8results about How to "Reliable estimate" patented technology

This invention discloses a method for simulating the underwater topography of natural lakes based on shoreline topographic features, belonging to the field of earth science technology. This invention focuses on "simulating the natural process of lake level drop," fully utilizing the lakeside topographic information and morphological parameters extracted from DEM data to achieve reliable estimation of water depth and water storage in natural lakes without actual measured data. The main advantages are as follows: 1) This method can automatically adjust the calculation formula for underwater topography based on lake morphological parameters, thereby achieving autonomous adjustment for water depth prediction of different types of lakes, effectively improving the accuracy and reliability of the simulation results; 2) In the underwater topography simulation process, the reshaping effect of lake sediments on the underwater topography is considered, and a correction formula is introduced into the slope calculation module, making the slope calculation results closer to the actual lakebed morphology, further improving the accuracy of the simulation process.

The invention discloses a railway hopper car remote state estimation method, device and equipment, and the method comprises the steps: modeling a railway hopper car sensor network into a discrete time random system comprising a plurality of nodes, and obtaining a state space model corresponding to the railway hopper car sensor network; establishing a packet loss and spoofing attack model of a sensor measurement signal under an amplification forwarding relay strategy; wherein a measurement signal of each sensor is transmitted to the remote estimator through the amplification forwarding relay; determining a remote estimation model according to the state space model and the packet loss and spoofing attack model; and calculating an estimator gain matrix by using the remote estimation model, and outputting a railway hopper car operation state estimation value according to the estimator gain matrix. According to the method, the running state of the railway hopper car is accurately and reliably estimated, and the safety and the operation efficiency of railway freight are improved.

The invention relates to the technical field of anti-collision early warning, and discloses a transformer substation crane three-dimensional anti-collision early warning system and method based on a millimeter wave radar, and the system comprises a millimeter wave radar module which is used for obtaining point cloud observation data; the Beidou satellite positioning module is used for acquiring absolute geographic coordinates of the crane; the processing module is used for estimating the motion states of the crane jib and the hoisted object based on the point cloud observation data and the absolute geographic coordinates, quantifying the dynamic collision risk based on the estimated motion states of the crane jib and the hoisted object, and generating an early warning signal; and the early warning module is used for receiving the early warning signal and sending out early warning information. According to the method, the crane jib, the hoisted object and the point cloud observation data are modeled into the random finite set, the probability hypothesis density filter is adopted for processing, and meanwhile, the point cloud observation data are obtained through the millimeter wave radar, so that the motion states of the crane jib and the hoisted object can be stably and reliably estimated, and the view blind area is eliminated.

The utility model discloses an air flotation test device for solid-liquid separation of biogas slurry, which relates to the technical field of anaerobic fermentation product treatment and comprises an air flotation tank, a water collecting tank, a residue collecting tank, a residue scraping mechanism, a reflux pump, a bubble machine and a bubble water releaser, the water collecting tank is communicated with the air flotation tank, and the water collecting tank can receive residue-removed biogas slurry in the air flotation tank; the bubble water releaser is fixedly arranged in the treated biogas slurry in the air flotation tank and can fill bubble water into the treated biogas slurry, a liquid inlet of the reflux pump is communicated with the water collecting tank, a liquid outlet of the reflux pump is communicated with a liquid inlet of the bubble machine, a liquid outlet of the bubble machine is communicated with the bubble water releaser, and the bubble water releaser is communicated with the water collecting tank. The slag collecting tank is fixedly arranged at the top of the inner side wall of the air floatation tank, the slag scraping mechanism is arranged above the air floatation tank, and the slag scraping mechanism can scrape biogas residues on the liquid level of treated biogas slurry in the air floatation tank into the slag collecting tank; according to the utility model, reliable evaluation and pre-estimation can be provided for the actual removal effect of the biogas residue in the biogas slurry doped with the biogas residue by the air flotation equipment.

