CN116933083A - Ionosphere total electron content prediction method, ionosphere total electron content prediction system, electronic equipment and medium - Google Patents
Ionosphere total electron content prediction method, ionosphere total electron content prediction system, electronic equipment and medium Download PDFInfo
- Publication number
- CN116933083A CN116933083A CN202310907810.4A CN202310907810A CN116933083A CN 116933083 A CN116933083 A CN 116933083A CN 202310907810 A CN202310907810 A CN 202310907810A CN 116933083 A CN116933083 A CN 116933083A
- Authority
- CN
- China
- Prior art keywords
- data
- ionosphere
- observation
- training
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000005433 ionosphere Substances 0.000 title claims abstract description 101
- 238000000034 method Methods 0.000 title claims abstract description 95
- 238000012549 training Methods 0.000 claims abstract description 83
- 230000008569 process Effects 0.000 claims description 47
- 238000009826 distribution Methods 0.000 claims description 42
- 239000011159 matrix material Substances 0.000 claims description 25
- 238000013499 data model Methods 0.000 claims description 24
- 230000000694 effects Effects 0.000 claims description 13
- 238000012545 processing Methods 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 9
- 238000012795 verification Methods 0.000 claims description 8
- 238000003860 storage Methods 0.000 claims description 4
- 238000004422 calculation algorithm Methods 0.000 description 20
- 230000008901 benefit Effects 0.000 description 9
- 238000004364 calculation method Methods 0.000 description 9
- 230000008859 change Effects 0.000 description 8
- 238000004891 communication Methods 0.000 description 7
- 238000001914 filtration Methods 0.000 description 6
- 238000012952 Resampling Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 230000002123 temporal effect Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000013398 bayesian method Methods 0.000 description 3
- 238000013476 bayesian approach Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 238000002360 preparation method Methods 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 241001123248 Arma Species 0.000 description 1
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 238000012614 Monte-Carlo sampling Methods 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000007429 general method Methods 0.000 description 1
- BJRNKVDFDLYUGJ-RMPHRYRLSA-N hydroquinone O-beta-D-glucopyranoside Chemical compound O[C@@H]1[C@@H](O)[C@H](O)[C@@H](CO)O[C@H]1OC1=CC=C(O)C=C1 BJRNKVDFDLYUGJ-RMPHRYRLSA-N 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000001373 regressive effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000001932 seasonal effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computational Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Probability & Statistics with Applications (AREA)
- Algebra (AREA)
- Evolutionary Biology (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention discloses a method, a system, electronic equipment and a medium for predicting total electronic content of an ionized layer, and relates to the field of the prediction of the total electronic content of the ionized layer, wherein the method comprises the following steps: firstly, multi-source observation data of a station t to be predicted is obtained, and data assimilation is carried out on the multi-source observation data of the station t to be predicted by utilizing an ionosphere total electron content prediction model, so that a true value of the ionosphere total electron content is obtained. The ionosphere total electron content prediction model is obtained by training a layered Bayesian network by using a training data set. The method improves the accuracy of ionosphere total electron content prediction.
Description
Technical Field
The present invention relates to the field of ionosphere total electron content prediction, and in particular, to a method, a system, an electronic device, and a medium for ionosphere total electron content prediction.
Background
Ionosphere is an important component of the near-earth space environment. The severe spatial and temporal variations thereof can severely degrade the quality of global short wave communications and the accuracy of satellite navigation and positioning. The ionospheric total electron content (total electron content, TEC) is one of the important parameters characterizing the spatial and temporal variations of the ionosphere. How to accurately acquire the characteristics and the change rule of the distribution of the ionized layer TEC, a system which not only comprises internal physics, but also reflects real observation is established, real-time monitoring of a space environment is simultaneously met, high-precision quasi-real-time ionized layer correction information is provided, and the system becomes one of the key research contents in the ionized layer field.
The ionosphere TEC assimilation can comprehensively utilize various observation means, such as a foundation and space-based global navigation satellite system (Global Navigation Satellite System, GNSS), an ionosphere altimeter, a Jason satellite and an incoherent scattering radar, and meanwhile, a physical model or an empirical model is combined to obtain a high-precision and high-time-resolution multi-scale ionosphere assimilation model. This is of great significance for research and application of ionosphere science.
First, studying ionosphere TEC data assimilation may help one to better understand the physical properties and spatial structure of the ionosphere. By comprehensively analyzing the data of different observation means, the characteristics of global/regional distribution, high-low latitude difference, daily variation, seasonal variation and the like of the ionized layer TEC can be obtained. This helps to further study the mechanisms of ionosphere formation and evolution, exploring the interactions between the earth's atmosphere and solar activity.
Secondly, research on ionosphere TEC assimilation can also be used for improving satellite navigation positioning accuracy. The ionosphere has great influence on the propagation of radio waves, and the non-uniformity of the distribution of the ionosphere TEC can cause the phenomena of diffraction, scattering, absorption and the like of radio waves, thereby influencing the reliability and the accuracy of communication and navigation. By assimilating the data of different observation means, more accurate ionosphere TEC distribution can be obtained, thereby improving the satellite navigation and positioning performance.
