CN112232590B - Integral evaluation method, system and storage medium for multi-source power meteorological fusion data - Google Patents

Integral evaluation method, system and storage medium for multi-source power meteorological fusion data Download PDF

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CN112232590B
CN112232590B CN202011203989.8A CN202011203989A CN112232590B CN 112232590 B CN112232590 B CN 112232590B CN 202011203989 A CN202011203989 A CN 202011203989A CN 112232590 B CN112232590 B CN 112232590B
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邸悦伦
郭俊
章国勇
叶钰
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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 Hunan Electric Power Co Ltd
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Abstract

The invention relates to the field of protection of power grids, and discloses a method, a system and a storage medium for integrally evaluating multi-source power and weather fusion data, so as to optimize the selection of the multi-source data and ensure the quality of data fusion. The method comprises the following steps: dividing multisource power meteorological fusion data into six types of data including satellite, radar, on-line monitoring device, manual observation, release of meteorological department and calculation of historical data; and dividing the data quality corresponding to various data into: accurate, erroneous, missing, and other four conditions; and calculating occurrence probabilities of various data under four conditions respectively, and then respectively carrying out first, second and third evaluations of data fusion credibility based on a D-S evidence theory method, wherein the first evaluation comprises grouping calculation of normalization constants and grouping calculation of occurrence probabilities of various data characteristics to form a final quantized evaluation result.

Description

多源电力气象融合数据整体评估方法、系统及存储介质Overall evaluation method, system and storage medium of multi-source power meteorological fusion data

技术领域technical field

本发明涉及电网的防护领域，尤其涉及一种多源电力气象融合数据整体评估方法、系统及存储介质。The invention relates to the field of protection of power grids, in particular to an overall evaluation method, system and storage medium for multi-source power meteorological fusion data.

背景技术Background technique

随着电网建设规模不断扩大，电网运行保障对专业化气象服务的要求正在不断提高。电力气象作为提供电力运维技术支撑的特殊技术领域，需要扎实的观测数据基础。为了有效掌握输电线路附近气象实况信息，电力气象实况数据充分应用卫星观测、雷达观测等遥感观测手段和在线监测装置监测、人工现场观测的常规观测方式，同时吸纳气象部门发布的气象信息以及历史气候数据信息，形成了多源融合的电力气象数据集。With the continuous expansion of the scale of power grid construction, the requirements for professional meteorological services for power grid operation guarantee are constantly increasing. As a special technical field that provides technical support for power operation and maintenance, power meteorology requires a solid foundation of observation data. In order to effectively grasp the real-time meteorological information near the transmission line, the real-time meteorological data of electric power fully utilize remote sensing observation methods such as satellite observation and radar observation, online monitoring device monitoring, and conventional observation methods of manual on-site observation, and at the same time absorb meteorological information issued by the meteorological department and historical climate The data information forms a multi-source fusion power meteorological data set.

不同来源的观测数据、相同来源不同特征的观测数据都存在观测质量良莠不齐的情况，对融合数据整体的可信程度也造成了不可避免的影响。Observational data from different sources and observational data from the same source with different characteristics all have uneven observation quality, which has an inevitable impact on the overall credibility of the fusion data.

现有的数据融合技术类型较多，但融合数据整体效果评估方法仍然无法全面配合业务需求。特别由于电力气象数据数量大、更新迭代速度快，客观上要求一种快速有效的整体评估方法。There are many types of existing data fusion technologies, but the overall effect evaluation method of fusion data still cannot fully meet business needs. Especially due to the large amount of power meteorological data and the fast update iteration speed, a fast and effective overall evaluation method is objectively required.

发明内容Contents of the invention

本发明的主要目的在于公开一种多源电力气象融合数据整体评估方法、系统及存储介质，以优化多源数据的选取并确保数据融合的质量。The main purpose of the present invention is to disclose an overall evaluation method, system and storage medium for multi-source power meteorological fusion data, so as to optimize the selection of multi-source data and ensure the quality of data fusion.

