598 results about "Nonlinear coupling" patented technology

The invention relates to the technical field of fan blade fatigue damage prediction. The invention provides a fan blade fatigue damage prediction method and system. The method comprises the following steps: constructing a coupling finite element model based on blade anisotropy parameters; blade surface three-dimensional strain field data, blade vibration acceleration signals, environment temperature and humidity and wind speed and direction data are obtained in real time, and a multi-dimensional monitoring data set is constructed; based on the multi-dimensional monitoring data set, nonlinear coupling features of all the load components are extracted, a multi-dimensional feature tensor is obtained, and a reference stress field matched with the current working condition is generated; inputting the multi-dimensional feature tensor and the reference stress field into a bidirectional long-short-term memory network, and establishing a data-physics combined driven damage evolution model; and positioning a damage area based on a damage probability distribution diagram output by the damage evolution model. The problems that in the prior art, prediction errors are obvious, sensitivity to early damage is insufficient, the false alarm rate is high, and accurate positioning of the damage position and quantitative prediction of the residual life are difficult to achieve are solved.

The invention relates to the technical field of agricultural information perception and decision making, in particular to a multi-source perception fused agricultural monitoring method and system. The method comprises the following steps: converting a multi-source heterogeneous agricultural sensing signal into a space-time tensor, constructing a semantic resonance field to simulate nonlinear coupling between modals, and driving multi-modal data to adaptively aggregate by using a gravitational evolution mechanism to form a fused semantic field; calculating non-linear response to generate an agricultural state emergence index, and according to the index, identifying a potential risk area and constructing a binary risk map; for the risk area, semantic disturbance is mapped into an agricultural variable disturbance vector through a modal decoupling matrix, a minimum intervention strategy is generated in combination with sparse optimization of an operation response matrix, and feasible operation suggestions are output after verification of an agricultural knowledge graph; a drift potential energy function is constructed based on strategy execution feedback, strategy parameters are dynamically updated through gradient descent, and closed-loop self-evolution optimization is achieved in combination with trend prediction. According to the invention, full-link adaptive optimization from multi-source sensing to regulation and control decision is realized.

The invention relates to an intelligent early warning method and system for urban ground collapse based on multi-source factor fusion, and belongs to the technical field of urban disaster early warning. A PS-InSAR and an SBAS-InSAR are adopted to process and calculate deformation values respectively, deformation time sequences extracted through the two processing methods are verified and analyzed, and a settlement graph is generated through vector results which are verified to be qualified; setting a settlement rate threshold value, and carrying out preliminary ground collapse early warning identification according to deformation; dividing a dynamic factor and a static factor for the deformation time sequence and the collected multi-source data, constructing a multi-channel weighted space-time diagram structure taking a monitoring area grid unit as a node, and performing multi-source data fusion and predicting a comprehensive risk probability by using a space-time diagram neural differential attention network; and carrying out dual-channel fusion study and judgment. According to the method, the nonlinear coupling and space-time dynamic relation between disaster-inducing factors is comprehensively described in the fusion process, and high-precision, low-false-alarm and strong-generalization prediction of the urban ground collapse risk can be achieved.

The invention discloses a major hazard source risk dynamic calculation system based on AI large model enabling, and relates to the technical field of risk calculation, and the system comprises a multi-modal data collection and preprocessing module which is used for achieving the synchronous collection of original data through the butt joint of an industrial protocol with a sensor; the heterogeneous data space-time fusion engine module is used for constructing a space-time diagram model and analyzing a nonlinear coupling relationship of multi-source data; the online incremental learning and model fine tuning module is used for triggering dynamic parameter adjustment based on the real-time data flow; a risk conduction probability calculation module; a multi-modal knowledge self-evolution module; and a self-adaptive threshold management and alarm module. According to the method, sliding window dynamic confidence interval calculation is combined with a time-varying confidence coefficient adjustment mechanism, self-adaptive fitting of a threshold value to real environment disturbance is achieved by embedding a periodic correction term, the bidirectional contradiction between detection sensitivity and false alarm suppression is effectively cracked, and a complete technical closed loop from dynamic sensing and cross-domain verification to rapid linkage is formed.

