CN112131990B - Millimeter wave network rainfall inversion model construction method suitable for complex scene - Google Patents

Millimeter wave network rainfall inversion model construction method suitable for complex scene Download PDF

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CN112131990B
CN112131990B CN202010964571.2A CN202010964571A CN112131990B CN 112131990 B CN112131990 B CN 112131990B CN 202010964571 A CN202010964571 A CN 202010964571A CN 112131990 B CN112131990 B CN 112131990B
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郑鑫
杨涛
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Hohai University HHU
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Abstract

本发明公开了一种适用于复杂场景的毫米波网络降雨反演模型构建方法，其具体步骤为：第一步，获得毫米波网络中各链路信号衰减值A；第二步，建立考虑复杂场景的毫米波降雨反演模型，第三步，对各链路，根据历史实测衰减值AC以及同时间尺度雨量站历史降雨数据R构建历史数据集，通过参数率定的方式确定第五步中的各链路的模型参数s、d、m、c、τ。本发明的一种适用于复杂场景的毫米波网络降雨反演模型构建方法，可以适用于不同气候区、不同链路情况的毫米波降雨反演，且无需前期进行湿天线影响及杂波去除等噪声处理步骤，提高了训练数据集的可靠性。

Figure 202010964571

The invention discloses a method for constructing a millimeter wave network rainfall inversion model suitable for complex scenarios. The specific steps are as follows: the first step is to obtain the signal attenuation value A of each link in the millimeter wave network; the second step is to establish a complex considering complex The millimeter-wave rainfall inversion model of the scene, the third step, for each link, build a historical data set according to the historical measured attenuation value A C and the historical rainfall data R of the rainfall stations at the same time scale, and determine the fifth step by parameter calibration The model parameters s, d, m, c, and τ of each link in . A method for constructing a millimeter-wave network rainfall inversion model suitable for complex scenarios of the present invention can be applied to millimeter-wave rainfall inversion in different climate zones and different link conditions, and does not require wet antenna influence and clutter removal in the early stage. Noise processing step, which improves the reliability of the training dataset.

