WO2020192367A1 - 一种平流雾测报系统及测报方法 - Google Patents

一种平流雾测报系统及测报方法 Download PDF

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Publication number
WO2020192367A1
WO2020192367A1 PCT/CN2020/077567 CN2020077567W WO2020192367A1 WO 2020192367 A1 WO2020192367 A1 WO 2020192367A1 CN 2020077567 W CN2020077567 W CN 2020077567W WO 2020192367 A1 WO2020192367 A1 WO 2020192367A1
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rssi
advection fog
fog
advection
lora base
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PCT/CN2020/077567
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English (en)
French (fr)
Inventor
李效东
周宏围
任雍
彭云峰
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厦门龙辉芯物联网科技有限公司
福建省大气探测技术保障中心
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Application filed by 厦门龙辉芯物联网科技有限公司, 福建省大气探测技术保障中心 filed Critical 厦门龙辉芯物联网科技有限公司
Priority to US17/263,903 priority Critical patent/US11372133B2/en
Priority to EP20778640.1A priority patent/EP3951439B1/en
Publication of WO2020192367A1 publication Critical patent/WO2020192367A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
    • G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/02—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/10—Devices for predicting weather conditions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W2001/006—Main server receiving weather information from several sub-stations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W2203/00—Real-time site-specific personalized weather information, e.g. nowcasting
    • 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

Definitions

  • the invention relates to the field of meteorological applications, in particular to an advection fog forecasting system and a forecasting method thereof.
  • Advection fog is the fog formed when warm and humid air flows advection on a colder underlying surface, and the lower part cools down. Advection fog often has the characteristics of strong suddenness, wide impact, high intensity, and long duration, which pose a serious threat to road traffic and navigation.
  • the measurement and reporting of advection fog mainly use lidar, visibility sensor, etc.
  • the measuring distance of lidar is only 8-10km, and its measuring distance is short, so it can only be observed at close range.
  • the coverage of advection fog may reach hundreds of kilometers.
  • multiple lidars and visibility sensors need to be deployed to ensure effective measurement and forecasting of advection fog, but this undoubtedly increases the amount of advection fog forecasting. Cost, while deploying lidar or visibility sensors in wide seas is also unrealistic.
  • the inventor of the present invention has in-depth ideas for many problems existing in advection fog forecasting, and then developed the present invention.
  • the purpose of the present invention is to provide an advection fog measuring and reporting system and method with a wide measuring range and low measuring cost.
  • An advection fog measurement and reporting system which includes two or more LoRa base stations, a meteorological data interface for accessing meteorological data, and a data analysis server;
  • the LoRa base station is used to transmit and receive radio signals, and collect the actual received radio signal strength value RSSI 1 , and transmit the radio signal strength value RSSI 1 to the data analysis server; the LoRa base station is established in advection fog Within the observation area, and the distance between two adjacent LoRa base stations is 5-100km;
  • the meteorological data interface is used to receive the meteorological data in the advection fog forecasting area and transmit it to the data analysis server; the meteorological data includes humidity, wind speed and rainfall;
  • the data analysis server judges the advection fog situation between the two LoRa base stations based on the obtained meteorological data and combined with the received radio signal strength value RSSI 1 between the two LoRa base stations: when the RSSI 1 is less than the advection fog reporting threshold RSSI When 0 and no rain, it is judged that there is advection fog in the area between the two LoRa base stations, and the advection fog concentration W is obtained;
  • RSSI 0 ⁇ + Log based d – Log basex X – Log basey Y
  • ⁇ is the RSSI signal distance adjustment parameter
  • d is the distance between two LoRa base stations
  • based is the base of the logarithm of the distance between the LoRa base stations in the advection fog measurement area
  • basex is the base of the logarithm of the humidity adjustment factor in the advection fog measurement area
  • X Is the humidity value of the advection fog observation area
  • basey is the base of the logarithm of the wind speed adjustment factor in the advection fog observation area
  • Y is the wind speed value in the advection fog observation area.
