618 results about "Baseline model" patented technology

The invention provides an enterprise employee behavior analysis and safety risk early warning monitoring method and system, and relates to the technical field of enterprise risk management, and the method comprises the steps: collecting employee terminal operation behavior data, building an activity thermal distribution diagram based on office area grid behavior association intensity, and training a behavior evaluation model. And inputting the behavior scoring matrix into a deep neural network to extract target behavior characteristics, calculating an abnormal behavior risk weight coefficient in combination with a department security policy, performing classification and analyzing a risk diffusion probability, and generating a risk situation index to determine an early warning level. Starting a response strategy according to the early warning level, blocking high-risk early warning in real time, tracking associated accounts, establishing a risk association map, identifying potential risk propagation nodes and performing active protection, finally generating an early warning report and feeding back effective protection rules to a behavior baseline model, and realizing continuous optimization of a risk early warning mechanism. And the accuracy and effectiveness of safety risk early warning in the enterprise are improved.

The invention discloses a model training method and device, electronic equipment and a computer storage medium. The model training method comprises the following steps: performing supervision fine tuning on an initial baseline model based on a first training sample set in a preset training sample set to obtain a supervision fine tuning model and a preference data sample; performing optimization training on the initial reward model based on the preference data sample to obtain a target reward model, and performing optimization training on the supervision fine tuning model based on the preference data sample to obtain a preference optimization model; generating a first reward signal based on the preference optimization model and the first training sample set, and generating a second reward signal based on the preference optimization model, the target reward model and a second training sample set in the preset training sample set; and performing optimization training on the preference optimization model based on the first reward signal, the second reward signal and the second training sample set to obtain a target object generation type pre-training model. By adopting the method, the generalization ability of the model can be improved.

The invention relates to the technical field of audio and video analysis and data processing, in particular to an AI-based multimedia equipment operation and maintenance management system, which comprises a data acquisition module, an analysis module, an equipment state modeling module, a fault prediction module, a maintenance strategy optimization module, a resource allocation module and a visual platform. The data acquisition module acquires temperature, vibration, audio and video feature data of the equipment through the sensor and the video equipment, and the data are preprocessed and then transmitted to the cloud; the equipment state modeling module constructs a dynamic baseline model based on historical data, abnormal signals are generated through real-time comparison, and the visual platform realizes multi-dimensional state monitoring through 3D topology rendering, AR auxiliary diagnosis and early warning linkage. The system solves the problems of response lag and low resource utilization rate caused by dependence on manual operation and maintenance in the prior art through a closed-loop management mechanism driven by a data stream, and improves the fault diagnosis precision and the operation and maintenance efficiency.

The invention relates to the technical field of line arc abnormity monitoring, in particular to a data center operation and maintenance service environment monitoring system which comprises a multi-mode sensing unit, a dynamic baseline modeling unit, a transition state abnormity extraction unit and a grading early warning unit. The dynamic baseline modeling unit builds a three-layer safety baseline model, the base layer builds harmonic references in a segmented mode according to the load rate, the environment layer generates voiceprint feature template libraries of different temperature and humidity intervals, the time layer fits a 24-hour change trend envelope line, and the transition state anomaly extraction unit separates and decouples an arc voiceprint feature frequency band through a blind source and calculates the energy ratio. The harmonic abrupt change phase deviation amplitude is analyzed in combination with window sliding correlation, the abnormal evolution index is generated through fusion, the graded early warning unit triggers response according to the index, transition state abnormity is accurately recognized, the perspectiveness and reliability of operation and maintenance early warning are improved, and the method is suitable for safe operation and maintenance of data center equipment.

