70 results about "Uncertainty model" patented technology

The invention relates to the technical field of anti-collision systems, in particular to an offshore wind power construction safety pre-warning system based on AIS (automatic identification system) data, which comprises a ship dynamic uncertainty modeling module for acquiring AIS signal updating frequency and positioning precision marks of a target ship and generating a ship future position probability ellipse. According to the invention, the position coordinate, the speed value and the course angle of the ship are obtained in real time based on the AIS data, the reliability and the motion trend of ship position prediction are determined by combining the AIS signal updating frequency and the positioning precision mark, and the future position probability ellipse of the ship is accurately generated; a protection area is dynamically constructed, comprehensive calculation is carried out in combination with the spatial relation between a ship future position probability ellipse and the positions of surrounding fan pile foundations, and a hourly dynamic collision risk index sequence is obtained; and further according to the statistical deviation between the current motion state of the ship and the standard parameter of the function partition, combining with path difference analysis to obtain a navigation abnormity comprehensive index.

The invention discloses a mangrove forest protection effect intelligent evaluation and scene prediction system, and relates to the field of ecological environment information intelligence, and the system comprises a key driving factor recognition module which is used for determining a core variable influencing the protection effect and a dynamic response interval of the core variable based on a statistical analysis and nonlinear fitting method; the protection effect quantitative evaluation module is used for calculating a net improvement effect of protection intervention by comparing ecological index differences inside and outside the protection area; the causal effect analysis module is used for separating independent contributions of natural factors and artificial protection by adopting a dual machine learning framework; the space-time dynamic modeling module is used for performing uncertainty modeling on the space-time evolution of the ecological indexes by using a Bayesian hierarchical structure; and the multi-scene prediction module is used for realizing ecological response prediction under multi-factor driving and supporting input and simulation of user-defined scenes. According to the scheme, multi-dimensional and high-precision evaluation and future trend reliable prediction of mangrove forest protection effects can be realized.

The invention discloses a self-adaptive micro-grid operation optimization method, system and device and a medium, and belongs to the technical field of micro-grid operation optimization, and the method comprises the steps: obtaining the operation state data of a micro-grid; performing depth feature coding and space-time attention processing on the micro-grid operation state data through a state sensing module to generate unified state representation; carrying out online strategy learning and optimization through a strategy evolution module based on unified state representation, and outputting a strategy optimization result; a new energy uncertainty model is adopted to generate multi-scene prediction data, the prediction data and a strategy optimization result are combined, and a prediction control problem is solved through a rolling optimization module; and according to a real-time risk assessment result, the decision weights of the strategy evolution module and the rolling optimization module are adjusted, and a micro-grid operation scheduling instruction is output. According to the method, a new state deep perception-dynamic strategy learning-robustness optimization normal form is constructed, and intelligentization and adaptive learning of the micro-grid are realized.

The invention provides a power grid dispatching reserve capacity calculation method considering new energy and load uncertainty and a related device, and aims to solve the problems of uncertainty coping, N-1 security constraint satisfaction and calculation efficiency under high-proportion new energy grid connection. The method comprises the following steps: establishing a wind power ellipsoid uncertainty model and a load ellipsoid constraint model containing a spatial smoothing effect; introducing a participation factor to construct a generator adaptive response model, and realizing fluctuation compensation; constructing a main objective function by using conventional operation constraints (power balance, climbing rate and the like), and solving an initial scheduling scheme; the N-1 fault robustness is verified through an auxiliary objective function, and the constraint is iteratively updated; and the optimal reserve capacity is solved by adopting Benders decomposition. The method can accurately quantify the multi-source uncertainty, improve the wind power consumption capability, ensure the safe and economical operation of the system, is high in calculation efficiency, and is suitable for the calculation of the reserve capacity of a novel power system.