The invention discloses an integrated scheduling system for realizing a PCS, an EMS and a BMS, and relates to the technical field of power control, and the system comprises a multi-dimensional performance evaluation module which constructs a battery aging dynamic model, carries out the training, carries out the health state pre-judgment through the battery aging dynamic model based on a standardized state vector, and generates a multi-dimensional performance evaluation index; the multi-objective optimization module is used for generating a collaborative scheduling strategy set by combining a fuzzy analytic hierarchy process with a multi-objective optimization solver of an improved genetic algorithm based on the multi-dimensional performance evaluation indexes; the dynamic derating module is used for generating an executable instruction queue with security constraints by combining an industrial internet of things protocol stack with a dynamic derating coefficient algorithm based on the collaborative scheduling strategy set; according to the invention, through the physical driving characteristic layer and the dynamic parameter calibration layer, the nonlinear coupling modeling of the cyclic attenuation and calendar aging mechanism in the battery aging dynamic model is realized.

The invention discloses a charge and discharge controllable system and method for a decommissioned battery, and relates to the technical field of intelligent charge control. By collecting the capacity fading rate, the internal resistance value, the cycle index and the environment temperature data of the decommissioned battery in real time, a health degree parameter is calculated by adopting a nonlinear coupling algorithm; and the future health degree evolution trend is predicted in combination with the LSTM neural network. And dynamically generating a grading label according to a preset scene threshold matrix, and matching the charging demand thermodynamic diagram with the battery grading label through a dynamic scheduling algorithm to realize intelligent distribution of charging and discharging power. And introducing a photovoltaic-battery-power grid cooperative power supply model, predicting and dynamically adjusting the power supply proportion based on the environment temperature and the photovoltaic output, and deploying to a target scene. And through a dynamic health degree evaluation and scene adaptive matching mechanism, the utilization rate of the retired battery is improved, the deployment cost of charging facilities is reduced, and the power supply reliability under multiple scenes is remarkably improved.

The invention relates to the field of tunnel engineering geology and support design, and discloses a weak surrounding rock tunnel deformation risk discrimination method based on shear expansion-shear constitutive, which comprises the following steps: constructing a nonlinear coupling model between a shear expansion angle and shear stress, normal stress and joint parameters, and obtaining the shear expansion angle; establishing a volumetric strain rate discrimination formula; inverting an initial crustal stress tensor field; reconstructing an irregular tunnel boundary; constructing a risk level discrimination model, and outputting a risk level; supporting schemes such as supporting rigidity, anchor rod parameters and spraying layer thickness are matched according to the risk grades; establishing a model to predict a risk trend; a support adjustment suggestion is generated; collecting monitoring data to dynamically correct model parameters; and all the modules are integrated in a deployment system. According to the method, the coupling relation between the shear expansion angle and the shear strength is introduced, the coupling type constitutive discrimination model is established, the risk grading system and the support correction strategy associated with the support response are constructed, and active early warning of the high-risk section and dynamic adjustment of the support rigidity are achieved.

The invention belongs to the technical field of data processing, and discloses a multi-modal project data analysis method, which comprises the following steps: collecting heterogeneous modal project data, carrying out denoising and timestamp correction on each modal project data based on an adaptive multi-scale filtering technology, carrying out cross-modal time alignment, and outputting a multi-modal signal. Constructing a multi-level Boltzmann machine energy network, and modeling the joint probability distribution of the multi-modal signals; a dynamic complexity measurement mechanism based on topological entropy is designed and used for representing project structure complexity, and a topological entropy dynamic field is generated; the topological entropy dynamic field is used as a constraint condition, a multi-dimensional covariant field frame is used, nonlinear coupling and space-time propagation between multi-modal signals are simulated, the multi-modal signals are abstracted into a string vibration mode, local topological defects in the topological entropy dynamic field are recognized, and an abnormal fluctuation map is generated; and the analysis of the complex multi-modal data in the project operation process is more intelligent and controllable.

