39results about How to "Improve analytical accuracy" patented technology

A quantitative detection method of thallium content in converter graphite ball, steps include: 1) the pretreatment of converter graphite ball; 2) the preparation of standard solution series; 3) the detection of thallium content in converter graphite ball.The application finds that the suitable pretreatment method of converter graphite ball is carbon removal by burning, and then high-chloric acid and dilute nitric acid are used to dissolve salt, by which the converter graphite ball can be completely dissolved, thereby the stability of quantitative determination of thallium content in converter graphite ball is improved, the pretreatment method is simple, fast and operable; the method is suitable for the quantitative detection of thallium content in converter graphite ball with thallium content not less than 0.0062 μg / L, the recovery rate of thallium is as high as 99.1%, the analysis accuracy is high, the detection lower limit is as low as ppb level, the sensitivity is high, and the method has the advantages of simple operation, simple analysis condition and low labor intensity.Therefore, the application has significant economic benefits and the advantages of simplicity and economy.

This invention belongs to the field of spectral data processing technology, specifically a spectral data processing system and method based on in-situ data sensing and intelligent analysis. This invention employs in-situ optical imaging, absorption spectroscopy, fluorescence spectroscopy, and PL mapping to acquire real-time three-dimensional spectral data of time, wavelength, and intensity for real-time monitoring of material crystallization, defects, and luminescence properties. It uses an automatic invalid value detection and cleaning algorithm to address data quality issues; it extracts peak position and half-peak width (WHM) characteristic parameters from the spectral data using peak position extraction and WHM calculation methods; it uses a nonlinear optimization algorithm to perform multi-peak Gaussian fitting on the spectral data; and it employs a signal-slot mechanism to achieve real-time synchronization of data presentation and fitting analysis, forming a complete closed loop of in-situ testing, data sensing, processing, analysis, synchronization, and export. This invention achieves real-time acquisition, efficient processing, accurate analysis, and automatic synchronization of spectral data, providing technical support for standardized processing and traceable analysis of spectral data.

The application discloses an energy consumption metering method and device based on digital twinning, which comprises the following steps: collecting building energy consumption related data based on a pre-constructed building energy consumption digital twinning model; analyzing the energy consumption of the building according to the building energy consumption related data, and giving topological correlation characteristics, multi-scale time characteristics, equipment health characteristics and environmental lag characteristics; combining a pre-constructed energy consumption coefficient prediction model to analyze the topological correlation characteristics, multi-scale time characteristics, equipment health characteristics and environmental lag characteristics, and giving environmental influence coefficients, topological correlation coefficients and space utilization coefficients; determining a benchmark energy consumption prediction model through the environmental influence coefficients, topological correlation coefficients and space utilization coefficients, combining an energy consumption compensation coefficient, and giving building target energy consumption, so as to realize accurate prediction of benchmark energy consumption and dynamic compensation of actual energy consumption, realize accurate metering of building energy consumption, and improve the analysis accuracy of building energy consumption.

The application provides a method and device for detecting the root cause of antenna feeder abnormalities, electronic equipment and a computer readable storage medium, and relates to the field of wireless communication. The detection method comprises: obtaining an abnormal event in a wireless call record, and extracting target index data of an abnormal event occurrence area, wherein the target index data comprises coverage and interference type index data; determining whether the occurrence of the abnormal event is attributed to coverage interference according to the target index data; if yes, detecting whether there is an abnormality in the lobe angle and / or the antenna azimuth angle according to a root cause detection algorithm. At least the problems of diagnosis and analysis of antenna feeder problems in the related art relying on manual operation of experts, time and manpower consumption, and insufficient experience solidification of first-line personnel in positioning problems, which leads to low problem positioning accuracy, are solved.

The embodiment of the invention provides a multi-source threat intelligence analysis method and device, electronic equipment and a storage medium, and relates to the field of network security. The method comprises the steps of obtaining multi-source heterogeneous threat intelligence data, and performing normalization processing on the multi-source heterogeneous threat intelligence data to obtain a standardized data set; partitioning the standardized data set to obtain a plurality of data blocks, and dynamically partitioning based on a preset word segmentation device to obtain structured integrated data; extracting dominant threat information and attack intention tags through global analysis of a preset large language model to obtain a preliminary analysis list; and constructing a knowledge graph according to the preliminary analysis list and storing the knowledge graph in a preset graph database, and combining the knowledge graph and the preliminary analysis list to generate an intelligence analysis result. The method is used for realizing end-to-end structured processing and effective association analysis of multi-source heterogeneous threat intelligence, improving intelligence analysis efficiency and accuracy, and providing reliable support for network security defense.

