15 results about "Exponentially weighted moving average" patented technology

This invention relates to an integrated method for bearing condition monitoring and fault diagnosis under complex noise conditions, belonging to the field of mechanical equipment condition monitoring and fault diagnosis. This method extends the classic noise-resistant correlation (NRC) method to resist interference from complex noise. The extended NRC is fused with the frequency domain energy ratio to form a composite correlation coefficient. Integrating the composite correlation coefficient through a sliding window yields a health characteristic index that monotonically changes with bearing degradation. Subsequently, an exponentially weighted moving average (EWMA) control chart is used to track this index to indicate early anomalies. Simultaneously, this method highlights fault-related periodic signals while achieving monitoring, realizing the integration of monitoring and diagnosis.

This invention discloses a method and system for monitoring the operating status of power transmission and transformation equipment. First, real-time operating data of the power transmission and transformation equipment is collected and standardized for preprocessing. Then, feature extraction is performed on the preprocessed data to obtain feature data of the power transmission and transformation equipment. A pre-trained least squares support vector machine model is used to obtain the equipment operating status prediction result. Next, the equipment operating status prediction result is converted into a state confidence score. The state confidence scores are then fused using an improved D-S evidence theory method based on Euclidean distance to obtain a comprehensive confidence score for the equipment operating status. Finally, based on the comprehensive confidence score, an exponentially weighted moving average algorithm is used to construct a dynamic threshold for graded early warning of the power transmission and transformation equipment. This invention solves the technical problem that simple models cannot cope with the nonlinear correlation of multiple parameters in power transmission and transformation, and accurately adapts to the actual operation and maintenance needs of multi-parameter collaborative monitoring of power transmission and transformation systems.

This invention relates to the field of intelligent manufacturing and industrial data analysis technology, specifically to a method, system, and storage medium for identifying production bottlenecks based on multi-dimensional data fusion. The method includes: acquiring production data and constructing a multi-dimensional data vector; updating the weights of a linear prediction model based on the multi-dimensional data vector; performing multi-dimensional data fusion using the updated weights to obtain a comprehensive index value; obtaining a production bottleneck severity score based on the comprehensive index value; determining the production bottleneck status based on the production bottleneck severity score and the comprehensive index value; and identifying factors influencing the production bottleneck based on weight changes. This invention dynamically adjusts the weights using a gradient descent method to score the bottleneck, and dynamically updates the judgment threshold using an exponentially weighted moving average. Finally, it performs multi-dimensional cause analysis by comprehensively considering the weight contribution, change trend, and data anomaly, achieving adaptive and accurate location and cause identification of production bottlenecks.

The application discloses an intelligent evaluation system and method for a knowledge base question answering application, and relates to the technical fields of artificial intelligence, natural language processing and multi-agent collaborative system. The system comprises a data preprocessing module, a data synthesis module, a data screening module, a data scoring module and an adaptive weight adjustment module. The data preprocessing module performs data preprocessing on enterprise original documents and generates a data knowledge base. The data synthesis module repeatedly asks and answers each paragraph of the data knowledge base by using a large language model to generate a question and answer pair. The data screening module screens out high-quality samples through screening agent collaboration and a MACA framework mechanism. The data scoring module scores the question and answer pair through scoring agent collaboration and a MACA framework mechanism. The adaptive weight adjustment module dynamically updates the weight of each screening and scoring dimension by using an exponentially weighted moving average algorithm.

This invention discloses a wind turbine fault detection method and system that integrates seasonal features and an attention mechanism. The method includes: acquiring historical SCADA data of the wind turbine for preprocessing and introducing seasonal labels to construct a standardized input vector; inputting the standardized input vector into an attention-enhanced bidirectional long short-term memory network to output predicted values ​​of normal behavior for each key component of the wind turbine; performing seasonal standardization mapping on the original residual sequence based on the corresponding seasonal labels to obtain a deseasonalized standardized residual sequence; calculating the small offsets of each key component in the standardized residual sequence using an exponentially weighted moving average operator; and issuing a fault alarm for the corresponding key component when the offset statistic exceeds the detection threshold of that component. This invention integrates seasonal features and a deep attention mechanism, using bidirectional long short-term memory to model component thermal response and seasonally standardized residuals to eliminate environmental interference, significantly improving the accuracy of early fault diagnosis for key wind turbine components.