The invention provides a digital twinning application-oriented electrical equipment electromagnetic heat flow coupling rapid calculation method, and belongs to the technical field of electrical digital data processing.The method comprises the steps that firstly, a three-dimensional model of electrical equipment is acquired and preprocessed, and a full-order electromagnetic heat flow coupling calculation model is established and verified through a temperature rise test; generating an experimental point matrix by using a Latin hypercube sampling method, and constructing a current temperature power density relational data set; performing regional division on the power density field by applying a K-means clustering algorithm, and constructing an electromagnetic response surface model through a radial basis function; establishing a heat flow field order reduction model based on an intrinsic orthogonal decomposition technology, and extracting a dominant mode primary function; bidirectional coupling of an electromagnetic field and a heat flow field reduced-order model is achieved, an improved Lagrange multiplier method and a fixed point iteration method are adopted for processing the nonlinear coupling problem, finally, a software development kit supporting an open platform communication unified architecture protocol is packaged, and the electromagnetic heat flow coupling rapid calculation capacity needed by digital twinning application is achieved.

The invention relates to the technical field of artificial intelligence processing, in particular to a manufacturing and processing system based on artificial intelligence simulation control parameters. According to the manufacturing and processing system, three-dimensional space-time coding is carried out on vibration, temperature and deformation time-varying signals through a multi-mode sensing module; generating a composite feature body including an equipment rigidity distribution matrix, a thermal deformation gradient vector and a material residual stress tensor, and solving the problems of spatial-temporal asynchronization and physical field feature splitting of multi-source data; the parameter resolving module constructs a parameter incidence matrix based on a fractal neural network, combines dynamic constraint conditions of thermal deformation gradient vectors, performs multi-objective optimization on a rigidity distribution matrix and residual stress tensor, generates an initial parameter solution set for eliminating time-lag deviation, and breaks through the nonlinear coupling optimization bottleneck of a traditional static model; and the state dissociation module calls a historical wear feature library to construct a performance attenuation reference surface, and tool accumulated wear and instantaneous thermal deformation disturbance are synchronously inhibited through convolution operation and projection correction of a dynamic compensation factor.

The invention relates to a source-containing power distribution network multi-target collaborative voltage optimization control method based on an FACMAC algorithm, and belongs to the technical field of photovoltaic inversion control. According to the technical scheme, a power distribution network physical system is composed of a plurality of feeder lines, a transformer, a line and a plurality of grid-connected photovoltaic inverters, and each inverter can measure operation information such as local voltage and current in real time; the data acquisition and communication system is used for acquiring node operation data and realizing low-delay communication; the multi-agent reinforcement learning control system is composed of a plurality of distributed agents and factorization centralized Critic modules, and whole-network voltage optimization decision can be carried out in training and execution stages. And the execution unit adjusts the reactive power output of the inverter in real time according to the control instruction. According to the method, the whole-network cooperative regulation and control capability is improved, the training efficiency bottleneck in a high-dimensional scene is relieved, the expression capability on a complex nonlinear coupling relationship is enhanced, and efficient, stable and extensible power distribution network voltage optimization control is realized.

The invention discloses a partial discharge detection method based on multi-source data fusion, and particularly relates to the technical field of discharge detection. Multi-mode partial discharge signals of partial discharge target equipment are collected, unified time reference alignment and multi-mode data structure normalization processing are carried out, and multi-source partial discharge observation data are generated; constructing a cross-modal discharge event response sequence, identifying response time delays and amplitude differences among modal signals, and extracting inter-modal response coupling feature data; constructing a non-linear feature alignment mapping function, performing time domain and frequency domain joint mapping on the multi-source observation data to obtain discharge feature multi-dimensional tensor data after non-linear coupling compensation, and performing inter-modal weight reconstruction and feature redistribution to generate a partial discharge fusion feature map; accurate identification of the partial discharge type and the spatial position is realized through the classification discrimination model, and a partial discharge detection result is output; and the accuracy of partial discharge detection is effectively improved.

The invention relates to the technical field of photovoltaic energy storage, and discloses a photovoltaic energy storage intelligent management system based on a micro-service architecture, which comprises the following modules: an environmental parameter acquisition module in which G is irradiance, [delta] T is temperature difference, RH is humidity, [alpha], [beta] and [gamma] are attenuation coefficients, dynamic influence of environment on power generation efficiency is quantified, and the environmental parameter acquisition module is used for acquiring environmental parameters; outputting the power correction factor to the cooperative control module; a power generation efficiency correction module; an edge calculation optimization micro-service module; an optical-storage-network cooperative control micro-service module; a self-adaptive prediction micro-service module; the dynamic influence of environmental factors on the power generation efficiency is accurately quantified through a nonlinear coupling model; a power correction factor can be calculated in real time, the influence of complex working conditions such as environment humidity sudden rise and irradiance instantaneous change in cloudy weather on photovoltaic efficiency is effectively captured, a scientific basis is provided for system decision making, and it is ensured that anomaly detection and energy efficiency optimization are close to actual working conditions.

