WO2023056682A1 - Algorithme de prédiction de couche d'ozone basé sur l'intelligence artificielle - Google Patents

Algorithme de prédiction de couche d'ozone basé sur l'intelligence artificielle Download PDF

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WO2023056682A1
WO2023056682A1 PCT/CN2021/129850 CN2021129850W WO2023056682A1 WO 2023056682 A1 WO2023056682 A1 WO 2023056682A1 CN 2021129850 W CN2021129850 W CN 2021129850W WO 2023056682 A1 WO2023056682 A1 WO 2023056682A1
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ozone
neural network
value
day
concentration
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PCT/CN2021/129850
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Chinese (zh)
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史剑
杜辉
张文
郭海龙
曾智
张雪艳
汪浩笛
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中国人民解放军国防科技大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0418—Architecture, e.g. interconnection topology using chaos or fractal principles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/08—Fluids
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
    • 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

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  • the invention belongs to the technical field of ozone layer forecasting, and in particular relates to an artificial intelligence-based ozone layer forecasting algorithm.
  • the ozone layer is the stratosphere of the atmosphere where the concentration of ozone is high. The part with the greatest concentration is located at an altitude of 20-25 kilometers. If the ozone of the ozone layer is corrected to the standard situation, its thickness is only about 3 millimeters on average. Ozone content varies with latitude, season and weather. Ultraviolet radiation is absorbed by ozone at high altitudes, which has a warming effect on the atmosphere. At the same time, it protects the living things on the earth from the damage of far ultraviolet radiation. The small amount of ultraviolet radiation that passes through has a bactericidal effect and is of great benefit to living things.
  • the purpose of the present invention is to provide an artificial intelligence-based ozone layer forecasting algorithm to solve the problems raised in the above-mentioned background technology.
  • the ozone layer prediction algorithm based on artificial intelligence comprises the following steps:
  • Establish an ozone concentration monitoring station select a location that has no impact on the surrounding environment and the discharge of active pollutants as the ozone concentration monitoring station;
  • S2. collect and obtain historical meteorological data: obtain the daily concentration data of each ozone concentration monitoring site history every day, the daily concentration data predicted every day of each ozone concentration monitoring site history and the reference corresponding to the history of each ozone concentration monitoring site every day As a result, the reference result corresponding to the history of each ozone concentration monitoring site every day is the difference between the daily concentration data of each ozone concentration monitoring site history and the daily concentration data of each ozone concentration monitoring site history every day;
  • Select influencing factors select yesterday's O 3 -8h value, PM2.5 daily average concentration, NO2 daily average concentration, forecasted daily average air pressure, daily average wind speed and daily average temperature as influencing factors;
  • Preliminary prediction of the O 3 -8h value in one day by fitting, the predicted value and the measured value of the meteorological parameters are substituted into the prediction equation, and the to-be-predicted value of each ozone concentration monitoring station is obtained by obtaining the equation Predict the O 3 -8h value of a day;
  • the network is divided into three parts: an input layer, a hidden layer and an output layer, and in the network, the neurons of the input layer, the hidden layer and the output layer are fully connected;
  • the chaotic artificial neural network is to mix the new hyperchaotic system with the artificial neural network, and the chaotic artificial neural network is to carry out the ozone concentration through the artificial neural network, BP and multiple linear regression model predict;
  • the pollutant concentration data in S1 includes sulfur dioxide SO2, nitrogen dioxide NO2, O 3 , O 3 -8h, fine particulate matter with an aerodynamic diameter of 2.5 microns or smaller PM2.5 and aerodynamic diameter PM10 is fine particulate matter 10 microns or smaller.
