1216 results about "Risk prediction models" patented technology

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine safety data comprehensive analysis and early warning system which comprises a data integration module, a three-dimensional visualization module, a risk assessment module, a linkage control module, a model training module and a central processing unit and can further comprise a decision support module, a storage cluster and a communication gateway. The data integration module constructs a multi-source heterogeneous data acquisition channel and performs dynamic topology modeling; the three-dimensional visualization module dynamically renders the monitoring data based on the space-time reference axis; the risk assessment module generates a danger situation map through space-time correlation analysis; the linkage control module establishes a multi-level response mechanism; the model training module optimizes the risk prediction model; and the central processing unit schedules each module to operate. According to the system, integrated analysis, dynamic visualization, risk prediction and cross-system linkage disposal of coal mine safety data are achieved, and the intelligent level and emergency capacity of coal mine safety monitoring are improved.

The invention relates to the technical field of geological disaster monitoring and early warning, and discloses a Beidou-based geological disaster intelligent monitoring and early warning method and system. Beidou high-precision monitoring equipment is deployed by selecting a geological disaster prone area, earth surface displacement, settlement and inclination deformation data are collected in real time, and a multi-modal database is constructed in combination with environmental parameters. And performing alignment and noise correction on the spatio-temporal data by adopting Kalman filtering and a weighted evidence theory, extracting short-term and long-term deformation characteristics by utilizing a DBSCAN spatial clustering algorithm, and realizing multi-scale abnormal change pattern recognition in combination with a GeoHash grid index. Dimensional differences are eliminated through Z-score standardization processing, a geological stability index and change rate model is established, a causal reasoning framework is further constructed based on a Bayesian network, and a risk prediction model is trained in combination with a space-time neural network. The system can dynamically adjust a monitoring period threshold value and automatically trigger graded early warning, and supports hidden danger rectification whole-process tracing and multi-level gridding management. According to the scheme, the limitation of traditional single-source monitoring is broken through, the full-chain prevention and control of geological disasters from deformation feature extraction, causal relationship modeling to dynamic risk prediction is realized, and the early warning timeliness and accuracy are remarkably improved.

The invention relates to the technical field of hazardous chemical substance safety management and control, and particularly provides a hazardous chemical substance storage accident risk analysis method, which comprises the following steps: collecting and preprocessing time sequence data and spatial topological data of a storage area; inputting the time sequence data into a time dynamic risk prediction model, and extracting time sensitive features to output a risk level time sequence curve with a risk storage unit label; labeling a high-risk period window in the curve; constructing a spatial diffusion risk assessment model based on the spatial topological data, extracting risk diffusion features of a target storage unit, associating adjacent units, and outputting a dynamic risk spatial distribution result; performing space-time coupling analysis on the high-risk time period and the spatial distribution, and outputting early warning information after correction; and finally, calling the management and control rule base to generate a risk management and control decision instruction. According to the method, time-space two dimensions are covered, dynamic risk assessment, early warning and management and control are realized, the comprehensiveness and management and control accuracy of hazardous chemical substance storage risk analysis are effectively improved, and a scientific support is provided for safety management of a storage area.

The invention relates to the technical field of safety monitoring, in particular to a building construction safety monitoring method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-source heterogeneous data of a construction site, carrying out the distributed feature extraction of the multi-source heterogeneous data through employing a federal learning framework, and generating time-space correlated construction site state representation data; based on a preset dynamic risk prediction model, risk prediction is carried out by using the construction site state representation data, a multi-level risk prediction result is output, and the preset dynamic risk prediction model is constructed based on a construction safety knowledge graph and a space-time diagram neural network; and triggering an adaptive feedback mechanism according to the risk level corresponding to the prediction result, generating visual early warning information and an equipment control instruction, and linking a construction site control system to execute emergency response operation. The problems that a traditional monitoring method is tedious in data processing, insufficient in real-time performance, high in cost, lack of prediction capacity and the like are solved.

