39results about How to "Precise inversion" patented technology

The invention discloses a method for inverting key parameters in an underground water pollutant biodegradation numerical model based on a neural network algorithm. Carrying out preprocessing and refined dimension reduction on the data, and processing high-dimensional multi-scale data; building a multilayer neural network architecture under a physical information neural network framework, embedding a related reaction equation of a pollutant natural attenuation process, and coupling a biochemical reaction mechanism and a data driving model; a neural network model is trained, errors in the training process are calculated, and the accuracy of an inversion result is improved by using a sub-when optimization algorithm; integrating observation data and a model prediction result, determining final parameter setting of a numerical model, and carrying out underground water damage real object quantification; comparative verification of a neural network parameter inversion model is carried out, and feasibility and accuracy of the parameter inversion method are ensured. According to the method, the problem that the key parameters of the biodegradation numerical model are difficult to accurately invert in the real object quantification process of underground water pollution under complex geological conditions is solved.

The invention discloses an under-cloud aerosol extinction coefficient laser radar detection inversion method, and the method comprises the steps: obtaining an aerosol extinction coefficient corresponding to an echo signal of a laser radar in a horizontal direction, and taking the aerosol extinction coefficient as a first calibration value EXTI; the height of a calibration point at the cloud bottom is judged according to the distance correction square signal at the set moment, and an atmospheric extinction coefficient corresponding to the height of the calibration point at the cloud bottom is obtained and serves as a second calibration value EXT0; obtaining a second extinction coefficient profile based on the second calibration value EXT0, obtaining an extinction coefficient at the first height X according to the second extinction coefficient profile, and taking the extinction coefficient as a second calibration value EXTX; the first height X is greater than the height of the blind area; the first calibration value EXTI and the second calibration value EXTX are compared, when EXTX-EXTI is larger than EXTI * delta, the second calibration value EXT0 is adjusted, the step of obtaining the second extinction coefficient profile is executed again based on the adjusted second calibration value EXT0 till EXTX-EXTI is smaller than EXTI * delta, the second calibration value EXTX is output, and delta is a relative error. The method has the advantage that the aerosol extinction coefficient can be accurately inverted even in the presence of clouds based on the method.

The invention provides a method and a device for inverting heat flow of a divertor based on a laser speckle image, and belongs to the technical field of nuclear fusion engineering thermal hydraulic testing. The device comprises a fusion reactor high heat flow simulation loading system, a laser speckle effect test system, a divertor water cooling simulation system and a test database system. A divertor target plate heat flow and loading surface strain corresponding database is established through the steps of adjusting cooling water flow conditions of a divertor test piece, carrying out series high heat flow loading on the divertor, measuring the divertor by adopting a laser speckle digital correlation technology and the like. In the actual reactor application, the surface strain of the same parts in the reactor is measured through the laser speckle technology, and the loaded heat flow inversion is realized through the corresponding query database. According to the method and the device for inverting the heat flow of the divertor based on the laser speckle image, quantitative inversion of the loaded heat flow of the divertor in an actual fusion reactor service environment is realized, and the operation safety of a nuclear fusion reaction device is improved.

The invention discloses a floating transportation towing physical model test system for yaw and deviation correction simulation, which comprises a tug power simulation system, the tug power simulation system comprises a floating transportation towing platform and a jet flow loading facility, and the main body structure of the floating transportation towing platform is composed of a fixed guide rail, a movable trailer and a power supply guide rail. The movable trailer is erected on the fixed guide rail and is driven by a motor, height-adjustable horizontal slideways are erected on cross beams on the front side and the rear side of the movable trailer, hooks with pulleys are arranged on the horizontal slideways, the hooks are connected with the floating transportation component model through traction cables, and the jet flow loading facility is carried on the movable trailer. A mechanical arm, a high-pressure plunger pump and a jet nozzle are arranged, and the jet nozzle and a water supply pipeline are connected to the tail end of the mechanical arm. The invention further discloses a test method adopting the test system. The device has the function of flexibly simulating the transverse loading of the tug, and can realize the complete similar simulation of the component response in the floating yaw process and the deviation correction process.

