449 results about "Risk classification" patented technology

The invention relates to the technical field of respiratory system risk prediction, and provides a respiratory system risk prediction method and system based on a graph neural network, and the method comprises the steps: collecting the multi-modal medical data of a patient, and constructing a multilayer heterogeneous graph based on the multi-modal medical data; constructing a weighted adjacency matrix and a node feature vector through the multi-layer heterogeneous graph; matrix product operation and convolution operation are carried out based on the weighted adjacent matrix and the node feature vector, splicing combination with historical moment state information is carried out, graph state representation is obtained, weighted aggregation of time dimensions is carried out, and time sequence attention features are obtained; performing coding processing based on the clinical examination data to obtain multi-modal fusion features; and inputting the multi-modal fusion features into a risk classifier for classification calculation to obtain a respiratory system risk level prediction result, generating a risk assessment report, and outputting respiratory risk early warning information. The accuracy and clinical practicability of respiratory system risk prediction are improved.

The invention belongs to the field of multi-modal artificial intelligence risk identification. The core comprises a multi-source heterogeneous data parallel acquisition module; a modal exclusive feature extraction module; a graph attention driven dynamic fusion module; and a risk classification module with an attention mechanism. A real-time fusion weight is generated through a cross-modal incidence matrix, adaptive feature weighting and hidden risk association mining are realized, and the recognition accuracy and interpretability of a complex scene are remarkably improved. The method is applied to the fields of financial risk control and industrial monitoring.

The invention discloses an adaptive neural network flood routing simulation and risk assessment method. The method comprises the following steps: step 1, generating multi-source hydrometeorological preprocessing data; 2, constructing a river network topological graph, generating node feature vectors and edge feature vectors, and forming time-space diagram input data; 3, inputting the liquid state time constant neural network to execute continuous time state updating, and generating a node hydrological state prediction vector set; step 4, injecting hydrodynamic physical consistency residual errors to generate a physical constraint hydrological state prediction vector set; 5, executing adaptive lag compensation, and generating a lag compensation hydrological state prediction vector set; 6, mapping to generate flow, water level and flood peak arrival time prediction data; and step 7, outputting a risk grading graph and early warning threshold list data. According to the invention, accurate prediction of the flood process and dynamic generation of the risk map are realized, and early warning precision and response efficiency are improved.

The invention provides an environment detection method and system based on a sample target detection model. The method comprises the following steps: synchronously acquiring an environment image, a video stream and physical parameters by using a multi-mode sensor; decomposing the data into image features and environmental parameter components through a dual-time sequence control signal, and realizing space-time alignment by adopting a linear phase filter; constructing a foreground region template based on the depth information, and generating target recognition feature representation containing an abnormal blurred target; adversarial training is carried out on the lightweight target detection network in combination with a transfer learning strategy, the network integrates convolutional features and a Transform attention mechanism, and the weight is dynamically adjusted through environmental parameters; fusing a target result and sensor data in real-time detection, and inputting a decision tree model for risk grading; and after the early warning is triggered, reconstructing a false detection sample through an online learning mechanism and iteratively optimizing the model. The system correspondingly comprises a multi-modal data acquisition module, a data enhancement and annotation module, a model training module, a real-time detection and fusion module and an early warning and optimization module. According to the invention, through multi-source data fusion, dynamic data enhancement and an adaptive compensation mechanism, the small target detection precision, the environmental adaptability and the real-time early warning capability are significantly improved.

The invention relates to the technical field of aquatic product quality inspection and traceability, and discloses a block chain enabled aquatic product traceability and quality evaluation method. According to the method, complete cycle data of aquatic products is divided into a block chain storage layer, a circulation traceability layer and a quality inspection evaluation layer, the block chain storage layer comprises a plurality of distributed nodes, the circulation traceability layer records chain type circulation events from cultivation to sales, and the quality inspection evaluation layer integrates multiple types of quality inspection indexes. The method comprises the following steps: judging whether a block hash value in a block chain storage layer is abnormal or not, and if so, extracting a breeding environment parameter, a transportation temperature and humidity track and a sales inspection report in a circulation traceability layer; calculating a multi-source quality index fusion weight of the quality inspection evaluation layer, and analyzing the abnormal signal intensity of each detection node; and finally, performing quality risk classification by combining the information and a preset evaluation threshold, and generating a quality grade label. According to the method, credible traceability and scientific evaluation of complete-cycle data of aquatic products are realized, and an effective means is provided for quality control.

