47 results about "Exponentially weighted moving average" patented technology

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 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 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.

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 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.

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.

A water toxicity detection system (100) and method (200) employ bivalve organisms (104) as biological indicators to monitor aquatic environments in real-time. The system (100) includes sensors (106) configured to measure gape behavior of multiple bivalve organisms (104), generating corresponding gape measurements that are processed by a computing system (122). The processor (108) normalizes gape measurements and calculates exponentially weighted moving average (EWMA) and exponentially weighted moving variance (EWMV) values to assess short-term behavioral patterns. A detection module (120) uses EWMA and EWMV as state-space variables to identify deviations indicative of exposure to toxic substances, specifically detecting gape closing (GC) events characterized by increased activity followed by shell closure. The system (100) generates system-level alarms when a predetermined fraction of individual bivalves simultaneously exhibits abnormal behavior patterns consistent with toxicity exposure. The technology enables early detection of waterborne contaminants including heavy metals, organic compounds, industrial chemicals, and algal toxins.

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.

The invention relates to the technical field of temperature monitoring, in particular to a mining flame-proof temperature measurement monitoring method based on optical fiber sensing, and the method comprises the steps: obtaining a temperature data sequence of a target optical fiber monitoring node within a preset time period from the end to the current moment; according to the temperature data difference between the adjacent moments and the temperature data sequence, performing temperature abnormal change trend to obtain a temperature abnormal trend characteristic factor, a temperature instantaneous trend degree and a local high-frequency energy ratio at the current moment so as to obtain a self-adaptive smoothing coefficient at the current moment; and exponentially weighted moving average processing is performed on the temperature data at the current moment by using the self-adaptive smoothing coefficient at the current moment to obtain de-noised temperature data at the current moment, so that the de-noising processing precision of the temperature data and the robustness of EWMA smoothing processing are improved, and the de-noising accuracy of the temperature data is improved. And stable and credible basic data are provided for subsequent temperature abnormity identification, over-temperature early warning and equipment state evaluation.

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.

The invention provides a signal compensation method and system for pre-stress monitoring and an anchor cable sensor, and the method comprises the following steps: monitoring the stress change of a pre-stress component through a ceramic thick-film force-sensitive resistor sensor, and generating a bridge output signal; performing temperature compensation on the bridge output signal based on the reference signal, the temperature value and the cycle index to obtain a correction signal; measuring the current zero position output to obtain a zero position drift amount, and combining the cycle index to obtain a compensation bias voltage; and outputting a final output signal after zero compensation in combination with the compensation bias voltage and the correction signal. Temperature drift is separated based on differential measurement, exponential weighted moving average trend prediction is carried out, temperature compensation of bridge output signals is achieved, and the problem of temperature drift in the prior art is solved; and the zero drift is adaptively compensated through the digital potentiometer in combination with the cycle index, and finally an accurate stress value is output, so that online zero self-compensation is realized, the problem of insufficient long-term stability is solved, and the system reliability is improved.

This invention belongs to the field of medical imaging technology and discloses a prostate ultrasound image segmentation method based on a bidirectional exponentially weighted moving average algorithm. The specific steps are as follows: S1, preliminary localization: The transformation localization matrix is ​​obtained by using a set localization convolutional network to obtain the transformation localization matrix coefficients, and the average template is obtained by using a point distribution model and principal component analysis. The two are combined for preliminary localization. This invention improves upon the normal vector contour boundary operator proposed by Hodge, and uses a boundary operator that comprehensively considers the joint information of the neighborhood normal vector to segment the prostate. It also proposes a method based on bidirectional exponentially weighted moving average to segment the shape of the prostate. Based on the boundary operator, the normal vector index value is used as the data input. This method achieves better segmentation results with a single input of data than ordinary multi-iteration segmentation and can better preserve features in non-noise areas, enabling accurate and fast ultrasound image segmentation.

The invention provides a dual-mode communication load monitoring system and method based on adaptive switching of HPLC and HRF, and the system comprises a dual-mode communication module which is used for the parallel transmission and dynamic switching of two communication modes of HPLC and HRF; the load monitoring unit is used for collecting load data of the power equipment in real time; the self-adaptive switching control unit is used for evaluating the communication quality and a preset dynamic weight and generating a switching instruction; the dynamic priority scheduling unit is used for adjusting task estimation running time in real time and optimizing communication resource distribution by adopting an improved HRF dynamic priority scheduling algorithm based on load data in an HRF communication mode; and the load prediction correction unit dynamically corrects a load prediction value through an exponential weighted moving average and resource demand modeling method in combination with historical load data and a real-time monitoring result, and feeds back the load prediction value to the adaptive switching control unit. And the continuity of load data transmission is ensured.

