604 results about "Fuzzy inference" patented technology

The invention provides a transformer life prediction method and system based on machine learning and the Internet of Things, and relates to the technical field of transformer life prediction, and the method comprises the steps: collecting and preprocessing the multi-modal operation data of a transformer, constructing a multi-modal data fusion model based on a graph neural network, and carrying out the prediction of the life of the transformer; and mapping the dimensionality-reduced feature data into a graph structure, inputting transformer multi-modal operation data collected in real time into an optimized fuzzy inference system to obtain a health state evaluation result and a final residual life prediction result of the transformer, and obtaining a prediction result of the residual life of the transformer on the basis of transformer maintenance suggestions and historical maintenance records. Constructing a transformer digital twinborn model for maintenance strategy optimization, inputting the health state evaluation result, the residual life prediction result and the transformer maintenance suggestion into the transformer digital twinborn model, performing fault simulation and predictive maintenance analysis, and based on the fault simulation and predictive maintenance analysis result, obtaining a maintenance strategy of the transformer. And generating an intelligent maintenance scheme in combination with the expert knowledge base.

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 discloses an unmanned ship multi-mode perception and decision support method and system based on fuzzy logic and a rule engine, and the method comprises the steps: obtaining multi-source sensor data, carrying out the standardization processing, calculating a dynamic weight coefficient based on information entropy, and carrying out the data fusion; mapping the fusion data to a feature space, analyzing feature correlation, and evaluating an environmental threat degree; performing fuzzy partitioning on the feature space and generating an initial rule, and optimizing a rule set through information gain; a fuzzy inference system is constructed and cooperatively operated with a rule engine, and collaborative decision parameters are optimized; and performing multi-level risk assessment and generating a level decision, and optimizing a decision instruction through reinforcement learning. According to the method, the sensing accuracy and decision reliability of the unmanned ship in a complex sea condition environment are improved through multi-modal data adaptive fusion and collaborative decision of the fuzzy logic and the rule engine, and the method has good engineering application value.

The invention provides an electric power spot market price prediction method and system based on artificial intelligence, and relates to the technical field of electric power price prediction, and the method comprises the steps: calculating an impact factor weight through employing a deep reinforcement learning optimization fuzzy reasoning system based on historical transaction data and external association data; performing wavelet transform decomposition on the data to obtain a feature fluctuation sequence, and performing weighted fusion to generate a market feature fusion sequence; the method comprises the following steps: constructing a bidirectional feedback neural network by using a graph attention network to extract market transaction features and generate feature correction parameters, generating layered early warning signals in combination with a cascade forest algorithm to construct a dynamic market behavior feature map, and inputting an optimized feature map into a prediction model group comprising a plurality of prediction sub-models, and realizing information sharing and complementary learning through a cross-modal feature interaction module, generating an initial prediction result, performing constraint correction according to an intelligent clearing rule, correcting a correction prediction result, and finally outputting a predicted price of the electric power spot market.

The invention provides a private domain traffic peak identification and route scheduling method and system based on time sequence prediction, and relates to the technical field of private domain traffic management, and the method comprises the steps: predicting traffic data and confidence through a multi-dimensional heterogeneous graph network and a causal reasoning mechanism, and generating a dynamic threshold based on empirical mode decomposition and a generalized extreme value distribution model, and predicting a peak value by combining a fuzzy inference device. And then, constructing a distributed collaborative early warning mechanism, and generating graded early warning signals and early warning grades according to the multi-dimensional early warning indexes and node consensus. And finally, constructing a multi-target routing optimization model, performing routing resource grouping based on the link reliability score, searching candidate paths by using multi-agent reinforcement learning, determining a distribution proportion according to early warning information and a peak prediction result, optimizing a path selection strategy in a peak time period, and scheduling private domain traffic to a selected path. According to the method, the private domain flow peak value can be effectively identified, the service quality is guaranteed through dynamic routing scheduling, and the user experience is improved.

