30 results about "Wavelet transform analysis" patented technology

The invention belongs to the technical field of diving state monitoring, and relates to a diver state monitoring method based on biofeedback, and the method comprises the steps: aligning a sign signal with environment data through a timestamp, extracting motion features through acceleration data, constructing a motion noise model, and removing signal artifacts in real time; in the aspect of EEG signal purification, a db4 wavelet basis is adopted for carrying out wavelet packet decomposition, water pressure related frequency band characteristics are extracted, low-frequency drift is removed, meanwhile, heart rate signals are analyzed through nonlinear dynamics, a Lyapunov index is extracted to evaluate HRV changes, breathing and EEG # imgabs0 # wave coupling are analyzed through wavelet transformation, and the risk evaluation precision is further enhanced; in the aspect of environment compensation, through compensation of a depth and SpO2 relational expression and water temperature on respiratory frequency, in combination with SLTM network extraction time sequence features and a deep causal convolutional layer, risk trend prediction is performed, a diver risk level and a prediction curve are output in real time, a comprehensive risk index is finally calculated, the health state and potential risk of the diver are comprehensively evaluated, and safety is improved.

The invention provides an underground water environment automatic monitoring super station state supervision method and system. The method comprises the following steps: collecting a multi-source dynamic time sequence data set and carrying out time-space alignment; generating a dynamic characteristic index set by using a nonlinear dynamic characteristic extraction method; based on the index set, constructing a self-adaptive cooperative measurement network to perform anomaly monitoring, and generating an anomaly detection result and a cooperative control instruction; generating real-time monitoring and early warning information by using a dynamic threshold adjustment algorithm; and generating an adaptive control instruction and a dynamic resource allocation scheme by using a neural network adaptive control strategy and a resource scheduling optimization algorithm. According to the method, a dynamic characteristic index set is generated, a C-C method is adopted to reconstruct a high-dimensional phase space, a Wolf algorithm and a G-P algorithm are combined to calculate related indexes and dimensions, and a multi-scale fractal mode is analyzed through an R / S analysis method and wavelet transform. The methods capture complex dynamic behaviors of the underground water system, and solve the problem that the traditional method is insufficient in non-linear feature extraction capability.

The invention discloses a traditional Chinese medicine diagnosis and treatment aided decision-making method and system, and relates to the technical field of traditional Chinese medicine intelligent diagnosis and treatment aided decision-making, and the method comprises the steps: carrying out the wavelet transformation analysis of pulse condition waveform time series data, extracting a pulse condition dynamic change feature sequence, inputting a tongue image color change rate and a pulse condition feature change rate into a space-time convolution network, and obtaining a time-space convolution network; generating a tongue pulse dynamic association index and outputting a preliminary syndrome classification; fourier transform analysis is carried out on the spleen channel acupoint impedance spectrum, false-true state judgment is carried out in combination with tongue pulse dynamic correlation indexes, preliminary syndrome classification is corrected, and a false-true state quantification result and corrected syndrome classification are output; and matching the corrected syndrome classification with the traditional Chinese medicine knowledge map, adjusting the dosage according to a virtual-real state quantification result, reversely verifying the drug-syndrome specificity through a curative effect database, screening an optimal treatment scheme, and outputting a personalized diagnosis and treatment scheme. According to the method, the syndrome classification precision is improved, and an objective basis is provided for personalized dose adjustment.

The invention discloses a direct current charging pile detection system and method applied to the field, and relates to the technical field of power equipment on-line monitoring, and the method comprises the steps: inputting a three-dimensional data set into a Toeplitz cyclic measurement matrix for compressed sampling, reconstructing the three-dimensional data set into a high-resolution signal through employing an improved orthogonal matching pursuit algorithm, and carrying out the detection of the high-resolution signal; analyzing and calculating a local variable coefficient through a sliding window, marking abnormal positions, and integrating the abnormal positions into an abnormal position set; inputting the abnormal position set into a pre-trained long and short term memory prediction model, evaluating the insulation residual life and the fault risk level, and calculating the maximum allowable output current of the DC charging pile in real time; the load impedance is dynamically adjusted and detected through the memristor array, the output current of the direct current charging pile is collected in real time, and synchronous compression wavelet transform is adopted to analyze a harmonic ridge line and extract a harmonic component; the accuracy of fault early warning is remarkably improved, rapid dynamic adjustment of the output current is achieved, and the technical problem of multi-physics field coupling monitoring of the rapid charging pile is effectively solved.