  • a method for forecasting advection fog which includes the following steps:
  • Step 1 Establish two or more LoRa base stations in the area where advection fog observation and reporting is required, and set the distance between two adjacent LoRa base stations to 5-100km; use the meteorological data interface to obtain meteorological data in the advection fog observation area ;
  • the LoRa base station collects the actual received radio signal strength RSSI 1 and transmits it to the data analysis server, and obtains the weather data of humidity, wind speed, and rainfall through the weather data interface and transmits it to the data analysis server; through the data of more than 30 days Accumulation, and at least 5-8 times of advection fog weather should occur in the accumulation of data for more than 30 days, and then perform statistical analysis on the collected data to obtain the forecast threshold RSSI 0 of advection fog to obtain the parameters ⁇ , based in the model , Basex, basey, to determine the RSSI 0 acquisition model;
  • RSSI 1 meteorological data, and whether it is advection fog weather, construct a model for obtaining the forecast threshold RSSI 0 , as follows:
  • RSSI 0 ⁇ + Log based d – Log basex X – Log basey Y
  • ⁇ is the RSSI signal distance adjustment parameter
  • d is the distance between two LoRa base stations
  • basex is the base of the logarithm of the humidity adjustment factor in the advection fog measurement and reporting area
  • X is the humidity value of the advection fog measurement and reporting area
  • basey is the base of the logarithm of the wind speed adjustment factor in the advection fog forecasting area
  • Y is the wind speed value of the advection fog forecast area
  • Step 3 Perform advection fog forecasting
  • the LoRa base station obtains the actual received radio signal strength RSSI 1 and transmits it to the data analysis server; obtains the humidity, wind speed, and rainfall in the advection fog observation area through the meteorological data interface, and transmits it to the data analysis server;
  • meteorological data combined with the advection fog reporting threshold RSSI 0 , according to the advection fog concentration formula:
  • W RSSI 1 -RSSI 0 , W represents the concentration of advection fog
  • the present invention adopts the principle that the wireless communication signal is affected by weather parameters, and collects radio transmission signals that may form a fog area.
  • the radio signal is affected by the fog and will produce a certain fog attenuation.
  • the RSSI change curve of the signal strength received from the radio is used to measure the advection fog with reference to the humidity, wind speed, rainfall and other weather parameters in the measurement area.
  • the long-distance LoRa base station is used to transmit and receive radio signals during advection fog forecasting. Within the same measurement range, the number of LoRa base stations that need to be deployed will be reduced, thereby reducing the cost of advection fog forecasting.
  • the present invention can realize the effective forecasting of advection fog in a certain area, and has a wide range and low cost.
  • Figure 1 is a block diagram of the principle of the advection fog forecasting system of the present invention
  • FIG. 2 is a diagram of the RSSI change rule
  • FIG. 3 is a block diagram of the principle of the advection fog measuring and reporting system according to the first embodiment of the present invention
  • FIG. 4 is a block diagram of the principle of the advection fog measuring and reporting system according to the second embodiment of the present invention.
  • Fig. 5 is a functional block diagram of the advection fog measuring and reporting system according to the third embodiment of the present invention.
  • the present invention discloses an advection fog forecasting system, which includes two or more LoRa base stations, a weather data interface and a data analysis server.
  • the LoRa base station is used to transmit and receive radio signals, and collect the actual received radio signal strength value RSSI 1 , and transmit the radio signal strength value RSSI 1 to the data analysis server; the LoRa base station is established in the advection fog measurement report Within the area, and the distance between two adjacent LoRa base stations is 5-100km.
  • the meteorological data interface is used to receive the meteorological data in the advection fog forecasting area and transmit it to the data analysis server; the meteorological data includes humidity, wind speed and rainfall;
  • the data analysis server judges the advection fog situation between the two LoRa base stations based on the meteorological data obtained and combined with the received radio signal strength value RSSI 1 between the two LoRa base stations: when the RSSI 1 is less than the advection fog reporting threshold RSSI 0 and When there is no rain, it is determined that there is advection fog in the area between the two LoRa base stations, and the advection fog concentration W is obtained;
  • ⁇ is the RSSI signal distance adjustment parameter
  • d is the distance between two LoRa base stations
  • based is the base of the logarithm of the distance between the LoRa base stations in the advection fog measurement area
  • basex is the base of the logarithm of the humidity adjustment factor in the advection fog measurement area
  • X Is the humidity value of the advection fog observation area
  • basey is the base of the logarithm of the wind speed adjustment factor in the advection fog observation area
  • Y is the wind speed value in the advection fog observation area.
  • the present invention also discloses a advection fog forecasting method, which specifically includes the following steps:
  • Step 1 Establish two or more LoRa base stations in the area where advection fog observation and reporting is required, and set the distance between two adjacent LoRa base stations to 5-100km; use the meteorological data interface to obtain meteorological data in the advection fog observation area ;
  • the LoRa base station collects the actual received radio signal strength RSSI 1 and transmits it to the data analysis server, and obtains the weather data of humidity, wind speed, and rainfall through the weather data interface and transmits it to the data analysis server; through the data of more than 30 days Accumulation, and at least 5-8 times of advection fog weather should occur in the accumulation of data for more than 30 days, and then perform statistical analysis on the collected data to obtain the forecast threshold RSSI 0 of advection fog to obtain the parameters ⁇ , based in the model , Basex, basey, to determine the RSSI 0 acquisition model;
  • RSSI 1 meteorological data, and whether it is advection fog weather, construct a model for obtaining the forecast threshold RSSI 0 , as follows:
  • RSSI 0 ⁇ + Log based d – Log basex X – Log basey Y
  • ⁇ is the RSSI signal distance adjustment parameter
  • d is the distance between two LoRa base stations
  • basex is the base of the logarithm of the humidity adjustment factor in the advection fog measurement and reporting area
  • X is the humidity value of the advection fog measurement and reporting area
  • basey is the base of the logarithm of the wind speed adjustment factor in the advection fog forecasting area
  • Y is the wind speed value of the advection fog forecast area
  • Step 3 Perform advection fog forecasting
  • the LoRa base station obtains the actual received radio signal strength RSSI 1 and transmits it to the data analysis server; obtains the humidity, wind speed, and rainfall in the advection fog observation area through the meteorological data interface, and transmits it to the data analysis server;
  • meteorological data combined with the advection fog reporting threshold RSSI 0 , according to the advection fog concentration formula:
  • W RSSI 1 -RSSI 0, W represents the concentration of advection fog
  • the invention adopts the principle that the wireless communication signal is affected by weather parameters, collects the radio transmission signal that may form a fog area, and the radio signal is affected by the fog will produce a certain fog attenuation.