The application provides a self-evolving intelligent fund analysis method, relates to the field of data processing, and comprises the following steps: receiving a natural language analysis instruction, combining a large language model decomposition task, extracting account fund flow characteristics and matching with historical cases, and generating a weighted execution instruction; identifying a transfer path based on a fund correlation graph, extracting a priority path subgraph structure according to a user identifier, and forming a directional analysis model; performing node risk scoring on the fund correlation graph, outputting a risk account identifier and an analysis report, and realizing intelligent and accurate fund risk identification.

This invention relates to the field of digital twin technology, and more particularly to a legal compliance full lifecycle management platform based on digital twins. By setting up a model building module, a unit invocation module, a behavior analysis module, an anomaly adjustment module, and a compliance verification module, a digital twin model is constructed and adjusted. Several behavioral data points are input into the model, and the behavioral data is analyzed to run the execution unit. The accuracy of the model's operation is judged based on the execution results. The anomaly adjustment module is invoked to determine the adjustment method based on abnormal operating states, and the adjusted model is subjected to compliance verification to determine its usability. The adjusted model is then put into use. This invention, by constructing a general digital twin model, performing targeted analysis and adjustment of single and complex behaviors, and determining the usability and specificity of the model, improves the accuracy of judgment and analysis efficiency of complex behaviors.

The invention discloses a power distribution area line loss analysis application scene evaluation method, system and equipment. The method comprises the following steps: acquiring electric quantity data of each monitoring node according to a topological structure of a power distribution area under different application scenes; calculating based on the electric quantity data of each monitoring node in the topological structure to obtain the line loss rate of the power distribution area and determine a line loss abnormal event; on the basis of the line loss abnormal event, the line loss simulation device is used for measuring the line loss of each device or measuring the electric energy data of each device to calculate the line loss of each device; performing calculation based on the line loss of each device and the line loss of a standard electric energy meter to obtain a line loss rate calculation error of each device; according to the invention, the electric energy data is obtained based on multiple scenes for line loss analysis, so that the accuracy and resolution of line loss analysis can be greatly improved; digital sensing of electric energy data of each node can be realized, and elements with excessive loss can be found out; whether the actual line loss is true or not can be assessed and the degree of unknown loss can be determined by calculating the line loss rate calculation error.

This invention relates to a method and system for analyzing machine-inspected images. The method includes: acquiring no-anomaly data corresponding to each strategy as a training dataset, wherein the no-anomaly data includes machine-inspected data where the machine inspection conclusion for the images is no anomaly, and the strategy is set in correspondence with product information and / or business information; performing the following stitching process on the training dataset: if multiple image features are selected from each machine-inspected image in the training dataset, the multiple image features are stitched together; if only one image feature is selected from each machine-inspected image, no stitching is performed; and training a discrete analysis model using the stitched training dataset and saving it in correspondence with the strategy. According to this application, historical data can be utilized, the training model can be effectively managed, thereby improving the accuracy of the analysis.

The application relates to an unmanned aerial vehicle intelligent flight method and system based on energy data analysis, and relates to the field of energy management technology.The method comprises the following steps: acquiring a required inspection position; constructing a monitoring time window and acquiring a monitoring wind receiving vector of a wind measuring point in the monitoring time window; determining a standard wind receiving vector according to the monitoring wind receiving vector; constructing a simulated inspection path according to a starting point and the required inspection position, and constructing a historical time window; determining a path single-point vector of each point on the simulated inspection path according to the standard wind receiving vector in the historical time window, and determining a required charging position according to the path single-point vector, and constructing a simulated inspection task according to the required charging position and the simulated inspection path; determining a task suitability coefficient according to the simulated inspection task, and defining the simulated inspection task with the largest task suitability coefficient as a use inspection task, and controlling the unmanned aerial vehicle to fly according to the use inspection task. The application has the function of improving the unmanned aerial vehicle inspection effect.