The invention relates to the technical field of automatic identification, and particularly discloses a self-adaptive deformation correction and identification system for a flexible curved surface bar code, which can accurately capture a complex deformation condition by monitoring the three-dimensional shape change and strain distribution of a bar code area on a flexible surface in real time, and can calculate a curvature characteristic value by using a K-means clustering algorithm, so as to realize the self-adaptive deformation correction and identification of the flexible curved surface bar code. Strain data are analyzed through Haar wavelet transform to determine deformation characteristic values, so that the deformation degree of the bar code and the influence of the deformation degree on the recognition process are comprehensively evaluated, a random forest model is constructed based on the characteristic values for deformation degree prediction and deformation score output, high-precision recognition is ensured, and when uncorrectable serious deformation is detected, the recognition accuracy is improved. The system automatically triggers an alarm mechanism, including sound alarm, visual signal and network notification, to inform management personnel in time, so that the stability and long-term reliability of the system are guaranteed.

The invention provides a real-time monitoring and control method and system for pressure in a catheter in breast duct endoscopy, and relates to the technical field of medical equipment monitoring and control, and the method comprises the following steps: forming a multi-dimensional parameter characteristic matrix by collecting temperature, conductivity and image data in the catheter, and carrying out adaptive filtering analysis to extract fluid characteristic parameters; pressure distribution characteristics in the conduit are calculated, fluid flow state parameters are monitored in combination with a microfluid sensor array to identify a local turbulence area, when the turbulence intensity exceeds an early warning threshold value, wavelet transform is adopted to analyze and calculate the turbulence development trend, and the pressure in the conduit is controlled within a safe range through an active pulsation compensation technology; and the safety and the accuracy of breast duct endoscope examination are improved.

The present invention relates to the field of automated identification technology, and specifically discloses an adaptive deformation correction and identification system for flexible curved barcodes. By real-time monitoring of the three-dimensional shape changes and strain distribution of the barcode area on the flexible surface, the system can accurately capture complex deformation situations, and utilizes a K-means clustering algorithm to calculate curvature eigenvalues, and Haar wavelet transform to analyze strain data to determine deformation eigenvalues, thereby comprehensively evaluating the degree of deformation of the barcode and its impact on the identification process. Based on these eigenvalues, a random forest model is constructed to predict the degree of deformation and output a deformation score to ensure high-precision identification. When severe, uncorrectable deformation is detected, the system automatically triggers an alarm mechanism, including an audible alarm, a visual signal, and a network notification, to promptly inform management personnel and ensure the stability and long-term reliability of the system.

The invention relates to the technical field of building construction, and discloses a fabricated pipeline construction progress prediction method based on deep learning. According to the method, multi-source engineering data, including structured construction parameters, unstructured construction logs and the like, of fabricated pipeline construction are collected, and a standardized feature set is generated through preprocessing; extracting feature vectors of each data source by using a depth feature extraction model, constructing a construction progress knowledge base, generating global fusion features through multi-source feature fusion, and predicting a construction process sequence and progress by combining knowledge base data and by means of a time sequence prediction algorithm. Meanwhile, analyzing sensor data by adopting wavelet transform, and correcting a process sequence by utilizing a grey system theory when the sensor data is abnormal; and constructing a construction specification knowledge graph, and verifying the consistency of the process sequence and the specification by using a graph embedding algorithm. The method can accurately predict the construction progress, responds to abnormal conditions in real time, ensures that the construction accords with specifications, and improves the construction management level of the fabricated pipeline.

