142 results about "Dynamic assessment" patented technology

The invention relates to the technical field of risk assessment, in particular to an AI enterprise financial risk dynamic assessment method and system. The method comprises the following steps: obtaining enterprise multi-dimensional financial data, operation activity data, industry environment data and macroeconomic index data, carrying out cleaning, standardization and time sequence alignment processing, and integrating the data into a financial risk assessment multi-dimensional feature matrix; constructing a dynamic weight distribution model based on the financial risk assessment multi-dimensional feature matrix, endowing differentiated weights, and generating a time sequence weighted feature vector; risk level mapping and comparative analysis are carried out on the time sequence weighted feature vectors, risk points exceeding a threshold value are identified, and feature sources of the risk points are traced; and constructing a risk conduction path map based on the risk points and feature sources thereof, simulating risk evolution trends under different intervention measures, and evaluating and generating a dynamic evaluation report containing risk early warning levels, key influence factors and intervention measure suggestions. According to the invention, the enterprise financial risk assessment efficiency can be improved.

The invention relates to the technical field of geological engineering, and discloses a geological safety risk dynamic assessment method based on multi-source data fusion, which comprises the following specific steps: step 1, collecting and standardizing multi-source geological data; 2, performing semantic fusion and conflict resolution on the geologic features; 3, constructing a dynamic risk assessment model; 4, risk situation real-time updating and early warning are carried out; the satellite remote sensing system in the first step adopts the synthetic aperture radar interference measurement technology, the spatial resolution is better than 3 meters, the revisit period is shorter than 7 days, and the earth surface deformation monitoring precision reaches the millimeter level. Through standardized processing and semantic fusion of the multi-source geological data, the problem of heterogeneous data integration is effectively solved, the data basic quality of risk assessment is improved, a space-time coupling neural network model is adopted, nonlinear features and space-time correlation characteristics of geological risk evolution are accurately captured, and prediction precision and timeliness are improved.

The invention relates to the technical field of government affair services, in particular to a government affair service policy effect intelligent evaluation system based on multi-source data fusion. The system comprises a data acquisition module, a data fusion module, an evaluation model module and a result output module. The data acquisition module acquires policy-related data from a plurality of heterogeneous data sources; the data fusion module performs cleaning, normalization and semantic fusion processing on the multi-source data; the evaluation model module analyzes fusion data by using an intelligent evaluation model based on a causal inference framework, and quantitatively evaluates multiple dimensions of a policy effect; and the result output module generates a structured evaluation report. According to the method, the causal relationship between policy intervention and effects is accurately identified through a causal inference technology, multi-source data semantic fusion is realized by adopting the knowledge graph, the method has dynamic assessment and feedback calibration capabilities, and the accuracy and practicability of policy effect assessment are effectively improved.

The invention discloses an earthquake disaster loss dynamic assessment method and system based on multi-source disaster situation data, and relates to the technical field of earthquake disaster loss assessment, and the method comprises the following steps: S1, obtaining the multi-source original data of an earthquake disaster, and carrying out the preprocessing, the multi-source original data comprises seismic oscillation parameter data and multi-source disaster data; and S2, based on the preprocessed multi-source data, constructing a coupling driving model of the earthquake influence field and the multi-source disaster situation data, and generating a regional disaster damage thermodynamic map. According to the earthquake disaster loss dynamic evaluation method and system based on the multi-source disaster situation data, the earthquake multi-source disaster situation data is acquired, the coupling driving model of the earthquake influence field and the multi-source disaster situation data is constructed, and the influence of various factors on the earthquake disaster loss is comprehensively considered, so that the limitation that a traditional method depends on a single data source is overcome; the real situation of earthquake disaster loss can be reflected more comprehensively and accurately, and a more reliable basis is provided for rescue decision making.

The invention relates to the technical field of fire safety monitoring and early warning, in particular to a fire risk dynamic assessment and early warning method based on deep reinforcement learning, comprising the following steps: acquiring multi-source sensing data of a target area; a dynamically updated fire risk assessment state space is constructed based on multi-source sensing data, and a real-time state in the state space is processed and a risk assessment decision is generated through a deep reinforcement learning agent optimized based on an accumulated award function weighted by an accuracy award, a false alarm punishment and a missing alarm punishment; the deep reinforcement learning agent adopts a deep deterministic strategy gradient network structure; mapping the continuous output of the risk assessment decision into a discrete dynamic fire risk level; and triggering an early warning signal matched with the dynamic fire risk level according to the dynamic fire risk level. Through multi-source data standardization, dynamic state space, reinforcement learning optimization and grading early warning binding, accurate dynamic evaluation, reliable early warning and efficient emergency response are realized.

