203 results about "Change analysis" patented technology

The invention relates to the technical field of information traceability, in particular to a digital information traceability management system. The system comprises an information fingerprint extraction module, an origin node consensus module, a circulation path mapping module, an information integrity verification module and a behavior consensus feedback module, and can be used for acquiring original information; performing text semantic feature extraction on the original information to obtain semantic features; performing information fingerprint extraction on the semantic features to obtain semantic fingerprints; performing consensus processing on the semantic fingerprint based on an origin node to obtain a consensus result; generating a source point voucher according to the consensus result to obtain the source point voucher; performing circulation behavior triggering according to the source point voucher to obtain a to-be-recorded behavior; and performing content change analysis according to the to-be-recorded behavior to obtain a changed semantic fingerprint. According to the method, through fusion of semantic hash and behavior consensus, the source tracing robustness, safety, efficiency and applicability are improved.

The invention discloses a hardware fault real-time detection method and system based on cooperation of a CPU (Central Processing Unit) and a BMC (Baseboard Management Controller), and the method comprises the following steps: respectively collecting high-frequency state data and tendency indexes by establishing a communication mechanism between the CPU and the BMC; the processor uses a CUSUM algorithm to carry out abrupt change analysis on the periodically collected operation state to generate an abrupt change event; and the management controller uses an EWMA algorithm to model the trend data, and extracts abnormal changes. And the system performs fusion analysis on the mutation event and the trend anomaly to form a fusion anomaly vector. The risk assessment module calculates a risk score based on a preset rule, determines a fault level, positions a target component, and outputs a processing strategy. And triggering a response action according to the strategy and recording an execution state. And the fusion data and the response record are input into the adaptive module together for dynamically adjusting CUSUM and EWMA parameters, so that adaptive updating of the algorithm is realized, and finally a fault detection signal is generated.

The invention provides an early-stage automatic identification method and system for acute kidney injury related to cardiac surgery, and relates to the technical field of physiological parameter detection. The method comprises the following steps: acquiring average arterial pressure, cardiac blood discharge volume, central venous pressure and blood oxygen saturation, regularly recording and extracting data of adjacent time points, calculating a variation range, screening a mutation region, judging whether a reference range is exceeded or not, analyzing a multi-parameter synchronous variation condition, and marking a hemodynamics abnormal state. According to the method, abnormal changes of hemodynamics and urine metabolism are accurately recognized through real-time monitoring and multi-parameter analysis, the urine flow rate and the metabolite excretion rate are combined, misjudgment of a single index is avoided, the fineness of renal function change analysis is improved, the high-risk time period is accurately positioned through time cross comparison, the diagnosis accuracy is improved, and the diagnosis accuracy is improved. Trend analysis and risk grade division enable the change of the illness state to be more visual, and a real-time early warning system effectively monitors the fluctuation trend, optimizes clinical management and improves the timeliness of medical response.

The invention belongs to the technical field of power file rule extraction and change comparison, and particularly relates to a policy document intelligent rule extraction and change comparison method for electricity charge checking, which comprises the following steps: analyzing, reconstructing and optimizing an electricity charge related policy document, and retaining original context and logic level information; designing a core statement recognition Prompt template and a rule extraction and conversion Prompt template, constructing a policy and rule extraction agent, guiding a large language model to recognize core statements, extracting rule element information and forming standardized policy and rule description; according to the method, similar policy rules are retrieved from a historical policy rule description knowledge base, a policy rule comparison agent is constructed, and deep semantic comparison and change analysis are performed on current standardized policy rule description and historical similar policy rule description, so that automatic identification of newly added and modified contents of current rules and historical rules is realized; and a key technical support is provided for automation and intelligentization of electricity charge checking business rule generation.

The invention belongs to the technical field of inspection equipment control systems, and discloses an inspection equipment control system, method and device, and the system comprises a difficulty recognition module, a path density adjustment module, a state monitoring module, a boundary compensation module and a control linkage module. According to the method, a trend included angle is obtained through numerical value change analysis of a track section structure, an angle matching ratio is introduced to realize dynamic adjustment of a path section interval, a three-period sequence is constructed in cooperation with gyroscope data to extract main direction change, disturbance rhythm change is accurately captured, disturbance amplitude is extracted through a minimum interval distance trend, and the disturbance accuracy is improved. A time axis turning position is reconstructed by combining data of a path trigger, a boundary offset reference is positioned, linkage control of the distance between path points and an average path is realized, a processing logic establishes a multi-dimensional linkage relation of path adjustment, disturbance identification and boundary compensation, and path response flexibility and equipment operation stability under environment change are enhanced. And the inspection precision and adaptability in a high-disturbance scene are improved.

