361 results about "Early identifications" patented technology

The invention belongs to the technical field of coal mine risk early warning, and particularly relates to a coal mine goaf multi-risk comprehensive early warning method and system based on machine learning, and the method comprises the steps: collecting mine pressure, gas and hydrological real-time data in real time through a multi-temporal-spatial-scale sensor, and obtaining a dynamic coupling relation basic data set based on the real-time data; preprocessing noise and missing values according to the dynamic coupling relationship basic data set, and modeling node connection between a geological structure and mine pressure change by adopting a graph neural network to obtain space-time heterogeneous feature representation; non-linear features are analyzed through spatial-temporal heterogeneous feature representation, and a multi-scale dynamic mode is determined; acquiring a risk conduction path in the multi-scale dynamic mode, and acquiring an early recognition signal of a potential disaster chain; based on the early recognition signal, a long-short-term memory network is used for processing a sequential sequence, and the probability of the compound disaster is judged; a high-risk area is extracted from the composite disaster probability, and real-time early warning model parameters are obtained; and generating alarm output according to the real-time early warning model parameters.

The invention provides a mine abnormal event real-time identification method and system based on time sequence characteristics, and relates to the technical field of mode identification, and the method comprises the steps: carrying out the time-space alignment and semantic annotation of multi-modal monitoring data, and constructing a time sequence knowledge graph; calculating a dynamic association weight between entities, and analyzing a risk propagation path; predicting a risk situation based on a historical evolution rule; and dynamically generating a differential early warning strategy and establishing a closed-loop tracking system. According to the invention, early identification, accurate prediction and efficient disposal of mine safety risks can be realized, and the mine safety management level is improved.

The invention relates to the technical field of fire early-stage recognition, in particular to a multi-source information-based conveying belt fire early-stage recognition and early-warning system, which comprises a sudden change detection module, a synchronous analysis module, a spatial trend recognition module, a coupling fluctuation screening module and a probability evaluation module. According to the invention, through multi-source information linkage acquisition, collaborative monitoring of parameters such as along-line temperature, smoke, gas, images, load and heat source temperature difference, combined characteristic analysis among parameters and synchronous response trend determination are driven, and active revelation of early risk hidden dangers and dynamic discrimination of spatial distribution consistency and fluctuation continuity are realized. Potential abnormal focusing locking under a high-interference complex working condition is promoted, collaborative fluctuation between a load and a heat source temperature difference further eliminates environmental noise influence, risk weight dynamic adjustment strengthens classification sensitivity of probability identification, fire risk clustering division promotes accurate mastering of distribution of tiny initial hidden dangers, and the probability identification accuracy is improved. And the reliability of fire early warning in a coal mine area conveying belt scene is obviously improved.

The invention relates to the technical field of health management, in particular to a psychological risk early warning method and system based on multi-dimensional emotion data of a user. The method comprises the following steps: acquiring user multi-dimensional emotion data corresponding to a user emotion expression image, a user emotion voice signal and a user emotion physiological index, and performing multi-source time sequence alignment processing on the user multi-dimensional emotion data to obtain user multi-dimensional time sequence synchronous emotion data; an emotion three-level feature extraction framework is constructed, modal emotion feature extraction and cross-modal fusion are carried out, and cross-modal fusion psychological features of the user are obtained; and obtaining user environment data and user social network data, and performing emotional psychological dynamic graph reasoning and psychological risk early warning on the user cross-modal fusion psychological features based on the user environment data and the user social network data to generate corresponding user emotional psychological risk early warning response measures. According to the invention, early recognition and intervention of psychological health problems can be realized.

The invention discloses a crop disease diffusion prediction method and system based on multi-modal fusion, and the method comprises the following steps: S1, collecting and preprocessing an RGB image sequence and a sensor data sequence of a crop growth environment, and generating an RGB image time sequence difference result and a sensor difference result through time difference processing; s2, mapping the RGB image time sequence difference result and the sensor difference result to a shared time sequence space through a time alignment algorithm, and generating a sensor alignment result and an RGB alignment result; s3, an FD-ViT prediction model is constructed; inputting the sensor alignment result and the RGB alignment result into an FD-ViT prediction model for prediction, and generating a prediction result; and S4, generating a disease diffusion thermodynamic diagram and early warning information according to a prediction result. According to the method, RGB image data and sensor network data are fused, a Transform-based time sequence prediction model is constructed, and early recognition and diffusion trend prediction of crop diseases are realized.