The application discloses a mass spectrum combined characterization qualitative and quantitative method and system of unified hidden state, relates to the cross field of analytical chemistry, bioinformatics and artificial intelligence data processing, the method obtains original data and instrument / method metadata containing LC-MS / MS, encodes to form a condition vector and is used for conditional modulation, constructs wave encoder and particle encoder, respectively Tokenizes continuous chromatographic signal and discrete fragment peak set into wave encoder and particle encoder to obtain wave representation and particle representation, realizes cross-modal interaction through wave-particle fusion module, forms unified hidden state or object-level unified hidden state set, adopts two-stage training of self-supervised pre-training and task fine-tuning, and applies conditional quantitative operator and qualitative operator to unified hidden state or object-level unified hidden state set in the inference stage, and parallelly outputs quantitative results and identification score / probability, and is suitable for end-to-end qualitative and quantitative analysis in DDA / DIA scene.

The invention relates to a method for detecting the content of aluminum and potassium in cesium-containing aluminum brazing flux, and belongs to the technical field of chemical detection and analysis. The detection method provided by the invention comprises the following steps: S1, adding water into a cesium-aluminum-containing brazing flux sample, uniformly mixing, then adding concentrated nitric acid with the volume of V1 and perchloric acid with the volume of V2, boiling to concentrate the total volume to V2, cooling, diluting, and filtering to obtain a to-be-detected solution; and S2, respectively analyzing the concentration of the aluminum element and the concentration of the potassium element in the to-be-detected liquid at the wavelength of 396.150-396.154 nm and the wavelength of 766.488-766.494 nm by adopting an inductively coupled plasma emission spectrometry to obtain the aluminum content and the potassium content in the cesium-containing aluminum soldering flux sample. The detection method provided by the invention solves the problems of instrument corrosion caused by high fluorine in the aluminum soldering flux, serious cesium matrix interference and low determination accuracy, and is high in accuracy, good in precision and high in sensitivity.

The invention provides a multi-source data fused power equipment fault root cause analysis method and system. The method comprises the steps of performing credibility evaluation on multi-source power equipment data; the power equipment comprises sensing monitoring data, operation and maintenance record data and power grid topology data; calculating a fusion weight according to a credibility evaluation result; fusing the power equipment data by using the fusion weight to obtain an equipment fault feature vector; performing correlation calculation on the fault root causes and the key features to obtain a correlation matrix; performing root cause matching degree calculation and screening according to the incidence matrix and the equipment fault feature vector to obtain candidate root causes; constructing a Bayesian network according to the candidate root causes and the corresponding key features, dynamically initializing the conditional probability of the Bayesian network by using real-time data update factors according to an incidence matrix, and adjusting the Bayesian network by using fault reasoning knowledge of a fault knowledge graph; and performing root cause screening on the equipment fault feature vector by adopting a Bayesian network to obtain a power equipment matching fault root cause and a corresponding confidence coefficient. Through the scheme, the accuracy and the disposal efficiency of fault root cause analysis in a complex scene can be improved, and the operation and maintenance cost is reduced.

The present disclosure relates to a failure cause analysis method, device and equipment of a target detection algorithm, and belongs to the technical field of target detection. The method comprises: training and / or testing the target detection algorithm through a data set, and obtaining failure data of the target detection algorithm in the training and / or testing process, each piece of failure data comprising a failure type and measurement values of a plurality of failure influence factors; performing causal reasoning on the failure process of the target detection algorithm based on the failure data to obtain failure causal relationships of the target detection algorithm under at least one failure type, the failure causal relationships comprising causal relationships between the plurality of failure influence factors; and iteratively calculating failure weight values of the plurality of failure influence factors based on the failure causal relationships corresponding to each failure type in the at least one failure type. The present disclosure can qualitatively and quantitatively analyze the failure causes of the target detection algorithm.

In order to improve the accuracy of performance detection and evaluation and improve the effectiveness of service state reliability evaluation, the invention aims to solve the problems that the existing evaluation method cannot obtain the evolution condition of a complex working condition, the prediction evaluation accuracy is poor, and the influence of a real state is not comprehensively considered. According to the model-based multi-source detection data fusion evaluation and evolution prediction method provided by the invention, the evaluation model is frequently corrected and trained by using the corresponding relationship between the actual physical test detection data and the load working condition, so that the strong association between the actual quality state parameters and the performance prediction evaluation is realized; evolutionary prediction of key parameters such as stress state, internal defect and morphology precision of a large-scale structure is realized, and accurate evaluation of service performance of a product with a complex structure is realized.