The invention discloses an intestinal cancer metastasis prediction method and device based on spatial omics, a medium and equipment, and the method comprises the steps: collecting original multi-omics data, and carrying out modal alignment and quality control processing to obtain pre-processed multi-omics data comprising second spatial transcriptome data, second single-cell RNA sequencing data and second pathological image data; performing cross-modal semantic embedding on the second spatial transcriptome data based on the second single-cell RNA sequencing data to generate a spatial enhanced expression profile; performing multi-scale graph construction on the second spatial transcriptome data and the second pathological image data, and extracting spatial heterogeneity features; inputting the spatial enhancement expression spectrum and the spatial heterogeneity features into a pre-trained metastasis risk prediction model, and outputting a liver metastasis probability spatial heat map and a key driving feature list; and finally generating a clinical prediction report containing high-risk area positioning. According to the method, through dynamic optimization of spatial resolution and multi-scale feature collaborative modeling, the sensitivity of early transfer detection is remarkably improved.

The invention discloses an electric power operation risk early warning method and system based on knowledge enhancement and multi-modal fusion. The method comprises the steps that video monitoring data, sensor monitoring data and service system data are collected in real time through multi-source sensing equipment deployed on an electric power operation site; the method comprises the following steps of: extracting entities and relationships from unstructured texts such as regulation documents and job logs by utilizing a natural language processing technology based on deep learning, extracting behavior characteristics from video streams by adopting a computer vision algorithm, and constructing an electric power security knowledge graph with dynamic updating capability; designing a multi-modal feature fusion algorithm based on an attention mechanism, and effectively integrating visual features, text features and sensor data; a graph neural network is adopted to train a dynamic risk prediction model to carry out risk prediction, intelligent research and judgment of electric power operation risks are realized, accurate management and control of the risks are realized through a grading early warning mechanism, and closed-loop management from risk perception to early warning treatment is formed.

The invention discloses an intelligent fire-fighting risk prediction and dynamic early warning method and system based on multi-source data fusion, and relates to the technical field of intelligent fire-fighting risk prediction, and the method comprises the steps: collecting multi-source fire-fighting data, and constructing a fire-fighting safety state multi-dimensional data matrix; constructing a risk prediction model based on the fire safety state multi-dimensional data matrix to carry out real-time risk scoring; and executing dynamic early warning through a risk prediction result. According to the method, through the multi-source data acquisition and fusion module, multi-type fire-fighting data are comprehensively acquired and standardized, a structured and multi-dimensional fire-fighting safety state matrix is constructed, and the problems of data dispersion and information splitting are solved. In combination with a risk collaborative prediction and scoring module, the risk level of each region is dynamically output based on multi-factor analysis, and the accuracy and foresight of risk prediction are improved. Through a dynamic early warning generation and linkage module, real-time grading early warning and linkage of a fire fighting system are realized, and rapid response and intelligent prevention and control of a high-risk area are realized.

The invention discloses a postoperative drainage device for clinical nursing and a monitoring system, and belongs to the technical field of biomedical engineering. The problems that an existing device manually monitors drainage liquid data, consequently, the monitoring precision is insufficient, and the complication probability cannot be predicted are solved, drainage liquid and patient vital sign data are collected in real time through the micro spectrograph, the character multi-parameter sensor, the micro pressure sensor and the intelligent bracelet and transmitted to the remote monitoring platform, and the monitoring accuracy is improved. Medical staff can check real-time data of a patient at any time and adjust a treatment scheme in time, so that the nursing efficiency and quality are improved; multi-parameter data such as color, metering and property of drainage liquid and vital sign data are analyzed through a risk prediction model, intervention measures are dynamically adjusted according to different complication types and probabilities in combination with patient condition changes and real-time monitoring data, accurate intervention on the complication is achieved, and the occurrence rate and severity of the complication are reduced.

