718 results about "Risk prediction models" patented technology

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 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 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 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 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.

The invention discloses an anomaly detection method and system for transaction flow data, and relates to the technical field of big data analysis. The method comprises the following steps: based on transaction flow data, acquiring operation behavior data of a target user in a transaction process and interaction behavior data after the transaction is completed, and sorting according to timestamps to form a user operation behavior sequence and a user interaction behavior sequence; inputting the sequence into a pre-trained abnormal risk prediction model, and outputting an initial risk coefficient, wherein the model integrates an individual behavior baseline and an adaptive weight module; acquiring a group behavior baseline of the similar user group, and correcting the initial risk coefficient in combination with a matching result of the sequence and the group baseline to obtain a final risk coefficient; and performing classification abnormity early warning operation on the final risk coefficient based on a preset risk threshold. According to the method, through whole-process behavior sequence analysis, individual and group baseline dual calibration and dynamic weight adjustment, the accuracy and timeliness of transaction flow data anomaly detection are effectively improved.

The invention relates to the field of automatic driving and traffic safety, in particular to a fine-grained multi-task driving risk prediction method fused with a trajectory prediction auxiliary task. Comprising the following steps: step 1, automatically labeling risk labels based on trajectory data; 2, designing a main task of the driving risk prediction model; 3, auxiliary task design of the driving risk prediction model; and 4, constructing and training a driving risk prediction model. An experiment result based on a disclosed NGSIM data set shows that the precision and robustness of a driving risk prediction model can be remarkably improved by introducing trajectory prediction as an auxiliary task.

The invention discloses a switchable graded sampling pre-construction control method for dust and a detection device. The method comprises the following steps: synchronously acquiring data through multiple types of sensors, constructing a condensation risk prediction model, calculating a condensation risk index RI and dividing risk grades; based on the risk level, dynamically adjusting the target power of the heating wire, the blowback period of the air pump and the airflow speed, and cooperatively triggering an electrode cleaning and trapping filter screen switching program; after cleaning is completed, follow-up control parameters are dynamically adjusted; and closed-loop control is formed. According to the method, the temperature and humidity sensor is arranged in the external environment to obtain background parameters, the airflow velocity sensor is used for sensing the working condition change of the air pump, and a dynamic condensation risk prediction model is established, so that excessive heating is avoided fundamentally, and evaporation loss of semi-volatile substances is prevented; therefore, the accuracy of a dust concentration detection result is remarkably improved in an extreme humidity environment.

The invention relates to the technical field of medical information, and discloses a pancreatic cancer risk prediction method based on machine learning and multi-modal data, and the method comprises the steps: obtaining the multi-modal data of a target user; performing time sequence deduction on the molecular biological detection data to generate a virtual molecular time sequence; time sequence signals are extracted from the virtual molecule time sequence and the time sequence behavior monitoring data; calculating the dynamic coupling strength between the two time sequence signals to obtain a space-time coupling coefficient; weighted fusion is carried out on the features, and unified multi-modal feature representation is constructed; carrying out multi-modal feature representation training to obtain a special risk prediction model for the target user; and obtaining a risk quantitative score, and identifying a key risk driving factor which contributes to the score most. According to the invention, through multi-modal time sequence fusion and personalized modeling, early-stage, dynamic and explainable and evaluable pancreatic cancer risks are realized.

The invention provides a BIM-based 3D building digital monitoring method and system, and belongs to the technical field of building engineering monitoring, and the method comprises the steps: carrying out the digital conversion and processing of a building design drawing of a target building, extracting the geometric and attribute information of the target building, and generating a standard BIM model; verifying and calibrating the basic multi-dimensional data and the standard BIM model when the target building is completed, and generating a reference BIM model; performing spatial registration and association mapping on the acquired multi-dimensional monitoring data and the reference BIM model to generate an association data set; comparing based on the associated data set to generate a comparison result; obtaining a potential risk type and a risk evolution trend of the target building through a risk prediction model based on a comparison result, historical monitoring data and a component static attribute in the reference BIM model; and generating a target monitoring strategy based on the potential risk type and the risk evolution trend. The building operation and maintenance efficiency is improved, and the service life of the building is prolonged.