The invention discloses a high-precision dynamic error real-time compensation method based on five-axis linkage, and relates to the technical field of machine manufacturing, and the method comprises the following steps: multi-source dynamic error perception: collecting geometric errors of a machine tool through a grating ruler, collecting temperature field distribution through an infrared thermal imager, and collecting cutting force disturbance through a piezoelectric force sensor; the method comprises the steps of constructing a grating ruler, constructing a thermal infrared imager, constructing a multi-source data acquisition system, carrying out nonlinear coupling modeling, and establishing a dynamic error model based on improved Volterra series, and has the advantages that the multi-source data acquisition system is constructed through the grating ruler, the thermal infrared imager and the piezoelectric force sensor, geometric, thermal and force multi-source dynamic errors are sensed synchronously, and the dynamic error detection accuracy is improved. The nonlinear coupling characteristic of the thermal-force-geometric error is accurately quantified by combining an improved Volterra series model and Lyapunov exponent analysis, the problem that a traditional single-source error model cannot accurately describe a complex error field is solved, and the accuracy of error prediction is remarkably improved.

The invention discloses a shield tunnel muck improvement parameter prediction method and system, and relates to the technical field of tunnel engineering construction.The method comprises the steps that a multi-modal sensor is arranged at a key part of a shield tunneling machine, and tunneling parameters, stratum parameters, multi-modal sensor signals, muck physical properties and modifier injection parameters are collected; then, extracting change characteristics, calculating mutation sensitive factors and carrying out working condition judgment; constructing a normal prediction sub-model library and a sudden change quick response model, calculating a stratum model adaptation index and performing model suitability judgment; establishing a working condition coupling prediction model by using a graph neural network, extracting nonlinear coupling characteristics, calculating a working condition coupling index and judging prediction stability; actual construction performance data are collected and compared with a model prediction result, a prediction correction index is calculated, deviation analysis is carried out, and a corresponding correction strategy is triggered. According to the method, intelligent prediction and dynamic correction of shield tunnel muck improvement parameters can be achieved, and the safety and stability of the construction process are improved.

The invention relates to the technical field of electrical digital data processing, and discloses a train ice melting simulation optimization method of an electromagnetic thermal coupling model fused with a deep learning method, which comprises the following steps of: inputting a geometric representation tensor and a physical working condition parameter vector containing an electromagnetic excitation frequency and a reference environment temperature into a feature mapping neural network; outputting a dual-channel space source item tensor containing basic heat source power density and a heat source to temperature change sensitivity distribution matrix through nonlinear convolution operation; constructing a heat conduction discrete numerical value evolution operator configured with an active item linear correction interface; time stepping operation is executed according to the heat conduction time scale, a basic heat source is corrected in real time through the Hadamard product of a sensitivity distribution matrix and temperature deviation, and an operator is substituted for solution. On the premise that electromagnetic-thermal nonlinear coupling characteristics are reserved, decoupling of the time scale is achieved, and the calculation efficiency in high-frequency physical field simulation is effectively improved.

The invention discloses a multi-target blending optimization method for refined oil of a refining enterprise, and particularly relates to the technical field of target optimization. Constructing an original physical property feature set by identifying non-linear response sensitive variables in the blending component proportion; respectively constructing an oil product performance prediction model and a blending component interaction influence model based on the original physical property feature set; utilizing an oil product performance prediction model to analyze the influence of non-linear coupling of simultaneous change of a plurality of blending component proportions on the change trend of key performance indexes of the refined oil product; analyzing the influence of the proportion change of a single or individual blending component on the stability of the key performance index of the product oil by utilizing the blending component interaction influence model; and according to the key performance change trend characteristics and the fluctuation sensitivity indexes, constructing a constraint equation, generating a blending solution set, performing target performance achievement degree evaluation and global optimal solution screening, obtaining an optimal blending scheme, improving the blending prediction precision, and realizing stable optimization control of the product oil quality.