The invention belongs to the technical field of computer-aided project management, and discloses an electric power engineering design project management system and method, and the system comprises a data integration and processing module, a multilayer dynamic knowledge graph data storage library, a graph-based automatic compliance check engine, and a graph neural network-based predictive risk engine. And an active design change impact analysis module. According to the method, a unified and intelligent project data core is constructed, heterogeneous data sources such as design files and law and regulation texts are integrated in a multi-layer dynamic knowledge graph, automatic compliance review is realized by utilizing graph query, data-driven risk prediction is realized by utilizing a graph neural network GNN, and the risk prediction efficiency is improved. And the linkage influence of design change can be simulated and quantitatively evaluated. According to the method, the problems of data islanding, passive compliance examination, subjective risk management, difficulty in change analysis and the like in the prior art are solved, and the management efficiency, accuracy and initiative of the electric power engineering design project can be remarkably improved.

The invention discloses a health trend analysis early warning method based on big data, and relates to the technical field of data processing, and the method comprises the steps: analyzing female physiological cycle data based on big data, and building a health trend analysis model; the method comprises the steps of obtaining a physiological cycle type of a target user, collecting a sleep state, a physiological cycle and physical sign data of the target user, generating standard health monitoring data, initializing an analysis model according to basic information of the user, performing sleep quality change analysis, evaluating a health trend of the user according to an analysis result in combination with physiological cycle characteristics, and providing personalized sleep improvement suggestions. The technical problem that traditional health management in the prior art neglects the influence of the female physiological cycle on the health state and the sleep quality and lacks pertinence and individuation is solved, and individualized health management is provided by combining the female physiological cycle data and the sleep state data. And the sleep quality of women in different physiological cycle stages is improved.

The invention discloses a tobacco appearance quality control method and related equipment based on microenvironment intelligent maintenance, a tobacco appearance quality control model is constructed, an appearance feature extraction network is utilized, feature extraction is performed on appearance time sequence data of tobacco based on aligned timestamps, and features reflecting appearance changes of the tobacco are obtained; and performing feature extraction on the time sequence data of the environment where the tobacco leaves are located based on the aligned timestamps through an environment feature extraction network to obtain features of environment change. The appearance change characteristics and the environment change characteristics are mapped into a control network for appearance quality change analysis, the influence of the appearance change characteristics and the environment change characteristics on appearance quality change is found through the change relation between the appearance change characteristics and the environment change characteristics after alignment and time stamping, and the environment parameters are adjusted based on the appearance quality change analysis. And the tobacco appearance quality change speed is accurately regulated and controlled, so that the tobacco is in the best appearance state during use.

Solutions for integration and ongoing management of enterprise applications operating in a networked environment are disclosed. An AI transformer extracts, transforms, and manages integration point information from various sources, including documents, UMLs, and other system diagrams. Instead of relying on traditional documentation, the invention utilizes blockchain-based NFTs (non-fungible tokens) to store and manage integration point information. NFTs are created using smart contracts, which define the rules and conditions for the NFT. A metadata engine prepares metadata for generating the Integration Point NFTs. An NFT manager creates the integration point NFTs. A change analyzer determines the impact of changes to integration points on different applications and functionalities. A release compliance component is integrated with DevOps and is responsible for ensuring that integration point changes are incorporated into all impacted applications according to the release defined in the NFT.

The embodiment of the invention provides a geological disaster recognition method and system combined with a remote sensing technology, and the method comprises the steps: firstly obtaining a multi-time-sequence remote sensing image data set of a target region, which comprises remote sensing images collected at different times; then, performing topographic feature extraction processing on the data set to obtain a topographic feature set, covering earth surface coverage features, slope features and elevation change features, then, performing multi-time-sequence change analysis on the topographic feature set to generate a dynamic change feature set, and performing abnormal region identification on the dynamic change feature set by using a geological disaster identification model to obtain an abnormal region identification result; according to the method and the device, the abnormal region set with the geological disaster risk is determined, and finally, the disaster early warning signal is generated according to the abnormal region set and is sent to the disaster monitoring terminal, so that comprehensive and dynamic monitoring and accurate early warning of the geological disaster risk region are realized, and the accuracy and the efficiency of geological disaster monitoring and early warning are improved.

