CN111078909A - A method and system for assimilation modeling of power grid wildfire satellite monitoring images based on tetrahedron model - Google Patents

A method and system for assimilation modeling of power grid wildfire satellite monitoring images based on tetrahedron model Download PDF

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CN111078909A
CN111078909A CN201911241717.4A CN201911241717A CN111078909A CN 111078909 A CN111078909 A CN 111078909A CN 201911241717 A CN201911241717 A CN 201911241717A CN 111078909 A CN111078909 A CN 111078909A
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satellite
data
monitoring
power grid
assimilation
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李波
章国勇
周秀冬
罗晶
何立夫
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State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Disaster Prevention and Mitigation Center of State Grid Hunan Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Disaster Prevention and Mitigation Center of State Grid Hunan Electric Power Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
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Abstract

The invention discloses a tetrahedral model-based power grid mountain fire satellite monitoring image synchronization modeling method and system, wherein 4 facets are used for respectively describing basic attributes, data characteristics, disaster characteristics, original images and other elements of unstructured data of a power grid disaster satellite monitoring image and describing internal relations among the elements, and structured data synchronization operation is carried out on multi-time satellite images, so that rapid retrieval of power grid disaster data in multi-time satellite remote sensing monitoring images is realized. The method is clear in thought, convenient to operate and high in practicability, can provide a technical basis for unified storage and associated operation of power grid disaster multi-source monitoring data, and achieves multi-source heterogeneous data assimilation modeling in power grid disaster monitoring.