The invention relates to the technical field of network security and data protection, and discloses a data leakage prevention method and system based on user behavior perception, and the method comprises the steps: obtaining historical operation data, and constructing a personalized behavior reference library; monitoring a current access behavior in real time by sliding a time window, calculating a deviation degree and triggering anomaly detection; performing multi-level feature analysis on the abnormal behavior and calculating a comprehensive abnormal score; dynamically adjusting the access authority according to the score, recording an abnormal behavior and carrying out relevance matching; optimizing the reference model through feedback learning; and evaluating the credibility and the risk level based on an accurate modeling result, and adaptively adjusting permission configuration. According to the method, the user behavior rule can be accurately captured, the data leakage risk can be efficiently identified, the access permission can be dynamically adjusted, the false alarm rate can be reduced, adaptive protection can be realized, and data security and business smoothness can be guaranteed.

The invention provides an AI-based carbon footprint and supply chain carbon emission collaborative management method and platform, and the method comprises the steps: collecting various types of carbon-related data from each link of a supply chain in real time, and carrying out the standardization processing to form a unified data flow. An intelligent sensing technology is combined with a dynamic reference model, and sudden carbon emission fluctuation generated in production operation is automatically recognized. The propagation influence and potential risk of these carbon emission fluctuations in the entire supply chain are analyzed. The risk propagation condition and the processing capacity of each node are comprehensively considered, and an optimal carbon emission distribution processing scheme is formulated through an optimization algorithm. A distributed account book technology and an automatic execution mechanism are utilized, and an excitation system is combined to ensure that each participant completes a corresponding carbon processing task according to an optimal scheme. According to the method, the intelligent level and the overall effect of supply chain carbon management can be effectively improved, and a technical support is provided for an enterprise to realize a carbon emission reduction target.

The invention provides a learning data accurate mining method based on cognitive calculation driving. The learning data accurate mining method comprises the following steps of S1, performing multi-modal learning behavior data acquisition and heterogeneous integration; s2, a dynamic feature weight optimization step based on calculus; s3, performing cognitive state differential equation modeling; s4, a cognitive diagnosis hybrid model based on statistics; s5, incremental construction of the dynamic knowledge graph is carried out; s6, constructing a federated learning framework for privacy protection; s7, a cognitive intervention strategy is generated; s8, constructing a multi-granularity effect evaluation system; s9, a step of constructing an interpretability enhancement module; s10, a step of carrying out adaptive iterative optimization; the learning data accurate mining method based on cognitive calculation driving has the following advantages that the data utilization rate breaks through the limitation of a traditional method through federated learning and heterogeneous graph fusion; the attention prediction error is reduced through differential equation modeling, and the method is superior to all existing ARIMA / LSTM baseline models.

The invention provides a patient vital sign anomaly detection method based on an artificial intelligence technology, and relates to the technical field of data processing, and the method comprises the steps: collecting original sign data of a patient; calculating multi-dimensional characteristic parameters; establishing an individual baseline model, and determining a comprehensive reference interval in the model; the vital sign features monitored in real time are constructed into multi-dimensional feature vectors, the multi-dimensional feature vectors are input into the individual baseline model, and the deviation degree of the real-time multi-dimensional feature vectors in the comprehensive reference interval is calculated; performing a clustering analysis to identify an anomalous aggregation region; performing trend analysis, calculating change direction consistency and continuous change amplitude of the multi-dimensional feature vector, and generating a trend analysis result; calculating an accumulated change index in a continuous time window according to a trend analysis result to obtain a dynamic confidence score; when the dynamic confidence score continuously exceeds an adaptive threshold value, determining that a vital sign abnormal event exists; the autonomy and accuracy of the method for detecting the vital sign abnormity of the patient are improved.

