50 results about "Wavelet neural network" patented technology

The invention relates to the technical field of equipment detection management, and discloses an equipment detection management system and method based on the Internet of Things. The system comprises a multi-source data acquisition unit which acquires heterogeneous time sequence data and synchronizes time-frequency domain signals; the state feature extraction unit is used for extracting features by using a deep wavelet neural network and generating sparse codes; the anomaly detection unit is used for constructing a graph attention network model to calculate a dynamic anomaly threshold value; the service life prediction unit is used for predicting the residual service life of the equipment through the multi-task time sequence convolutional network; and the resource optimization unit is used for optimizing resource allocation based on the improved ant colony algorithm. According to the method, the problems of incomplete data acquisition, inaccurate feature extraction, false alarm and missing alarm of anomaly detection, low service life prediction precision, unreasonable resource allocation and the like in traditional equipment detection management are solved, accurate equipment state monitoring, timely anomaly discovery, accurate service life prediction and efficient resource utilization are realized, the equipment operation reliability is improved, and the maintenance cost is reduced.

The invention discloses an urban intelligent water affair risk dynamic identification and early warning method based on deep learning, and the method comprises the following steps: S1, collecting the water pressure, flow, residual chlorine concentration, elevation, rainfall, valve state, pump station state and accident label of each node in a water supply network, and constructing a time alignment data sequence; s2, constructing a dynamic adjacency matrix according to the pipe network connection relation and the event state information; s3, inputting the data sequence and the dynamic adjacency matrix into an improved space-time diagram wavelet neural network to generate space-time feature representation; s4, multi-scale features are extracted and fused through the high-frequency branches and the low-frequency branches; s5, constructing a hyperedge set, executing graph structure propagation, and generating a risk representation tensor; s6, inputting the risk representation tensor into the risk prediction network, and outputting a node risk probability and a confidence interval; and S7, determining a risk level according to the risk probability and the confidence interval, and generating a corresponding early warning signal. According to the invention, fine modeling and dynamic early warning of urban water supply risks are realized.

The invention relates to the technical field of power distribution network fault detection, and provides a neutral point small current grounding power distribution network single-phase grounding fault section positioning method based on a space-time diagram wavelet neural network. The method comprises the following steps: constructing a training data set covering multiple types of single-phase earth fault scenes, and carrying out time sequence electrical quantity sampling; data preprocessing is completed through operations such as data cleaning and missing completion; constructing a spatial-temporal feature extraction module fusing a graph wavelet neural network and a gating circulation unit network, and carrying out spatial-temporal joint modeling on node voltage and current time sequence information; and outputting a node-level fault positioning probability through a full connection layer and a Softmax classifier. According to the method, the response capability of the model to topological disturbance is enhanced through frequency domain wavelet filtering, the information transmission efficiency is improved in combination with a residual mechanism, the fault section identification precision under the conditions of sparse observation data and weak electrical disturbance characteristics is effectively improved, and the method is suitable for a small current grounding fault positioning task in a complex operation environment of a power distribution network.

The invention provides a dynamic sequence recommendation mechanism for multi-scale wavelet transform of an intelligent maritime reconnaissance instrument, which belongs to the technical field of communication information services and comprises an embedded layer, a multi-scale wavelet decomposition layer, a wavelet neural network layer, a multi-view contrast learning module and a final recommendation layer. According to the method, the discrete wavelet transform is introduced to replace the traditional Fourier transform and discrete cosine transform, so that the dynamic interest change in the user behavior sequence is more accurately captured. According to the method, time and frequency localization analysis can be carried out on the signals at the same time, and the method is especially good at processing non-stable user behavior data containing mutation, so that long-term stable interests and short-term sudden interests of users are extracted and distinguished on different time scales. In addition, a wavelet neural network and an enhanced multi-view contrast learning mechanism are introduced, and the feature processing ability, generalization ability and recommendation precision of the model are further improved.

