22 results about "Geomagnetic storm" patented technology

The invention discloses an ionosphere TEC forecasting method, system, medium and equipment fused with a geomagnetic field, and belongs to the field of ionosphere TEC forecasting, the method comprises the following steps: obtaining current ionosphere TEC grid data, solar activity index data and global geomagnetic field observation data, and constructing to obtain multi-channel fusion data; based on a preset ConvLSTM model, processing the multi-channel fusion data, and outputting an ionized layer TEC forecast result; wherein the ConvLSTM model is obtained by training an input sequence in historical multi-channel fusion data and a corresponding target sequence; the input sequence is composed of multi-channel fusion data of a plurality of consecutive time steps, and the target sequence is composed of ionized layer TEC grid data immediately after the input sequence. By implementing the method, the forecasting precision of the ionized layer TEC under complex space weather conditions such as magnetic storm can be improved.

The invention discloses a Dst index monitoring method, system and equipment based on a Zhang-Heng-1 satellite, a medium and a program product. The method comprises the following steps: acquiring magnetic field data acquired by the Zhang-Heng-1 satellite; performing data preprocessing on the magnetic field data to obtain satellite-borne observation data; carrying out background magnetic field deduction processing on the satellite-borne observation data by utilizing a global geomagnetic field model to obtain a satellite-borne magnetic field residual error; and finally, the satellite-borne magnetic field residual error is input into a ground-based Dst index mapping model, a corrected satellite-borne Dst index is obtained, and the ground-based Dst index mapping model is a multiple linear regression model obtained through segmented fitting according to different magnetic storm grades based on historical Dst index data and solar radiation indexes. According to the method, high-timeliness and high-precision monitoring of the global geomagnetic storm index is realized through high-precision mapping from the satellite-borne original observation residual error to the foundation standard Dst index.

The invention belongs to the technical field of GNSS precise point positioning cycle slip detection, and discloses a DI-based adaptive GF cycle slip detection threshold model method, and the method comprises the steps: obtaining IGS tracking station data during an extra-large magnetic storm; preprocessing the data, and calculating an inter-epoch difference GF value and a DI value; obtaining 99.7 percentile in each interval section according to a 3 sigma criterion by using distribution of a time domain difference GF value and a DI value; performing quadratic polynomial fitting on the percentile to obtain a final threshold model; the GF adaptive threshold model based on the DI avoids the limitation that the ROTI is influenced by cycle slip, and effectively solves the problem of false detection.

A method for dynamic adaptive threshold orbit maintenance of low-Earth orbit satellites based on space environment parameter sensing includes the following steps: S1, Offline mapping model construction: With the goal of optimizing global fuel efficiency, a mapping model between space environment parameters and the optimal orbit maintenance threshold is generated through simulation and optimization algorithms; S2, Online sensing and control: Space environment parameters are sensed in real time, and the adaptive threshold under the current space environment is dynamically determined based on the mapping model. The satellite orbit decay is monitored, and when the orbit decay reaches or exceeds the adaptive threshold, an orbit maintenance maneuver is triggered. By constructing an intelligent mapping model of "space environment - optimal threshold", the trigger threshold for orbit maintenance is upgraded from a fixed value to a variable that dynamically adapts to space weather, thereby achieving optimization of fuel consumption at the global level, while effectively balancing control efficiency during calm space weather periods and orbit safety under extreme events such as geomagnetic storms.

The invention belongs to the technical field of power systems, and particularly relates to a power grid GIC waveform uncertainty quantification method and system based on a semi-invariant. The method comprises the following steps: acquiring earth surface magnetic field monitoring data in a geomagnetic storm period, calculating a waveform of an earth surface induction geoelectric field based on the earth surface magnetic field monitoring data in the geomagnetic storm period, and determining an equivalent injection current of each bus node of a power grid according to the waveform; acquiring a reference value of the grounding resistance of the transformer substation, and calculating a reference value of the GIC waveform of the power equipment in combination with the equivalent injection current; obtaining the semi-invariant of each order of the grounding resistance of the transformer substation, and determining the semi-invariant of each order of the waveform output of the power grid GIC in combination with the sensitivity matrix of the power equipment GIC to the grounding resistance of the transformer substation; the probability distribution of the power grid GIC time domain waveform is calculated according to each order of semi-invariant output by the power grid GIC waveform in combination with the reference value of the GIC waveform, and the problem of uncertainty quantification of the power grid GIC waveform under the condition of strong randomness of the transformer substation grounding resistance is solved.