The invention discloses a disaster risk assessment early warning method and system based on multi-source heterogeneous data, and relates to the technical field of data fusion and processing, and the method comprises the steps: obtaining a multi-source heterogeneous disaster risk data stream; constructing a normalized disaster feature matrix; generating a multi-source feature incidence matrix; generating a disaster risk index set according to the disaster risk prediction model; generating a regional disaster risk distribution thermodynamic diagram; constructing a disaster risk time sequence model; and generating a disaster research and judgment report, and performing visual early warning on the disaster research and judgment report through a three-dimensional simulation technology. The technical problem that traditional disaster risk assessment early warning depends on a single or few data sources, data are one-sided and lack of real-time performance, and consequently assessment early warning is inaccurate is solved, comprehensive real-time analysis based on multi-source heterogeneous data is achieved, the accuracy of disaster risk assessment and the timeliness of early warning are improved, and the risk assessment early warning efficiency is improved. And a reliable basis is provided for disaster emergency decision making.

The invention relates to the technical field of medical monitoring and early warning, in particular to a tumor intervention postoperative intelligent nursing monitoring system, which comprises an individualized data acquisition module for outputting a standardized data packet and a historical feature index table; the dynamic feature processing module generates three-dimensional feature tensors of coding time, physiological features and pathological mark dimensions; the individual self-adaptive modeling module generates an individual risk prediction model embedded with a genetic and pathological response function; the dynamic threshold optimization module encodes a historical baseline fluctuation range into chromosome gene loci, and iteratively corrects an abnormal judgment boundary in combination with a genetic algorithm; the intelligent early warning decision module calls a clinical knowledge graph to generate a three-level early warning instruction, and constructs a double-closed-loop feedback channel: a first closed loop calibrates and judges boundary parameters through a false alarm feedback signal, and a second closed loop converts disposal effectiveness into weight correction vector optimization model parameters; and full-link closed-loop management of individual dynamic physiological variation from feature fusion and model adaptation to decision optimization is realized.

The invention discloses an intelligent supply chain risk prediction and decision optimization method based on a knowledge graph, and belongs to the field of supply chain management. The intelligent supply chain risk prediction and decision optimization method based on the knowledge graph comprises supply chain knowledge graph construction, supply chain risk prediction model construction based on the knowledge graph and supply chain decision optimization based on the knowledge graph. The supply chain knowledge graph construction comprises data acquisition and preprocessing, knowledge graph construction and a dynamic updating mechanism. The construction of the supply chain risk prediction model based on the knowledge graph comprises feature engineering, model construction and model training and optimization. According to the method, the data integration and analysis capability can be improved, the risk early warning efficiency of the supply chain is improved, global optimization decision is realized, the quick response capability is enhanced, and support is provided for stable and efficient operation of the supply chain.

The invention discloses a tumor prognosis prediction method and a tumor prognosis prediction system, which are used for constructing a multi-modal fusion model based on image-pathology to improve the prognosis prediction efficiency of tumors, especially pancreatic cancer, and providing reference information for clinical decision-making. According to the technical scheme, the method comprises the following steps: S1, preprocessing an original tumor enhanced CT image, segmenting a tumor region, extracting radiomics features and depth image features of the tumor region, and establishing a CT image feature set; s2, after feature preprocessing is carried out on the tumor clinical data, clinical features with statistical significance are screened out, and a clinical feature set is established; s3, carrying out Hamp; e, preprocessing the pathological image, segmenting a tissue region, extracting spatial relation features, and generating a pathological spatial feature set; and S4, based on a feature interaction method, carrying out multi-modal fusion on the CT image features, the clinical features and the pathological spatial features, inputting a full-connection neural network, constructing a tumor survival risk prediction model, and outputting a tumor survival risk probability through the tumor survival risk prediction model.

The invention provides a risk control cross-domain risk prediction method and system in combination with transfer learning, and the method comprises the steps: carrying out the domain difference measurement of a source domain and target domain risk control scene, generating a domain difference measurement matrix, building a hierarchical knowledge transfer channel based on the domain difference measurement matrix, and carrying out the prediction of the risk control cross-domain risk. And selectively migrating the source domain risk prediction model parameters, generating a target domain initialization risk prediction model, and carrying out dynamic adaptive training on the target domain initialization risk prediction model by adopting a target domain risk sample to obtain a cross-domain risk prediction model. And inputting the to-be-evaluated sample of the target domain into the cross-domain risk prediction model to generate a preliminary risk prediction score, and finally performing cross-domain calibration on the preliminary score based on the domain difference metric matrix to generate a final risk prediction result, thereby improving the accuracy of risk control cross-domain risk prediction.