The invention discloses a map database-based land type map spot storage and analysis method and system. The method comprises the following steps of: acquiring and processing data; storing data; monitoring pattern spot type distribution analysis; performing annual change investigation pattern spot and land type change analysis; and reading and displaying data. According to the method, spatial analysis is carried out on land type change monitoring pattern spots and annual change investigation pattern spots, and a method based on a pattern database is designed to store analysis results; the incidence relation and important attributes of the change monitoring pattern spots and the annual change pattern spots can be expressed on a visual platform, and the monitoring type distribution condition and the annual change survey of the change conditions of different land types in a certain space range are analyzed; according to the storage and analysis method, the internal relation between the change monitoring pattern spots and the annual change pattern spots is visually presented, the effectiveness of change monitoring pattern spot analysis is improved, the data value of the monitoring pattern spots is deeply mined, natural resource data analysis and application are facilitated, and planning and decision making of related departments of the natural resource industry are supported.

The invention discloses a systematic monitoring method for the electrical safety performance of a cable, and relates to the field of electrical safety, and the method comprises the steps: collecting sensor data to an edge calculation node, preprocessing the data, and outputting the data as real-time calculation temperature data, local discharge point data, resistance and capacitance data and vibration data; the data are sent to a hidden danger classification model and a fault positioning model in the cloud, and the models output early warning information to an operation and maintenance project for processing; the sensor data sending process is judged according to the data frequency, when it is judged that the frequency is larger than 1 kHz, a 1D-CNN electric arc detection model is loaded, when it is judged that the frequency is smaller than 1 kHz, a Light GBM feature classification model is loaded, and 24-hour sensor data smaller than 1 kHz are periodically loaded into an LSTM-Attention gradient analysis model; and related data of partial discharge generated by judgment of the 1D-CNN arc detection model is sent to an LSTM-Attention gradient analysis model for evaluating the partial discharge trend of the global cable. Classification model output is used as prior knowledge of a positioning model, and the positioning accuracy can be improved.

The invention relates to the technical field of medical information, in particular to an anomaly recognition system for trend characteristics of chronic disease monitoring data. The system comprises a chronic disease monitoring data integration module, a chronic disease feature change analysis module, a chronic disease composite feature analysis module and a chronic disease trend feature anomaly recognition module, and can perform chronic disease classification and associated chronic disease integration according to real-time chronic disease monitoring data. Generating integrated associated chronic disease data; performing tissue structure mapping reconstruction of the chronic disease patient and disease mutation characteristic factor analysis of the chronic disease patient according to the integrated and associated chronic disease data, and generating disease mutation characteristic factor data of the chronic disease patient; according to the disease mutation characteristic factor data of the chronic disease patient, performing disease composite characteristic analysis on the chronic disease patient, and performing chronic disease trend abnormal characteristic analysis to generate chronic disease trend abnormal characteristic data. According to the invention, accurate identification of chronic disease trend abnormity is realized.

The invention relates to the technical field of engineering management, in particular to an AI-based engineering quantity list automatic compiling method and system, and the method comprises the following steps: constructing a model through analyzing a construction task dependency relationship, tracking the progress in real time, and predicting changes through a neural network to generate change analysis; and optimizing delay task adjustment time and resource configuration in combination with constraint conditions, evaluating a weather risk level and dynamically adjusting a construction period, and finally dynamically updating a project quantity list based on resource and risk results. According to the method, a task network structure with a quantifiable scoring mechanism is constructed by analyzing the dependency relationship between the tasks, so that the logic association between the tasks not only stays on the static hierarchy presentation, but also introduces the difference weight of time and resource dimensions to realize dynamic modeling on the dependency of key paths; and depth support is provided for subsequent prediction and adjustment. Tracking of task progress changes does not depend on a static progress comparison mode any more, but deep extraction of actual construction data is carried out.