The invention relates to the technical field of spectral analysis and concentration quantitative detection, and discloses an LIBS quantitative analysis method, device and system based on a thin plate spline regression algorithm. The method comprises the steps that multiple sets of sample data are obtained, and each set of sample data comprises corresponding target sample concentration, laser energy and spectral intensity; constructing a concentration quantitative model about the concentration, the laser energy and the spectral intensity based on a spline function; determining an optimal penalty parameter by adopting a cross validation method; based on the optimal penalty parameter, solving parameters of the concentration quantitative model through a minimization objective function to obtain a constructed concentration quantitative model; and performing quantitative analysis on a sample with unknown concentration by using the constructed concentration quantitative model. Thus, the output energy of the laser is incorporated into the core model, and an I-E-C nonlinear coupling relationship is constructed, so that the problem of calibration deviation caused by laser energy fluctuation is reduced, and the precision improvement of concentration quantification in a complex scene is realized.

The invention discloses a bimodal adaptive immersion phase change charging station thermal management system, and relates to the technical field of charging station thermal management, and the system comprises a circulation management module which carries out the circulation mapping and optimization operation of a phase change temperature result, and generates a circulation scheme; the immersion cooling module is used for intelligently regulating and controlling the nanometer phase change slurry to flow in an immersion cooling tank in a self-adaptive manner by utilizing a circulation scheme, and dynamically switching between a single-phase liquid cooling mode and a phase change boiling mode to generate heat absorption data; the heat exchange module is used for inputting the heat absorption data into a plate heat exchanger for heat exchange to generate cooling liquid temperature data, and carrying out heat dissipation and waste heat recovery operation of a dry cooler to generate backflow heat data; feature extraction and multi-dimensional nonlinear coupling analysis are carried out on the initial working condition data of the charging station, and iterative optimization is carried out in combination with a pre-training model, so that high-precision prediction of dynamic and multi-modal temperature changes is realized, and a reliable basis is provided for circulation management and cooling mode selection.

The invention relates to the technical field of boiler combustion, and particularly discloses a boiler combustion chamber multi-parameter intelligent regulation and control method, which comprises the following steps: constructing a holographic combustion state sensing network, collecting a multi-mode signal of a boiler combustion chamber in real time, and generating a holographic feature vector through time-frequency collaborative feature fusion; constructing a working condition migration map based on the holographic feature vector, capturing a combustion parameter nonlinear coupling relationship by using a map attention neural network, and outputting CEP, FRP and a nonlinear coupling feature matrix; according to the method, a holographic combustion state sensing network and a time-frequency collaborative feature fusion technology are adopted, multi-mode signals of the boiler combustion chamber are collected in real time, and holographic feature vectors containing combustion stability indexes and NOx generation trend factors are generated, so that powerful support is provided for accurately describing the state and performance of the combustion chamber; and a nonlinear coupling relationship of combustion parameters is captured by using a working condition migration map and a map attention neural network, so that the accuracy of combustion efficiency prediction and fault risk assessment is improved.

The invention relates to a cable joint resonance real-time diagnosis method and device and a medium, and belongs to the technical field of power equipment state monitoring, and the method comprises the following steps: obtaining a high-frequency current signal collected by a high-frequency current sensor and an ultrasonic vibration signal collected by an ultrasonic sensor array; performing signal synchronization on the high-frequency current signal and the ultrasonic vibration signal, and extracting a high-frequency current feature and an ultrasonic array feature; calculating a dominant frequency synchronization index, a normalized phase stability index and a nonlinear coupling index based on the high-frequency current characteristics and the ultrasonic array characteristics, and performing weighted summation to determine a fusion confidence coefficient function; and judging whether resonance exists or not based on the fusion confidence coefficient function, and if so, carrying out resonance source positioning. Compared with the prior art, the method has the advantages of high precision, low false alarm and high adaptability, and is suitable for key power system scenes such as new energy grid connection, urban smart power grids and wind power.

