342 results about "Uncertainty analysis" patented technology

An icing detection method based on tension and tilt angle sensors. Two kinds of sensor data are comprehensively utilized, cross validation and mutual compensation are realized, and the precision of an icing monitoring result is improved; compared with a single sensor, data fusion of a multi-source heterogeneous sensor improves the robustness and the anti-interference ability of ice volume assessment, and improves the reliability of icing detection; an uncertainty analysis model for icing thickness is established, a confidence interval of a monitoring result is given, and more sufficient reference information is provided for related decisions; by setting a scientific and reasonable monitoring and early-warning mechanism, intelligent monitoring and early-warning of an icing condition are realized, and the intelligent level of the technology is improved; and by establishing an icing monitoring model, the icing condition of wires can be accurately predicted, and the early-warning and monitoring capabilities are improved to ensure the safe operation of a power grid. Also disclosed are an icing detection system based on tension and tilt angle sensors, a computer device and a computer-readable storage medium.

The invention discloses a deep learning-based intelligent identification method and system for rock and soil mechanical parameters, and aims to accurately identify the elastic modulus, strength parameter and permeability coefficient of rock and soil and support the decision and design of engineering projects under complex geological conditions. And starting from data demand analysis, determining a parameter identification target and planning a field test layout. A basic data set is constructed by comprehensively collecting and integrating multi-source data including laboratory and field test data, remote sensing information and the like. The data is pre-processed and analyzed to remove noise and explore associations between the data. The method comprises the following core steps: constructing a rock-soil mechanical model by using a deep learning algorithm, and realizing high-precision inversion of parameters by defining and optimizing an objective function. And verifying an inversion result and carrying out uncertainty analysis. According to the method, an analysis result is fed back to engineering design adjustment and decision support, and scheme optimization is achieved. The model and strategy are dynamically adjusted through continuous monitoring and real-time data, and safety and high efficiency of engineering implementation are guaranteed.

The invention belongs to the technical field of atmospheric pollution monitoring, and discloses a method for realizing VOCS inversion of satellite remote sensing column concentration by using machine learning. Comprising the following steps: preprocessing multi-source data to obtain a multi-source feature data set; performing feature hierarchical extraction and association analysis based on the multi-source feature data set to obtain a feature association graph; performing dynamic feature embedding and relation modeling based on the feature association graph to obtain a component feature space; based on the component feature space, constructing a multi-task deep learning inversion model, and performing weight optimization to obtain an optimized inversion model; based on the optimized inversion model, space-time interpolation and uncertainty analysis are carried out on satellite remote sensing column concentration data, and a high-resolution VOCs component inversion graph is obtained; and multi-scale verification and dynamic correction are carried out based on the high-resolution VOCs component inversion graph and the ground VOCs component monitoring data, and a VOCs component inversion result is finally obtained, so that the refinement level of regional atmosphere quality management is improved.

The invention discloses a flowmeter self-calibration method and system based on big data analysis. The method comprises the steps that multi-source data collection is conducted, and multiple kinds of data are collected and stored in a big data platform with a block chain evidence storage function; data preprocessing: adopting multiple algorithms to improve data quality; feature extraction and analysis: identifying key factors and monitoring fluid states by means of multiple technologies; calculating calibration parameters, and considering various coefficients and uncertainty analysis; performing automatic calibration, encrypting and writing parameters after digital twin simulation, and reserving a copy; and verifying the calibration effect by adopting a multi-mode verification mechanism. The system comprises a plurality of functional modules which work cooperatively. According to the invention, the data acquisition comprehensiveness is improved, the calibration period is shortened, the production efficiency is improved, the data integrity and reliability are ensured, key factors are accurately identified, the fluid state is monitored in real time, the calibration parameter adjustment is simulated, and the calibration effect is verified in a multi-mode manner, so that the reliability and accuracy of a flowmeter measurement system are improved.

The invention provides a method and system for measuring resistance in an underwater vehicle towing test, and relates to the technical field of underwater vehicle hydrodynamic performance testing, and the method comprises three links of gravity center adjustment, measurement data processing and uncertainty analysis. Firstly, it is ensured that the gravity center and the buoyancy center of a test model are consistent in height, model attitude stability is ensured, and resistance measurement errors caused by attitude changes are eliminated. Secondly, measuring the sailing resistance of the test model in the towing test, and post-processing the measured data to obtain the total resistance coefficient of the test model at the test temperature; and finally, quantizing errors of each link and synthesizing expansion uncertainty by adopting a deviation limit and precision limit combined uncertainty analysis method. Compared with a traditional single-index evaluation method, the method not only can reveal error sources and quantify the influence of each link on the measurement result, but also can accurately reflect the composite effect of errors, and ensures the reliability of the resistance measurement result.