The embodiment of the invention relates to the field of artificial intelligence, and provides a model context protocol injection attack protection method and device based on dynamic semantic analysis, computer equipment and a storage medium, and the method comprises the steps: obtaining request data corresponding to a call request initiated by a user through a model context protocol, carrying out the preprocessing of the request data, obtaining the preprocessed request data; performing semantic vectorization on request text and context historical information in the request data through a lightweight bidirectional encoder representation model to output an initial risk score; correcting the initial risk score according to context historical information carried in a model context protocol to obtain a corrected final risk score; and performing hierarchical defense decision according to the final risk score, and determining risk grading information corresponding to each piece of request data so as to execute a protection action corresponding to each piece of risk grading information. By adopting the method, the accuracy of identifying the protocol injection attack can be improved.

The invention discloses a cold region tunnel freeze injury diagnosis, risk grading and control system and method, and relates to the technical field of tunnel engineering. The system comprises a basic data layer for storing disease, index and measure databases; the core analysis layer is used for diagnosing a frost heaving mechanism, identifying single-factor, double-factor and three-factor frost heaving types, calculating freezing depth and frost heaving force and analyzing sensitivity; the risk level evaluation layer is used for dividing a disease zone, a frost heaving level, a freezing injury risk and a freezing injury risk response level; and the intelligent decision-making layer constructs an intelligent comprehensive decision-making analysis platform to realize risk-measure accurate matching and closed-loop management and control. The method comprises the four steps of data acquisition and storage, frost heaving mechanism diagnosis and quantification, risk grade evaluation and intelligent decision measure matching, through multi-source data fusion, multi-factor coupling analysis and zoning and grading prevention and control, the whole-process accurate management and control of the cold region tunnel frost damage is realized, and the frost damage treatment efficiency and the tunnel operation safety are improved.

The invention provides an enterprise production safety monitoring and checking system based on multi-source data fusion, and the system is characterized in that the system comprises a data collection module which collects production environment data, processes the data, and generates a time-space alignment data set; the feature screening module is used for screening features highly related to security from the space-time alignment data set to generate a feature matrix; the model construction module is used for constructing a risk assessment model, calculating a dynamic risk value according to the multi-dimensional features, and performing risk grading on the dynamic risk value; the response control module is used for starting a corresponding response strategy according to the risk classification and outputting an early warning instruction set and an equipment control signal; the monitoring calculation module is used for monitoring control, comparing risk value changes before and after treatment and calculating response efficiency; and the adjusting and optimizing module is used for carrying out dynamic adjustment according to the response efficiency and optimizing the response strategy. And the limitation of traditional single-dimensional monitoring is broken through, accurate correlation analysis of equipment, environment and personnel risks is realized, and the composite hidden danger recognition capability is remarkably improved.

The invention discloses a driver controller detection system, which comprises an acquisition module, an acquisition module, a processing module, an identification module, a judgment module, an analysis module and a generation module, the acquisition module captures contact information and electromagnetic interference data of contacts in real time by means of a high-dynamic voltage sensor and a broadband EMC probe; the acquisition module is triggered by a locomotive signal and records dynamic working condition data; the processing module aligns the data, performs denoising and extracts fault parameters; the identification module scans the voltage waveform and judges the transient contact failure; the judgment module monitors EMC data and compares an instruction position, and judges misoperation; the analysis module analyzes fault association, judges a composite type and triggers an alarm; the generation module generates a detection report and a log. According to the system, a multi-dimensional monitoring network is constructed under a dynamic working condition, data reliability is guaranteed, faults are accurately identified, a compound fault source is positioned, and the safety and stability of locomotive operation are improved through risk grading early warning and automatic reporting.

