CN112232590B - Integral evaluation method, system and storage medium for multi-source power meteorological fusion data - Google Patents
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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
Technical Field
The invention relates to the field of protection of power grids, in particular to a method, a system and a storage medium for integrally evaluating multi-source power and weather fusion data.
Background
Along with the continuous expansion of the construction scale of the power grid, the requirements of the operation guarantee of the power grid on specialized weather services are continuously increasing. The electric power meteorological is used as a special technical field for providing electric power operation and maintenance technical support, and a solid observation data base is needed. In order to effectively master weather live information near a power transmission line, the electric power weather live data fully uses remote sensing observation means such as satellite observation, radar observation and the like and conventional observation means of on-line monitoring device monitoring and manual on-site observation, and simultaneously absorbs weather information and historical weather data information released by a weather department, so that a multi-source fusion electric power weather data set is formed.
The observation data of different sources and the observation data of different characteristics of the same source have the condition of good observation quality, and the integral credibility of the fusion data is inevitably influenced.
The existing data fusion technology is more in types, but the overall effect evaluation method of the fusion data still cannot fully match with service requirements. Particularly, because the quantity of the electric power meteorological data is large and the updating iteration speed is high, a rapid and effective overall evaluation method is objectively required.
Disclosure of Invention
The invention mainly aims to disclose a method, a system and a storage medium for integrally evaluating multi-source power meteorological fusion data, so as to optimize the selection of the multi-source data and ensure the quality of data fusion.
In order to achieve the above purpose, the invention discloses a method for integrally evaluating multi-source power meteorological fusion data, which 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; the occurrence probability of each type of data under four conditions is calculated;
combining satellite and radar occurrence probability data into a remote sensing observation group, combining an online monitoring device and manual observation occurrence probability data into a near-end observation group, and combining meteorological department release and historical data calculation occurrence probability data into other source groups; calculating occurrence probability data of each group corresponding to four conditions based on a D-S evidence theory method as a first evaluation result of fusion reliability;
combining the occurrence probability data of remote sensing observation and near-end observation into a direct observation group, and calculating the occurrence probability data of the direct observation group corresponding to four conditions respectively based on a D-S evidence theory method to serve as a second evaluation result of fusion reliability;
combining the direct observation group and the occurrence probability data of other sources, and calculating to obtain the occurrence probability data of all sources under the condition that the fusion data of all sources respectively correspond to four conditions based on a D-S evidence theory method as a third evaluation result of the fusion reliability.
Corresponding to the method, the invention also discloses a multi-source power meteorological fusion data overall evaluation system: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the above method when the computer program is executed.
Similarly, the invention also discloses a computer storage medium, on which a computer program is stored, wherein the program is executed by a processor to implement the steps in the above method.
The invention has the following beneficial effects:
the D-S (Dempster/Shafer) evidence theory is a probability analysis method aiming at the condition of multi-source interference, and can quickly master the overall characteristics to form a simple and visual evaluation result. Therefore, the D-S evidence theory is introduced into the reliability evaluation work of the electric power weather multisource fusion data, the information can be rapidly analyzed, the selection of multisource data, the taking of fusion data and the research direction of electric power weather can be optimized according to the evaluation result, the reliability of the electric power weather data is improved, and the power grid guarantee work efficiency is also improved.
The invention will be described in further detail with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 is a flowchart of a method for overall evaluation of multi-source power weather fusion data in accordance with a preferred embodiment of the present invention.
Detailed Description
Embodiments of the invention are described in detail below with reference to the attached drawings, but the invention can be implemented in a number of different ways, which are defined and covered by the claims.
Example 1
The embodiment discloses a method for integrally evaluating multi-source power meteorological fusion data, which is shown in fig. 1 and comprises the following steps:
step S1, dividing multisource power meteorological fusion data into six types of data, namely 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.
In this step, if the "other" type of data generation is considered to be affected by "error" or "missing", then "other" is a function of "error" and "missing".
The probability of occurrence of data quality types for six sources of data is as follows in table 1:
table 1:
s2, combining satellite and radar occurrence probability data into a remote sensing observation group, combining an online monitoring device and manual observation occurrence probability data into a near-end observation group, and combining meteorological department release and historical data calculation occurrence probability data into other source groups; and calculating occurrence probability data of each group corresponding to four conditions based on a D-S evidence theory method to serve as a first evaluation result of the fusion reliability.