Systems, methods, and devices that relate to assessing uncertainty associated with entities are disclosed. In one example aspect, the method receives artifacts relating to an entity and categories for assessing uncertainty. For each category, a generative model retrieves and standardizes data points from the artifacts. A rule-based model inputs the standardized data points to output a rating. The generative model then generates an assessment of the rating and data points according to a predefined structure. The method outputs a summary, rating, and standardized data points for each category. These outputs can be used by other systems for assessing the uncertainty of the entity and taking action based on the assessment.

The invention relates to the technical field of exploration, and discloses a water depth inversion method and system based on a multispectral remote sensing image, which utilizes an independent verification sample set to carry out hierarchical precision evaluation, and analyzes model performance differences according to dimensions such as a water depth range and a substrate type. Error propagation of links such as atmospheric correction and water level correction is quantified through full-chain uncertainty analysis, an uncertainty quantification model of ensemble learning is constructed, and a pixel-level precision distribution diagram is generated. Therefore, according to the technical scheme, a closed-loop dynamic adaptation framework is formed by systematically integrating multi-temporal data dynamic modeling and an uncertainty quantification mechanism, a spatio-temporal variation compensation mechanism is embedded in the whole process from data acquisition to result verification, the interference of spatial-temporal heterogeneity of water optical characteristics on water depth inversion is effectively dealt with, and the accuracy of water depth inversion is improved. The problems of parameter mismatch and precision reduction of the water depth inversion model caused by dynamic change of optical characteristics of a coastal water body in seasonal and tidal scales are solved.

The invention discloses an ecological restoration effect evaluation method and system based on satellite remote sensing, and the method comprises the steps: collecting the satellite remote sensing data and field soil data of a preset region, and taking the remote sensing data of the preset region within a specified time as to-be-analyzed data; calculating a time sequence deviation and a contrast ratio based on the satellite remote sensing data to obtain a grading region; performing deviation correction on the satellite remote sensing data according to the field soil data in the grading region, and inputting the corrected satellite remote sensing data into a space-time ecological development prediction model to obtain ecological development prediction data; performing restoration uncertainty analysis according to the ecological development prediction data and the field soil data to obtain an ecological restoration effect confidence coefficient, and performing elastic capability analysis according to the ecological restoration effect confidence coefficient and the satellite remote sensing data to obtain an ecological elastic index; and constructing a time-varying ecological restoration effect evaluation model according to the ecological elasticity index, and outputting an evaluation result based on the to-be-analyzed data.

The invention provides a contaminated site remediation technology scoring system which comprises a multi-dimensional evaluation index system construction module, a situational weight dynamic adjustment module, a remediation technology applicability scoring module, an uncertainty analysis and sensitivity test module, a scoring result visualization module, a technical scheme sorting and screening unit and the like. Through four innovations of a dynamic weight adaptation mechanism, a multi-model fusion scoring algorithm, a technology combination scheme evaluation module and uncertainty robust analysis, the problems of single dimension, weight solidification, difficulty in quantification of a synergistic effect and the like in pollution remediation technology evaluation are systematically solved; and an objective, efficient and transparent intelligent decision-making tool is provided for repairing a complex polluted site.

The invention discloses a power load prediction method, apparatus and device, and a storage medium. The method comprises the steps of obtaining a first power load sequence of a power system; performing uncertainty analysis on the first power load sequence to obtain uncertainty characteristic data; predicting a power load according to the first power load sequence and the uncertainty feature data to obtain a first predicted power load sequence, and predicting a power load predicted value at the target prediction moment; determining a power load residual sequence according to the first power load sequence and the first predicted power load sequence; predicting a power load residual error according to the power load residual error sequence to obtain a power load residual error prediction value; and determining a target power load prediction result according to the power load prediction value and the power load residual prediction value. According to the method and the device, the uncertainty analysis is performed on the historical power load sequence, and the power load is predicted based on the uncertainty analysis result, so that the accuracy of power load prediction is improved.