The present application belongs to the technical field of bicycle power meter, and specifically discloses a zero point automatic calibration method for a bicycle power meter, which comprises the following steps: recording the current time as the initial time, collecting strain gauge data and acceleration data and storing them in a data buffer; judging whether the bicycle enters a stable state; performing initial calibration of the zero point in the stable state; judging whether to enter a first mode or a second mode according to a user zero point calibration instruction; periodically calculating a refined zero point at a first time interval in the first mode, and performing calibration according to the refined zero point; updating a temporary zero point in the second mode, and performing calibration based on the updated temporary zero point when the time difference is not less than a second time interval. The present application effectively filters out interference factors such as wind vibration, slight collision and misoperation, so that the zero point estimation is more stable and reliable. The present application can automatically judge the zero point estimation opportunity, and significantly reduces the risk of zero point error caused by misoperation.

The present application belongs to the technical field of hydrogeological parameter measurement, and discloses a kind of spatio-temporal quantitative expression method of hydrogeological parameter based on non-contact observation method, spatio-temporal quantitative expression method of hydrogeological parameter based on non-contact observation method includes: using the NMR geophysical prospecting method of different field source form, GPR geophysical prospecting method, TEM geophysical prospecting method collects different types, different structure geophysical data;And based on the different types, different structure geophysical data inversion obtains hydrogeological parameter;Based on D-S evidence theory, the hydrogeological parameter obtained by inversion is merged with the collected hydrogeological data to obtain the best estimate of the hydrogeological parameter.The present application merges the measured different types, different structure geophysical data and the collected hydrogeological data to obtain the best estimate of the hydrogeological parameter, and quantifies the uncertainty factor.

The invention discloses an intelligent fusion navigation filtering method and system for complex environment transition. Navigation data and environment data are collected by a sensor arranged on ocean equipment, preliminary navigation state estimation is obtained after the navigation data passes through a strapdown inertial navigation module, and local error state estimation and filter health degree are obtained after the navigation data and the navigation data are input into a parallel sub-filter; combining the navigation data and the environment data with the health degree of the filter to serve as fusion data, inputting the fusion data into a reinforcement learning agent to obtain a dynamic fusion weight, performing weighted fusion on the plurality of local error state estimations by using the dynamic fusion weight, and superposing the fusion data on the initial navigation state estimation to generate a final navigation result. According to the method, the optimization mapping relation between the environment characteristics and the filtering weight is directly learned through the reinforcement learning agent, integrated self-adaptive adjustment from environment perception to fusion decision is achieved, and the estimation precision and robustness of a navigation system in dynamic complex environments such as a bay-ocean transition section are effectively improved.

This invention discloses a joint estimation method for power system state and parameters based on Newton-Raphson guided machine learning, relating to the fields of smart grid optimization and power system analysis. The method includes: addressing the problems of low iteration efficiency and machine learning physical mapping distortion in traditional state estimation and parameter identification, embedding the fixed-step Newton-Raphson method as a differentiable module into the machine learning training framework to construct an end-to-end model for parameter and state co-optimization; establishing a PV / PQ node hybrid input-output mapping mechanism that conforms to actual measurement scenarios, using the admittance matrix as an optimizable parameter, and achieving gradient backpropagation under physical constraints through automatic differentiation; designing a node-level state estimation loss function, combined with physical regularization methods for conductance / susceptance, to ensure the feasibility and stability of parameter estimation; and balancing computational efficiency and accuracy by dynamically adjusting the number of Newton iteration steps, ultimately achieving high-precision state prediction and grid parameter reconstruction. Verified on the IEEE-118 node system, this method converges within three Newton iterations, with a state estimation error below 1.2e⁻³, an admittance reconstruction error reduced by 41% compared to the baseline, and strong generalization ability under multiple operating conditions. This invention can be applied to the construction of digital twins for power grids, online security assessment, and fault diagnosis, significantly improving the efficiency and accuracy of power system analysis.