The application relates to the technical field of market share analysis, in particular to a drug market share analysis method and system based on multi-source heterogeneous data. First, drug sales data is collected, a unified data set is constructed through time alignment and unit standardization processing; the regional occurrence time and propagation order of the sudden event are identified based on the daily average sales sudden increase rate index, the regional drug sales is taken as the overall demand signal, the wavelet transform technology is used to realize demand signal decomposition, and the regional panic demand component and the actual medical demand baseline are obtained; the regional dynamic market share is calculated through weighted synthesis, and the final prediction result is obtained by correcting the historical data; through multi-source data fusion and wavelet transform analysis, the accuracy of market share analysis is improved.

The application relates to the technical field of mechanical processing control, and discloses an alloy saw blade production and processing automatic control method and system.The method comprises the following steps: determining abnormal frequency distribution by acquiring an original vibration signal and converting the original vibration signal into a frequency domain spectrum; extracting a feature vector and determining an abnormal separation vector when an energy proportion exceeds a limit; calculating the deviation of the feature vector from a normal mode by using a support vector machine, combining a fault mechanism topology graph to determine a coupling influence index; adjusting the rotating speed and the feed amount according to a leading abnormal source and a machine tool stiffness characteristic to determine an adjustment scheme and update equipment control instructions; and adopting wavelet transform to analyze signal multi-scale components, and realizing minimum coupling influence control of the whole intermittent cutting process through an iterative optimization cycle condition. The method can solve the problem of low early abnormal identification precision in the prior art.

The invention provides air conditioning equipment which comprises a detection device and a processing device, and the detection device is configured to obtain air parameters of an air-conditioned room so as to generate a detection signal; the processing device is provided with an analysis unit, a prediction unit, and a generation unit, and the analysis unit is configured to perform set wavelet transform analysis based on a time sequence of the detection signal and decompose the detection signal into an approximate component signal and a detail component signal; the prediction unit is configured to predict the change trend of the approximate component signal to obtain a predicted approximate component signal and predict the change trend of the detail component signal to obtain a predicted detail component signal; the generation unit is configured to generate a prediction detection signal based on the prediction approximation component signal and the prediction detail component signal. Through real-time detection of the air parameters and prediction of the change trend, intelligent prediction and control of the future air state of the air-conditioned room are achieved, the intelligent level of an air conditioning system is improved, and better use experience is provided.

The invention discloses a continuous production control method and system based on gravure multi-station linkage, and relates to the technical field of production line automatic control, and the method comprises the steps: collecting the tension, speed, position and other state quantity information of adjacent control nodes, carrying out the compensation processing of the upstream node state quantity according to the material transmission characteristics, and obtaining the state coupling deviation value between the nodes; performing frequency domain conversion and wavelet transform on the deviation value, and analyzing an energy gathering area to identify a dynamic fluctuation type; constructing a state sequence by using the state coupling deviation value, and obtaining inter-node transfer characteristics through online recursion calculation; and selecting reference parameters from the self-learning control strategy according to the fluctuation type, performing self-adaptive optimization in combination with the transfer characteristics, generating a control instruction, and sending the control instruction to an execution mechanism for adjustment. The state coupling relation between the stations can be accurately recognized, self-adaptive coordination control is achieved, and the control precision and the response speed are improved.

This application belongs to the technical field of diving state monitoring, and relates to a diver state monitoring method based on biofeedback. By aligning the physiological signs signal and environmental data through timestamps, and using acceleration data to extract motion features, a motion noise model is constructed to remove signal artifacts in real time; in terms of EEG signal purification, db4 wavelet basis is used for wavelet packet decomposition, features of water pressure-related frequency bands are extracted and low-frequency drift is removed. At the same time, the heart rate signal is analyzed by nonlinear dynamics, Lyapunov exponents are extracted to evaluate HRV changes, and wavelet transform is used to analyze the coupling between respiration and EEG #imgabs0# waves to further enhance the accuracy of risk assessment; in terms of environmental compensation, through the relationship between depth and SpO2 and the compensation of water temperature for respiratory rate, combined with the SLTM network to extract temporal features, and a deep causal convolution layer is used for risk trend prediction, the diver risk level and prediction curve are output in real time, and finally the comprehensive risk index is calculated to comprehensively evaluate the diver's health status and potential risks and improve safety.