The invention discloses a meteorological observation equipment-oriented multi-modal data anomaly monitoring method, and relates to the technical field of meteorological monitoring, and the method comprises the steps: creating independent nodes through a quantum path based on a turbulence compensation data set, and obtaining a causal edge set through a time sequence; obtaining a dynamic node state value through Kalman filtering according to the causal edge set and the turbulence compensation data set to construct a dynamic causal graph; positioning an abnormal node through three-stage detection based on the dynamic causal graph and the dynamic node state value, and generating an abnormal node association path through a space-time proximity diffusion algorithm; generating a path attenuation rate sequence through reset simulation based on the state value of the dynamic node and the abnormal node association path; and obtaining the ID and coordinates of the fault source equipment through sudden drop node detection according to the path attenuation rate sequence. According to the method, the problem of meteorological variable nonlinear coupling distortion is solved, accurate tracing and automatic positioning of the composite fault are also realized, and the normal form change of meteorological monitoring from experience driving to mechanism driving is promoted.

The invention discloses a hybrid nonlinear floating fan hydrodynamic load coupling calculation method. The method comprises the steps of obtaining a velocity potential of a wave field in a computational domain through OceanWave3D numerical simulation (a wave field calculation model), and performing post-processing to obtain wave field data; dividing an instantaneous wet surface according to the six-degree-of-freedom motion, calculated based on an OpenFAST (floating fan calculation model), of the floating fan in each time step, and performing pressure integration to obtain a Front-Krylov force; updating the hydrostatic stiffness matrix according to the real-time position change of the buoyancy center of the fan, and calculating hydrostatic restoring force; linear radiation force and diffraction force are calculated through time domain convolution of frequency domain data, nonlinearity of the radiation force and the diffraction force is considered based on an instantaneous wetting volume change coefficient, and hydrodynamic load and structural motion response calculation in the current time step is completed after the OpenFAST is coupled. According to the method, coupling calculation between the open source code OpenFAST and OceanWave3D is achieved, and compared with commercial software at the present stage, the method is more accurate in wave load calculation under the extreme wave sea condition.

The invention relates to the technical field of power distribution networks, in particular to a power distribution network resource self-adaptive scheduling method, system and device and a medium. Using a support vector machine algorithm to construct an agent model representing a nonlinear coupling relationship between the novel power factor real-time parameters and the initial data set of the power distribution network; decomposing a multivariable interaction effect in the agent model by using a global sensitivity analysis method to obtain a sensitivity index; adaptively correcting the sampling space of the agent model according to the sensitivity index to obtain an optimized agent model; and on the basis of the optimization agent model and the real-time monitoring data of the power distribution network, generating a power distribution network resource adaptive scheduling strategy by adopting a particle swarm optimization algorithm, thereby carrying out optimal configuration on the power distribution network resources in the novel power factor access scene. According to the method, efficient and accurate optimal configuration of the power distribution network resources in a novel power element access scene is realized, and the operation performance and stability of the power distribution network are remarkably improved.

The invention provides a simulation test key factor screening-oriented method and system and a computer program product. The method comprises the following steps: generating a structured configuration file; constructing a supervised learning data set based on the structured configuration file; based on the supervised learning data set, training a machine learning classification model to obtain a current key factor set; and repeating the steps, judging whether the current key factor set is converged or not, and if the current key factor set is converged, stopping simulation and outputting the current key factor set of the last round as a final key factor. According to the technical scheme of the invention, a high-dimensional, nonlinear and high-coupling complex system can be automatically processed; key factors are objectively recognized through machine learning, a small-batch iteration verification mechanism is adopted, and the screening accuracy and efficiency are improved while the number of times of simulation is remarkably reduced.

The invention discloses a wide temperature range compensation method for resistive current of a lightning arrester, which belongs to the technical field of online monitoring of power equipment, and comprises the following steps: acquiring waveform data under a reference low-temperature working condition, and establishing an initial capacitive harmonic base; acquiring working condition data at any temperature in real time, performing harmonic decomposition, and analyzing to generate a nonlinear distortion feature vector; dynamically correcting the initial base by using the feature vector, and carrying out subtraction operation on the total current harmonic wave to obtain a final resistive current harmonic wave vector; and reconstructing an instantaneous resistive current waveform through inverse transformation and calculating a characteristic value. According to the technical scheme, the initial capacitive harmonic wave base is established, the nonlinear distortion feature vector is generated based on the harmonic distortion of the real-time working condition quantification temperature and valve plate nonlinear coupling, and dynamic feedback correction is conducted on the base, so that the real resistive current waveform in the wide temperature range can be accurately separated and reconstructed, and the accuracy of the real resistive current waveform in the wide temperature range is improved. And the accuracy and reliability of lightning arrester state evaluation are improved.