The invention discloses a steel structure construction safety risk dynamic assessment method and system based on artificial intelligence. The method comprises the steps of obtaining a multi-dimensional data set in real time; based on the multi-dimensional data set, a deep learning algorithm is adopted to fuse an image and a sensor signal, a structure defect is accurately identified, and a defect feature vector is extracted; when the defect feature vector exceeds a preset threshold value, a machine learning classifier is introduced to construct a risk assessment model, classification quantification of defects is realized, and an initial risk level is output; for the initial risk level, analyzing the space-time fusion data by using a probability state transition model, simulating the dynamic evolution trend of the risk and judging the stability of the risk; a mapping model is constructed based on a virtual simulation technology, dynamic simulation deduction is carried out on risk changes, and a visual visualization report and a graded early warning signal level are generated. According to the invention, full-chain intelligent closed loop from defect perception to risk prospective early warning is realized, and the safety management and control level and accident prevention capability of a construction site are greatly 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 discloses a big data analysis-based occupational health management system optimization method. The method comprises the following steps of 1, collecting and preprocessing multi-source health information data of employees; 2, mapping the multi-modal data set into a unified-dimension employee health feature tensor; 3, constructing a health risk isomerism map based on the employee health feature tensor; 4, inputting the health risk heterogeneous atlas into the improved GATv2 model to obtain a health risk embedded representation vector; 5, performing risk assessment on the health risk embedded representation vector, and identifying a high-risk employee node; step 6, constructing an intervention action candidate set for the high-risk employee nodes, and generating an optimal intervention strategy by adopting an MOPSO algorithm; and step 7, executing the optimal intervention strategy and generating a score of an intervention effect. According to the invention, the improved GATv2 model and the MOPSO algorithm are fused, and dynamic assessment and accurate intervention of occupational health are realized.

The invention relates to the technical field of laboratory safety risk assessment, and discloses a laboratory safety risk dynamic assessment method based on machine learning, and the method comprises the steps: S1, collecting the multi-source real-time data of a laboratory environment and equipment; s2, carrying out preprocessing and time sequence synchronization on the multi-source real-time data; s3, extracting time sequence features from the time sequence data based on a sliding time window; s4, inputting the feature vector into a machine learning model with an online learning capability; s5, distributing according to the risk prediction result and the current data; and S6, when the risk level reaches a preset condition, triggering laboratory safety early warning and linkage control. Incremental updating is carried out on the machine learning model by inputting feature vectors in real time, adaptive iteration adjustment is carried out on a to-be-updated parameter set based on prediction error changes of a continuous time window, risk judgment is corrected in time along with environment changes, and the real-time performance, sensitivity and stability of model risk prediction are improved.

The invention discloses a chronic obstructive pulmonary disease high risk dynamic assessment model system based on an artificial intelligence algorithm, relates to the technical field of chronic obstructive pulmonary disease high risk assessment, and aims to solve the technical problem that the timeliness and accuracy of an assessment result of an existing assessment model are limited. The medical data acquisition module is used for acquiring medical data of a subject through a wearable device, an electronic medical record system or a manually input multi-source channel, generating standardized preprocessed medical data after performing format cleaning, missing value filling and abnormal value filtering on the data, and transmitting the standardized preprocessed medical data to the memory of the cloud database; and the artificial intelligence dynamic assessment model construction module is used for constructing an artificial intelligence assessment model fusing time sequence characteristics and a nonlinear relationship based on the preprocessed medical data, and generating an intermediate result and a final parameter of risk assessment through multi-level calculation. The method has the advantage that the high risk of chronic obstructive pulmonary disease can be dynamically evaluated in real time.