The invention relates to the technical field of new energy power grid connection, and discloses a new energy grid-connected line fault positioning system, which comprises a distributed architecture module which is formed by cascading a management chassis, an expansion chassis, a distributed terminal and a fault positioning master station through an optical fiber or wireless mode and supports multi-node collaborative data processing and nanosecond time synchronization; the software and hardware platform module comprises a hardware module and a software function module and is used for acquiring and processing fault signals and outputting alarms; and the synchronous time service module is integrated with a GPS / BD dual-mode receiver and a crystal oscillator compensation algorithm, and outputs an IRIG-B code time synchronization signal. According to traveling wave propagation characteristics and wavelet transform characteristics, the system utilizes a novel double-end traveling wave fault positioning method in which traveling wave arrival time is determined by traveling wave feature point positions extracted by appropriate wavelet transform and traveling wave propagation speed is determined by structural parameters of a power transmission line and analysis scales of corresponding wavelet transform. And the requirement of accurate fault positioning of the power transmission line can be met.

This invention relates to a method for monitoring and evaluating the utilization rate of urban public service facilities, comprising: acquiring nighttime light satellite data, GIS data, POI data, and EVI data of the study area; extracting the spatial range of urban public service facilities based on the GIS data and POI data, and delineating an external buffer zone for the spatial range of urban public service facilities; calculating the time-series data of the difference in nighttime light intensity between the spatial range of urban public service facilities and the corresponding external buffer zone; analyzing the time-frequency characteristics of the difference time-series data using a three-level discrete wavelet transform based on the Haar function to obtain the residual sequence and high-frequency detail sequence of the nighttime light intensity difference time-series data; identifying outliers in the residual sequence and high-frequency detail sequence located outside a preset confidence interval, thereby achieving monitoring of abnormal use of urban public service facilities. This invention significantly reduces monitoring costs while improving monitoring accuracy and real-time performance, achieving efficient and automated monitoring of the utilization rate of urban public service facilities.

The invention discloses a wave velocity field inversion method for a deep mining environment, and the method comprises the steps: obtaining seismic data of a target region, and carrying out the preprocessing of the obtained seismic data; constructing an initial model; according to the observation data and the synthetic data, introducing cross wavelets to construct a joint objective function; solving the wave equation to obtain synthetic data; performing wavelet transform analysis on the observation data and the forward modeling data, and calculating a coherent difference between the observation data and the forward modeling data in a time-frequency domain; using an adjoint state method to jointly solve the traditional waveform and the wavelet error gradient and update model parameters; and carrying out visual display on the obtained velocity model, and completing wave velocity field inversion of the deep mining environment of the target area. The invention also discloses a system for realizing the wave velocity field inversion method for the deep mining environment. According to the method, the wave velocity field inversion for the deep mining environment is realized, the reliability is higher, and the accuracy is better.

The invention relates to an urban public service facility monitoring and utilization rate evaluation method. The method comprises the following steps: acquiring noctilucent satellite data, GIS data, POI data and EVI data of a research area; according to the GIS data and the POI data, an urban public service facility space range is extracted, and an external buffer area of the urban public service facility space range is delimited; calculating noctilucent intensity difference time sequence data of the urban public service facility space range and the corresponding external buffer area; analyzing time-frequency characteristics of the difference time sequence data by using three-layer discrete wavelet transform based on a Haar function, and obtaining a residual sequence and a high-frequency detail sequence of the noctilucent intensity difference time sequence data; and identifying abnormal values of the residual error sequence and the high-frequency detail sequence outside a preset confidence interval, and realizing monitoring of abnormal use of the urban public service facility. According to the invention, the monitoring precision and real-time performance are improved while the monitoring cost is greatly reduced, and efficient and automatic monitoring of the utilization rate of urban public service facilities is realized.