The invention discloses a steel-concrete nondestructive testing method and device based on time-varying analysis, and relates to the field of steel-concrete structure detection.The steel-concrete nondestructive testing device comprises an acquisition module, a data processing module, a data processing module and a data processing module, collecting a stress wave signal, a vibration response signal, a sound wave propagation signal and an elastic wave signal of the structure at a preset interval continuous moment and a temporary moment of stress change; the processing module is used for receiving the signals acquired by the acquisition module, performing noise reduction and time sequence alignment processing on the signals, and synchronously establishing association mapping between the signals and corresponding acquisition moments; when the system is applied to steel-concrete structure detection, multi-dimensional signals of key stress positions can be accurately collected, signal time-varying laws are mined after processing, health state parameters are matched to generate visual health assessment, data can be dynamically updated, structural abnormity can be early warned in time, core influence factors can be marked, potential damage can be effectively prevented and controlled, and the system is high in practicability. Long-term safety of the structure is guaranteed, and service life is prolonged.

The invention relates to the technical field of data communication, and discloses a network flow deep packet inspection multi-task classification method and system, and the method comprises the steps: firstly obtaining a target network flow data flow, recognizing the corresponding protocol and load content of the target network flow data flow, and generating a flow topological graph related to the target network flow data flow and the load content; collecting a topological data sequence in the atlas, analyzing a corresponding data transmission type, analyzing a type guiding mode, and fitting a flow transmission curve; identifying an abnormal traffic peak value and a core transmission hotspot in the curve, calculating a traffic throughput threshold and analyzing a network cache state; then, a deep packet detection framework is constructed based on the cache state, a processing delay value and a classification misjudgment rate of a real-time classification path in the framework are collected, and a traffic coordination index is calculated; and finally, reconstructing a data coordination task according to the index, identifying a task classification protocol, and generating a multi-task-oriented classification result of the flow data flow. According to the invention, the detection and classification efficiency of multi-task traffic in a complex network environment can be improved.

The utility model relates to rock blasting block size analysis technical field, and disclose an image acquisition device for rock blasting block size analysis, including unmanned aerial vehicle holder and fixed focus industrial camera, the bottom of unmanned aerial vehicle holder installs the support, the bottom of support is fixed with backplate, both sides of backplate bottom are all fixed with the frame, fixed focus industrial camera is located between two frame, the middle part of both sides of fixed focus industrial camera all is equipped with the limit slot, the frame sliding connection has with the frame fitting limiting plate of limit slot, through installing unmanned aerial vehicle holder on unmanned aerial vehicle, utilize unmanned aerial vehicle to be able to drive fixed focus industrial camera to fly over complex, dangerous or difficult to reach the blast heap area, has reduced the security risk, realized the full coverage collection of true sense, the height is convenient for flexible control, optimization precision and efficiency, and can monitor the shooting height of fixed focus industrial camera through height measurement subassembly.

This invention provides a flow cytometry mass spectrometry analysis method, comprising the following steps: (A1) labeling cells with a metal element and selecting a nuclide based on the metal element; adding the nuclide when preparing the cell solution; (A2) ionizing the metal element and the nuclide respectively and sending them to a time-of-flight mass spectrometer; (A3) the time-of-flight mass spectrometer outputs a first type of information on the change of the metal element intensity over time and a second type of information on the change of the nuclide intensity over time, compressing the second type of information over time to obtain the rate of change of the second type of information after compression in adjacent time periods; (A4) determining whether the rate of change exceeds a threshold; if it exceeds the threshold, the detection data of the metal element and nuclide corresponding to the adjacent time period are invalid; if it does not exceed the threshold, the detection data of the metal element and nuclide corresponding to the adjacent time period are valid. This invention has the advantages of low cost and high precision.