The invention discloses a water area chlorophyll a concentration remote sensing inversion method and system, a medium and equipment, relates to the technical field of environmental monitoring, and provides an improved genetic algorithm for feature selection based on Sentinel-2 remote sensing satellite images and actually measured Chl-a concentration data of a Wuliang vegetarian sea by taking the Wuliang vegetarian sea as a research area, and an XGBoost model is optimized in combination with a dung beetle optimization algorithm DBO. And inversion is carried out on the Chl-a concentration of the ouyew vegetarian sea. A remote sensing characteristic wave band of Chl-a concentration is screened through an improved genetic algorithm, the remote sensing characteristic wave band is input into a DBO-XGBoost model, and a DBO-XGBoost Wuji prime sea Chl-a concentration inversion model is established. The result shows that under the same inversion model, redundant information between wave bands is effectively reduced, the complexity of the model is reduced, and the prediction performance of the model is improved at the same time. The decision coefficient R2 of the-DBO-XGBoost model is 0.9379, and the decision coefficient R2 of the- The root-mean-square error (RMSE) is 2.2566 [mu] g / L, the mean absolute error (MAE) is 1.9019 [mu] g / L, and powerful support is provided for monitoring of the concentration of the Wuliang vegetarian sea Chl-a.

The invention belongs to the technical field of machine vision automatic identification, and particularly relates to a radioactive article automatic identification method based on machine vision, and the method comprises the steps: labels on radioactive articles are automatically identified and clamped, and the labels are square labels with the same size, different from the background color and different in color; according to the method, the radioactive article is measured, the radiation data of the radioactive article is measured, and the radiation field is reconstructed according to the radiation data, so that the problems that working personnel are affected and the measured and acquired data are insufficient when the space dose rate in the retired environment is acquired are solved. According to the radioactive substance clamping and measuring device, the complexity and danger of radioactive substance processing in the nuclear industry are dealt with, clamping and measuring of radioactive substances are carried out through the mechanical arm, the clamping accuracy is improved, the working efficiency is greatly improved, and the safety of workers is guaranteed.

The invention relates to an inversion method and an inversion system for uneven stress distribution in heterogeneous rocks, and belongs to the technical field of deformation and damage of heterogeneous rocks. Aiming at the problem that the existing numerical simulation method is difficult to accurately reflect the deformation and failure characteristics of the heterogeneous rock, the method comprises the following steps: selecting and pretreating a heterogeneous rock sample, and carrying out nanoindentation test on the heterogeneous rock sample to obtain the elasticity modulus of composition minerals of the sample; the method comprises the following steps: extracting composition mineral types and distribution ranges of a heterogeneous rock sample to obtain an elastic modulus distribution matrix, monitoring a test deformation process of the heterogeneous rock sample, calculating a strain field of the heterogeneous rock sample to obtain a strain field matrix, and calculating a non-uniform stress distribution field through the elastic modulus distribution matrix and the strain field matrix. And comparing the calculated stress value with the applied stress value, optimizing a threshold interval of composition minerals of the sample, and outputting a non-uniform stress distribution field. Compared with a numerical simulation method, the method can evaluate the stress field more visually and accurately, and has important physical significance.

The invention belongs to the related technical field of printing material mechanics research, and discloses a discrete element modeling method and system for the compaction behavior of a printing material, and the method comprises the steps: S1, building a discrete element model of the compaction behavior of the printing material, and determining to-be-optimized parameters in the model and the range of the to-be-optimized parameters; establishing an objective function according to a deviation between a numerical calculation result obtained by discrete element model simulation and a test measurement result; s2, establishing a distributed reinforcement learning model: designing a state space according to the target function value and each parameter, and setting a reward structure and an action space of each to-be-optimized parameter, the reward structure being a reciprocal of the target function value; and S3, parameter inversion is carried out through the distributed reinforcement learning model, each optimized parameter is obtained, and discrete element modeling is completed. According to the method, the model parameters are optimized and determined through parameter inversion in the discrete element model based on the distributed reinforcement learning algorithm, the speed and efficiency of parameter inversion are improved, and the parameter inversion is more accurate.