The invention relates to the technical field of smart power grids, and particularly discloses a power grid marketing side system platform data processing and analyzing method based on edge computing, which collects power consumption and power factor data of a user in real time through a smart electric meter, and performs efficient processing by using an edge computing node close to a data source. Analyzing the power consumption change rate by adopting Haar wavelet transform, analyzing the power factor fluctuation degree by adopting fast Fourier transform, constructing a power consumption change rate characteristic value and a power factor characteristic value so as to evaluate the power utilization stability of a power grid and the stability of a power system, and integrating the characteristic values into a comprehensive characteristic vector so as to evaluate the power utilization stability of the power grid and the stability of the power system. And as the input of the support vector machine model, the power grid marketing response speed is evaluated, and a power grid operator can dynamically adjust a marketing strategy according to an evaluation result.

The application provides a duct-in-pressure real-time monitoring and control method and system in a mammary ductoscopy, relates to the technical field of medical equipment monitoring and control, and comprises the following steps: collecting temperature, conductivity and image data in a duct to form a multi-dimensional parameter feature matrix, performing adaptive filtering analysis to extract fluid characteristic parameters, calculating duct-in-pressure distribution features, combining with microfluid sensor array monitoring fluid flow state parameters to identify local turbulent flow areas, when the turbulent flow intensity exceeds a warning threshold, using wavelet transform analysis to calculate the turbulent flow development trend, and controlling the duct-in-pressure in a safe range through active aortic pulsation compensation technology, so that the safety and accuracy of the mammary ductoscopy are improved.

The invention relates to the technical field of market occupancy analysis, in particular to a drug market occupancy analysis method and system based on multi-source heterogeneous data. The method comprises the following steps: firstly, collecting drug sales volume data, and constructing a unified data set through time alignment and unit standardization processing; the regional occurrence time and the propagation sequence of emergencies are identified based on the daily average sales volume sudden increase rate index, the regional drug sales volume serves as an overall demand signal, the wavelet transform technology is used for achieving demand signal decomposition, and regional panic demand components and actual medical demand baselines are obtained; the regional dynamic market share is calculated through weighted synthesis, and correction is carried out in combination with historical data to obtain a final prediction result; through multi-source data fusion and wavelet transform analysis, the accuracy of market share analysis is improved.

The application relates to a stereotactic electroencephalogram brain network generation method based on multi-source information, and relates to the field of brain science research. 1) stereotactic electroencephalogram signals, magnetic resonance imaging (MRI) and CT imaging data after implantation of electrodes are acquired; 2) electrode positioning is performed to obtain the position coordinates of each electrode point in the brain and the brain area to which the electrode point belongs; 3) data processing is performed to obtain preprocessed electroencephalogram signals; 4) wavelet transform is used to analyze the time domain and the frequency domain of the stereotactic electroencephalogram signals, corresponding wavelet coefficients are extracted, and a local brain network is constructed; 5) a multi-source self-encoder model network is trained by using a backward propagation parameter updating mode; 6) multi-layer perception machine model is used to obtain multi-source global latent features by combining the global multi-source electrode spatial coordinates and the local spatial coordinates in a high-dimensional latent space; and global electroencephalogram signals are obtained by decoding through a decoder and relevant functional networks are generated. Global latent features are effectively obtained, and the application has great potential in the application of cognitive function research.

The invention relates to the technical field of meteorological monitoring and thunder and lightning early warning, in particular to a thunder and lightning early warning system based on an HHT (Hilbert Huang Transform) and a time sequence difference method, which comprises the following contents: a multi-site atmospheric electric field instrument network used for collecting atmospheric electric field signals in real time; the signal processing module is used for performing signal denoising, time sequence differential analysis and HHT (Hilbert-Huang Transform); and the early warning module is used for generating early warning information according to the difference threshold value and the time-frequency spectrum energy threshold value and outputting the early warning information to the display terminal. The method is applied to de-noising processing of original atmospheric electric field signals based on a wavelet transform analysis method, and electric field changes caused by the lightning activity process are reserved. By selecting seven wavelet functions and four threshold analysis methods to compare and analyze the denoising effects of the respective analysis methods, it is found that the effect of the Rigorous SURE threshold method of the sym5 wavelet analysis method is optimal.