Figure 202010964571

Description

Millimeter wave network rainfall inversion model construction method suitable for complex scene
Technical Field
The invention relates to a millimeter wave network rainfall inversion model construction method suitable for complex scenes, and belongs to the technical field of meteorological element monitoring.
Background
The real-time rainfall monitoring by utilizing the millimeter wave network is a novel rainfall monitoring technology. Currently, inversion models are mainly classified into two types: an ITU rain failure model with a physical foundation and a model established based on a machine learning method. The ITU model does not need previous label data, but has lower inversion accuracy along with different link characteristics and application areas, and cannot be well applied to millimeter wave networks in various areas; the machine learning method is to perform learning training by combining data of the rainfall station with surrounding millimeter wave metadata and attenuation data, and the model obtained by the machine learning method has strong ambiguity because the data set is constructed by using the whole millimeter wave network data, and the inversion accuracy of each link still needs to be improved. On the other hand, the prior processing of the data by the existing millimeter wave rainfall inversion method comprises the steps of dry-wet period discrimination, basic attenuation deduction, wet antenna influence deduction and the like, new errors are introduced by the addition of the steps, and the methods cannot effectively process periodic signals existing in millimeter wave signals.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides the millimeter wave network rainfall inversion model construction method suitable for the complex scene, which can be suitable for millimeter wave rainfall inversion in different climatic regions and different link conditions, and noise processing steps such as wet antenna influence, clutter removal and the like are not required to be performed in the early stage, so that the reliability of a training data set is improved.
The technical scheme is as follows: in order to solve the technical problem, the invention provides a millimeter wave network rainfall inversion model construction method suitable for complex scenes, which comprises the following specific steps:
the method comprises the steps of firstly, obtaining signal strength TSL of each link signal transmitting end and signal strength RSL of a receiving end in a millimeter wave network, preprocessing data, interpolating lost signal strength, removing singular values and interpolating to obtain synchronous data of rainfall stations nearby links;
step two, subtracting the signal strength RSL of the receiving end from the signal strength TSL of the transmitting end of each link after preprocessing to obtain a signal attenuation value A;
thirdly, obtaining the attenuation A of the basic signal by using the minimum value of the sliding windowBLAnd subtracting the base signal attenuation A from the signal attenuation value ABLObtaining a corrected attenuation value ACThe size of the sliding window is set to be tau, and for the t time of the ith link, the specific form is as follows:
Figure BDA0002681763940000021
Figure BDA0002681763940000022
by calculating correction values for all T moments
Figure BDA0002681763940000023
Then, a corrected attenuation value A of the ith link is obtainedC；
Fourthly, the attenuation value A after the millimeter wave link data is correctedCTime resampling is carried out, the time precision is consistent with rainfall data of a rainfall station, and the corrected attenuation value after resampling is recorded as A'CThe attenuation value for the ith link is
Figure BDA0002681763940000024
Fifthly, respectively establishing a millimeter wave rainfall inversion model considering a complex scene for each link in the millimeter wave network,
for the ith link, i is a positive integer, and the model concrete form is as follows:
Figure BDA0002681763940000025
wherein R isiThe rainfall intensity of the ith link; si、di、mi、ci、τiFor the ith link model parameter, si、diDepending on the link frequency, polarization, mi、ciRelated to regional environment, tau is a sliding window parameter; a'CCorrecting the ith link obtained in the fourth step by resampling to obtain a basic attenuation amount; l isiIs the ith link length;
sixthly, for each link, actually measuring an attenuation value A 'according to the history'CAnd constructing a historical data set by historical rainfall data R of the rainfall station in the same period, and determining model parameters s, d, m, c and tau of each link in the fifth step in a parameter calibration mode.
Preferably, in the fourth step, the average value of the modified attenuations in the time step is selected as the modified attenuation value at the end of the time period by resampling the modified attenuation data of the millimeter wave link.
Preferably, in the sixth step, the SCE-UA algorithm is used to calibrate the model parameters, and the objective function is the nash efficiency coefficient NSE.
Has the advantages that: according to the millimeter wave network rainfall inversion model construction method suitable for the complex scene, the influence of regional environment elements on millimeter wave attenuation is considered in the model structure, corresponding inversion models can be provided for different regions and different link attributes, the requirement of millimeter wave rainfall inversion under the complex scene can be met, and the method has stronger applicability compared with other technologies; according to the method, the millimeter wave attenuation data are corrected by using the minimum value of the sliding window, and noise is filtered through local attenuation characteristics, so that compared with other technologies, the data processing steps are simplified, the introduction of errors is avoided, the millimeter wave periodic signal change can be effectively removed, and the reliability of a data set is improved; the sliding window parameters adopted by the millimeter wave attenuation data are determined by adopting a calibration mode, and the requirements of objective facts of different link frequencies, lengths and polarization modes of a millimeter wave network are met.
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FIG. 1 is a schematic flow chart of the present invention.