The invention provides a puree production line microbial pollution risk real-time monitoring and early warning method, which comprises the following steps: deploying multiple types of sensors, an industrial camera and an operation log interface, collecting environmental temperature and humidity, pH value, dissolved oxygen, image and operation behavior multi-modal data in a fermentation tank in real time, and carrying out edge calculation and standardized preprocessing to obtain the microbial pollution risk of a puree production line. Data missing, abnormity and time sequence difference are eliminated; multi-modal features and a dynamic knowledge graph constructed based on a production process and a pollution event are fused, and a deep learning model and a graph neural network are utilized to realize microbial pollution risk probability intelligent prediction; risk grading and a dynamic weighting algorithm are introduced, grading early warning signals and disposal suggestions are automatically generated, and a production control system is linked to execute response measures; according to the subsequent production state, continuous feedback is carried out, the model and the early warning threshold are adjusted in a self-adaptive mode, and the accuracy, response efficiency and production safety of pollution early warning are remarkably improved.

The invention discloses a road risk grading early warning method and system based on a Beidou satellite system, and the method comprises the steps: firstly carrying out the real-time positioning of a vehicle through Beidou dual-frequency signals, inertial navigation data, road side unit differential data and vehicle-mounted sensor data, and obtaining the precise position information; fusing the real-time position of the vehicle with meteorological data, vehicle-mounted OBD parameters and social media public opinion data to generate a dynamic risk factor matrix; a fuzzy rule base is constructed based on expert experience, the fuzzy weight of each risk factor in a matrix is obtained, meanwhile, the time sequence weight of each factor is predicted by means of an LSTM model, and a final road risk score is obtained through dynamic weighting. And performing graded early warning on the road risk in combination with the driver portrait, and feeding back and updating the fusion parameter, the fuzzy rule base or the LSTM model parameter according to the early warning effect to form a closed-loop optimization mechanism. Dynamic coupling analysis of multi-dimensional risk factors is realized, and the real-time performance and accuracy of road risk early warning are improved.

The invention relates to an elderly patient falling early warning method and system, and the method comprises the steps: collecting a motion signal, a physiological signal and an environment signal of a target patient, carrying out the preprocessing of the collected signal data, and generating a standardized multi-modal time series data flow and a data quality identifier; according to the standardized multi-modal time sequence data stream and the data quality identifier, constructing and storing an individualized baseline description of the target patient; outputting an abnormal tag and a corresponding evidence snapshot based on the individualized baseline description; according to the abnormal label, the evidence snapshot and the recent time sequence data of the target patient, generating a trigger factor description; according to the long-term behavior file and the short-term falling risk level, fusing to form a layered comprehensive risk file, and outputting a risk level, a priority and a suggested action list based on the comprehensive risk file; and implementing falling early warning and real-time closed-loop intervention on the target patient according to the priority and the suggested action list. According to the invention, the accuracy of early warning of falling of the elderly patient can be improved.

The invention relates to the technical field of image recognition, in particular to a burn scar hyperplasia risk assessment method combining semantic segmentation and texture feature analysis, which comprises the following steps: acquiring an image gray gradient, color jump and texture variance, calculating pixel mutation, extracting a mutation boundary, constructing a periodic direction field, and extracting a continuous offset region. According to the method, by extracting the pixel gray gradient, the color jump and the texture variance, calculating the boundary sudden change intensity and generating the layer, the structure change characteristics can be refined, the image anomaly perception precision can be enhanced, the semantic boundary can be identified based on the sudden change sequence, and the physical continuity is prevented from interfering the segmentation accuracy. A periodic evolution record is constructed through direction gradient, the dynamic trend of the structure is disclosed, an expansion area is locked in combination with direction continuous offset and change stability, a classification label is constructed through point location density, direction consistency and a gradient module value, and the interpretability and accuracy of risk identification are improved.