Preferably, the step specifically includes:
2.1 grouping calculation normalization constant
Firstly, calculating a normalization constant K of observation credibility of a satellite and a radar Remote sensing ，
Similarly, a normalization constant K of the observation reliability of the on-line monitoring device and the manual observation is calculated Proximal end Normalization constant K of data credibility of release and historical data calculation of meteorological department Data ，
2.2 grouping calculation of probability of occurrence of various types of data characteristics
(1) Probability of occurrence of "accurate" fused by observation
Firstly, calculating the occurrence probability of the observation fusion of the satellite and the radar into' accurate
Similarly, the occurrence probability of 'accurate' is calculated by combining the on-line monitoring device and the manual observation
The occurrence probability of the data fusion of the release of the meteorological department and the calculation of the historical data into 'accurate'>
(2) Observing the probability of occurrence of fusion into "error
Similarly, the occurrence probability of the observation fusion of the satellite and the radar into error is calculated respectively
The observation of the on-line monitoring device and the manual observation is fused into the occurrence probability of error>
The probability of occurrence of error is fused by the data released by the meteorological department and calculated by the historical data +.>
(3) Observation fuses to "lack of probability of occurrence of survey
Calculating occurrence probability of satellite and radar observation fusion as 'missing detection' respectively
The observation of the on-line monitoring device and the manual observation is fused into the occurrence probability of' lack of detection>
The release of meteorological department and the calculation of historical data are fused into' missing test"probability of occurrence>
(4) Observation fuses to "other" probability of occurrence
Calculating the occurrence probability of the observation fusion of satellite and radar into 'other' respectively
The observation of the on-line monitoring device and the manual observation is fused into the occurrence probability of' other +.>
Meteorological department release and historical data reckoning data are fused into 'other' occurrence probability ++>
The first evaluation results of data fusion are shown in table 2 below:
table 2:
and S3, combining the occurrence probability data of the remote sensing observation and the near-end observation into a direct observation group, and calculating the occurrence probability data of the direct observation group corresponding to the four conditions respectively based on a D-S evidence theory method to serve as a second evaluation result of the fusion reliability.
Preferably, the step specifically includes:
3.1, grouping calculation normalization constant
Calculating normalization constant K of observation credibility of remote sensing class and near end class Observation ，
Normalized constant K of data type fusion data Data Remain unchanged.
3.2, grouping calculation of occurrence probability of various types of data characteristics
(1) Probability of occurrence of "accurate" fused by observation
Calculating the occurrence probability of 'accurate' by fusing 'remote sensing' and 'near-end' observation
The occurrence probability of fusing information of other source data types into accurate
Remain unchanged.
(2) Observing the probability of occurrence of fusion into "error
Calculating the occurrence probability of fusion of ' remote sensing class ' and ' near-end class ' observation into ' error
Information fusion of "other Source data class" into "error" probability of occurrence>
Remain unchanged.
(3) Observation fuses to "lack of probability of occurrence of survey
Calculating occurrence probability of 'missing test' fused by 'remote sensing' and 'near-end' observation
Information fusion of other source data class into occurrence probability of lack test>
Remain unchanged.
(4) Observation fuses to "other" probability of occurrence
Calculating the occurrence probability of fusion of ' remote sensing class ' and ' near-end class ' observation into ' other
Information fusion of "other Source data class" into "other" probability of occurrence>
Remain unchanged.
The results of the second evaluation of the data fusion are shown in Table 3 below:
table 3:
and S4, combining the direct observation group and the occurrence probability data of other sources, and calculating to obtain occurrence probability data of all sources under the condition that the fusion data of all sources respectively correspond to four conditions based on a D-S evidence theory method, wherein the occurrence probability data is used as a third evaluation result of the fusion reliability.
Similarly, the steps may specifically include:
4.1 calculating normalization constant
Calculating a normalization constant K of the data credibility of the observation class and the data class,
4.2, calculating the credibility of various data characteristics
(1) Probability of occurrence of "accurate" fused by observation
Calculating the occurrence probability M of ' observation type ' and ' data type ' data fusion into ' accurate R ，
(2) Observing the probability of occurrence of fusion into "error
Calculating the occurrence probability M of 'error' fused by 'observation type' and 'data type' data W 。
(3) Observation fuses to "lack of probability of occurrence of survey
Calculating the occurrence probability M of ' observation type ' and ' data type ' data fusion into ' missing test M 。
(4) Observation fuses to "other" probability of occurrence
Calculating the occurrence probability M of ' observation class ' and ' data class ' data fusion into ' other O 。
The results of the third evaluation of the data fusion are shown in Table 4 below:
table 4:
| full source fusion data trustworthiness | |
| Accurate and accurate | M R |
| Errors | M W |
| Lack of measurement | M M |
| Others (error, missing measurement) | M O |
And S5, judging whether the third evaluation result of the fusion reliability accords with the expectation, if not, optimizing at least one of six types of initial multi-source data, and reevaluating until all optimized source fusion data respectively correspond to occurrence probability data under four conditions to accord with the expectation.