The invention discloses a marine engine room data monitoring method and system based on wavelet transform analysis, and the method comprises the steps: obtaining the operation data of marine engine room equipment, and carrying out the data preprocessing, and obtaining the preprocessed operation data of the marine engine room equipment; performing time sequence wavelet analysis on the preprocessed operation data of the marine engine room equipment to obtain a time sequence of the operation data of the marine engine room equipment; performing time sequence abrupt change point detection on the time sequence of the operation data of the marine engine room equipment, and determining a time abrupt change point of the operation data of the marine engine room equipment; and based on the time variation point of the operation data of the marine engine room equipment, combining a preset early warning mechanism to realize marine engine room data monitoring. According to the embodiment of the invention, the method can accurately find the potential fault of the equipment, evaluates the state of the equipment in a period of time in the future, and improves the safety of ship navigation. The method can be widely applied to the technical field of ship automation.

The invention discloses a method and system for rapidly calibrating performance parameters of a mobile phone mainboard, and relates to the technical field of electronic manufacturing automation, and the method comprises the steps: carrying out the rapid feature scanning of a to-be-calibrated mainboard, and obtaining an initial feature vector; inputting the initial feature vector into a deep Bayesian network to obtain a prediction result set of multiple times of sampling, and performing uncertainty quantization processing and calibration processing to generate a prediction value of each parameter to be calibrated and a corresponding prediction uncertainty measure; based on the predicted value of each to-be-calibrated parameter and the corresponding prediction uncertainty measure, constructing multi-dimensional risk field distribution, and executing multi-scale wavelet transform analysis to generate a calibration quality risk comprehensive index; and obtaining production line state information in real time, fusing the production line state information with the calibration quality risk comprehensive index, and generating a calibration strategy threshold through a meta reinforcement learning decision maker. According to the method, the problems of excessive calibration and insufficient calibration are effectively avoided, so that resource use is optimized, and the consistency of product quality is ensured.

The application discloses a kind of direct current charging pile detection systems and methods applied to field, it is related to the online monitoring technical field of electric power equipment, including, three-dimensional data set is input Toeplitz cyclic measurement matrix and is compressed sampling, and high-resolution signal is reconstructed using improved orthogonal matching pursuit algorithm, local variation coefficient is analyzed and calculated by sliding window, and abnormal position is marked, and is integrated into abnormal position set;Abnormal position set is input pre-trained long short-term memory prediction model, assesses the remaining life of insulation and fault risk level, and real-time calculation direct current charging pile maximum allowable output current;Through memristor array dynamic adjustment detection load impedance, real-time acquisition direct current charging pile output current, and using synchronous compression wavelet transform analysis harmonic ridge line and extract harmonic component;Significantly improve the accuracy of fault early warning, and realize the rapid dynamic adjustment of output current, effectively solve the technical problem of fast charging pile multi-physical field coupling monitoring.

The present invention discloses a charging pile life prediction system and method, which relates to the technical field of new energy equipment monitoring. It includes collecting multi-dimensional data signals during the operation of the charging pile, performing time alignment according to a preset data synchronization time window, and constructing a complete life characteristic index system; using wavelet transform to analyze the degradation trends of various signals, combining deep learning algorithms to perform spatio-temporal alignment and feature fusion on the collected multi-source heterogeneous data, establishing a degradation rate model to achieve feature extraction and health assessment of the life state; performing life feature extraction based on the results output by the degradation rate model, constructing a life state assessment model to achieve dynamic prediction and regular correction of the remaining life of the charging pile, and outputting optimization suggestions for equipment use according to the analysis results of life influencing factors. The present invention not only overcomes the prediction deviation problem caused by traditional single data sources, but also realizes all-round and multi-dimensional monitoring and evaluation of the health state of the charging pile.