  • the RSSI variation curve of the received signal strength also refers to the weather parameters such as humidity, wind speed, and rainfall in the measurement area for advection fog measurement.
  • the long-distance LoRa base station is used to transmit and receive radio signals during advection fog forecasting. Within the same measurement range, the number of LoRa base stations that need to be deployed will be reduced, thereby reducing the cost of advection fog forecasting.
  • the five LoRa base stations are the first LoRa base station, the second LoRa base station, the third LoRa base station, the fourth LoRa base station, and the fifth LoRa base station.
  • the second LoRa base station, the third LoRa base station, and the The four LoRa base stations and the fifth LoRa base station are respectively deployed in the four directions of the first LoRa base station, southeast, northwest, and the distance from the first LoRa base station is 50km.
  • the second LoRa base station, the third LoRa base station, the fourth LoRa base station, and the fifth LoRa base station are used to transmit radio signals, while the first LoRa base station is used to receive radio signals, and the first LoRa base station is connected to the data analysis server, and the data is analyzed at the same time
  • the server is also connected to the meteorological data interface.
  • the second LoRa base station, the third LoRa base station, the fourth LoRa base station, and the fifth LoRa base station transmit radio signals, which carry the corresponding LoRa base station ID, and the first LoRa base station receives the remaining four LoRa Base station’s radio signal and obtain the corresponding radio signal strength values RSSI 12 , RSSI 13 , RSSI 14 , RSSI 15 , where RSSI 12 is the received radio signal strength value transmitted by the second LoRa base station, and RSSI 13 is the received first 3.
  • the first LoRa base station sends the acquired radio signal strength values RSSI 12 , RSSI 13 , RSSI 14 , and RSSI 15 to the data analysis server.
  • the meteorological data interface obtains the meteorological observation information such as humidity, wind speed, and rainfall in the same period in the advection fog prediction area, and transmit the humidity, wind speed, and rainfall to the data analysis server.
  • the data analysis server obtains the advection fog reporting threshold RSSI 02 , RSSI 03 , RSSI 04 , and RSSI 05 between the first LoRa base station and the remaining LoRa base stations according to the weather observation information and the radio signal strength value between the two LoRa base stations.
  • the second to fifth LoRa base stations are configured to transmit radio signals, and the first LoRa base station is used to receive radio signals.
  • the second to fifth LoRa base stations can also be set to receive radio signals, while the first LoRa base station is used to transmit radio signals.
  • the data analysis server is connected to the fifth LoRa base station, and advection fog prediction can also be performed by obtaining the radio signal strength received by the second to fifth LoRa base stations.
  • the first to fifth LoRa base stations can also be set to both transmit and receive radio signals.
  • the five LoRa base stations are all connected to the data analysis server, any two Advection fog can be judged between LoRa base stations. In this case, the forecast of advection fog will be more accurate.