The invention discloses a centralized heat supply network pipe pressure monitoring method and system based on artificial intelligence, and relates to the technical field of heat supply system monitoring, and the method comprises the steps: firstly laying optical fibers and point pressure sensors on a network pipe, and collecting acoustic strain and pressure data; constructing a digital twin grid through same-frequency alignment and Delaunay triangulation, and reconstructing a continuous pressure field; calculating the residual error between the reconstruction pressure and the measured value at the monitoring node; extracting multi-scale residual features to form a feature matrix based on a wavelet operator of a pipe network topological structure map; inputting the features into a pre-trained spectrogram wavelet neural network, and outputting a normalized abnormal score; and finally positioning a fault pipe section according to the score and performing graded warning. Physical modeling and deep learning are fused in the whole process, closed-loop monitoring from data collection to fault diagnosis is achieved, and the precision and timeliness of pipe network anomaly detection are remarkably improved. Manual detection is not needed, and the monitoring process is more flexible.

The invention discloses a gearbox fault identification method based on a Gabor wavelet neural network, and the method comprises the steps: inputting a gearbox vibration signal into a wavelet neural network, generating a Gabor wavelet, and converting the Gabor wavelet into a wavelet kernel; synchronously extracting a real component and an imaginary component of a wavelet kernel by adopting a dual-channel complex filtering mechanism, and performing normalization operation on the wavelet kernel by combining an energy normalization strategy; in each layer of the wavelet neural network, carrying out time-frequency feature extraction through forward propagation, carrying out iterative optimization on a wavelet kernel through back propagation, and outputting a complex feature map extracted from the time-frequency features of each layer; and inputting the plurality of feature maps into a convolutional neural network for advanced abstract feature extraction to obtain a fault diagnosis result of the gearbox. According to the method, wavelet transform is embedded into the deep network end to end, adaptive modeling of time-frequency characteristics of input signals is realized, various fault characteristics of the gearbox can be adaptively and accurately matched, and the resolution and accuracy of time-frequency analysis are remarkably improved.

The invention provides a power supply system operation and maintenance monitoring method and platform based on a neural network, and relates to the technical field of power supply system operation and maintenance. Historical operation data of a power supply system is collected, and an operation and maintenance management database is constructed according to the historical operation data to mark historical operation states of power supply stations in the power supply system; based on the historical sensing detection images and the historical operation and maintenance management parameters, the current sensing detection images and the operation and maintenance management parameters are integrated into a multi-source splicing feature map through a wavelet neural network model; fault feature classification is carried out on the multi-source splicing feature map based on a softmax classifier, and the operation state of each power supply station is updated in real time according to the classification result of a jump convolution network model, so that the real-time operation state of each power supply station is obtained; and comparing the historical operation state of each power supply station with the real-time operation state, and generating an operation and maintenance report and fault trend prediction of the corresponding power supply station. According to the invention, supervision efficiency and reliability of operation and maintenance of the power supply system are improved.

The invention provides a gait evaluation method and system based on a human body nonlinear system analysis technology, and the method comprises the steps: collecting a gait cycle six-channel high-precision time sequence signal outputted by a wearable inertial measurement unit, eliminating noise and gait difference through wavelet threshold denoising and Z-score standardization, extracting a chaotic feature vector composed of a Lyapunov index spectrum and a Kolmogorov entropy value, and carrying out the recognition of a gait signal, a wavelet neural network is adopted to realize nonlinear mapping of chaotic features and phase-space reconstruction parameters, and a self-adaptive feedback mechanism is introduced to dynamically optimize modeling parameters, so that the accuracy and personalized matching capability of gait pattern recognition and stability evaluation are effectively improved; quantitative characterization of the gait chaos level and real-time online model optimization can be achieved, and high-robustness support is provided for rehabilitation training and exercise aided decision making.