The invention relates to the technical field of power switch equipment, in particular to a quick breaking device of a vacuum circuit breaker. According to the technical scheme, the quick breaking device of the vacuum circuit breaker comprises a multi-source signal acquisition and preprocessing module, a high-speed measurement and control decision module, a quick execution driving module, a fault diagnosis and self-adaptive protection module, an intelligent communication and state monitoring module and a power management and self-inspection maintenance module, according to the invention, through collaborative optimization of the high-speed measurement and control decision module and the rapid execution driving module, the FPGA core board is adopted to replace a traditional microprocessor, and nanosecond acquisition and wavelet transform analysis of current signals are realized; a triple modular redundancy check mechanism is introduced to ensure that a decision instruction is verified and output within 50 microseconds, thereby effectively limiting the damage of electrodynamic force to an equipment structure, and remarkably improving the short-circuit resistance of a power system.

The invention discloses a life prediction method, device, equipment, medium and product of a rotating mechanical component, and relates to the technical field of state monitoring and fault prediction of mechanical equipment, and the method comprises the steps: obtaining an operation monitoring signal of a target rotating mechanical component in a current time period, and extracting a preset key feature; analyzing the characteristic stability through multi-scale wavelet transform to obtain a current steady-state index, and determining the health state of the rotary mechanical part according to the current steady-state index; and inputting the characteristics into the corresponding steady-state or non-steady-state working condition life prediction model according to the state, and outputting a residual life prediction value. According to the method, calculation redundancy is reduced by screening key features, working condition self-adaptive division is realized by using multi-scale wavelet transform, and differentiated prediction models are constructed for different health states, so that the precision and robustness of service life prediction of the rotary mechanical part are remarkably improved under complex working conditions, and the service life prediction efficiency of the rotary mechanical part is improved. And light-weight and high-reliability intelligent maintenance decision support is realized.

The present application belongs to the field of medical and health informatization technology, and discloses an infectious disease prevention and control big data AI resource scheduling system and method; by constructing a load temperature field model of a computing power node cluster, the node load intensity is mapped to a temperature distribution, and the temperature gradient between adjacent nodes is calculated to identify hot and cold node pairs. Based on the data dependency topology, the energy consumption cost and time delay penalty of task migration are analyzed, and a multi-path heat diffusion network is constructed combined with the temperature gradient to realize the optimized shunting of tasks from hot spots to multiple cold nodes. The system analyzes the periodic fluctuations and sudden peak characteristics of resource occupation through wavelet transform, dynamically adjusts the task decomposition granularity and load distribution weight, and adapts to changes in resource usage patterns. The present application realizes global heat balance and efficient utilization of computing power resources in epidemic analysis, improves computing efficiency, and enhances the system's ability to respond to sudden computing demands.

The invention provides air conditioning equipment. The air conditioning equipment comprises a carbon dioxide sensor and a processing device; the processing device comprises an analysis unit, a reconstruction unit and an identification unit; the analysis unit is configured to perform set wavelet transform analysis based on the original carbon dioxide concentration time sequence so as to execute multi-stage decomposition, and the original carbon dioxide concentration time sequence is decomposed into multi-stage approximate components and detail components; the reconstruction unit is configured to reconstruct and generate an effective carbon dioxide concentration time sequence after multi-stage decomposition; the recognition unit is configured to recognize the statistical characteristics of the effective carbon dioxide concentration time sequence and presume the scene type of the air-conditioned room according to the statistical characteristics of the effective carbon dioxide concentration time sequence. According to the method, the wavelet transform analysis technology and the statistical feature recognition technology are combined, accurate analysis and scene recognition of the carbon dioxide concentration time sequence in the air-conditioned room are achieved, and the method has the advantages of being good in real-time performance and high in accuracy and intelligent degree.