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Abstract

一种平流雾测报系统及测报方法，采用无线通信信号受天气参数影响的原理，采集可能形成雾区的无线电传输信号，无线电信号受雾的影响将产生一定的雾衰减，衰减值表现在无线电接收信号强度RSSI上，从无线电接收信号强度RSSI变化曲线同时参照测量区域内的湿度、风速、雨量等天气参数进行平流雾的测报。且在进行平流雾测报时采用传输距离远的LoRa基站进行无线电信号的发射与接收，在相同的测量范围内，需要布设的LoRa基站的数量就会减少，由此降低平流雾测报的成本。

Description

一种平流雾测报系统及测报方法 技术领域
本发明涉及气象应用领域，具体涉及一种平流雾测报系统及其测报方法。
 背景技术
平流雾，是当暖湿空气平流到较冷的下垫面上，下部冷却而形成的雾。平流雾往往具有突发性强、影响范围广、强度大、持续时间长等特点，对公路交通、航海等构成严重威胁。
目前平流雾测报主要采用激光雷达、能见度传感器等来测量，激光雷达测量距离仅为8-10km，其测量距离短，只能进行近距离观察。而平流雾的覆盖范围可能达到数百公里，按照目前的测量方法则需要布设多个激光雷达、能见度传感器，才能够保证对平流雾的有效测量及测报，但这无疑就增加了平流雾测报的成本,同时在广阔的海域部署激光雷达或能见度传感器也是不现实的。
有鉴于此，本发明人针对平流雾测报存在的诸多问题而深入构思，进而开发出本发明。
 技术问题
针对上述问题，本发明的目的在于提供一种测量范围广、测量成本低的平流雾测报系统及测报方法。
 技术解决方案
为实现上述目的，本发明采用的技术方案是：
一种平流雾测报系统，其包括两个以上的LoRa基站、一用于接入气象数据的气象数据接口和一数据分析服务器；
所述LoRa基站，用于发射和接收无线电信号，并采集其实际接收到的无线电信号的强度值RSSI 1，并将该无线电信号强度值RSSI 1传送至数据分析服务器；该LoRa基站建立在平流雾测报区域内，且两相邻LoRa基站之间的距离为5-100km；
所述气象数据接口用于接收平流雾测报区域内的气象数据，并传送至数据分析服务器；该气象数据包括湿度、风速和雨量；
[根据细则91更正 15.04.2020]　
所述数据分析服务器,根据获得的气象数据并结合两LoRa基站之间的接收到的无线电信号强度值RSSI 1，判断两LoRa基站之间的平流雾情况：当RSSI 1小于平流雾的测报阈值RSSI 0且无雨时，判断为两LoRa基站之间的区域内有平流雾，并获得平流雾浓度W；
[根据细则91更正 15.04.2020]　
计算平流雾的浓度W公式如下：
[根据细则91更正 15.04.2020]　
W=RSSI1-RSSI0
[根据细则91更正 15.04.2020]　
[根据细则91更正 15.04.2020]　
平流雾的测报阈值RSSI 0  = α+ Log based d – Log basex X – Log basey Y
其中，α为RSSI信号距离调整参数；d为两LoRa基站之间的距离；based为平流雾测报区域LoRa基站距离对数的底数；basex为平流雾测报区域的湿度调整因子对数的底数；X为平流雾测报区域的湿度值，basey为平流雾测报区域的风速调整因子对数的底数； Y为平流雾测报区域的风速值。
 一种平流雾测报方法，其包括以下步骤：
步骤1、在需要进行平流雾测报的区域内建立两个以上的LoRa基站，且两相邻的LoRa基站之间的距离设置为5-100km；利用气象数据接口获取平流雾测报区域内的气象数据；
步骤2、LoRa基站采集其实际接收到的无线电信号强度RSSI 1并传送至数据分析服务器，以及通过气象数据接口获取湿度、风速、雨量的气象数据并传送至数据分析服务器；通过30天以上的数据累计，且在30天以上的数据累计中至少要出现5-8次平流雾的天气，然后对采集的数据进行统计分析，求出平流雾的测报阈值RSSI 0求取模型中的参数α、based、basex、basey，由此确定RSSI 0的求取模型；
根据RSSI 1、气象数据以及是否平流雾天气的情况，构建测报阈值RSSI 0的求取模型，具体如下：
RSSI 0 = α+ Log based d – Log basex X – Log basey Y
其中，α为RSSI信号距离调整参数；
d为两LoRa基站之间的距离；
based为平流雾测报区域LoRa基站距离对数的底数；basex为平流雾测报区域的湿度调整因子对数的底数；
X为平流雾测报区域的湿度值；
basey为平流雾测报区域的风速调整因子对数的底数；
 Y为平流雾测报区域的风速值；
步骤3、进行平流雾测报；
LoRa基站获取其实际接收到的无线电信号强度RSSI 1，并传送至数据分析服务器；通过气象数据接口获取平流雾测报区域的湿度、风速、雨量，并传送至数据分析服务器；
根据LoRa基站实际接收的无线电信号强度RSSI 1、气象数据并结合平流雾的测报阈值RSSI 0，依照平流雾的浓度公式：
[根据细则91更正 15.04.2020]　
W=RSSI1-RSSI0，W表示平流雾的浓度；
[根据细则91更正 15.04.2020]　
[根据细则91更正 15.04.2020]　
[根据细则91更正 15.04.2020]　