为达上述目的，本发明公开一种多源电力气象融合数据整体评估方法，包括：In order to achieve the above purpose, the present invention discloses an overall evaluation method of multi-source power meteorological fusion data, including:

将多源电力气象融合数据划分为卫星、雷达、在线监测装置、人工观测、气象部门发布和历史数据推算六类数据；并将各类数据所对应的数据质量划分为：准确、错误、缺测和其他四种情况；并计算各类数据分别对应四种情况下的发生概率；Divide the multi-source power meteorological fusion data into six types of data: satellite, radar, online monitoring device, manual observation, meteorological department release and historical data calculation; and divide the data quality corresponding to each type of data into: accurate, wrong, missing and the other four situations; and calculate the probability of each type of data corresponding to the four situations;

将卫星与雷达发生概率数据合为遥感观测组，将在线监测装置与人工观测发生概率数据合为近端观测组，以及将气象部门发布和历史数据推算发生概率数据合为其他来源组；基于D-S证据理论方法计算各组分别对应四种情况下的发生概率数据作为融合可信度第一次评估结果；Combining satellite and radar occurrence probability data into a remote sensing observation group, combining online monitoring device and manual observation probability data into a near-end observation group, and combining the occurrence probability data released by meteorological departments and estimated from historical data into other source groups; based on D-S Evidence theory method is used to calculate the occurrence probability data of each group corresponding to the four situations as the first evaluation result of fusion credibility;

将遥感观测与近端观测发生概率数据合为直接观测组，基于D-S证据理论方法计算该直接观测组分别对应四种情况下的发生概率数据作为融合可信度第二次评估结果；Combining the occurrence probability data of remote sensing observation and near-end observation into a direct observation group, and calculating the occurrence probability data corresponding to the four situations of the direct observation group based on the D-S evidence theory method as the second evaluation result of fusion credibility;

将直接观测组和其他来源发生概率数据合并，基于D-S证据理论方法计算得出全部来源融合数据分别对应四种情况下的发生概率数据作为融合可信度第三次评估结果。Combining the direct observation group with the probability data from other sources, and calculating based on the D-S evidence theory method, the fusion data from all sources corresponded to the occurrence probability data of the four cases as the third evaluation result of the fusion credibility.

与上述方法相对应的，本发明还公开一种多源电力气象融合数据整体评估系统：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，其中，所述处理器执行所述计算机程序时实现上述方法的步骤。Corresponding to the above method, the present invention also discloses an overall evaluation system for multi-source power meteorological fusion data: including a memory, a processor, and a computer program stored in the memory and operable on the processor, wherein the processor The steps of the above method are realized when the computer program is executed.

同理，本发明还公开一种计算机存储介质，其上存储有计算机程序，其中，所述程序被处理器执行时实现上述方法中的步骤。Similarly, the present invention also discloses a computer storage medium on which a computer program is stored, wherein when the program is executed by a processor, the steps in the above method are realized.

本发明具有以下有益效果：The present invention has the following beneficial effects:

D-S(Dempster/Shafer)证据理论是一种针对多源干扰条件下的概率分析方法，能够迅速掌握整体特征，形成简洁直观的评估结果。因此，将D-S证据理论引入电力气象多源融合数据可靠性评估工作中，能实现信息的快速分析，根据评估结果，可以优化多源数据的选取、融合数据的取用并指导电力气象的研究方向，提高了电力气象数据可靠性，也提升了电网保障工作效能。D-S (Dempster/Shafer) evidence theory is a probability analysis method for multi-source interference conditions, which can quickly grasp the overall characteristics and form concise and intuitive evaluation results. Therefore, introducing the D-S evidence theory into the reliability evaluation of multi-source fusion data of power meteorology can realize the rapid analysis of information. According to the evaluation results, the selection of multi-source data, the acquisition of fusion data can be optimized and the research direction of power meteorology can be guided , improve the reliability of power meteorological data, and also improve the efficiency of power grid protection work.

下面将参照附图，对本发明作进一步详细的说明。The present invention will be described in further detail below with reference to the accompanying drawings.

附图说明Description of drawings

构成本申请的一部分的附图用来提供对本发明的进一步理解，本发明的示意性实施例及其说明用于解释本发明，并不构成对本发明的不当限定。在附图中：The accompanying drawings constituting a part of this application are used to provide further understanding of the present invention, and the schematic embodiments and descriptions of the present invention are used to explain the present invention, and do not constitute an improper limitation of the present invention. In the attached picture:

图1是本发明优选实施例的多源电力气象融合数据整体评估方法流程图。Fig. 1 is a flow chart of the overall evaluation method of multi-source power meteorological fusion data in a preferred embodiment of the present invention.