The invention provides an island shoreline erosion risk prediction method based on computer vision, and belongs to the technical field of island shoreline erosion risk analys.The island shoreline erosion risk prediction method comprises the steps that firstly, a high-resolution remote sensing image is obtained, island contour information is extracted, and a shoreline initial model is constructed through a small grid approximation method; generating multi-level grid representation through grid merging operation and storing the multi-level grid representation as a shoreline feature matrix; then collecting a multi-time sequence remote sensing image, calculating a change matrix set of adjacent time points, and analyzing a shoreline evolution mode; establishing a coast erosion kinetic equation model by combining the wave action intensity, the tide amplitude, the sea level rising rate, the geological structure coefficient and the shoreline slope angle; a shoreline change rule is analyzed by applying a map attention grid evolution model, and finally, an uncertainty analysis module is introduced, a multi-scale erosion risk assessment map is generated, a high-risk area is identified and output as a prediction result, so that the technical problem that the complicated island shoreline erosion risk is difficult to accurately predict is solved.

The invention discloses a forest carbon sink acquisition method and system for driving an ecological carbon mode through remote sensing observation, and the method and system are corresponding schemes: in the scheme, a set of high-precision and multi-dimensional satellite-ground collaborative observation data system is formed by combining multi-source observation data, and a solid foundation is laid for subsequent carbon sink inversion; a high-resolution prior carbon concentration field of a research area can be generated by using a machine learning method, and a high-precision posterior carbon concentration field can be obtained; on the basis, through inversion analysis and a correction link, high-precision, wide-coverage and low-uncertainty analysis of forest carbon sequestration in a research area is finally realized, and the problems that an existing carbon sequestration estimation method is insufficient in space coverage, high in model uncertainty, limited in multi-source data fusion and the like are solved.

The invention relates to the technical field of power system scheduling, and discloses an intra-day look-ahead scheduling rapid solving method considering large-scale new energy cluster power generation volatility. Comprising the following steps of S1, new energy cluster space-time fluctuation scene generation based on a neuron cellular automaton, S2, power grid dynamic security domain definition and simplification based on a physical information neural network, S3, scheduling rapid optimization solution based on model prediction path integration, and S4, scheduling scheme dynamic elasticity and stability evaluation based on a Kupman operator theory. The new energy cluster space-time fluctuation scene generation method based on the neuron cell automaton can effectively generate a space-time scene reflecting large-scale new energy cluster power generation volatility, supports uncertainty analysis, has the advantages of being high in calculation efficiency and scene authenticity, and is suitable for large-scale new energy cluster power generation. The problem that scene generation is inaccurate due to the fact that a traditional statistical model ignores space-time coupling is solved.

The invention discloses a liquid cooling adaptive control method and system based on a single-phase immersion liquid cooling data center, and relates to the technical field of liquid cooling control, and the method comprises the steps: reading real-time power consumption data of the data center, and synchronously starting a temperature monitoring sensor to read chip temperature data and environment temperature data; configuring a short-time predictor by using the historical load sequence, and executing information gain ratio and prediction uncertainty analysis of preset candidate granularity; performing matching evaluation of single-phase immersion liquid cooling and the data center by using the calibrated prediction granularity; and if the triggering threshold value is met, self-adaptive optimization under local-global control coordination constraint of single-phase immersion liquid cooling control is executed, and a liquid cooling response scheme is output. The technical problems that in the prior art, the cooling capacity of single-phase immersion liquid cooling is low in matching degree with the load requirement, response lags behind, and energy consumption control is insufficient are solved, and the technical effects of self-adaptive optimization control based on real-time and prediction data, energy consumption reduction and heat dissipation efficiency and temperature control precision improvement are achieved.

The invention provides a dangerous rock early warning method, system and device and a medium. Extracting local features of the geological point cloud data, aggregating the local features to generate global features, and obtaining dangerous rock features according to the global features; constructing a dangerous rock three-dimensional model according to the dangerous rock characteristics; performing stability and change trend prediction according to the dangerous rock three-dimensional model to obtain dangerous rock early warning information; according to the method, the change trend can be accurately predicted by using the dangerous rock three-dimensional model, the accuracy of dangerous rock early warning is improved, the uncertainty analysis of dangerous rock disaster time is met according to the dynamic expression of the personalized characteristics of the dangerous rock hidden danger, the reliability and accuracy of dangerous rock disaster early warning are improved, and a data basis is provided for disaster prevention and reduction work of geological disasters.