The invention relates to an earth pressure balance shield construction safety toughness dynamic evaluation system and method based on an extended cloud model and a network analysis method, and the system comprises an index system construction module which builds a multi-level toughness evaluation system based on a literature measurement and factor analysis method; an entropy weight TOPSIS weight calculation module objectively calculates the initial weight of the index through an information entropy theory; the ANP network weight optimization module corrects and optimizes the global weights of the indexes by constructing an inter-index nonlinear dependency network and a feedback mechanism; and the extended cloud toughness evaluation module quantifies qualitative indexes into membership degrees for different toughness levels based on expectation, entropy, hyper-entropy and other digital characteristics, comprehensively integrates weights and the membership degrees, and finally outputs soil pressure balance shield construction safety toughness levels and targeted optimization strategies. According to the method, multi-dimensional toughness quantitative evaluation and dynamic prevention and control of the construction safety risk of the earth pressure balance shield (EPB) can be realized under the complex stratum condition.

The invention discloses a forest fire risk dynamic assessment and early warning system based on multi-source data fusion and artificial intelligence, and belongs to the technical field of forest fire prevention and disaster early warning. The system aims at solving the technical problems that in an existing forest fire monitoring technology, evaluation lags behind, data is single, and risk prospective prediction cannot be achieved. According to the technical scheme, satellite microwave remote sensing, optical remote sensing and near real-time meteorological and topographic data are integrated through a multi-source data acquisition module, and are fused and processed through a feature extraction module to generate a fire danger influence factor feature set containing the water content, the load capacity, the meteorological conditions and the topographic features of combustible materials; a dynamic risk distribution diagram in a future preset time period is output through a machine learning model of the dynamic risk assessment module, and finally automatic early warning is achieved through the early warning and decision making module. The system is mainly used for realizing scientific and accurate assessment and early warning of forest fire risks, and provides technical support for forest fire prevention decision making.

A rock burst risk dynamic assessment method based on a Bayesian method comprises the steps that a stope working face is divided into M * M regular grids, and each grid serves as an independent assessment unit; collecting geological and mining data, selecting a plurality of influence factors based on historical data and expert knowledge, and quantifying the plurality of influence factors by adopting a static evaluation model; determining weights of a plurality of influence factors by using an analytic hierarchy process, and calculating an initial risk score of each evaluation unit by using a comprehensive index method; normalizing the initial dangerousness score into prior distribution by adopting an S-type function, and constructing an initial dangerousness prior probability field; a power law attenuation model is introduced to calculate influence weights among the evaluation units, and an influence weight matrix is constructed; real-time monitoring data are obtained through a micro-seismic monitoring system, the risk probability of each evaluation unit is updated in real time based on the Bayesian theorem, and dynamic evaluation and evaluation result output are achieved. According to the method, the accuracy and the real-time performance of rock burst risk assessment can be effectively improved.

The invention specifically relates to an automobile fire risk dynamic assessment method and system based on multi-source data fusion, and relates to the technical field of automobile safety, and the method comprises the steps: employing an improved analytic hierarchy process to determine an index weight, building an initial assessment model based on a random forest algorithm, and outputting a 0-100 score risk value. According to the invention, road traffic and environmental meteorological data are incorporated into an evaluation system, and through verification of tens of thousands of cases, the risk identification accuracy can be effectively improved, and the missing report rate can be reduced; through a double-layer mechanism of five-minute short-term updating and daily model evolution, the risk response delay is less than 10 seconds, the problem of evaluation lag during scene switching is solved, and the method is particularly suitable for frequent switching scenes of urban expressways and suburban road sections.

The invention relates to a regional fire risk dynamic assessment and early warning method for an urban tunnel. The method comprises the following steps of: gridding the tunnel by taking tunnel infrastructure as a reference object; obtaining a tunnel fire accident database, and constructing a tunnel fire risk assessment index system; considering the influence of the index on the fire risk and the influence of the mobile index on spatial distribution, and determining an index weight; determining an index membership threshold, calculating an index membership degree, and obtaining a real-time initial fire risk assessment result of each region; early warning parameters are reasonably selected, the initial fire risk assessment result is corrected in combination with the single index risk, and a final regional fire risk assessment result is obtained; setting an early warning threshold value of each index under different evaluation results based on the final evaluation result of the regional fire risk occurrence probability; and comparing the index early warning value according to the index data measured by the risk sensing equipment, and outputting a single-index fire risk early warning result to realize single-index fire risk early warning. The method is high in pertinence, high in accuracy and high in sensitivity.