The present invention discloses a nonlinear inversion method and system of apparent resistivity based on the EConvNet‑C algorithm, including: obtaining apparent resistivity data of a set landfill leachate water level through high-density electrical measurement; inputting the apparent resistivity data into a trained EConvNet‑C leachate water level recognition model to obtain inverted landfill resistivity data. The present invention integrates a multi-layer perceptron and an EConvNet‑T into an EConvNet‑C leachate water level recognition model. The multi-layer perceptron can transform an undefinable potential space into a low-resolution feature image space, thereby realizing nonlinear inversion of potential differential data carrying leachate information to true resistivity; providing guidance for later pollution control and soil remediation of landfills.

This invention relates to a multi-temporal radiation signal inversion method based on Conditional Transformer, belonging to the field of satellite remote sensing data processing / spatial information science and technology. The method includes: acquiring meteorological time-series data of the target area to establish a dataset; designing a Conditional Transformer deep learning network; combining the target time-series data and meteorological data encoded by time and meteorological code generators with the output of the Encoder layer, and inputting this combination into the Decoder layer to train the Conditional Transformer deep learning network to obtain a surface radiation signal inversion model; inverting surface radiation brightness temperature data and evaluating the inversion accuracy; efficiently capturing long-distance spatiotemporal dependencies of radiation signals; flexibly inverting radiation changes according to the target time and meteorological conditions; removing nonlinear interference from the atmosphere on radiation; and more accurately inverting the true radiation characteristics of the surface.

The present invention provides a method and device for inverting the heat flow of a divertor based on laser speckle images, and belongs to the technical field of thermal hydraulic testing of nuclear fusion engineering. The device includes a fusion reactor high heat flow simulation loading system, a laser speckle effect testing system, a divertor water cooling simulation system, and a test database system. By adjusting the cooling water flow conditions of the divertor specimen, performing a series of high heat flow loading on the divertor, and measuring the divertor using laser speckle digital correlation technology, a database corresponding to the heat flow of the divertor target plate and the strain of the loading surface is established. In actual reactor applications, the surface strain of the same component in the reactor is measured by laser speckle technology, and the loading heat flow inversion is realized by corresponding query database. The present invention adopts the above-mentioned method and device for inverting the heat flow of a divertor based on laser speckle images, realizes the quantitative inversion of the divertor loading heat flow in the actual fusion reactor service environment, and improves the safety of the operation of the nuclear fusion reaction device.

The present application relates to the technical field of complex radiation field inversion, and particularly relates to a complex radiation field inversion system and method based on a multi-layer source grid, wherein a multi-layer source grid division subsystem is used to perform multi-layer discrete approximation on a complex source term with a known position and shape to be inverted, a detector partition arrangement subsystem is used to set detectors for a radioactive source, all coarse grid volume sources and all fine grid volume sources respectively in different manners for detection, a source term inversion subsystem is used to perform inversion on source intensity distribution of the complex source term according to the radioactive source, all coarse grid volume sources, all fine grid volume sources and first, second and third measurement values, and a radiation field calculation subsystem is used to calculate the radiation field distribution of the inverted complex source term. The technical problem that the complex source cannot be inverted in the traditional inversion is overcome, and the radiation field under the complex source distribution can be quickly and accurately inverted.