当RSSI 1小于RSSI 0时获得平流雾浓度W,判断为两LoRa基站之间的区域内有平流雾,平流雾浓度W为雾浓度。
 有益效果
采用上述方案后，本发明采用无线通信信号受天气参数影响的原理，采集可能形成雾区的无线电传输信号，无线电信号受雾的影响将产生一定的雾衰减，该衰减值表现在无线电接收信号强度RSSI上，从无线电接收信号强度RSSI变化曲线同时参照测量区域内的湿度、风速、雨量等天气参数进行平流雾的测报。且在进行平流雾测报时采用传输距离远的LoRa基站进行无线电信号的发射与接收，在相同的测量范围内，需要布设的LoRa基站的数量就会减少，由此降低平流雾测报的成本。总之，本发明能够实现某一区域内的平流雾情况的有效测报，且其范围广、成本低。
 附图说明
图1为本发明平流雾测报系统原理框图；
图2为RSSI变化规律图；
图3为本发明第一实施例的平流雾测报系统原理框图；
图4为本发明第二实施例的平流雾测报系统原理框图；
图5为本发明第三实施例的平流雾测报系统原理框图。
 本发明的实施方式
如图1和图2所示，本发明揭示了一种平流雾测报系统，其包括两个以上的LoRa基站、一气象数据接口和一数据分析服务器。
其中，LoRa基站用于发射和接收无线电信号，并采集其实际接收到的无线电信号的强度值RSSI 1，并将该无线电信号强度值RSSI 1传送至数据分析服务器；该LoRa基站建立在平流雾测报区域内，且两相邻LoRa基站之间的距离为5-100km。
气象数据接口用于接收平流雾测报区域内的气象数据，并传送至数据分析服务器；该气象数据包括湿度、风速和雨量；
[根据细则91更正 15.04.2020]　
数据分析服务器,根据获得的气象数据并结合两LoRa基站之间的接收到的无线电信号强度值RSSI 1，判断两LoRa基站之间的平流雾情况：当RSSI 1小于平流雾的测报阈值RSSI 0且无雨时，判断为两LoRa基站之间的区域内有平流雾，并获得平流雾浓度W；
[根据细则91更正 15.04.2020]　
计算平流雾的浓度W公式如下：
[根据细则91更正 15.04.2020]　
W=RSSI1-RSSI0
[根据细则91更正 15.04.2020]　
[根据细则91更正 15.04.2020]　
上述平流雾的测报阈值RSSI 0  = α+ Log based d – Log basex X – Log basey Y ；
其中，α为RSSI信号距离调整参数；d为两LoRa基站之间的距离；based为平流雾测报区域LoRa基站距离对数的底数；basex为平流雾测报区域的湿度调整因子对数的底数；X为平流雾测报区域的湿度值，basey为平流雾测报区域的风速调整因子对数的底数； Y为平流雾测报区域的风速值。
基于上述平流雾测报系统，本发明还揭示了一种平流雾测报方法，其具体包括以下步骤：
步骤1、在需要进行平流雾测报的区域内建立两个以上的LoRa基站，且两相邻的LoRa基站之间的距离设置为5-100km；利用气象数据接口获取平流雾测报区域内的气象数据；
步骤2、LoRa基站采集其实际接收到的无线电信号强度RSSI 1并传送至数据分析服务器，以及通过气象数据接口获取湿度、风速、雨量的气象数据并传送至数据分析服务器；通过30天以上的数据累计，且在30天以上的数据累计中至少要出现5-8次平流雾的天气，然后对采集的数据进行统计分析，求出平流雾的测报阈值RSSI 0求取模型中的参数α、based、basex、basey，由此确定RSSI 0的求取模型；
根据RSSI 1、气象数据以及是否平流雾天气的情况，构建测报阈值RSSI 0的求取模型，具体如下：
RSSI 0 = α+ Log based d – Log basex X – Log basey Y
其中，α为RSSI信号距离调整参数；
d为两LoRa基站之间的距离；
based为平流雾测报区域LoRa基站距离对数的底数；basex为平流雾测报区域的湿度调整因子对数的底数；
X为平流雾测报区域的湿度值；
basey为平流雾测报区域的风速调整因子对数的底数；
 Y为平流雾测报区域的风速值；
步骤3、进行平流雾测报；
LoRa基站获取其实际接收到的无线电信号强度RSSI 1，并传送至数据分析服务器；通过气象数据接口获取平流雾测报区域的湿度、风速、雨量，并传送至数据分析服务器；
根据LoRa基站实际接收的无线电信号强度RSSI 1、气象数据并结合平流雾的测报阈值RSSI 0，依照平流雾的浓度公式：
[根据细则91更正 15.04.2020]　
W=RSSI1-RSSI0，W表示平流雾的浓度
[根据细则91更正 15.04.2020]　
[根据细则91更正 15.04.2020]　
[根据细则91更正 15.04.2020]　
当RSSI 1小于RSSI 0时获得平流雾浓度W,判断为两LoRa基站之间的区域内有平流雾,平流雾浓度W为雾浓度。
本发明采用无线通信信号受天气参数影响的原理，采集可能形成雾区的无线电传输信号，无线电信号受雾的影响将产生一定的雾衰减，该衰减值表现在无线电接收信号强度RSSI上，从无线电接收信号强度RSSI变化曲线同时参照测量区域内的湿度、风速、雨量等天气参数进行平流雾的测报。且在进行平流雾测报时采用传输距离远的LoRa基站进行无线电信号的发射与接收，在相同的测量范围内，需要布设的LoRa基站的数量就会减少，由此降低平流雾测报的成本。