The invention discloses an environmental component detection data processing method and system in occupational health, and belongs to the technical field of environmental data processing, and the method comprises the steps: obtaining the original detection data of a multi-source detection device, obtaining the real-time position information of a worker, carrying out the matching, generating a structured monitoring matrix, and calculating a dynamic risk value; generating a real-time risk vector, identifying a combined pollution scene, calculating an enhancement effect, generating a superposition risk index, comparing the superposition risk index with an occupational contact limit value standard, generating a risk grading signal, carrying out short-term exposure trend analysis, and predicting future risk grade change. And generating an early warning decision instruction containing the current risk level and the predicted risk level to execute a double-path intervention strategy, and generating an equipment control signal for driving the physical equipment to act. According to the invention, multi-source monitoring data is dynamically associated with personnel positions, and multi-dimensional risk calculation, trend prediction and closed-loop control are carried out, so that accurate dynamic assessment and prospective early warning intervention of individual risks can be realized.

A soft rock roadway reinforced roof anchoring thickness collaborative design method comprises the steps that the proportion of grout filling cracks in a grouting reinforcement area is counted, a grouting dispersion uniformity index is calculated, and grouting effect evaluation is conducted; measuring the actual boundary depth of the grouting reinforcement area by adopting a method of combining drilling radar scanning and rock core sampling; mechanical parameters of the grouted rock mass are obtained, and a grouting enhancement coefficient is calculated; a stress transfer efficiency evaluation model is established, and the critical cooperative bearing surface depth is determined by analyzing the stress attenuation law at the boundary of the grouting reinforcement area; the critical anchoring layer thickness is calculated under the constraint condition that it is ensured that an anchoring system can effectively penetrate through a grouting strengthening area and go deep into a stable rock stratum; the cooperative work performance of a grouting reinforcement area and an anchoring system is quantitatively evaluated by calculating a system cooperation degree index, and a cooperative bearing efficiency evaluation system based on dynamic risk grading and control is established. According to the method, collaborative optimization of grouting and anchoring parameters can be achieved, and formation of a bolting-grouting integrated bearing structure can be ensured.

The invention provides an intelligent monitoring and risk early warning method for urban road settlement, and belongs to the technical field of urban management based on machine learning. The method comprises the following steps: firstly, collecting four types of multi-source space-time monitoring data, including urban road settlement data, underground environment data, pavement structure data and dynamic load and environment data; secondly, constructing a multi-source data space-time completion model, performing unsupervised completion on sparse monitoring area data, and generating space-time continuous settlement field data; thirdly, constructing a road settlement health index prediction model fusing multiple factors, inputting complementation data and original features, and outputting a health index of a 0-1 continuous interval; and finally, in combination with the health index and the road function level, four-level risk classification and dynamic early warning are realized. According to the method, the problems of space-time faults and data islands of a traditional method are solved, urban road global real-time monitoring is achieved, and early warning upgrading from qualitative judgment to quantitative grading is achieved.

The invention relates to a human body health state assessment system and assessment method based on multi-point acquisition, and the system comprises an event sensing module which is used for collecting an image data signal, a physiological signal and an environment data signal of a human body, and outputting an asynchronous pulse event flow when the signal change is detected; the pulse coding module is used for generating a space-time pulse sequence through a preset adaptive threshold coding technology; the neural network processing module is used for performing event driving processing by utilizing leakage integral distribution neurons in a preset pulse neural network according to the space-time pulse sequence to obtain a current health characteristic pulse mode; and the health state decoding module is used for obtaining a health state score and a risk classification result of the current human body through a preset pulse distribution rate analysis algorithm and a preset time sequence decoding technology according to the current health characteristic pulse mode. Therefore, the problems of high power consumption, high delay, low data transmission efficiency, low early pathological feature recognition accuracy and the like of a human health state evaluation system are solved.