The invention relates to the technical field of deformation measurement, in particular to a building deformation monitoring system based on fusion and intersection of Beidou and machine vision technologies, and the system comprises a pose calculation module which is used for setting anchoring nodes, matching Beidou three-dimensional coordinates and inclination angle data of the anchoring nodes obtained in real time with two-dimensional pixel coordinates of the anchoring nodes in a visual image, and calculating the pose of the anchoring nodes; resolving a real-time pose, and converting the real-time pose into a deformation measurement result; the three-dimensional deformation field generation module is used for processing the topological graph of the building structure by adopting a graph calculation model, fusing multi-source heterogeneous information by aggregating and updating node features, and decoding to generate a three-dimensional deformation field of the whole building; and the measurement early warning module is used for constructing a normal behavior baseline model to predict a normal deformation state, carrying out deformation state evaluation and generating deformation early warning information by comparing deformation deviation degrees and combining context analysis and a deformation trend, so that the precision and comprehensiveness of building deformation monitoring are improved.

The invention discloses a cross-platform job resource integrated management system and method based on the Internet, and belongs to the technical field of job resource management. A baseline model is constructed, and whether resource consumption data is abnormal or not is judged; extracting an abnormal resource type, obtaining a corresponding resource demand, identifying tasks having resource competition with the current job, and obtaining an abnormal competition task set; defining job attributes, determining a resource association relationship between tasks, and constructing an abnormal competition task relationship graph; determining the latest execution time of each competition task; analyzing the maximum bearing capacity of the current resource meeting all task execution under the conflict-free condition; calculating the quantity of resources needing to be added; subtracting the actually added resource quantity from the resource quantity needing to be added to obtain a vacant resource quantity; when personnel resources are vacant, the competition tasks are sorted, and the competition tasks are preferentially allocated to high-priority exclusive jobs in combination with the maximum bearing capacity; when the device resources are vacant, idle special devices are preferentially allocated for exclusive jobs.

The invention discloses a line aging monitoring system applied to a safe electric device, and the system comprises the steps: collecting time series data of line state parameters (such as multi-point temperature, key node current and voltage, and leakage current), and training a dynamic baseline model through employing historical data collected under a normal operation condition; the model can learn and predict an expected parameter value after considering the influence of factors such as load and environment in a normal operation state of the line; then, comparing a line state parameter time sequence acquired in real time with a parameter baseline value time sequence predicted by the dynamic baseline model, and calculating a residual vector sequence between the two time sequences; furthermore, the abnormal state of the line is accurately judged by analyzing the mode characteristics of the residual error sequence in a period of time, so that the problem that normal fluctuation and real aging abnormity are difficult to distinguish by a traditional method is effectively solved, and the accuracy and reliability of line aging monitoring are remarkably improved.

The invention relates to the technical field of air conditioner simulation optimization, in particular to an efficient machine room multi-subsystem cooperative control method based on intelligent simulation, and the method comprises the steps: building a digital twinborn model and a virtual simulation environment of central air conditioner key equipment; performing simulation analysis on the health state and the operation performance of key equipment in the digital twin model through a virtual simulation environment, and establishing a performance reference model; acquiring an external data source to obtain equipment operation parameters; in the virtual simulation environment, the equipment operation parameters are analyzed based on the performance reference model, and health state parameters and operation performance parameters are obtained; dynamically updating the digital twin model based on the health state parameters and the operation performance parameters to obtain an equipment state twin model; and based on the equipment state twin model, performing simulation design in a virtual simulation environment to obtain equipment compensatory cooperative control parameters. According to the invention, through simulation analysis of the digital twin model, cooperative control of the central air-conditioning subsystem is realized.

The invention provides a leasing equipment full life cycle monitoring method and system based on the Internet of Things, and belongs to the technical field of equipment operation and maintenance monitoring, and the method comprises the steps: obtaining real-time state data of leasing equipment in an operation process through a multi-source sensor; performing feature extraction on the real-time state data to obtain various feature vectors, and constructing a multi-dimensional feature vector set; inputting the multi-dimensional feature vector set into a random forest model, and judging whether the leasing equipment is in an abnormal state or not in combination with a space-time clustering algorithm; if yes, fault prediction is carried out on the leasing equipment in combination with an abnormal detection result, historical operation data and a baseline model, and a fault probability value of the leasing equipment is obtained; the future position of the leasing equipment is predicted based on the position data, an electronic fence boundary is set for the leasing equipment, if the future position exceeds the electronic fence boundary, an early warning notification of trajectory deviation is generated, and the system finally realizes trajectory and fault visual presentation in a thermodynamic diagram mode, so that the equipment operation and maintenance intelligence and management precision are improved.