在实际的平流雾测报时，可以仅布设两个LoRa基站来预测两个LoRa基站之间的平流雾情况，也可以布设三个以及三个以上的LaRa基站来预测更大区域的平流雾情况。以下将列举一实施例对本发明进行进一步的解释说明，在该实施例中，平流雾预测区域内布设了五个LoRa基站。
如图3所示，五个LoRa基站分别为第一LoRa基站、第二LoRa基站、第三LoRa基站、第四LoRa基站和第五LoRa基站，其中，第二LoRa基站、第三LoRa基站、第四LoRa基站、第五LoRa基站分别布设在第一LoRa基站的东南西北四个方向，且其与第一LoRa基站的距离为50km。第二LoRa基站、第三LoRa基站、第四LoRa基站和第五LoRa基站用于发射无线电信号，而第一LoRa基站用于接收无线电信号，且第一LoRa基站连接数据分析服务器，同时该数据分析服务器还连接气象数据接口。
在进行平流雾测报时，第二LoRa基站、第三LoRa基站、第四LoRa基站和第五LoRa基站发射无线电信号，该无线电信号携带有相应的LoRa基站ID，第一LoRa基站接收其余四个LoRa基站的无线电信号并获取相应的无线电信号强度值RSSI 12、RSSI 13、RSSI 14、RSSI 15，其中，RSSI 12为接收到的第二LoRa基站发射的无线电信号强度值，RSSI 13为接收到的第三LoRa基站发射的无线电信号强度值，RSSI 14为接收到的第四LoRa基站发射的无线电信号强度值，RSSI 15为接收到的第五LoRa基站发射的无线电信号强度值。第一LoRa基站将获取的无线电信号强度值RSSI 12、RSSI 13、RSSI 14、RSSI 15发送至数据分析服务器。
同时，通过气象数据接口获取平流雾预测区域内的同时段的湿度、风速、雨量等气象观测信息，并将湿度、风速、雨量传送至数据分析服务器。数据分析服务器根据这些气象观测信息以及两LoRa基站之间的无线电信号强度值获取出第一LoRa基站与其余LoRa基站之间的平流雾测报阈值RSSI 02、RSSI 03、RSSI 04、RSSI 05。
然后，将无线电信号强度RSSI 12与平流雾测报阈值RSSI 02进行比较，即可知第一LoRa基站与第二LoRa基站之间是否有雾；将无线电信号强度RSSI 13与平流雾测报阈值RSSI 03进行比较，即可知第一LoRa基站与第三LoRa基站之间是否有雾；将无线电信号强度RSSI 14与平流雾测报阈值RSSI 04进行比较，即可知第一LoRa基站与第四LoRa基站之间是否有雾；将无线电信号强度RSSI 15与平流雾测报阈值RSSI 05进行比较，即可知第一LoRa基站与第五LoRa基站之间是否有雾。若第一LoRa基站与其余四个LoRa基站之间均判断为有雾，那么，这五个LoRa基站所涵盖的区域内就判断为有雾。
上述实施例是将第二至第五LoRa基站设置为用于发射无线电信号的，而第一LoRa基站是用来接收无线电信号的。而在具体的平流雾测报过程中，如图4所示，也可以将第二至第五LoRa基站设置为用来接收无线电信号，而第一LoRa基站用来发射无线电信号，此时，第二至第五LoRa基站连接数据分析服务器，通过获取第二至第五LoRa基站接收到的无线电信号强度也可以进行平流雾预测。当然，如图5所示，也可以将第一至第五LoRa基站均设置为既用于发射无线电信号也用来接收无线电信号，此时，五个LoRa基站均连接数据分析服务器，任意两个LoRa基站之间均可进行平流雾判断，该情况下，平流雾的测报会更加准确一些。
以上所述，仅是本发明实施例而已，并非对本发明的技术范围作任何限制，故凡是依据本发明的技术实质对以上实施例所作的任何细微修改、等同变化与修饰，均仍属于本发明技术方案的范围内。

Claims (2)

  1. [根据细则91更正 15.04.2020]　
    一种平流雾测报系统，其特征在于：所述系统包括两个以上的LoRa基站、一用于接入气象数据的气象数据接口和一数据分析服务器；
    所述LoRa基站，用于发射和接收无线电信号，并采集其实际接收到的无线电信号的强度值RSSI 1，并将该无线电信号强度值RSSI 1传送至数据分析服务器；该LoRa基站建立在平流雾测报区域内，且两相邻LoRa基站之间的距离为5-100km；
    所述气象数据接口用于接收平流雾测报区域内的气象数据，并传送至数据分析服务器；该气象数据包括湿度、风速和雨量；
    所述数据分析服务器,根据获得的气象数据并结合两LoRa基站之间的接收到的无线电信号强度值RSSI 1，判断两LoRa基站之间的平流雾情况：当RSSI 1小于平流雾的测报阈值RSSI 0且无雨时，判断为两LoRa基站之间的区域内有平流雾，并获得平流雾浓度W；
    平流雾的浓度公式如下：
    W=RSSI1-RSSI0；
    平流雾的测报阈值RSSI 0  = α+ Log based d – Log basex X – Log basey Y
    其中，α为RSSI信号距离调整参数；d为两LoRa基站之间的距离；based为平流雾测报区域LoRa基站距离对数的底数；basex为平流雾测报区域的湿度调整因子对数的底数；X为平流雾测报区域的湿度值，basey为平流雾测报区域的风速调整因子对数的底数；Y为平流雾测报区域的风速值。
  2. [根据细则91更正 15.04.2020]　
    一种平流雾测报方法，其特征在于：所述测报方法包括以下步骤：