The invention discloses an industrial multi-source heterogeneous data feature fusion and dynamic modeling method and system, and relates to the technical field of industrial multi-source heterogeneous data processing, and the system comprises a data collection module, an event triggering type local space-time alignment module, a time sequence data set generation module, a feature fusion module and an industrial equipment state model generation module. According to the method, an event-triggered local space-time alignment module is arranged, an event-driven dynamic space-time anchoring mechanism is adopted, and in a preset tolerant time window, an image feature time sequence is constructed through industrial camera superframe sampling to be matched with sensor time sequence data, so that feature dislocation caused by sampling frequency difference is avoided; compared with an existing interpolation method, the non-linear coupling relation between the process parameter data and the sensor time sequence data is obtained, and the problem of feature fusion distortion caused by the time granularity difference is solved by performing non-linear interpolation on the process parameter data through Gaussian process regression and generating a continuous proxy curve synchronous with the sensor time sequence data.

The invention relates to an energy efficiency optimization method for a dynamic load of a central air conditioner, which comprises the following steps of: establishing a response relationship among a cold and heat source, a transmission and distribution system and end equipment, identifying a nonlinear decoupling characteristic of a cold and heat response behavior by monitoring operation deflection of each level of equipment under a low-load working condition, and defining and updating a cooling capacity response deviation threshold in real time; on the basis of the cooling capacity response deviation threshold, delay compression control is conducted on response actions of the refrigerant side and the air medium side, and by adjusting the air side response time sequence in advance, dynamic thermodynamic equilibrium is formed between refrigerant transfer delay and air medium response in advance; by establishing the response relationship among the cold and heat source, the transmission and distribution system and the end equipment and introducing a cold capacity response deviation threshold dynamic updating mechanism, nonlinear coupling mismatch in a cold and heat transmission chain can be accurately identified, so that the system is more flexible to operate under low-load and variable-load conditions, frequent start and stop or redundant operation of the cold and heat source is avoided, and the system reliability is improved. And therefore, the overall COP value of the system is remarkably increased, and unit cooling capacity consumption is reduced.

The invention discloses an AI-driven dynamic body temperature monitoring system and aims to solve the problem that existing body temperature detection equipment is difficult to adapt to individual differences and environment dynamic changes. The system comprises a temperature sensing unit, an environment acquisition unit and a heart rate sensing unit which are respectively used for acquiring skin temperature, environment parameters and heart rate variation characteristics; the system constructs a body temperature and environment incidence matrix, extracts a nonlinear coupling region, and fuses heart rate features to generate a three-mode state tensor; abnormal sensitivity is evaluated through an attention mechanism and a graph neural network model, and an individualized fever judgment threshold is dynamically generated in combination with a historical stable state, so that an accurate health response decision is realized; and the system further outputs behavior suggestions based on the decision result, such as water replenishing or medical treatment prompting, so that the intelligence and practicability of body temperature monitoring are improved.

The invention provides a vibration signal comprehensive evaluation method for determining a vertical spindle machine tool vibration sensor arrangement scheme, and relates to the technical field of measurement and processing in the field of intelligent manufacturing. A plurality of sensor position pre-selection schemes are generated, the same vertical spindle machine tool which is normal and has a chip clamping problem is selected, and sensors are arranged respectively; the vibration signals are normal and have a chip clamping problem; analyzing a steady-state response condition of a normal constant-rotating-speed idling vibration signal and a vibration interference condition of a vibration signal during normal trial switching, and screening out a standard reaching scheme under a normal condition; analyzing non-linear coupling inconsistency of the constant-rotating-speed idling vibration signals with the chip clamping problem, and generating corresponding indexes; identifying a chip clamping area based on the index, comparing the difference between the chip clamping area and a real chip clamping area, and screening out a standard reaching scheme under the chip clamping problem; and selecting a scheme which reaches the standard under the normal condition and under the normal condition.