The invention discloses an urban occupational health risk real-time supervision method and system based on data analysis, and the method comprises the steps: continuously collecting the behavior data flow of a target employee and the physical environment dynamic data flow of an associated working region, and generating an individual-environment dynamic data sequence; detecting a behavior abnormal signal based on the historical behavior mode of the target employee; in combination with the physical environment dynamic data flow at the current moment, the association strength between the physical environment dynamic data flow and the occupational health risk is evaluated, and a personalized micro-intervention instruction is generated and triggered; capturing the instant feedback response of the employee to the micro-intervention instruction in real time, updating the personal behavior baseline model of the target employee in real time, and calibrating the evaluation rule of the association strength; and dynamically generating a real-time occupational health risk portrait of the employee based on the individual behavior baseline model, the association strength evaluation rule and the individual-environment dynamic data sequence. According to the embodiment of the invention, accurate early warning and personalized intervention of occupational health risks can be realized.

The invention relates to the technical field of heat transmission, in particular to a heat transmission data storage and management system based on an industrial big data platform, and the system comprises a self-adaptive acoustic baseline modeling module which generates a self-adaptive baseline model library for storing the mapping relation between a working condition area and a model; the abnormal deviation degree calculation module is used for calculating and generating an abnormal deviation degree; the health state evaluation module is used for generating a comprehensive health index representing the long-term service performance of the pipe network; and the closed-loop correction and scheduling module is used for determining a comprehensive risk level according to the comprehensive health index and the change trend thereof, generating an operation and maintenance scheduling instruction for dynamically adjusting an abnormal deviation degree calculation process and pipe network operation parameters, and realizing closed-loop feedback control. According to the invention, accurate identification and positioning of abnormal events such as leakage, third-party damage and the like are realized.

The invention relates to the technical field of seismic station network operation and maintenance management, and particularly discloses a seismic station network operation and maintenance edge computing system and a seismic station network operation and maintenance edge computing device, which efficiently access a multi-source original data stream of seismic station network terminal equipment by using edge computing nodes, construct a data-equipment-environment ternary association graph, realize comprehensive integration and dynamic weaving of data, and improve the efficiency of seismic station network operation and maintenance. The integrity and the real-time performance of the data are ensured; by adopting a cross-modal attention mechanism, multi-source features such as earthquakes, equipment and environments are effectively fused, and the effectiveness and relevance of feature expression are enhanced; by constructing a multi-factor dynamic baseline model, the system can accurately quantify the abnormal risk level, and a basis is provided for operation and maintenance decision making; and automatic scheduling and effect evaluation of operation and maintenance tasks are realized through the intelligent response module, and the operation and maintenance efficiency and the response speed are improved.

The invention belongs to the technical field of graph classification, discloses a graph classification method based on subgraph integration and position sensing, and relates to a graph classification method SIPA combining substructure embedding and node position sensing. The SIPA firstly extracts sub-graphs through two different strategies so as to capture multi-sample sub-structures in the graphs, the structural features of the sub-structures are coded by adopting a graph convolutional network, and information of different sub-graphs is effectively fused through an attention mechanism. Then, anchor nodes are introduced to calculate relative position information of nodes, and the information is embedded into node representation to better capture global position features. And finally, a graph information bottleneck mechanism is used for optimizing node representation and removing redundant information irrelevant to a classification task. The method not only effectively learns the local structure information, but also enhances the perception capability of the relative position information of the nodes. Experimental results show that SIPA is superior to an existing baseline model in five data sets, and the superiority of SIPA in a graph classification task is verified.