The invention discloses a high and cold arid region slope soil stability safety risk evaluation system and method, and relates to the technical field of slope engineering. The state of each side slope under multi-dimensional indexes such as freeze-thaw cycle frequency, dry-wet alternation index, wind erosion strength, shear strength, water content, porosity and fracture density is quantified, the feature similarity between any two side slopes is calculated, and then a side slope feature similarity matrix is formed. Based on the matrix, an unsupervised clustering algorithm (such as spectral clustering, similarity propagation and the like) can be adopted to divide a plurality of side slopes into similar subsets with structural characteristics similar to environmental response, and category attribution of risks is achieved. On the basis, structural variation analysis, historical instability statistics and central risk difference extraction are performed on similar slope samples, so that the internal instability tendency of the slope can be identified, and a risk prediction model suitable for the type of slope can be constructed through feature training.

The invention discloses a terrain and time sequence-based power transmission line icing risk prediction method, system and device, and a medium. The prediction method comprises the following steps: acquiring meteorological elements, topographic features and time sequence features, unifying the meteorological elements, the topographic features and the time sequence features to the same time step length and geographic coordinate grid, and performing topographic correction on the meteorological elements according to the topographic features; constructing and optimizing an icing risk prediction model based on the meteorological elements after terrain correction and the time sequence features as input; inputting real-time weather forecast data into the optimized icing risk prediction model, dividing icing risk grades according to model output, and performing spatial visualization and early warning through a GIS platform; and quantizing the contribution of each element to a prediction result, optimizing model parameters and a feature extraction method, and carrying out interpretable driven model iteration. According to the invention, refined prediction and dynamic early warning of the icing risk are realized, accurate operation and maintenance of a power system are effectively supported, and the icing disaster risk is reduced.

The invention belongs to the technical field of intelligent AI for rainfall analysis and landslide early warning, and discloses an intelligent AI-based rainfall data analysis highway landslide early warning system, which comprises a slope hydrologic monitoring module, a slope damage detection module, an anti-slide pile dynamic monitoring module, a landslide risk analysis module and a risk early warning output module, a slope landslide risk prediction model is constructed by collecting rainfall, water seepage amount, slope structure damage state parameters, pressure difference value of two sides of an anti-slide pile and pile-soil gap variation key data in real time; and the pressure difference value of the two sides of the anti-slide pile, the structural damage state parameters and the rainfall capacity are subjected to cross dynamic association through time sequence characteristics, the water seepage amount and the pile-soil gap variation are fused, and the landslide risk level is output through the dynamic risk amplification factor and the soil saturation critical index. The real-time performance and accuracy of landslide early warning can be remarkably improved, and the method is suitable for highway slope safety monitoring under complex geological conditions.

The invention discloses a VTE real-time monitoring and intelligent prevention and treatment system, and belongs to the technical field of intelligent prevention and treatment, and the system comprises a baseline construction module which is used for collecting multi-dimensional VTE parameters, calculating the normal fluctuation interval of each parameter to form an initial individualized baseline, and constructing a self-adaptive individualized baseline through threshold calibration; the trend identification module is used for dynamically setting a sliding window duration, calculating a VTE trend slope and a VTE product deviation, constructing a trend constraint and a product deviation constraint, and when the two constraints are not met at the same time and the continuous deviation duration is exceeded, judging that the deviation is continuous abnormal deviation; the time sequence risk prediction module is used for calculating deviation values of various parameters of the target patient, constructing a space-time fusion feature matrix, inputting a time sequence risk prediction model and outputting a VTE risk probability; and the early warning and intervention module is used for performing double judgment and intervention, setting three-level early warning and intervention measures, calculating an improvement rate to verify a prevention and control effect in real time, and realizing VTE early recognition and intelligent prevention and control.