The invention relates to the technical field of equipment fault prediction, in particular to an on-line monitoring analysis and fault pre-judgment method and system for a switch cabinet of a switch cabinet, and the method comprises the following steps: collecting a voltage sequence, recognizing a main peak, recording the main peak, extracting a rear section of the main peak, analyzing amplitude change, judging discontinuous features, constructing a track trend sequence, recognizing continuous offset, and carrying out fault prediction. According to the method, through extraction and serialization processing of voltage main peak characteristics in the contact conduction period and combination of comparison of change amplitudes of sampling sections after main peaks, accurate identification of transient anomalies is achieved, and through dip angle change analysis of main peak tracks in the continuous periods, accurate identification of the transient anomalies is achieved, so that the accuracy of the transient anomalies is improved, and the accuracy of the transient anomalies is improved. According to the method, the deviation behavior of the running trend is recognized, abnormal sections which are consistent in direction and are distributed in a concentrated mode are positioned through cross comparison of abnormal periods and track deviation, the deviation trend is comprehensively judged based on the period number and direction characteristics, and the timeliness and accuracy of fault pre-judgment are effectively improved.

The invention relates to the technical field of sensing data processing, and discloses an old people home safety decision service system fusing multi-mode sensing data, which comprises a data interaction analysis unit, a data change analysis unit, a data calculation unit, a safety analysis unit and a safety decision unit. The hidden association of the multi-modal indexes is accurately captured, the early degradation of the collaboration level can be perceived through the graph condensation degree and the graph equilibrium value, the collaboration degradation track can be used for analyzing the collaboration state, and the finally generated safety decision instruction can perform early warning in advance. According to the scheme, single-mode threshold dependence is broken through, hidden dangers hidden in data correlation evolution are recognized in advance, and the lagging of home safety decision of old people is solved.

The invention provides a change analysis method and device based on cross-temporal frequency domain information interaction and a readable storage medium thereof. The method comprises the following steps: acquiring a dual-temporal remote sensing image and partitioning the dual-temporal remote sensing image; extracting spatial features through a shared encoder; fourier transform is carried out on the double-tense features in a feature domain, high and low frequency components are divided according to a step length r, low frequency information is exchanged, and after inverse transform, splicing is carried out to obtain a fusion feature F; performing same frequency domain interaction on an original image in a data domain, and modeling through a video encoder to obtain a feature E; and finally fusing F and E, and decoding and outputting a change prediction map. According to the method, the high and low frequency information proportion is dynamically regulated and controlled through the frequency domain step length, dual interaction of the data domain and the feature domain is combined, structural difference perception is enhanced, pseudo change interference is suppressed, and change detection precision and robustness are remarkably improved. Experiments show that the method is superior to the prior art in F1 score, intersection-union ratio and other indexes, and is suitable for the fields of smart cities, disaster monitoring and the like.

The invention discloses an embedded system integration test case multiplexing method and system, and the method is characterized in that the multiplexing of a test case is carried out based on the existing software assets: firstly, based on the existing software requirements and the test case, an existing requirement-test case tracking chain is constructed; performing demand change analysis on the new demand activity graph information; performing change influence range analysis based on a change analysis result in combination with a demand-test case tracking chain, and determining a test case reuse and generation scheme; and finally, executing the test case multiplexing and generating scheme. According to the method, the tracking relation between existing requirements and test case assets is established, meanwhile, the execution cost of downstream test tasks caused by requirement change is reduced through multiplexing, and the test efficiency of integration testing is improved.

The invention relates to the technical field of machine learning, and discloses an outsourcing service quality evaluation method based on deep learning, which comprises the following steps: capturing operational micro habit dynamics of a service subject through lightweight representation data, establishing a self-adaptively updated reference normal state model, monitoring deviation in real time, generating an early disturbance signal, and determining the outsourcing service quality according to the early disturbance signal; and performing correlation analysis on the multiple signals to identify a predefined disturbance mode, and finally dynamically evaluating the service quality based on mode matching. According to the method, early-stage risk early warning which cannot be captured by a traditional macroscopic index is realized through fine-grained perception and modeled association of micro habits, and meanwhile, a judgment threshold is dynamically calibrated in combination with a situation beacon, so that the early-warning precision and timeliness are remarkably improved, and the early-warning effect is improved. And the multi-dimensional monitoring capability from individual anomaly to collective out-of-order is expanded by means of group behavior entropy change analysis.

The invention provides an automatic management interaction method, system and device of a whole vehicle software version and a medium, and relates to the technical field of vehicle software engineering.According to the method, functions of version monitoring, change analysis, test management and version management are provided through an integrated main interface, and a user can check the software version state and tasks to be done in real time; and performing multi-dimensional version comparison and risk assessment to generate a detailed change and risk report. In addition, the method supports configuration of a test strategy, execution of an automatic test and generation of a test report and a defect positioning list, and the test efficiency and accuracy are effectively improved. And in the aspect of version management, the whole process operation from creation to release is realized, the timeline and historical report of the whole life cycle can be displayed, and the transparency and traceability of version control are ensured. On the whole, the method effectively improves the efficiency and reliability of whole vehicle software version management, simplifies the operation process, and enables the version control to be more efficient, more accurate and easier to manage.

