80 results about "Exponentially weighted moving average" patented technology

The invention discloses a self-adaptive multi-dimensional adjustment metering box based on the Internet of Things, and relates to the technical field of intelligent monitoring of power equipment. The problem of measurement distortion caused by high fixed threshold false triggering rate, weak anti-aliasing capability and multi-source data asynchronization in a high-dynamic industrial scene in the prior art is solved. Multi-parameter acquisition is realized through the sensing acquisition module and the annular buffer area; a reconfigurable FIR filtering module is adopted to dynamically switch a low-pass / band elimination mode to suppress aliasing interference; noise features are extracted by combining spectral kurtosis analysis and concept drift detection, and a dynamic threshold value is generated by using exponentially weighted moving average and a sliding window standard deviation; a multi-channel time sequence is aligned through an IEEE 1588 protocol, and cloud parameter closed-loop optimization is realized based on extended Kalman filtering and a particle swarm algorithm; according to the method, the threshold fault tolerance, the high-frequency transient signal capturing precision and the multi-source heterogeneous data fusion reliability in a high-noise environment are remarkably improved.

The invention discloses an integrated power distribution cabinet energy efficiency dynamic optimization method based on multi-modal data fusion, and relates to the technical field of intelligent power grids. The problems of failure of an energy efficiency optimization model and poor long-term operation stability caused by data time sequence misalignment and error accumulation of a multi-source sensor in the prior art are solved. According to the scheme, the time offset is dynamically corrected through the adaptive time sequence deviation prediction model; compensating missing data by adopting a non-uniform time step reconstruction algorithm and Kalman filtering; detecting an error drift trend through an exponentially weighted moving average model, updating a feature weight, and inhibiting long-term error accumulation; constructing a self-adaptive time sequence attention fusion network model, and fusing physical constraints and a data driving mechanism to generate an optimization decision; bayesian optimization is utilized to quantify parameter uncertainty, closed-loop feedback execution data is carried out, and model parameters are dynamically updated; according to the invention, the precision, long-term stability and equipment safety of energy efficiency optimization of the power distribution cabinet are remarkably improved, and efficient and reliable operation under multi-physics field coupling constraint is ensured.

The invention discloses a CAN bus intrusion detection method based on adaptive unscented Kalman filtering, and the method comprises the steps: obtaining real-time message data, and carrying out the preprocessing of the real-time message data, and obtaining a time sequence feature vector; constructing a nonlinear state space model based on the time sequence feature vector; performing unscented Kalman filtering state prediction based on the nonlinear state space model; inputting the time sequence feature vector as an actual observation value, calculating a Kalman gain to correct an unscented Kalman filtering state prediction result, and outputting a state estimation residual error; dynamically updating a process noise covariance matrix through exponentially weighted moving average based on the state estimation residual, and adjusting a measurement noise covariance matrix according to the measurement innovation sequence; calculating the mahalanobis distance of the state estimation residual error, comparing the mahalanobis distance with a self-adaptive anomaly detection threshold value, and judging whether an intrusion behavior occurs or not; and if the abnormal score exceeds a threshold value, triggering a multi-level alarm mechanism, recording a suspicious message and executing a safety protection operation.

Disclosed is an explainable method for monitoring a state of a generator of a wind turbine generator system on the basis of a spatio-temporal graph. The method includes: S1: acquiring data collected by a supervisory control and data acquisition (SCADA) system; S2: carrying out data understanding on the SCADA data, selecting features associated with the generator, and carrying out data preparation on the selected feature data, and obtaining valid data; S3: embedding the SCADA data, and forming a directed spatio-temporal graph data sequence; and S4: carrying out modeling of a normal behavior model of the generator on the constructed directed spatio-temporal graph data sequence, computing a full-graph-level residual and a node-level residual, computing a residual through an exponentially weighted moving average (EWMA) control chart method, carrying out full-graph-level state monitoring on the generator, forming a fault information transmission chain relation, and enhancing explainability and robustness of a monitoring result.