The invention discloses a building equipment maintenance demand linkage detection and evaluation system and method, and relates to the technical field of intelligent operation and maintenance of building equipment, and the system comprises an isomorphic equipment cluster division module, a group data flow collection module, a group behavior baseline learning module, an individual deviation degree calculation module, and a maintenance decision generation module. Equipment with consistent function attributes and operation environments is classified into the same cluster, data are classified according to different working conditions on the basis of historical operation data provided by a group data flow acquisition module, group average operation parameters, parameter fluctuation ranges and probability distribution characteristics under each working condition are calculated, a dynamic group behavior baseline model is generated, and the dynamic group behavior baseline model is established. With the continuous updating of new data, the normal behavior mode change of the equipment under different working conditions can be reflected in real time, and when the individual deviation degree calculation module calculates the individual equipment deviation degree according to the model, the accuracy and timeliness of the calculation result can be ensured.

The invention discloses a building equipment energy consumption statistics and optimization method, and relates to the technical field of building energy consumption optimization, and the method comprises the steps: collecting the energy consumption data of building equipment, and carrying out the preprocessing, and the energy consumption data comprises the equipment power consumption, the operation time, and environment parameters; inputting the preprocessed energy consumption data into the constructed energy consumption reference model to obtain an energy efficiency deviation index; calculating an equipment energy efficiency score based on the energy efficiency deviation index in combination with the environmental parameters; and performing hierarchical management on the building equipment according to the equipment energy efficiency score, and when the equipment energy efficiency score is lower than a preset threshold value, triggering equipment maintenance early warning and generating an equipment optimization scheme. According to the invention, by establishing a dynamic grading threshold and a differentiated management and control mechanism, intelligent grading management of equipment is realized, and management standards can be adaptively adjusted according to historical data distribution characteristics.

A computer implemented method for on-line monitoring a structure, the method comprising: receiving (151) at least dynamics data (204) of dynamic variables representing a dynamic response of the structure from a plurality of sensors (10), the dynamic response of the structure being generated when the structure has been subjected to an excitation; detecting an impulse (152) created by said excitation by identifying a free load response (208) of the structure from said at least dynamic data (204); obtaining (153) a set of poles (210) comprising pairs of a natural frequency and a damping factor of the structure from the free load response (208); generating (154), using a non-supervised machine learning algorithm, a probability-based baseline model for the structure using a plurality of said poles (210) obtained along a certain period of time, the probability-based baseline model associating in a probabilistic way the dynamic variables affecting the structure and providing a plurality of baseline parameters (214); every time a new impulse is detected and a new set of poles (211) are obtained from the free load response of said impulse, feeding (155) the new set of poles (211) to a concept drift algorithm to obtain a KPI (217) of the structure.

The invention discloses an abnormality diagnosis method for an electric energy metering device, and the method comprises the steps: building a dynamic baseline model which reflects the real normal state change rule of a target user through the full mining of the historical multi-dimensional and fine-grained data of the target user, and carrying out the refined comparison through the data collected in real time at high frequency, thereby achieving the abnormality diagnosis of the target user. Sensitive and reliable anomaly criteria are formed by comprehensively considering absolute and relative deviations, so that possible problems of the metering device can be quickly and accurately identified in an actual operation environment. Through the mode, the adaptive capacity of the diagnosis result to individual differences and behavior change trends is remarkably improved, and efficient data security guarantee and operation and maintenance support are provided for a large-scale intelligent distribution network system.

The invention discloses a model-based question and answer test method and system, a medium and a product, and relates to the technical field of data processing, question and answer questions are respectively input into a target model and a reference model, and a first question and answer result and a second question and answer result are correspondingly obtained. And calling a corresponding scoring mechanism according to the type of the question, performing scoring comparison on the first question-answering result and the second question-answering result, and generating a first scoring result of the first question-answering result and a second scoring result of the second question-answering result. Corresponding scoring mechanisms are selected according to different question and answer question type characteristics, and the scoring mechanisms of the dual-channel question and answer test are realized and are mutually independent. And carrying out aggregation comparison on the first scoring result and the second scoring result according to each capability dimension system, determining a question and answer test result of the target model, and carrying out multi-angle arrangement and presentation on accumulated evaluation data, so that the advantages and disadvantages of the target model and the reference model in each capability dimension can be known.