Finally, research of ionosphere TEC data assimilation can also be used to improve the predictive ability of solar storms and spatial weather. Solar storms and spatial weather have great influence on the ionosphere, and can cause ionosphere disturbance to influence the normal operation of important facilities such as satellite navigation, communication, a ground power system and the like. Therefore, the method accurately predicts the influence of solar storm and space weather on the ionosphere, and has important significance for maintaining national security and guaranteeing life and property security of people. By assimilating the data of different observation means, more accurate space-time distribution of the ionosphere TEC can be obtained, and the early warning capability of the space environment is further improved.
Data assimilation algorithms have made great progress in the past decades, but the following three problems remain today with the traditional data assimilation methods represented by kalman filtering:
(1) At present, due to the limitation of calculation force, in the process of assimilating large-scale data, in order to facilitate calculation of large-scale matrix inversion and reduce calculation time, it is generally assumed that state quantity space is independent, that is, state quantity between adjacent positions is not relevant, which is generally not true in practical situations, and related information is lost, so that model accuracy is reduced.
(2) In conventional assimilation algorithms, there are typically parameters defined as constants, which require strong a priori knowledge of the definition of the parameters, such as the error variance of the observed data and model predictions. The setting of the sizes of the parameters often needs repeated verification, influences the production efficiency, and is set to be constant, so that the parameters limit the consideration of the algorithm in terms of uncertainty to a great extent, and the accuracy of data assimilation is reduced.
(3) Data assimilation, when faced with observed data composed of multiple data sources and the data temporal spatial resolutions are different, often requires resampling, i.e., resampling the data to the same temporal spatial resolution. The negative effects of resampling are quite obvious, and the accuracy after assimilation is often greatly affected.
Disclosure of Invention
The invention aims to provide a method, a system, electronic equipment and a medium for predicting the total electronic content of an ionized layer, so as to improve the accuracy of the prediction of the total electronic content of the ionized layer.
In order to achieve the above object, the present invention provides the following solutions:
an ionospheric total electron content prediction method comprising:
acquiring multi-source observation data of a station t to be predicted; the observation data is the total electron content of the ionized layer; multisources include global navigation satellite systems, ionosphere altimeters, jason satellites and incoherent scattering radars;
predicting the true value of the total electronic content of the ionized layer at the moment t of the station to be predicted by utilizing an ionosphere total electronic content prediction model according to the multisource observation data at the moment t of the station to be predicted; the ionosphere total electron content prediction model is obtained by training a layered Bayesian network by using a training data set; the training data set comprises multi-source observation data of an observation station for training and output data of an ionosphere physical model; the observed data is an ionosphere total electron content observed value; the layered bayesian network includes a data model, a process model, and a parameter model.
Optionally, training the layered bayesian network by using the training data set specifically includes:
carrying out missing data processing on multi-source observation data of any observation station for training by using a distance crossing matrix to obtain multi-source continuous observation data;
determining the true value of the total electronic content of the ionized layer of the observation station for training by taking the multi-source continuous observation data and the output data of the ionized layer physical model as the input of the current layered Bayesian network;
determining the true value of the total electronic content of the ionized layer of the observation station for training and the mean square error of multi-source observation data of the observation station for training by means of mean square error verification;
judging whether the maximum iteration times are reached;
if not, the parameters of the current layered Bayesian network are adjusted, and the step of carrying out missing data processing on the multi-source observation data of any observation station for training by utilizing a distance crossing matrix to obtain multi-source continuous observation data is returned;
if yes, taking the current layered Bayesian network corresponding to the minimum mean square error as the ionosphere total electron content prediction model.
Optionally, the determining process of the output data of the ionosphere physical model specifically includes:
acquiring geomagnetic indexes and solar activity indexes of observation stations for training;
determining output data of the ionosphere physical model by taking the geomagnetic index and the solar activity index as inputs of the ionosphere physical model; the output data is a continuous value of the total electron content of the ionized layer.
Optionally, the data model is:
Z t ＝O t +ε t the method comprises the steps of carrying out a first treatment on the surface of the Wherein Z is t The multi-source observation data of all the observation stations for training at the moment t; o (O) t True value and epsilon of total electronic content of ionized layer of observation station for training at t moment t And (5) the observation error items of all the observation stations for training at the time t.
Optionally, the process model is:
O t ＝ξ+ρO t-1 +(β 0 +β S )X t +η t the method comprises the steps of carrying out a first treatment on the surface of the Wherein O is t The total electron content true value of all the observation stations for training in the t moment is calculated; xi is the trend of the total electron content distribution of the ionosphere in the observation area; ρO is t-1 The influence of the total electronic content of the ionized layer at the time t-1 on the total electronic content of the ionized layer at the time t is represented; o (O) t-1 The total electron content of the ionized layer of all observation stations for training at the time t-1; x is X t Is the output data of the ionosphere physical model; beta 0 Is a constant that does not vary spatially over time; beta S Is a spatially varying variable; η (eta) t Is a residual term.
An ionospheric total electron content prediction system, comprising:
the data acquisition module is used for acquiring multi-source observation data of a station t to be predicted; the observation data is the total electron content of the ionized layer; multisources include global navigation satellite systems, ionosphere altimeters, jason satellites and incoherent scattering radars;
the prediction module is used for predicting the true value of the total electronic content of the ionized layer at the moment t of the station to be predicted by utilizing the total electronic content prediction model of the ionized layer according to the multi-source observation data at the moment t of the station to be predicted; the ionosphere total electron content prediction model is obtained by training a layered Bayesian network by using a training data set; the training data set comprises multi-source observation data of an observation station for training and output data of an ionosphere physical model; the observed data is an ionosphere total electron content observed value; the layered bayesian network includes a data model, a process model, and a parameter model.