The invention discloses a main bearing fault diagnosis and degradation prediction method for multi-source coupling signal sparse decoupling, and belongs to the technical field of fault diagnosis. The method comprises the following steps: acquiring a multi-source vibration signal of a rotating machinery main bearing; performing variable working condition anomaly detection based on a causal decoupling theory to obtain a health state index with independent working conditions; performing signal sparse separation and decoupling by using a multi-band wavelet sparse decomposition network; performing frequency domain feature enhancement and impact response sparse diagnosis based on a wavelet neural network, and identifying a fault type; and predicting a degradation trend by combining a deep transfer learning model, and estimating the remaining service life. The problems of poor adaptability to complex working conditions, weak noise interference suppression, insufficient signal sparse processing, difficulty in weak feature extraction and the like in the prior art are solved, the fault diagnosis precision and the prediction reliability are remarkably improved, and the method is suitable for health management of the rotating machine main bearing.

The invention relates to an English phoneme m recognition method based on a spline interpolation wavelet neural network, and solves the problems that a traditional recognition method is low in calculation speed and low in precision and stability. Audio input is converted into an analyzed discrete signal set by using a sample-and-hold technology, and a standardized audio data set is obtained through normalization and threshold processing to serve as input of a neural network; a six-order spline interpolation wavelet is selected, and a criterion function of a neural network is constructed according to mapping coefficients of the interpolation wavelet and a common wavelet; taking a wavelet as an excitation function of the neural network; constructing a training feedback matrix by using the mapping coefficients of the interpolation wavelet and the common wavelet; the wavelet neural network training has global convergence, and an output layer weight is a corresponding wavelet coefficient; performing discrete Fourier transform on the wavelet coefficient to obtain a frequency spectrum of the wavelet coefficient, and identifying a feature point and a distribution characteristic thereof as a signal identification condition; and judging whether the input phonemes contain consonants m or not by comparing the feature points with the distribution characteristics of the feature points.

The application provides a power line carrier channel noise prediction method and system considering type matching deviation, and the method comprises the following steps: collecting original noise data of power line carrier channel noise; calculating the matching similarity between the original noise data and each noise type respectively, and determining the noise type; calculating the matching deviation of the corresponding noise type; inputting the original noise data into a pre-trained noise prediction model, and realizing the prediction of the power line carrier channel noise based on the output of the model; wherein the model is trained by a preset wavelet neural network based on the wavelet neural network prediction error of the corresponding noise type and the matching deviation. Compared with the prior art, by identifying the type of the power line carrier channel noise, training the preset wavelet network model based on the matching deviation, realizing the prediction of the power line carrier channel noise, the type of the noise and the prediction model can be adapted, and the prediction accuracy and precision are effectively improved.

The present application relates to a kind of discrete multi-motor servo system fuzzy wavelet neural network control method with event triggering mechanism, belong to multi-motor servo system control field, comprising the following steps: S1: the discrete time system model of multi-motor servo system is established;S2: design two type fuzzy wavelet neural network to estimate unknown nonlinear function caused by external interference and internal parameter perturbation;S3: based on the backstepping control framework, introduce event triggering mechanism with dead zone operator, design discrete time fuzzy wavelet neural network controller;S4: using the discrete time fuzzy wavelet neural network controller, control multi-motor servo system is carried out.

The present application relates to a cross-stitch network-based tool multi-task high-synergy monitoring method, comprising the following steps: S1, collecting tool vibration signals, constructing a training data set and a test data set; S2, using a wavelet neural network for feature extraction for the anomaly detection task, and using a gated recurrent network for feature extraction for the RUL prediction task; S3, constructing a cross-stitch network, performing deep feature extraction and shunt-type feature fusion; S4, constructing a regularization loss function, inputting the training data set, and using an automatic weight adjustment strategy to adaptively optimize the loss weights of different tasks, so that the regularization loss function reaches a minimum, and the network training is completed; S5, inputting the test data set into the trained network model to obtain the results of the anomaly detection task and the RUL prediction task. The present application innovatively introduces and optimizes and improves the cross-stitch network and applies it to tool multi-task monitoring, significantly improves the tool multi-task synergy, can achieve higher classification and prediction accuracy, and has good stability.

