CN112232590A - Multi-source electric power meteorological fusion data overall evaluation method and system and storage medium - Google Patents
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Abstract
The invention relates to the field of protection of power grids, and discloses a multi-source electric power meteorological fusion data overall evaluation method, a system and a storage medium, which are used for optimizing selection of multi-source data and ensuring quality of data fusion. The method comprises the following steps: dividing multi-source electric power meteorological fusion data into six types of data including satellites, radars, online monitoring devices, artificial observation, meteorological department release and historical data calculation; and dividing the data quality corresponding to each type of data into: accuracy, error, lack of testing and other four conditions; and calculating occurrence probabilities of various types of data under four conditions respectively, and then performing primary, secondary and tertiary evaluation on data fusion credibility respectively based on a D-S evidence theory method, wherein the primary, secondary and tertiary evaluation comprises grouping calculation of normalization constants and grouping calculation of occurrence probabilities of various types of data characteristics to form a final quantitative evaluation result.
Description
Technical Field
The invention relates to the field of protection of power grids, in particular to a multi-source electric power meteorological fusion data overall evaluation method, a multi-source electric power meteorological fusion data overall evaluation system and a storage medium.
Background
With the continuous expansion of the construction scale of the power grid, the requirements of the operation guarantee of the power grid on specialized meteorological services are continuously improved. The electric power meteorology is a special technical field for providing electric power operation and maintenance technical support, and needs a solid observation data foundation. In order to effectively master the weather actual information near the power transmission line, the electric power weather actual data is fully applied to remote sensing observation means such as satellite observation, radar observation and the like and conventional observation modes such as on-line monitoring device monitoring and manual on-site observation, and simultaneously absorbs weather information and historical weather data information issued by a weather department, so that a multi-source fused electric power weather data set is formed.
The observation data from different sources and the observation data from the same source with different characteristics have the condition of poor observation quality, and the integral credibility of the fusion data is inevitably influenced.
The existing data fusion technology has more types, but the method for evaluating the overall effect of the fused data still cannot be comprehensively matched with the service requirements. Particularly, due to the large quantity of electric power meteorological data and the high updating iteration speed, a fast and effective overall evaluation method is objectively required.
Disclosure of Invention
The invention mainly aims to disclose a multi-source electric power meteorological fusion data overall evaluation method, a multi-source electric power meteorological fusion data overall evaluation system and a storage medium, so that the selection of multi-source data is optimized and the quality of data fusion is ensured.
In order to achieve the purpose, the invention discloses a multi-source electric power meteorological fusion data overall evaluation method, which comprises the following steps:
dividing multi-source electric power meteorological fusion data into six types of data including satellites, radars, online monitoring devices, artificial observation, meteorological department release and historical data calculation; and dividing the data quality corresponding to each type of data into: accuracy, error, lack of testing and other four conditions; calculating the occurrence probability of each kind of data corresponding to the four conditions respectively;
combining the occurrence probability data of the satellite and the radar into a remote sensing observation group, combining the occurrence probability data of the on-line monitoring device and the artificial observation into a near-end observation group, and combining the occurrence probability data issued by a meteorological department and calculated by historical data into other source groups; calculating occurrence probability data of each group corresponding to four conditions respectively based on a D-S evidence theory method to serve as a first evaluation result of fusion credibility;
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 respectively corresponding to four conditions based on a D-S evidence theory method to serve as a second evaluation result of fusion credibility;
and combining the occurrence probability data of the direct observation group and other sources, and calculating to obtain occurrence probability data of all source fusion data respectively corresponding to four conditions based on a D-S evidence theory method as a third evaluation result of the fusion credibility.
Corresponding to the method, the invention also discloses an integral evaluation system of the multi-source electric power meteorological fusion data, which comprises the following steps: 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 executing the computer program.
Similarly, the present invention also discloses a computer storage medium having a computer program stored thereon, wherein the program realizes the steps of the above method when being executed by a processor.
The invention has the following beneficial effects:
the D-S (Dempster/Shafer) evidence theory is a probability analysis method aiming at the multisource interference condition, can rapidly master the overall characteristics, and forms a concise and intuitive evaluation result. Therefore, the D-S evidence theory is introduced into the reliability evaluation work of the multi-source fusion data of the electric power meteorological phenomena, the information can be quickly analyzed, the selection of the multi-source data, the taking of the fusion data and the research direction of the electric power meteorological phenomena can be optimized according to the evaluation result, the reliability of the electric power meteorological phenomena is improved, and the work efficiency of power grid guarantee is also improved.
The present invention will be described in further detail below with reference to the accompanying drawings.
Drawings
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 flow chart of the overall evaluation method of multi-source electric power meteorological fusion data according to the preferred embodiment of the 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
The embodiment discloses an overall evaluation method of multi-source electric power meteorological fusion data, as shown in fig. 1, comprising the following steps:
step S1, dividing multi-source electric power meteorological fusion data into six types of data including satellite, radar, online monitoring device, artificial observation, meteorological department release and historical data calculation; and dividing the data quality corresponding to each type of data into: accuracy, error, lack of testing and other four conditions; and calculating the occurrence probability of each kind of data corresponding to the four conditions respectively.