The application relates to the technical field of quasi-brittle material mechanical property testing and constitutive inversion, in particular to an inversion analysis method of a quasi-brittle material tensile softening constitutive under monotonic loading, which comprises the following steps: collecting unilateral notched beam specimen size parameters and monotonic loading three-point bending test data; establishing a two-dimensional numerical model comprising an undamaged area and a cohesive crack area; constructing a segmented linear tensile softening constitutive subroutine comprising an elastic stage and a softening stage; integrating the two-dimensional numerical model and the subroutine, iteratively inverting constitutive parameters through an optimization algorithm, and outputting a final segmented linear tensile softening constitutive after the simulation data and the test data reach a preset fitting degree. The method can improve the constitutive inversion accuracy, adapt to the nonlinear fracture characteristics of quasi-brittle materials, and provide reliable support for material performance evaluation.

This invention discloses a method and system for inverting active power and wind speed in wind farms. The method includes: dividing historical active power and center-point wind speed time-series data into multiple continuous sub-series segments, aggregating them using a Transformer encoder to obtain a correlation feature vector of nonlinear coupling between active power and wind speed; concatenating the correlation feature vector with the current active power, mapping it using a linear regression head, and applying physical constraints using the wind farm's equivalent power curve to obtain a center-point wind speed inversion sequence; concatenating the center-point wind speed inversion sequence with static spatial features, feeding it into a selective state-space model based on the Mamba architecture, and outputting a time-varying correlation feature vector at the turbine location level; finally, designing a linear regression head mapping for each turbine location to obtain a regional wind speed inversion time series, and applying physical constraints using a single-turbine power curve to form a wind speed inversion link from the center point to the region. This achieves accurate inversion of wind speed at the wind farm's center point and in the region, improving the precision and reliability of wind speed sensing.

The invention relates to the technical field of surface temperature extraction, and provides an automatic scorching ethylene cracking furnace tube surface temperature extraction method, which comprises the following steps of: firstly, accurately positioning a furnace tube in a swinging state in a complex infrared thermal radiation environment by combining a deep neural network with an attention mechanism; the method comprises the following steps of: establishing a homogeneous background, separating the homogeneous background from the homogeneous background, establishing a lookup table (LUT) based on an infrared radiation transmission theory, fully utilizing priori knowledge of three physical parameters, namely furnace tube surface temperature, equivalent background temperature and emissivity, and accurately deducting the influence of reflection radiation, range radiation, emissivity and the like of the background on infrared radiation receiving of a detector; therefore, the surface temperature distribution of the furnace tube is accurately and quantitatively calculated.

This invention discloses a system and method for intelligent detection of deep rock properties and geological formation inversion, relating to the field of mine safety assessment technology. The system includes a terahertz incoherent detection module, a multi-dimensional external field control module, a signal acquisition and control module, and an intelligent inversion and disaster prediction module. The terahertz incoherent detection module includes a terahertz incoherent wave source, a wavefront optimization unit, and a sample coupling unit. The multi-dimensional external field control module includes a magnetic field control unit and a light field control unit. The signal acquisition and control module includes an intrinsically safe synchronous controller, a high-precision current detector, and a signal preprocessing unit. The intelligent inversion and disaster prediction module includes a feature extraction unit, a correlation model library, and a disaster prediction output unit. This invention achieves high-precision, rapid in-situ detection of rock properties and geological formation conditions, as well as accurate early warning of disaster risks, effectively supporting the safe and efficient mining of deep resources.

A portal crane stress inversion method comprises the following steps: designing a portal crane stress data acquisition and fusion module to realize reasonable layout of stress sensors and multi-dimensional fusion of virtual and real stress data; designing a stress inversion deep learning module of the portal crane, and constructing a dynamic reconstruction and primary negative feedback deep learning unit by taking virtual and real stress data as input to realize stress inversion deep training of the portal crane; and designing a stress inversion verification module of the portal crane, constructing a secondary negative feedback unit oriented to the actually measured stress, the inversion stress and the deep learning unit of the portal crane, and realizing verification and correction based on the inversion method of the actually measured stress. The problem of incomplete stress monitoring of the current portal crane can be solved to a certain extent, and the stress inversion confidence coefficient and the safety management and control capability of the portal crane are improved.