  • the collection and acquisition of historical meteorological data in S2 also includes obtaining the point source emission characteristics corresponding to any historical moment of each ozone concentration monitoring station, and the point source emission characteristics corresponding to any historical moment of each ozone concentration monitoring station Source emission characteristics are obtained through the following steps:
  • the sensitive area includes all cities bordering on the city where each ozone concentration monitoring site is located;
  • the prediction equation of fitting by way of fitting in said S4 is:
  • c is the predicted value of O 3 -8h on the day to be predicted
  • x1, x2, ..., x6 respectively represent the average concentration of NO2, average temperature, average air pressure, average wind speed, and average concentration of PM2.5 on the day to be predicted
  • the predicted value of , and the predicted or measured value of yesterday's O 3 -8h of the day to be predicted, a0, a1, ..., a6 are regression coefficients.
  • the new hyper-chaotic system in the S5 is described by the following formula:
  • the corresponding hyperchaotic complex system is given by:
  • ⁇ 1 u1+ju2
  • ⁇ 2 u3+ju4
  • ⁇ 3 u5
  • j -1-0.5
  • the bar above the variable represents the complex conjugate of the variable
  • the formula of S8 using chaotic artificial neural network for long-term and short-term forecasting is as follows:
  • n the number of samples
  • xobs,i the observed value
  • xpre,i the predicted value
  • Var the sample variance
  • the chaotic artificial neural network in the S7 adopts CANN operation, and the CANN operation is usually divided into two steps: first, setting parameters, selecting an activation function and an operation mode, and using a sigmoid function for the activation function, and setting The operation mode is set to regression; second, 70%-80% of the sample data are selected for training, and the rest are used as test data, and the weights and thresholds are automatically set in CANN.
  • an early warning of ozone pollution is also included, and the early warning of ozone pollution includes an early warning module, which is used for early warning of ozone pollution and determining the interval and pollution level of ozone pollution.
  • the method for ozone pollution early warning comprises the following steps:
  • the physical parameters include air temperature, total solar radiation irradiance, cloud cover, particle concentration, and nitrogen oxide concentration;
  • S202 Determine the inflection point value of each physical parameter described in S201 according to atmospheric ozone generation conditions, and use the inflection point value to perform interval division and assign a value to each interval;
  • the present invention adopts the CANN operation similar to other neural networks in that it does not depend on the complex relationship between parameters and outputs, but it relies on constant changes in weights, so that parameters and outputs are closely related, avoiding cumbersome mathematical modeling.
  • the present invention has high generalization, can maintain high prediction accuracy while reducing some input parameters, can achieve good results in long-term and short-term ozone prediction, and can accurately and efficiently predict ozone Concentration is conducive to the protection of the environment.
  • Fig. 1 is a flowchart of the present invention.
  • the present invention provides a kind of technical scheme: the ozone layer prediction algorithm based on artificial intelligence comprises the following steps:
  • Establish an ozone concentration monitoring station select a location that has no impact on the surrounding environment and the discharge of active pollutants as the ozone concentration monitoring station;
  • S2. collect and obtain historical meteorological data: obtain the daily concentration data of each ozone concentration monitoring site history every day, the daily concentration data predicted every day of each ozone concentration monitoring site history and the reference corresponding to the history of each ozone concentration monitoring site every day As a result, the reference result corresponding to the history of each ozone concentration monitoring site every day is the difference between the daily concentration data of each ozone concentration monitoring site history and the daily concentration data of each ozone concentration monitoring site history every day;