The invention discloses a hydrogen fuel cell hybrid electric vehicle energy management method based on robust optimization, and the method comprises the following steps: building a vehicle model: building a longitudinal dynamic model of a hydrogen fuel cell hybrid electric vehicle, determining a vehicle demand power model, and building an evaluation function of instantaneous hydrogen consumption, constructing an output power model of the hydrogen fuel cell and the capacitor; performing uncertainty analysis on the hydrogen fuel cell hybrid electric vehicle, determining uncertain parameters in the vehicle model, and constructing an uncertainty set of the uncertain parameters; the method comprises the following steps: constructing an sLSTM vehicle speed prediction model through vehicle historical speed data, predicting a future driving speed, analyzing the uncertainty of the prediction model, and outputting a prediction speed error in a current prediction time domain; and according to the optimization target, constructing a target function for minimizing the hydrogen consumption, solving the target function by applying a robust optimization algorithm, and outputting a first control result of the optimal control sequence.

The invention discloses an intelligent prediction and uncertainty analysis method for pose errors of a six-degree-of-freedom motion platform, and the method comprises the steps: taking an expected pose in a high-precision six-degree-of-freedom motion platform control system as network input data, and obtaining an actual pose through employing a binocular vision measurement system; collecting multiple groups of data, normalizing the data, and dividing the data into a training set and a test set; constructing a pose error prediction network model taking a gated circulation unit neural network as a basic framework, designing a translation and rotation feature coupling attention module, introducing a sparse attention mechanism guided by domain knowledge, and improving the capturing capability of the model for the real mechanical coupling characteristics of the six-degree-of-freedom platform; and inputting a test set into the trained model, and evaluating data of the test set by adopting a Monte Carlo Dropout method to obtain a pose error prediction result and an uncertainty analysis result. According to the method, the static pose precision of the six-degree-of-freedom motion platform can be effectively improved, a visual and reliable network prediction confidence level is provided, and the precision measurement requirement in the metering field is met.

The invention provides an uncertainty analysis method and device for a livestock target detection model. The method comprises the following steps: acquiring an RGB image of a livestock target; constructing an uncertainty analysis model of the livestock target detection model, and calling the uncertainty analysis model to detect the livestock target in the RGB image to obtain the category of the livestock target and a detection frame coordinate; and respectively calculating a cognitive uncertainty index and a random uncertainty index of the livestock target detection model according to the category and the coordinates of the detection frame. The method is used for solving the problems that in the prior art, an uncertainty analysis method is not comprehensive enough, uncertainty result attribution is lacked, and the uncertainty evaluation effect is poor.

The invention relates to a Bayesian KAN fused aircraft rapid multidisciplinary uncertainty analysis method, and belongs to the technical field of spacecraft manufacturing and application. According to the method, Bayesian learning is combined with a Colmogorov-Arnodel network, a Bayesian KAN neural network which can cope with high-dimensional input uncertainty, can quantify model cognitive uncertainty and is higher in fitting capacity is provided, a BKAN network agent model is constructed for a subsubject analysis model, time-consuming numerical simulation of each subsubject is replaced, and the calculation amount is reduced; with reference to a basic framework of a global sensitivity equation GSE of a deterministic field coupling multidisciplinary system, input random input and model cognition uncertainty are introduced, and expressions of mean values and covariances of disciplinary coupling variables and system responses are deduced on the GSE framework; a rapid semi-analytical multidisciplinary random and model cognition hybrid uncertainty propagation method is established, efficient multidisciplinary uncertainty analysis is achieved, and the problems that an existing method is poor in precision and large in calculation amount are solved.

The invention discloses a mineral resource metallogenic law simulation and target prediction system based on big data, and relates to the technical field of geological exploration. The multi-source data acquisition and standardization module supports acquisition of five types of data, and the data are preprocessed and then transmitted to the distributed database; mining key elements by using an improved Apriori algorithm, and constructing a correlation graph; building a model based on Unity 3D, and integrating three types of dynamic simulation; performing three-level prediction by using a CNN-LSTM model; three-dimensional rendering and multifunctional display are supported; and automatically updating data, models and rules to form an optimized closed loop. According to the method, multi-source data is integrated to improve ore-forming element recognition comprehensiveness, dynamic simulation fits geological reality, AI prediction is accurate, uncertainty analysis is included, visual interaction is practical, iterative optimization continuously improves efficiency, invalid exploration cost is reduced, and mineral exploration is efficiently guided.