The invention relates to an optical parameter detection device and method, in particular to an optical system wavefront detection device and method based on phase modulation. In order to solve the problems in the prior art that a multi-frame acquisition strategy adopted by a phase recovery method based on a point spread function is difficult to adapt to a dynamic test scene, a single-frame acquisition strategy is difficult to stably invert wavefront with conjugate symmetry, and iterative inversion operation time is relatively long and may fall into a local optimal solution, the invention provides a phase recovery method based on a point spread function. The invention discloses an optical system wavefront detection device based on phase modulation. The optical system wavefront detection device comprises a module, a first imaging lens, a first plane mirror, a beam splitter, a microscope objective, a second plane mirror, a partition phase plate, a second imaging lens and a photoelectric detector, and asymmetric phase coding is achieved through the partition phase plate. Based on the device, according to the method, the unsupervised deep neural network is combined with the physical driving layer, so that accurate inversion of different optical systems to be detected is realized.

The present invention discloses a multi-baseline forest height inversion method and system for P-band heavy track PolInSAR, and a storage medium. First, SAR image data is acquired; SKP algorithm is used to decompose different scattering mechanisms of forests, and the influence of ground time incoherence and residual ground scattering is removed; then, the fuzzy interval of pure body coherence coefficient is calculated in combination with the set multi-baseline, and solution space optimization is carried out to remove impossible solution combinations; finally, multi-baseline forest height is inverted, and the coherence strength of pure body coherence coefficient is reduced by assuming body time incoherence, and forest height inversion is realized by using cost function. The method provided by the present invention cooperates with SKP algebraic synthesis method and vegetation coherent scattering model to form a multi-baseline PolInSAR forest height inversion method, focusing on removing the joint influence of the above three aspects of interference, realizing accurate inversion of forest height, and ensuring that the method has high robustness under different regions and time baselines, thereby providing technical support for large-scale forest height and AGB inversion mapping.

The present invention provides a new method for characterizing complex fracture systems in deep shale gas reservoirs, including: dividing single wells into natural fracture single wells, artificial fracture single wells and single wells coupled with natural fractures and artificial fractures through fracture information of single wells, using Monte Carlo method combined with discrete fracture model to generate fracture network model diagram of single wells, dividing single wells into inner and outer zones based on the permeability of fractured low-porosity shale reservoirs using radial composite model, combining dual porosity medium model and discrete fracture model according to continuity and heterogeneity of reservoir fractures, and characterizing complex fracture systems of natural fractures, artificial fractures and coupled with natural fractures and artificial fractures. The present invention forms a method for characterizing complex fracture systems of single wells, which can more accurately invert reservoir parameters and fracture parameters, improve the reliability of fracture characterization, and effectively support efficient exploration and development.

The invention relates to the technical field of power system monitoring, and discloses an icing torsion inversion method, system and device based on a physical constraint neural network, and a medium, and the method comprises the steps: collecting the torsion angle data, environmental parameter data and historical icing thickness data of a transmission conductor; establishing a physical constraint model between the torsion angle and the icing thickness based on a wire mechanics principle; constructing a first neural network based on the physical constraint model, and defining a composite loss function including a data driving loss function and a physical constraint loss function; training a first neural network by using historical icing thickness data, and optimizing network parameters through a back propagation algorithm to minimize a composite loss function; and inputting the torsion angle data and the environmental parameter data into a trained first neural network for prediction to obtain an icing thickness prediction value of the current wire. According to the method, the mechanical physical model is embedded into the neural network training process, so that accurate inversion from the wire torsion angle to the icing thickness is realized.