The present application relates to the technical field of power system operation analysis and regulation, and specifically discloses a kind of VSC-HVDC system power flow regulation capability evaluation method, comprising the following steps: S1, the power flow regulation range mathematical model of establishing containing back-to-back flexible HVDC transmission system BTB;S2, establish new energy output uncertainty model;S3, construct power flow over-limit risk and risk adaptive power regulation mechanism;S4, generalized polynomial chaos matrix method is used to evaluate the two-way power flow regulation range;S5, output power flow regulation range evaluation results and visual index.The present application uses the above-mentioned VSC-HVDC system power flow regulation capability evaluation method, realizes the collaborative quantification of new energy randomness, operation risk and BTB regulation capacity, improves the accuracy and engineering practicability of the evaluation results, and provides a decision basis for power system planning and design, operation control.

This invention relates to the fields of tunnel engineering and 3D geological modeling technology, specifically to an intelligent modeling method and system for 3D geological uncertainty in tunnels based on transition probability geostatistics and sparse boreholes. The method includes: S1, integrating multi-source data to construct a 3D geological conceptual model; S2, statistically characterizing a one-dimensional transition probability matrix, calculating and fitting a spatial continuity and 3D anisotropic variability function model; S3, calculating the prior spatial probabilities and transition adjustment factors for various lithologies, obtaining the posterior lithology distribution of nodes through Bayesian intelligent updating, and initially assigning lithology categories to nodes through random sampling; S4, assigning the most probable lithology category to each grid node, calculating the variance of lithology values ​​across all implementations, and measuring model uncertainty; S5, outputting the optimal 3D uncertainty model for the tunnel; and S6, verification and evaluation. This invention can effectively achieve 3D heterogeneous modeling and explicit quantification of uncertainty under strong geological constraints.

This invention discloses a steerable chassis trajectory tracking control method based on composite time delay estimation and compensation, comprising the following steps: establishing a discretized vehicle dynamics model considering steering execution lag, clarifying the difference between steering lag and stochastic network delay, and providing a basis for compensation design; designing an adaptive prediction time-domain function, dynamically calculating the optimal prediction time domain based on the current vehicle speed, road curvature, and network delay state; designing a control quantity considering steering lag and constructing a time delay uncertainty model, using the polyhedral method to handle the parameter uncertainty caused by time delay; performing rolling optimization solution, dynamically adjusting the optimization weights based on the uncertainty model, the adaptive time domain, and an online weight adaptive mechanism of reinforcement learning to obtain the optimal control increment. This invention solves the problems in existing technologies that simplify complex internal time delays into a single, fixed model, failing to distinguish and process their physical causes, resulting in limited compensation accuracy, poor system dynamic adaptability, and insufficient robustness.

This application discloses a method for calculating the thrust envelope of a solid rocket motor based on servo uncertainty, relating to the field of solid rocket motor technology. This method focuses on the gas valve servo system of a throat-operated solid rocket motor, performing uncertainty modeling of the motor drive system and transmission system, analyzing the main influencing parameters of servo system uncertainty, including transmission clearance, eccentric shaft friction coefficient, and other uncertainty parameters and their probability distributions; generating random parameter samples based on the uncertainty model, and using the servo system control model to solve for the actual throat displacement response under random samples, obtaining the engine thrust time history under single-valve and multi-valve cooperative working modes; and fitting the thrust uncertainty envelope by calculating the mean and standard deviation of the thrust response through Monte Carlo simulation. This application quantitatively characterizes the degree of influence of internal factors of the servo mechanism on thrust performance, providing a reference for ensuring precise thrust control of solid rocket motors.

The invention provides a carbon emission reduction accounting method and system based on multi-modal uncertainty quantization, and relates to the technical field of carbon emission reduction accounting, and the method comprises the following steps: collecting and preprocessing the multi-modal data of a reconstructed building, and obtaining a preprocessed multi-modal data set; uncertainty modeling is carried out on the preprocessed multi-modal data set, and uncertainty distribution is obtained; based on the preprocessed multi-modal data set and uncertainty distribution, performing inversion by adopting a Bayesian framework to obtain a multi-modal data posterior probability; and performing Monte Carlo simulation on the posterior probability of the multi-modal data to obtain an uncertainty weight, and calculating the carbon emission reduction based on the uncertainty weight. According to the method, quantitative modeling is carried out on the uncertainty of the multi-modal data, the probability transmission and fusion of the uncertainty are realized by using the Bayesian framework, and finally the carbon emission reduction amount accounting result with the confidence interval is output, so that the scientificity and the credibility of the accounting are remarkably improved.