The invention discloses a cloud platform maintenance method for the health state of feeding system equipment, and the method comprises the following steps: S1, collecting an output signal of a weighing sensor in a unit time range, and relates to the technical field of industrial Internet of Things equipment state monitoring. According to first time sequence data formed by collecting output signals of a weighing sensor and sorting according to time, second time sequence data formed by monitoring and obtaining near-real-time geomagnetic activity data and sorting according to time, normalization and segmented aggregation approximate processing are firstly carried out on the two types of data to obtain dimension reduction data fragments, and the dimension reduction data fragments are subjected to dimension reduction processing; after combining into a joint data sample set, selecting a selected value based on cumulative probability distribution and an information entropy maximization principle, and generating a corresponding sensor symbol sequence and a geomagnetic symbol sequence; and according to symbol matching scores, mismatching score penalty and vacancy score penalty rules, a scoring matrix is constructed by adopting a dynamic programming algorithm, and common-mode interference caused by geomagnetic storm to a weighing sensor in a feeding system is identified by combining comprehensive confidence scores.

The invention provides an earth stray current type labeling method, device and system and a medium, and the method comprises the following steps: A, obtaining the related data of earth stray current, the related data at least comprises the active power data of each pole of a DC power transmission line, the earth stray current data of a transformer substation, and geomagnetic storm data, the geomagnetic storm data comprises geomagnetic storm level or K index on-site report information; b, preprocessing the related data obtained in the step A to obtain direct current transmission line characteristics, transformer substation stray current characteristics and geomagnetic storm characteristics; and C, marking the type of the ground stray current according to the data preprocessed in the step B. According to the method, multiple influence factors are comprehensively considered, the ground stray current generated by the direct current transmission project and the geomagnetic storm is accurately marked, accurate training sample data can be provided for traceability of the ground stray current, and treatment of the stray current is facilitated.

The invention discloses a method for grading geomagnetic storm disasters caused by CME, and relates to the technical field of space weather disaster monitoring, and the method specifically comprises the steps of data collaborative collection, data standardization preprocessing, parameter fusion inversion, trigger source positioning and grade pre-judgment and correction. According to the method, multi-source satellite data are synchronously acquired through a solar dynamics observatory, a sun-earth relation observatory and a Parker solar detector, and through a standardized preprocessing process, a CME comprehensive parameter inversion formula is fused with the multi-source satellite data; comprehensive inversion of multi-dimensional key parameters such as CME explosion time, source region position, three-dimensional propagation direction, initial speed, acceleration, mass and magnetic flux is realized, the innovation point directly aims at the defects that a traditional method is single in data source and incomplete in inversion parameter, and through a multi-source data fusion and mutual verification mechanism, the multi-dimensional key parameters of the CME are obtained. The precision and reliability of CME parameter inversion are improved, and data support is provided for subsequent geomagnetic storm trigger source positioning and disaster grading.

The application discloses a small-resistor configuration method and system for uncertainty geomagnetic disturbance containing a wind power system, and belongs to the cross field of geomagnetic storm and power system stability research. The application applies transformer reactive power loss caused by induced geomagnetic field sample to the power system as a load, causes node voltage change, thereby links the geomagnetic disturbance to the small disturbance stability of the system, and is used for evaluating the influence of the geomagnetic storm on the power grid safety; through constructing the joint sensitivity characteristic space of voltage sensitivity and damping ratio sensitivity, the installation position of the small resistor is optimized, and the small disturbance instability accident caused by the geomagnetic disturbance can be effectively prevented.