And S6, after the fusion data of all sources are evaluated to meet the expectations, executing corresponding data fusion processing.
[ operation example ]
The probability of occurrence of data quality types for six types of data is as follows in table 5:
the first evaluation results are shown in Table 6 below:
the second evaluation results are shown in Table 7 below:
| air temperature observation | Direct observation data credibility | Confidence of other source data |
| Accurate and accurate | 0.947497949 | 0.625 |
| Errors | 0.04511895 | 0.354166667 |
| Lack of measurement | 0.007280558 | 0.010416667 |
| Others (error, missing measurement) | 0.000102543 | 0.010416667 |
The third evaluation results are shown in table 8 below:
| air temperature observation | Full source fusion data trustworthiness |
| Accurate and accurate | 0.972669121 |
| Errors | 0.027078238 |
| Lack of measurement | 0.000250887 |
| Others (error, missing measurement) | 0.000002 |
Example 2
Corresponding to the method, the embodiment discloses a multi-source power meteorological fusion data overall evaluation system: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the steps of the above method when said computer program is executed.
Example 3
Similarly, the present embodiment discloses a computer storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above method.
In summary, the method, the system and the computer storage medium for evaluating the whole multi-source power meteorological fusion data disclosed in each embodiment of the invention have at least the following advantages:
the D-S evidence theory is a probability analysis method aiming at the multi-source interference condition, and can quickly master the overall characteristics to form a concise and visual evaluation result. Therefore, the D-S evidence theory is introduced into the reliability evaluation work of the electric power weather multisource fusion data, the information can be rapidly analyzed, the selection of multisource data, the taking of fusion data and the research direction of electric power weather can be optimized according to the evaluation result, the reliability of the electric power weather data is improved, and the power grid guarantee work efficiency is also improved.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. The method for integrally evaluating the multi-source power meteorological fusion data is characterized by comprising the following steps of:
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; the occurrence probability of each type of data under four conditions is calculated;
combining satellite and radar occurrence probability data into a remote sensing observation group, combining an online monitoring device and manual observation occurrence probability data into a near-end observation group, and combining meteorological department release and historical data calculation occurrence probability data into other source groups; calculating occurrence probability data of each group corresponding to four conditions based on a D-S evidence theory method as a first evaluation result of fusion reliability;
combining the occurrence probability data of remote sensing observation and near-end observation into a direct observation group, and calculating the occurrence probability data of the direct observation group corresponding to four conditions respectively based on a D-S evidence theory method to serve as a second evaluation result of fusion reliability;
combining the direct observation group and the occurrence probability data of other sources, and calculating to obtain the occurrence probability data of all sources under the condition that the fusion data of all sources respectively correspond to four conditions based on a D-S evidence theory method as a third evaluation result of the fusion reliability.
2. The method for integrally evaluating multi-source power meteorological fusion data according to claim 1, wherein the method based on the D-S evidence theory specifically further comprises:
before calculating the occurrence probability data of four cases corresponding to the occurrence probability data after being combined into groups, calculating the normalization constant corresponding to the current combined groups.
3. The multi-source power weather fusion data overall assessment method according to claim 1 or 2, further comprising:
and judging whether the third evaluation result of the fusion reliability accords with the expectation or not, if not, optimizing at least one of six types of initial multi-source data, and reevaluating until all optimized source fusion data respectively correspond to occurrence probability data under four conditions to accord with the expectation.
4. The method for integrally evaluating multi-source power weather fusion data as claimed in claim 3, further comprising:
after evaluating that all source fusion data are in line with expectations, a corresponding data fusion process is performed.
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 executable on the processor, characterized in that the processor implements the steps of the method according to any of the preceding claims 1 to 4 when the computer program is executed.
6. A computer storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, realizes the steps in the method of any of the preceding claims 1 to 4.
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