The invention relates to a renal transplantation post-operation rejection risk monitoring and early warning method and a renal transplantation post-operation rejection risk monitoring and early warning system. The method comprises the steps of collecting recipient nursing monitoring data, laboratory indexes and transplanted kidney ultrasonic blood flow parameters in a follow-up visit period, performing timestamp alignment, deletion processing and standardization on multi-source data, extracting features to construct a time sequence feature sequence, inputting the time sequence feature sequence into a pre-training risk prediction model, outputting the rejection reaction occurrence probability of the next period, and forming a risk trend. Calculating a nursing sensitive index contribution weight based on the model contribution information, and screening a target nursing monitoring index; and establishing an individualized baseline model to obtain a baseline value and an allowable fluctuation interval, extracting characteristics such as deviation amplitude, direction, rate, fluctuation and continuous deviation duration and the like, and performing individualized calibration on the occurrence probability to obtain a calibration risk score. When the threshold value is not reached and the trend is not triggered, generating a nursing monitoring suggestion of the next period; and pushing early warning and generating grading intervention suggestions when a threshold value is reached or a trend is triggered.

The invention provides a geological environment monitoring method and system, and the method comprises the steps: constructing an air-space-earth-depth four-dimensional cooperative monitoring network, collecting multi-modal geological environment data, carrying out the preprocessing of the data, and obtaining multi-modal feature data and abnormal data points after dynamic load balance distribution of calculation resources; fusing the data to generate a comprehensive geological environment feature map containing space-time correlation features, abnormal hot spot distribution and a geologic body three-dimensional reconstruction model; and based on the map, performing geological risk prediction by using a geological risk prediction model to obtain a prediction probability. A monitoring network is constructed to integrate multi-modal geological data, resource allocation is optimized by combining edge calculation dynamic load balancing, geological risk prediction is realized by using a prediction model, the problems of single data, poor dynamic adaptability and weak model generalization ability of a traditional method are solved, the prediction precision is improved, the response time is shortened, and the prediction efficiency is improved. And a high-risk area is visually displayed through a three-dimensional risk thermodynamic diagram.

A method, device and system for active management and control of road traffic safety are disclosed. The method includes acquiring traffic data of a target road in real time, wherein the target road includes management and control sections; for each management and control section, judging whether there is a traffic accident according to the acquired traffic data; if so, formulating an emergency management and control strategy; if not, extracting traffic flow data from the traffic data, and generating predicted traffic flow data according to the traffic flow data by a traffic flow prediction model; generating a risk level according to the predicted traffic flow data by a risk prediction model; determining the current active management and control strategy of the management and control section according to the risk level and the predicted traffic data; and issuing the corresponding management and control strategy of each of the management and control sections.

The invention discloses a machine learning-based intra-operative bleeding risk prediction method and device for spinal surgery. The machine learning-based intraoperative bleeding risk prediction method for spinal surgery comprises the following steps: acquiring information of a patient to be predicted; obtaining a trained hemorrhage risk prediction model; and inputting the information of the patient to be predicted into the trained massive hemorrhage risk prediction model so as to obtain a prediction result. According to the method, the high-precision prediction model is trained through large-scale patient data (including basic information, operation parameters, blood indexes and the like), so that the accuracy and the stability of intraoperative SBL risk prediction are improved. And a Cell Saver use suggestion based on a risk threshold is provided, and blood resource allocation is optimized. Clinical decision-making efficiency is improved through an automatic tool, blood transfusion related complications (such as infection and immune response) are reduced, and patient prognosis is improved.