具体实施方式Detailed ways

以下结合附图对本发明的实施例进行详细说明，但是本发明可以由权利要求限定和覆盖的多种不同方式实施。The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways defined and covered by the claims.

实施例1Example 1

本实施例公开一种多源电力气象融合数据整体评估方法，如图1所示，包括：This embodiment discloses an overall evaluation method for multi-source power meteorological fusion data, as shown in Figure 1, including:

步骤S1、将多源电力气象融合数据划分为卫星、雷达、在线监测装置、人工观测、气象部门发布和历史数据推算六类数据；并将各类数据所对应的数据质量划分为：准确、错误、缺测和其他四种情况；并计算各类数据分别对应四种情况下的发生概率。Step S1. Divide the multi-source power meteorological fusion data into six types of data: satellite, radar, online monitoring device, manual observation, release by the meteorological department, and historical data calculation; and divide the data quality corresponding to each type of data into: accurate, wrong , missing test and other four situations; and calculate the occurrence probability of each type of data corresponding to the four situations.

在该步骤中，如果认为“其他”类型的数据产生是受到了“错误”或“缺测”的影响，则“其他”是“错误”和“缺测”的函数。In this step, if the generation of data of type "Other" is considered to be affected by "Error" or "Missing", then "Other" is a function of "Error" and "Missing".

六种来源数据的数据质量类型发生概率如下表1：The data quality type occurrence probability of the six sources of data is shown in Table 1:

表1：Table 1:

Figure BDA0002756412230000031
Figure BDA0002756412230000031

步骤S2、将卫星与雷达发生概率数据合为遥感观测组，将在线监测装置与人工观测发生概率数据合为近端观测组，以及将气象部门发布和历史数据推算发生概率数据合为其他来源组；基于D-S证据理论方法计算各组分别对应四种情况下的发生概率数据作为融合可信度第一次评估结果。Step S2. Combining satellite and radar occurrence probability data into a remote sensing observation group, combining online monitoring device and manual observation probability data into a near-end observation group, and combining the occurrence probability data released by the meteorological department and estimated from historical data into other source groups ; Based on the D-S evidence theory method, calculate the occurrence probability data of each group corresponding to the four situations as the first evaluation result of fusion credibility.

优选地，该步骤具体包括：Preferably, this step specifically includes:

2.1、分组计算归一化常数2.1, group calculation normalization constant

首先计算卫星和雷达的观测可信度的归一化常数K遥感，First calculate the normalization constant K remote sensing of the satellite and radar observation reliability,

Figure BDA0002756412230000032
Figure BDA0002756412230000032

同理，计算在线监测装置和人工观测的观测可信度的归一化常数K近端、气象部门发布和历史数据推算的数据可信度的归一化常数K数据，In the same way, calculate the normalization constant K near-end of the observation reliability of the online monitoring device and manual observation, and the normalization constant K data of the data reliability released by the meteorological department and estimated from historical data,

Figure BDA0002756412230000033
Figure BDA0002756412230000033

Figure BDA0002756412230000034
Figure BDA0002756412230000034

2.2、分组计算各类型数据特征的发生概率2.2. Calculate the probability of occurrence of various types of data features in groups

(1)、观测融合为“准确”的发生概率(1) The occurrence probability of observation fusion being "accurate"

首先计算卫星和雷达的观测融合为“准确”的发生概率

Figure BDA0002756412230000035
First calculate the probability of occurrence of fusion of satellite and radar observations as "accurate"
Figure BDA0002756412230000035

Figure BDA0002756412230000036
Figure BDA0002756412230000036

同理，计算在线监测装置和人工观测的观测融合为“准确”的发生概率

Figure BDA0002756412230000037
气象部门发布和历史数据推算的数据融合为“准确”的发生概率/>
Figure BDA0002756412230000038
In the same way, calculate the occurrence probability that the observation fusion of online monitoring device and manual observation is "accurate"
Figure BDA0002756412230000037
Data released by the meteorological department and calculated from historical data are fused into an "accurate" probability of occurrence/>
Figure BDA0002756412230000038