The invention discloses a disaster recovery resource scheduling method and device based on a heterogeneous cloud environment, and the method comprises the steps: constructing a multi-dimensional resource portrait model and a cross-cloud network topological graph in the heterogeneous cloud environment, and generating a standardized resource vector set and a topological affinity scoring matrix; aggregating the local SLA default risk prediction model of each cloud node through federated learning, and performing fine tuning through transfer learning to obtain a global prediction model; calculating load fluctuation entropy based on the SLA constraint template and real-time load data, and inputting the load fluctuation entropy into a global model to predict and obtain an SLA default probability set; constructing and solving a dynamic weight multi-objective optimization function to obtain an optimal takeover cloud node list and a migration strategy identifier set; a target cloud node and a migration strategy are determined through SLA simulation verification, and an adaptive execution adapter is called to complete virtual machine incremental snapshot migration or cross-cloud arrangement deployment of containerized services. According to the invention, intelligent and automatic scheduling of disaster recovery resources is realized, and the fault takeover speed and the cross-cloud resource utilization rate are improved.

The invention discloses an intelligent vehicle safety decision-making method considering a conflict spatio-temporal evolution law, and the method comprises the steps: enabling an intelligent vehicle to obtain vehicle trajectory data from a cloud platform, building risk multi-scale characteristic indexes based on the vehicle trajectory data, and enabling the indexes to comprise a vehicle-to-longitudinal and transverse conflict index, a vehicle group order conflict index, and a vehicle cluster interaction conflict index, respectively measuring the vehicle microscopic risk, the vehicle group mesoscopic risk and the traffic flow macroscopic risk; constructing a directed association rule reflecting a different-scale risk conflict directed chain relationship; and constructing a linear CRF risk prediction model, fusing current microscopic, mesoscopic and macroscopic risk states and directed association rules among the states, predicting a microscopic risk state at a future moment, and triggering a corresponding early warning mechanism according to a prediction result to realize driving safety decision.

The invention relates to the technical field of tunnel construction geological disaster early warning, and discloses a tunnel construction geological disaster early warning method and system. According to the method, multi-source geological monitoring data and environment dynamic parameters of a tunnel construction area are collected; and then through data fusion and feature engineering processing, data contradiction and redundancy are eliminated, and a standardized training data set is formed. And then training and verifying a disaster risk prediction model by using the data set and adopting a machine learning algorithm so as to obtain an optimized model to accurately predict the disaster risk. And performing risk value prediction on different geological structure units by applying the optimization model, generating a risk distribution map, and determining an optimal geological structure unit through a search algorithm. Based on this, numerical simulation of geological disaster triggering and propagation is executed, a disaster evolution rule is analyzed, and a path simulation data set is output. And finally, performing multi-objective optimization on the data set according to a safety early warning objective, generating a geological disaster early warning scheme, and performing iterative calibration.

The invention discloses a data-driven early-diabetic future diabetes-related disease progress risk prediction model and a construction method thereof, and the method comprises the steps: collecting clinical index data, including age, gender, BMI, WHR, HOMA-IR, HDL-C, TG, SBP, DBP, SCR and ALT, of early-diabetic patients in a training and verification queue; using an unsupervised soft clustering method combining dimension reduction based on UMAP, graph clustering and a Gaussian mixture model to identify the phenotypic heterogeneity of the prediabetes mellitus; obtaining the probability of the individual phenotype characteristics, evaluating the association between the probability and the development risk of the future diabetes related diseases in the early stage of diabetes, and constructing a development risk prediction model of the future diabetes related diseases in the early stage of urine diseases; and performing model optimization and robustness verification in the verification queue. According to the method, the heterogeneity of the prediabetes mellitus can be effectively identified, and accurate risk stratification and personalized prevention are realized.

The invention relates to the technical field of cleaning robots, in particular to a property cleaning robot operation planning and monitoring system based on digital twinning, and the system comprises a multi-mode sensing module which carries out the real-time collection of multi-mode environment data; the smudginess risk prediction module is used for outputting a smudginess risk thermodynamic diagram according to the multi-modal environment data and a smudginess risk prediction model; the twinborn body simulation module is used for performing multi-granularity simulation on the environment data twinborn body according to the dirt risk thermodynamic diagram; the dynamic operation planning module is used for carrying out multi-target optimization processing according to robot body state data and simulation result data in the multi-modal environment data; and the self-adaptive execution monitoring module is used for automatically executing the dynamic operation plan by the property cleaning robot, carrying out twinborn real-time fusion and locally optimizing the dynamic operation plan. According to the invention, the intelligent, refined and predictable management capability of the cleaning work of the property cleaning robot is improved.