The invention provides a construction site collision risk assessment method based on a digital twinborn scene, and relates to the technical field of building simulation and collision detection. According to the method, after collision risk evaluation factors are screened out by constructing a construction site digital twinborn scene, correlation analysis is carried out by using a weighted Heraonian average operator, and a determined weight is obtained; constructing a membership function of each collision risk evaluation factor, performing fuzzy reasoning, establishing a construction site collision risk evaluation mechanism, and outputting an evaluation result; dynamically generating and updating the working space of the digital twinborn scene according to the evaluation result, reconstructing the digital twinborn scene, and executing voxelization collision detection to obtain an accurate collision detection result; and according to an accurate collision detection result, in combination with the generated dynamic working space, calculating a collision time-space amount to obtain a final risk value. The method is high in collision detection speed and accurate, is applied to a dynamic complex environment of a construction site, and can achieve objective and accurate evaluation of space-time collision risks.

The invention discloses an engineering project progress multi-dimensional monitoring system and method based on an information technology, and belongs to the field of monitoring. The engineering project progress multi-dimensional monitoring system comprises an initial construction plan construction module, a data acquisition and environment monitoring module, a data analysis and plan optimization module, a construction progress prediction module and a risk management module. By collecting multi-dimensional data of a construction site in real time and applying the intuitionistic fuzzy reasoning algorithm, a construction plan can be dynamically adjusted to cope with constantly changing site conditions and external environment factors, the intuitionistic fuzzy reasoning algorithm is utilized to analyze requirements and bottlenecks of a construction project, and the construction efficiency is improved by combining construction logic and physical constraints. Resource configuration can be carried out more accurately; fuzzy and uncertain conditions are processed through the fuzzy logic controller, it is ensured that a project meets all related building and safety standards, and project quality and safety are ensured through continuous monitoring and timely adjustment.

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 discloses a mine ecological environment restoration evaluation method and system based on deep learning, and the method comprises the steps: obtaining related data and evaluation indexes of a mine ecological environment restoration effect, carrying out the fuzzification of the indexes through employing a fuzzy mathematical theory, and obtaining a fuzzy evaluation index set; constructing a fuzzy evaluation index system according to the fuzzy evaluation index set, and obtaining a fuzzy score matrix of each evaluation index and fuzzy information of the fuzzy score matrix; based on fuzzy information of the fuzzy scoring matrix, high-level features of the fuzzy information are extracted; determining different evaluation grades and standards according to the clustering result, and representing the matching degree between the evaluation object and each evaluation grade by adopting a fuzzy membership degree to obtain an evaluation result; establishing a fuzzy reasoning rule base by using a fuzzy reasoning technology; and performing information fusion on the fuzzy scores of the evaluation indexes by adopting a fuzzy integral method, and comprehensively considering the interaction influence and the importance degree difference between the evaluation indexes to obtain a final comprehensive evaluation result of the mine ecological restoration effect.

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 full-automatic three-dimensional live-action modeling method based on an unmanned aerial vehicle oblique photography technology, and particularly relates to the technical field of unmanned aerial vehicle photography, and the method comprises the following steps: firstly, dividing a modeling region into a plurality of unmanned aerial vehicle oblique photography regions, and presetting an initial flight route and a parameter combination; an initial image is collected according to a preset route, terrain and shelter analysis is carried out, and a region complexity level is output through fuzzy reasoning; determining whether the initial image is adopted or not according to the classification, and if not, re-collecting the image based on an optimization strategy; and finally, gathering and summarizing the adopted images, and inputting the collected images into three-dimensional modeling software to generate an accurate three-dimensional model. According to the method, the flight strategy of the unmanned aerial vehicle is dynamically optimized, and especially in complex terrains and shelter dense areas, the coverage degree and quality of image acquisition are ensured, so that the detail retention degree of a three-dimensional model is remarkably improved, resources of the unmanned aerial vehicle are more effectively utilized, unnecessary flight and data redundancy are reduced, and the modeling efficiency is improved.