An electronic device, comprising: the system comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic equipment to execute the ionosphere total electron content prediction method.
A computer readable storage medium storing a computer program which when executed by a processor implements the ionospheric total electron content prediction method described above.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the ionosphere total electron content prediction method, the ionosphere total electron content prediction system, the electronic equipment and the medium, multi-source observation data of a station t to be predicted are firstly obtained, and data assimilation is carried out on the multi-source observation data of the station t to be predicted by utilizing an ionosphere total electron content prediction model, so that a true value of the total electron content of the ionosphere is obtained. The ionosphere total electron content prediction model is obtained by training a layered Bayesian network by using a training data set. The method utilizes the trained layered Bayesian network to assimilate the multi-source observation data, predicts the true value of the total electronic content of the ionized layer more accurately, and improves the accuracy of the prediction of the total electronic content of the ionized layer.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for predicting total electron content in an ionosphere according to the present invention;
fig. 2 is a flow chart of data assimilation using a layered bayesian network according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method, a system, electronic equipment and a medium for predicting the total electronic content of an ionized layer, so as to improve the accuracy of the prediction of the total electronic content of the ionized layer.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
The invention discloses an ionosphere total electron content prediction method, which is an ionosphere data assimilation method based on a layered Bayesian network algorithm. The theoretical basis of the layered bayesian network algorithm is a conditional probability distribution, the goal of which is to obtain probability distributions of other unknown parameters given known observation data. Meanwhile, the idea of independent conditions is utilized, the complex problem is split into a plurality of relatively simple models step by step, and each model is split according to the idea of independent conditions, so that the complex posterior probability reasoning is converted into a series of relatively simple conditional probability reasoning. Because of the great advantages and application potential of layered bayesian networks in terms of data space-time analysis, layered bayesian networks have been successfully used in the medical, environmental, economic, marine and atmospheric fields at present with a certain achievement.
However, until now, the layered bayesian network algorithm is not widely applied to data assimilation, and is still in a scientific experimental stage, mainly due to the complexity of algorithm mechanisms and contents, and the specific analysis has the following three reasons:
1. the computational complexity is high. The layered Bayesian method needs to perform a large amount of probability calculation and model updating, and has higher calculation complexity. And formula derivation is cumbersome, requiring a significant amount of mathematical and physical background knowledge associated therewith. And when the data amount is large, the computational complexity increases exponentially, resulting in computational difficulties.
2. The parameters are difficult to select. The layered bayesian approach requires the determination of a plurality of prior probability distributions and conditional probability distributions, the parameters of which are to be estimated from the actual data. The choice and definition of the prior probability directly affects the convergence of the final samples, requiring strong prior knowledge, however, different parameter choices may lead to different results, and thus how to select the appropriate parameters becomes a difficulty.
3. Error transfer. The layered bayesian method estimates the system state by means of a priori probability distribution and a conditional probability distribution, the errors of which are transferred to the final estimation result. If there is an error in the prior probability distribution or the conditional probability distribution, the final estimation result will be affected, thereby reducing the accuracy of the estimation.
4. Overfitting problems. The layered bayesian approach requires the selection of a priori probability distribution and a conditional probability distribution, which are typically determined by minimizing the error. If the distribution chosen is too complex, it may lead to over-fitting problems, i.e. good performance on training data but poor performance on new data.
5. The most important part of the process model of the layered bayesian network algorithm, the definition of which must exhibit the characteristics of parameters over time and space, is generally dependent on understanding the physical process mechanisms and data analysis, and has no simplified general method.
For the global or regional, the ionosphere total electron content (total electron content, TEC) data acquired by multisource observation data (foundation and space-based GNSS, ionosphere altimeter, jason satellite and incoherent scattering radar) are assimilated into an ionosphere physical model or an empirical model to construct a high-precision multiscale (global or regional) ionosphere TEC model. Compared with the data assimilation method of three-dimensional variation and Kalman filtering, the method can realize high-precision ionosphere data assimilation.
As shown in FIG. 1, the ionosphere total electron content prediction method provided by the invention comprises the following steps:
step 101: acquiring multi-source observation data of a station t to be predicted; the observation data is the total electron content of the ionized layer; multisources include global navigation satellite systems, ionosphere altimeters, jason satellites, and incoherent scattering radars.
Step 102: predicting the true value of the total electronic content of the ionized layer at the moment t of the station to be predicted by utilizing an ionosphere total electronic content prediction model according to the multisource observation data at the moment t of the station to be predicted; the ionosphere total electron content prediction model is obtained by training a layered Bayesian network by using a training data set; the training data set comprises multi-source observation data of an observation station for training and output data of an ionosphere physical model; the observed data is an ionosphere total electron content observed value; the layered bayesian network includes a data model, a process model, and a parameter model.
Ionospheric physical/empirical models are a class of mathematical models built using observation data that can be used to predict some key parameters in the ionosphere. Common ionospheric physical/empirical models include the international reference ionosphere model, the NeQuick model, and the URSI model. In the present invention, a NeQuick model is used.