The invention discloses a fire safety intelligent evaluation system and a closed-loop management and control method, and the method comprises the steps: analyzing national standard and local standard texts through a natural language processing technology, disassembling terms into a three-stage quantifiable index system, and constructing a dynamic index library comprising at least 200 subdivisions; a dynamic rule base is generated based on a BERT-NER model, a standard parameter threshold value is automatically extracted, and version difference comparison and real-time updating are supported; deploying a fire-fighting special BERT model to analyze an unstructured document, extracting violation events and converting the violation events into structured evaluation factors, aggregating multi-source sensor data and video streams through an edge computing gateway, and adding space-time tags to realize millisecond-level real-time monitoring; generating a comprehensive score in combination with the static weight and the dynamic weight, constructing a risk prediction model based on an Attention-LSTM network, inputting historical hidden danger data, environment variables and enterprise operation data, and outputting future risk probability distribution; according to the invention, compliance management is efficient, assessment accuracy is improved, risk response is real-time, and a management and control process is digital.

The invention relates to the technical field of medical health management, in particular to a chronic kidney disease and diabetes collaborative nutrition monitoring and early warning method and system.The method comprises the steps that multi-dimensional physiological indexes, nutrition intake and sleep data of a patient are obtained, and a dynamically-coupled metabolic state topological space is constructed; based on the space, constructing a dual-disease metabolism coupling matrix, analyzing stability characteristics of a metabolism state trajectory, identifying stable and unstable attractors, and calculating a multi-dimensional risk score; a sleep-metabolism synchronism risk prediction model is introduced, and a personalized early warning threshold value is dynamically adjusted; when the risk score exceeds a threshold value, early warning is triggered, and intervention suggestions are generated, the interactive influence of protein metabolism and glycometabolism is quantitatively described from the metabolic mechanism level, and the contradiction of chronic kidney disease and diabetic patients in nutrition management is solved; limitation of a static threshold is broken through, and dynamic risk assessment is realized; sleep factors are creatively integrated, and a new way for improving the metabolic state is provided.

The invention relates to the technical field of team cooperation, and discloses a team cooperation priority adjustment method based on a situation, and the method comprises the following core steps: collecting task attribute parameters, member state parameters and environment interference parameters in real time, and generating task initial priority data and situation influence weight data; the task initial priority data and the situation influence weight data are fused for conflict detection analysis, task conflict feature data are output, when the task conflict feature data exceed a preset threshold value, dynamic priority sequence data are generated, and a risk prediction model is constructed based on historical cooperation data; and performing cooperation risk prediction processing in combination with the dynamic priority sequence data, generating team cooperation risk early warning data, integrating the dynamic priority sequence data and the team cooperation risk early warning data to perform decision report generation processing, and outputting team cooperation optimization guidance report data. The task execution deviation is reduced, and the overall efficiency, stability and risk response capability of team cooperation are improved.

The invention relates to an unstable slope monitoring and early warning method, system and device and a medium, and belongs to the technical field of geological disaster monitoring, and the monitoring and early warning method comprises the steps: collecting original monitoring data of a target slope region, carrying out the edge preprocessing, and generating a standardized data package; transmitting the standardized data packet to a cloud server for integrity verification and logic verification, and generating a verified data set; performing multi-modal data fusion on the verified data set, constructing a displacement-rainfall-microseismic time sequence incidence matrix, extracting displacement acceleration, rainfall cumulative intensity and microseismic event frequency features, and generating a fusion feature vector set; inputting the fusion feature vector set into a pre-trained risk prediction model for real-time risk prediction, and outputting a landslide risk index and a displacement trend prediction result; and determining a corresponding early warning level according to the landslide risk index, and triggering an automatic response strategy corresponding to the early warning level. According to the invention, the real-time performance and accuracy of landslide early warning can be improved.

The invention discloses an environmental event dynamic risk assessment method and system based on a Bayesian network, belongs to the technical field of artificial intelligence, and aims to solve the technical problems that in existing environmental risk assessment, dynamic data adaptability is poor, multi-source heterogeneous data fusion is difficult, and real-time performance is insufficient. Comprising the following steps: acquiring environment data through a sensor cluster deployed in an environment, and uploading the environment data to an edge computing node; performing data preprocessing on the environmental data through the edge computing node; extracting time series data fragments from the standardized data stream based on a predefined sliding time window; constructing a risk prediction model based on the dynamic Bayesian network; and taking the extracted time series data fragments as input, performing risk level analysis through a risk prediction model in combination with particle filter reasoning, predicting and outputting a risk level, a risk level probability value and a risk conduction path as prediction results, and constructing a visual risk conduction map based on the prediction results.

