315 results about "Early warning signs" patented technology

The utility model discloses a gas pipeline anti -damage early warning line fixed structure relates to gas pipeline technical field, including pressure regulating cabinet and cell -phone mobile terminal. Through set up's audible -visual integrated alarm, segmented early warning processor, video break -in monitor, detection line host computer and pipeline damage early warning detection line, make video break -in monitor can carry out monitoring early warning to the personnel and equipment of entering pipeline upper area, and send personnel break -in early warning signal to cell -phone mobile terminal through segmented early warning processor, audible -visual integrated alarm carries out audible -visual alarm and reminds personnel and mechanical equipment to pay attention to entering gas pipeline area, when mechanical equipment excavates on pipeline upper, pipeline damage early warning detection line is destroyed, and detection line host computer detects excavation signal, and segmented early warning processor receives excavation signal and dials a call early warning reminder to cell -phone mobile terminal, and audible -visual integrated alarm carries out audible -visual alarm and reminds construction personnel and mechanical equipment to stop construction immediately, guarantees the accuracy of pipeline early warning.

The present application relates to the technical field of distributed system testing and operation and maintenance monitoring, and particularly relates to a server test environment automatic monitoring method and system, comprising: collecting a micro-service cluster directed call topology structure, constructing a distributed cell layer with node and edge vector spaces; parsing data serialization rules to extract constraint restriction mapping matrices, assembling a Hodge-Dirac operator; obtaining real-time business state vector groups to splice into zero-order co-chains, using the operator to solve an algebraic Laplace matrix to calculate a quadratic form energy functional; when determining that the quadratic form energy functional is greater than zero, generating an early warning signal, constructing and solving a discrete layer diffusion dynamics equation based on the algebraic Laplace matrix, obtaining a state compensation update matrix; finally, analyzing the state compensation update matrix into reverse rollback and cache erasure instructions, and submitting the instructions to a service mesh data plane proxy component for execution. The present application realizes code-free cross-node semantic conflict quantification and distributed consistency state self-healing closed loop.

This invention discloses a weak fault diagnosis method based on chaotic signals of wind turbine stator current. The diagnostic method includes: acquiring the current signal of the wind turbine stator and preprocessing the current signal, the preprocessing including row-sequence component transformation, adaptive fundamental suppression, and detrending processing; calculating a chaos index based on the preprocessed signal; calculating a health index based on the chaos index and classifying the health status according to the health index; and outputting a diagnostic result including the health index, fault type, and graded early warning signal based on the aforementioned results.

The utility model relates to refrigerating system circulating water tank equipment technical field discloses a water tank device of refrigerating system, including water tank, the inboard mounting of water tank has the support, the upside threaded sleeve joint of support has the sealing cylinder, the inboard sliding connection of sealing cylinder has pressure sensor, the upside fixed connection of pressure sensor has the push rod, the upper end of push rod is provided with the slot, the utility model discloses through the buoyancy ball, utilizes the buoyancy and gravity in water, when the water level in the box is below the safety threshold, push rod is under the action of buoyancy ball gravity, pushes push rod and pressure sensor and moves down, and pressure sensor can form water level early warning signal and sends to liquid level switch, and liquid level switch starts and supplements water in the box, realizes the function of automatic water replenishment, guarantees the water level of whole box to be always in normal water level to improve the safety, reliability of whole refrigerating system.

This invention discloses a method and system for dynamic data credibility assessment that integrates multi-dimensional indicators, relating to the field of big data processing. The method includes: formulating multi-dimensional credibility assessment indicators based on business needs; acquiring raw data streams from power acquisition terminals and calculating real-time indicator values; based on an integrated assessment model, merging and calculating the real-time indicator values ​​and weights to output a comprehensive credibility score; triggering an early warning signal when the comprehensive credibility score is lower than a credibility threshold, and locating the specific indicator type and associated equipment causing the low credibility based on indicator contribution analysis; matching and executing a preset repair strategy based on the location results, and iteratively re-executing the assessment process after repair until the comprehensive credibility score meets the credibility threshold. This solves the problems of incomplete data dimension coverage and insufficient dynamic adaptability to massive multi-source data in existing power big data processing scenarios, which lead to one-sided credibility assessment results and data distortion.