The application discloses a kind of laboratory safety risk intelligent early warning method, system, medium and product, it is related to risk early warning field.The method comprises the following steps: calibrating laboratory digital twin model based on real-time monitoring data, generating initial probability distribution based on laboratory digital twin model and fusing preset equipment failure probability model and personnel behavior uncertainty model;The probability density distribution change of initial probability distribution at a plurality of time points in the future is deduced by numerical algorithm, and the corresponding state prediction probability distribution is obtained;The probability value of each dangerous state developed at each time point in the future is obtained based on the state prediction probability distribution of each future time point;When the probability value of any dangerous state evaluated exceeds the preset threshold value, the optimal control operator is solved reversely by optimization algorithm, and the intervention action corresponding to the optimal control operator is executed.The above technical scheme can predict laboratory safety risk in advance.

This invention discloses a method for optimizing the dynamic transmission error distribution characteristics of a harmonic reducer, comprising: establishing a static transmission error probability model to obtain the probability distribution of the overall static transmission error; constructing a dynamic transmission error mathematical model considering static transmission error and dynamic parameters; constructing a high-precision surrogate model of dynamic transmission error including the probability distribution of static transmission error and the range of dynamic parameters; obtaining the probability distribution of dynamic transmission error; and dynamically adjusting the range of dynamic parameters based on a particle swarm optimization strategy to find the parameter range that optimizes the dynamic transmission error distribution. By introducing static transmission error into the dynamic model of the harmonic reducer, considering the influence of the processing and assembly of the harmonic reducer on the transmission error, a probability-range hybrid uncertainty model is used to mathematically describe the probability distribution characteristics of static transmission error and the range of dynamic parameters, and a Chebyshev polynomial method is used to construct an approximate model of dynamic transmission error.

This invention discloses a structural topology optimization design method considering the uncertainty of multi-peak loads, belonging to the field of uncertain structural optimization design. It mainly includes three parts: establishing a multi-peak load uncertainty model, solving for the random response, and solving for the optimization formula. This invention describes load uncertainty using a Gaussian mixture model, solves for the Gaussian mixture model coefficients based on the EM algorithm, and establishes a multi-peak load distribution probability model. By decorrelating random variables, sparse grid technology is used to solve for the mean, standard deviation, and sensitivity of the response, thereby solving for the topology optimization model considering the multi-peak load uncertainty. This method addresses the problem of low structural product reliability that may occur in structural designs considering multi-peak load uncertainty by establishing an accurate probabilistic model of multi-peak load uncertainty. This method is simple and easy to implement, readily applicable in engineering, and can improve the design efficiency of structural designers considering complex load conditions.

This invention provides a method for predicting the dominant frequency of blasting vibration by integrating parameter fluctuation processing and the Dream Optimization Algorithm (DOA), belonging to the field of underground engineering safety control technology. The system includes a data acquisition and uncertainty modeling module, a data preprocessing and feature construction module, a hyperparameter optimization module, and a prediction model training and result output module. The method obtains relevant parameters through on-site investigation and monitoring, and generates an extended sample set using a combination of probabilistic perturbation modeling and fuzzy triangular modeling. The data is preprocessed and features are selected. The DOA algorithm is used to globally search the hyperparameters of the Support Vector Regression (SVR) model, and the optimal hyperparameter combination is selected by combining a robustness fitness function. The SVR model is trained, and prediction results and uncertainty prediction intervals are generated. This invention can explicitly characterize the uncertainty of geological parameters, achieve efficient global optimization of hyperparameters, improve prediction accuracy, robustness, and generalization ability, and is applicable to different geological conditions and blasting scenarios. It can be extended to various blasting dynamic response prediction tasks.