The invention discloses a community fire risk dynamic assessment method and system based on AI prediction, and belongs to the technical field of fire prevention and control, and the method comprises the steps: S1, collecting and preprocessing multi-source data of a community, and constructing a comprehensive risk factor database; s2, constructing a risk prediction model, taking the preprocessed multi-source data as input, identifying a key risk factor, and outputting a fire risk probability; s3, calculating the dynamic weight of each index by fusing the static expert experience and the real-time risk contribution degree; s4, based on the dynamic weight and the risk probability, calculating a comprehensive risk value and dividing risk levels; s5, constructing a prevention and control strategy library based on the risk level and the key risk factor; according to the community fire risk dynamic assessment method and system based on AI prediction, the change rule of the fire risk factors in the community can be captured in real time, potential fire hazards can be predicted in advance, and a targeted prevention and control strategy can be automatically generated.

The invention belongs to the technical field of order fulfillment process monitoring, and discloses an order fulfillment process delay early warning method and system based on multi-node state acquisition, and the method comprises the steps: collecting the dynamic state data of multiple nodes in a fulfillment process in real time; matching judgment is carried out through the node rule base, primary early warning is triggered, and an early warning context data set is constructed; inputting the data set into a pre-trained delay risk prediction model, and outputting an overall risk probability and a key influence node identifier; issuing a grading early warning signal according to the risk probability; carrying out interpretable root cause positioning through a root cause analysis rule chain based on directed acyclic graph representation; incremental learning is carried out on the prediction model based on early warning feedback data to form an optimized closed loop; the system correspondingly comprises a data acquisition module, a rule judgment module, a risk prediction module, an early warning release module, a root cause positioning module, a feedback learning module and the like. According to the invention, the early-stage, accurate and automatic early warning and root cause analysis of the full-process risk of order fulfillment are realized.

The invention relates to the technical field of medical information processing, in particular to an acute gastrointestinal hemorrhage secondary sepsis risk prediction system and method, and the method comprises the following steps: retrospectively collecting multi-source clinical data of an acute gastrointestinal hemorrhage patient, and constructing a data set; in the training set, preprocessing the multi-source clinical data and screening key features related to the sepsis risk; based on the key features, adopting a machine learning algorithm or a deep learning algorithm to construct an acute gastrointestinal hemorrhage secondary sepsis risk prediction model; and inputting multi-source clinical data of a patient with acute gastrointestinal hemorrhage to be predicted into the optimized prediction model, and outputting a risk probability value of secondary sepsis of the patient with acute gastrointestinal hemorrhage to be predicted. According to the method, the acute gastrointestinal hemorrhage secondary sepsis risk prediction model based on multi-source clinical data fusion is constructed, so that the sepsis risk of a specific patient group is accurately evaluated.

The invention discloses a cerebral hemorrhage patient tracheotomy risk prediction method and system based on machine learning, and the method comprises the steps: obtaining an initial clinical data set of a target patient, calculating laboratory inspection data according to a predefined rule, and constructing a composite physiological state index to generate a feature vector for prediction; inputting the feature vector for prediction into a risk prediction model pre-trained based on an ensemble learning algorithm to obtain a risk quantitative index; the model interpretation module generates an individualized prediction contribution decomposition result based on an SHAP value calculation framework, and explains the specific influence of each feature on the risk index; and finally comprehensively generating a risk prediction report. According to the method, the feature representation and model prediction capability is enhanced by constructing the composite indexes, and meanwhile, the decision process is transparent and credible by utilizing interpretability analysis, so that clinical risk assessment and decision support are effectively assisted.