The application discloses a kind of based on digital twinning waste incineration grate wear prediction and jam early warning method, belong to solid waste treatment and industrial automation monitoring technical field.The method includes: the displacement, velocity, contact force and acceleration data of grate mechanism are collected in real time by sensor group;Establish the three-dimensional geometric model of grate mechanism and define kinematic pair constraint relationship, material attribute and contact parameter, construct multi-body dynamics digital twinning model, real-time data are mapped to the model to realize virtual and real synchronous;The time-varying contact force and sliding speed signal output by digital twinning model are acquired, and the wear rate is dynamically calculated based on the modified Archard wear model and the remaining useful life is predicted, and when the prediction deviation exceeds the threshold, the dynamic wear coefficient is adaptively corrected;Real-time monitoring of grate motion parameters and comparison with normal working condition benchmark value, according to stroke deviation, speed fluctuation, acceleration peak value, trigger jam early warning signal gradually;The wear amount and remaining life of each grate piece are summarized, and a health assessment report and differentiated repair recommendations are generated.The application time-varying coupling of digital twinning and wear prediction is realized, the wear rate is calculated dynamically step by step, and three-level grading early warning is realized, which can improve the wear prediction accuracy of waste incineration grate and the early warning ability of jam failure.

The embodiment of the present application provides a kind of intra-abdominal pressure and muscle relaxation association monitoring device, it is related to the technical field of medical monitoring technology technology.The device includes: data acquisition synchronization module, for synchronously obtaining intra-abdominal pressure time series data and muscle relaxation state time series data;Signal processing module is connected with the data acquisition synchronization module, for determining pressure stability characteristic parameter based on the intra-abdominal pressure time series data, and determining muscle relaxation trend characteristic parameter based on the muscle relaxation state time series data;Evaluation warning module is connected with the signal processing module, for generating associated risk index based on the pressure stability characteristic parameter and the muscle relaxation trend characteristic parameter, and triggers early warning signal in the case where the associated risk index exceeds preset risk threshold value.Through the present application, the problem of low monitoring precision of intra-abdominal pressure is solved, and the effect of improving the monitoring precision of intra-abdominal pressure is achieved.

The invention discloses an XGBoost-based power generation quotation rationality evaluation method, system and equipment and a medium, and relates to the technical field of power market operation and supervision, and the method comprises the steps: collecting historical operation and market environment data, obtaining five evaluation indexes through a prediction model, elastic analysis, matching degree calculation and clustering analysis, fusing the indexes to construct a feature vector, and obtaining a power generation quotation rationality evaluation result. And training a classification model to evaluate the rationality of quotation, and generating a monitoring early warning signal when the matching degree is too low, the price response is too strong, the quotation deviation is too large and the behavior mode is abnormal by continuously tracking the change trend of key indexes. According to the invention, by fusing the multi-dimensional dynamic features and adopting the elastic time sequence matching and interpretable machine learning model, a power generation quotation behavior intelligent supervision closed loop integrating comprehensive evaluation, accurate quantification and active early warning is constructed, and the comprehensiveness, accuracy, objectivity and timeliness of market supervision are significantly improved.

The present application relates to the technical field of control system, especially to an intelligent control system of ultrapure water machine, the present application collects water quality data, membrane surface data and filtration pressure through a monitoring module; a state analysis module divides the filtration characteristic state according to the fluctuation of the membrane surface data; a control unit executes a control strategy according to the filtration characteristic state, including, obtaining the filtration pressure, identifying whether there is an abnormal value in the filtration pressure, determining whether to send a warning signal; or, obtaining the water quality data, calculating the fluctuation of the water quality data in each cycle, judging whether the fluctuation is consistent; and executing an adjustment strategy according to the consistency determination result, including, adjusting the water inflow, or sending a warning signal; the present application can improve the pertinence and reliability of system control, effectively delay membrane pollution, reduce membrane loss, and ensure the continuous and stable operation of the ultrapure water machine.

This invention relates to the field of rockburst early warning technology, specifically to a method for sensing early warning precursors of rockbursts based on causal mechanisms. The method includes: constructing a multi-dimensional feature matrix based on multi-source heterogeneous data; identifying the true causal relationships between day-level, hour-level, and grade-level parameters using a conditional independence test; determining causal directions through structural analysis to construct day-level, hour-level, and grade-level causal transmission networks; quantifying the causal intensity of each causal transmission path; extracting the corresponding key causal transmission paths at the day-level, hour-level, and grade-level; obtaining day-level rockburst early warning signals; issuing hour-level rockburst early warning signals after obtaining day-level rockburst early warning signals; and issuing grade-level emergency rockburst early warning signals after obtaining hour-level rockburst early warning signals. This invention constructs a complete time-gradient rockburst early warning system from day-level early warning to graded imminent warning, forming a complete early warning time chain.