This invention discloses a battery state cycle monitoring method and system based on charge / discharge behavior, belonging to the field of battery state monitoring technology. It configures state attribute codes describing abnormal charge / discharge states of the battery, forming stage-specific labels. Within one state recording cycle of battery charge / discharge, it performs several cyclic captures of abnormal charge / discharge states of the battery under monitoring. It counts the captured abnormal states, quantifies the marginal probabilities of each stage's label and the joint probabilities between labels from different stages, uses a normalized point mutual information model to measure the first charge / discharge correlation between two stage labels, and uses an exponentially weighted moving average model to obtain the second charge / discharge correlation. Based on the comparison results with a preset threshold, it generates a battery state monitoring report. This invention achieves accurate identification and correlation analysis of abnormal patterns at different stages during battery charge / discharge, improving the accuracy and reliability of battery state monitoring.

This invention discloses a closed-loop management method driven by a holographic ledger for supplementary medical reimbursement in enterprises, comprising: initializing holographic ledger records containing a finite state machine instance and a hash chain storage area; performing hash verification before each step starts, and starting the step after passing the verification, outputting the hash and writing it into the hash chain to form an immutable evidence chain, which is automatically migrated by the state machine; extracting the fiber texture of the invoice and the microscopic imprint features of the papermaking equipment and combining them into a joint physical fingerprint, which is bound to the business data hash value via HMAC-SHA256 to generate a two-layer fusion verification vector; comparing this vector with historical records in multiple dimensions and verifying the integrity of the hash chain, distinguishing the anomaly type and triggering differentiated processing; intercepting and triggering review when an anomaly occurs, and adaptively adjusting the judgment threshold based on the review result using an exponentially weighted moving average method to form a ledger-driven closed-loop traceability chain; this invention realizes ledger active driving, data hash chain closed-loop locking, two-layer traceability of invoice physical features, and adaptive optimization of detection parameters.

This invention relates to the field of water meter status monitoring technology, specifically an online monitoring method and system for water meter operation status based on the Internet of Things (IoT). The method includes: when the water meter microcontroller unit is in sleep mode, an accelerometer collects vibration signals using a dual-threshold hysteresis comparison method. When the ratio of short-time energy to long-time energy exceeds an energy ratio threshold and the duration exceeds a set duration, a first-level wake-up signal is output to wake up the coprocessor. The coprocessor performs first-order differential processing on the vibration signal, calculates the zero-crossing rate and peak-to-average power ratio (PAPR) of the differential sequence, and uses the weighted sum of the zero-crossing rate and PAPR as a water flow characteristic factor. This invention addresses false alarm anomalies by using an exponentially weighted moving average mechanism to smoothly correct the wake-up threshold and judgment boundary. This allows the system to continuously improve its identification benchmark based on historical operating data during long-term service, enhancing the long-term reliability of water meter operation status monitoring and the overall endurance of the equipment.

This invention relates to the field of hydrogen peroxide production technology, and more specifically, to a method for monitoring and early warning of hydrogen peroxide production based on big data. The method includes: calculating the material imbalance vector of a device node based on the law of conservation of mass; adjusting the smoothing coefficient of an exponentially weighted moving average algorithm according to the change in the material imbalance vector of the device node, and calculating the target smoothing coefficient of the device node to obtain the corrected sensor reading of the device node at each moment; taking the weighted sum of the corrected sensor readings of each device node in the set of upstream adjacent nodes of the device node at a moment as the theoretical sensor reading of the device node at that moment; and determining the upstream and downstream influence of the device node at each moment based on the difference between the theoretical sensor reading and the corrected sensor reading, thereby achieving abnormal monitoring and early warning of the production process and effectively improving the accuracy of monitoring and early warning.

This invention belongs to the field of agricultural intelligent monitoring technology, and particularly relates to a method and system for early warning of soil parameter anomalies based on multi-source data fusion. First, it integrates multi-source data from soil sensors, remote sensing platforms, weather stations, and geographic information databases in the target area, preprocessing them into a standardized dataset. Then, it divides the data into high-frequency and low-frequency node sets according to the acquisition frequency, calculates weights using an attention mechanism, and extracts features using a 1D convolutional neural network and a multilayer perceptron, respectively, before fusing them to obtain a soil parameter sequence. Next, it constructs an anomaly early warning model, combining historical quantiles and exponentially weighted moving averages to generate dynamic thresholds. An improved antlion optimization algorithm is used to optimize an ensemble multi-classifier composed of random forest, gradient boosting tree, and support vector machine, jointly outputting the early warning level. Finally, the data is pushed through multiple channels and visualized interactively. This invention can solve the problems of single data source, low fusion efficiency, and insufficient accuracy of anomaly early warning in existing soil parameter monitoring methods.