The invention provides a pipe gallery disease monitoring and diagnosis method based on multi-source heterogeneous data, and relates to the technical field of disease monitoring and diagnosis, and the method comprises the steps: carrying out the data collection through a multi-mode sensor network disposed in an underground pipe gallery; fusing the multi-source heterogeneous data set based on a graph neural network to generate a dynamic sensing characteristic spectrum of the whole domain of the pipe gallery; carrying out disease evolution mode identification, and determining a pipe gallery disease risk level; and triggering the self-adaptive early warning strategy, executing the self-adaptive early warning strategy to generate a decision instruction set, and pushing the decision instruction set to the visual monitoring platform. The technical problem that potential problems are difficult to find in time and the operation efficiency of the pipe gallery is affected due to the fact that detection and risk assessment of the pipe gallery diseases depend on periodicity is solved, real-time monitoring and early recognition of the underground pipe gallery diseases are achieved through effective integration and processing of the multi-source heterogeneous data, and the method and the device have the advantages of being high in practicability and the like. And the efficiency and the safety of pipe gallery management are improved.

The invention discloses a multi-agent large-model disease diagnosis knowledge reasoning system based on data dual drive, and relates to the technical field of artificial intelligence assisted medical diagnosis. The system collects patient symptom follow-up records, laboratory test results, observation diagnosis probabilities and expert diagnosis recommendation results in a multi-source manner; time sequence evolution characteristics are extracted, a time sequence diagnosis sensitivity coefficient is calculated, and early recognition of disease risks is achieved; in combination with anti-fact simulation and statistical reasoning, a causal consistency coefficient is obtained and is used for verifying causal reasonability of observation diagnosis and contrast results; based on agent group consensus analysis, calculating a game consistency coefficient for judging the credibility of a diagnosis conclusion; positioning and multi-level verification are carried out on abnormal reasoning steps and knowledge fragments, so that the reliability and safety of a result are guaranteed; continuous optimization of the diagnosis model is realized through a log analysis and knowledge backflow mechanism; according to the invention, the accuracy, interpretability and safety of disease diagnosis can be obviously improved.

The invention discloses a coronary artery calcification early warning system for type 2 diabetes patients, and relates to the technical field of medical detection. A data acquisition module is used for acquiring continuous physiological parameter data of a user; the risk modeling module is combined with coronary artery calcification evolution characteristics in historical clinical samples to construct a multi-parameter dynamic association model; an index weight calculation unit generates a risk influence factor vector based on a sensitivity analysis result of the physiological indexes on risk prediction; the machine learning analysis module performs iterative training on the prediction model by adopting an integrated learning algorithm, and performs prediction updating by utilizing a risk influence factor vector; the early warning trigger module dynamically generates a graded early warning signal according to the grading trend and a set threshold value; the weak item positioning module carries out contribution degree analysis and anomaly recognition on the key risk indexes and automatically generates personalized intervention suggestions; according to the invention, early recognition and dynamic early warning of coronary artery calcification progress can be realized, and the method is suitable for intelligent early warning management scenes of chronic disease cardiovascular risks.

The invention discloses an intelligent building fire identification and simulation early warning method and system, belongs to the technical field of building informatization and disaster prevention and control, and aims to solve the technical problems of how to realize fire early identification, fire intelligent prediction and evacuation path dynamic planning, improve fire identification accuracy and response speed, and improve the safety and reliability of a building. According to the technical scheme, the method comprises the steps of BIM modeling, wherein a high-precision three-dimensional space model is established based on building information modeling, and the high-precision three-dimensional space model is deeply coupled with an FDS fire numerical simulation engine; multi-modal data acquisition: performing data acquisition by adopting a visual, environmental and spatial multi-modal sensor, and performing feature fusion on the acquired data by using a multi-channel deep convolutional network to generate unified space-time fire characterization; a video-sensing-geometric data collaborative sensing network is constructed on the basis of space-time fire characterization, so that the recognition robustness in a complex environment is improved; intelligent identification and decision making; performing early warning control linkage; and post-disaster assessment feedback.