The invention discloses a highway intelligent risk monitoring method and system based on multi-modal information, and the method comprises the steps: collecting multi-modal traffic data, constructing a road network holographic portrait through a road network holographic portrait risk prediction model, positioning a potential risk point through a dual time-space mask anomaly detection model, and carrying out the detection of the potential risk point. Lane-level risk assessment is completed by means of a lane-level risk dynamic assessment algorithm, and risk information is output after multi-dimensional fusion analysis is carried out through a traffic risk intelligent research and judgment platform. All the models and algorithms are operated cooperatively, data integration, feature mining, anomaly recognition, risk assessment and fusion research and judgment are achieved step by step, a whole-process monitoring system from data collection to result output is constructed, refined and dynamic risk monitoring from the global road network to the local lane is achieved, the risk recognition accuracy and monitoring comprehensiveness are effectively improved, and the risk monitoring efficiency is improved. And technical support is provided for safe operation of the expressway.

The invention discloses a chronic obstructive pulmonary disease dynamic risk prediction method based on a time sequence convolutional network, and the method comprises the steps: obtaining static baseline data and dynamic time sequence data of a patient through multi-source data collection, and achieving the data synchronization through timestamp alignment; carrying out one-hot coding on the static data, constructing a feature matrix for the dynamic data, and carrying out standardization processing; a personalized context vector is constructed based on the static features, and multi-source dynamic time sequence feature adaptive fusion is realized by using an attention mechanism; performing time sequence dependency feature extraction by adopting a causal expansion convolutional network, and capturing a long-term dependency relationship through residual block stacking and exponential expansion rate design; and inputting the extracted time sequence features into a classifier, and outputting the dynamic risk probability of acute exacerbation of the chronic obstructive pulmonary disease patient. According to the method, self-adaptive feature fusion is realized in combination with personalized context vectors and an attention mechanism, a long-term time sequence dependency relationship is captured by adopting a causal expansion convolutional network, and a high-precision chronic obstructive pulmonary disease dynamic risk prediction model is constructed.

The invention provides a safety monitoring method and system for deep well heat damage, relates to the technical field of deep well safety, and solves the technical problems of high energy consumption and insufficient monitoring precision of deep well high-temperature heat damage treatment in the prior art. The method comprises the following steps: constructing a heat damage risk prediction model based on a machine learning algorithm, inputting real-time deep well environment data into the heat damage risk prediction model, and outputting a current heat damage risk level; based on the current heat damage risk grade, refrigerating equipment and a ventilation system are dynamically controlled, and the environment is cooled; and waste heat generated during operation of the refrigeration equipment and the mine equipment is recycled, and stepped recycling and power generation treatment are conducted on the waste heat. The method is used for the full-period operation process of deep well mineral resource exploitation, and is particularly suitable for core production links such as the deep well tunneling stage and the stoping operation stage.

The invention discloses a 3D printing defect real-time correction system based on AI visual inspection, and relates to the field of 3D printing defect real-time correction, and the 3D printing defect real-time correction system comprises a region division module which is used for carrying out region division to form at least one printing monitoring block; the image acquisition equipment is used for synchronously acquiring a top image and a side image of a printed piece in the printing monitoring block through layer-frame; the image preprocessing module is used for acquiring a preprocessed image of the printing monitoring block; the feature extraction module is used for extracting printing defect feature vectors; the risk prediction module is used for constructing a 3D printing risk prediction model and predicting the probability of occurrence of irreversible defects in future M-layer printing; the instruction generation module is used for generating a parameter fine tuning instruction and an emergency intervention instruction; and the dynamic adjusting module is used for adjusting the printing parameters in real time and dynamically switching the working modes. The method has the advantages that accurate intervention can be carried out before 3D printing irreversible defects occur, and printing success is effectively guaranteed.

The invention discloses a construction method of an obstructive sleep apnea feature recognition model. The obstructive sleep apnea feature recognition model is used for recognizing obstructive sleep apnea features, and comprises the following steps: collecting an apnea hypopnea index and a front face video of a subject; performing frame sampling and image enhancement on the front face video; performing automatic positioning and geometric normalization on face key points; constructing a weighted adjacency graph, and dividing mutually exclusive functional subnets; extracting multi-scale topological features based on the weighted adjacency graph and the functional subnet; splicing the multi-scale topological features, the facial anatomical features and the clinical variables to form comprehensive feature vectors, and screening the comprehensive feature vectors; and adopting an OSA risk prediction model to predict the probability of the individual suffering from obstructive sleep apnea. According to the method, screening can be completed in a non-invasive and low-cost manner under common illumination and natural postures.

