7results about How to "Precise inversion" patented technology

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.

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.

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.

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 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.

This invention discloses a method and system for detecting and evaluating the settlement of buried pipelines based on ground-penetrating radar (GPR). The detection method acquires B-Scan mask images of shallow-buried pipelines, extracts the overall and local characteristics of target hyperbolic reflection signal feature points in the images, removes singular points, performs curve fitting on all valid target hyperbolic reflection signal feature points, and calculates and scores the depth and wave velocity values ​​corresponding to the target feature points to obtain the pipeline burial depth. The evaluation method, based on the pipeline settlement detection results, identifies the location of maximum settlement deformation by using the angle formed by the height change between two points on the pipeline, and predicts the location where the risk of fracture in the detected section may occur. The detection system implements this detection method, and the evaluation system implements this evaluation method. This application solves the problem in engineering measurements where hyperbolic reflection signals with characteristic damage caused by electromagnetic wave attenuation and coupling cannot be effectively calculated, significantly improving the data's processability and analyzability.

This invention discloses a method for dynamic monitoring of forest resources and automatic database updating based on multi-source remote sensing data, belonging to the field of forestry informatization. This method addresses the inconsistencies in resolution, imaging time, and band coverage of multi-source remote sensing data by dynamically generating multi-scale grids to achieve spatial alignment and fusion of data. Utilizing temporal residual discriminative convolutional networks and sparse attention mechanisms, it intelligently detects changes in forest resources, achieving efficient and automatic attribute inversion. The system also includes automatic differential database updates and multi-version management to ensure information synchronization and reliability. Combined with external climate and fire data, it provides alarms for abnormal changes and suggestions for auxiliary intervention, forming an intelligent closed loop from monitoring to management. This method improves the automation, intelligence, and precision of forest resource monitoring and is suitable for large-scale, dynamic forest resource management scenarios.