Description

Power grid mountain fire satellite monitoring image assimilation modeling method and system based on tetrahedral model
Technical Field
The invention belongs to the technical field of electric power weather, and particularly relates to a power grid disaster situation multi-time satellite image synchronization modeling method and system of a tetrahedral model.
Background
Along with the deepening of the informatization and the intelligentization construction of the electric power system, the monitoring data stored in the terminal server is multiplied, and the monitoring data far exceeds the category of the traditional power grid disaster monitoring. In the electric power meteorological disaster monitoring data, satellite image data, video monitoring image data and the like belong to unstructured data, part of observation data are stored in a graph-text mixed semi-structured mode, unified data storage, management and analysis are not facilitated to be directly performed, a large amount of observation data cannot be compared and analyzed in a unified mode, standardized conversion needs to be performed on semi-structured and unstructured electric power meteorological observation data, and quick standardized conversion and storage of the semi-structured and unstructured electric power meteorological data are achieved. At present, the meteorological satellite monitoring image data volume of a power grid company reaches a PB level, a wind cloud four-weather synchronous satellite updates every 15 minutes, the data volume reaches 5G every time, and the multi-time satellite image unstructured data has the characteristics of large scale, high timeliness and the like.
Disclosure of Invention
The invention provides a tetrahedral model-based power grid mountain fire satellite monitoring image simultaneous modeling method and system which are convenient to operate and high in practicability, and aims at the storage requirements of large data volume and high timeliness of electric power meteorological satellites. The rapid storage, query and retrieval of the power grid disaster data in the multi-time mass satellite remote sensing monitoring image can be realized, and the assimilation modeling of the structured data and the unstructured data in the power grid disaster monitoring is realized.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a power grid mountain fire satellite image assimilation modeling method based on a tetrahedral model comprises the following steps:
acquiring meteorological satellite image data, extracting basic attribute information of a satellite image, and describing by using a basic attribute xml file;
step (2), acquiring the data characteristics of the current satellite according to the meteorological satellite image data acquired in the step (1), and describing by using a data characteristic xml file;
step (3), selecting different mountain fire identification models to perform pixel-by-pixel mountain fire identification according to the meteorological satellite image data obtained in the step (1), and describing disaster monitoring characteristics by using a table text file;
and (4) constructing a meteorological satellite image tetrahedral model by taking the original satellite image data of the meteorological satellite, the basic attribute obtained in the step (1), the data characteristic obtained in the step (2) and the disaster monitoring characteristic obtained in the step (3) as 4 facets, simultaneously writing a unique identification code into the basic attribute xml file, the data characteristic xml file and the disaster monitoring characteristic table text file, naming the original satellite image data of the meteorological satellite by the unique identification code for local storage, and constructing a query index association relationship among different facets.
As a further improvement of the above technical solution:
preferably, the unique identification code is a satellite transit time.
Preferably, the basic attributes include a satellite name, a satellite sensor type, a satellite transit time, and a satellite transit zenith angle.
Preferably, the data characteristics include latitude and longitude ranges of the satellite monitoring images, the provinces of the networks included in the satellite monitoring images and a projection mode of the satellite monitoring images.
Preferably, the disaster monitoring features include longitude and latitude coordinates of fire pixels in the satellite monitoring image forest fire identification model and the satellite forest fire identification result, brightness temperature values of mid-infrared wave bands and far-infrared wave bands of the fire pixels, and underlying surface classification results of the fire pixel region.
Preferably, the original satellite image data includes an original file of unstructured data, and the file format of the original satellite image is maintained.
The invention also provides a power grid mountain fire satellite monitoring image assimilation modeling system based on the tetrahedral model, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the steps of any method when executing the computer program.
The invention has the following beneficial effects:
the power grid mountain fire satellite monitoring image synchronization modeling method and system based on the tetrahedral model are convenient to operate and high in practicability, based on the incidence relation in a tetrahedron, 4 facets are utilized to respectively describe the basic attributes, data characteristics, disaster monitoring characteristics, original image data and other components of power grid disaster satellite monitoring image unstructured data, the query index incidence relation among different facets is constructed, structured data synchronization operation is carried out on multiple times of satellite images, the efficiency of storage, query and retrieval of power grid disaster data in multiple times of mass satellite remote sensing monitoring images is improved, a technical basis can be provided for unified storage and association operation of power grid disaster data, and structured data and unstructured assimilation modeling in power grid disaster monitoring is achieved. The search range can be quickly narrowed, and the target data can be accurately positioned.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic flow diagram of the process of the present invention.
FIG. 2 is a tetrahedral model diagram of a meteorological satellite image constructed in the present invention.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Example 1
As shown in fig. 1, the power grid mountain fire satellite monitoring image assimilation modeling method based on the tetrahedral model specifically comprises the following steps:
acquiring unstructured image data of a meteorological satellite at a certain time, and constructing a tetrahedral model of the meteorological satellite at the time (as shown in fig. 2):
1) basic properties: the method comprises the steps of describing a satellite name, a satellite sensor type, satellite transit time, a satellite transit zenith angle and the like by using an xml file;
for example, Fengyun three # C star, VIRR sensor, entrance time Beijing time 2019, 10 months and 28 days 09: 43: 00, exit time beijing time 2019, 10 months, 28 days 09: 58: 00 and a satellite transit zenith angle of 92.5 degrees.
2) Data characteristics: the method comprises the steps of describing a longitude and latitude range of a satellite monitoring image, a network province contained in the satellite monitoring image and a projection mode of the satellite monitoring image by using an xml file;
for example, longitude ranges of east longitude 93-119 degrees, latitude ranges of north latitude 18-46 degrees, border crossing network provinces Jiangsu, Zhejiang, Anhui, Shanghai, Jiangxi, Hunan, Hubei, Fujian, Guizhou, Sichuan, Shaanxi, Shanxi, Hebei, Tianjin, Gansu, Xinjiang, Liaoning, Shandong, Guangxi and Yunnan, and equal longitude and latitude projection is adopted.
3) Disaster monitoring characteristics: the method comprises the steps of describing a satellite monitoring image forest fire identification model, longitude and latitude coordinates of fire point pixels in satellite forest fire identification results, brightness temperature values of middle infrared wave bands and far infrared wave bands of the fire point pixels and underlying surface classification results of fire point pixel areas by using a text file; and selecting a context forest fire identification model, identifying 29 forest fire pixels in total, and storing the result of each pixel into a table text.
4) Original satellite image data: original files of unstructured data keep the file format of original satellite images.
Constructing a meteorological satellite image tetrahedral model by using original satellite image data of a meteorological satellite, basic attributes acquired in step 1), data characteristics acquired in step 2) and disaster monitoring characteristics acquired in step 3) as 4 facets, simultaneously writing a unique identification code into a basic attribute xml file acquired in step 1), a data characteristic xml file acquired in step 2) and a disaster monitoring characteristic table text file acquired in step 3), taking satellite transit time as the unique identification code, for example 20191028094300, writing the satellite transit time into the 2 xml files and the table text, naming the original meteorological satellite image data as the unique identification code for local storage, and constructing query index association relations among different facets.
Query example:
1) "retrieve the general situation of mountain fire in Hunan province 4 months and 5 days of Qingming festival in 2019", carry on the year, month and day matching of the satellite transit time in the basic attribute on the basis of the date, carry on the satellite in the data characteristic and cross the network province matching of the satellite in Hunan province, the direct correlation gives the correspondent disaster monitoring characteristic data, summarize many times satellite data of each time quantum, get the mountain fire monitoring general situation of Hunan province on the day of Qingming festival;
2) "13: 00 +/-800 kV binin gold thread 1214 tower accessory mountain fire trips in 4/5/2019, mountain fire satellite monitoring images in that period of time are retrieved", satellite transit time accurate matching in basic attributes is carried out based on date, +/-800 kV binin gold thread 1214 tower longitude and latitude coordinates are inquired by a related circuit pole tower table, fire pixel longitude and latitude matching in disaster monitoring characteristic data is carried out based on the longitude and latitude coordinates, a matching result is given, and basic attributes (satellite name, satellite sensor type and the like) of the satellite passing the border nearby at the tripping moment and inquiry conditions of whether mountain fire is identified are output.
Example 2
The invention also provides a power grid mountain fire satellite monitoring image assimilation modeling system based on the tetrahedral model, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the steps of the method embodiment 1 are realized when the processor executes the computer program.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (7)