The invention provides an adaptive equalization method for a PON, and the method comprises the steps: decomposing an input unbalanced signal into a preset number of layers through employing a Mallat decomposition algorithm, and obtaining a high-frequency detail coefficient and a low-frequency approximation coefficient of each layer; performing denoising processing on the high-frequency detail coefficient of each layer: calculating a GCV score of the high-frequency detail coefficient of the current layer by using a generalized cross validation threshold method, determining an optimal denoising threshold value for denoising processing based on the GCV score, and performing denoising processing on the high-frequency detail coefficient based on the optimal denoising threshold value by using a nonlinear contraction function; based on the denoised high-frequency detail coefficient of each layer and the denoised low-frequency approximation coefficient of the corresponding layer, performing layer-by-layer iterative reconstruction by using a Mallat reconstruction algorithm to obtain a denoised data symbol sequence; and taking the denoised data symbol sequence as the input of a pre-trained embedded wavelet neural network, and outputting an equalized signal.

The invention relates to a converter end point carbon content prediction method based on a wavelet neural network, which comprises the following steps: firstly, carrying out data preprocessing on original data, sequentially finishing blank value elimination, abnormal value screening and normalization processing, then screening out weak correlation process parameters through Pearson's correlation coefficient analysis in combination with a metallurgy mechanism, and finally, carrying out prediction on the end point carbon content of a converter. Subsequently, dimensionality reduction is carried out through principal component analysis, and finally an optimized input parameter set is formed to replace original full-amount parameters. After the optimization parameter set is substituted into the wavelet neural network model, the model complexity is greatly reduced, and the training efficiency is improved. And finally, inputting production data into the wavelet neural network prediction model to obtain an end point carbon content prediction result. According to the method, modeling is carried out based on the wavelet neural network, deep learning driving replaces traditional mechanism driving, complex reactions and nonlinear relations in automatic learning data can be achieved, and the prediction precision of the converter end point carbon content is improved.

The invention relates to a wavelet neural network dynamic construction method, which comprises the following steps of: setting a nonlinear system, and processing an unknown nonlinear function in the nonlinear system by adopting a self-adaptive scaling wavelet network approximation framework; processing the non-linear function, namely determining an initial wavelet space for approaching the non-linear function by utilizing an initial wavelet frequency estimator of the adaptive scaling wavelet network approaching framework; on the basis of a scaling parameter self-adaptive adjustment mechanism, scaling parameters are dynamically updated on line so as to adjust the frequency bandwidth of the wavelet basis function and enable the frequency bandwidth to be matched with the spectrum distribution of the nonlinear function; and based on a wavelet basis function increasing and cutting mechanism of the adaptive scaling wavelet network approximation framework, increasing and cutting a wavelet basis function on the basis of the initial wavelet space so as to further approach the nonlinear function.

The invention discloses a vehicle energy management method, device and equipment and a storage medium, and belongs to the technical field of intelligent control of new energy automobiles, and the method comprises the steps: obtaining vehicle data of a target vehicle; wherein the vehicle data comprises historical vehicle speed information and current environment information; inputting the vehicle data into the trained prediction model to obtain the vehicle speed and the vehicle flow of the target vehicle at N future time points; wherein the prediction model is determined according to a wavelet neural network and a particle swarm optimization algorithm; according to the vehicle speed and the vehicle flow of the target vehicle at each future time point, determining an energy management instruction corresponding to the target vehicle at each future time point; and managing the energy of the target vehicle according to the energy management instruction corresponding to the target vehicle at each future time point. According to the invention, efficient energy management of the target vehicle in a dynamic scene is realized, the energy consumption of the target vehicle in a driving process is reduced, and the service life of key parts of the target vehicle is prolonged.