The Nequick model is a semi-empirical, digitized model for predicting the electron content in the global ionosphere. The model was developed by the European radio communication planning Committee (CEPT) under support of the European Union in 1996, the initial version being called the QUICK program. The development of the Nequick model mainly depends on data such as ground and satellite observation data, the earth magnetic field and solar activity indexes. The principle of the Nequick model is based on a three-dimensional Maxwell-Boltzmann system of equations for calculating position, solar activity and vertical and horizontal IP (ionosphere) electron content. The method has the core ideas that the influence of path delay, aliasing and the like of electric wave propagation is simulated by modeling the electronic content of an ionized layer, so that the method is further used for optimizing communication and navigation applications such as satellite navigation, television broadcasting, wireless communication, radar communication and the like.
The NeQuick model includes two types: neQuick-1 and NeQuick-2. The NeQuick-1 is mainly used for calculating the ionosphere partition and the ionosphere layer number, and the NeQuick-2 is used for calculating and predicting the electronic content in the global scope. The main output parameters of the Nequick model include parameters such as electron content, vertical ionization delay, total ionospheric delay, satellite antenna phase center and electron delay errors, satellite user position errors, signal to noise ratio, and the like.
Wherein, the data model is:
Z t ＝O t +ε t (1)。
wherein Z is t The multi-source observation data of all the observation stations for training at the moment t; o (O) t True value and epsilon of total electronic content of ionized layer of observation station for training at t moment t And (5) the observation error items of all the observation stations for training at the time t.
The process model is as follows:
O t ＝ξ+ρO t-1 +(β 0 +β S )X t +η t (2)。
wherein O is t The total electron content true value of all the observation stations for training in the t moment is calculated; xi is the trend of the total electron content distribution of the ionosphere in the observation area; ρO is t-1 The influence of the total electronic content of the ionized layer at the time t-1 on the total electronic content of the ionized layer at the time t is represented; o (O) t-1 The total electron content of the ionized layer of all observation stations for training at the time t-1; x is X t Is the output data of the ionosphere physical model; beta 0 Is a constant that does not vary spatially over time; beta S Is a spatially varying variable; η (eta) t Is a residual term.
The flow of data assimilation using layered Bayesian network is shown in figure 2, and consists of three parts, namely data preparation, network learning check and prediction. The task of data preparation is to prepare the input data required by the layered bayesian network, and for multi-source observation data, the main data processing includes: and processing the data missing from the sites, and selecting training points, check points and format conversion according to the observation conditions and data conditions of each site. For ionospheric physical model or empirical model data, the primary data processing includes: model driving data is prepared, the space-time distribution characteristics of parameters are analyzed according to the existing data, and the model driving data is processed and stored according to the data format required by the layered Bayesian network. The network learning verification is to train the layered Bayesian network by using training site observation data, evaluate the network learning result by using VMSE (Validation Mean Squared Error, mean square error verification), and adjust certain key parameters. When the verification precision meets the requirement, the TEC of any point can be predicted by using the layered Bayesian network.
In practical application, training the layered bayesian network by using the training data set specifically includes:
and carrying out missing data processing on the multi-source observation data of any observation station for training by using the distance crossing matrix to obtain multi-source continuous observation data.
Distance cross matrix (distance cross-matrix) refers to a process of calculating distances between any two objects in a group of objects, and combining the distances into one symmetric matrix. The distance may be measured in various ways, such as Euclidean distance, manhattan distance, cosine similarity, jaccard similarity, mahalanobis distance, and so forth. Distance intersection matrices are commonly used in a number of fields of clustering, dimension reduction, similarity searching, and the like. The size of the distance-crossing matrix is n×n, where n represents the number of objects. The ith row of the distance intersection matrix represents the distance of the ith object from the other objects, and the jth column represents the distance of the jth object from the other objects. Several basic properties of the distance intersection matrix are as follows:
1. non-negativity: d, d i,j ≥0。
2. Symmetry: d, d i,j ＝d j,i 。
3. Zero distance performance: d, d i,i ＝0。
4. Triangle inequality: for any i, j, k has d i,j ≤d i,k +d k,j 。
In practical application, the missing data processing by using the distance intersection matrix is divided into two steps: and calculating a distance crossing matrix and a predicted missing value. Firstly, calculating a distance crossing matrix by using the existing data, wherein the specific formula is as follows:
d i,j ＝dist(x i ,x j ) (3)。
in which x is i And x j Respectively data of different positions. Assuming a total of n samples, each data source has p features, and the matrix formed by the data is recorded as X n×p Where the ith row represents the ith sample and the jth column represents the jth feature. And obtaining the distance crossing matrix D of the data through a calculation formula of the distance crossing matrix. Next, the missing values of the matrix are complemented using the distance-crossing matrix D. Assuming that there is a miss in the jth data of the ith sample, the formula for complementing that data is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,predictive value, ω, representing missing data of multisource observation data of observation site for training i,k Represents the distance, X, between the ith sample and the kth sample k,j The j value of the kth sample. Several sample points closest to the ith sample, e.g. the first k closest samples, may be selected to calculate a weighted average, wherein the weight of each sample is assigned by the inverse of the distance. The estimated value thus calculated is a predicted value of the missing value. And complementing the missing data of the multi-source observation data of any observation station for training by using the predicted value of the missing value.
And determining the true value of the total electronic content of the ionized layer of the observation station for training by taking the multi-source continuous observation data and the output data of the ionized layer physical model as the input of the current layered Bayesian network.