1.一种基于四面体模型的电网山火卫星影像同化建模方法，其特征在于，包括以下步骤：1. a power grid mountain fire satellite image assimilation modeling method based on tetrahedron model, is characterized in that, comprises the following steps: 步骤(1)，获取气象卫星影像数据，提取卫星图像的基本属性信息，用一个基本属性xml文件进行描述；Step (1), obtaining meteorological satellite image data, extracting the basic attribute information of the satellite image, and describing it with a basic attribute xml file; 步骤(2)，依据步骤(1)获得的气象卫星影像数据，获取该时次卫星的数据特征，用一个数据特征xml文件进行描述；Step (2), according to the meteorological satellite image data obtained in step (1), obtain the data feature of the satellite at this time, and describe it with a data feature xml file; 步骤(3)，依据步骤(1)获得的气象卫星影像数据，选用不同的山火判识模型进行逐像素的山火判识，将灾情监测特征用一个表格文本文件进行描述；Step (3), according to the meteorological satellite image data obtained in step (1), select different wildfire identification models to carry out pixel-by-pixel wildfire identification, and describe the disaster monitoring feature with a table text file; 步骤(4)，以气象卫星的原始卫星影像数据、步骤(1)获取的基本属性、步骤(2)获取的数据特征以及步骤(3)获取的灾情监测特征为4个刻面构建气象卫星影像四面体模型，并向基本属性xml文件、数据特征xml文件以及灾情监测特征表格文本文件中同时写入一个唯一标识码，将气象卫星的原始卫星影像数据以所述唯一标识码命名进行本地存储，构建不同刻面间的查询索引关联关系。Step (4), using the original satellite image data of the meteorological satellite, the basic attributes obtained in step (1), the data features obtained in step (2), and the disaster monitoring features obtained in step (3) as four facets to construct a meteorological satellite image. The tetrahedral model is written into the basic attribute xml file, the data feature xml file and the disaster monitoring feature table text file at the same time, and the original satellite image data of the meteorological satellite is named with the unique identification code for local storage, Build query index associations between different facets. 2.根据权利要求1所述的基于四面体模型的电网山火卫星监测影像同化建模方法，其特征在于，所述唯一标识码为卫星过境时间。2 . The tetrahedral model-based assimilation modeling method for satellite monitoring images of mountain fires in power grids according to claim 1 , wherein the unique identification code is satellite transit time. 3 . 3.根据权利要求2所述的基于四面体模型的电网山火卫星监测影像同化建模方法，其特征在于，所述基本属性包括卫星名称、卫星传感器类型、卫星过境时间以及卫星过境天顶角。3. The tetrahedral model-based assimilation modeling method for satellite monitoring images of mountain fires in power grids, wherein the basic attributes include satellite name, satellite sensor type, satellite transit time, and satellite transit zenith angle . 4.根据权利要求2所述的基于四面体模型的电网山火卫星监测影像同化建模方法，其特征在于，所述数据特征包括卫星监测影像经纬度范围、卫星监测影像包含的网省以及卫星监测影像投影方式。4 . The assimilation modeling method for power grid mountain fire satellite monitoring images based on tetrahedral model according to claim 2 , wherein the data features include the latitude and longitude range of the satellite monitoring images, the network province included in the satellite monitoring images, and the satellite monitoring images. 5 . Image projection method. 5.根据权利要求2所述的基于四面体模型的电网山火卫星监测影像同化建模方法，其特征在于，所述灾情监测特征包括卫星监测影像山火判识模型和卫星山火判识结果中火点像素的经纬度坐标、火点像素的中红外波段和远红外波段的亮温值、火点像素区域下垫面分类结果。5 . The assimilation modeling method for power grid mountain fire satellite monitoring images based on tetrahedral model according to claim 2 , wherein the disaster monitoring feature comprises a satellite monitoring image mountain fire identification model and a satellite mountain fire identification result. 6 . The latitude and longitude coordinates of the middle fire pixel, the brightness temperature value of the mid-infrared band and the far infrared band of the fire pixel, and the classification result of the underlying surface of the fire pixel area. 6.根据权利要求2所述的基于四面体模型的电网山火卫星监测影像同化建模方法，其特征在于，所述原始卫星影像数据包括非结构化数据的原始文件，保持原有卫星影像的文件格式。6. The tetrahedral model-based assimilation modeling method for satellite images of mountain fire monitoring in power grids, wherein the original satellite image data comprises an original file of unstructured data, and the original satellite image data is maintained. file format. 7.一种基于四面体模型的电网山火卫星监测影像同化建模系统，包括存储器、处理器以及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现权利要求1至6任一所述方法的步骤。7. A power grid mountain fire satellite monitoring image assimilation modeling system based on a tetrahedron model, comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor executes The computer program implements the steps of the method of any one of claims 1 to 6.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116047546A (en) * 2022-07-07 2023-05-02 北京玖天气象科技有限公司 Mountain fire monitoring method based on multi-source satellite data