The present invention discloses a method for improving the grinding accuracy of a worm wheel grinding machine. On the one hand, a thermal characteristic simulation model is established, and it is found that the relative thermal deformation between the tool and the workpiece spindle increases linearly with the rotational speeds of the worm wheel grinding wheel and the workpiece spindle. By optimizing the thermal characteristics of the worm wheel grinding machine, thermal balance design is achieved and uniform distribution of the temperature field is ensured, thereby improving the ability to resist thermal deformation and enhancing thermal stability. On the other hand, a thermal error prediction model is created, and the parameters of the autoregressive wavelet neural network are optimized using the chaotic sparrow search algorithm. In the chaotic sparrow search algorithm of the present invention, Bernoulli chaotic sequences and perturbations, elite opposition-based learning, and sine-cosine search algorithm are combined. The created thermal error prediction model can effectively improve the prediction accuracy, and the predicted thermal error is used to compensate for the thermal error of the worm wheel grinding machine. Thus, by implementing thermal characteristic optimization and error compensation, the grinding accuracy of the worm wheel grinding machine can be effectively improved.

The invention is suitable for the technical field of three-dimensional object classification, and provides a three-dimensional object classification method of a hypergraph wavelet neural network based on a smooth spline, and the method comprises the following steps: inputting and preprocessing data; constructing a hypergraph and scoring node importance; optimizing node features by applying a smooth spline; and carrying out multi-scale feature extraction, feature fusion and classification through a hypergraph wavelet neural network based on a smooth spline. According to the invention, by introducing a smooth spline method and a node importance scoring mechanism, the robustness of the model to noise is enhanced, and the smoothness of features is improved; the integrated feature fusion strategy effectively fuses different features, more meaningful information is captured, and node embedding representation with higher discrimination is obtained. Experimental results show that the model provided by the invention is superior to an existing 3D object classification model in classification accuracy, and verifies the effectiveness of the hypergraph wavelet neural network based on the smooth spline and the adopted fusion strategy.

The application relates to a UPFC device fault prediction method based on a wavelet neural network, and comprises the following steps: defining a fault variable of a unified power flow controller; classifying the fault variable, and defining a data acquisition structure for device state monitoring and fault prediction; acquiring signal data of each node of the unified power flow controller, classifying and coding the signal data after filtering and A / D conversion; constructing a device fault prediction model by adopting a wavelet neural network; inputting real-time signal data into the trained device fault prediction model to obtain a device state prediction result output by the device fault prediction model; judging whether a device fault exists according to the device state prediction result; and issuing a fault early warning signal for the predicted device fault. The application realizes multi-dimensional and all-around UPFC online state monitoring, fault diagnosis and fault early warning, predicts specific fault information of the UPFC device in advance, effectively protects the operation safety of the device, and avoids damage to other power electronic elements caused by the UPFC device fault.

The invention relates to the field of Internet of Things production, in particular to a production equipment monitoring method, system and equipment based on the industrial Internet of Things and a medium, and the method comprises the following steps: obtaining coupling change information of a monitoring part obtained through combined measurement based on a displacement sensor and an eddy current sensor; the coupling change information comprises a change rate signal and an eddy current energy signal, and the monitoring part is a part needing to be monitored on the target production equipment; acquiring surface feature information of the monitored part obtained based on a phase-controlled eddy current sensor; fusing the coupling change information with the surface feature information to obtain a comprehensive feature signal; inputting the comprehensive characteristic signal into a preset wavelet neural network prediction model to output a risk level of the target production equipment; through dynamic fusion of feature enhancement driven by a physical mechanism and data driving, the dependence of traditional time domain analysis on a single signal amplitude is broken through, and the cross-physics coupling effect of weak damage is amplified from multiple dimensions.

The invention relates to the technical field of urban rail train automatic driving, in particular to an improved urban rail train speed trajectory adaptive prediction function control method, which comprises the following steps: step 1, establishing an urban rail train speed trajectory control system model; 2, controlling an urban rail train speed track adaptive prediction function; step 3, designing an adaptive prediction function of the speed track of the urban rail train; and 4, performing an urban rail train speed trajectory tracking control HIL experiment. According to the method, on the basis of jointly selecting a step function and a wavelet function as a primary function, an urban rail train speed control softening factor adaptive adjustment strategy considering a fuzzy satisfaction index and urban rail train operation characteristics is designed, and an additional resistance calculation method based on a multi-mass-point model is introduced. Besides, an effective softening factor adaptive adjustment function parameter setting method is also designed based on a wavelet neural network and an entropy weight method, so that the control quality is further improved, and the improvement of more than 25% can be realized.

The invention discloses an English syllable u feature wavelet coefficient extraction method based on a spline interpolation wavelet neural network, and the method comprises the steps: firstly carrying out the normalization and threshold preprocessing of an audio signal, and effectively removing the interference of environment noise; secondly, constructing a three-layer neural network feedback matrix based on a six-order spline wavelet function, and generating a feedback matrix through transposition operation and inverse matrix calculation of a global matrix psi; then, inverse discrete Fourier transform is utilized to construct a criterion function containing frequency domain errors; and finally, dynamically adjusting the weight of an output layer through iterative training, and terminating training when the modulus value of the criterion function is smaller than a training error iteration ending condition, so as to obtain a wavelet coefficient set representing the characteristics of the longhairy sound u. According to the method, the frequency domain localization characteristic of the six-order spline wavelet is innovatively combined with the adaptive learning of the neural network, the anti-noise performance is improved while the feature extraction precision is ensured, and the problem of individual pronunciation difference is solved.

The invention relates to the technical field of electric power data processing, in particular to an electric energy metering data anomaly detection method, system and device and a medium, and the method comprises the steps: carrying out the moving average filtering and standardization processing of original electric energy metering data, and generating preprocessing data; dynamically selecting a target wavelet basis function according to the spectrum kurtosis value of the preprocessed data, performing multilayer wavelet packet decomposition on the preprocessed data, and extracting a high-frequency detail component and a low-frequency approximate component; energy class features of the high-frequency detail components and statistical features of the low-frequency approximate components are extracted, fusion and normalization are carried out, and multi-dimensional feature vectors are constructed; inputting the multi-dimensional feature vector into a wavelet neural network model, and outputting an abnormal probability value of the current time point; and comparing the abnormal probability value of the current time point with a dynamic threshold value to judge whether the current time point is an abnormal point or not. The method has the beneficial effects that the accuracy, the stability and the self-adaptive capability of the abnormal detection of the electric energy metering data are remarkably improved.

The present invention relates to the technical field of equipment detection and management, and discloses an equipment detection and management system and method based on the Internet of Things. The system includes a multi-source data acquisition unit, which collects heterogeneous time series data and synchronizes time-frequency domain signals; a state feature extraction unit, which uses a deep wavelet neural network to extract features and generate sparse codes; an anomaly detection unit, which constructs a graph attention network model to calculate dynamic anomaly thresholds; a life prediction unit, which predicts the remaining life of the equipment through a multi-task time series convolutional network; and a resource optimization unit, which optimizes resource allocation based on an improved ant colony algorithm. This invention solves the problems of incomplete data collection, inaccurate feature extraction, false positives and omissions in anomaly detection, low life prediction accuracy, and unreasonable resource allocation in traditional equipment detection and management, and achieves accurate monitoring of equipment status, timely detection of anomalies, accurate life prediction, and efficient resource utilization, thereby improving equipment operation reliability and reducing maintenance costs.

The invention discloses a dissolved oxygen concentration control method based on a pipeline recursive wavelet neural network, and relates to the field of artificial intelligence. Firstly, a control factor of the DO concentration is analyzed based on a reaction mechanism; and then, constructing a pipeline recursive wavelet neural network to control the DO concentration. Meanwhile, an improved adaptive learning rate algorithm is adopted to train network parameters. Therefore, the DO control precision in the sewage treatment process is improved.