The invention discloses a full-cycle path chronic disease management system and method based on artificial intelligence, and belongs to the technical field of intelligent medical treatment, and the method comprises the steps: generating a model result based on an artificial intelligence chronic disease risk prediction model and a prescription through an active diagnosis and treatment module, and carrying out the active recognition, intervention and management of a target group; the personalized diagnosis and treatment module is used for providing refined follow-up visit, prescription and screening services according to health states and risk characteristics of different individuals; and semantic search, index generality identification and multi-source data integration capabilities of clinical data are provided through an intelligent data governance and decision support module. According to the invention, an intelligent medical new mode of active, personalized and intelligent chronic disease management is constructed, clinical decision is assisted, the management efficiency is improved, the chronic disease management level is improved, reasonable flow of medical resources is promoted, and chronic disease prevention, management and referral are promoted to develop towards the full-life-cycle management direction.

The invention discloses a method and a device for monitoring the running state of a railway overhead line system. The method comprises the following steps: receiving environmental data acquired by various sensors; the environment data comprises at least one of humidity data, wind speed data, temperature data, ultraviolet intensity data and electromagnetic induction intensity signals; predicting the environmental data through a pre-established risk prediction model to obtain a predicted contact network risk level; wherein the risk prediction model is established based on historical environment data; based on historical fault data, the environment data and an actual detection result, the contact network state is evaluated, and a contact network state evaluation grade is obtained; and generating early warning information according to the contact network risk level and the contact network state evaluation level. According to the invention, operation state monitoring, risk level discrimination and intelligent early warning linkage based on multi-source data can be realized, so that the safety and operation and maintenance efficiency of the contact network system are improved.

The invention provides a cloud AI data leakage risk prediction and control method and system based on a flow map, and relates to the technical field of cloud computing data security. Based on the collected multi-source log data, constructing a time series data flow knowledge graph; inputting the knowledge graph into a space-time diagram risk prediction model, and obtaining a dynamic risk score of an entity and a predicted potential data leakage path by fusing a space-time diagram neural network and risk conduction simulation; according to the dynamic risk score and the potential data leakage path, gradient dynamic security management and control measures are generated and executed, and the measures comprise differentiated access control actions triggered according to the risk level; and generating a visual audit report of the data access link based on the risk prediction result and the security management and control process. Through a time sequence graph reflecting dynamic flow of data and utilizing STGNN to carry out risk conduction modeling and prediction, the active defense capability of cloud AI data security is improved, the accuracy of risk identification is effectively improved, and service interference is effectively reduced.

The invention provides a breast cancer recurrence risk prediction method, system and device based on an ultrasonic image, and relates to the field of intelligent medical treatment, the method uses a deep convolutional neural network to perform deep network feature extraction on a breast ultrasonic image, and uses a deep learning semantic segmentation algorithm to perform accurate positioning and automatic segmentation on a breast tumor region of interest, thereby improving the accuracy of breast cancer recurrence risk prediction. Meanwhile, habitat analysis is carried out on the ultrasonic images to extract tumor heterogeneity features, multi-level and multi-mode features such as deep learning features, radiomics features and habitat analysis features are fused, a breast cancer recurrence risk prediction model is constructed, breast cancer recurrence risk prediction is carried out, and a breast cancer recurrence risk assessment result is output. And a quantitative basis is provided for clinical treatment decisions. The accuracy and robustness of recurrence risk prediction are remarkably improved through multi-feature fusion, and standardization and objectification of breast cancer prognosis evaluation are achieved. In addition, the method further has the advantages of being easy and convenient to operate, low in cost, noninvasive, nonradiative, good in repeatability and the like.