The invention discloses a data-driven ship abnormal behavior online detection and early warning method and system. The method comprises the following steps: carrying out data cleaning on original ship AIS (Automatic Identification System) trajectory data; compressing the cleaned trajectory data by adopting a trajectory data compression algorithm, calculating trajectory similarity with all ships based on a time sequence similarity algorithm, and constructing a trajectory similarity measurement matrix; performing clustering analysis on the trajectory similarity measurement matrix by using a trajectory clustering algorithm to obtain a clustering result; backtracking the cleaned trajectory data based on a clustering result, and determining a ship navigation area by using a boundary extraction algorithm; utilizing a historical average ship trajectory prediction algorithm to generate a ship prediction trajectory under a time length threshold value; and in combination with the ship navigation area and the ship prediction trajectory, ship abnormal behavior detection is realized through angle difference change analysis and matching of the ship trajectory point and the ship navigation area, and early warning is carried out. According to the invention, real-time, efficient and accurate judgment and early warning of the ship trajectory can be realized.

The invention discloses a teaching scheme generation method and system based on student score change analysis. The method comprises the steps of obtaining multiple pieces of historical score data of a target student at multiple historical time points; constructing a plurality of score change curves corresponding to the target student according to the historical score data and the corresponding historical time points; predicting score influence factors corresponding to the target student according to the plurality of score change curves based on a graph neural network; and according to the score influence factors and the score change curve, based on an LLM algorithm model, generating a teaching scheme corresponding to the target student. Therefore, accurate score influence factor analysis and personalized teaching scheme making based on the historical score trend can be realized, and the learning effect of students and the utilization efficiency of educational resources are improved.

The invention relates to the technical field of intelligent management, and discloses an intelligent management system for a virtual power plant, and the system is characterized in that a parameter obtaining module divides a safety management region into a plurality of sub-safety management regions, and obtains the electric power parameters of each sub-safety management region in a plurality of time intervals; a first determination module performs change analysis on the electric power parameters and determines an electric power change coefficient corresponding to each time period; a second determination module determines an electric power change index corresponding to the time interval according to the electric power change coefficient; the risk calculation module performs sequence division on the electric power change indexes and calculates a comprehensive electric power risk management value; the risk management module judges whether the virtual power plant meets safety management or not according to the relation between the comprehensive power risk management value and a preset comprehensive power risk management value, so that the risk management precision and efficiency of the virtual power plant are ensured, and real-time monitoring and active early warning of the operation state of the virtual power plant are realized; therefore, the security and stability of the virtual power plant are improved.

The invention relates to the technical field of automobile state monitoring, in particular to an automobile electric drive state intelligent recognition system which comprises a data acquisition module, a change analysis module, an association recognition module, a state layering module and a state recognition module. According to the method, the temperature and flow velocity dynamic states of the cooling liquid at the inlet, the outlet and the middle part of the channel are captured in real time, the time sequence characteristics reflecting the heat dissipation process of the electric drive equipment are established, the dynamic change rate of the electric power of the electric drive equipment is synchronously associated, and the linear coupling strength between the electric power input and the heat flow response output is deeply analyzed; a multi-dimensional joint feature fusing electric power change, heat flow dynamic response and response delay between the electric power change and the heat flow dynamic response is constructed, the similarity between the current operation state and various preset typical working condition features is calculated through clustering analysis, and finally affiliation judgment of the current operation state is output in a probability form. The problem of recognition fuzziness caused by complex and changeable working conditions is effectively solved.

The invention discloses an incremental template generation method, system and equipment based on modularization and version control and a medium, belongs to the technical field of low-code development of software engineering, and aims to solve the technical problem of how to realize staged generation, incremental output and automatic combination of codes. In order to improve the efficiency and reliability of a large-scale system in agile development and multi-person collaborative development, the adopted technical scheme is as follows: module configuration identification: an application structure is split into independent functional modules through a low-code designer, each function is abstracted into a standardized JSON configuration structure, and the standardized JSON configuration structure is provided with a unique identifier of the module; template registration and version identification: identifying newly added, modified and deleted contents through version control to realize differentiated code generation; carrying out difference identification and change analysis; performing incremental template rendering and merging; and outputting a result and integrating the project.