In this step, "other" is a function of "error" and "absence" if "other" types of data production are considered to be affected by either "error" or "absence".
The data quality type occurrence probabilities of the six source data are shown in the following table 1:
table 1:
step S2, combining the satellite and radar occurrence probability data into a remote sensing observation group, combining the on-line monitoring device and the artificial observation occurrence probability data into a near-end observation group, and combining the data issued by the meteorological department and the historical data calculation occurrence probability data into other source groups; and calculating occurrence probability data of each group corresponding to four conditions respectively based on a D-S evidence theory method to serve as a first evaluation result of the fusion credibility.
Preferably, the steps specifically include:
2.1 group calculation normalization constants
Firstly, a normalization constant K of observation credibility of the satellite and the radar is calculatedRemote sensing，
Similarly, a normalization constant K for calculating the observation reliability of the on-line monitoring device and the manual observationProximal endNormalization constant K of data reliability of data issued by meteorological department and calculated by historical dataData of，
2.2, grouping and calculating the occurrence probability of each type of data characteristics
(1) The occurrence probability of 'accurate' is combined by observation
Firstly, the occurrence probability that the observation fusion of the satellite and the radar is accurate is calculated
Similarly, the occurrence probability of 'accurate' combination of the observation of the on-line monitoring device and the manual observation is calculated
The data released by the meteorological department and calculated by historical data are fused into accurate occurrence probability
(2) Probability of occurrence of errors by fusion of observations
Similarly, the probability of occurrence of errors caused by the fusion of the observations of the satellite and the radar into the same is calculated respectively
The on-line monitoring device and the observation of manual observation are combined into the occurrence probability of error
The data released by the meteorological department and calculated by historical data are fused into the occurrence probability of errors
(3) Probability of occurrence of 'lack of measurement' combined with observation
Calculating the probability of occurrence of observation fusion of satellite and radar into' lack of measurement
The on-line monitoring device and the observation of manual observation are combined into the occurrence probability of' lack of measurement
Probability of occurrence of 'missing detection' by integrating meteorological department release and historical data calculation data
(4) Probability of occurrence of "other" by fusion of observations
Calculating the probability of occurrence of the integration of the observations of the satellite and of the radar into "others" separately
The on-line monitoring device and the observation of manual observation are combined into the occurrence probability of other
The data released by the meteorological department and calculated by historical data are fused into the occurrence probability of other data
Data fusion the first evaluation results are given 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 respectively corresponding to four conditions based on a D-S evidence theory method to serve as a second evaluation result of the fusion credibility.
Preferably, the steps specifically include:
3.1 group calculation normalization constants
Calculating a sum of remote sensing classes "Normalization constant K of near-end class observation credibilityObservation of，
Normalization constant K for "data class" fused dataData ofRemain unchanged.
3.2, grouping and calculating the occurrence probability of each type of data characteristics
(1) The occurrence probability of 'accurate' is combined by observation
Calculating the occurrence probability of 'accurate' combining 'observation of' remote sensing class 'and' near-end class
Probability of occurrence of fusion of "other source data class" information into "accurate
Remain unchanged.
(2) Probability of occurrence of errors by fusion of observations
Calculating the probability of occurrence of errors formed by fusing 'remote sensing class' observation and 'near-end class' observation
Fusion of "other source data class" information into probability of occurrence of "error
Remain unchanged.
(3) Probability of occurrence of 'lack of measurement' combined with observation
Calculating the occurrence probability of 'lack of measurement' formed by the fusion of 'remote sensing' observation and 'near-end' observation
Probability of occurrence of merging ' other source data class ' information into ' absence survey
Remain unchanged.
(4) Probability of occurrence of "other" by fusion of observations
Calculating the occurrence probability of combining the observation of the remote sensing class and the observation of the near-end class into other observation
Probability of occurrence of merging "other Source dataclass" information into "other
Remain unchanged.
Data fusion the results of the second evaluation are given in table 3 below:
table 3:
and S4, merging the occurrence probability data of the direct observation group and other sources, and calculating to obtain occurrence probability data of all source fusion data respectively corresponding to four conditions based on a D-S evidence theory method as a third evaluation result of the fusion credibility.