And determining the true value of the total electronic content of the ionosphere of the observation station for training and the mean square error of the multi-source observation data of the observation station for training by means of mean square error verification.
And judging whether the maximum iteration times are reached.
If not, the parameters of the current layered Bayesian network are adjusted, and the step of carrying out missing data processing on the multi-source observation data of any observation station for training by utilizing a distance crossing matrix to obtain multi-source continuous observation data is returned.
If yes, taking the current layered Bayesian network corresponding to the minimum mean square error as the ionosphere total electron content prediction model.
The determining process of the output data of the ionosphere physical model specifically comprises the following steps:
and acquiring geomagnetic indexes and solar activity indexes of the observation sites for training.
Determining output data of the ionosphere physical model by taking the geomagnetic index and the solar activity index as inputs of the ionosphere physical model; the output data is a continuous value of the total electron content of the ionized layer.
In practical application, before training the layered bayesian network, the layered bayesian network is first constructed, specifically as follows:
step 1: establishing a data model, wherein a true value O exists at any time t t The ultimate goal of data assimilation is to infer O t Posterior probability distribution of (2) to find O t Maximum posterior probability. A data model of the multi-source observation data is defined, as in formula (1), in the formula, t=1, 2,3, T; o (O) t ＝[(S 1 ,t),(S 2 ,t),···,(S N ,t)]Represents the true value epsilon of the ionized layer TEC of all observation stations for training at the moment t t ＝((S 1 ,t),(S 2 ,t),···,(S N ,t)) T Is an observation error item of all observation stations for training at the moment t, and obeys the distribution0 is a vector with all elements 0, I N Is an N-dimensional identity matrix>Is constant and is constant in space-time range, i=1, 2,3, N, N is the number of observation sites for training. Output data X of ionospheric physical model or empirical model t As O (S) i Covariates of t), output data X of ionospheric physical model or empirical model t And true value O (S) i The relation of t) is shown in the formula (2).
Step 2: a process model is established, wherein the purpose of the process model is to describe the spatial distribution and change of the total electronic content TEC of the ionized layer at any time, and a process model is defined as a formula (2), wherein xi represents the trend of the TEC distribution in the observation area and is a constant which does not change with the time and the space. ρO is t-1 The effect of TEC at time t-1 on TEC at time t is shown. 0 < ρ < 1, and sequential data assimilation is achieved by ρ. Beta 0 Is a constant that does not vary spatially over time. Beta S Is a spatially varying variable such that equation (2) is a non-stationary model, beta S ＝[β(S 1 ),β(S 2 ),···,β(S N )] T 。η t Is a residual term, which varies with spatial position. Beta S ～N(0,∑ β )，η t ～N(0,∑ η )，∑ η 、∑ β Is the covariance matrix. The covariance function may be defined as an exponential function for ease of calculation. Sigma (sigma) η Each element of the matrix isexp(-φ β d(S i ,S j ) For the observation site S i ,S j The distance between the two points. Likewise sigma η Each element of the matrix is +.>For convenience of subsequent presentation, < > for>
Step 3: establishing a parameter model, wherein parameters used by the data model and the process model are as followsIn theory, in order to obtain the most accurate posterior probability distribution, all parameters should be obtained through Bayesian inference, but this greatly increases the calculation amount, and it is difficult to implement real-time data assimilation update, so in order to reduce the calculation amount, the parameter model only defines ∈ ->The prior probability distribution of the five parameters is obtained by data analysis and model verification of other parameters. ρ obeys normal distribution with mean value 0 and huge variance, and its value is [0,1]Between them. Zeta is analyzed by autoregressive moving average model (auto regressive moving average model, ARMA) according to site data, and variance parametersObeys the IG distribution.
Step 4: after the definition of the data model, the process model and the parameter model is finished, the construction of the HBN (hierarchical Bayesian network, layered Bayesian network) is finished, and in order to make up the deficiency of priori knowledge and obtain posterior probability distribution of other parameters, the layered Bayesian network is firstly subjected to learning training. The method aims at continuously adjusting the parameters of the layered Bayesian network by using the training data, so that the layered Bayesian network is suitable for the current training data. In the layered Bayesian network learning process, VMSE is used as a main index for evaluating the performance of the layered Bayesian network, and the definition of VMSE is shown in formula (5). The main method is to train the layered Bayesian network by utilizing multisource observation data and ionosphere physical or experience model output data in the assimilation period, predict TEC posterior probability of check points and compare with VMSE as standardDifference between predicted value and observed data, and adjusting phi β ，φ η Value until VMSE is minimum.
Wherein n is v The number of all check data of check points in the assimilation period; z is Z * (S i T) is the predicted value (true value) of the total electron content of the ionosphere; z (S) i T) is the observation of the total electron content of the ionosphere (multisource observations at the moment t of the station to be predicted) such that VMSE is minimal β ，φ η Is the optimal parameter value. In order to verify the network parameters, it is necessary to first obtain a signal at the observation site S i Predicted value Z at * (S i T). According to equation (1), the station S 'to be predicted' i Predicted value Z at time t * (S′ i The distribution obeyed by t) is as in formula (6):
O(S i ′t)＝ξ+ρO(S i ′，t-1)+(β 0 +β(S i ′))X(S i ′，t)+η(S i ′，t) (7)。
wherein O (S) i ' t-1) is the station S ' to be predicted at the previous moment ' i And thus O (S) i ' t) can only be achieved by sequential stepwise iteration. Definition of O (S) i ′，[t]) Represent S i All true values at the' point t times.