The invention belongs to the technical field of multi-modal data processing, and discloses a multi-modal data-oriented event pulse feature extraction method and device. The method solves the problems that in the prior art, a fixed sliding window consumes computing resources, cross-modal information complementarity is ignored through single-modal analysis, event instantaneous pulse characteristics are difficult to capture, and the real-time performance error report and missing report rate is high due to the fact that multi-modal data fusion is difficult. Judging an event window through an event detection function in combination with an adaptive threshold value based on exponentially weighted moving average; when events are detected in multiple modes, optimal time offset calibration is calculated through a kernel function; according to the method, the computing resource consumption is reduced, the detection robustness and the time synchronization precision are improved, edge equipment is adapted, and real-time decision making of scenes such as industrial Internet of Things automatic driving safety monitoring is supported.

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.

The invention relates to the technical field of fault early warning systems, and discloses a safety early warning method and system for a distribution box, and the method comprises the steps: obtaining multi-dimensional monitoring data collected in a continuous time window in the distribution box; an isolation tree set is constructed for the multi-dimensional monitoring data, and the process of constructing a single isolation tree is as follows: at non-leaf nodes of the tree, non-uniform selection probability distribution is generated according to statistical characteristics of each feature of a data sample in the nodes, and segmentation features are selected based on the selection probability distribution; calculating a basic abnormal score; obtaining an exponentially weighted moving average value obtained through calculation of historical data points and a time change rate of the exponentially weighted moving average value, and calculating an adjustment factor; obtaining a comprehensive risk index by using the basic abnormal score and the adjustment factor; and when the comprehensive risk index exceeds a preset risk threshold value, generating an early warning signal. According to the method, early faults caused by key parameter collaborative change can be monitored, continuously deteriorated potential faults can be identified, and the risks of missing report and false report caused by trend judgment errors are reduced.

The invention relates to the technical field of edge computing, in particular to a gas flowmeter real-time fault diagnosis method based on edge computing, which comprises the following steps: continuously acquiring gas flow, pipeline pressure and medium temperature data at an edge node, updating an exponential weighted moving average value at the previous moment by adopting a preset weight coefficient, and calculating the fault of a gas flowmeter according to the exponential weighted moving average value; and calculating a difference value between the currently acquired data and the updated exponentially weighted moving average value, accumulating the difference value to a preorder accumulated sum to form a statistical control quantity, and carrying out numerical comparison on the statistical control quantity and an early warning line constructed according to a historical data standard deviation. According to the method, the hierarchical calculation and state transition strategy is executed at the edge nodes, energy consumption is optimized, the method operates in a monitoring state with extremely low power consumption in most of time, lightweight statistic accumulation and comparison are only performed on gas flow, pipeline pressure and medium temperature data, and energy consumption is reduced. According to the design, the high-power-consumption deep analysis task is strictly limited at the necessary moment when the statistics deviate from the early warning line in the early stage.

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.

System for determining the driving behavior of a vehicle (10), wherein the system comprises: Processor (30) comprising an input (36) configured to receive lateral acceleration data from at least one onboard lateral acceleration sensor, wherein the processor (30) is configured (i) to calculate an output signal (125) from the received lateral acceleration data, and (ii) to compare the output signal (125) with at least one output threshold value to determine the driving behavior of the vehicle (10), and Processor (30) comprising an output (38) configured to send a control signal (127) to prevent the vehicle (10) from lowering its ride height, the control signal indicating the vehicle's (10) driving style, wherein the processor (30) has at least one exponentially weighted moving average filter (46) configured to calculate a moving average of the lateral acceleration data at predetermined intervals and to assign exponential weights to the calculated moving averages to calculate the output signal (125), and wherein the processor (30) is configured to apply a gain value to the output signal (125) each time the lateral acceleration data exceeds the at least one lateral acceleration threshold value for a predetermined period, and the amplified output signal (125) decays according to the exponentially weighted moving average filter (46) applied to the lateral acceleration data.