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120036142A1 (en) * 2008-12-10 2012-02-09 Telespazio S.P.A. Optimized Satellite Image Retrieval
CN102591896A (en) * 2011-01-05 2012-07-18 北京大用科技有限责任公司 System, implementation, application, and query language for a tetrahedral data model for unstructured data
CN108538014A (en) * 2018-04-24 2018-09-14 国网安徽省电力有限公司 Electric transmission line channel mountain fire control method and system based on satellite check frequency
CN109598236A (en) * 2018-12-04 2019-04-09 国网湖南省电力有限公司 A kind of fiery put of automation sentences knowledge method and system
CN109656905A (en) * 2018-11-15 2019-04-19 全球能源互联网研究院有限公司 A kind of processing method, device and the shared system of power grid meteorological disaster data

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120036142A1 (en) * 2008-12-10 2012-02-09 Telespazio S.P.A. Optimized Satellite Image Retrieval
CN102591896A (en) * 2011-01-05 2012-07-18 北京大用科技有限责任公司 System, implementation, application, and query language for a tetrahedral data model for unstructured data
CN108538014A (en) * 2018-04-24 2018-09-14 国网安徽省电力有限公司 Electric transmission line channel mountain fire control method and system based on satellite check frequency
CN109656905A (en) * 2018-11-15 2019-04-19 全球能源互联网研究院有限公司 A kind of processing method, device and the shared system of power grid meteorological disaster data
CN109598236A (en) * 2018-12-04 2019-04-09 国网湖南省电力有限公司 A kind of fiery put of automation sentences knowledge method and system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
曾文英,蔡报勤,王晓庆,王明文,殷剑敏,黄淑娥,章毅之: "卫星遥感森林火点监测系统的设计与实现" *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116047546A (en) * 2022-07-07 2023-05-02 北京玖天气象科技有限公司 Mountain fire monitoring method based on multi-source satellite data
CN116047546B (en) * 2022-07-07 2024-02-27 北京玖天气象科技有限公司 Mountain fire monitoring method based on multi-source satellite data

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