The invention discloses a scheduling method and device for a new energy power system containing small hydropower stations, equipment and a medium, and the method comprises the steps: constructing a hydropower space-time matrix model of the system based on a topological structure and water flow motion parameters between nodes of a watershed; substituting the load prediction value and the operation parameter prediction value of each unit into a deterministic model to obtain a reference value of a system optimization target; and according to the uncertainty model, the reference value and the certainty model of the system, constructing an IGDT scheduling model of the system and solving the IGDT scheduling model to obtain an output plan and an optimization target of each unit in the system. By constructing a hydropower space-time matrix model of the system, a topological structure and a water flow motion state between each small hydropower station and a conventional hydropower station are considered when a decision is made, and the accuracy and the reliability of a scheduling result are improved; the influence of water load uncertainty on a scheduling result is quantified through an information gap decision theory, a decision maker provides scheduling strategies under different risks and different effects, and the reliability and economical efficiency of a final scheduling scheme are guaranteed.

The application provides a multi-micro-grid system optimization method, device and storage medium, relates to the new energy power supply field, and the method comprises the following steps: determining the topological structure of the multi-micro-grid system in a shared energy storage mode, establishing an economic scheduling model based on the optimization scheduling process of the multi-micro-grid system, determining a source-load uncertainty model and a constraint condition, establishing a fuzzy opportunity constraint based on the source-load uncertainty model, constructing a game model to solve the economic scheduling model under the fuzzy opportunity constraint, and obtaining the optimization result of the multi-micro-grid system; and the device and the storage medium are used for realizing the method. The application has the beneficial effects that the source-load uncertainty in the multi-micro-grid system is considered, the scheduling precision is improved, and the application has important theoretical and application values for solving the multi-micro-grid system optimization problem.

The invention discloses a low-altitude aircraft path optimization method for civil aviation emergency rescue, and relates to the technical field of unmanned aerial vehicles, and the method comprises the following steps: obtaining task information and real-time data, and building an error model for wind field prediction; dividing a flight area into three-dimensional grids, and defining a cost function for managing the extreme risk of the wind field; dividing a flight airspace into three-dimensional grids, and defining a path cost function; searching a path with the lowest cost from the starting point to the end point in the grid by using an improved search algorithm; dynamically updating the model in flight according to an actually measured wind field, and performing local path re-planning when the deviation is too large; and outputting a final optimized path, submitting the final optimized path to the aircraft for execution, and continuously monitoring to ensure safety. According to the method, the conditional value-at-risk is introduced as a core optimization index of path planning, and the wind field uncertainty model and the real-time dynamic adjustment mechanism are constructed, so that a flight path with high reliability in a complex wind field environment can be generated.

The invention provides a robust GeNeRF three-dimensional reconstruction method for a dynamic interference scene. The robust GeNeRF three-dimensional reconstruction method comprises the following steps: collecting and preprocessing a multi-view image data set containing transient interferents; selecting a matched source view for each target view according to the camera pose proximity to form a source view set; extracting structural features of the source view set and extracting semantic features of the target view; constructing a source view uncertainty model based on the structural features; constructing a target view uncertainty model based on the semantic features; integrating the output of the source view uncertainty model and the output of the target view uncertainty model, and constructing a heterovariance reconstruction loss function; and training the GeNeRF model by using the loss function. According to the method, through collaborative modeling of two types of complementary uncertainty and construction of the heterovariance loss function, the model can adaptively adjust the regional supervision intensity to effectively suppress transient interference, the reconstruction precision and robustness of the GeNeRF method in a complex dynamic scene are improved, and meanwhile, reliable guarantee is provided for rapid deployment of the GeNeRF method in practical application.