This invention discloses a method and device for early warning of energy consumption in air conditioning refrigeration systems based on genetic algorithms. It aims to solve the problems of traditional energy consumption prediction models, such as single feature set, weak nonlinear modeling ability, reliance on experience for parameter tuning, and susceptibility to local optima. The method first integrates operating parameters such as indoor and outdoor temperature difference, humidity, compressor performance, and refrigerant charge to construct input features that comprehensively reflect the system state. Second, it designs a wavelet neural network with Morlet wavelet function as the activation function and introduces a genetic algorithm to globally optimize network weights, scaling factors, and translation factors, automatically searching for the optimal parameter combination to improve model convergence speed and prediction accuracy. Third, it dynamically sets energy consumption thresholds based on historical data, compares the prediction results in real time, and triggers an early warning when the threshold is exceeded. This method ensures high accuracy while possessing good generalization ability and engineering applicability, providing effective support for intelligent operation and maintenance and energy-saving management.

This invention discloses a method for classifying coral reef substrate based on the fusion of lidar and side-scan sonar data, mainly including the following steps: (1) By analyzing the structure of the photon-counting lidar scanning system, a precise calculation model for the coordinates of underwater depth sounding points of UAV-borne lidar is constructed; (2) Through fine processing steps such as seabed line tracking and joint correction of radiation distortion, a geocoded side-scan sonar image is constructed; (3) The backscattering intensity of lidar and side-scan sonar is fused to construct a seabed topographic image; (4) The standard wavelet BP neural network is improved; (5) Based on the fused image, coral reef species samples are extracted, and feature parameters with high self-cohesion and resolution are selected for sample training. The sample training results are used to classify the coral reef substrate in the fused image. Through the above steps, coral reef substrate classification based on the fusion of lidar and side-scan sonar data can be realized, providing technical support for identifying coral reef types.

The application relates to the technical field of intelligent equipment manufacturing, and specifically discloses an AI-based equipment manufacturing process energy consumption optimization and carbon footprint tracking system, which comprises a central collaborative scheduler, and the central collaborative scheduler is communicatively connected with the following modules: a data self-checking reconstruction module, a wavelet neural network optimized by using an improved particle swarm algorithm is used to construct a data self-checking model, and time sequence analysis and correlation comparison are carried out on multi-source heterogeneous sensing data of an equipment manufacturing process; the application can dynamically identify and correct inherent errors of sensing data by deploying a multi-source heterogeneous sensor network, combining a precise time synchronization protocol, constructing a time sequence alignment sequence, using a wavelet neural network optimized by using an improved particle swarm algorithm to construct a data self-checking model, outputting a high-fidelity data stream, and solving the problem of insufficient reliability of carbon footprint accounting data in a traditional manufacturing process from the source, thereby providing a data basis for energy consumption optimization and carbon emission tracking, and ensuring the authenticity and effectiveness of accounting results.

The invention relates to the technical field of motion control, and discloses a high-precision two-dimensional motion error prediction compensation iteration method, which comprises the following steps of: acquiring real-time two-dimensional motion data of a target object through a sensing device, and decomposing through wavelet transform to obtain a low-frequency motion trend and a high-frequency noise component; a hybrid prediction model containing a wavelet neural network and a Bayesian filtering module is utilized to predict and calculate residual errors to generate dynamic error compensation parameters, and a compensation matrix is constructed after optimization by means of a genetic algorithm. Based on a hierarchical control architecture, error compensation is completed through global compensation, local correction and an execution layer driving execution mechanism. And an online learning module is also arranged to update model parameters in real time. The method improves motion error prediction accuracy and compensation precision, is widely applied to the fields of precision machining, robot control and the like, and improves the performance of a motion system.