The invention relates to a spoil field three-dimensional form change analysis method, which comprises the following steps: constructing an air-space-ground collaborative multi-scale three-dimensional monitoring network by adopting an unmanned aerial vehicle carrying a laser radar system; dynamic construction of a multi-period digital elevation model and a digital terrain model is realized through a weighted fusion algorithm based on point cloud density difference and an improved progressive triangulation network encryption algorithm; performing time sequence alignment on the digital terrain models in different periods, and constructing a digital elevation difference model; and through a digital elevation difference model and a self-adaptive threshold change detection technology, the space-time differentiation characteristics and evolution rules of a slope curvature sudden change area, a surface roughness abnormal zone and a deformation rate of the spoil site are disclosed. According to the scheme provided by the invention, high-precision three-dimensional form dynamic modeling and deformation process precise monitoring of the spoil ground can be realized, and reliable technical support is provided for ecological safety situation assessment and geological disaster advanced early warning of the spoil ground.

The invention discloses a cooking robot control system based on voice processing, and relates to the technical field of intelligent robotics.The cooking robot control system comprises a steady-state locking subsystem, a dynamic detection subsystem, a change analysis subsystem and a feedback subsystem, firstly, a cooking robot sends a rotating speed control instruction to a motor corresponding to a stirring paddle by recognizing a voice command of a user, and the rotating speed control instruction is sent to the motor corresponding to the stirring paddle; the method comprises the following steps: collecting pot wall temperature data in a pot cavity, monitoring operation data of a motor of a stirring paddle in the pot cavity to determine a steady-state control mode, identifying the time for performing centrifugal inrush flow on food in the pot cavity based on the steady-state control mode and the push resistance rate monitored in real time to form centrifugal inrush flow and sauce redistribution operation, and then acquiring the pot wall temperature data in real time to obtain a sauce redistribution result. The temperature difference change before and after the short-time pulse acceleration is executed is analyzed to obtain an execution change temperature difference, an adjustment mechanism is triggered based on the execution change temperature difference, and finally, after the adjustment mechanism is executed, the temperature difference change is continuously fed back until the cooking operation is completed.

The invention relates to the technical field of air tightness detection, in particular to an air tightness detection method and system for a plastic pipe connecting piece, and the method comprises the steps: obtaining real-time pressure data and associated temperature data of the interior of a detected plastic connecting piece in an inflation and pressure maintaining process; inputting the real-time pressure data into a temperature and pressure coupling model for pressure value compensation correction to obtain a compensated pressure value only reflecting the leakage effect; performing time sequence change analysis on the compensated pressure value, and identifying and determining a starting time point when the pressure enters a stable state; extracting the dynamic attenuation characteristic of the compensated pressure value; and performing matching degree calculation on the dynamic attenuation characteristics and the reference model of the leakage-free basis behavior to generate an air tightness detection result of the plastic pipe connecting piece. According to the invention, the problem of interference of non-leakage factors such as environment temperature in air tightness detection is effectively solved, the inhibition capability of interference of non-leakage factors such as temperature is enhanced, and the accuracy and reliability of a detection result are improved.

Systems, devices, and methods related to change analysis for application deployment are provided. An example method performed by a computer system includes receiving a dependency graph associated with an application or an update to the application for deployment in a target environment. The dependency graph indicates resources within the target environment and dependency relationships among the resources. The method further includes identifying a change in resources caused by the application and one or more impact paths from the dependency graph. Each one of the impact paths indicates one or more changed resources associated with the application and one or more impacted resources originating from the one or more changed resources across the dependency graph. The method further includes generating a change impact summary indicating the one or more impact paths and outputting the change impact summary.

The invention belongs to the technical field of network security, and provides a detection method and system applied to an APT attack behavior, and the technical scheme is that an early warning upgrading mechanism is reconstructed through a triple decision engine, and on the basis of retaining an original confidence scoring system, an early warning is obtained by combining newly added threat entropy change analysis and attack feature matching. The primary early warning is verified by integrating multiple factors, frequent triggering of the primary early warning due to erroneous judgment is avoided, meanwhile, it is ensured that early warning can be rapidly upgraded in time for abnormal behaviors really having threats, the reliability of early warning is improved, and safety personnel can more accurately judge the threat degree and reasonably arrange countermeasures.