This invention relates to the field of material analysis and detection technology, specifically disclosing a method for determining the lithium isotope ratio of spodumene using laser ablation-multiple-receiver inductively coupled plasma mass spectrometry (ICP-MS). The method includes: adding water vapor to the laser carrier gas to achieve wet plasma conditions; performing laser ablation with a laser spot size of 24-45 μm and a repetition frequency of 2-4 Hz; using natural spodumene megacrystalline crystals or synthetic spodumene glass with uniform lithium isotope composition as calibration reference materials, alternating with the sample to be tested for ablation analysis, and using a cross-injection method to correct instrument mass deviations. This invention systematically characterized four candidate reference materials, whose δ... 7 Recommended Li values ​​were 12.85±0.31‰, 12.29±0.31‰, 12.37±0.35‰, and 4.74±0.30‰, respectively. This method optimizes the water vapor addition rate and laser ablation parameters, which can reduce carrier gas consumption, decrease mass fractionation, and δ... 7 The accuracy of Li determination has been significantly improved, making it suitable for in-situ micro-area analysis in the mineralization study of pegmatite-type lithium deposits.

The application discloses a low-cost high-accuracy e-commerce goods-carrying video analysis method and system, and belongs to the technical field of data processing, and comprises the following steps: performing multidimensional availability checking on a to-be-analyzed goods-carrying video, then demultiplexing the video into a video audio stream and a video picture stream; performing streaming voice transcription on the video audio stream to generate a text semantic sequence, performing dynamic analysis on the video picture stream to extract a motion intensity coefficient, dynamically dividing video clips based on the two, and generating picture frame sets corresponding to each semantic segment, and simultaneously performing cross-segment summary aggregation to generate a video overall summary; performing parallel reasoning analysis on the picture frame sets of each segment, the audio text and the overall summary, generating multidimensional text analysis results, structurally processing each dimension analysis result, and integrating and concatenating in time sequence to generate a complete video structured description document, so that low-cost, high-accuracy e-commerce goods-carrying video automatic analysis is realized.

The embodiment of the invention discloses a rock slice image analysis method and device, equipment and a storage medium, and the method comprises the steps: obtaining a rock slice image of a target rock slice; inputting the rock slice image into a pre-trained target image segmentation model to obtain a rock slice segmentation result of the target rock slice; and determining a rock slice analysis result of the target rock slice according to the rock slice segmentation result. According to the technical scheme of the embodiment of the invention, the problem of poor segmentation precision of the carbonate slice image in the prior art is solved, the image can be segmented based on the pre-trained target image segmentation model, the image segmentation precision is improved, and the analysis accuracy of the rock slice image is further improved.

The application specifically relates to a circuit board fault diagnosis system, comprising a temperature acquisition module, a data processing module and a fault mode library of various types of circuit boards; the temperature acquisition module is used for collecting thermal radiation signals and visible light signals of the circuit board, converting the thermal radiation signals of the circuit board into temperature distribution dot array data, and sending the temperature distribution dot array data and the visible light signals of the circuit board to the data processing module; the data processing module is used for analyzing the received visible light signals of the circuit board, completing identification of the type of the circuit board, processing the received temperature distribution dot array data of the circuit board and completing fault diagnosis of the circuit board; the application also relates to a circuit board fault diagnosis method; the collected thermal radiation signals of the circuit board are processed and analyzed, comprehensive, efficient and accurate diagnosis of the circuit board fault is realized, and the application has strong popularization and universality.

The application provides a walking data filtering method and related equipment. The method comprises the following steps: traversing all obtained walking data matrices, determining a maximum value corresponding to each coordinate point to obtain a maximum value matrix; traversing four edges of the maximum value matrix to determine edge points; the edge point is a first non-zero coordinate point in the traversal; determining a filtering area based on the edge point by using a flood filling method; and filtering the walking data matrix based on the filtering area. Through fitting a maximum pressure matrix, filtering edge abnormal data and adaptively adjusting a processing range, the application improves the accuracy and reliability of plantar pressure data, adapts to individual differences and optimizes gait analysis effects.

This application discloses a unified latent state-based mass spectrometry joint characterization qualitative and quantitative method and system, involving the interdisciplinary fields of analytical chemistry, bioinformatics, and artificial intelligence data processing. The method acquires raw data from LC-MS / MS and instrument / method metadata, encodes it into conditional vectors, and uses them for conditional modulation. A wave encoder and a particle encoder are constructed, and continuous chromatographic signals and discrete fragment peak sets are tokenized and input into the wave encoder and particle encoder respectively to obtain wave and particle characterizations. Cross-modal interaction is achieved through a wave-particle fusion module to form a unified latent state or an object-level unified latent state set. A two-stage training process of self-supervised pre-training and task fine-tuning is employed. During the inference stage, conditional quantitative and qualitative operators are applied to the unified latent state or object-level unified latent state set, outputting quantitative results and identification scores / probabilities in parallel. This method is suitable for end-to-end qualitative and quantitative analysis in DDA / DIA scenarios.