The invention relates to the technical field of building carbon emission management, in particular to a building full-life-cycle carbon emission management system and method based on a calculation management system. The system comprises a data management module for acquiring and processing carbon emission data in a full life cycle stage of a building, mapping the processed carbon emission data with a factor database through a uniform interface and then storing the processed carbon emission data in the database; the carbon emission accounting module is used for executing carbon emission accounting and simulation and uncertainty analysis; the intelligent management module is used for carrying out real-time monitoring, carbon emission trend analysis, abnormity early warning and visual display on the carbon emission data; and the optimization emission reduction module operates an optimization algorithm, generates an emission reduction scheme through iterative processing and performs carbon asset management. According to the building full-life-cycle carbon emission management method based on the calculation, management and reduction system, closed-loop cooperation of accurate accounting, intelligent management and emission reduction optimization is achieved.

The invention discloses an electricity-hydrogen coupling system stability evaluation method, which belongs to the field of energy system stability evaluation, and comprises the following steps: S1, considering nonlinear characteristics of an electrolytic bath and a fuel cell, and constructing a dynamic mathematical model of an electricity-hydrogen coupling system; s2, parameter uncertainty analysis is carried out; s3, generating a unified stability score; s4, obtaining a multi-objective optimization model; s5, performing simulation verification to obtain a final dynamic weight; and S6, inputting the real-time operation data of the electricity-hydrogen coupling system and the final dynamic weight into the step S3 to generate a stability score under the current working condition, performing matching verification in combination with a stability criterion, and outputting an evaluation result. By the adoption of the stability evaluation method for the electricity-hydrogen coupling system, through a technical closed loop of dynamic modeling-intelligent screening-collaborative optimization, breakthrough improvement is achieved in the dimensions of model precision, calculation efficiency, system robustness and the like, and full-working-condition stability guarantee is provided for the electricity-hydrogen coupling system.

The invention relates to the technical field of new energy power distribution planning, in particular to a new energy power distribution planning intelligent simulation system oriented to a novel power system. Comprising a multi-source data dynamic modeling unit; the multi-objective optimization decision-making unit is used for realizing collaborative optimization of economy and reliability of the power distribution network and generating a planning scheme of equipment configuration, energy storage layout and network reconstruction by improving a heuristic algorithm; and the uncertainty analysis unit is used for quantifying the influence of the wind and light output fluctuation on the planning scheme and outputting a risk assessment index through a scene generation technology and a probability sampling method. Through multi-modal data fusion, time sequence model construction and a self-adaptive updating mechanism of the multi-source data dynamic modeling unit, the dynamic adaptation capability of the model to new energy power fluctuation and load characteristic change can be improved, and meanwhile, the space-time correlation characteristics between the source and the load are mined, so that the power grid model can be synchronously evolved along with the time sequence data change; and the deviation between the model and the actual operation condition is reduced.

The invention belongs to the technical field of flatness identification and correction, and discloses a flatness identification and correction method based on microscopic system uncertainty analysis, comprising the following steps: introducing a deep neural network with an uncertainty reasoning function to predict a defocus distance, and using output variance to quantify prediction uncertainty; judging whether the global uncertainty of the target focal plane enters a correction process or not; performing local analysis on the target focal plane to determine a high-uncertainty region; according to the coordinate points of the high-uncertainty area, five-axis adjustment parameters are solved, and the position and angle of an adjustment sample are determined; and after the initial focus is adjusted, capturing a new target focal plane by the network, entering the step S101 for re-evaluation, controlling the attitude of the target focal plane to reach the standard through feedback, and exiting the correction process.

The invention discloses a Tube MPC-based hybrid electric vehicle layered energy management method. The method comprises the following steps of performing longitudinal dynamic modeling on a hybrid electric vehicle, determining a vehicle demand power formula, constructing an evaluation function of equivalent fuel consumption, and constructing an output power model of a vehicle engine and a battery; carrying out uncertainty analysis on the energy management system, determining uncertain parameters in the vehicle model, quantifying the uncertainty of the uncertain parameters based on probability distribution, and constructing an uncertainty set of the uncertain parameters; on the basis of a dynamic programming algorithm, under the global working condition, the minimum amount of equivalent fuel consumption serves as a target function, an optimal control sequence of the engine is output, and an SOC reference trajectory is generated; and performing tracking solution on the control variables and the state variables in the uncertainty set by applying a Tube model predictive control (Tube MPC) method to obtain an optimal control sequence under uncertain factors, and outputting a first control variable.

