243 results about "Dynamic Bayesian network" patented technology

The invention provides a circuit board production yield root cause tracing method, which comprises the following steps of: acquiring process parameters, equipment states, environment variables and quality detection results of a whole production process, and constructing a multi-dimensional time sequence database; extracting a typical manufacturing process modeling unit through a sliding time window and dynamic time warping; establishing a cross-process dynamic causal relationship graph in combination with nonlinear Granger causal test, a structural equation model and a dynamic Bayesian network; an intervention and anti-factual reasoning method is applied, the causal effect and path stability under parameter disturbance of each process are evaluated, and the influence of a key causal path is quantified; according to the method, the accuracy of defect rate root cause positioning can be improved, and powerful support is provided for circuit board production process optimization and quality improvement.

The invention relates to the technical field of robot decision making, and provides a robot multi-modal fusion autonomous decision making method and system based on a large language model.The method comprises the steps that a robot obtains multi-modal environment information through a visual sensor, a touch sensor, an auditory sensor and a laser radar which are carried by the robot; performing preliminary filtering and noise reduction processing on the original sensor data, and synchronously recording all the sensor data by timestamps; performing space-time semantic alignment on the preprocessed multi-modal data, mapping pixel coordinates of a target in a visual target coordinate quantization original image to a robot coordinate system, performing uncertainty evaluation on a multi-modal signal through a dynamic Bayesian network, and taking entropy or variance as an uncertainty quantitative evaluation index. According to the method, the information quality is improved from a data fusion source, accurate and reliable basic support is provided for subsequent decision making, and decision making errors caused by data deviation are greatly reduced.

The invention relates to the technical field of computer systems based on specific calculation models, and discloses a blood oxygen change monitoring algorithm fused with dynamic Bayesian modeling, which comprises the following steps: quantizing signal uncertainty by calculating entropy of pulse waveform harmonic energy distribution; and on the basis of the entropy value, driving a dynamic Bayesian network to carry out confidence coefficient evaluation and weighting processing on the blood oxygen estimation model, and finally outputting a decision pair containing a blood oxygen estimation value and the confidence coefficient thereof. According to the method, the signal uncertainty is converted into a computable entropy index, so that the system can autonomously distinguish real physiological changes and measurement noise, the problem of misjudgment caused by motion interference in traditional blood oxygen monitoring is avoided, meanwhile, non-inductive personalized calibration is achieved by utilizing the continuous learning ability of the Bayesian network, and the accuracy of the system is improved. And the reliability and the practicability of the medical wearable equipment are remarkably improved.

The invention discloses a geological disaster monitoring and early warning method based on multi-factor dynamic coupling, belongs to the technical field of geological disaster monitoring and early warning, and aims to solve the problems that a traditional method cannot fuse multi-source factors in real time, is low in early warning precision, lags in response and the like. A geological environment static background factor is combined to construct a susceptibility evaluation model, a dynamic weight is analyzed and calculated by adopting a time sequence, a dynamic Bayesian network is utilized to carry out coupling analysis, and a geological disaster risk probability value is output in real time, so that a corresponding early warning level and an emergency response are triggered. The method is mainly used for real-time monitoring, accurate risk assessment and timely early warning of geological disasters.

The invention discloses a ship desulfurization system reliability evaluation method and system based on multi-source information fusion, and the method comprises the steps: collecting multi-source information data of a hybrid desulfurization system, and carrying out the self-adaptive preprocessing; generating a fusion feature vector; constructing a dynamic Bayesian network based on multi-source fusion features, performing real-time reasoning by adopting data-driven transition probability learning and particle filtering, describing transient behaviors of system state evolution and mode switching, and performing dynamic multi-state reliability modeling; a fault mode is automatically extracted, and data-driven systematic risks are identified and quantitatively analyzed; a multi-resolution digital twinborn architecture is constructed, dynamic simulation prediction is carried out, a self-adaptive updating mechanism is adopted to keep the model synchronous with a physical system, and a virtual verification environment for reliability evaluation is provided; according to the method, an intelligent decision optimization system is constructed, self-adaptive generation and dynamic adjustment of a maintenance strategy are realized, closed-loop feedback is carried out, and the accuracy of reliability evaluation of the hybrid desulfurization system is improved.