FIG. 2 is an example of link basis attenuation, attenuation correction, and rainfall comparison determined by the method of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
As shown in fig. 1, a millimeter wave network rainfall inversion model construction method suitable for complex scenes includes the following steps,
the method comprises the steps of firstly, obtaining signal intensity TSL of each link signal transmitting end and signal intensity RSL of a receiving end in a millimeter wave network, collecting microwave data once every 10s, preprocessing the data, interpolating lost signal intensity, removing and interpolating singular values, and performing linear interpolation by using data before and after the lost values; acquiring rainfall intensity data of rainfall stations nearby a link in the same period of 15 min;
step two, subtracting the signal strength RSL of the receiving end from the signal strength TSL of the transmitting end of each link after pretreatment to obtain a signal attenuation value A, and obtaining the attenuation value A of the ith linki＝TSLi-RSLi；
Thirdly, obtaining the attenuation of the basic signal by using the minimum value of the sliding windowABLAnd subtracting the base signal attenuation A from the signal attenuation value ABLObtaining a corrected attenuation value ACThe size of the sliding window is set to be τ equal to 91, that is, the minimum of 91 data is used as the base attenuation of the 91 th time, and for the t time of the ith link, the specific form is:
Figure BDA0002681763940000031
Figure BDA0002681763940000032
by calculating correction values for all T moments
Figure BDA0002681763940000033
Then, a corrected attenuation value A of the ith link is obtainedC；
Fourthly, the attenuation value A after the millimeter wave link data is correctedCTime resampling is carried out, the time precision is 15min, the corrected attenuation values within 15min are averaged to be used as the corrected attenuation values at the time of the end of the period and are kept consistent with rainfall data of a rainfall station, and the corrected attenuation values after resampling are recorded as A'C；
And fifthly, respectively establishing a millimeter wave rainfall inversion model considering a complex scene for each link in the millimeter wave network, wherein for the ith link, the model concrete form is as follows:
Figure BDA0002681763940000034
wherein R isiThe rainfall intensity of the ith link; si、di、mi、ci、τiFor the ith link model parameter, si、diDepending on the link frequency, polarization, mi、ciRelated to regional environment, tau is a sliding window parameter; a'CPassing the ith link obtained in the fourth step through a duplicateThe sampled corrected base attenuation; l isiIs the ith link length;
sixthly, for each link, actually measuring an attenuation value A according to the historyCAnd constructing a data set by using the historical rainfall data R of the time scale rainfall station, carrying out rating on the model parameters s, d, m, c and tau of each link by using an SCE-UA algorithm and combining a rating data set, and selecting a Nash efficiency coefficient NSE by using an objective function.
In the invention, through the fourth step, all parameters in the model in the third step are obtained, and in practice, only the resampled corrected attenuation value A 'needs to be utilized'CAnd obtaining the link rainfall intensity data R. When the millimeter wave link is used for rain measurement, due to the fact that the millimeter wave link is located in different climatic regions and different combination modes of link frequency and length, data of all links cannot be calculated completely by using one formula, no matter an inversion model with a physical basis or a machine learning model, differential modeling is not performed according to the millimeter wave network condition in a complex scene, the inversion model provided by the invention not only considers the attribute of the millimeter wave link, but also introduces the influence of environmental elements, and the millimeter wave link has stronger applicability compared with other models. Before the millimeter wave attenuation data is subjected to rainfall inversion, the steps of dry-wet period discrimination, basic attenuation determination, wet antenna influence removal and the like are often required to be carried out, the processing modes of the steps are different, great uncertainty is brought, unreasonable processing often brings great errors, the existing method uses the same processing mode for each link, the requirement of a complex millimeter wave network cannot be met, in addition, obvious periodicity exists in millimeter wave signals, other methods cannot be effectively solved, in the invention, the millimeter wave attenuation data is corrected by using the minimum value of a sliding window, the minimum value of the sliding window is the minimum value in a period of time, for the millimeter wave attenuation variable caused by rainfall, a random process can be considered in a local range, the minimum value of the random process meets the expectation and the variance is 0, and the attenuation caused by non-rainfall environmental factors such as humidity and wet antennas is not a random variable, but rather a local constant, the sliding window minimum may therefore represent attenuation induced by non-rainfall elements, i.e. the minimum already contains wet antenna effects and other non-rainfallCompared with other technologies, the method simplifies the data processing steps, avoids the introduction of errors, can effectively remove millimeter wave periodic signal changes, and improves the reliability of a data set; for different links, because the link frequency, the length and the polarization mode are different, and the window size is also different, the sliding window parameters adopted by the millimeter wave attenuation data in the invention are determined by adopting a calibration parameter mode, thereby meeting the objective fact requirements of different link frequencies, lengths and polarization modes of the millimeter wave network.
As shown in fig. 2, the invention can effectively filter the periodic attenuation of the millimeter wave signal, and the attenuation obtained by the invention after correction has a very high matching degree with the rainfall data of the actual rainfall station.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (3)