The invention provides a cancer early-stage dynamic risk prediction method and device, equipment and a storage medium, and relates to the technical field of data management and risk assessment. The method comprises the following steps: acquiring health medical data, key gene data and behavioral habit data of a target user; configuring a dynamic risk prediction model based on the key gene data and the behavior habit data to obtain a dynamic risk prediction model corresponding to the target user; and inputting the health medical data into a dynamic risk prediction model corresponding to the target user to obtain predicted health medical data of the target user, and performing cancer early-stage dynamic risk prediction on the target user based on the predicted health medical data. According to the method, the health condition of an individual can be comprehensively analyzed from multiple dimensions, the change of the physical condition of a patient can be better adapted, and a more reliable basis is provided for early diagnosis and treatment of cancers.

The invention provides a rockburst intelligent early warning method based on cross-modal fusion of while-drilling sensing data and micro-seismic monitoring data, and belongs to the technical field of tunnel and underground engineering safety and disaster prevention and control. The rockburst intelligent early warning method comprises the steps that while-drilling parameters are acquired in real time, and drilling three-dimensional track coordinates are recorded; microseismic event signals induced by drilling disturbance are collected in real time, and seismic source parameters are inverted; preprocessing and time-space alignment are carried out on the collected while-drilling parameters and the microseismic event signals, and an aligned multi-modal feature sequence is generated; inputting the aligned multi-modal feature sequence into a trained rockburst risk prediction model, and outputting a rockburst risk grade probability distributed along the axis of each drilling track; and performing spatial interpolation on the rockburst risk level probability distributed along the axis of each drilling track, generating a three-dimensional rockburst risk probability field in front of the tunnel face, performing three-dimensional visual reconstruction on the three-dimensional rockburst risk probability field, performing risk level judgment according to a three-dimensional risk cloud picture in front of the tunnel face, and automatically issuing a corresponding early warning signal.

The invention discloses an AI auxiliary radiotherapy dose intelligent optimization device, which comprises a multi-modal image fusion module, a dose distribution prediction module, a dynamic adaptive optimization module and a prognosis model integration module, and is characterized in that the multi-modal image fusion module is used for aligning anatomical features of different images, eliminating respiratory motion artifacts and predicting the dose distribution of the different images; the dose distribution prediction module is used for rapidly predicting radiotherapy dose distribution based on anatomical features and historical data, the dynamic adaptive optimization module is used for monitoring anatomical changes in real time and dynamically adjusting a dose plan, and the prognosis model integration module is used for quantifying correlation between dose distribution and radioactive injury risks. High-precision alignment and respiratory motion artifact elimination of an anatomical structure are achieved through the multi-modal image fusion module, three-dimensional dose calculation is rapidly and accurately conducted through the dose distribution prediction module, organ displacement and deformation are effectively coped with through the dynamic self-adaptive optimization module through a real-time image monitoring and reinforcement learning algorithm, and the accuracy of the three-dimensional dose calculation is improved. And the prognosis model integration module constructs an individualized risk prediction model.

The invention relates to the field of rainfall pollution detection, in particular to an automatic rainfall sampling method and device for a high-density built-up area. Geographic information parameters and rainfall dynamic parameters of the high-density built-up area are collected; performing format unification, space-time alignment and normalization processing on the obtained multi-source data, and constructing a standardized multi-dimensional feature data set; inputting the standardized multi-dimensional feature data set into a pollution risk prediction model, and dynamically calculating and outputting a pollution risk level of a rainfall event and a time window of a key pollution stage; generating a composite dynamic sampling strategy based on the pollution risk level and the time window; the method comprises the following steps: collecting and storing a water sample according to a composite dynamic sampling strategy, and carrying out preliminary analysis on the collected water sample by using an integrated water quality sensor to generate actually measured concentration data; and carrying out weighted compensation on the generated actually measured concentration data by utilizing geographic information parameters of the high-density built-up area, and calculating the equivalent pollution concentration representing the comprehensive pollution level of the high-density built-up area.