The application provides a kind of biomass energy participates in peak shaving green township power distribution network regional collaborative autonomy method, the method first obtains the source and load prediction data of each regional microgrid of green township, energy supply equipment basic parameters and local grid purchase and sale electricity price data;Then, according to the time scale characteristics of source and load prediction data, an uncertainty model of source and load is established;Then, a double-layer optimization model of green township power distribution network is constructed, including a regional microgrid energy trading price model based on the energy supply and consumption conditions of each regional microgrid as the upper layer strategy, and a two-stage robust optimization model of regional microgrid considering comprehensive constraint conditions with economic efficiency and clean energy consumption rate as optimization objectives based on the uncertainty model of source and load and regional microgrid energy trading price as the lower layer strategy;Finally, the double-layer optimization model of green township power distribution network is iteratively solved, and the distributed energy output, energy storage charging and discharging power and regional interaction power that make the system economic efficiency and clean energy consumption rate optimal are obtained.

The specification describes a positioning technology for a global navigation satellite system (GNSS), aiming at improving the positioning accuracy in a complex environment. According to the technology, based on a corrected random model of least square variance component estimation (LS-VCE), the uncertainty of observation data is estimated more accurately, so that the positioning accuracy is remarkably improved. The technology comprises the following key steps: firstly, performing uncertainty modeling on GNSS observation data, and estimating variance and covariance components of the observation data by using an LS-VCE method; thirdly, defining a weight matrix to reflect the importance or reliability of different observation data, constructing an observation equation system, and associating the observation data with model parameters; and then, through iterative solution, optimal parameter estimation and variance component estimation are obtained by using a least square principle. And finally, verifying the effectiveness of the LS-VCE method through a simulation experiment, and optimizing the model according to an experiment result.

The present application relates to the field of new energy power system and optimal scheduling, in particular to a kind of multi-time scale energy management method of light storage grid-connected system, comprising S1, based on day-ahead operation forecast data, under the condition that energy storage operation constraint, grid-connected constraint and system power balance constraint are satisfied, to establish day-ahead scheduling model with light storage grid-connected system operation cost, S2, introduce uncertainty modeling mechanism in day-ahead scheduling model, generate day-ahead scheduling scheme considering uncertainty, S3, construct intraday operation optimization model under shorter time scale, and combine system real-time operation state, to the rolling correction of day-ahead scheduling scheme, S4, construct penalty function to the deviation of intraday operation decision relative to day-ahead scheduling scheme, combine day-ahead scheduling scheme and output the final operation control instruction of light storage grid-connected system, the present application system effectively suppresses minute-level power fluctuation, while ensuring the safe operation of system, reduces the transaction cost with power market and prolongs the service life of energy storage.

The invention provides a blasting vibration peak velocity prediction method fusing parameter uncertainty and data-driven optimization, which comprises the following steps of: firstly, acquiring data such as blasting parameters, lithologic indexes, geological conditions and actually measured vibration peak velocity (PPV), and establishing a basic database; a prior probability model is constructed, a joint uncertainty model is established in combination with probability disturbance and a fuzzy triangular number, a multi-dimensional disturbance sample is generated through a joint central value and a joint standard deviation and is fused with original data, and an extended database is formed. Feature analysis is carried out on the fused data, and input variables which have obvious influence on the vibration peak velocity (PPV) are screened; and constructing a BP neural network model based on the screened features, carrying out global optimization on a network weight and a threshold by adopting a PSO algorithm, and then carrying out local fine tuning by utilizing Adam. Finally, through training and verification, prediction of the vibration peak velocity (PPV) is realized, and model precision is evaluated through RMSE, MAE, MAPE, Rand other indexes. The influence of rock and soil parameter uncertainty on prediction precision can be effectively processed, and the reliability and applicability of blasting vibration prediction are improved.

The application relates to the technical field of intelligent driving, in particular to a vehicle obstacle avoidance and path planning method based on intelligent driving. The application proposes the following scheme: an environment data is collected through a multi-modal sensor, a dynamic target probability space-time confidence field is constructed, a target category and a geometric state probability distribution are output by using a Bayesian deep neural network, and a multi-modal future trajectory prediction set is generated in combination with a historical trajectory. Further, an evolution scene is constructed through trajectory sampling, a forward simulation is performed on a candidate self trajectory, an optimal driving path is generated based on a conditional risk value evaluation, and the optimal driving path is converted into a control instruction to drive the vehicle. The application has uncertainty modeling capability and high risk identification capability, and the safety and robustness of path planning are improved.