Disclosed in the present invention are a low-altitude air route risk map construction method and system based on hexagonal grid cells. The method comprises: on the basis of management and control requirements for different types of airspace, forming low-altitude airspace three-dimensional hexagonal hierarchical grids by means of multi-scale partitioning of an airspace horizontal plane and multi-scale partitioning of an airspace vertical plane; constructing an unmanned aerial vehicle flight risk assessment indicator system for the low-altitude airspace three-dimensional hexagonal hierarchical grids, and after optimization, constructing an unmanned aerial vehicle operational risk assessment model based on a dynamic Bayesian network, so as to generate multi-scale grid risk values; and combining the multi-scale grid risk values with geographic location information of a region, designing a hexagonal grid code index, and generating a dynamic multi-scale low-altitude air route three-dimensional hexagonal risk map. The present invention can improve the accuracy and real-time performance of unmanned aerial vehicle flight risk assessment of grids, quickly obtain low-risk grids of an unmanned aerial vehicle in a target region, and improve the safety of air route planning.

The invention discloses a soft start control method for intelligent temperature control of a glue injection mold, and particularly relates to the technical field of glue injection molds. Initial temperatures of a plurality of heating areas of the mold are collected, and a temperature distribution model is constructed; calculating a thermal inertia score value based on the thermal response characteristic and the thermal capacity parameter of each region; predicting temperature rise rates of different areas by using the thermal inertia score value, the thermal coupling degree abnormal value and the historical control deviation frequency, and setting differentiated soft start amplitude limiting parameters; temperature feedback is collected in real time in the heating process, a temperature rise rate error sequence is constructed, and the future deviation risk is predicted through the dynamic Bayesian network; when the risk value exceeds the limit, the PWM duty ratio or the conduction angle of the heating area is dynamically corrected, and power output self-adaptive adjustment is achieved; the method has the advantages of being high in predictability, accurate in response and intelligent in control, and is suitable for high-precision heating control application of the complex glue injection mold.

The invention provides an intelligent decision-making method based on multi-dimensional risk coupling modeling of new energy ships and related equipment. The method comprises the following steps: fusing multi-modal data of each new energy ship to obtain a target multi-modal feature vector; mining risk implicit association strength by using a large language model, and constructing a risk knowledge graph; performing risk time sequence evolution prediction by adopting a dynamic Bayesian network based on the atlas to obtain a single-ship risk prediction result; constructing a graph structure according to the single ship risk and the operation parameters, and carrying out space coupling modeling by adopting a graph convolutional network to obtain a cluster risk prediction result; and generating an optimal operation and maintenance strategy by adopting reinforcement learning based on a cluster risk prediction result and a reward function by taking a high-fidelity digital twinborn body as a virtual environment. Therefore, according to the method, the problem of closed-loop adaptive control from risk deduction to decision response in a complex navigation scene is effectively solved by constructing a unified modeling mechanism of multi-dimensional risk coupling and linking real-time control strategy generation.

The invention provides a chemical potential safety hazard intelligent identification and early warning method. Relates to the field of chemical safety, and discloses a chemical potential safety hazard intelligent identification and early warning method comprising the following steps: S1, preprocessing multi-modal data through a multi-modal feature self-calibration fusion algorithm; s2, constructing a dynamic Bayesian network time-varying coupling evaluation model based on a protection layer theory; s3, related knowledge documents are retrieved by adopting an enhanced RAG technology; s4, carrying out hidden danger identification based on the knowledge-enhanced large model; s5, four-level intelligent early warning is generated based on the risk value calculation model; and S6, optimizing model parameters through deep reinforcement learning. The chemical potential safety hazard intelligent identification and early warning method based on the protective layer theory and the large model technology has the advantages that dynamic risk perception can be realized, cross-modal potential hazards can be accurately identified, an early warning scheme can be quickly generated, and the initiative and scientificity of chemical safety management can be improved.

The embodiment of the invention provides a dynamic risk assessment management method and system based on business risk control, and belongs to the technical field of business risk monitoring. Comprising the following steps: acquiring behavior data of a user, and performing statistical feature extraction on the behavior data; constructing a dynamic Bayesian network according to the statistical characteristics of the behavior data; performing incremental updating on a conditional probability table of a dynamic Bayesian network according to the real-time behavior data; constructing and training an LSTM model and a random forest model by adopting the behavior data; acquiring a current comprehensive risk assessment result according to the real-time behavior data, the dynamic Bayesian network, the LSTM model and the random forest model; a multi-model fusion mode is adopted, so that the risk assessment precision can be effectively improved; and finally, an adversarial sample is constructed, and the dynamic Bayesian network and the LSTM model are trained offline according to the adversarial sample, so that the generalization ability for an unknown attack mode can be improved, and the security is higher.