The application provides a crane safety monitoring system and method, the system comprising: an airborne monitoring terminal for real-time acquisition of multi-dimensional operation data of the crane; an operation and maintenance monitoring platform comprising an operation monitoring module for fusion processing of the pre-processed multi-dimensional operation data by using a Kalman filtering algorithm to obtain real-time operation state estimation; and an updated model is established for each movement mechanism of the crane to calculate a theoretical operation state; an alarm signal of the corresponding movement mechanism is generated by comparing the real-time operation state estimation with the theoretical operation state, and a historical operation database is constructed; a warning prediction model is trained by using the historical operation data to predict a fault risk probability and generate a warning signal of the corresponding movement mechanism; the system realizes equipment state monitoring, fault warning, safety protection and management by comprehensively monitoring and analyzing the operation parameters, energy consumption data and operation behavior of the crane, and effectively improves the operation safety and operation and maintenance efficiency of the crane.

This invention discloses an artificial intelligence-based method for monitoring and early warning of plasma burner wall temperature, relating to the field of wall temperature monitoring technology. The method includes the following steps: dividing the plasma burner wall to obtain a monitoring zone sequence, establishing a state operation dictionary, and constructing a joint characterization framework for wall temperature operation status based on the monitoring zone sequence and the state operation dictionary; collecting historical wall temperature data, constructing a wall temperature correlation stability baseline generation model, inputting the joint characterization framework for wall temperature operation status and historical wall temperature data into the wall temperature correlation stability baseline generation model, outputting a wall temperature correlation stability baseline, and generating a wall temperature deviation description; constructing a wall temperature monitoring model, collecting real-time wall temperature data and inputting it into the wall temperature monitoring model, outputting the correlation damage degree and the ranking of contributing factors; generating a risk level based on the correlation damage degree, and generating a plasma burner wall temperature monitoring and early warning signal based on the risk level and the ranking of contributing factors.

This invention relates to the field of grounding wire management technology and discloses a monitoring and management system for intelligent grounding wires based on the BeiDou Navigation Satellite System. The system includes installing a wireless sensor on the top of a grounding operation rod to construct a set of grounding wire status parameters; using a decimeter-level single-frequency RTK matrix to construct a set of grounding wire positioning parameters; preprocessing the data; and then numbering and storing the preprocessed grounding wire status parameter set and grounding wire positioning parameter set. The system calculates status consistency, positioning matching, temporal correlation, and lifecycle; verifies the operation process and data validity based on the calculation results; and issues early warning signals based on the verification results. This system achieves intelligent monitoring, programmed control, and information-based early warning throughout the entire grounding wire usage process, replacing traditional manual operation and verification, avoiding human negligence and non-standard operation problems, eliminating hidden dangers such as missed removal, missed installation, and incorrect sequence, and ensuring personal and equipment safety.

This invention discloses an automatic early warning method and device for external damage prevention of transmission lines based on deep learning. It constructs a 3D image of the external damage source corresponding to each external damage source image using the IGEV algorithm, obtains and predicts the feature image sequence to obtain a predicted 3D image sequence of external damage sources. It acquires first 3D point cloud data corresponding to each actual external damage source and second 3D point cloud data corresponding to each actual transmission line in the external damage source 3D image sequence, and calculates a first distance between the actual external damage source and the actual transmission line. It acquires third 3D point cloud data corresponding to each predicted external damage source and fourth 3D point cloud data corresponding to each predicted transmission line in the predicted external damage source 3D image sequence, and calculates a second distance between the predicted external damage source and the predicted transmission line. Based on the first and second distances, a target early warning signal is triggered. Compared with existing technologies, the technical solution of this invention can achieve timely prediction of external damage source behavior and realize real-time early warning.