The invention discloses a high-precision space target orbit forecasting method based on numerical fitting, and relates to the technical field of space target orbit forecasting, frequency domain decomposition is combined with a harmonic compensation model, and seasonal system drift caused by sun-moon gravity is thoroughly eliminated; high-frequency components peel off attitude noise through light pressure parameterization and smooth difference, and perturbation coupling error transmission is avoided; a harmonic order adaptive mechanism captures a main disturbance term with the minimum calculation amount; carrying out least square batch estimation to realize coefficient primary solution; the template library pre-loading technology effectively compresses the time consumed for new target initialization; in addition, a geomagnetic modulation factor is introduced to dynamically suppress compensation distortion during a magnetic storm period, a sliding window mechanism guarantees real-time updating of a coefficient, and stable forecasting is still maintained under an extreme event; attitude measurement is replaced by luminosity curve inversion, so that non-cooperative target forecasting becomes possible; tangential component directional compensation ensures the stability of an orbital plane, and reduces the fuel consumption of satellite position protection.

The invention relates to a transformer direct current magnetic bias monitoring and diagnosis system based on multi-source sensing, which belongs to the technical field of intelligent monitoring and diagnosis of power equipment and comprises a current sensor, a temperature sensor, a noise sensor and a vibration sensor. The system further comprises a data processing and collecting module, a model reasoning module, a state output module and a communication protocol module. According to the invention, real-time monitoring, early warning and severity accurate classification of the direct-current magnetic bias state of the transformer are realized, and the method has stronger anti-interference capability and adaptability. The system is especially suitable for power transformer DC magnetic bias monitoring and intelligent operation and maintenance influenced by high-voltage DC power transmission, rail transit stray current and geomagnetic storm, is helpful for improving the safety and reliability of power grid operation, and meets the urgent demands of a power system for intelligent fault diagnosis and risk early warning.

The application discloses a high-precision space target orbit prediction method based on numerical fitting, and relates to the technical field of space target orbit prediction.The frequency domain decomposition of the application is combined with a harmonic compensation model to completely eliminate seasonal system drift caused by the gravitation of the sun and the moon; high-frequency components are stripped of attitude noise through light pressure parameterization and smooth difference to avoid the transmission of perturbation coupling error; a harmonic order adaptive mechanism captures the main disturbance term with the least amount of calculation; least square batch estimation realizes one-time solution of coefficients; template library preloading technology effectively compresses the time consumption of new target initialization; in addition, the application introduces a geomagnetic modulation factor to dynamically suppress compensation distortion during magnetic storms, and a sliding window mechanism guarantees real-time updating of coefficients, and stable prediction is still maintained under extreme events; photometric curve inversion is adopted instead of attitude measurement to make non-cooperative target prediction possible; tangential component directional compensation ensures the stability of the orbit plane and reduces satellite position fuel consumption.

A low earth orbit satellite dynamic adaptive threshold orbit keeping method based on space environment parameter perception comprises the following steps: S1, off-line mapping model construction: with global fuel efficiency optimization as a target, generating a mapping model between a space environment parameter and an optimal orbit keeping threshold through a simulation and optimization algorithm; and S2, online sensing and control: sensing space environment parameters in real time, dynamically determining a self-adaptive threshold value in the current space environment based on the mapping model, monitoring the orbit attenuation of the satellite, and triggering the orbit to keep maneuvering when the orbit attenuation reaches or exceeds the self-adaptive threshold value. By constructing an intelligent mapping model of'space environment-optimal threshold ', a trigger threshold maintained by an orbit is upgraded from a fixed value to a variable dynamically adaptive to space weather, so that the optimization of fuel consumption is realized on the global level; and meanwhile, the control efficiency of the space weather calm period and the track safety under extreme events such as magnetic storm are effectively considered.