The invention belongs to the field of medical artificial intelligence, and particularly relates to a multi-modal data acquisition and fusion method for Alzheimer's disease. The method comprises the following steps: firstly, synchronously acquiring eye movement, expression, voice, gait and grip strength data of a subject through a virtual reality multi-task normal form, and combining with an MoCA scale to score a result; then preprocessing and feature extraction are carried out on each modal data, and unified feature representation is constructed; on the basis, a cross-modal attention mechanism is adopted to realize interaction and weighted fusion of multi-modal features, and a unified fusion feature vector table is generated; and finally, outputting structured data organized according to task fragments for auxiliary evaluation and modeling of cognitive impairment. The method can effectively solve the problems that in the prior art, single-mode information is insufficient, and multi-mode data are difficult to align and fuse, has the advantages of being low in cost, easy to popularize and high in detection accuracy, and can be widely applied to early recognition and auxiliary diagnosis of the Alzheimer's disease.

The invention discloses a switch cabinet latent fault remote diagnosis system, method, device and medium, and belongs to the technical field of fault diagnosis, and the system comprises a multi-dimensional sensor sensing module, a data transmission module, a data fusion analysis module, and a data storage and fault detection module. The multi-dimensional sensor sensing module is used for acquiring multi-dimensional data of the operation state of the switch cabinet; the data transmission module is used for transmitting the multi-dimensional data to the data fusion analysis module; the data fusion analysis module is used for performing data preprocessing on the multi-dimensional data to obtain preprocessed multi-dimensional data, and determining a fault classification result according to the preprocessed multi-dimensional data; and the data storage and fault detection module is used for performing fault identification based on the real-time monitoring data and the fault classification result. According to the invention, early recognition and accurate diagnosis of the latent fault of the switch cabinet are realized, and the downtime and maintenance cost of equipment are reduced.

The invention discloses a multi-mode mental health risk intelligent early identification system and method. On the basis of awareness and voluntary, the system can collect 9 types of data sources in an anonymization mode, a quintuple privacy protection architecture is constructed, and the quintuple privacy protection architecture specifically comprises irreversible code ID generation, national cryptographic algorithm and quantum key dual encryption, federal learning localization processing, block chain evidence storage and dynamic desensitization erasing. The innovation point is that based on a multi-modal fusion model and a dynamic scoring algorithm, a dynamic psychological scale is used for scoring, and graded early warning of psychological health risks is realized. By means of a federated learning framework, continuous optimization and cooperation of parameters of the cross-calibration scale can be realized. On the premise of strictly protecting privacy, early recognition and grading suggestion of student mental health risks can be achieved, medical diagnosis is not involved, and the requirements of GDPR and other data protection laws and regulations are met.

The invention relates to the technical field of remote sensing, and discloses a forest land health state analysis method and system based on multi-source remote sensing image analysis. The system comprises a multi-source remote sensing acquisition module, a feature extraction and fusion module, a health assessment module, a traceability analysis module, a strategy matching module, a visual reconstruction module, an early warning decision module and an execution feedback module, and constructs a multi-modal forest land observation data set by fusing multi-source remote sensing data of multispectrum, hyperspectrum, radar and thermal infrared. The comprehensive extraction of multi-dimensional information such as vegetation coverage, canopy biochemical characteristics, under-forest structures and surface thermal environments is realized, the limitation of single data source analysis is overcome, and the comprehensiveness and accuracy of forest land health condition evaluation are improved; through dynamic comparison of multi-stage remote sensing images and health risk level mapping, early identification and early warning of forest growth abnormity, degeneration trend and pest and disease risk are realized.

The invention relates to the technical field of synthetic aperture radars, discloses a method for monitoring activity of a high-order loose body based on multi-source SAR data, and aims to solve the problem that an existing high-order loose body activity identification mode is poor in accuracy. Obtaining multi-source SAR data and carrying out adaptive preprocessing; extracting a maximum back scattering feature and quantifying activity; carrying out activity identification and multi-scale feature analysis on the high-position loose body; and comprehensively evaluating the multi-dimensional risk of the movable high-position loose body. Through organic combination of multi-track collaboration, fine feature extraction and multi-dimensional model evaluation, an effective scheme is provided for solving the problems that early recognition of the high-position loose body in the alpine and valley region is difficult and risk evaluation is inaccurate, and the accuracy and timeliness of monitoring and early warning are remarkably improved.

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 the technical field of state monitoring, in particular to a special equipment state real-time detection system and method, and the system comprises a data processing module, an association construction module, an abrupt change extraction module, a state recognition module and a trend prediction module. According to the method, by combining signal continuity analysis and denoising processing, the original data quality is improved, the influence of noise interference on state evaluation is reduced, multi-cycle data feature extraction and clustering analysis are utilized, a dynamic correlation model is constructed, the state classification accuracy under different working conditions is enhanced, and a trend direction and fluctuation form combined analysis mechanism is adopted; the method is advantaged in that abnormal signal period characteristics are accurately captured, early-stage identification efficiency of potential faults is improved, a multi-dimensional correlation characteristic comparison verification method is integrated, a composite state discrimination system is established, single-dimension misjudgment risks are reduced, amplification proportion sequence and persistence analysis technologies are fused, and a probabilistic prediction model is combined. And early warning of the abnormal state time node of the special equipment is realized.

The invention discloses a black pig breeding disease intelligent monitoring management method based on big data, and relates to the technical field of animal husbandry intelligent management and animal disease monitoring, and the method comprises the following steps: obtaining multi-dimensional data information of pigs in a black pig breeding scene in real time through a sensor network, a video monitoring system and a physiological information collection device; the multi-dimensional data collected in real time is preprocessed and standardized, and the original data quality and the data analysis effectiveness are improved. According to the invention, by introducing an adaptive adjustment mechanism, the problem of pathological data loss caused by excessive elimination of abnormal values in the prior art is solved, and accurate retention and dynamic tracking of early disease signals of pigs are realized. The method integrates multi-dimensional feature extraction and intelligent evaluation, has pathological trend perception and processing strategy adaptive adjustment capabilities, effectively improves the early recognition sensitivity and discrimination accuracy of a disease early warning system, and provides more scientific health management support for farms.

The invention is applicable to the technical field of petroleum drilling engineering, and provides a drilling overflow early recognition method based on deep reinforcement learning, which comprises the following steps of: firstly, collecting and preprocessing data, and then constructing and optimizing a drilling overflow early recognition model based on a near-end strategy optimization (PPO) algorithm, and finally, collecting data in real time in drilling operation, preprocessing the data, inputting the data into the model, analyzing the change of the characteristic quantity according to a PPO algorithm, judging whether an overflow sign exists according to an overflow drilling process flow, and if so, early warning according to a Reward function strategy. According to the method, a traditional threshold value method and machine learning are combined, a deep reinforcement learning algorithm is used for optimizing drilling parameter threshold values, characteristic quantity is monitored in real time to capture changes before overflow, historical data are self-learned to recognize an overflow mode, and dependence on field operators is reduced; meanwhile, model decision logic is displayed, understanding and trust of engineers are enhanced, more reliable overflow monitoring guarantee is provided for drilling operation, accident risks are reduced, and wellbore safety is guaranteed.

The invention discloses a building defect recognition method and system based on image recognition, and particularly relates to the technical field of computer vision. Comprising a data acquisition and preprocessing module, a building defect knowledge graph construction module, a building defect identification module, a building defect quantitative analysis module, a building defect risk assessment module and a building defect risk early warning module. According to the method, the extracted building defect information and structured domain knowledge are deeply fused, the reason for forming the defect can be further reasoned, deep understanding and diagnosis of the defect are achieved, the interpretability of model output is remarkably improved, quantitative parameters, diagnosis causes, component importance and historical change trends of the defect are calculated, and the accuracy of the model output is improved. According to the method, the quantitative building defect risk index is obtained, early warning of different levels is automatically triggered according to the preset value, early recognition and predictive maintenance of the structure risk are achieved, and active early warning and risk prediction of the building defect are achieved.

The invention discloses a medical image automatic identification system based on a neural network, and relates to the technical field of medical image identification. The method is used for solving the problem that early recognition of neurodegenerative diseases is difficult due to medical image and genome data splitting and poor model interpretability in the prior art. The method comprises the following steps: firstly, extracting multi-scale features of a brain structure through a three-dimensional convolutional neural network and a self-attention mechanism, calculating a multi-gene risk score based on a risk site, and encoding the score into a feature vector; secondly, using a cross attention mechanism to take gene features as query vectors, fusing the gene features with image features, and generating brain structure anomaly features under gene regulation; then, gradient weighting class activation mapping is applied to generate a visual thermodynamic diagram, and gene-image association weight weighting is combined to construct a brain region risk distribution diagram; and finally, a high-risk brain region space coordinate set is extracted through threshold segmentation, and an accurate quantification basis is provided for early recognition.

The invention discloses an elevator operation and maintenance full-life-cycle fault monitoring and diagnosing method and system, and relates to the technical field of data analysis, and the method comprises the following steps: collecting fault action data of known elevator component parts, and establishing an elevator component part fault tree map; marking a historical operation process instruction of the target elevator equipment, analyzing the matching between the historical operation process instruction and the assembled building, and evaluating the current state of each component of the target elevator equipment; running data of all parts of the real-time target elevator equipment are obtained, similar fault screening is conducted on the running data and the known elevator component fault tree atlas, an undetermined fault set of all the parts of the real-time target elevator equipment is obtained, an elevator part-fault confidence coefficient function is established, and fault types of all the parts of the target elevator equipment are generated. The method has the advantages that the fault early recognition capability and the fault diagnosis accuracy are improved, the fault processing time is shortened, and the reliability of elevator equipment is improved.

The invention relates to the technical field of material detection, and discloses an identification analysis and method for recessive degradation of a flexible conductive composite material, and the system comprises a perturbation excitation module, a response collection module, a time sequence feature extraction module, a micro-response trend consistency analysis module, and a state evaluation and early warning module. Multiple periods of small-signal capacitance excitation are applied to the material, pi-pi stacking slippage and interface polarization behaviors in a conductive network are excited, electrical response data are collected to construct a time sequence feature vector, and the deviation degree of the evolution trend is further calculated. And based on the trend deviation index, performing health state scoring and triggering early warning, thereby realizing early recognition and evaluation of material recessive degradation.

The invention discloses a rockburst early recognition method and device based on physical information injection and a medium, and belongs to the technical field of underground engineering disaster monitoring and intelligent early warning. The method comprises the following steps: acquiring acoustic emission, microseismic, stress and displacement data, and performing space-time alignment in combination with lithology, joints and other static geological attributes to generate a high-dimensional feature cube; cubic input physical information is injected into a converter for modeling, and intermediate features such as energy accumulation rate and three-dimensional danger confidence distribution are output; retrieving a rockburst case library based on the intermediate features, and fusing similar cases to generate an induction mechanism interpretation and prevention and control suggestion text; a potential fracture surface position is extracted for visualization; triggering an edge alarm based on the risk level and recording execution feedback; input distribution and physical residual distribution are monitored, and increment fine tuning is triggered according to the drift state and feedback. According to the method, the technical effects of reducing the false report and missing report rate and improving the early warning timeliness and the spatial positioning precision are achieved.

The invention discloses a deep venous thrombosis early warning method and system based on multi-modal physiological signals, and relates to the technical field of health monitoring. The method is used for realizing thrombus early recognition and graded early warning. The method comprises the following steps: synchronously acquiring physiological signals of lower limbs on two sides through a multi-modal sensing device, performing motion artifact elimination and signal quality screening, and generating preprocessed time sequence data; performing dynamic time warping and feature extraction on the time series data, calculating a bilateral symmetry index and an instantaneous and accumulated time-varying difference degree, and fusing the bilateral symmetry index and the instantaneous and accumulated time-varying difference degree with absolute physiological parameters to form a comprehensive risk assessment vector; establishing a dynamic baseline based on the historical risk vector, calculating a current deviation index, and recognizing an abnormal state in combination with a double-side cooperation mode; and according to the deviation index and the duration, in combination with the abnormality type and the severity, generating a graded early warning signal containing a risk grade and a disposal suggestion through multi-stage judgment, thereby realizing early warning and risk management of thrombus.

The invention discloses a turning gear monitoring system and a control method thereof, and aims to solve the problem that an existing turning gear lacks comprehensive real-time monitoring and fault early warning. The system comprises a monitoring module, a data processing module, a control module and an early warning execution module, wherein the monitoring module, the data processing module, the control module and the early warning execution module are connected in sequence; the monitoring module is used for monitoring operation data of the turning gear; the data processing module is used for monitoring and diagnosing the turning gear by utilizing a preset health database according to the operation data and generating a diagnosis result; the control module is used for generating a corresponding early warning instruction based on preset graded early warning according to the diagnosis result; and the early warning execution model is used for executing a corresponding alarm action according to the early warning instruction. Through cooperative work of multiple modules, the system can integrate various operation parameters and mechanical state data, early recognition of complex faults is achieved, and the early warning efficiency of early faults of the turning gear is improved.

The invention discloses a construction method of a risk prediction model for early diagnosis of liver cancer. The construction method of the risk prediction model comprises the following steps: 1, preparing clinical data; 2, processing, analyzing and learning clinical data, and constructing a project database of a hepatocellular carcinoma clinical diagnosis path based on big data; 3, determining main diagnosis key points in the disease timing sequence diagnosis scheme and related indexes influencing the operation, and formulating a diagnosis and treatment path extraction rule; 4, constructing a quality-efficiency evaluation index system for the diagnosis and treatment path set, and calculating the early diagnosis probability and postoperative recurrence probability of the liver cancer; and 5, establishing a Markov model for early screening and diagnosis of liver cancer and risk prediction of postoperative recurrence. According to the method, the ANN principle is utilized, an HCC early diagnosis risk prediction model and an HCC clinical diagnosis and treatment path evaluation system are established, HCC high-risk groups are subjected to early recognition, and the HCC early diagnosis rate is increased; meanwhile, an optimal clinical diagnosis and treatment decision is provided for HCC treatment.

The invention provides a high and steep slope dangerous rock mass recognition and stability evaluation method and system, and the method comprises the steps: building a dangerous rock mass recognition model according to the fall point cloud data of a high and steep slope based on a point cloud density mutation detection algorithm and a genetic neural network, and carrying out the recognition of the three-dimensional boundary of the dangerous rock mass; according to the three-dimensional boundary of the dangerous rock body, any dangerous rock body contour point cloud set is formed, a dangerous rock body geometric feature parameter extraction model is established, and dangerous rock body geometric feature parameters are obtained through extraction; and establishing a dangerous rock mass stability evaluation model comprising a rock mass parameter set, an environment data set and a stability algorithm method set, coupling the dangerous rock mass geometric characteristic parameters with the rock mass parameter set and the environment data set as input conditions, inputting the input conditions into the stability algorithm method set, and evaluating the stability of the dangerous rock mass. According to the method, the data precision, the recognition efficiency, the analysis accuracy and the response real-time performance are greatly improved, and a theoretical basis is provided for early recognition and stability evaluation of the slope dangerous rock mass in the high and steep environment of water conservancy and hydropower engineering.

The invention relates to a feeding intolerance burst risk early warning method and device based on multi-modal dynamic monitoring. The method comprises the steps that a gastrointestinal tract peristaltic wave image sequence under the abdominal skin surface of a patient is collected; collecting borborygmus data; collecting physical sign physiological data of the patient; monitoring behavior and posture data of the patient in real time; tracking the infinitesimal displacement and deformation of the gastrointestinal wall in the gastrointestinal tract peristaltic wave image sequence, and extracting and quantifying the speed, direction and wave crest and trough change rate of the peristaltic wave and the regularity index of the peristaltic wave; calculating a physiological deviation degree between the fusion feature at the current moment and an expected normal mode generated by the physiological feature baseline model of the patient; and inputting the physiological deviation degree, the change rate of the physiological deviation degree in the time dimension and the accumulated deviation duration into a conditional variation risk assessment model, and outputting a potential spatial distance. Through the non-invasive and highly quantified feeding intolerance risk model, the early recognition capability and clinical intervention efficiency of feeding intolerance are remarkably improved.

The invention discloses a rapid progression type nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and relates to the field of medical informatics crossing. The invention provides a rapid progressive nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and aims to solve the problem that a rapid progressive nasopharyngeal carcinoma patient is difficult to recognize in time by depending on TNM staging and experience judgment in the prior art. According to the method, historical case data collection, missing value filling and standardization preprocessing, core feature determination through feature screening, class imbalance correction, feature coding and feature matrix construction are sequentially carried out, an artificial neural network model is trained and optimized under a cross validation framework, and performance and threshold values are determined on a validation set. During clinical application, patient features are input, and the model outputs a rapid progress risk probability and a risk level. Compared with a conventional staging or linear model, the method can improve the prediction accuracy, and achieves the early recognition and individualized treatment of a high-risk patient.

The invention provides a highway soft rock slope three-dimensional intelligent monitoring and disaster early-stage identification early-warning method, and belongs to the technical field of slope monitoring, and the method comprises the following steps: shooting a highway roadbed by using remote sensing equipment, obtaining corresponding remote sensing image data, and identifying a remote sensing image; the image is input into a machine model for feature comparison learning, hidden danger point screening and disaster body recognition are carried out, a soft rock area is sampled, a soft rock mass three-dimensional space model is established, data in the soft rock mass are collected in real time by arranging a collecting device, the collected data serve as dynamic data of the soft rock mass three-dimensional space model, and the dynamic data are stored in a storage device. Remote sensing data and real-time collected data are input into an on-line early warning system, whether instability occurs in a soft rock area or not is judged, early warning information is sent to an area where instability or landslide possibly occurs, and management personnel are notified to check on site and take protective measures.