Figure BDA0002756412230000041
Figure BDA0002756412230000041

Figure BDA0002756412230000042
Figure BDA0002756412230000042

(2)、观测融合为“错误”的发生概率(2), the probability of observation fusion being "error"

同理，分别计算卫星和雷达的观测融合为“错误”的发生概率

Figure BDA0002756412230000043
在线监测装置和人工观测的观测融合为“错误”的发生概率/>
Figure BDA0002756412230000044
气象部门发布和历史数据推算的数据融合为“错误”的发生概率/>
Figure BDA0002756412230000045
In the same way, the probability of occurrence of "error" in the fusion of satellite and radar observations is calculated separately
Figure BDA0002756412230000043
Occurrence probability of "error" fusion of observations from online monitoring devices and manual observations/>
Figure BDA0002756412230000044
Probability of occurrence of "error" in the fusion of data released by the meteorological department and estimated from historical data/>
Figure BDA0002756412230000045

(3)、观测融合为“缺测”的发生概率(3) The probability of observation fusion being "missing"

分别计算卫星和雷达的观测融合为“缺测”的发生概率

Figure BDA0002756412230000046
在线监测装置和人工观测的观测融合为“缺测”的发生概率/>
Figure BDA0002756412230000047
气象部门发布和历史数据推算数据融合为“缺测”的发生概率/>
Figure BDA0002756412230000048
Separately calculate the probability of fusion of satellite and radar observations as "missing observations"
Figure BDA0002756412230000046
Occurrence probability of "missing detection" from online monitoring device and manual observation fusion
Figure BDA0002756412230000047
Probability of occurrence of "missing" data fusion released by the meteorological department and estimated by historical data/>
Figure BDA0002756412230000048

(4)、观测融合为“其他”的发生概率(4) The occurrence probability of observation fusion as "other"

分别计算卫星和雷达的观测融合为“其他”的发生概率

Figure BDA0002756412230000049
在线监测装置和人工观测的观测融合为“其他”的发生概率/>
Figure BDA00027564122300000410
气象部门发布和历史数据推算的数据融合为“其他”的发生概率/>
Figure BDA00027564122300000411
Calculate the probability of occurrence of fusion of satellite and radar observations as "Other" separately
Figure BDA0002756412230000049
Occurrence probability of fusion of observations from online monitoring devices and manual observations as "Other"/>
Figure BDA00027564122300000410
The data released by the meteorological department and the data calculated by historical data are fused into the probability of occurrence of "other"/>
Figure BDA00027564122300000411

数据融合第一次评估结果如下表2：The results of the first evaluation of data fusion are shown in Table 2:

表2：Table 2:

Figure BDA00027564122300000412
Figure BDA00027564122300000412

步骤S3、将遥感观测与近端观测发生概率数据合为直接观测组，基于D-S证据理论方法计算该直接观测组分别对应四种情况下的发生概率数据作为融合可信度第二次评估结果。Step S3, combine the occurrence probability data of the remote sensing observation and the near-end observation into a direct observation group, and calculate the occurrence probability data of the direct observation group corresponding to four situations respectively based on the D-S evidence theory method as the second evaluation result of fusion reliability.

优选地，该步骤具体包括：Preferably, this step specifically includes:

3.1、分组计算归一化常数3.1, group calculation normalization constant

计算“遥感类”和“近端类”观测可信度的归一化常数K观测，The normalization constant K observations used to calculate the reliability of observations of "remote sensing class" and "near-end class",

Figure BDA0002756412230000051
Figure BDA0002756412230000051

“数据类”融合数据的归一化常数K数据保持不变。 The normalization constant K of the "data class" fused data remains unchanged.

3.2、分组计算各类型数据特征的发生概率3.2. Calculate the probability of occurrence of various types of data features in groups

(1)、观测融合为“准确”的发生概率(1) The occurrence probability of observation fusion being "accurate"

计算“遥感类”和“近端类”观测融合为“准确”的发生概率

Figure BDA0002756412230000052
Calculate the probability of occurrence of fusion of "remote-sensing class" and "near-end class" observations as "accurate"
Figure BDA0002756412230000052

Figure BDA0002756412230000053
Figure BDA0002756412230000053

“其他来源数据类”信息融合为“准确”的发生概率

Figure BDA0002756412230000054
保持不变。Probability of "accurate" fusion of "other source data" information
Figure BDA0002756412230000054
constant.