The invention discloses an electric power equipment product carbon footprint uncertainty analysis method and system based on AHP-PD, and belongs to the technical field of product carbon footprint evaluation and uncertainty analysis. The method comprises the following steps: constructing a carbon footprint uncertainty hierarchical structure model comprising a target layer, a criterion layer and an index layer; constructing a judgment matrix based on the hierarchical structure model; calculating a weight set W and a local weight set Wi; synthesizing a global weight set omega according to the weight set W and the local weight set Wi; the uncertainty of the carbon footprint source is quantified; obtaining probability distribution of the total uncertainty of the carbon footprint of the electric power equipment product; and adopting the statistical characteristics of the probability distribution as the uncertainty analysis result of the carbon footprint in the full life cycle of the electric power equipment product. According to the method, the final result is in a probability form (such as a confidence interval), the real situation can be better reflected compared with a single determined value, and risk decision is supported.

The invention discloses a multi-axial fatigue life prediction method based on physical information, aiming at the problem of multi-axial fatigue damage of a material, an MLP-Attention model provided by the invention adopts a multi-layer sensor MLP to process a complex nonlinear relationship, and meanwhile, aiming at the characteristics of complex data and strong nonlinearity of a multi-axial fatigue experiment, the multi-axial fatigue life prediction method based on the physical information can be used for predicting the multi-axial fatigue life of the material. A self-attention mechanism Attention is introduced, and the accuracy of a model prediction result is improved through adaptive feature weighting; for the difficulty that the multi-axial fatigue life has large discreteness, Gaussian process regression is selected and fused to perform uncertainty analysis on the prediction result so as to predict the fatigue life interval, the practicability and prediction precision of the prediction model are greatly improved, and the problem that uncertainty cannot be quantified in the traditional technology is solved.

The invention relates to the technical field of random geological modeling and numerical simulation integration, in particular to an automatic shield numerical simulation method and device considering geological uncertainty, and the method comprises the steps: extracting geological interface position information based on a pre-constructed voxelization three-dimensional geological model; obtaining structured construction information, and constructing a building informatization BIM model by using the geological interface position information and the construction information; key information of the BIM model is extracted, and a computational grid model and a computational instruction file are respectively generated according to the key information, so that the purpose of calculation integration of the BIM and the numerical simulation software is achieved. Therefore, the problem that according to a design method in the related technology, due to the fact that random geological modeling and numerical calculation analysis are separated from each other, geological uncertainty analysis in shield engineering is difficult to efficiently complete is solved.

The invention discloses an ASIC special password intelligent chip aging prediction method, and relates to the field of chip aging prediction, and the method comprises the steps: carrying out the feature fusion coding through obtaining chip power supply wiring layer data and collecting environment parameters, obtaining an aging sensitivity feature vector, carrying out the fractional order aging dynamics modeling based on the aging sensitivity feature vector, and carrying out the aging prediction. Performing chip aging state prediction based on the chip aging state change equation to obtain an aging track prediction sequence, extracting an aging sensitivity feature vector component and the aging track prediction sequence to perform residual life probability evaluation, and obtaining a residual life cumulative distribution function; on the basis of the residual life cumulative distribution function, uncertainty analysis is carried out on chip aging prediction to obtain a two-factor risk index, a coupling aging rule between hardware structure weakness and environmental stress is grasped, real aging sensitive points of the chip can be accurately reflected, and accurate reference can be provided for preventive maintenance of the chip.

The invention discloses a natural electric field-electric field joint detection and inversion method based on GMM prior, and relates to the technical field of tunnel engineering geological detection, and the method comprises the steps: obtaining the resistivity and natural potential observed in a to-be-inverted region; an equivalent body source of a natural potential is parameterized into a body source vector on a grid unit, weighted L1 sparse regular constraint is introduced into the body source vector, a GMM prior model is used as underground electrical structure prior shared across physics fields, and a multi-task joint inversion objective function of a shared underground electrical structure label is established; and performing inversion iteration by adopting maximum posteriori, updating parameters of the GMM prior model and the underground electrical structure label by adopting an expectation maximization algorithm until an end condition is met, and obtaining a joint inversion imaging result of the tunnel water-rich anomalous body. The advantages of a multi-source electrical method are fused, underground electrical structure priori is introduced, Bayesian uncertainty analysis is combined, and high-resolution and high-reliability recognition of the water-rich anomalous body in front of the tunnel is achieved.