective potential field, a subjective modulator and a multi-agent rejection potential field with dynamically adjustable parameters are fused, a total potential energy function of the system is constructed, and a collaborative guidance strategy for all users and conflict avoidance is determined and output by solving the minimum value of the function. According to the invention, efficient and personalized parking guidance with macroscopic cooperation capability can be provided, and the user experience and the operation efficiency of the parking lot are significantly improved.

The invention relates to the technical field of computing power resource allocation, and particularly discloses a computing power resource allocation method and system based on multi-dimensional dynamic evaluation and a product. The method comprises the steps of obtaining evaluation index data of a to-be-scheduled task in multiple dimensions; dynamically configuring a weight value of each evaluation index according to a task attribute and a system state; the evaluation index data is fuzzified by using a preset membership function, a fuzzy evaluation matrix is constructed in combination with the weight value, and comprehensive calculation is performed through a fuzzy inference rule to obtain fuzzy comprehensive evaluation data of the task, so that an accurate evaluation result is generated; modeling a task execution process by adopting a multi-layer perceptron model, and predicting the execution performance of the task; and based on the evaluation result and the prediction result, dynamically selecting a proper strategy from a plurality of predefined task scheduling strategies, and scheduling the tasks to optimize computing power resource allocation. According to the method, the resource utilization efficiency is remarkably improved through multi-dimensional evaluation and a dynamic scheduling mechanism.

The invention provides a vehicle fault diagnosis method and device based on multi-source data fusion and a storage medium. The method comprises the following steps: collecting real-time state data and real-time alarm data of a target part, and obtaining external environment data and map navigation data; preprocessing the data, fusing the preprocessed data to obtain a multi-source data set target feature, inputting the target feature extracted from the multi-source data set into an adaptive neural fuzzy inference system, mapping the target feature to a plurality of fuzzy sets, and fuzzifying by using a member function; calculating the rule weight of each target feature to reflect the fuzzy intensity; and training an inference system by adopting a neural network learning algorithm, adjusting a member function and a rule weight, and converting fuzzy output into a specific numerical value or a fault level by utilizing an output member function in a defuzzification process so as to judge the health state of the part. According to the invention, high-interpretability fault diagnosis output is provided, the accuracy is improved, and fault diagnosis can be carried out in a wide vehicle operation environment.

The invention discloses a power equipment fault diagnosis method based on adaptive fuzzy reasoning. The method comprises the following steps: S1, constructing a multi-source normalized feature vector set; s2, constructing a power system diagram; s3, executing an attention graph convolution operation, and outputting a node embedding state vector set; s4, obtaining an edge synapse updating weight matrix; s5, obtaining a fuzzy rule base; s6, executing a multi-stage fuzzy reasoning process, and outputting a fault diagnosis judgment result; s7, writing a fault diagnosis judgment result into a state memory unit of a corresponding node to form a node state memory record and an edge state evolution record; and S8, when the confidence coefficient of a fault diagnosis judgment result is lower than a set threshold value, calling a node state memory record and an edge state evolution record, executing time sequence sliding window backtracking processing, updating a node embedding state vector set, and repeating the step S6 to execute re-judgment. According to the invention, the graph neural network and adaptive fuzzy reasoning are fused, and intelligent diagnosis of power faults is realized.

The invention provides a storage cluster fault diagnosis method and system based on a spiking neural P calculation model, and the method comprises the steps: collecting operation indexes containing static data and dynamic data from a monitoring system of a storage cluster, defining a neuron for each operation index after preprocessing through the construction and training of the spiking neural P calculation model, and carrying out the fault diagnosis of the storage cluster. Initializing an initial state value of each neuron according to the collected data; and performing state updating and reasoning by using pulse signal propagation and fuzzy reasoning through a trained spiking neural membrane calculation model, judging a fault according to a state value of a neuron, and when the state value of a certain neuron exceeds a set fault threshold value, considering that an index has a fault or a potential fault, and identifying a specific fault type in the storage cluster through state combination of the plurality of neurons. According to the invention, efficient fault diagnosis of the distributed storage system is realized.