(2)、观测融合为“错误”的发生概率(2), the probability of observation fusion being "error"

计算“遥感类”和“近端类”观测融合为“错误”的发生概率

Figure BDA0002756412230000055
“其他来源数据类”信息融合为“错误”的发生概率/>
Figure BDA0002756412230000056
保持不变。Calculate the probability of fusion of "remote-sensing class" and "near-end class" observations as "error"
Figure BDA0002756412230000055
Occurrence probability of "error" fusion of "other source data type"information/>
Figure BDA0002756412230000056
constant.

(3)、观测融合为“缺测”的发生概率(3) The probability of observation fusion being "missing"

计算“遥感类”和“近端类”观测融合为“缺测”的发生概率

Figure BDA0002756412230000057
“其他来源数据类”信息融合为“缺测”的发生概率/>
Figure BDA0002756412230000058
保持不变。Calculate the probability of fusion of "remote sensing" and "proximal" observations into "missing"
Figure BDA0002756412230000057
The probability of "missing test" information fusion from "other sources of data"/>
Figure BDA0002756412230000058
constant.

(4)、观测融合为“其他”的发生概率(4) The occurrence probability of observation fusion as "other"

计算“遥感类”和“近端类”观测融合为“其他”的发生概率

Figure BDA0002756412230000059
“其他来源数据类”信息融合为“其他”的发生概率/>
Figure BDA00027564122300000510
保持不变。Calculate the probability of fusion of "remote-sensing class" and "near-end class" observations into "other"
Figure BDA0002756412230000059
Probability of fusion of "other source data type" information into "other"/>
Figure BDA00027564122300000510
constant.

数据融合第二次评估结果如下表3：The results of the second evaluation of data fusion are shown in Table 3:

表3：table 3:

Figure BDA00027564122300000511
Figure BDA00027564122300000511

步骤S4、将直接观测组和其他来源发生概率数据合并，基于D-S证据理论方法计算得出全部来源融合数据分别对应四种情况下的发生概率数据作为融合可信度第三次评估结果。Step S4. Merge the occurrence probability data of the direct observation group and other sources, and calculate based on the D-S evidence theory method to obtain the occurrence probability data corresponding to the four situations of all source fusion data as the third evaluation result of fusion credibility.

同理，该步骤具体可包括：Similarly, this step may specifically include:

4.1、计算归一化常数4.1. Calculate the normalization constant

计算“观测类”和“数据类”数据可信度的归一化常数K，Calculate the normalization constant K of the data reliability of "observation class" and "data class",

Figure BDA0002756412230000061
Figure BDA0002756412230000061

4.2、计算各类型数据特征的可信度4.2. Calculate the credibility of various types of data features

(1)、观测融合为“准确”的发生概率(1) The occurrence probability of observation fusion being "accurate"

计算“观测类”和“数据类”数据融合为“准确”的发生概率MR，Calculate the occurrence probability M R of "observation class" and "data class" data fusion as "accurate",

Figure BDA0002756412230000062
Figure BDA0002756412230000062

(2)、观测融合为“错误”的发生概率(2), the probability of observation fusion being "error"

计算“观测类”和“数据类”数据融合为“错误”的发生概率MW。Calculate the occurrence probability M W of the fusion of "observation class" and "data class" data into "error".

(3)、观测融合为“缺测”的发生概率(3) The probability of observation fusion being "missing"

计算“观测类”和“数据类”数据融合为“缺测”的发生概率MM。Calculate the occurrence probability M M of the fusion of "observation class" and "data class" data into "missing test".

(4)、观测融合为“其他”的发生概率(4) The occurrence probability of observation fusion as "other"

计算“观测类”和“数据类”数据融合为“其他”的发生概率MO。Calculate the occurrence probability M O of the fusion of "observation class" and "data class" data into "other".