The invention, which relates to the technical field of artificial intelligence, discloses an artificial intelligence-based education evaluation and feedback system comprising a data acquisition module, a vector generation module, a prediction module, an error region positioning module and a feedback module. The system constructs a unified high-dimensional cognitive state vector by collecting answering behaviors, eye movement tracks, facial micro-expressions, voices and intonations and electroencephalogram signals of students; generating a learning evolution path map based on a dynamic Bayesian network and a causal reasoning mechanism, and predicting future learning bottleneck nodes; an error region is recognized through semantic deconstruction and graph matching, and context-associated personalized feedback content is generated in combination with a generative language model; according to the system, an evaluation feedback closed loop of cognitive state modeling, accurate identification of an erroneous region and intelligent feedback pushing is realized, and the accuracy of education evaluation and the effectiveness of intervention are improved.

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

The invention discloses an SVG valve hall cooling efficiency evaluation method and system based on a probabilistic graph model, and the method comprises the steps: obtaining original time sequence data which is obtained through the collection of a cooling system multi-parameter monitoring sensor group disposed in an SVG valve hall in continuous T sampling periods; preprocessing the original time series data to obtain a credible time series data set; constructing a Bayesian network topological structure comprising three-level nodes of an environment layer, a component layer and an efficiency layer and causal dependence edges, and optimizing parameters of the Bayesian network topological structure by adopting a maximum likelihood estimation method to form a dynamic Bayesian network model after parameter calibration; and the credible time sequence data set is used as an evidence variable to be input into the Bayesian network model after parameter calibration, calculation is carried out through a belief propagation reasoning algorithm, a final control instruction set is generated through probability weighted scoring processing, the final control instruction set is fed back to a valve group monitoring system, and early warning and automatic load reduction are achieved. The problems of large evaluation deviation and early warning lag in the prior art are solved.

The invention relates to the technical field of stage equipment intelligent control, provides a stage equipment linkage control method and system based on time sequence arrangement and protocol adaptation, and aims to solve the problem of poor stage control dynamic adaptability caused by hard coding binding of art time sequence logic and an equipment protocol in traditional stage control. The method comprises the following steps: constructing an artistic effect causal graph based on an artistic effect sequence, and deconstructing the artistic effect causal graph into a physical executable constraint set; calculating the capability confidence coefficient of the stage equipment in real time, performing dynamic Bayesian network deduction by taking the physical executable constraint set as an observation target and the capability confidence coefficient as a conditional probability, generating a layered executable plan set, and selecting an execution base plan from the layered executable plan set; the execution base plan is compiled into a target equipment protocol instruction, and the target equipment protocol instruction is distributed and executed through a corresponding protocol adapter, so that the stage control adaptive robustness and the art presentation guarantee capability under stage equipment heterogeneity and state dynamic change are remarkably improved.

The invention discloses an intelligent substation virtual loop online transmission verification method based on GOOSE / SV closed loop test, and relates to the technical field of intelligent substation operation and maintenance. Through precise clock synchronization and an improved cross-correlation algorithm, in combination with wavelet noise reduction and spectral clustering analysis, nanosecond synchronization quality evaluation of GOOSE / SV signals is realized, and the hidden transmission risk discovery time is shortened from regular maintenance to real-time monitoring; a hierarchical hidden Markov model is adopted to analyze equipment-level to system-level behavior modes, real-time probabilistic reasoning is realized in combination with a dynamic Bayesian network and particle filtering, a multi-dimensional evaluation system is constructed, the reliability evaluation capability under complex working conditions is remarkably improved, an optimization scheme is generated based on network path characteristic analysis and bottleneck identification, and the reliability of the system is improved. Multi-scene closed-loop verification is carried out by means of a digital twin technology, safety and reliability of parameter optimization are ensured, and full-process intelligent operation and maintenance of the virtual circuit of the intelligent substation from state perception to optimization verification are realized.