o(S i ′，[t])＝[O(S i ′，1)，O(S i ′，2)，…，O(S i ′，t)]′ (8)。
Namely Z (S' i The posterior probability distribution of t) is:
p[Z(S i ′，t)|z]＝∫p(Z(S i ′，t)|O(S i ′，[t])，σ 2 )p(O(S i ′，[t]|β(S i ′)，w，θ)p(β(S i ′)|θ)
dO(S i ′,[t]dβ(S i ′)dθdw,i＝1,2,···,Num (9)。
w＝(O t ,O t ′,Z t ') represents all variables to be inferred and represents all parameters in the HBN, z= (X) t ,Z t ) Representing all known observations. Beta (S) in formula (9) i ' is a variable with spatial position, β (S) i ') conditional probability is:
in which β= (β) 1 ,β 1 ,···,β n ' is learning site β= (S) i ) Value, S β,12 Is a 1 XN-dimensional row vector representing the site S to be predicted i ' and observation station for training (S 1 ,S 2 ,···,S N ) The correlation between the two is expressed by a distance function; s is S β,21 Is an N x 1 column vector, S β,12 Transpose of S β,12 The i elements in (a) are:
S β,12 (i,j)＝exp(φ β d(S i ,S′ j )),i＝1,2,···,N (11)。
wherein d (S) i ,S′ j ) Representing a site S' to be predicted and an observation site S for training i Distance between them. Beta (S) is expressed by the following formulas (10) and (11) i ') is as in equation (12):
o (S 'of the same formula (10)' i The posterior probability distribution of t) is:
wherein O is t ＝[O(S 1 ,t),O(S 2 ,t)···O(S N ,t)]' represents the true value of the learning site at the time t, S η,12 Is a 1 XN-dimensional row vector representing the site S to be predicted i ' and observation station for training (S 1 ,S 2 ···S N ) The correlation between the two changes along with the change of the distance, S β,21 Is an N x 1 column vector, S β,12 Transpose of S η,12 The i elements in (a) are:
S η,12 (i)＝exp(φ η d(S i ,S′ j )),i＝1,2,···,N (14)。
in the formula (14), d (S i ,S′ j ) Representing a station S to be predicted i ' and training observation site S i Distance between them.
Finally, according to the formula (13) and the formula (14), O (S' i The posterior probability distribution of t) is shown in equation (15), i.e., data assimilation is complete.
p[O(S′ i ,t)∣β(S′ i ),O t ,θ,w]～N(χ,Λ) (15)。
The data model assumes that the data conditions are process dependent so that HBN data assimilation is applicable to observed data having multiple data sources. In addition, the HBN data assimilation requirement on the data is more in line with the real situation in the nature, and the requirement on the data independence is reduced from being independent to being independent of the condition.
The data assimilation algorithm has been greatly developed in the past decades, and the invention provides a layered Bayesian network algorithm aiming at the data assimilation method represented by Kalman filtering.
Aiming at the problems existing in the Kalman filtering data assimilation method, the layered Bayesian network algorithm has excellent algorithm mechanism and advantages. In data assimilation, the layered Bayesian network algorithm is a more efficient and accurate method, and compared with the traditional Kalman filtering series algorithm and variation algorithm, the layered Bayesian network algorithm has the advantages of being free of linear change of state and constraint of error Gaussian distribution assumption, and has the advantages of Bayesian network and layered modeling theory, and the layered Bayesian network algorithm is mainly expressed in the following four aspects.
(1) The layered modeling theory splits the data assimilation problem step by step, and the space dependence relation of the state quantity is represented by using the conditional probability, so that the limitation of the space independence requirement is eliminated, and the complex problem is split into a plurality of simple problems. Meanwhile, in the Bayesian network, parameters representing the spatial dependence relationship can also change with time, so that the layered Bayesian network has great application potential in space-time analysis.
(2) The traditional data assimilation algorithm (such as Kalman filtering algorithm) can only estimate the mean value and variance of the state quantity, and the layered Bayesian network can obtain posterior probability distribution of the target state quantity and parameters by sampling through a Monte Carlo method, and can describe the law of the nonlinear system changing along with time more accurately.
(3) The layered bayesian network is capable of processing data of different sources and different resolutions without the need to forcibly change the resolution of the data by resampling.
(4) In a layered Bayesian network, the complex posterior probability solution problem is converted into a series of simple posterior probability solutions, which are easy to realize by a Monte Carlo sampling method.
Under the layered Bayesian theory framework, data assimilation and resolution are divided into three layers of data, process and parameters. The data comprise site observation data, remote sensing observation data and land process model simulation data, the process represents the space-time distribution and change of parameters to be assimilated, and the parameters comprise data acquisition, a process model and all parameters in the assimilation process. The layered bayesian theory defines data, processes and parameters as three random variables and builds conditional probability distribution models, respectively, as in formulas (19) - (21), converting the data assimilation problem into posterior probability distributions for reasoning processes and parameters under existing data conditions, as in formula (18).
p (procedure, parameter data) ≡18.
(first layer) p (data|procedure, parameters) (19).
(second layer) p (process|parameter) (20).
(third layer) p (parameter) (21).