This invention discloses a method for calculating the sub-satellite point based on the SGP4 orbital model, relating to the field of satellite navigation and orbit prediction technology. The polar spherical projector of this invention eliminates longitude jumps in latitude >75°, ensuring continuous trajectory output for scenarios such as polar scientific expeditions and glacier monitoring. By directly mapping three-dimensional radial information to generate latitude, it avoids the iterative convergence problem of the ellipsoidal model, ensuring natural symmetry of the North and South Poles coordinates and continuous longitude domain. Second-order differential compensation of the mean anomaly angle, combined with dynamic correction of the Earth's rotational angular velocity, strictly aligns the UTC timestamp with the orbital position. It suppresses the cumulative error generated by traditional linear interpolation at perigee. It enhances adaptability to complex perturbation environments; atmospheric drag vectorization compensation captures real-time aerodynamic direction through relative velocity cross terms, combined with continuous processing of the density index model, significantly improving the orbital attenuation prediction accuracy of low-Earth orbit satellites during geomagnetic storms.

This invention discloses a method, system, equipment, medium, and program product for monitoring the Dst index based on the Zhangheng-1 satellite. The method includes: acquiring magnetic field data collected by the Zhangheng-1 satellite; preprocessing the magnetic field data to obtain onboard observation data; subtracting the background magnetic field from the onboard observation data using a global geomagnetic field model to obtain the onboard magnetic field residual; and finally, inputting the onboard magnetic field residual into a ground-based Dst index mapping model to obtain the corrected onboard Dst index. The ground-based Dst index mapping model is a multiple linear regression model obtained by fitting historical Dst index data and solar radiation index in segments according to different geomagnetic storm levels. This invention achieves high-timeliness and high-precision monitoring of global geomagnetic storm indices through high-precision mapping from the original onboard observation residuals to the ground-based standard Dst index.

The invention belongs to the technical field of oil and gas pipe networks, and relates to an oil and gas pipe network magnetic storm response statistical evaluation method and system based on a conditional random field, and the method comprises the steps: obtaining a horizontal one-dimensional layered geoelectric structure model and geomagnetic field time sequence data at a plurality of discrete measurement points of the earth surface, and calculating a geomagnetic storm induction geoelectric field at each measurement point; further determining a Gaussian process regression model of the induced earth electric field; according to the Gaussian process regression model of the induction ground electric field, respectively obtaining a conditional mean value and a conditional covariance matrix of the induction ground electric field along the pipeline; further determining a conditional random field model, and sampling and generating multiple groups of samples distributed in the induced electric field space along the pipeline based on the conditional random field model; and voltage and current responses of the pipeline network under the action of the geomagnetic storm are calculated for each sample by adopting a node admittance matrix method, and statistical characteristics of the pipeline responses are obtained after summarizing by adopting a Monte Carlo method. The problem of uncertainty quantification of the pipeline network magnetic storm response under the ground electrical parameter sparse measurement condition is solved.

The invention belongs to the technical field of ionosphere total electron content forecasting, and relates to a global ionosphere total electron content forecasting method and system based on a graph neural network, and the method comprises the steps: obtaining global ionosphere TEC data of the first 24 hours; obtaining geomagnetic and solar activity index data of the first 24 hours; carrying out sequence conversion and stacking on the obtained data; inputting the stacked data into a pre-trained graph neural network to obtain a prediction result of the global ionosphere TEC in the next 24 hours; the graph neural network simulates the three-dimensional spherical surface of the ionized layer, adopts an encoder-processor-decoder architecture, and introduces a regular icosahedron and a subdivided regular polyhedron thereof to simulate local and global information transmission of the electron content of the ionized layer. According to the method, a regular polyhedron structure is introduced, three-dimensional spherical simulation is directly carried out by utilizing the graph neural network, and a relatively good prediction effect is achieved in a geomagnetic quiet period or a magnetic storm period.

The invention provides a ground stray current characteristic site determination method, device and system and a medium, and the method comprises the following steps: A, obtaining related data of ground stray current, the related data comprising the ground stray current of each transformer substation and a corresponding stray current type, the stray current type comprises urban rail transit stray current, direct current transmission project stray current, geomagnetic storm stray current and a negative sample; b, preprocessing the data according to the related data of the ground stray current obtained in the step A; c, determining an optimal ground stray current characteristic site according to the data preprocessed in the step B; by comprehensively considering the missing rate and the abnormal rate of the monitoring data of the ground stray current of each transformer substation, the generalization performance of the training model and the like, whether the transformer substation can be used as a characteristic site of the ground stray current or not is determined, a data basis can be provided for identification of a ground stray current event, and treatment of the ground stray current is assisted.