    步骤1、在需要进行平流雾测报的区域内建立两个以上的LoRa基站，且两相邻的LoRa基站之间的距离设置为5-100km；利用气象数据接口获取平流雾测报区域内的气象数据；
    步骤2、LoRa基站采集其实际接收到的无线电信号强度RSSI 1并传送至数据分析服务器，以及通过气象数据接口获取湿度、风速、雨量的气象数据并传送至数据分析服务器；通过30天以上的数据累计，且在30天以上的数据累计中至少要出现5-8次平流雾的天气，然后对采集的数据进行统计分析，求出平流雾的测报阈值RSSI 0求取模型中的参数α、based、basex、basey，由此确定RSSI 0的求取模型；
    根据RSSI 1、气象数据以及是否平流雾天气的情况，构建测报阈值RSSI 0的求取模型，具体如下：
    RSSI 0 = α+ Log based d – Log basex X – Log basey Y
    其中，α为RSSI信号距离调整参数；
    d为两LoRa基站之间的距离；
    based为平流雾测报区域LoRa基站距离对数的底数；basex为平流雾测报区域的湿度调整因子对数的底数；
    X为平流雾测报区域的湿度值；
    basey为平流雾测报区域的风速调整因子对数的底数；
    Y为平流雾测报区域的风速值；
    步骤3、进行平流雾测报；
    LoRa基站获取其实际接收到的无线电信号强度RSSI 1，并传送至数据分析服务器；通过气象数据接口获取平流雾测报区域的湿度、风速、雨量，并传送至数据分析服务器；
    根据LoRa基站实际接收的无线电信号强度RSSI 1、气象数据并结合平流雾的测报阈值RSSI 0，依照平流雾的浓度公式：
    W=RSSI1-RSSI0，W表示平流雾的浓度
    当雨量等于0且RSSI 1小于 RSSI 0 时获得平流雾浓度W,判断为两LoRa基站之间的区域内有平流雾,平流雾浓度W为雾浓度。
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Publication number Priority date Publication date Assignee Title
CN109932758B (zh) * 2019-03-28 2023-08-04 厦门龙辉芯物联网科技有限公司 一种平流雾预报系统及预报方法
CN111785094B (zh) * 2020-07-31 2021-12-07 上海眼控科技股份有限公司 平流雾检测方法、装置、计算机设备和可读存储介质
CN113093311B (zh) * 2021-03-24 2022-09-27 中国联合网络通信集团有限公司 降雨分布观测方法及装置、基站、系统
CN115097548B (zh) * 2022-08-08 2023-04-07 广东省气象公共服务中心(广东气象影视宣传中心) 基于智能预测的海雾分类预警方法、装置、设备及介质

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100946386B1 (ko) * 2009-05-28 2010-03-08 부경대학교 산학협력단 안개 발생 가능성 평가 시스템 및 방법
CN101819286A (zh) * 2010-04-09 2010-09-01 东南大学 一种基于图像灰度直方图的雾天检测方法
KR101162006B1 (ko) * 2011-02-24 2012-07-03 부산대학교 산학협력단 일출 전후의 안개 탐지 방법
CN102540277A (zh) * 2012-01-16 2012-07-04 武汉大学 基于面向对象和时序影像的白天陆地辐射雾检测方法
CN103926634A (zh) * 2014-03-12 2014-07-16 长江水利委员会长江科学院 一种基于面向对象分类的白天陆地辐射雾的遥感监测方法
CN206331578U (zh) * 2017-01-04 2017-07-14 路永明 一种基于无线扩频技术的低功耗遥测雨量系统
KR101810405B1 (ko) * 2017-02-09 2018-01-25 주식회사 제이컴스 다중 주파수를 이용한 안개 및 미세입자 검출 시스템
CN108107486A (zh) * 2018-01-16 2018-06-01 昆明理工大学 一种基于LoRa的车载式交通气象嵌入式实时监测装置
KR101880616B1 (ko) * 2017-08-03 2018-07-23 한국해양과학기술원 해상풍과 해무 위성정보를 이용한 해무 예측 방법
KR101919814B1 (ko) * 2017-09-29 2018-11-19 주식회사 미래기후 안개 예측 시스템 및 이를 이용한 안개 예측 방법
CN109932758A (zh) * 2019-03-28 2019-06-25 厦门龙辉芯物联网科技有限公司 一种平流雾预报系统及预报方法

Family Cites Families (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3606736B2 (ja) * 1998-04-30 2005-01-05 松下電器産業株式会社 気象衛星データ受信機能付カーナビゲーション装置