The invention discloses an air-railway combined transportation path optimization method based on XGBoost and a space-time attention network, and particularly relates to the field of intelligent transportation systems.According to the method, a traffic network basis is constructed by integrating flight and train historical data, topological information and weather data, delay time and consumed time of a critical path are predicted by means of an XGBoost model, and the time consumption of the critical path is predicted by means of the XGBoost model; a space-time dependency relationship is modeled through a space-time attention network, and complex space-time association is captured in combination with a space and time attention module and a multi-head mechanism; an optimal path is generated based on a weighted multi-objective function (covering time, economy, reliability and comfort), and users are supported to dynamically adjust weights to adapt to personalized requirements; and meanwhile, real-time adjustment of model parameters is realized by adopting an exponential weighted moving average and self-adaptive updating strategy, so that the prediction precision is remarkably improved, the reliability of the path and the user satisfaction are optimized, the real-time performance is ensured through a lightweight closed-loop updating mechanism, and a high-precision, personalized and real-time response intelligent solution is provided for air-railway combined transportation.

The invention discloses a photovoltaic power station generating capacity calculation method and device, electronic equipment and a storage medium, and relates to the field of artificial intelligence and big data, and the method comprises the steps: obtaining the geographic position data and historical power generation data of a target power station; determining a geographic area to which the target power station belongs, iteratively selecting a reference power station in the geographic area, calculating a spatial correlation weight of the reference power station, and calculating a first theoretical power generation amount of the target power station in the target time period based on the spatial correlation weight of the reference power station and the real-time power generation data; inputting the historical power generation data into the exponential weighted moving average model, and outputting a second theoretical power generation amount of the target power station in the target time period; and calculating the theoretical generating capacity of the target power station in the target time period based on the first theoretical generating capacity and the second theoretical generating capacity. According to the invention, the technical problem of low calculation result accuracy of a mode of calculating the theoretical generating capacity of the photovoltaic power station according to meteorological data in the prior art is solved.

The invention provides a branch diameter real-time measurement method and system based on a depth camera, and relates to the technical field of data processing, and the method comprises the steps: extracting a skeleton center line based on a final contour, dynamically laying measurement points along a skeleton line, and calculating a unit normal vector, scanning the contour boundary along the normal direction to determine the intersection point coordinates of the normal and the contour; calculating three-dimensional space coordinates according to the intersection point coordinate set and the corresponding depth value to obtain a section diameter measurement value; and on the basis of the section diameter measurement value, real-time smoothing processing is carried out through an exponential weighted moving average filtering algorithm for dynamically adjusting parameters, instantaneous errors are eliminated in combination with an anomaly detection mechanism, and a smoothed diameter data stream is output. The accuracy of contour extraction and the practicability of the measurement method are improved.

The invention discloses a polling arbitration method and a polling arbitration system for dynamically updating weight by an EWMA algorithm, and the method comprises the following steps: monitoring and receiving a request signal sent by each AHB bus channel on a matrix bus in real time; taking a preset arbitration period as a time window, collecting the current request access times of each AHB bus channel, reading the historical request times stored in the last period, calculating the request variation of each channel, and performing uniform quantization; and on the basis of the historical weight value and the quantized value, an exponentially weighted moving average value is calculated through an EWMA algorithm. When the method is used, the actual weight value of each AHB channel can be adaptively and dynamically adjusted every fixed arbitration period number, resource allocation and system fairness are improved, and it is ensured that an arbiter can more accurately reflect the actual demand of each AHB channel on a matrix bus.

The present invention discloses a method, system, and storage medium for oil and gas pipeline safety assessment based on stress monitoring and intelligent prediction, which relates to the field of oil and gas pipeline safety monitoring and assessment. The method aims to address the problem in the prior art of lacking a method for oil and gas pipeline safety monitoring and assessment based on stress monitoring and intelligent prediction, making it difficult to effectively identify potential safety hazards in pipelines. The method comprises: S100, using finite element analysis to perform numerical simulations on the stress distribution of pipelines under different working conditions to determine stress-sensitive locations in the pipeline; S200, using stress sensors to collect stress data from stress-sensitive locations in the pipeline in real time; S300, constructing an XLSTM model, which uses a gating mechanism and residual connections, and uses a Hippo optimization algorithm to optimize the model's hyperparameters, and predicting stress trends based on the optimized model; S400, using an exponentially weighted moving average control chart for stress anomaly warning, introducing an adaptive threshold adjustment mechanism, performing anomaly detection on real-time stress data, and identifying potential safety risks.