Similarly, the steps may specifically include:
4.1, calculating normalization constant
Calculating a normalization constant K of the credibility of the data of the observation class and the data class,
4.2, calculating the credibility of each type of data characteristic
(1) The occurrence probability of 'accurate' is combined by observation
Calculating the occurrence probability M of the fusion of the observation class data and the data class data into accurate dataR，
(2) Probability of occurrence of errors by fusion of observations
Calculating the occurrence probability M of the data fusion of the observation class and the data class into the errorW。
(3) Probability of occurrence of 'lack of measurement' combined with observation
Calculating the occurrence probability M of integrating ' observation class ' data and ' data class ' data into ' missing measurementM。
(4) Probability of occurrence of "other" by fusion of observations
Calculating the occurrence probability M of merging the ' observation class ' data and the ' data class ' data into ' otherO。
Data fusion third evaluation results are given in table 4 below:
table 4:
| whole source fused data credibility | |
| Accurate and accurate | MR |
| Error(s) in | MW |
| Absence survey | MM |
| Others (error, absence measurement) | MO |
And step S5, judging whether the third evaluation result of the fusion credibility accords with the expectation, if not, optimizing at least one of the six types of initial multi-source data, and re-evaluating until all optimized source fusion data respectively correspond to occurrence probability data in four conditions to accord with the expectation.
And step S6, executing corresponding data fusion processing after all the source fusion data are evaluated to be in accordance with the expectation.
[ example of operation ]
The data quality type occurrence probability of the six types of data is as follows:
the first evaluation results are shown in table 6 below:
the results of the second evaluation are shown in table 7 below:
| temperature observation | Direct observation data reliability | Confidence of data from other sources |
| Accurate and accurate | 0.947497949 | 0.625 |
| Error(s) in | 0.04511895 | 0.354166667 |
| Absence survey | 0.007280558 | 0.010416667 |
| Others (error, absence measurement) | 0.000102543 | 0.010416667 |
The results of the third evaluation are shown in Table 8 below:
| temperature observation | Whole source fused data credibility |
| Accurate and accurate | 0.972669121 |
| Error(s) in | 0.027078238 |
| Absence survey | 0.000250887 |
| Others (error, absence measurement) | 0.000002 |
Example 2
Corresponding to the above method, the embodiment discloses an overall evaluation system for multi-source electric power meteorological fusion data: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.
Example 3
Similarly, the present embodiment discloses a computer storage medium having a computer program stored thereon, which when executed by a processor implements the steps of the above method.
In summary, the overall evaluation method, system and computer storage medium for multi-source electric power meteorological fusion data respectively disclosed in the above embodiments of the present invention at least have the following beneficial effects:
the D-S evidence theory is a probability analysis method under the multisource interference condition, can quickly master the overall characteristics, and forms a concise and intuitive evaluation result. Therefore, the D-S evidence theory is introduced into the reliability evaluation work of the multi-source fusion data of the electric power meteorological phenomena, the information can be quickly analyzed, the selection of the multi-source data, the taking of the fusion data and the research direction of the electric power meteorological phenomena can be optimized according to the evaluation result, the reliability of the electric power meteorological phenomena is improved, and the work efficiency of power grid guarantee is also improved.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by 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 (6)
1. The integral evaluation method of the multi-source electric power meteorological fusion data is characterized by comprising the following steps:
dividing multi-source electric power meteorological fusion data into six types of data including satellites, radars, online monitoring devices, artificial observation, meteorological department release and historical data calculation; and dividing the data quality corresponding to each type of data into: accuracy, error, lack of testing and other four conditions; calculating the occurrence probability of each kind of data corresponding to the four conditions respectively;
combining the occurrence probability data of the satellite and the radar into a remote sensing observation group, combining the occurrence probability data of the on-line monitoring device and the artificial observation into a near-end observation group, and combining the occurrence probability data issued by a meteorological department and calculated by historical data into other source groups; calculating occurrence probability data of each group corresponding to four conditions respectively based on a D-S evidence theory method to serve as a first evaluation result of fusion credibility;
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 respectively corresponding to four conditions based on a D-S evidence theory method to serve as a second evaluation result of fusion credibility;
and combining the occurrence probability data of the direct observation group and other sources, and calculating to obtain occurrence probability data of all source fusion data respectively corresponding to four conditions based on a D-S evidence theory method as a third evaluation result of the fusion credibility.
2. The overall evaluation method for the multi-source electric meteorological fusion data according to claim 1, wherein the D-S evidence theory-based method specifically further comprises the following steps:
before calculating the occurrence probability data under four conditions respectively corresponding to the occurrence probability data after the occurrence probability data are combined into a group, calculating a normalization constant corresponding to the currently combined group.
3. The overall evaluation method for the multi-source electric power meteorological fusion data according to claim 1 or 2, characterized by further comprising:
and judging whether the third evaluation result of the fusion credibility accords with the expectation, if not, optimizing at least one of the six types of initial multi-source data, and re-evaluating until all optimized source fusion data respectively correspond to occurrence probability data in four conditions to accord with the expectation.
4. The overall evaluation method for the multi-source electric meteorological fusion data according to claim 3, characterized by further comprising:
and executing corresponding data fusion processing after all the source fusion data are evaluated to be in accordance with the expectation.
5. A multi-source electric power meteorological fusion data overall evaluation system comprises: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any of the preceding claims 1 to 4 are performed by the processor when executing the computer program.
6. A computer storage medium having a computer program stored thereon, wherein the program is adapted to perform the steps of the method of any one of claims 1 to 4 when executed by a processor.
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