KR100894482B1 (ko) * 2007-10-05 2009-04-22 부산대학교 산학협력단 기상위성을 이용한 안개 탐지시스템 및 그를 사용한 안개탐지방법
KR20130055788A (ko) * 2011-11-21 2013-05-29 한국전자통신연구원 위성시스템에서의 강우감쇠/강우강도 데이터 통합수집 장치
KR20130107424A (ko) * 2012-03-22 2013-10-02 한국건설기술연구원 실시간 안개상황 감지 및 예측 방법, 시스템 및 이를 이용한 제어시스템
JP6184148B2 (ja) * 2013-04-04 2017-08-23 隆治 相馬 霧状態予測装置、霧状態予測プログラム及び記録媒体
CN104345355B (zh) * 2014-09-30 2016-05-18 天青公司 一种采集与处理天气数据和图像的装置、方法和系统
JP5950422B2 (ja) * 2014-12-08 2016-07-13 国立研究開発法人防災科学技術研究所 視程予測システム及び視程予測方法
CN106772697B (zh) * 2016-11-21 2019-07-05 元江哈尼族彝族傣族自治县气象局 云海自然景观预报方法及系统
CN106950614B (zh) * 2017-02-28 2019-03-22 中船重工鹏力（南京）大气海洋信息系统有限公司 一种区域自动气象站小时雨量数据质量控制方法
CN107479112B (zh) * 2017-07-07 2020-02-11 北京中交华安科技有限公司 浓雾预测方法、装置及系统
AT520436B1 (de) * 2017-09-13 2019-04-15 UBIMET GmbH Verfahren zur Ermittlung zumindest einer meteorologischen Größe zur Beschreibung einer Zustandsform atmosphärischen Wassers
US10962680B2 (en) * 2017-11-03 2021-03-30 Climacell Inc. Real-time weather forecasting for transportation systems
CN107920086B (zh) * 2017-12-11 2024-06-11 上海启诺信息科技有限公司 会议相关设备管理装置及系统
CN108375808A (zh) * 2018-03-12 2018-08-07 南京恩瑞特实业有限公司 Nriet基于机器学习的大雾预报方法
CN111785094B (zh) * 2020-07-31 2021-12-07 上海眼控科技股份有限公司 平流雾检测方法、装置、计算机设备和可读存储介质
CN112633595B (zh) * 2020-12-31 2021-07-23 南京师范大学 一种基于雷达降雨数据挖掘的雨量站观测网络设计方法

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100946386B1 (ko) * 2009-05-28 2010-03-08 부경대학교 산학협력단 안개 발생 가능성 평가 시스템 및 방법
CN101819286A (zh) * 2010-04-09 2010-09-01 东南大学 一种基于图像灰度直方图的雾天检测方法
KR101162006B1 (ko) * 2011-02-24 2012-07-03 부산대학교 산학협력단 일출 전후의 안개 탐지 방법
CN102540277A (zh) * 2012-01-16 2012-07-04 武汉大学 基于面向对象和时序影像的白天陆地辐射雾检测方法
CN103926634A (zh) * 2014-03-12 2014-07-16 长江水利委员会长江科学院 一种基于面向对象分类的白天陆地辐射雾的遥感监测方法
CN206331578U (zh) * 2017-01-04 2017-07-14 路永明 一种基于无线扩频技术的低功耗遥测雨量系统
KR101810405B1 (ko) * 2017-02-09 2018-01-25 주식회사 제이컴스 다중 주파수를 이용한 안개 및 미세입자 검출 시스템
KR101880616B1 (ko) * 2017-08-03 2018-07-23 한국해양과학기술원 해상풍과 해무 위성정보를 이용한 해무 예측 방법
KR101919814B1 (ko) * 2017-09-29 2018-11-19 주식회사 미래기후 안개 예측 시스템 및 이를 이용한 안개 예측 방법
CN108107486A (zh) * 2018-01-16 2018-06-01 昆明理工大学 一种基于LoRa的车载式交通气象嵌入式实时监测装置
CN109932758A (zh) * 2019-03-28 2019-06-25 厦门龙辉芯物联网科技有限公司 一种平流雾预报系统及预报方法

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