  • Select influencing factors select yesterday's O3-8h value, PM2.5 daily average concentration, NO2 daily average concentration, forecasted daily average air pressure, daily average wind speed and daily average temperature as the influencing factors;
  • Preliminary prediction of the O 3 -8h value in one day by fitting, the predicted value and the measured value of the meteorological parameters are substituted into the prediction equation, and the to-be-predicted value of each ozone concentration monitoring station is obtained by obtaining the equation Predict the O 3 -8h value of a day;
  • the network is divided into three parts: an input layer, a hidden layer and an output layer, and in the network, the neurons of the input layer, the hidden layer and the output layer are fully connected;
  • the chaotic artificial neural network is to mix the new hyperchaotic system with the artificial neural network, and the chaotic artificial neural network is to carry out the ozone concentration through the artificial neural network, BP and multiple linear regression model predict;
  • the pollutant concentration data in S1 includes sulfur dioxide SO2, nitrogen dioxide NO2, O3 , O3-8h , fine particulate matter PM2.5 with an aerodynamic diameter of 2.5 microns or less and fine particulate matter PM10 with an aerodynamic diameter of 10 microns or less.
  • the collection and acquisition of historical meteorological data in S2 also includes obtaining the point source emission characteristics corresponding to any historical moment of each ozone concentration monitoring site, and any of the ozone concentration monitoring sites
  • the point source emission characteristics corresponding to historical moments are obtained through the following steps:
  • the sensitive areas include all cities bordering on the city where each ozone concentration monitoring site is located;
  • the prediction equation of fitting by way of fitting in said S4 is:
  • c is the predicted value of O 3 -8h on the day to be predicted
  • x1, x2, ..., x6 respectively represent the average concentration of NO2, average temperature, average air pressure, average wind speed, and average concentration of PM2.5 on the day to be predicted
  • the predicted value of , and the predicted or measured value of yesterday's O 3 -8h of the day to be predicted, a0, a1, ..., a6 are regression coefficients.
  • the new hyper-chaotic system in the S5 is described by the following formula:
  • the corresponding hyperchaotic complex system is given by:
  • ⁇ 1 u1+ju2
  • ⁇ 2 u3+ju4
  • ⁇ 3 u5
  • j -1-0.5
  • the bar above the variable represents the complex conjugate of the variable
  • the formula of S8 using chaotic artificial neural network for long-term and short-term forecasting is as follows:
  • n the number of samples
  • xobs,i the observed value
  • xpre,i the predicted value
  • Var the sample variance
  • the chaotic artificial neural network in S7 adopts CANN operation, and the CANN operation is usually divided into two steps: the first, setting parameters, selecting activation function and operation mode, for activation function, use sigmoid function, and set the operation mode to regression; second, select 70%-80% of the sample data for training, and the rest as test data, and the weights and thresholds are automatically set in CANN.
  • an early warning of ozone pollution is also included, and the early warning of ozone pollution includes an early warning module, and the early warning module is used for early warning of ozone pollution, and determining the interval and pollution level of ozone pollution.
  • the method for ozone pollution early warning comprises the following steps:
  • the physical parameters include air temperature, total solar radiation irradiance, cloud cover, particle concentration, and nitrogen oxide concentration;
  • S202 Determine the inflection point value of each physical parameter described in S201 according to atmospheric ozone generation conditions, and use the inflection point value to perform interval division and assign a value to each interval;
  • the present invention adopts CANN operation similar to other neural networks in that it does not depend on the complex relationship between parameters and outputs, but it relies on the constant change of weights, so that parameters and outputs are closely related , avoiding cumbersome mathematical modeling;
  • the present invention has high generalization, can reduce some input parameters while maintaining high prediction accuracy, and can achieve good results in long-term and short-term ozone prediction, and The ozone concentration can be accurately and efficiently predicted, which is beneficial to the protection of the environment.