The invention discloses an active power distribution network operation risk assessment method and system considering element uncertainty, and the method comprises the steps: inputting basic data, and inputting the basic data into a pre-constructed power distribution network wind-solar output and load demand uncertainty model; constructing a multi-level active power distribution network risk assessment index system; the method comprises the following steps: taking wind power, photovoltaic and load output data as input, generating an operation condition scene by adopting LHS sampling of global sensitivity analysis, carrying out scene reduction by utilizing a K-medoids clustering algorithm to obtain an appropriate scene number and a probability of each scene, calculating a power flow in an s-th operation scene, and obtaining an output node voltage matrix and a line active and reactive power matrix; and after all scenes are traversed, the operation risk is calculated according to a pre-constructed risk index system. The method has the advantages that accurate evaluation and weak link positioning of the operation risk of the power distribution network are achieved, and the problem that a traditional risk evaluation method is insufficient in precision in the complex active power distribution network is effectively solved.

The invention relates to a risk early warning method, system and device for a power distribution network and a medium, and the method comprises the steps: obtaining the historical operation data of the power distribution network, and constructing a multi-stage risk assessment matrix according to the historical operation data; obtaining multi-source operation data at the current moment, performing uncertainty modeling on the multi-source operation data based on the multi-level risk assessment matrix, and generating a multi-source evidence set; performing evidence fusion operation on the multi-source evidence set to obtain a first risk assessment result; under the condition that the first risk assessment result does not meet a preset stable condition, knowledge retrieval reasoning is executed based on the first risk assessment result and the multi-source operation data to obtain a knowledge reasoning result, and then the knowledge reasoning result and the multi-source evidence set are combined to obtain an enhanced evidence set; and performing secondary evidence fusion operation on the enhanced evidence set to obtain a final risk early warning result. The method has the effect of improving the risk assessment accuracy of the power distribution network.

The application discloses a method and device for evaluating the radiation emission level of power electronic equipment based on a reverberation chamber, a medium and equipment. The method comprises the following steps: obtaining a reverberation chamber radiation receiving power sample and a transmission coefficient sample; obtaining a radiation power calculation model of the power electronic equipment to be measured, constructing a radiation power function of the power electronic equipment to be measured, obtaining a probability density function of a radiation power estimation value based on the radiation power function of the power electronic equipment to be measured, and obtaining an uncertainty model; evaluating the first radiation emission level of the power electronic equipment to be measured based on the radiation power calculation model and the uncertainty model; and determining the second radiation emission level according to the radiation power function of the power electronic equipment to be measured. The method can accurately describe the statistical distribution characteristics of the electromagnetic field inside the reverberation chamber, quantitatively analyze the influence of the number of independent samples on the measurement uncertainty, and effectively evaluate the statistical characteristics and measurement uncertainty of the radiation emission of the power electronic equipment.

The application belongs to the technical field of satellite-ground communication and physical layer security, and particularly relates to a satellite sensing assisted secure communication method based on dynamic uncertainty modeling. The method comprises the following steps: constructing a sensing-integrated transmission architecture, using a dual-function beam to actively detect potential threat areas while transmitting data to downlink users; performing primary positioning by capturing target echo signals, and dynamically calculating the uncertainty boundary of positioning in combination with echo signal-to-noise ratio; establishing a multi-dimensional channel uncertainty model, including a user-side angle uncertainty model established for the high-speed movement of low-orbit satellites, and a norm-bounded uncertainty region constructed for eavesdroppers based on the above boundary; taking the maximization of the worst-case secrecy rate as a global optimization goal, jointly designing optimal beamforming and radar receiving filters under the constraints of sensing, power and security; and using semi-positive relaxation and convex-concave process algorithms to convert the non-convex problem into a convex optimization sub-problem for iterative solution. The application realizes a closed loop of active sensing and mathematical modeling, and significantly improves the dynamic adaptability and robustness of satellite secure communication.