The invention discloses a walking dysfunction intelligent evaluation system and method based on a flexible electronic technology, and the method comprises the following steps: S1, setting a multi-modal flexible sensor array, and synchronously collecting multi-modal data; s2, synchronously aligning the multi-channel signals by using an edge calculation unit to obtain a standardized gait feature tensor; s3, based on the dynamic Bayesian network, forming a high-dimensional hidden state probability distribution sequence of a complete gait cycle; s4, outputting a gait anomaly typing result and a walking dysfunction risk level based on the echo state network; s5, in combination with a knowledge rule engine and historical gait data, performing multiple verification and dynamic Bayesian network model parameter adaptive updating on a gait anomaly typing result; and S6, generating a rehabilitation evaluation report according to the gait anomaly typing result and the walking dysfunction risk level. According to the method, dynamic Bayesian network time sequence modeling and an echo state network intelligent discrimination algorithm are combined, and active detection and grading evaluation of walking dysfunction are achieved.

The invention relates to the technical field of intelligent regulation and control and energy conservation of hospital environment air quality, in particular to a big data management and intelligent evaluation system for the hospital environment air quality. The system comprises a multi-modal data acquisition module, a data processing module, a dynamic transmission modeling module, a dynamic Bayesian network risk modeling module and a prospective risk hedging and energy consumption optimization module. The system calculates a dynamic air transmission coefficient by collecting environment and people flow data, and constructs a dynamic Bayesian network model to calculate a cross-region propagation risk probability; the method is characterized in that when the risk probability exceeds a threshold value, a system actively solves a multi-objective optimization problem with minimization of energy consumption as an objective, and an optimal HVAC control instruction is generated; according to the method, the conversion from lagging evaluation to prospective risk hedging is realized, and the risk can be actively identified and regulated before the pollution exceeds the standard.

The invention belongs to the technical field of electric vehicle thermal management system testing, and discloses an electric vehicle thermal management system testing method and system, and the method comprises the steps: obtaining time sequence data of thermosensitive modules, such as a battery pack, an inverter and a motor, of an electric vehicle under a set thermal load change condition, then, a graph structure with the thermosensitive modules as nodes and the thermal influence relation as edges is constructed, a thermal coupling relation is generated through a graph neural network based on message passing, a dynamic Bayesian network is constructed based on time sequence data and the thermal coupling relation, the temperature response time dependency relation between the modules is obtained, and then a thermal lag distribution diagram is generated; and finally, evaluating the response coordination and the adaptation degree of the thermal management control strategy under the extreme thermal load condition based on the thermal lag distribution diagram.

The invention discloses an intelligent English learning auxiliary system based on artificial intelligence. The system comprises a data acquisition module, a data preprocessing module, a user portrait construction module, a learning path planning module and an intelligent learning auxiliary module. The invention relates to the technical field of intelligent education, in particular to an intelligent English learning auxiliary system based on artificial intelligence, and the system achieves the efficient cleaning of data through a self-adaptive noise injection adversarial generator and hierarchical multi-mode alignment. Knowledge point structure association is mined through a causal attention graph convolutional network, a dynamic Bayesian network is combined to capture mastery time sequence evolution, and user portrait robustness is improved through meta learning and credibility calibration; a knowledge graph is constructed based on a portrait and a real-time mastering state, an optimal path is automatically generated, a reinforcement learning strategy network is introduced for continuous optimization, a mastering probability and a sub-path are dynamically corrected according to practice feedback, and efficient learning assistance with high individuation and dynamic response is realized.

The invention relates to a gas discharge detection method, system and equipment based on an ultraviolet spectrum and a medium. The method comprises the following steps: transmitting a frequency-modulated ultraviolet light beam to a gas chamber to be detected, synchronously collecting and processing a transmission light signal in a branching manner, and simultaneously obtaining a modulated spectrum signal for gas absorption analysis and a discrete photon pulse signal for detecting a discharge event; respectively processing the two paths of signals, and extracting sulfur dioxide concentration information and photon radiation characteristics; fusing the two types of features to construct a dynamic feature vector, and obtaining a discharge state grade by using a dynamic Bayesian network diagnosis model; and finally, combining the concentration and state information, and outputting the membership degree of the discharge duration through fuzzy logic. By adopting the method, the fast process and the slow process of partial discharge can be cooperatively monitored, and the early detection sensitivity and the diagnosis reliability are improved.

The invention discloses a tight coupling laser inertial vision fusion method based on a merged probability voxel map, and relates to the technical field of vision fusion. Comprising the steps of performing display parametric modeling based on radar measurement noise, and constructing a probability voxel plane model; combining the planes which may have a coplanar relationship, and adding the laser radar point clouds in the determined effective voxel planes into a global map; projecting the global map into an image frame to obtain a tracking point, tracking by using an LK optical flow method, and meanwhile, further eliminating an abnormal point by using a random sampling consensus algorithm based on a dynamic Bayesian network; and based on image information acquired by the camera, optimizing and maintaining the global map by minimizing frame-to-frame pixel errors and frame-to-map color errors and updating the global map. According to the method, the performance of the laser inertial visual odometer is improved by combining a framework of efficient coupling radar, IMU and camera information of the probability voxel map.

The invention discloses a multi-domain battlefield task decision-making method and system based on a knowledge graph. The multi-domain battlefield task decision-making method based on the knowledge graph comprises the steps that the relation between battlefield elements and tasks in a database is analyzed, and situation entity entries of the battlefield elements are extracted; utilizing a predefined relation template to construct a relation between the situation entity entries; constructing a decision knowledge base based on fuzzy reasoning; based on the decision knowledge base, constructing a knowledge reasoning model by using a dynamic Bayesian network technology; and receiving a combat instruction, generating a multi-dimensional decision suggestion by using the knowledge reasoning model based on a combat rule matched with the knowledge graph in the decision knowledge base, and displaying a decision result through the user interaction module. According to the method, multi-source information can be integrated in a multi-domain combat environment, cross-domain collaborative decision is realized, and the adaptability of a complex battlefield is improved; the knowledge graph and the fuzzy reasoning technology are combined, so that the uncertainty and fuzziness of the battlefield situation are effectively processed, and the decision-making ability is remarkably enhanced.

The invention discloses a network security attack and defense strategy generation method, system and device fused with a knowledge graph and a medium, and belongs to the technical field of network security attack and defense strategies, and the method comprises the steps: dynamically constructing a network security knowledge graph containing an attack path and vulnerability association relationship based on multi-source data of a public vulnerability library, dynamic mapping of real-time asset change information and a graph entity is realized through an entity link mechanism, and consistency verification is performed on a mapping relation by using a graph neural network model so as to filter anomalies; structured and unstructured attack feature data are collected in real time from channels such as network traffic, and preprocessing is completed through regular cleaning, feature selection and data quality evaluation; real-time data and a knowledge graph are subjected to multi-dimensional matching, an attack and defense target function of a defense cost target is combined, a candidate strategy set is generated through a genetic algorithm, an incomplete information game model is introduced to calculate sub-game perfect Nash equilibrium to determine an optimal strategy, and a dynamic Bayesian network is utilized to update a strategy transition probability based on historical data.

The invention discloses a structure safety assessment method based on a dynamic Bayesian network and adaptive variable weight, and the method comprises the steps: correcting the theoretical value probability distribution of a physical model through employing the monitoring data of each index, carrying out the nondimensionalization according to the corrected theoretical value probability distribution, obtaining a score, and determining the variable weight coefficient of the weight according to the score; and integrating the indexes to obtain an evaluation score of the safety of the structure. According to the method provided by the invention, the safety result of the structure can be quickly and accurately provided.

The invention belongs to the technical field of environmental monitoring and ecological risk assessment, and particularly relates to a Yangtze River Delta basin water ecological risk prediction method and system based on a Bayesian network model. The method comprises the following steps: firstly, constructing a water ecology risk assessment system, and preprocessing sample data; sub-basins are divided based on DEM data, and the sub-basins which are adjacent in space and similar in data feature are aggregated into non-overlapping data blocks; constructing a dynamic Bayesian network model by adopting a random variational inference method, and inputting meteorological prediction data to obtain probability distribution of risk levels of each sub-basin; constructing a risk space correlation model according to sub-basin division, and simulating conduction and superposition processes of risks in a basin network; and finally, evaluating the comprehensive risk state of each sub-basin in combination with a network topology index. By constructing the dynamic Bayesian network model and the risk space correlation network, the spatial-temporal dynamic assessment of the watershed water ecological risk is realized, and the propagation path and the cumulative effect of the risk in the watershed can be predicted.