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Abstract

Est divulgué dans la présente invention un algorithme de prédiction de couche d'ozone basé sur l'intelligence artificielle, l'algorithme comprenant les étapes suivantes : S1, l'établissement d'un site de surveillance de la concentration d'ozone ; S2, la collecte et l'acquisition de données météorologiques historiques ; S3, la sélection d'un facteur d'impact ; S4, la réalisation d'une prédiction préliminaire sur des valeurs O3-8h d'un jour ; S5, l'établissement d'un nouveau système hyperchaotique ; S6, l'établissement d'un réseau neuronal artificiel ; S7, l'établissement d'un réseau neuronal artificiel chaotique ; et S8, l'utilisation du réseau neuronal artificiel chaotique pour effectuer une prédiction à long terme et à court terme. La présente invention est propice à simplifier un processus de recherche de procédés de prédiction météorologique numériques classiques ; et les procédés de prédiction météorologique numériques classiques tendent à être relativement complexes, et ont des exigences élevées pour le calcul. La similarité entre une opération CANN utilisée dans la présente invention et d'autres réseaux neuronaux est que l'opération CANN ne repose pas sur une relation complexe entre des paramètres et des sorties, mais repose sur le changement constant de poids, de telle sorte que les paramètres sont étroitement associés aux sorties, ce qui permet d'éviter une modélisation mathématique fastidieuse.
PCT/CN2021/129850 2021-10-09 2021-11-10 Algorithme de prédiction de couche d'ozone basé sur l'intelligence artificielle WO2023056682A1 (fr)

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CN116776745A (zh) * 2023-08-18 2023-09-19 南昌云宜然科技有限公司 基于边缘计算的污染物浓度和轨迹移动预测的方法与系统
CN117031582A (zh) * 2023-06-27 2023-11-10 华南理工大学 递归时空学习及模拟监测融合的臭氧小时浓度预报方法
CN117113089A (zh) * 2023-10-16 2023-11-24 北京英视睿达科技股份有限公司 基于一氧化碳的甲烷数据补全方法、装置、设备及介质
CN117265251A (zh) * 2023-09-20 2023-12-22 索罗曼(广州)新材料有限公司 一种钛扁条氧含量在线监测系统及其方法
CN117879789A (zh) * 2024-03-13 2024-04-12 数盾信息科技股份有限公司 基于高速加密的卫星通信报文数据传输方法
CN117972593A (zh) * 2024-03-27 2024-05-03 中科三清科技有限公司 一种典型重污染历史案例库分析的长期预报预警方法
CN117972593B (zh) * 2024-03-27 2024-06-04 中科三清科技有限公司 一种典型重污染历史案例库分析的长期预报预警方法

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Cited By (11)

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CN117031582A (zh) * 2023-06-27 2023-11-10 华南理工大学 递归时空学习及模拟监测融合的臭氧小时浓度预报方法
CN116776745A (zh) * 2023-08-18 2023-09-19 南昌云宜然科技有限公司 基于边缘计算的污染物浓度和轨迹移动预测的方法与系统
CN116776745B (zh) * 2023-08-18 2023-10-24 南昌云宜然科技有限公司 基于边缘计算的污染物浓度和轨迹移动预测的方法与系统
CN117265251A (zh) * 2023-09-20 2023-12-22 索罗曼(广州)新材料有限公司 一种钛扁条氧含量在线监测系统及其方法
CN117265251B (zh) * 2023-09-20 2024-04-09 索罗曼(广州)新材料有限公司 一种钛扁条氧含量在线监测系统及其方法
CN117113089A (zh) * 2023-10-16 2023-11-24 北京英视睿达科技股份有限公司 基于一氧化碳的甲烷数据补全方法、装置、设备及介质
CN117113089B (zh) * 2023-10-16 2024-01-23 北京英视睿达科技股份有限公司 基于一氧化碳的甲烷数据补全方法、装置、设备及介质
CN117879789A (zh) * 2024-03-13 2024-04-12 数盾信息科技股份有限公司 基于高速加密的卫星通信报文数据传输方法
CN117879789B (zh) * 2024-03-13 2024-05-14 数盾信息科技股份有限公司 基于高速加密的卫星通信报文数据传输方法
CN117972593A (zh) * 2024-03-27 2024-05-03 中科三清科技有限公司 一种典型重污染历史案例库分析的长期预报预警方法
CN117972593B (zh) * 2024-03-27 2024-06-04 中科三清科技有限公司 一种典型重污染历史案例库分析的长期预报预警方法

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