The invention provides a flame pulsating heat release rate determination method and device, equipment and a medium, and relates to the technical field of optical measurement, and the method comprises the steps: obtaining flame interference fringe images and phase diagrams of a combustion region at different continuous moments; extracting a plurality of background lines from the background area; performing phase inversion according to the phase difference of each pixel point at the moment to obtain the pulsating heat release rate of each pixel point at the moment; based on the pulsating heat release rate of each pixel point at different moments, determining a pseudo heat release rate time sequence of different background pixel points on each background line at each moment; according to the pseudo heat release rate time sequence of different background pixel points at each moment, spatial expansion is carried out, and the target heat release rate of each pixel point at each moment is determined; and based on the pulsating heat release rate of each pixel point at each moment and the target heat release rate of the pixel point at the corresponding moment, the flame pulsating heat release rate of the combustion area at each moment is obtained. According to the method, the accurate flame pulsating heat release rate can be obtained.

The invention discloses a natural gas pipeline cleanliness degree evaluation method and device, equipment and a medium, and relates to the technical field of natural gas pipeline monitoring, and the method comprises the steps: constructing a full-working-condition simulation database of a natural gas pipeline; generating a data-driven flow-roughness response model based on the pressure feature spatial index; performing pressure feature matching and roughness inversion on the actual pressure and the actual flow by using the flow-roughness response model; the roughness serves as an absolute index, a performance index is determined based on the planned flow and the actual flow, an economic index is determined based on the actual flow and the theoretical flow, the absolute index, the performance index and the economic index are input into a three-dimensional pipeline cleanliness evaluation system, and a comprehensive cleanliness degree score is calculated based on an evaluation method; the method is advantaged in that problems of strong physical model dependence, model misalignment, low calculation efficiency and poor multi-condition adaptability are solved, and reliability, accuracy and timeliness of natural gas pipeline cleanliness evaluation are improved.

This invention discloses a water level error correction system and method for steam generators, belonging to the field of industrial measurement and control. The system includes a differential pressure sensor with a Wheatstone bridge as its core, a precision resistor connected in series in its power supply circuit, and a multi-channel signal acquisition and processing unit. The method simultaneously acquires the output voltages of the two channels of the bridge and the voltage of the precision resistor, first calculating the sensor chip temperature; then constructing two characteristic quantities—voltage and voltage difference—and intelligently judging the system operating condition based on their changes; finally, adaptively calling a pre-stored compensation model according to the operating condition, calculating the system static pressure, and performing comprehensive dynamic correction of the original differential pressure for static pressure error and temperature drift, thereby calculating the accurate water level. This invention eliminates the need for additional sensors, achieving real-time compensation for the inherent error of the differential pressure sensor under all operating conditions solely through innovative signal fusion and adaptive algorithms, significantly improving the accuracy and stability of water level measurement, while also being low in cost and simple in structure.

The invention discloses a rape phenotype inversion model generation method and device, and relates to the technical field of model training, and the method comprises the steps: collecting rape data of a preset region in a preset time period through an unmanned plane and manual sampling in the preset region of rape growth, vegetation index data collected by the unmanned aerial vehicle and ground actual measurement phenotype data sampled manually are obtained; analyzing the ground actual measurement phenotype data to obtain a phenotype main influence factor; matching and associating the vegetation index data with corresponding ground actual measurement phenotype data to obtain an initial sample set; performing sample layering and sampling on the initial sample set based on the phenotype main influence factor to obtain a training sample set and a test sample set; training the initial machine learning model according to the training sample set to obtain an initial rape phenotype inversion model; verifying the initial rape phenotype inversion model through the test sample set to obtain a verification result; and optimizing the initial rape phenotype inversion model according to a verification result to obtain a target rape phenotype inversion model.

This invention provides a method for dynamic capacity expansion micro-meteorological simulation of transmission lines, belonging to the field of power system technology. The method includes: extracting features from wind field simulation data under different operating conditions based on intrinsic orthogonal decomposition to obtain corresponding modes and constructing a wind field order reduction model; using wind field data from sparse monitoring points as constraints, solving for the weight coefficients of each mode using the least squares method to reconstruct the baseline wind field for the entire line; calculating and correcting the local wind field error at non-monitoring points based on the deviation between the measured wind field data from monitoring points and the reconstructed baseline wind field, thus obtaining the wind field for the entire line; identifying dynamic thermal bottleneck points and determining the maximum allowable current carrying capacity based on the wind speed at these bottleneck points. Beneficial effects: Through offline physical cost reduction, online sparse reconstruction, and residual correction, a limited number of monitoring points can accurately invert the complex wind field of the entire line, achieving low-cost, high-precision micro-meteorological spatiotemporal simulation of transmission lines and improving the safety margin for dynamic capacity expansion.