数据融合第三次评估结果如下表4：The results of the third assessment of data fusion are shown in Table 4:

表4：Table 4:

全部来源融合数据可信度Fusion data credibility from all sources 准确precise MR M R 错误mistake MW M W 缺测missing test MM M M 其他(错误、缺测)Other (errors, missing tests) MO MO

步骤S5、判断融合可信度第三次评估结果是否符合预期，如果不符合，对初始多源的六类数据中的至少一类进行优化，再重新评估直至优化后的全部来源融合数据分别对应四种情况下的发生概率数据符合预期。Step S5. Determine whether the result of the third evaluation of the fusion credibility is in line with expectations. If not, optimize at least one of the six types of initial multi-source data, and then re-evaluate until the optimized fusion data from all sources correspond to The probability of occurrence data for the four cases is in line with expectations.

步骤S6、在评估全部来源融合数据符合预期后，执行相应的数据融合处理。Step S6, after evaluating that all source fusion data meet expectations, perform corresponding data fusion processing.

【运算实例】【Computation example】

六类数据的数据质量类型发生概率如下表5：The data quality type occurrence probability of the six types of data is shown in Table 5:

Figure BDA0002756412230000063
Figure BDA0002756412230000063

第一次评估结果如下表6：The results of the first evaluation are as follows in Table 6:

Figure BDA0002756412230000071
Figure BDA0002756412230000071

第二次评估结果如下表7：The results of the second evaluation are as follows in Table 7:

气温观测temperature observation 直接观测数据可信度Direct Observational Data Confidence 其他来源数据可信度Reliability of data from other sources 准确precise 0.9474979490.947497949 0.6250.625 错误mistake 0.045118950.04511895 0.3541666670.354166667 缺测missing test 0.0072805580.007280558 0.0104166670.010416667 其他(错误、缺测)Other (errors, missing tests) 0.0001025430.000102543 0.0104166670.010416667

第三次评估结果如下表8：The results of the third evaluation are as follows in Table 8:

气温观测temperature observation 全部来源融合数据可信度Fusion data credibility from all sources 准确precise 0.9726691210.972669121 错误mistake 0.0270782380.027078238 缺测missing test 0.0002508870.000250887 其他(错误、缺测)Other (errors, missing tests) 0.0000020.000002

实施例2Example 2

与上述方法相对应的，本实施例公开一种多源电力气象融合数据整体评估系统：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，所述处理器执行所述计算机程序时实现上述方法的步骤。Corresponding to the above method, this embodiment discloses an overall evaluation system for multi-source power meteorological fusion data: including a memory, a processor, and a computer program stored in the memory and operable on the processor, the processor executes the The computer program implements the steps of the above-mentioned method.

实施例3Example 3

同理，本实施例公开一种计算机存储介质，其上存储有计算机程序，所述程序被处理器执行时实现上述方法中的步骤。Similarly, this embodiment discloses a computer storage medium on which a computer program is stored, and when the program is executed by a processor, the steps in the above method are implemented.

综上，本发明上述各实施例所分别公开的多源电力气象融合数据整体评估方法、系统及计算机存储介质，至少具有以下有益效果：To sum up, the overall evaluation method, system and computer storage medium of the multi-source power meteorological fusion data respectively disclosed in the above-mentioned embodiments of the present invention have at least the following beneficial effects:

D-S证据理论是一种针对多源干扰条件下的概率分析方法，能够迅速掌握整体特征，形成简洁直观的评估结果。因此，将D-S证据理论引入电力气象多源融合数据可靠性评估工作中，能实现信息的快速分析，根据评估结果，可以优化多源数据的选取、融合数据的取用并指导电力气象的研究方向，提高了电力气象数据可靠性，也提升了电网保障工作效能。D-S evidence theory is a probability analysis method for multi-source interference conditions, which can quickly grasp the overall characteristics and form concise and intuitive evaluation results. Therefore, introducing the D-S evidence theory into the reliability evaluation of multi-source fusion data of power meteorology can realize the rapid analysis of information. According to the evaluation results, the selection of multi-source data, the acquisition of fusion data can be optimized and the research direction of power meteorology can be guided , improve the reliability of power meteorological data, and also improve the efficiency of power grid protection work.