Where p () represents a probability distribution; p (a|b) represents the conditional probability distribution of a given b. The first layer is a data model p (data|process, parameter), the second layer is a process model p (process|parameter), the third layer is a parameter model p (parameter), and the posterior probability can be inferred only by defining the three models. The data model, the process model and the layered model can be split step by step again, so that in the layered bayesian method, a complex model can be defined by using simple conditional probabilities. Since the layered bayesian theory defines data, processes and parameters as random variables, the posterior probability distribution of any one variable can be inferred, including processes and parameters that are not observed.
The data model shows the advantages that:
(1) The complex nature of the data X is determined by the corresponding process Y, with a conditional probability p (X|Y, θ D ) The definition of the data model is greatly simplified by avoiding the consideration of the characteristics of the data X.
(2) The biggest advantage of the data model is that the data model models multi-source data respectively, so that the layered Bayesian network algorithm has good applicability to multi-source data and scale differences in data assimilation.
(3)p(X|Y,θ D ) Defining data conditions depends on the corresponding process Y, and when there is multi-source data, it is more reasonable to assume that the conditions are independent from each other, i.e. that the mutual independence between the observed data is relaxed to the condition independence.
(4) When the input data are multi-source data with different resolutions, the layered Bayesian network respectively builds a data model for each sub-dataEach sub-number condition depends on the true of the corresponding scaleActual process Y i And parameters->Each scale can be embodied in a process model, so the layered bayesian network has certain advantages in solving scale differences.
The process model presents the advantages of:
when multi-source data with different resolutions of different platforms exist, a data model is built for each data, the conditions of the data model depend on the corresponding real process, and the task of the process model is to complete the process model of the corresponding process I of each scale. The process model is built with great flexibility, and whether the non-stationarity, the anisotropy, the spatial and time distribution and the variation separability of the state quantity can be selected according to specific problems in practical application.
Example two
In order to perform a corresponding method of the above embodiment to achieve the corresponding functions and technical effects, an ionospheric total electron content prediction system is provided below, including:
the data acquisition module is used for acquiring multi-source observation data of a station t to be predicted; the observation data is the total electron content of the ionized layer; multisources include global navigation satellite systems, ionosphere altimeters, jason satellites, and incoherent scattering radars.
The prediction module is used for predicting the true value of the total electronic content of the ionized layer at the moment t of the station to be predicted by utilizing the total electronic content prediction model of the ionized layer according to the multi-source observation data at the moment t of the station to be predicted; the ionosphere total electron content prediction model is obtained by training a layered Bayesian network by using a training data set; the training data set comprises multi-source observation data of an observation station for training and output data of an ionosphere physical model; the observed data is an ionosphere total electron content observed value; the layered bayesian network includes a data model, a process model, and a parameter model.
Example III
The invention provides an electronic device, comprising: the system comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic equipment to execute the ionosphere total electron content prediction method of the first embodiment.
Example IV
The present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the ionospheric total electron content prediction method of embodiment one.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. A method for ionospheric total electron content prediction, comprising:
acquiring multi-source observation data of a station t to be predicted; the observation data is the total electron content of the ionized layer; multisources include global navigation satellite systems, ionosphere altimeters, jason satellites and incoherent scattering radars;
predicting the true value of the total electronic content of the ionized layer at the moment t of the station to be predicted by utilizing an ionosphere total electronic content prediction model according to the multisource observation data at the moment t of the station to be predicted; the ionosphere total electron content prediction model is obtained by training a layered Bayesian network by using a training data set; the training data set comprises multi-source observation data of an observation station for training and output data of an ionosphere physical model; the observed data is an ionosphere total electron content observed value; the layered bayesian network includes a data model, a process model, and a parameter model.
2. The method for predicting total electron content in an ionosphere according to claim 1, wherein the training of the layered bayesian network using the training data set comprises:
carrying out missing data processing on multi-source observation data of any observation station for training by using a distance crossing matrix to obtain multi-source continuous observation data;
determining the true value of the total electronic content of the ionized layer of the observation station for training by taking the multi-source continuous observation data and the output data of the ionized layer physical model as the input of the current layered Bayesian network;
determining the true value of the total electronic content of the ionized layer of the observation station for training and the mean square error of multi-source observation data of the observation station for training by means of mean square error verification;
judging whether the maximum iteration times are reached;
if not, the parameters of the current layered Bayesian network are adjusted, and the step of carrying out missing data processing on the multi-source observation data of any observation station for training by utilizing a distance crossing matrix to obtain multi-source continuous observation data is returned;
if yes, taking the current layered Bayesian network corresponding to the minimum mean square error as the ionosphere total electron content prediction model.
3. The method for predicting total electron content in an ionosphere according to claim 1, wherein the determining process of the output data of the physical model in the ionosphere specifically comprises:
acquiring geomagnetic indexes and solar activity indexes of observation stations for training;
determining output data of the ionosphere physical model by taking the geomagnetic index and the solar activity index as inputs of the ionosphere physical model; the output data is a continuous value of the total electron content of the ionized layer.
4. The method of claim 1, wherein the data model is:
Z t ＝O t +ε t the method comprises the steps of carrying out a first treatment on the surface of the Wherein Z is t The multi-source observation data of all the observation stations for training at the moment t; o (O) t True value and epsilon of total electronic content of ionized layer of observation station for training at t moment t And (5) the observation error items of all the observation stations for training at the time t.