The invention relates to the technical field of state monitoring, in particular to an underground space infrastructure state monitoring management system based on the Internet of Things, which comprises a data acquisition marking module, a dynamic threshold adjustment module, a pattern analysis learning module, an anomaly identification prediction module, a task sorting scheduling module and a maintenance task management module. According to the method, through dynamic adjustment of the moving average and the exponentially weighted moving average and refining processing of data points, it is ensured that tiny variation is recognized in a changeable underground environment, so that the accuracy of fault prediction is greatly improved, and the fault prediction accuracy is improved by utilizing the long and short-term memory network and the sequence-to-sequence model. The system can automatically learn and identify the abnormal mode from complex multi-dimensional data, the response capability of early warning and the early warning accuracy are enhanced, the maintenance response process is optimized through intelligent task sorting and resource allocation, the time delay for coping with an emergency condition is effectively reduced, and the operation and maintenance efficiency is improved.

The invention provides an encrypted traffic anomaly detection method based on unsupervised learning, and relates to the field of artificial intelligence network security. In order to solve the problems of dependence on a large number of labeled abnormal samples, poor model generalization ability and poor dynamic adaptability of the existing encrypted traffic anomaly detection method, the invention provides an encrypted traffic anomaly detection method based on unsupervised learning, which comprises the following steps: extracting effective features of an encrypted traffic data packet, generating a state sequence by using a KMeans clustering algorithm, and carrying out unsupervised learning on the state sequence; calculating the occurrence probability of the state sequence in combination with an n-order homogeneous Markov chain model; a dynamic adaptive threshold is constructed based on exponential weighted moving average (EWMA) and a sliding window mechanism, and abnormality judgment is realized by comparing the occurrence probability of a state sequence with the dynamic threshold. According to the method, only normal encrypted traffic is utilized for training, and effective detection of all encrypted traffic including abnormal traffic can be realized.

The invention relates to the technical field of hydrogen peroxide production, in particular to a hydrogen peroxide production monitoring and early warning method based on big data, and the method comprises the steps: calculating a material imbalance vector of an equipment node based on the law of conservation of mass; adjusting a smoothing coefficient of an exponentially weighted moving average algorithm according to the material imbalance vector variation of the equipment node, and calculating a target smoothing coefficient of the equipment node to obtain a corrected sensor reading of the equipment node at each moment; taking the weighted sum value of the corrected sensor readings of the equipment nodes in the equipment node upstream adjacent node set at one moment as the theoretical sensor readings of the equipment nodes at the moment; the upstream and downstream influence of the equipment node at each moment is determined on the basis of the difference between the theoretical sensor reading and the corrected sensor reading of the equipment node at each moment, so that abnormal monitoring and early warning of the production process are realized, and the accuracy of monitoring and early warning is effectively improved.

The present invention provides a remote sensing detection method for sand dune movement and speed variation, which can be applied to the field of remote sensing image processing technology. The method includes: extracting dune edges from dune remote sensing observation time-series images to obtain a dune edge image sequence; preprocessing the dune edge image sequence to obtain a sand ridge line image sequence; constructing a velocity vector field from the sand ridge line image data of two adjacent frames in the sand ridge line image sequence using a dense optical flow method to obtain a space-time cube array of the sand ridge line movement velocity field; performing pixel-by-pixel time-series analysis on the space-time cube array of the sand ridge line movement velocity field using an exponentially weighted moving average control chart to obtain a time-series rate anomaly judgment result; and obtaining a dune movement and speed variation detection result by superimposing the time-series rate anomaly judgment result onto the dune remote sensing observation time-series images for identification. The present invention also provides a sand dune movement and speed variation remote sensing detection system, electronic equipment, and storage medium.

The invention provides a method for displaying the physical health state of a person before equipment operation based on an intelligent monitoring platform, and the method comprises the steps: firstly carrying out the cleaning, standardization and fusion processing of a collected multi-dimensional data source, generating multi-modal health data, and selecting the input variables of the health features of an operator through the Pearson correlation analysis; a convolutional neural network is combined with a gating circulation unit to extract spatial-temporal features of the monitoring feature quantity, and an attention mechanism is used to distribute corresponding weights for the gating circulation unit; repeatedly training the monitored health data by using exponential weighted moving average, and obtaining an alarm threshold value of a human body health state in combination with a model evaluation index root-mean-square error; based on a membership function combining a semi-trapezoid and a semi-ridge shape, determining a health state grade of the operator; and finally, the intelligent monitoring platform judges the body health state of the operator and whether the operator is allowed to operate the equipment or not before operating the equipment by the operator, and monitors and forcibly intercepts the operation authority of the person in an unhealthy state in real time.

The embodiment of the invention provides an oil and gas storage tank safety risk monitoring and early warning method and system, and belongs to the technical field of risk monitoring. The method comprises the steps that state parameters of a target oil and gas storage tank in a current period are collected, and key parameters are recognized based on the state parameters; calculating the volatility of each key parameter based on an improved exponentially weighted moving average method; historical parameter information of each key parameter is correspondingly collected, and a risk early warning model is obtained through training based on the historical parameter information and the volatility; and determining a target oil and gas storage tank early warning scheme based on the risk early warning model, and outputting alarm information based on the determined early warning scheme. According to the scheme, the comprehensive risk early warning method combining the historical data and the real-time data of the key operation parameters of the oil and gas storage tank can objectively reflect the safety risk state and timely and effectively carry out risk early warning.

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.

The present invention relates to the field of data generation statistics and discloses an automatic annotated map data generation algorithm based on DoubleGAN. The algorithm comprises a prediction model, which includes a generative model training and data expansion process. The generative model training includes S1: building a feature map generation model G1; S2: building a feature map discrimination model D1; S3: building an automatic annotation model G2; S4: building a generated label map discrimination model D2; S5: defining a loss function; S6: using an RMS optimizer to calculate the exponentially weighted moving average of the squared gradient; and S7: adversarial training. The algorithm uses U-net to build the automatic annotation model and uses Wasserstein and RMS optimizers to perform adversarial training on the DoubleGAN model. The algorithm automatically annotates randomly generated data, obtaining a large number of high-quality data samples and improving the generalization of downstream prediction models. The DoubleGAN algorithm also uses two GAN models with different structures to enhance existing data.

This invention relates to the field of power technology and discloses a smart electricity metering and prediction method and system based on big data. It acquires user electricity consumption, environmental parameters, and holiday data in real time through the Internet of Things (IoT) to form an initial dataset. A joint algorithm of sliding window and wavelet packet decomposition is employed, dynamically adjusting thresholds through an exponentially weighted moving average control chart, and combined with Kalman filtering for multi-stage signal reconstruction to obtain denoised data. Time features, meteorological features, and user behavior features are extracted from the denoised data, and principal component analysis is used for dimensionality reduction to obtain target feature data. An LSTM-GRU model is constructed and trained using the target feature data. An attention mechanism is used to strengthen the weight allocation of features related to electricity load prediction. The data to be predicted is input into the optimized LSTM-GRU model to obtain the predicted electricity load, which is then visualized. This invention improves the accuracy of electricity load prediction.

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.

The invention belongs to the technical field of lithium battery insulation detection, and particularly relates to a method and a system for monitoring metal foreign matters in a cylindrical lithium battery manufacturing process, and the method comprises the steps: collecting high-frequency response current data in a cylindrical lithium battery insulation test process, carrying out the preprocessing and delay coordinate embedding of the data, and constructing a high-dimensional phase space evolution track; recursively constructing an inertia trend vector at each moment by using an exponentially weighted moving average mechanism based on the historical state sequence of the phase-space evolution trajectory; the orthogonal escape intensity of the instantaneous velocity vector relative to the inertia trend vector is calculated to quantify non-inertia disturbance caused by the metal foreign matter; and based on the orthogonal escape intensity sequence, carrying out adaptive abnormity determination, and outputting a metal foreign matter monitoring result. According to the method, the non-inertial abrupt change component can be separated from the strong-amplitude periodic background signal, and accurate monitoring of the micron-sized metal foreign matter is realized.