以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims (6)

1.一种多源电力气象融合数据整体评估方法，其特征在于，包括：1. A method for overall evaluation of multi-source power meteorological fusion data, characterized in that, comprising: 将多源电力气象融合数据划分为卫星、雷达、在线监测装置、人工观测、气象部门发布和历史数据推算六类数据；并将各类数据所对应的数据质量划分为：准确、错误、缺测和其他四种情况；并计算各类数据分别对应四种情况下的发生概率；Divide the multi-source power meteorological fusion data into six types of data: satellite, radar, online monitoring device, manual observation, meteorological department release and historical data calculation; and divide the data quality corresponding to each type of data into: accurate, wrong, missing and the other four situations; and calculate the probability of each type of data corresponding to the four situations; 将卫星与雷达发生概率数据合为遥感观测组，将在线监测装置与人工观测发生概率数据合为近端观测组，以及将气象部门发布和历史数据推算发生概率数据合为其他来源组；基于D-S证据理论方法计算各组分别对应四种情况下的发生概率数据作为融合可信度第一次评估结果；Combining satellite and radar occurrence probability data into a remote sensing observation group, combining online monitoring device and manual observation probability data into a near-end observation group, and combining the occurrence probability data released by meteorological departments and estimated from historical data into other source groups; based on D-S The evidence theory method calculates the occurrence probability data of each group corresponding to the four situations as the first evaluation result of fusion credibility; 将遥感观测与近端观测发生概率数据合为直接观测组，基于D-S证据理论方法计算该直接观测组分别对应四种情况下的发生概率数据作为融合可信度第二次评估结果；Combining the occurrence probability data of remote sensing observation and near-end observation into a direct observation group, and calculating the occurrence probability data corresponding to the four situations of the direct observation group based on the D-S evidence theory method as the second evaluation result of fusion credibility; 将直接观测组和其他来源发生概率数据合并，基于D-S证据理论方法计算得出全部来源融合数据分别对应四种情况下的发生概率数据作为融合可信度第三次评估结果。Combining the direct observation group with the probability data from other sources, and calculating based on the D-S evidence theory method, the fusion data from all sources corresponded to the occurrence probability data of the four cases as the third evaluation result of the fusion credibility. 2.根据权利要求1所述的多源电力气象融合数据整体评估方法，其特征在于，所述基于D-S证据理论方法具体还包括：2. the multi-source power meteorological fusion data overall evaluation method according to claim 1, is characterized in that, described based on D-S evidence theory method specifically also comprises: 在计算发生概率数据合并成组后所分别对应的四种情况下的发生概率数据之前，先计算当前所合并组对应的归一化常数。Before calculating the occurrence probability data corresponding to the four situations after the occurrence probability data are merged into groups, the normalization constant corresponding to the currently merged group is firstly calculated. 3.根据权利要求1或2所述的多源电力气象融合数据整体评估方法，其特征在于，还包括：3. according to claim 1 and 2 described multi-source power meteorological fusion data overall evaluation method, it is characterized in that, also comprises: 判断融合可信度第三次评估结果是否符合预期，如果不符合，对初始多源的六类数据中的至少一类进行优化，再重新评估直至优化后的全部来源融合数据分别对应四种情况下的发生概率数据符合预期。Judging whether the result of the third evaluation of fusion credibility meets expectations, if not, optimize at least one of the six types of initial multi-source data, and then re-evaluate until the optimized fusion data from all sources corresponds to four situations The probability of occurrence data below is as expected. 4.根据权利要求3所述的多源电力气象融合数据整体评估方法，其特征在于，还包括：4. multi-source power meteorological fusion data overall assessment method according to claim 3, is characterized in that, also comprises: 在评估全部来源融合数据符合预期后，执行相应的数据融合处理。After evaluating that all source fusion data meet expectations, perform the corresponding data fusion processing. 5.一种多源电力气象融合数据整体评估系统：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上述权利要求1至4任一所述方法的步骤。5. A multi-source power meteorological fusion data overall evaluation system: comprising a memory, a processor and a computer program stored on the memory and operable on the processor, characterized in that, when the processor executes the computer program, it realizes The step of any one of the above-mentioned claims 1 to 4. 6.一种计算机存储介质，其上存储有计算机程序，其特征在于，所述程序被处理器执行时实现上述权利要求1至4任一所述方法中的步骤。6. A computer storage medium, on which a computer program is stored, wherein when the program is executed by a processor, the steps in the method of any one of claims 1 to 4 are implemented.
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