The invention relates to the technical field of data security, and discloses a data security risk quantitative dynamic evaluation method and system. The method comprises the following steps: collecting interaction data between nodes in real time, and constructing a preliminary risk distribution map; analyzing the atlas to recognize mutation nodes and defining a target area; evaluating a risk diffusion range of the target area, marking a high-risk propagation chain, extracting a key path, and calculating a risk accumulation effect of the key path to generate a monitoring frequency parameter; a monitoring strategy is adjusted and a risk assessment matrix is updated based on the parameters, so that an interaction strength synchronization demand between the nodes is analyzed, and a multi-node cooperative control scheme is determined; and finally, performing simulation verification on the scheme by constructing an interaction scene, judging whether the risk fluctuation frequency reaches a stable state or not, and outputting a final control scheme. According to the invention, the accuracy of multi-node data security risk early warning is improved.

According to the comprehensive evaluation method and system for the power production technical improvement project and the medium, expert preferences are quantized through joint analysis, historical project data rules are mined in combination with a deep neural network, deep fusion of subjective and objective information is achieved, authority of expert experience is reserved through a fuzzy integral fusion mechanism, and the comprehensive evaluation efficiency is improved. And objective laws in the data are fully utilized, so that comprehensive, deep and dynamic assessment and evaluation can be carried out on the power production technical improvement project, scientific, objective and prospective assessment and evaluation can be carried out on the project, the defects of a current method are effectively made up, and the method is suitable for popularization and application. And a more powerful, scientific and accurate support is provided for the management decision of a power production technical improvement project. The method is not only suitable for power production technical improvement project evaluation, but also can be expanded to complex project evaluation scenes of other industries, and has good universality and popularization value.

This invention relates to a multi-dimensional dynamic assessment method and system for waterway operation status, belonging to the field of waterway transportation operation monitoring technology. This invention fuses processed data at the data layer, feature layer, and decision layer to form a deep coupling of multi-modal data; constructs a multi-dimensional waterway operation status assessment index system; utilizes this index system for real-time dynamic assessment of multi-dimensional waterway operation status; trains a deep learning model using historical multi-modal time-series data; and uses the deep learning model for time-series prediction and graded early warning of waterway operation status. Based on the assessment and early warning results, it outputs executable decision-making schemes. This invention constructs an integrated assessment system encompassing navigation environment, vessel flow, waterway siltation, structural health, hydrometeorology, and navigation safety dimensions, achieving a comprehensive depiction of waterway operation status. It employs a three-level fusion strategy to break down data silos from multiple sources, improving the accuracy and reliability of status assessment.

The application discloses a device health degree dynamic evaluation method and system based on a self-encoder, relates to the technical field of health evaluation, and comprises the following steps: reading runtime sequence data; extracting data features after cleaning and screening; using the data features as training data to perform dynamic weight constraint learning on an LSTM self-encoder, and constructing a prediction model; receiving real-time data by using the model and calculating reconstruction error to establish a first score; configuring a sequence prediction branch, identifying gradual degradation by aligning short-term prediction error, and establishing a second score; identifying mutation points according to the reconstruction error, combining multi-scale sliding window analysis, and establishing a third score; and comprehensively evaluating the device health degree by using the three types of scores. The application solves the technical problems of high cost, poor adaptability and insufficient early warning capability caused by the dependence on invasive sensors and static rules in the prior art, and achieves the technical effects of improving the accuracy and foresight of device health evaluation.

The application relates to the technical field of medical information processing, and discloses a method and system for dynamically evaluating post-renal biopsy occult bleeding risk based on multi-source data fusion, wherein the method comprises the following steps: constructing a standardized uniform observation vector sequence; constructing a short-time instant risk model; constructing a mechanism to establish a long-time lag risk transfer model; generating a hierarchical treatment decision and a review scheduling plan; gain correcting the risk probability; and outputting a final occult bleeding risk grade. Compared with the scheme for postoperative bleeding risk in the prior art, which only performs static risk evaluation based on a single time section index, especially in the case that postoperative vital signs are temporarily stable but there is delayed hemoglobin decline, the technical problem that the continuous prediction of the risk transfer process of'short-time stability-long-time lag deterioration' cannot be realized is solved. Since the time-varying state transfer probability matrix is introduced in the application, and the risk transfer acceleration is determined, the sensitivity of postoperative risk identification is improved.