The invention belongs to the technical field of wind power plant SCADA (supervisory control and data acquisition) data detection, and particularly relates to a wind power SCADA data online self-adaptive abnormal value detection method considering concept drift, which comprises the following steps: S100, collecting actual wind power in real time through an SCADA system; carrying out pretreatment and rationality screening; s200, calculating the wind power of the unit through a power model, obtaining a prior wind power sequence, and constructing a residual sequence; introducing an input wind speed as a scaling factor to obtain a scaling residual sequence; processing the scaling residual error sequence, and marking an abnormal value according to a preset threshold value; s300, dynamically monitoring the scaling residual error sequence by adopting an exponentially weighted moving average method; triggering a concept drift candidate event when the EWMA value exceeds a control limit UCL; carrying out difference test on the current residual error distribution and the historical reference distribution by adopting KS test, and if the difference exceeds a preset threshold value, confirming that concept drift occurs; and S400, after judging that the concept drift occurs, updating the parameters of the power model.

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.

The invention discloses a flow early warning method for a micro-service interface and a related device, and relates to the technical field of data processing, and the method comprises the steps: determining the current flow data of the micro-service interface at the current monitoring moment; obtaining first historical traffic data of a time period corresponding to a preset short-term window before the current monitoring moment and second historical traffic data of a time period corresponding to a preset long-term window; based on the first historical traffic data, the second historical traffic data and the current traffic data, determining an average traffic ratio at the current monitoring moment; and the short-term fluctuation and the long-term trend of the flow are reflected. Based on the preset smoothing factor and the average flow rate at the current monitoring moment, the current flow threshold value is updated by using the exponential weighted moving average method, so that the threshold value is automatically adjusted according to the actual change of the flow, whether the flow is abnormal or not can be more accurately judged based on the average flow rate at the current monitoring moment and the current flow threshold value, and the flow monitoring accuracy is improved. Therefore, the reliability of flow early warning is improved.

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.

The invention relates to a deep learning-based portunus trituberculatus shipborne intelligent sorting model, and belongs to the technical field of intelligent sorting models.The deep learning-based portunus trituberculatus shipborne intelligent sorting model comprises a shipborne intelligent detection model used for detecting whether portunus trituberculatus is a portunus trituberculatus or not, and if the detection result is that the portunus trituberculatus is the portunus trituberculatus, the whole-shell width of The weight calculation model is used for updating the whole nail width of the portunus trituberculatus, determining the weight of the portunus trituberculatus according to the updated whole nail width, and grading the portunus trituberculatus according to the weight; the real-time counting system is used for counting the swimming crabs with different weight grades; wherein a FocalModulation module and an ASF-YOLO module are integrated in the shipborne intelligent detection model, and a dynamic scale calibration module and a multi-frame confidence updating module are further included in the weight calculation. The real-time counting system comprises an exponentially weighted moving average filtering module, an angle data processing module, a motion compensation module and a dynamic confidence threshold adjustment module. According to the invention, high-precision data support is provided for intelligent sorting.

The application provides a kind of vacuum press machine fault early warning method and system, obtain the multichannel sensor signal in the running cycle of vacuum press machine, constitute the signal matrix to be analyzed;Build by the first sub-dictionary of representing normal operating state and the second sub-dictionary of representing specific fault mode are spliced into the composite redundant dictionary;Based on the composite redundant dictionary, the sparse decomposition of the signal matrix to be analyzed is carried out by solving the regularization optimization problem, and the sparse coefficient matrix is obtained;The energy of the fault coefficient component corresponding to the second sub-dictionary in the sparse coefficient matrix is calculated at each time, and the energy is exponentially weighted moving average, to obtain the time-varying fault cumulative index;When the current value of the fault cumulative index exceeds the preset threshold, and the change rate in the preset time window exceeds the preset change rate threshold, generate fault early warning.

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.

The invention discloses an offshore wind power equipment abnormal state monitoring method and device, equipment, a medium and a product, and relates to the field of offshore wind power equipment abnormal state monitoring, and the method comprises the steps: inputting a sensor data set at each moment into a prediction model, and obtaining a target parameter prediction value at the ith moment; the prediction model is obtained by carrying out training on a CNN-LSTM-Attention model; the CNN-LSTM-Attention model comprises a one-dimensional convolutional neural network, a bidirectional long-short term memory network and an attention mechanism layer which are connected in sequence; calculating a loss value at the ith moment according to the target parameter predicted value and the target parameter true value at the ith moment; determining an exponentially weighted moving average control chart according to the loss value at the ith moment; and determining whether the offshore wind power equipment is in an abnormal state or not according to the loss value at the ith moment and the exponential weighted moving average control chart. According to the method, the abnormal state of the offshore wind power equipment can be efficiently and accurately monitored.