The invention relates to the field of resource optimization, and discloses a supply chain full-link-oriented logistics resource optimization and matching method and system, and the method comprises the steps: generating a multi-dimensional logistics state feature vector according to full-link multi-source data of a target supply chain, constructing a multi-target optimization model according to a fuzzy inference rule and a fuzzy weight system, and carrying out the optimization of the multi-target logistics state feature vector; performing preliminary matching degree analysis on the multi-dimensional logistics state feature vector, performing resource allocation on a target supply chain by using a preliminary logistics resource allocation scheme generated according to a comprehensive matching score, and performing weight online learning and dynamic self-adaptive adjustment on a fuzzy weight system according to a performance error value of an actual performance parameter value, so as to obtain a multi-dimensional logistics resource allocation scheme; and obtaining a target matching weight system, optimizing the preliminary logistics resource allocation scheme, and matching the logistics resources of the target supply chain to obtain a target resource matching result. According to the invention, in a dynamic scene of a full link of a supply chain, real-time learning of a multi-target tradeoff relation and adaptive updating of a fuzzy weight can be realized.

The invention discloses an intelligent electric energy meter operation state risk assessment method based on strong association rule identification and multi-model integration. The method comprises the following steps that historical operation data of an intelligent electric energy meter is collected in a multi-source mode, data preprocessing is carried out, and then a comprehensive feature database is constructed; using a strong association rule identification model to mine characteristic factors influencing the intelligent electric energy meter from the comprehensive characteristic database; performing fuzzy reasoning on continuous features in the feature factors by using a fuzzy reasoning system, and performing adaptive optimization in a fuzzy reasoning link to obtain a risk assessment value of the continuous features; calculating local saliency of discrete features in the feature factors in real time in a rolling time window, and performing weighted fusion on the local saliency and the reference saliency through an attenuation factor to obtain a risk assessment value of the discrete features; and carrying out weighted summation on the risk assessment values of the continuous features and the risk assessment values of the discrete features to obtain a final state risk assessment result. According to the invention, the state risk assessment accuracy is improved.

The invention discloses a cross-working-condition bearing fault diagnosis method and device based on a multi-scale convolutional fuzzy neural network migration model, and the method comprises the steps: inputting a vibration signal of a to-be-diagnosed bearing into a trained cross-working-condition bearing fault diagnosis model, and outputting a diagnosis result; wherein the trained cross-working-condition bearing fault diagnosis model is obtained by training a multi-scale convolutional fuzzy neural network migration model by adopting a training data set, and a total loss function in the training process comprises classification loss guided by error samples, depth domain adaptive loss and confusion loss of an adversarial network domain; the training data set comprises a vibration signal from the bearing under the source domain working condition and a corresponding fault label, and a vibration signal from the bearing under the target domain working condition; the migration model based on the multi-scale convolutional fuzzy neural network comprises a multi-scale convolutional neural network module, a gated recursive convolutional attention module, an improved adaptive fuzzy inference system and an adversarial network module which are connected in sequence. The objective of the invention is to solve the problem of poor bearing fault diagnosis accuracy under variable working condition migration conditions.

The invention discloses a big data unified analysis processing method and system based on cloud computing, and relates to the technical field of big data intelligent analysis, and the method comprises the steps: calculating the optimal score of each data source, selecting the data source with the highest optimal score, collecting original data, and preprocessing the original data; respectively extracting global and local features through a model combination mode based on the preprocessed data to form a feature data set, calculating feature importance scores based on the feature data set to generate a new feature data set, removing abnormal features in the new feature data set, and generating a frequent item group; and performing joint prediction and generating a task decision target based on the frequent item group in combination with fuzzy reasoning and adaptive regression, generating task decision data based on the task decision target, and transmitting the original data and the task decision data to a database for storage and backup. According to the method, by adopting frequent item set mining and association rule screening, potential modes in the data are automatically identified, low-efficiency modes are eliminated, and the interpretability of data analysis is improved.