The invention relates to the technical field of multi-modal behavior anomaly detection scheme design, in particular to a multi-modal behavior anomaly detection method and system under the condition that encrypted traffic is not decrypted. The method comprises the following steps: capturing a network encrypted traffic data packet in real time; on the premise that decryption is not carried out, multi-mode non-decryption features such as flow layer statistics, time sequence interaction, encryption handshake and context association are extracted in parallel; establishing a dynamic normal behavior baseline model of each feature based on historical data; the real-time features are compared with the baseline model, comprehensive judgment is carried out through a multi-mode correlation analysis algorithm, and an anomaly detection result is output; and when the abnormal condition is judged, the network control equipment is automatically linked for blocking. The method thoroughly gets rid of dependence on traffic decryption, realizes high-precision and self-adaptive detection and rapid automatic response to abnormal behaviors in encrypted traffic through multi-dimensional feature fusion and dynamic baseline technologies, and effectively solves the problem of failure of a traditional detection technology in an encrypted environment.

The invention discloses a non-stationary time sequence flow prediction method and device based on delay space-time dependence, and a medium, and relates to the technical field of intelligent traffic. The method comprises the following steps: constructing a prediction problem of non-stationary traffic flow, and decomposing a traffic flow time sequence into a time-varying component and a time-invariant component by using Fourier transform; based on the time-varying component and the time-invariant component, constructing a Moran operator and a spatial-temporal feature fusion framework, and extracting spatial and temporal features of traffic flow; and based on the space and time characteristics of the traffic flow, predicting the traffic flow and dynamically adjusting parameters by using function-to-function regression and a Bayesian Kriging optimization model. The method is superior to a traditional baseline model in a complex traffic flow prediction scene, and new technical support is provided for non-stationary traffic flow modeling and prediction optimization.

The invention discloses a single-sample time sequence knowledge graph reasoning method based on long sequence modeling, and belongs to the field of time sequence knowledge graph reasoning. According to the method, an attention mechanism and co-occurrence analysis are combined, features are aggregated from adjacent entities, and therefore tracking of the historical evolution trend is achieved, specifically, the attention mechanism is adopted to integrate relation information in entity neighborhoods, and the relevance between the adjacent entities and the importance of the neighborhoods of the adjacent entities are clearly explored in combination with the co-occurrence analysis. And furthermore, an encoder based on a state space model is adopted to encode the time sequence interaction of the entity, so that the method not only can efficiently model a long sequence, but also can capture long-term dependency. And finally, introducing a measurement network, applying the learned representation to the measurement network, evaluating the similarity between the support entity pair and the query entity pair from multiple perspectives, and ensuring that the possibility of reflecting future events is comprehensively evaluated. According to the invention, a comprehensive experiment on a widely used time sequence knowledge graph data set shows that the method is obviously superior to a baseline model.

The invention provides a manuscript review agent construction method and system embedded with a gradient evaluation system, and relates to the technical field of agents, and the method comprises the steps: pre-storing a constructed evaluation dimension knowledge base in a memory layer; activating a progressive review model in the plan layer; performing fine adjustment on a first reference model in the multi-layer progressive reference model through a predetermined tool set in the tool layer to obtain a first review model; storing the first manuscript review model to an execution layer, and performing manuscript review execution on the target paper through the execution layer to obtain a target manuscript review opinion; and the activation feedback layer analyzes the target manuscript review suggestions to obtain target modification suggestions. According to the method and the device, the technical problems that in the prior art, manuscript evaluation mainly depends on artificial experience judgment, so that a manuscript evaluation result has high subjectivity, machine manuscript evaluation is mostly limited to form review, and deep content evaluation is difficult to carry out can be solved; and objectivity and systematicness of manuscript review evaluation are improved.