A secondary equipment hidden danger intelligent identification method based on a wide area wave recording file comprises the following steps: in response to a power grid primary equipment fault event, carrying out multi-stage plant station topology analysis based on a power grid topology model, and determining peripheral plant stations associated with the fault event; automatically calling wave recording files of a fault plant station and peripheral plant stations at the fault moment; analyzing the recording file and extracting channel information and waveform image data for hidden danger identification; and based on the extracted channel information and waveform image data, carrying out parallel multi-dimensional hidden danger intelligent identification on the secondary equipment of the plant station through a rule engine and a deep learning model. According to the method, single-point post analysis is improved into wide-area linkage and multi-dimensional parallel intelligent screening, and particularly, AI identification based on waveform images is introduced, so that complex hidden dangers which are difficult to capture by a traditional method can be found, the transformation from post analysis to pre-warning is realized, and the efficiency and depth of secondary equipment hidden danger identification are remarkably improved.

The invention relates to the technical field of session data analysis, and discloses a time axis fusion analysis-based multi-modal session data analysis method and system, and the method comprises the steps: obtaining multi-modal session data, and enabling each modal session data in the multi-modal session data to have a corresponding modal session time axis; according to a preset anti-interference fitting algorithm, all modal session time axes are calibrated, and a unified wall clock time axis of the multi-modal session data is generated; identifying key information nodes in the multi-modal session data based on a unified wall clock time axis, and calculating an emotion vector corresponding to each moment in the multi-modal session data to obtain an emotion intensity curve of the multi-modal session data; and according to all the key information nodes and the emotional intensity curve, generating an interactive emotional map of the multi-modal session data. Therefore, by implementing the multi-modal session data analysis method, the traceability of the analysis result can be improved while the analysis accuracy and objectivity of the multi-modal session data are improved.

This invention discloses a dynamic visualization and intelligent analysis system and method for multi-source data, relating to the fields of data processing and intelligent analysis technology. It addresses the challenges of existing solutions in unifying and dynamically presenting cross-source data and accurately identifying hidden and complex changes. The method integrates, parses, unifies the timeframes, maps objects, spatially links, and standardizes attributes of equipment monitoring data, business record data, spatial location data, and external supplementary data to form a fused feature dataset. Based on this dataset, it constructs layered data such as points, regions, floors, trajectories, and timelines, generating dynamic frames and achieving dynamic visualization through differential updates. Simultaneously, it identifies complex disturbances, classifies objects, and provides feedback output based on attribute sequences, temporal misalignments, spatial transmission, field responses, and dynamic benchmarks, thereby enhancing the collaborative display and intelligent analysis capabilities of multi-source data.

The application relates to the technical field of data analysis, and discloses an insurance data analysis method, device, equipment and medium, which comprises the following steps: obtaining insurance application verification information uploaded by an applicant, and analyzing the text into structured information in JSON format. Based on a cross-modal attention mechanism, image data and structured information are semantically aligned to identify logical consistency conflicts in time and space. Conflict fields, types, severity and application value fields are extracted, and a rule engine is used to map a confidence level. If the confidence level does not exceed a threshold value, it is determined that the verification is passed and a report is generated; otherwise, the information is sent to an artificial review client, and a report is generated according to feedback data. The scheme can be applied to a financial technology business system platform and can realize confidence analysis of insurance data. The application improves the efficiency and accuracy of insurance data analysis.

This application provides a method, apparatus, electronic device, and storage medium for analyzing SoC signal events. The method involves acquiring an input waveform file to be analyzed, a configuration file, and an analysis processing time range; preprocessing the configuration file to determine at least one target signal to be analyzed; parsing the waveform file to be analyzed; extracting the time points and transition values ​​corresponding to at least one signal transition event of the target signal within the analysis processing time range; executing signal expressions at the time points corresponding to each signal transition event; recording accumulated events or target values ​​based on a counting mode; and outputting an SoC signal event analysis report for the waveform file to be analyzed based on the accumulated events or target values. This reduces the error rate and processing time, thus improving the efficiency and accuracy of SoC signal event analysis.