The embodiment of the invention provides a college intelligent system based on cloud native and multi-modal data fusion, and the system comprises a multi-modal data collection layer which is used for obtaining a multi-modal behavior data sequence supporting a to-be-tested object to be in a specific behavior state through a campus global data collection platform in a current time period; the data fusion and intelligent analysis layer is used for analyzing the multi-modal behavior data sequence by adopting a depth evidence uncertainty model to obtain belief quality and uncertainty of supporting the to-be-detected object to be in a specific behavior state at each time point in the current time period; and the micro-service business middle platform is used for predicting the belief trend of the to-be-tested object in the specific behavior state in the future time period according to the belief quality and uncertainty of the to-be-tested object in the specific behavior state supported at each time point in the current time period, and managing the to-be-tested object in the future time period according to the belief trend. According to the invention, the refinement, intelligence and individuation level of campus management can be improved.

The invention discloses a comprehensive energy carbon measurement and calculation method meeting multi-type carbon measurement requirements. The method comprises the steps that the multi-type carbon measurement requirements are recognized, and energy using objects are graded; establishing a multi-energy carbon emission calculation model and a corresponding uncertainty model; establishing a full-link cost model of metering, communication, storage and computing power; establishing a cost minimization optimization model under the constraint of the uncertainty of the total carbon emission, and solving to obtain the optimal acquisition precision and sampling strategy of each energy consumption object; and the optimal configuration is issued to metering and monitoring equipment of the comprehensive energy utilization system, and dynamic adjustment is performed according to the system operation state. According to the method, unified modeling and precision analysis can be carried out on carbon emission of multiple energy sources of electricity, gas, heat and hydrogen in a comprehensive energy utilization system, and quantitative modeling and optimization can be carried out on the precision grade, the sampling time interval and the data processing strategy of a metering device, so that on the premise that the overall carbon emission measurement and calculation precision constraint is met, the measurement and calculation efficiency is improved. And the comprehensive cost of the monitoring system is obviously reduced.

The invention discloses a laboratory safety risk intelligent early warning method and system, a medium and a product, and relates to the field of risk early warning. The method comprises the following steps: calibrating a laboratory digital twinborn model based on real-time monitoring data, and calculating and generating initial probability distribution based on the laboratory digital twinborn model and by fusing a preset equipment fault probability model and a personnel behavior uncertainty model; deducing probability density distribution change of the initial probability distribution at a plurality of time points in the future through a numerical algorithm to obtain corresponding state prediction probability distribution; based on the state prediction probability distribution of each future time point, obtaining a probability value of developing to each dangerous state at each time point in the future; and when the evaluated probability value of any dangerous state exceeds a preset threshold value, reversely solving the optimal control operator through an optimization algorithm, and executing an intervention action corresponding to the optimal control operator. By implementing the technical scheme, the laboratory safety risk can be predicted in advance.

The invention provides a full-heart CT image segmentation method and system for coping with contrast agent induction domain offset. The method comprises the following steps: S1, acquiring a to-be-processed heart CT angiography three-dimensional image and preprocessing the to-be-processed heart CT angiography three-dimensional image; s2, inputting the preprocessed image into a coding path of a segmentation model to extract an intermediate feature, executing a space consistency compensation operation on the intermediate feature, and inputting the feature after space consistency compensation into a decoding path of the segmentation model to obtain an initial segmentation prediction result and a corresponding deep feature; s3, introducing an uncertainty modeling mechanism based on an evidence theory to construct a reliability mask for an initial segmentation prediction result; and S4, under the constraint of a reliability mask, carrying out sample-level online calibration by using the deep features output in the step S2 to obtain a sample-level calibration prediction result, and carrying out weighted fusion on the sample-level calibration prediction result and the initial segmentation prediction result to obtain a final segmentation result. According to the method, stable segmentation of the non-contrast mode can be realized under the condition of no target domain labeling.