The invention discloses an urban low-altitude landing risk assessment method based on three-dimensional space grids, and belongs to the technical field of low-altitude flight risk assessment. The objective of the invention is to solve the problem of accurate assessment of the low-altitude falling risk in a complex city scene. The method comprises the steps of performing three-dimensional rasterization processing on a low-altitude airspace of an evaluation airspace to obtain voxels of the evaluation airspace, and obtaining a voxel set of the evaluation airspace; building shelter formed by population density grids or mobile signaling inversion population, sensitive facility vectors, tree canopy shielding and three-dimensional building models is quantified, values are assigned to voxels of an evaluation airspace, and a voxelization environment parameter database covering the whole domain is obtained; constructing an aircraft failure rate model; carrying out an aircraft falling trajectory uncertainty simulation test to obtain landing coverage probability density distribution and tail end speed of the aircraft; and carrying out voxel risk synthesis by considering the probability of collision with people, and carrying out risk grading on the obtained voxel risk, thereby completing urban low-altitude landing risk assessment based on the three-dimensional space grid.

The invention discloses a risk assessment system and method for analyzing abnormal sleep breathing of children based on CBCT dual-channel, and the method comprises the steps: collecting local three-dimensional image data of a maxillofacial region through CBCT, and extracting the sagittal area, volume and multi-dimensional angle and distance parameters of an upper airway through head correction and anatomical mark positioning; and constructing a logistic regression clinical prediction model combining single-factor and multi-factor logistic regression to realize risk prediction. Standardization and equal-interval sampling are synchronously carried out on image data, a multi-channel three-dimensional image data cube is generated, and deep learning classification is realized by inputting the multi-channel three-dimensional image data cube into a 3D ResNet network based on identical fast connection. And finally, performing weighted collaborative analysis on results of the two diagnosis channels, and outputting children obstructive sleep apnea risk classification. Through the parameter driving and image learning dual-channel fusion design, the accuracy and applicability of early screening and risk assessment of children obstructive sleep apnea are improved.

The invention provides an underground space multi-disaster coupling effect quantitative evaluation and quantitative control system and method. The system is characterized in that a multi-source sensing module, a data fusion and preprocessing module, a coupling risk evaluation module and a quantitative control decision module are connected in sequence; the method comprises the following steps: collecting multi-source heterogeneous monitoring data for a long time based on the multi-source sensing module; performing space-time registration, denoising and missing value interpolation on the multi-source heterogeneous monitoring data, and calculating an original disaster intensity index; performing normalization processing on different disaster intensity indexes, constructing a time-varying coupling factor between disasters, and calculating a comprehensive risk index by using a coupling risk assessment module; the quantitative control decision module performs dynamic risk grading based on the comprehensive risk index; and the quantitative control decision module calls and executes a quantitative control strategy from a preset strategy library based on the risk grading result and the dominant disaster mode combination. According to the invention, accurate monitoring and early warning of mine underground engineering disasters can be realized, and corresponding control decisions can be automatically generated.

The invention discloses an emergency treatment high-risk patient real-time grading method based on an improved multi-mode Transform algorithm. The method comprises the following steps: S1, generating a time synchronization multi-mode event sequence; s2, obtaining a corresponding single-mode feature representation tensor; s3, obtaining a risk weighted cross-modal attention matrix; s4, in the improved multi-modal Transform fusion network, generating a fusion feature representation tensor by using a time sequence sliding window cache and incremental updating mechanism, and outputting a risk grading label and a corresponding risk grading confidence coefficient based on the fusion feature representation tensor; s5, inputting the risk grading label and the risk grading confidence into the interpretive sub-network, and generating clinical causal chain prompt information; and S6, synchronizing the risk grading label, the risk grading confidence and the clinical causal chain prompt information. According to the method, the accuracy of high-risk patient identification and the adaptability of the model to a clinical complex scene are remarkably improved, and a test result shows that the real-time identification accuracy of a high-risk case is improved compared with that of a conventional multi-modal model.