The invention discloses a crop soil moisture content remote sensing inversion method, device and system, and a storage medium. The method comprises the following steps: S1, obtaining an unmanned aerial vehicle multispectral remote sensing image of crops in a predetermined area; s2, removing a soil background and an image shadow from the multi-spectral remote sensing image of the unmanned aerial vehicle; s3, training a back propagation neural network model and a partial least square regression model according to the unmanned aerial vehicle multispectral remote sensing image after the soil background and the image shadow are removed to obtain an inversion model; and S4, inputting the unmanned aerial vehicle multispectral remote sensing image of the crops in the target area into the inversion model, and performing inversion of the soil moisture content of the crops in each growth period. By adopting the technical scheme provided by the invention, the crop soil water content can be accurately inverted.

The invention relates to a spaceborne synthetic aperture radar-based rainfall inversion method, equipment and medium, and the method comprises the steps: inverting a radar echo end point according to the slope of a radar echo, and correcting the radar echo in a radar echo signal coverage level range according to the inverted radar echo end point; inverting a ground rainfall capacity function and a rainfall end point in a rainfall range by using a double-layer VIE algorithm based on the corrected radar echo; inverting a rainfall starting point by using the ground rainfall capacity function and the rainfall end point, and inverting a rainfall level function by comparing the rainfall starting point with a prior model by using a minimum distance method; and calculating the ground rainfall by using the rainfall level function and the ground rainfall function. The equipment and the medium are used for implementing the method. Compared with the prior art, the method can be widely applied to inversion of various rainfall types.

The invention discloses a method, a device, a medium and equipment for detecting steel bar corrosion information in reinforced concrete, and relates to the technical field of concrete steel bar corrosion detection. The method comprises the following steps: acquiring semi-potential distribution of reinforced concrete; establishing a corrosion model of reinforcement corrosion potential distribution in the reinforced concrete according to the function relationship among the saturation degree, the oxygen diffusion coefficient and the concrete resistance; establishing a particle swarm related to the steel bar corrosion information, and based on the semi-potential distribution and corrosion model, determining the adaptive value of each particle in the particle swarm related to the steel bar corrosion information by adopting a particle swarm optimization algorithm; and based on the adaptive value of each particle in the particle swarm, performing inversion on the information of reinforcement corrosion in the reinforced concrete to obtain the position of reinforcement corrosion and the size of the corrosion area in the reinforced concrete. By means of the scheme, accurate quantitative information of steel bar corrosion can be obtained, and the limitation that only the steel bar corrosion probability can be provided through an existing half-cell potential method is broken through.

The present application relates to the technical field of oil and gas drilling engineering, and specifically discloses a multi-field coupling complex fractured formation fracture width distribution inversion method, based on the theory of porous elasticity, a drilling fluid loss model considering fluid-solid coupling effect is constructed, a roughness correction factor is introduced to correct the traditional cubic law, and the application of Darcy's law in the matrix is improved, a drilling fluid loss mathematical model coupled with the matrix and the fracture is established, through numerical simulation, the influence law of key parameters such as fluid viscosity, pressure difference and fracture width on drilling fluid loss is obtained, and on this basis, a fracture width multivariate linear regression inversion equation with fluid viscosity, pressure difference and cumulative loss as independent variables is established, the inversion equation has high prediction accuracy and practicability, can be used for quickly and accurately inverting the fracture width on site, and provides theoretical and technical support for the understanding of the loss mechanism of multi-scale fractured formation and the optimization of leakage prevention and treatment.