The invention relates to the technical field of artificial intelligence, and provides a mountain wind energy resource dynamic evaluation method and device, equipment and a medium, which can perform attention fusion on multi-dimensional initial data, so that fusion features consider feature importance difference and comprehensiveness. A mountain wind energy evaluation data set with a wind energy power density label is constructed, and a basis is provided for supervised training of the model; the state space completely covers core variables influencing mountain wind energy evaluation, and the problem that a traditional model is insufficient in state space dimension and cannot represent a complex coupling relation is solved. The action space is in non-static output, so that the adaptive capacity of the model is improved; the reward function solves the technical problem that precision and efficiency cannot be considered at the same time; a dual experience playback mechanism improves the evaluation precision of the model on a complex wind field scene; the target network delay updating strategy enables the target network to maintain parameter stability in an iteration period, and the problems of training oscillation and slow convergence caused by real-time fluctuation of target return are solved.

This invention relates to the field of urban ecological monitoring and optimization control technology, and discloses a method and system for dynamic assessment and optimization of urban carbon sinks. The method includes: acquiring multi-source green space characteristic data; constructing a light energy utilization model to output a photosynthetic carbon fixation sequence; using a long short-term memory network model based on a weighted trend-maintaining loss function and a DropBlock mechanism; setting scenario influence factors to construct multi-scenario evolution results; and using a particle swarm optimization algorithm to generate optimal green space management and control parameters. Compared to existing technologies, especially under conditions of frequent human management, which struggle to achieve high-frequency dynamic carbon sink assessment and optimization control for urban green spaces of different types, this application improves the real-time performance, accuracy, and efficiency of urban carbon sink monitoring and control optimization by introducing a trend-maintaining loss mechanism and time-segment DropBlock regularization to enhance model generalization ability, and by combining multi-scenario perturbation simulation.

The invention relates to the technical field of health risk assessment, and discloses an elderly health risk assessment method and system, and the method comprises the steps: collecting physiological parameter data and body function data of an elderly, and determining an initial health risk assessment value of the elderly; judging whether to adjust the initial health risk assessment value or not according to a comparison result of the collected real-time health data and a preset health threshold range; if yes, calculating a deviation value between the real-time health data and a preset health threshold range, and determining a final health risk assessment value according to the deviation value; according to a comparison result of the final health risk assessment value and a preset health risk range, judging whether potential risk analysis needs to be carried out or not; and if so, collecting psychological state data of the elderly to determine a potential risk value, and determining a health risk level according to a sum result of the potential risk value and the final health risk assessment value. Through multi-dimensional data integration, dynamic assessment adjustment and hierarchical risk analysis, systematic assessment of the health risk of the elderly is realized.

This invention relates to the field of construction monitoring technology, specifically to a method and system for monitoring structural offsets using BIM technology. The method involves using a multi-source sensor cluster to collect real-time location information of multiple components within the construction structure, obtaining real-time monitoring data. Based on the real-time monitoring data and the preset coordinates of the components in the BIM model, a first offset evaluation index for the target component is calculated. The component importance is then obtained. The first offset evaluation index is corrected based on the component importance score to obtain a second offset evaluation index. This second offset evaluation index is compared with a preset offset threshold to determine monitoring and early warning information. This approach enables dynamic assessment of offset risks, establishes an early identification mechanism for potential problems, and optimizes the early warning system. It can dynamically capture offset trends and comprehensively consider them, achieving early risk identification and optimized early warning, thereby improving the accuracy and efficiency of construction structures.

The invention relates to a safety critical system risk dynamic assessment system based on multi-modal fusion and uncertainty quantification, and belongs to the technical field of industrial safety and artificial intelligence crossing. The technical problems of limitation of a single prediction model, lack of uncertainty representation, coarse and shallow multi-modal information fusion and the like of an existing safety evaluation system are solved. According to the method, a system architecture integrating multi-source heterogeneous data acquisition, space-time alignment preprocessing, modal adaptive fusion, uncertainty dynamic quantification and risk interpretability output is constructed. According to the system, an improved conformal prediction framework is adopted, and a Bayesian neural network and deep evidence learning are combined, so that real-time dynamic assessment and uncertainty accuracy measurement of safety critical system risks are realized. The core technology comprises multi-modal time sequence feature extraction of a gated cycle unit network based on an attention mechanism, fused hysteresis drift failure modeling of introducing a modal confidence imbalance coefficient and a modal missing detection delay coefficient, and a personalized uncertainty quantification method of conditional risk coverage guarantee. The method is especially suitable for the safety key fields such as industrial process control and infrastructure management, can provide double indexes of risk level and confidence for decision makers, and significantly improves the reliability and operability of risk assessment.