The invention relates to an electric power virtual simulation optimization method based on a digital twin technology, and the method comprises the following steps: S1, deploying sensors at key nodes of an electric power system, and integrating an intelligent electric meter, weather forecast, social media information and market data to form a comprehensive data pool; s2, constructing a digital twinborn model of the power system; s3, optimizing the digital twin physical model based on a Bayesian optimization method and an adversarial neural network; s4, applying a bee colony algorithm to formulate a cooperation strategy, and optimizing transmission and load adjustment of power in different areas; s5, introducing an adaptive neural fuzzy inference system, and further optimizing a cooperation strategy; and S6, based on the optimized digital twinborn model and the operation strategy, performing large-scale distributed simulation by using cloud resources. According to the invention, accurate construction of the electric power simulation model and accurate implementation of electric power virtual simulation are realized.

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 discloses an intelligent and automatic transformer substation information acquisition system, which relates to the technical field of information acquisition, and is characterized in that an information change coefficient and an information priority coefficient are acquired and substituted into a logistic regression model to obtain an information potential evaluation coefficient, the information potential evaluation coefficient is analyzed with a preset potential threshold to obtain a comparison result, and the transformer substation information is acquired according to the comparison result. Dividing to obtain a real-time acquisition potential information set and a non-real-time acquisition potential information set, substituting into a default score combination mechanism, determining an actual classification acquisition result of each piece of information, obtaining a network state when each piece of information is uploaded, obtaining a network delay degree and a bandwidth utilization rate from the network state, and substituting into fuzzy logic for fuzzy reasoning to obtain a real-time acquisition potential information set and a non-real-time acquisition potential information set; according to the method, the reasonability result of the default score combination mechanism is determined, then the network delay degree and the bandwidth utilization rate when each piece of information is uploaded are substituted into set parameters in the default score combination mechanism, the score combination mechanism is determined and adjusted, and the information redundancy and the system burden are reduced.

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 discloses an underground water pollution path micro-analysis method based on a graph neural network. The method comprises the following steps: S1, acquiring a historical data set and a real-time monitoring data set of underground water pollution; s2, using a fuzzy membership function to represent the language level of the fuzzy variable; s3, constructing a fuzzy logic rule base based on the historical data set; s4, using a fuzzy inference system to process fuzzy variables in the historical data set; s5, constructing an underground water pollution path diagram structure model based on the historical data set and the real-time monitoring data set; s6, constructing an incidence matrix between the nodes; s7, based on the trained graph neural network model, inputting water flow velocity, pollutant concentration and geologic structure parameters in the real-time monitoring data set; s8, generating real-time prediction of a pollution diffusion path; and S9, outputting an analysis result of the groundwater pollution path. The problems that in the prior art, a pollution diffusion path is difficult to accurately predict under a complex geological structure, and real-time dynamic response is insufficient are solved.

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 provides a navigation mark early warning method and system based on multi-factor fuzzy comprehensive judgment. The method comprises the following steps: firstly, determining key factors influencing navigation safety, and distributing weights according to the importance of each factor; secondly, environment data are collected in real time through a sensor, data of nearby ships are collected through an automatic identification system (AIS), and cleaning and formatting processing are carried out; thirdly, on the basis of the judgment factors and the weights, constructing a fuzzy relation matrix, inputting data collected in real time, processing the data through a fuzzy logic algorithm, and outputting a fuzzy vector; and finally, according to the fuzzy vector, the current risk level is evaluated, and an early warning signal is generated and broadcasted through communication equipment. The system comprises a data collection module, a data preprocessing module, a fuzzification module, a fuzzy reasoning module, an optimization module, a defuzzification module and an early warning generation module. The fuzzy relation matrix is optimized by combining the genetic algorithm and the particle swarm optimization algorithm, and the accuracy and robustness of the early warning system are remarkably improved.

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.