The application discloses a kind of high slope stability evaluation and early warning method based on multivariate monitoring data fusion, belong to slope engineering monitoring early warning technical field.For the problems of existing monitoring data source isolation, early warning lag and high false alarm rate, the method includes: real-time parallel acquisition of the multi-source asynchronous monitoring data of slope;Data are time-synchronized and spatially mapped to align, construct a unified spatiotemporal coordinate system under the fusion dataset, and extract the spatiotemporal feature vector;The feature vector is input into the fusion evaluation model, and a multi-dimensional fusion stability index is obtained;Based on the real-time sequence and change trend of the index, combined with the multi-level linkage trigger mechanism, dynamic release of graded warning signal;According to the new monitoring data and engineering response, the fusion evaluation model is periodically or triggered to update.The method realizes the deep fusion and dynamic evaluation of multi-source data, improves the early warning accuracy and timeliness, and can be widely applied to the safety monitoring and early warning of high slope construction period.

The application relates to the technical field of device control, and in particular relates to a PLC-based feeding and discharging processing production control system and method, which comprises a multi-modal data sensing module that collects three-dimensional point clouds and image sequences of a work scene through laser radar and a visual sensor, combines vibration signals and current signals of a processing spindle, carries out feature extraction, and outputs a comprehensive state vector comprising a workpiece pose and a device health state. A digital twin collaborative planning module receives the comprehensive state vector, drives a digital twin model and a physical environment to be synchronous in a virtual space, uses a reinforcement learning algorithm to carry out Monte Carlo tree search, and generates a flexible control strategy with a time sequence mark. The digital twin collaborative planning module adjusts a task allocation strategy according to an early warning signal, and corrects digital twin model parameters based on an execution log. The application realizes collaborative optimization of a feeding and discharging system and a processing beat according to the operation process of the above module, and improves device utilization and production efficiency.

This application discloses an industrial video surveillance multi-sensor linkage early warning device, including multiple sensor modules (such as temperature sensors, smoke sensors, vibration sensors, and gas concentration sensors), a video surveillance module (camera), a data processing module, an early warning module, and a communication module. The multiple sensor modules collect industrial environmental parameters in real time, the video surveillance module performs video monitoring, the data processing module receives and processes the sensor data and video data, determines whether there are any abnormalities, and if an abnormality is detected, controls the early warning module to issue an early warning signal. Simultaneously, the communication module transmits the data and early warning information to a remote monitoring terminal. The sensor modules and video surveillance module are mounted on an adjustable bracket, and the device also includes a power supply module. This device achieves comprehensive, real-time monitoring and timely early warning of the industrial environment, improving the safety and reliability of industrial production.

This application discloses a frequency-limited electromyography (EMG) signal acquisition device and method, relating to the field of EMG signal acquisition technology. The method includes: acquiring raw EMG signals and environmental electromagnetic interference feature sets of a monitored subject under a preset movement mode; inputting the raw EMG signals into a frequency-limited filtering circuit and adjusting the frequency-limited filtering circuit to obtain an interference-compensated frequency-limited filtering circuit; performing frequency-limited processing on the raw EMG signals and outputting limited EMG signals; extracting the signal recovery rate characteristics of the limited EMG signals over multiple consecutive monitoring cycles and calculating the EMG response compensation coefficient of the monitored subject; dynamically correcting the limited EMG signals to obtain neuromuscular function monitoring values, comparing them with a functional decline threshold, and outputting an early warning signal for acquired muscle weakness. This solves the technical problem that existing EMG signal acquisition devices cannot adapt to dynamically changing electromagnetic environments, leading to unstable signal quality.

The present application relates to the technical field of thickness detection, and discloses a catenary dropper icing thickness monitoring and early warning method and system, which comprises the following steps: obtaining a to-be-detected image of a catenary dropper, converting the image to HSV and Lab color spaces, and generating a fused edge feature map; segmenting out an icing area of the dropper, and extracting a pixel width sample set; fitting the pixel width sample set, obtaining an initial icing thickness probability distribution, and extracting a measurement uncertainty factor; constructing a historical icing thickness prior distribution; determining a current measurement confidence, and calculating a fusion coefficient; performing conjugate updating on the initial icing thickness probability distribution and the historical icing thickness prior distribution, obtaining an icing thickness posterior probability distribution and a distribution variance; obtaining a real-time environmental wind speed, calculating a safety thickness threshold and an early warning probability threshold; and when the cumulative probability of the icing thickness posterior probability distribution in the part exceeding the safety thickness threshold is greater than the adjusted early warning probability threshold, outputting an early warning signal. The present application improves the accuracy of the icing thickness monitoring result.