The invention discloses an intelligent crowd monitoring system based on deep learning, and relates to the technical field of intelligent monitoring, and the system comprises a data collection module which collects monitoring video information in real time and generates a monitoring video stream; the video processing module is used for performing target detection on the acquired monitoring video stream and extracting crowd position and quantity information according to a target detection output result; the image recognition module is used for calculating the crowd density, tracking the movement track of the crowd to obtain movement track data, and calculating crowd distribution change data according to the crowd density; the crowd analysis module is used for predicting and analyzing crowd behaviors by using a deep learning model to obtain a predicted congestion path; and the decision adjustment module is used for judging whether the crowd advancing route needs to be dynamically adjusted based on fuzzy reasoning and warning the crowd to avoid the congestion area. The problem that multi-dimensional comprehensive analysis of crowd behaviors, flowing directions and the like is lacked in the prior art is solved, and the crowd monitoring accuracy and the congestion avoiding efficiency are improved.

The invention relates to the technical field of agricultural irrigation, in particular to an intelligent irrigation dynamic decision-making method and equipment, and constructs an intelligent irrigation fuzzy reasoning and dynamic decision-making method by fusing multi-source remote sensing data and rainfall data and combining water demand characteristics of crops in different growth stages. It can be understood that the method is based on regularly updated remote sensing data, the moisture state of crops is dynamically monitored, and plot-level irrigation decision suggestions are generated. A manager can scientifically formulate an irrigation strategy according to suggestions in combination with the local actual water resource condition and the irrigation system, precise irrigation regulation and control are achieved, and the irrigation efficiency and decision scientificity are improved.

The invention provides a high-speed rail insulation joint health monitoring and early warning method and system, and relates to the technical field of joint health monitoring, and the method comprises the steps: carrying out the dimension reduction of resistance data and environmental influence factors through a random projection algorithm, carrying out the anomaly detection of the resistance feature data after the dimension reduction through an isolated forest algorithm, and carrying out the early warning. And through weight adjustment and path length optimization, a resistance abnormal point is marked. Using an empirical mode decomposition method to carry out constrained decomposition on the abnormal point data, and extracting multi-scale intrinsic mode function feature data; and performing resistance change trend prediction by using a Gaussian process regression model to obtain a resistance change prediction value and a confidence interval thereof. And inputting the predicted value and the confidence interval into a fuzzy reasoning model to obtain a graded early warning result of the high-speed rail insulated joint. Through multi-level data processing and analysis, the accuracy and reliability of fault early warning are improved, and safe operation of a high-speed rail steel rail system is ensured.

The invention discloses a grinding wheel wear degree identification method and device based on adaptive neural fuzzy reasoning. The method comprises the steps that a grinding temperature signal, grinding force and the surface quality of a workpiece are obtained; determining the abrasion stage of the grinding wheel according to the surface quality, the grinding force and the grinding temperature; extracting the total number of thermal pulses in the grinding temperature signal as the number of dynamic effective abrasive particles; a grinding wheel surface topography graph is obtained, abrasive dust and abrasive particle abrasion in the grinding wheel surface topography graph are recognized and distinguished through an HSV segmentation method, the area rate is calculated, and the abrasive dust proportion and the abrasive particle abrasion proportion are obtained; and the abrasive dust proportion, the abrasive particle abrasion proportion and the dynamic effective abrasive particle number serve as input, the abrasion stage serves as output, the self-adaptive neural fuzzy inference system is trained, and the trained self-adaptive neural fuzzy inference system is obtained for abrasion degree recognition. According to the method, the abrasion condition of the grinding wheel can be accurately and conveniently judged by utilizing the self-adaptive neural fuzzy reasoning technology and synthesizing the multi-dimensional characteristic signals in the grinding process.

The invention relates to the technical field of fire alarm, and discloses a self-adaptive threshold fire-fighting buzzer alarm method and system based on fuzzy logic, and the method comprises the following steps: S1, environment data collection; s2, data preprocessing; s3, performing fuzzy processing; s4, performing fuzzy reasoning and alarm threshold calculation; s5, optimizing an alarm threshold value; s6, real-time feedback and dynamic adjustment: according to the real-time fire suppression result and the environment change data, adjusting an alarm threshold to form a dynamic feedback mechanism; s7, applying a fire propagation model; and S8, alarm execution. By adopting distributed data storage, redundant backup, encryption technology and automatic data management, efficient and safe data processing, reliable data recovery and accurate alarm decision are realized, and the response speed and stability of the system are improved.