The invention belongs to the technical field of image processing, and particularly relates to a graphite ore grade detection method based on an improved YOLO11 model, C3k2-CAS and Detect-SEAM modules are introduced, the feature extraction capability is enhanced, the expression of different scales of ore textures is optimized, and the perception capability of the model for grade difference is improved, so that the detection precision is remarkably improved; the attention mechanism of the CAS module is adopted to replace the traditional multiplication operation, the calculation complexity and the model parameter quantity are greatly reduced while the detection performance is maintained, and the improved model is more efficient than a baseline model and is more suitable for edge device deployment; a plurality of data enhancement strategies are combined, so that the model can maintain high robustness and stable detection capability in a complex industrial environment; the trained optimization model can be efficiently deployed to graphite ore grade detection intelligent equipment, real-time and accurate industrial field detection is achieved, the mineral separation efficiency is greatly improved, and an efficient and reliable computer vision solution is provided for the intelligent mining industry.

The invention discloses an oil and gas storage and transportation oil leakage detection system based on multi-source data analysis, and relates to the technical field of oil leakage detection. An original sensing data set is constructed, the original sensing data set is preprocessed, an alignment data set is generated, a pipeline digital twin baseline model in a leakage-free state is established, and a baseline feature set is generated; according to the method, micro-disturbance active detection and dynamic compliance inversion are implemented, an active feature set is generated, the diffusion trend of potential leakage liquid and an earth surface exposure time window are predicted, earth surface evidence features are generated, multi-modal features are fused, and a leakage event and a response scheme are output. A diffusion closed loop from the underground to the earth surface is realized through multiphase seepage and remote sensing verification, the detection precision is improved by utilizing adaptive fusion and dynamic threshold control, self-learning updating is realized through model recharge, and the method has the advantages of high real-time performance, strong anti-interference and adaptive optimization.

The invention relates to the technical field of big data security analysis and network information security, in particular to a communication data intelligent security supervision system based on big data, which comprises a data acquisition module used for extracting standardized entity behavior feature vectors from multi-source heterogeneous communication data; the baseline modeling module is used for dynamically generating a multi-dimensional behavior baseline portrait based on the historical sequence of the entity behavior feature vectors; the anomaly detection module is used for comparing the real-time entity behavior feature vector with the multi-dimensional behavior baseline portrait so as to calculate a micro anomaly score; the atlas construction module is used for screening the micro-anomaly events according to whether the micro-anomaly score exceeds a preset threshold value or not, and quantifying association confidence among the screened events so as to construct an attack chain atlas; the risk quantification module is used for aggregating the characteristics of the attack chain atlas to determine a systematic risk score; according to the method, the discovery capability and response efficiency of complex attacks such as advanced persistent threats and the like are greatly improved.

The invention relates to the technical field of information flow intelligent recommendation, and discloses a real-time interactive feedback information flow content adaptive optimization method and system.The real-time interactive feedback information flow content adaptive optimization method comprises the steps that user interaction data are collected and subjected to robust preprocessing to obtain standardized interaction feature vectors; constructing a user personalized interaction baseline model to obtain a multi-level behavior reference basis, identifying a real-time interest signal of a user and updating an interest state, dynamically adjusting a recall strategy and executing multi-path recall to obtain a candidate content set, adaptively adjusting a sorting feature weight and executing sorting, and dynamically controlling diversity to execute rearrangement to obtain a final recommendation list, and meanwhile, obtaining an interest prediction score in a sorting stage by adopting a hierarchical reasoning architecture and a model distillation technology. The interest change of the user can be captured in real time, the matching degree of the recommended content and the instant interest of the user is improved, and the user experience and the recommendation effect are improved.