This application provides a clinical method, system, device, and medium for detecting bowel and bladder movements. First, raw pulse rate time-series data of the patient is collected. The raw pulse rate time-series data undergoes quality verification and signal cleaning to obtain effective pulse rate time-series data. Based on the effective pulse rate time-series data, the mean pulse rate within the current detection window is calculated, and a pre-determined individualized resting pulse rate baseline value is obtained. The difference between the mean pulse rate within the current detection window and the individualized resting pulse rate baseline value is determined as the dynamic pulse rate offset. The dynamic pulse rate offset is judged using predefined bowel and bladder behavior detection rules, and the event confidence level for the current time period is output. The event confidence level is compared with a preset clinical decision threshold. If the event confidence level exceeds the clinical decision threshold, a graded early warning signal is generated and pushed to the clinical nursing system. The above scheme can achieve non-invasive detection of clinical bowel and bladder events.

The application is suitable for the technical field of water conservancy monitoring, and particularly relates to a water conservancy monitoring and early warning method and system based on edge computing. The method comprises the following steps: performing sound wave detection, collecting a response signal, extracting a corresponding defect signal from the response signal, and generating baseline data; transmitting a sound wave detection signal, receiving a sound wave signal, and extracting the arrival time of a reflected signal peak; constructing a time sequence of the arrival time of the reflected signal peak corresponding to the same preset defect, calculating the continuous change trend of a strain value with time and a strain rate; and issuing an early warning signal according to the comparison result of the strain rate and the strain cumulative value with a preset safety threshold. The application can realize deformation monitoring of each position of a dam surface layer by laying a two-dimensional grid sliding extra-sensing network on the dam surface, overcomes the limitation that a single-point sensor can only measure local information, improves the accuracy and precision of deformation monitoring, and has low laying cost and is convenient for later maintenance.

The application belongs to the technical field of data processing and agricultural heat damage monitoring, and particularly relates to a peanut flowering heat damage early warning method based on meteorological and soil factor data processing, meteorological data and soil data are collected; the meteorological data and the soil data are respectively preprocessed; dynamic heat tolerance threshold value calculation is performed based on the regulating effect of soil factors on peanut heat tolerance performance; real-time atmospheric temperature and dynamic heat tolerance threshold value comparison are performed, and heat damage risk index is calculated; four risk levels of low, medium, high and extremely high are divided according to the risk index, corresponding early warning signals are generated and output. Through the above technical scheme, the meteorological factors and the soil factors can be analyzed and fused, the dynamic heat tolerance threshold value can be adaptively adjusted according to the actual soil condition, the problems of early warning deficiency or excessive early warning caused by the fixed threshold value can be avoided, and the peanut variety adaptation, early warning strategy self-learning, multi-region collaborative early warning and early warning effect evaluation functions are realized.

The application discloses a high-purity hydrogen production safety monitoring method based on intelligent sensing technology, which comprises the following steps: obtaining a plurality of hydrogen concentration data and environmental data based on a detector deployed in a high-purity hydrogen production area; obtaining the composition and concentration of interfering gas in the high-purity hydrogen production area, and determining a plurality of interference factors based on the interference degree of the composition and concentration of interfering gas on the plurality of hydrogen sensors; determining a plurality of environmental compensation factors based on the influence degree of the environmental data on the plurality of hydrogen sensors; fusing the plurality of hydrogen concentration data based on the plurality of interference factors and the plurality of environmental compensation factors respectively to obtain a target hydrogen concentration value; and sending an early warning signal and an early warning instruction to a control terminal if the target hydrogen concentration value exceeds a warning threshold. The application reduces the error of a single sensor, can consider the influence of environmental data on hydrogen concentration and correct it, and improves the accuracy of hydrogen concentration detection.

This invention relates to the field of mining safety technology, specifically to an intelligent monitoring and active blocking device for the escape of harmful gases in deep-earth resource mining. The device includes: a gas monitoring module for real-time acquisition of the concentration of harmful gases in the working area of ​​deep-earth resource mining, serving as a first judgment condition; an auxiliary monitoring module for real-time acquisition of images of the working area and identification of whether gas clouds formed by the escape of harmful gases exist in the images, serving as a second judgment condition; and an early warning and blocking module for outputting early warning and blocking signals based on the first and second judgment conditions. This invention, through the dual-condition collaborative judgment of "gas concentration + gas cloud image," shortens the identification time lag of harmful gas escape, solves the problem of compressed safety windows caused by monitoring delays, reduces the risk of safety accidents such as explosions and poisoning, and provides a more suitable safety control solution for the complex environment of deep-earth resource mining.