This invention relates to an artificial intelligence-based data inspection quality control method, system, device, and medium. The method includes: cleaning and determining the distribution of raw inspection data to generate a qualified dataset; adaptively learning key model parameters such as the effective data range and optimal smoothing coefficient through iterative calculation and simulation optimization based on the dataset and its distribution type; training a long short-term memory neural network time-series prediction model using the qualified dataset, and setting quality control limits by combining the learned benchmark dispersion to construct a real-time quality monitoring model; inputting real-time inspection samples into the model, generating alarm signals by calculating an exponentially weighted moving average monitoring statistic and comparing it with the control limits; iteratively optimizing the model and parameters based on historical alarm signals and new data to achieve continuous self-updating of quality control, enabling automatic learning of quality control parameters and intelligent optimization of the model, and improving the real-time monitoring capability of minor system deviations during the inspection process.

This invention discloses a method and system for generating empathic responses based on the evolution of emotional states, belonging to the fields of artificial intelligence and affective computing. The method includes: acquiring multimodal emotional state vectors from multi-turn dialogues and storing them in a short-term emotional trajectory memory; smoothing the emotional intensity sequence using an exponentially weighted moving average to construct an emotional evolution trajectory and calculate evolutionary feature parameters; predicting the emotional intensity of the next round using an evolutionary trend prediction model and calculating the intervention urgency; comparing the intervention urgency with a dual warning threshold to trigger an active intervention mode; selecting an appropriate strategy from an empathic strategy library through a strategy selection function, encoding it as a structured prompt, and inputting it along with the emotional evolution trajectory summary and active intervention information into a large language model to generate an active intervention-style empathic response. This invention achieves a shift from passive reaction to active prevention, solving the problems of lack of perception of the emotional accumulation process and inability to actively intervene in existing empathic dialogues, significantly improving the quality of human-computer emotional interaction.

This invention discloses a spatiotemporal decoupling prediction and feedforward compensation method for visual servoing of a robotic arm, comprising the following steps: S1, target TF transformation acquisition and dynamic spatiotemporal sliding window construction; S2, spatiotemporal decoupling and spatial dimensionality reduction mapping of multi-degree-of-freedom poses; S3, low-dimensional Fourier modeling and fast optimization based on adaptive fundamental frequency estimation; S4, heterogeneous system composite delay calculation, absolute timestamp feedforward compensation, and pose reconstruction; S5, trajectory cascade shaping and closed-loop distribution based on exponentially weighted moving average (EMA). The feedforward compensation method proposed in this invention, which does not rely on expensive computing power for spatiotemporal decoupling, dimensionality reduction fitting, and filtering reconstruction, can achieve millisecond-level computation on ordinary industrial control computers, completing long-delay feedforward compensation up to 250ms, ensuring the high-frequency smoothness and physical safety of the robotic arm.

This invention proposes a method and system for early warning of faults in a synchronous condenser excitation system, belonging to the field of excitation system fault early warning technology. First, this invention constructs a multi-scale temporal series network model incorporating pre-set temporal attention. Multi-scale temporal features are extracted by parallel connecting a multi-branch temporal convolutional network and a long short-term memory network, and dynamic feature fusion is achieved using a time attention mechanism guided by prior knowledge. Then, the network model is trained using normal operating condition data to characterize the normal operating state of the excitation system. Finally, residual analysis is performed using an exponentially weighted moving average method to achieve early fault warning of the excitation system. This invention, combining a multi-scale temporal series network and pre-set temporal attention, significantly improves the prediction accuracy of the network model for the normal operating state of the excitation system, thereby achieving sensitive capture and early warning of early fault characteristics.

This application relates to the field of industrial automation and intelligent monitoring technology, and discloses a method and system for real-time monitoring and fault early warning of paper machine operation status. It aims to solve problems in existing technologies such as weak multi-source data fusion capabilities, insufficient fault feature extraction, delayed early warning, and high false alarm rates. The method includes: simultaneously acquiring vibration, temperature, acoustic emission, current, and tension signals through multiple types of sensors; performing time alignment, resampling, and noise reduction processing on the signals; extracting and fusing the physical domain features of each signal using a dedicated sub-network; generating a dynamic state vector through joint encoding using LSTM and an attention mechanism; achieving fault identification and source tracing by combining a fault classifier with historical operating condition matching; and dynamically adjusting the early warning threshold using an exponentially weighted moving average, triggering graded early warnings based on the deviation magnitude and duration. The system is integrated into an edge computing platform, supporting low-latency, high-precision predictive maintenance. This application significantly improves the reliability and maintenance efficiency of paper machine operation.