The invention relates to a restricted space accident risk dynamic assessment method and system based on the Internet of Things. The method comprises the following steps: acquiring multi-source heterogeneous time-space sequence data, constructing a time-space data cube based on the data, inputting the time-space data cube into a physical constraint generative adversarial network, creating a large amount of high-quality virtual risk scene data conforming to a physical rule, and combining the virtual risk scene data with real data to obtain a real risk scene. And training a risk assessment model, seamlessly connecting the trained model with a real-time data stream, and assisting with a smooth filtering technology to obtain a comprehensive accident risk dynamic assessment result. By adopting the method, small sample limitation can be overcome, a physically compliant virtual risk scene is generated, and accurate dynamic assessment of the accident risk in the limited space is realized.

The invention discloses a multi-modal lung cancer risk dynamic assessment system and method, and belongs to the technical field of medical data analys.The multi-modal lung cancer risk dynamic assessment system comprises a data acquisition module, a topological representation module, a self-adaptive attention gating module, a risk assessment module and a feedback optimization module; the method comprises the following steps: constructing a family history topological graph, a gene mutation topological graph and an environmental exposure topological graph, extracting multi-level topological features, generating a continuous bar code and a Betti number sequence based on continuous homology analysis, calculating an interaction coefficient, and triggering a cross-modal risk collaborative amplification mechanism when a gene mutation and environmental toxin coexistence mode is detected. The system is further provided with a three-level collaborative amplification mechanism, a risk decomposition report containing gene risks, environment risks, family risks and collaborative amplification factors is generated, dynamic parameter optimization is achieved by calculating the topological entropy change rate, deep fusion of multi-modal data is achieved, and the risk factor interaction effect is accurately quantified.

This application relates to an auxiliary analysis system based on neural activity synchronization, comprising: a cooperative task output module that outputs a multi-person cooperative task; a task execution module that adjusts the synchronous participation of each target object in the cooperative task within the same time window based on the multi-person cooperative task; a data acquisition module that collects brain function signals and behavioral data of each target object during the execution of the multi-person cooperative task; an interference control module that collects interference information; a group neural activity synchronization calculation module that, based on the collected brain function signals, behavioral data, and interference information of each target object, quantifies the neural activity synchronization of any two target objects during the cooperative task to obtain a neural activity synchronization index; a group synchronization index and a group synchronization network structure are obtained based on the neural activity synchronization index; and a risk monitoring module that obtains auxiliary analysis results. This system can achieve objective auxiliary identification, dynamic assessment, and closed-loop optimization of ASD risk status.

This invention discloses an adaptive conservation assessment system for vernacular architectural heritage based on digital narrative. The system first constructs a digital narrative knowledge graph integrating spatiotemporal attributes, linking intangible cultural information such as oral history and craft memories with a high-precision 3D model. Then, through dynamic binding of multi-source sensor data, it achieves continuous integration and updating of architectural physical state, environmental parameters, and social usage information. Based on this, a risk assessment model coupling physical risk and the degree of damage to the integrity of the cultural narrative is established, outputting a dual risk index of structural and cultural value. Finally, digital twin technology is used to simulate the long-term effects of alternative conservation strategies, supporting adaptive decision-making. This invention realizes a transformation from "static recording" to "dynamic assessment," and from "material protection" to "cultural continuity," enhancing the scientific rigor, predictability, and public participation in the conservation process.

This invention discloses a postoperative bile fistula risk prediction system for hepatobiliary surgery patients, belonging to the field of bile fistula risk data processing technology. The system includes an evidence-driven variable acquisition module, a standardization judgment module, a time-series feature generation module, a feature screening module, and a dynamic prediction module. It performs time-series reconstruction and evolutionary feature extraction for dynamic monitoring indicators of postoperative bile extravasation. Through a structural-biochemical consistency constraint screening method, it fuses and filters features of structural risk channels, biochemical risk channels, and time-series evolution channels. Based on core predictive feature data, it constructs a bile fistula risk prediction model and dynamically predicts the probability of bile fistula occurrence at different postoperative time points through a time-series rolling update mechanism, outputting risk levels and early warning results. This invention enables early identification, dynamic assessment, and individualized intervention support for bile fistula risk, improving the accuracy of prediction results and its clinical application value.