The application discloses a kind of key project construction progress and system and method for risk early warning, by obtaining the buffer capacity data of labor subsystem, mechanical subsystem, material subsystem, fund subsystem and management subsystem in construction system and the logical dependence relationship and planned construction period of each process in construction plan network diagram, based on the coupling strength data between each subsystem buffer capacity data and each subsystem, calculate system resilience entropy, based on the coupling strength between high influence process, the key degree of process, the number of redundant path, the total amount of float time and calculate topological elasticity coefficient;When system resilience entropy exceeds first preset threshold or topological elasticity coefficient is lower than second preset threshold, generate early warning signal;Further, in response to early warning signal, based on structural causal model, counterfactual reasoning is carried out to subsystem buffer capacity data, intervention scheme containing target subsystem, target buffer capacity adjustment amount and expected effect is generated and output, realize the automatic generation of early warning and intervention scheme.

This invention discloses an intelligent monitoring and early warning method and system for bridge expansion joints, comprising: collecting multi-dimensional data such as stress, strain, displacement, temperature, humidity, and load frequency through a distributed sensor array, transmitting the data to a big data analysis platform for classification, storage, and correlation mapping; calculating damage variables by calling a fatigue damage evolution model; extrapolating load time-series response using a dynamic load response prediction model; and performing feature matching and anomaly determination using an intelligent abnormal deformation diagnosis model. This invention generates early warning information including damage level, deformation trend, and risk range by integrating model output results, achieving multi-dimensional data linkage analysis and early warning signal output, providing comprehensive and accurate technical support for the health monitoring of bridge expansion joints, and improving the scientific nature and timeliness of operation and maintenance decisions.

The application discloses a passenger car carbon emission monitoring and regulation system and method based on multi-source space-time data, comprising: a data fusion module for processing multi-source data and outputting space-time aligned fusion data; a carbon emission tracing and user analysis module for calculating vehicle-level carbon emission contribution and dividing user groups based on the fusion data; a multi-level regional accounting and early warning module for receiving and based on the vehicle-level carbon emission contribution and user group division results, and generating early warning signals by comparing preset targets; a regulation simulation and feedback module for simulating and generating regulation instructions according to the early warning signals, and feeding back the effect data after the instruction execution to the carbon emission tracing and user analysis module. The application realizes the tracing of massive mobile source carbon emissions from a macro region to specific vehicles, driving sections and time segments by constructing a data fusion-vehicle-level carbon emission calculation-user group clustering analysis chain, and solves the industry problem.

This invention belongs to the field of medical anesthesia monitoring technology, specifically relating to a pediatric anesthesia safety monitoring system and equipment. It aims to address the technical problem of "static monitoring and delayed risk warnings in existing anesthesia safety monitoring systems." The system includes a developmental feature module, an anesthesia sub-stage module, a drug metabolism simulation module, a risk mining module, a risk prediction module, and an early warning module. By combining EEG signals with individualized physiological feature tags, it dynamically identifies the sub-stages of anesthesia and generates a specific monitoring threshold range for the current sub-stage; by combining individualized physiological feature tags with real-time anesthetic drug administration data, it outputs drug metabolism influencing factors; by combining multidimensional vital signs parameters, stage-specific monitoring thresholds, and drug metabolism influencing factors, it outputs a corrected coupled risk feature value; based on the coupled risk feature value, it calculates a real-time anesthesia risk value and predicts and outputs a risk development trend value; and it dynamically corrects the early warning level, outputting tiered early warning signals and treatment guidelines.

This invention relates to the field of digital archives management technology, specifically to an artificial intelligence-based system for analyzing and warning of abnormal archive behavior. The invention uses a graph construction module to transform archive access logs into a relational topology graph; an attribute propagation module performs weighted transfer and accumulation of weights based on a graph traversal algorithm to calculate the cumulative relational index value of nodes; a connectivity retrieval module retrieves cross-regional links based on partition isolation rules; and an early warning output module generates early warning signals based on index values ​​and link status. This invention utilizes the flow mechanism of feature values ​​in a graph structure to aggregate minute risky behaviors within discrete long-term windows, accurately identifying complex abnormal patterns where a single operation may not violate regulations but the long-term cumulative risk exceeds the limit. This solves the problem that existing static statistical rules are unable to detect hidden logical violations, effectively reducing false alarms and false negatives.