The embodiment of the invention discloses a GBIC-based multi-electric aircraft starting power generation system DBN model lightweight method, and relates to the technical field of reliability design and modeling of an equipment complex system.The GBIC-based multi-electric aircraft starting power generation system DBN model lightweight method comprises the steps that a DBN model of a starting power generation system is established, nodes in the DBN model correspond to key components and working states of the starting power generation system, and the nodes in the DBN model correspond to the key components and the working states of the starting power generation system; directed edges in the DBN model are used for describing the mutual relation between the nodes, a redundant structure in the DBN model is recognized, lightweight processing is carried out, and the fault state of the starting power generation system is recognized through the DBN model subjected to the lightweight processing. Aiming at the problems of complex reasoning calculation and low efficiency in the use process of the dynamic Bayesian analysis method of the equipment complex polymorphic system, the grey system theory and the Bayesian information criterion are introduced, the lightweight of the dynamic Bayesian network of the complex polymorphic system is realized, the redundant structure is eliminated, and the accuracy of the result is ensured.

A ranking system includes: an exposure module configured to, in response to receiving an input from a computing device via a network, determine, based on an anisotropic intensity value and using a Dynamic Bayesian Network (DBM) exposure model, an exposure value for ranking items for output via the computing device, where the anisotropic intensity value corresponds to a tradeoff between utility to users and fairness to item producers; a ranking module configured to generate a ranking of the items based on the exposure value; and a response module configured to transmit a response including the ranking of the items to the computing device via the network.

The invention belongs to the technical field of artificial intelligence and power system monitoring, particularly relates to a transformer fault intelligent diagnosis and life prediction system based on deep learning, and aims to solve the problems that transformer fault recognition is lagged, early defects are difficult to capture, life prediction precision is low and multi-source data fusion is insufficient. The system synchronously collects data of oil chromatography, partial discharge, vibration and the like through multi-mode sensing, feature extraction and attention mechanism fusion are performed through a deep neural network, and fine-grained diagnosis of 12 types of faults is realized; tracking a fault evolution trend in combination with a dynamic Bayesian network, constructing a probabilistic life prediction model based on a physical mechanism and a gating cycle unit, and outputting residual life distribution; cloud edge cooperation and online incremental learning are supported, the model adaptability and prediction precision are improved, and the power grid accident risk is reduced.

The invention relates to a cable tunnel bridge fire prediction method and device, electronic equipment and a storage medium. The cable tunnel bridge fire hazard prediction method comprises the following steps: establishing a cable tunnel bridge fire hazard numerical simulation model, establishing a cable tunnel bridge fire hazard simulation model based on FDS software, obtaining key parameters such as heat release rate, temperature distribution and flame spread boundary under different working conditions, and constructing a multi-time sequence sample data set; performing state discretization on continuous variables such as temperature and a spreading range, and estimating a node prior probability in combination with a statistical frequency; constructing a dynamic Bayesian network topological structure according to a causal relationship among a heat source, temperature and spread, and setting a cross-time slice node dependence path to realize time sequence modeling; inputting a training sample and performing parameter learning to generate a conditional probability table; in the prediction stage, multi-step reasoning is achieved through forward propagation, flame spreading range probability distribution and interval estimation of multiple time steps in the future are obtained, and the specific position where the flame arrives is predicted.

The invention discloses a spacecraft in-orbit operation health assessment method, and relates to the technical field of space flight and aviation. Environmental data such as gravity gradient, radiation intensity and dust particle density of an asteroid surface are collected in real time through a multi-mode sensor array, modeling and prediction are carried out by adopting a dynamic Bayesian network in combination with Kalman filtering, and the health assessment accuracy of the spacecraft in-orbit operation is improved. The method comprises the following steps: dynamically generating environment characteristic parameters including a risk level and an environment change trend, and in health state prediction, simulating stress distribution in a low-gravitation environment through digital twinborn body and finite element analysis, predicting a fatigue state and a failure risk of a contact device, and accurately evaluating the fatigue life of a high-stress area. The quantification capability of the failure risk is improved through the risk probability model; besides, according to a health prediction result, landing parameters including a damping mode and a landing speed are dynamically optimized, and a dynamic planning method is used for minimizing impact force and stress load, so that the detector can be in stable contact in a low-gravity environment.