The invention discloses an earthquake monitoring device which comprises a processor, the processor is connected with a network connection device and a computer, and the processor comprises a monitoring unit, an access unit and a cooperation unit.The invention relates to the technical field of earthquake monitoring. The method comprises the following steps: analyzing ionosphere height and electron concentration change, detecting local abnormal increase or decrease of ionosphere electron concentration TEC, and locking an ionosphere disturbance range possibly related to an earthquake to determine an earthquake region and generate a charge layer image, so that data information conditions can be more intuitively obtained through the charge layer image; and recording the aurora form to indirectly reflect the activity of the charge layer, inversing the electron density structure of the ionosphere, analyzing the disturbance of the ionosphere, and more accurately determining the state of the ionosphere, geomagnetic storm early warning and other information in combination with access information so as to complete earthquake monitoring.

The invention relates to a containerized high-fidelity inter-satellite link simulation method and system for a geomagnetic storm phenomenon, and belongs to the technical field of satellite communication simulation. According to the method, distributed autonomous satellite nodes are constructed by adopting a container technology, orbit parameters of the nodes are corrected in real time by simulating disturbance of geomagnetic storm to the orbit, the spatial positions of the nodes are solved accordingly, and virtual inter-satellite links between the nodes are established. And the system dynamically calculates the link propagation delay and the packet loss rate based on the link budget model, and injects the result into the virtual link to realize the fine control of the link behavior. Meanwhile, the system supports link dual-channel shunting, neighbor discovery and state broadcasting, bit error rate-packet loss rate conversion and network adaptive scheduling, and can truly reflect the influence of space environment disturbance on the satellite network performance. The simulation method has high expansibility and accurate physical layer modeling capability, and is suitable for application scenes such as constellation design verification, protocol testing and anti-interference mechanism evaluation.

The invention discloses a method for automatically identifying PC2 geomagnetic pulsation, electronic equipment and a computer storage medium, and the method comprises the steps: obtaining magnetotelluric (MT) time series data containing magnetic storm data, carrying out the split-window Fourier transform of electromagnetic field components, and obtaining a frequency spectrum array of each electromagnetic field component in a frequency domain; calculating data quality evaluation parameters based on the spectrum array, wherein the data quality evaluation parameters comprise electromagnetic field linearity, a phase difference between an electric field and a magnetic field, and an amplitude of an electromagnetic field spectrum as an energy parameter; and according to the high-quality data type related to the magnetic storm, determining the distribution characteristic of each parameter of the high-quality data of the measuring point. The geomagnetic pulsation event is automatically detected by identifying MT dead band high-quality data, widely distributed MT observation stations are converted into a geomagnetic pulsation monitoring network, existing infrastructures are fully utilized, and the space weather monitoring cost is reduced.

The invention provides a geomagnetic storm short-term probability forecast level test and evaluation method and system, and relates to the technical field of space weather forecast, and the method comprises the steps: determining a forecast time period, obtaining target geomagnetic storm short-term probability forecast data published in the forecast time period, the historical geomagnetic storm observation data corresponds to the forecast time period and takes a Kp index as a criterion; pairing the target geomagnetic storm short-term probability forecast data and the historical geomagnetic storm observation data according to a time sequence by adopting a unified time reference to form a paired data set; and based on the paired data set, analyzing the statistical characteristics of the target geomagnetic storm short-term probability forecast data, and meanwhile, based on the statistical characteristics, determining a probability threshold value for converting probability forecast into dichotomy event forecast, so that comprehensive and accurate test and evaluation of geomagnetic storm short-term forecast performance can be realized. And scientific guidance is provided for forecasting strategy optimization and business landing.