1.一种适用于复杂场景的毫米波网络降雨反演模型构建方法，其具体步骤为：1. A method for building a millimeter wave network rainfall inversion model suitable for complex scenarios, the specific steps of which are: 第一步，获得毫米波网络中各链路信号发射端信号强度TSL、接收端的信号强度RSL，并对数据进行预处理，对丢失信号强度进行插补，对奇异值进行去除并插补，获得链路附近雨量站同期数据；The first step is to obtain the signal strength TSL of each link signal transmitter in the millimeter-wave network and the signal strength RSL of the receiver, and preprocess the data, interpolate the lost signal strength, remove and interpolate singular values, and obtain Concurrent data of rainfall stations near the link; 第二步，利用预处理后各链路的发射端信号强度TSL减去接收端的信号强度RSL，得到信号衰减值A；In the second step, the signal attenuation value A is obtained by subtracting the signal strength RSL of the receiving end from the signal strength TSL of the transmitting end of each link after preprocessing; 第三步，利用滑动窗口最小值得到基础信号衰减ABL，并将信号衰减值A减去基础信号衰减ABL，得到修正后的衰减值AC,滑动窗口的大小设为τ，对于第i条链路的t时刻，其具体形式为:The third step is to use the minimum value of the sliding window to obtain the basic signal attenuation A BL , and subtract the basic signal attenuation A BL from the signal attenuation value A to obtain the modified attenuation value A C , and the size of the sliding window is set as τ. time t of a link, its specific form is:
Figure FDA0002681763930000011
Figure FDA0002681763930000011
Figure FDA0002681763930000012
Figure FDA0002681763930000012
 通过计算所有T个时刻的修正值
Figure FDA0002681763930000013
后，得到第i条链路的修正后衰减值AC；By calculating the correction value for all T moments
Figure FDA0002681763930000013
After that, the corrected attenuation value AC of the i - th link is obtained; 第四步，将毫米波链路数据修正后的衰减值AC进行时间重采样，时间精度与雨量站降雨数据保持一致，重采样后的修正后衰减值记为A'C；The 4th step, carries out time resampling of the attenuation value AC after the correction of the millimeter wave link data, the time precision is consistent with the rainfall data of the rain gauge station, and the attenuation value after the correction after the resampling is recorded as A'C ; 第五步，对毫米波网络中各链路分别建立考虑复杂场景的毫米波降雨反演模型The fifth step is to establish a millimeter-wave rainfall inversion model considering complex scenarios for each link in the millimeter-wave network. 对于第i条链路，i为正整数，其模型具体形式为：For the i-th link, i is a positive integer, and the specific form of the model is:
Figure FDA0002681763930000014
Figure FDA0002681763930000014
 其中，Ri为第i条链路的降雨强度；si、di、mi、ci、τi为第i条链路模型参数，si、di与链路频率、极化方式有关，mi、ci与区域环境有关，τ为滑动窗口参数；A'C为第四步中得到的第i条链路经过重采样的修正后基础衰减量；Li为第i条链路长度；Among them, R i is the rainfall intensity of the ith link; si , d i , m i , c i , τ i are the model parameters of the ith link, si , d i are related to the link frequency, polarization mode related, m i and c i are related to the regional environment, τ is the sliding window parameter; A' C is the basic attenuation of the i -th link obtained in the fourth step after re-sampling and correction; Li is the i-th link road length; 第六步，对各链路，根据历史实测衰减值A'C以及同期雨量站历史降雨数据R构建历史数据集，通过参数率定的方式确定第五步中的各链路的模型参数s、d、m、c、τ。In the sixth step, for each link, a historical data set is constructed according to the historical measured attenuation value A' C and the historical rainfall data R of the rainfall station in the same period, and the model parameters s, s and d, m, c, τ. 2.根据权利要求1所述的适用于复杂场景的毫米波网络降雨反演模型构建方法，其特征在于：第四步中，对于毫米波链路修正后衰减数据进行重采样方法选择取时间步长内的修正后衰减的平均值作为时段末的修正后衰减值。2. the millimeter wave network rainfall inversion model construction method that is applicable to complex scene according to claim 1, is characterized in that: in the 4th step, carry out the resampling method for the attenuation data after the correction of the millimeter wave link and select the time step The average value of the corrected decay in the long period is taken as the corrected decay value at the end of the period. 3.根据权利要求1所述的适用于复杂场景的毫米波网络降雨反演模型构建方法，其特征在于：第六步中，利用SCE-UA算法对模型参数进行率定，目标函数选用纳什效率系数NSE。3. the millimeter wave network rainfall inversion model construction method that is applicable to complex scene according to claim 1, is characterized in that: in the 6th step, utilize SCE-UA algorithm to calibrate model parameters, and objective function selects Nash efficiency Coefficient NSE.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101543400A (en) * 2009-04-23 2009-09-30 深圳先进技术研究院 Aanimal behavior detection and automatic analysis system and animal behavior analysis methods
CN110031916A (en) * 2019-03-07 2019-07-19 中国人民解放军国防科技大学 Rainfall intensity measurement method based on satellite-ground link attenuation effect
CN110133602A (en) * 2018-02-08 2019-08-16 英飞凌科技股份有限公司 Radar Sensing with Phase Correction
CN110163472A (en) * 2019-04-11 2019-08-23 中国水利水电科学研究院 A wide range of extreme drought emergency monitoring and impact evaluation method and system
CN110490228A (en) * 2019-07-15 2019-11-22 中山大学 A kind of Hydro-Model Parameter Calibration Technology dynamic rating method based on CPP

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109255100B (en) * 2018-09-10 2020-07-28 河海大学 Urban rainfall inversion method based on microwave attenuation characteristic response fingerprint identification

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101543400A (en) * 2009-04-23 2009-09-30 深圳先进技术研究院 Aanimal behavior detection and automatic analysis system and animal behavior analysis methods
CN110133602A (en) * 2018-02-08 2019-08-16 英飞凌科技股份有限公司 Radar Sensing with Phase Correction
CN110031916A (en) * 2019-03-07 2019-07-19 中国人民解放军国防科技大学 Rainfall intensity measurement method based on satellite-ground link attenuation effect
CN110163472A (en) * 2019-04-11 2019-08-23 中国水利水电科学研究院 A wide range of extreme drought emergency monitoring and impact evaluation method and system
CN110490228A (en) * 2019-07-15 2019-11-22 中山大学 A kind of Hydro-Model Parameter Calibration Technology dynamic rating method based on CPP

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Extraordinary capacitive deionization performance of highly-ordered mesoporous carbon nanopolyhedra for brackish water desalination;杨涛 等;《Environmental Science Nano》;20190228;第1-2页 *
基于微波链路的降雨场反演方法研究;姜世泰 等;《物理学报》;20130815;第62卷(第15期);第1-8页 *

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