The invention discloses a charging pile thermal runaway intelligent protection method and system based on edge calculation, and relates to the technical field of charging pile thermal runaway protection. Comprising the following steps: S1, collecting thermal runaway multi-mode sensing data in real time, and carrying out data preprocessing; a charging pile thermal runaway multi-mode abnormal state is judged, and an abnormal feature data packet is generated; s2, multi-modal abnormal feature vectors are constructed, the thermal runaway risk probability is evaluated, and thermal runaway risk grading protection of the charging pile is carried out; s3, the response effect of thermal runaway risk grading protection is quantified, and thermal runaway risk grading protection is adjusted; and S4, monitoring and feeding back the thermal runaway risk, and optimizing algorithm parameters and a thermal runaway risk grading protection strategy. The problems that an existing charging pile thermal runaway protection system is difficult to adapt to complex working conditions, multiple in false alarm and missing alarm, lagging in response and insufficient in protocol compatibility and data stability, and consequently the thermal runaway risk is difficult to recognize and protect timely and accurately are solved.

The invention discloses a live pig epidemic prevention and control flow regulation system and method based on big data, and relates to the technical field of animal health information, and the method comprises the steps: recognizing abnormal live pig signals and track intersection features of structured epidemic prevention feature data through an intelligent algorithm, comparing the virus gene difference degree, constructing a virus propagation network diagram, and carrying out the analysis of the virus propagation network diagram; outputting a high-risk propagation node map; constructing an aerosol dynamics three-dimensional space model by taking a super propagation hub node of the high-risk propagation node map as an original point and combining culture environment parameters, simulating a virus air diffusion path, and generating an environment propagation thermal distribution map; fusing the high-risk propagation node map and the environment propagation thermal distribution map, identifying a cross infection area through a multi-dimensional risk superposition model to perform risk dynamic grading, and outputting a grading early warning report; according to the invention, breakthrough improvement of the live pig epidemic prevention and control capability is realized, key nodes and potential risk areas of epidemic propagation can be accurately identified, and the pertinence of prevention and control measures is improved.

The invention discloses a shallow coal seam strong mine pressure dynamic early warning and cooperative control method and system based on multi-source data fusion, and relates to the technical field of intelligent control. Coal seam multi-source sensing data is collected, a strong mine pressure feature vector is constructed, and the strong mine pressure feature vector is input into a mixed model of a deep recurrent neural network and a graph neural network; outputting an energy release index and a risk probability value, judging a coal seam strong mine pressure risk level, determining a fracturing position and fracturing parameters of directional hydraulic fracturing by adopting a self-adaptive grid division algorithm when a red early warning signal is triggered, and determining a fracturing parameter of directional hydraulic fracturing by adopting a self-adaptive grid division algorithm when an orange early warning signal is triggered. The spraying area and thickness distribution of energy absorption material spraying are determined through a reinforcement learning model, when a yellow early warning signal is triggered, a supporting resistance adjusting scheme of a hydraulic support is determined through a gradient descent algorithm, risk grading control is achieved, and the accuracy and practicability of coal seam early warning are improved.

The invention discloses an underground water arsenic pollution range dynamic delimiting method based on multi-source data fusion, and the method comprises the steps: employing a fixed monitoring well and a mobile unmanned aerial vehicle water quality sensing device to cooperatively work, and constructing a dynamic monitoring network; hydrogeological data, hydrochemical parameters and meteorological data are integrated, a hydrochemical parameter inversion model is established, and arsenic form distribution is predicted through the Fe / S concentration ratio; carrying out three-dimensional pollution plume dynamic modeling by adopting an improved MT3DMS model, and introducing a time attenuation coefficient to correct adsorption-desorption kinetic parameters; establishing a pollution diffusion probability prediction model by using an XGBoost algorithm, and predicting a pollution diffusion boundary based on a machine learning algorithm; and performing visual output to generate a dynamic risk grading graph. According to the method, the arsenic pollution range can be dynamically updated, and the pollution range delimiting precision is effectively improved.

The invention provides a data compliance migration method which comprises the following steps: acquiring data protection regulation change information through a regulation monitoring module, extracting key terms and geographic position parameters, and generating a compliance parameter set; triggering data classification identification according to the regulation change information, analyzing content features and sensitive levels of stored data, and determining risk classification of data blocks; distributing the data blocks to different transmission queues according to the risk classification, and generating a data migration task list; dynamically scheduling a data migration time window and a transmission quantity according to the risk classification and the optimal migration path; starting a parallel data transmission process, and executing a data migration operation; carrying out integrity check on the transmission data through a check algorithm, and confirming a transmission state; and the monitoring module is used for detecting service response time in the migration process and adjusting the migration progress and the switching opportunity.

The invention relates to the technical field of automobile auxiliary driving, in particular to an intelligent driving auxiliary system and method based on multi-modal emotion recognition, and the method comprises the steps: building a driver baseline model; synchronously acquiring a face image, a biological signal and vehicle operation data of a target driver in real time, and performing preprocessing; performing feature extraction on the preprocessed facial image and biological signal of the driver, and constructing a fused emotion feature based on a driver baseline model; performing emotional state recognition and confidence calculation according to the fused emotional features; calculating an emotional risk index according to the emotional state, the confidence coefficient and the vehicle operation data, and performing risk grading; and executing a corresponding dynamic response strategy according to a risk grading result. According to the invention, accurate perception and graded active intervention on the emotion and risk of the driver are realized, and the driving safety is obviously improved.

The invention discloses a landslide risk monitoring method and system based on multi-source data, and relates to the field of geological monitoring early warning, and the method comprises the steps: obtaining the environment monitoring data of a to-be-monitored slope area, and constructing a multi-dimensional feature matrix according to the environment monitoring data; performing risk prediction classification based on the multi-dimensional feature matrix according to a risk classifier to obtain a landslide risk level, and performing prediction based on the multi-dimensional feature matrix according to a neural network model to obtain a landslide displacement prediction quantity; calculating a landslide stability coefficient according to the environmental monitoring data based on a limit equilibrium method, calculating a rainfall induction threshold by combining the rainfall data and the soil humidity data, and obtaining a landslide comprehensive risk coefficient by integrating the landslide stability coefficient and the rainfall induction threshold; and determining a landslide risk monitoring result of the to-be-monitored slope area in combination with the landslide risk grade, the landslide displacement prediction amount and the landslide comprehensive risk coefficient. According to the application, the technical problem that the existing landslide risk monitoring accuracy is poor can be solved.

The invention provides a postoperative bleeding risk monitoring method and system for a hepatobiliary patient. The method comprises the following steps: collecting real-time multi-modal data; constructing an LSTM-CNN hybrid model by using the time-frequency decomposition features, and obtaining a local tissue hypoxia index and a vasomotor function anomaly probability; the low-frequency impedance change rate and the albumin level are fused through a random forest algorithm, and the ascites occurrence probability and the effusion amount predicted value are obtained; and generating a bleeding point positioning coordinate and a thermodynamic diagram risk grade. And calculating a comprehensive bleeding risk probability and positioning a bleeding area. And generating graded early warning signals and recommending personalized treatment schemes or nursing suggestions. According to the invention, the LSTM-CNN hybrid model, the random forest algorithm and the three-dimensional convolutional neural network are adopted to deeply extract different data features, so that the limitation of single index evaluation is avoided. A causal relationship model is established through the Bayesian network, and the comprehensive risk probability calculation preciseness is improved; the early warning threshold is dynamically adjusted by combining the individual characteristics of the patient, and the traditional problem of easy misjudgment is solved.

The invention discloses a marine commutation platform anti-collision early warning system based on remote sensing monitoring and machine learning, and the system comprises a multi-source data collection module which obtains multi-source data and carries out the fusion of the multi-source data to generate a fusion data field; the target recognition module recognizes and extracts the spatial position, length, speed and course of the target based on the fused data field; the relative motion modeling module is used for constructing a relative velocity vector between the platform and the target, calculating a distance change rate between the platform and the target, and judging a potential threat target according to the distance change rate; the risk level evaluation module is used for predicting a future trajectory of the platform and the potential threat target, constructing a collision risk function and obtaining a corresponding risk integral; inputting the target feature vector set of the potential threat target into the trained risk classification model to output a predicted risk level; and fusing the risk integral and the predicted risk level by adopting a fusion rule to obtain a risk level result. Therefore, dynamic monitoring and intelligent early warning of the potential threat target are realized.