5. The method of claim 1, wherein the process model is:
O t ＝ξ+ρO t-1 +(β 0 +β S )X t +η t the method comprises the steps of carrying out a first treatment on the surface of the Wherein O is t The total electron content true value of all the observation stations for training in the t moment is calculated; xi is the trend of the total electron content distribution of the ionosphere in the observation area; ρO is t-1 The influence of the total electronic content of the ionized layer at the time t-1 on the total electronic content of the ionized layer at the time t is represented; o (O) t-1 The total electron content of the ionized layer of all observation stations for training at the time t-1; x is X t Is the output data of the ionosphere physical model; beta 0 Is a constant that does not vary spatially over time; beta S Is a spatially varying variable; η (eta) t Is a residual term.
6. An ionospheric total electron content prediction system, comprising:
the data acquisition module is used for acquiring multi-source observation data of a station t to be predicted; the observation data is the total electron content of the ionized layer; multisources include global navigation satellite systems, ionosphere altimeters, jason satellites and incoherent scattering radars;
the prediction module is used for predicting the true value of the total electronic content of the ionized layer at the moment t of the station to be predicted by utilizing the total electronic content prediction model of the ionized layer according to the multi-source observation data at the moment t of the station to be predicted; the ionosphere total electron content prediction model is obtained by training a layered Bayesian network by using a training data set; the training data set comprises multi-source observation data of an observation station for training and output data of an ionosphere physical model; the observed data is an ionosphere total electron content observed value; the layered bayesian network includes a data model, a process model, and a parameter model.
7. An electronic device, comprising: a memory for storing a computer program, and a processor that runs the computer program to cause the electronic device to perform the ionospheric total electron content prediction method of any one of claims 1-5.
8. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the ionospheric total electron content prediction method of any of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310907810.4A CN116933083A (en) | 2023-07-24 | 2023-07-24 | Ionosphere total electron content prediction method, ionosphere total electron content prediction system, electronic equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310907810.4A CN116933083A (en) | 2023-07-24 | 2023-07-24 | Ionosphere total electron content prediction method, ionosphere total electron content prediction system, electronic equipment and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116933083A true CN116933083A (en) | 2023-10-24 |
Family
ID=88378595
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310907810.4A Pending CN116933083A (en) | 2023-07-24 | 2023-07-24 | Ionosphere total electron content prediction method, ionosphere total electron content prediction system, electronic equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116933083A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118013869A (en) * | 2024-04-10 | 2024-05-10 | 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) | Four-dimensional ionosphere model construction method and application thereof |
-
2023
- 2023-07-24 CN CN202310907810.4A patent/CN116933083A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118013869A (en) * | 2024-04-10 | 2024-05-10 | 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) | Four-dimensional ionosphere model construction method and application thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10852439B1 (en) | Global ionospheric total electron content prediction system | |
| Särkkä | Recursive Bayesian inference on stochastic differential equations | |
| Abbasi et al. | A hybrid of Random Forest and Deep Auto-Encoder with support vector regression methods for accuracy improvement and uncertainty reduction of long-term streamflow prediction | |
| CN111045046B (en) | Short-term ionosphere forecasting method and device based on NARX | |
| Chen et al. | Space-time covariance structures and models | |
| CN113379107B (en) | Regional ionosphere TEC forecasting method based on LSTM and GCN | |
| Han et al. | Real-time short-term trajectory prediction based on GRU neural network | |
| CN111797573A (en) | Ionized layer electron concentration total content time sequence prediction method based on deep learning technology | |
| CN116933083A (en) | Ionosphere total electron content prediction method, ionosphere total electron content prediction system, electronic equipment and medium | |
| CN113139327B (en) | Ionized layer TEC single-point prediction method and system based on GRU network model | |
| CN116562414A (en) | Land water reserve change prediction method based on multisource hydrographic meteorological data | |
| CN115951430A (en) | Geomagnetic index forecasting method based on LSTM | |
| Wu et al. | A novel bayesian additive regression trees ensemble model based on linear regression and nonlinear regression for torrential rain forecasting | |
| Popkov et al. | A randomized algorithm for restoring missing data in the time series of lake areas using information on climatic parameters | |
| CN112949944A (en) | Underground water level intelligent prediction method and system based on space-time characteristics | |
| CN117131991A (en) | Urban rainfall prediction method and platform based on hybrid neural network | |
| Hu et al. | Multi-sensor Ensemble Kalman filtering algorithm based on observation fuzzy support degree fusion | |
| Rezaie et al. | Shrinked (1− α) ensemble Kalman filter and α Gaussian mixture filter | |
| CN113723006B (en) | LS-SVM (least squares-support vector machine) -based single-station earth change magnetic field modeling prediction method and system | |
| CN115545159A (en) | Average sea surface temperature forecasting method of deep neural network | |
| Zhang et al. | Short-term regional ionospheric TEC forecast using a hybrid deep learning neural network | |
| CN115204073A (en) | Tide prediction method based on kriging interpolation | |
| Zhang et al. | Pixel-CRN: A new machine learning approach for convective storm nowcasting | |
| Jiang et al. | Efficient and accurate TEC modeling and prediction approach with random forest and Bi-LSTM for large-scale region | |
| Ma et al. | A Method for Establishing Tropospheric Atmospheric Refractivity Profile Model Based on Multiquadric RBF and k-means Clustering |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |