CN109541728B - Meteorological early warning method for power grid engineering project - Google Patents
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Abstract
The invention provides a power grid engineering project weather early warning method, which comprises the following steps: constructing a vectorization grid aiming at a GIS map, associating the current meteorological data of each meteorological monitoring point within n hours and each power grid facility to the vectorization grid, calculating the meteorological data by using an interpolation algorithm to form a meteorological data vectorization grid, calculating the meteorological data by using a trainer, calculating the current meteorological data and the n-hour forecast meteorological data of each power grid facility site according to the meteorological data calculated by the interpolation algorithm and the meteorological data obtained by the trainer, and early warning. The comprehensive interpolation algorithm and the trainer respectively obtain the meteorological data of the power grid facility position to obtain the final meteorological data, and the algorithm is adjusted according to the data collected by the power grid facility field sensor, so that the accuracy of power grid facility meteorological prediction is improved.
Description
Technical Field
The invention relates to the technical field of disaster monitoring and early warning, in particular to a power grid engineering project weather early warning method.
Background
In recent years, the operation environment of a power grid becomes severe and complex, the infrastructure engineering management faces huge safety pressure, and the potential risk in the power grid engineering construction is increased due to the lack of effective early warning means when severe weather such as rain, snow, thunder, strong wind and the like occurs. The lack of study and judgment or inaccurate prediction of the meteorological conditions of the power grid facilities in the next n hours can cause the control center not to carry out disaster deployment in advance or cause great economic loss.
In the prior art, the analysis of the current meteorological data and the prediction of the meteorological data in the future n hours are often judged by manual experience or a system, but the modes have the problems of inaccurate prediction, large radius of a prediction range and instantaneity. Therefore, based on the power grid GIS map data and the power grid facility infrastructure construction project basic data, the meteorological data are effectively calculated and predicted on the power grid facility site, the capability of acquiring meteorological early warning information on the power grid project site is promoted, the control center personnel can deploy disasters in advance, the goal of power grid safety construction is met, and the problem which needs to be solved at present is solved urgently.
Disclosure of Invention
The purpose of the invention is realized by the following technical scheme.
In order to solve the problems, the invention provides a power grid engineering project weather early warning method, which comprises the following steps:
step 1): aiming at the province covered by the system, inputting the established power grid facility basic data and the power grid facility basic data in construction of the province into the system, and inputting the early warning threshold value of each meteorological data of each power grid facility;
further, the grid infrastructure data comprises: name, GIS three-dimensional map coordinate, altitude, jurisdiction area, start time, completion time, facility type, three-dimensional space data of each type of facility, personnel type and personnel number.
Further, the three-dimensional spatial data of each type of facility specifically includes: length, width, height of each type of facility.
Further, the meteorological data includes: fire, rainfall, snow, temperature, wind speed.
Step 2): acquiring coordinates of each meteorological monitoring point of the province, current meteorological data and n-hour forecast meteorological data, and recording the coordinates, the current meteorological data and the n-hour forecast meteorological data into the system in real time;
further, the value of n is 1h, 3h, 12h and 24 h.
Step 3): forming a vectorization grid according to the province GIS map, and respectively associating the meteorological data and the grid facility coordinates of each monitoring point to the vectorization grid;
further, forming a vectorization grid according to the provincial GIS map specifically includes: and carrying out grid division on the province by taking m by m as a unit area, wherein the value of m is 1KM, 2KM or 5 KM.
Step 4): computing the meteorological data by using an interpolation algorithm to form a meteorological data vectorization grid;
further, the interpolation algorithm is preferably an inverse distance weight method;
further, the computing the meteorological data by using an interpolation algorithm to form a meteorological data vectorization grid specifically includes:
setting grids corresponding to the meteorological monitoring points as reference points, and setting the rest grids as estimation points;
calculating the meteorological data of each type of facility of the estimation point where the power grid facility is located by using an inverse distance weight method according to the current meteorological data of the reference point in the grid to obtain the meteorological data related to each type of facility in the power grid facility at present;
and calculating the meteorological data of each type of facility of the estimation point where the power grid facility is located by using an inverse distance weighting method according to each predicted meteorological data of each reference point in the grid within n hours to obtain each predicted meteorological data related to each type of facility in each power grid facility.
Further, the calculating of the meteorological data of the estimation point where the power grid facility is located by using the inverse distance weighting method specifically includes:
wherein p is0Meteorological data of an estimation point where a power grid facility is located; p is a radical ofiMeteorological data for a reference point; lambda [ alpha ]iAre weights of the reference points.
Step 5): inputting current meteorological data and n-hour predicted meteorological data of each monitoring point into a trainer to obtain meteorological data of a grid where a power grid facility is located in a vectorized grid;
further, before inputting the current meteorological data and the n-hour predicted meteorological data of each monitoring point into the trainer, the method further comprises:
obtaining historical meteorological data to form a historical meteorological database;
training by taking meteorological data pairs of positions of meteorological monitoring points and power grid facilities in historical meteorological data as samples to obtain a trainer;
further, the meteorological data pair specifically includes: and (3) taking the meteorological data of each meteorological monitoring point and the meteorological data of the position of a power grid facility as a meteorological data pair.
Step 6): and calculating the current meteorological data and the n-hour forecast meteorological data of each power grid facility site according to each meteorological data calculated by the interpolation algorithm and each meteorological data obtained by the trainer, and performing early warning.
Further, the calculating of the current meteorological data and the n-hour predicted meteorological data of each power grid facility site according to each meteorological data calculated by the interpolation algorithm and each meteorological data obtained by the trainer specifically includes:
setting each meteorological data weighted value W calculated by interpolation algorithm1And setting the weighted value W of each meteorological data obtained by the trainer2；
And calculating the current meteorological data and the n-hour predicted meteorological data of the power grid facility by using a weighted average method.
Further, before calculating the n-hour forecast meteorological data of the power grid facility, the method further comprises:
obtaining sensor data for each grid facility site, comprising: temperature, wind speed, wind direction, air humidity;
comparing the data of each sensor on the site of the power grid facility with the meteorological data of the power grid facility calculated in the step 6), and adjusting the weight value W according to the comparison result1And W2The size of (2).
Step 7): judging whether the current meteorological data of each power grid facility exceeds a meteorological data early warning threshold, if so, alarming, starting a field camera of the power grid facility, and displaying disaster data and field video data in a GIS display interface, otherwise, executing a step 8);
step 8): and judging whether the meteorological data of each power grid facility in n hours exceeds a meteorological data early warning threshold, if so, giving an alarm, starting a field camera of the power grid facility, and displaying disaster data and field video data in a GIS display interface.
The invention has the advantages that:
(1) the power grid facility meteorological data are respectively calculated by combining an inverse distance weight method and a trainer, and the final power grid facility meteorological data are obtained by adopting a weighted average algorithm, so that the prediction result is more accurate;
(2) the weighted value of the weighted average algorithm is adjusted in real time by using the sensor data of the power grid facility site, so that the accuracy of the prediction result is improved.
(3) Obtaining a trainer by taking historical meteorological data as a sample, and calculating the meteorological data of the power grid facility to enable a prediction result to be closer to an actual value;
(4) the radius range of weather prediction is narrowed, and the prediction accuracy is improved;
(5) by only carrying out meteorological prediction on the position of the power grid facility, the complexity of data operation is reduced, and the real-time performance of system early warning is ensured.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 shows a flow chart of a power grid engineering project weather early warning method according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
According to an embodiment of the present invention, a power grid engineering project weather early warning method is provided, as shown in fig. 1, the method includes:
step 1): aiming at the province covered by the system, inputting the established power grid facility basic data and the power grid facility basic data in construction of the province into the system, and inputting the early warning threshold value of each meteorological data of each power grid facility;
further, the grid infrastructure data comprises: name, GIS three-dimensional map coordinate, altitude, jurisdiction area, start time, completion time, facility type, three-dimensional space data of each type of facility, personnel type and personnel number.
Further, the three-dimensional spatial data of each type of facility specifically includes: length, width, height of each type of facility.
Further, the meteorological data includes: fire, rainfall, snow, temperature, wind speed.
Step 2): acquiring coordinates of each meteorological monitoring point of the province, current meteorological data and n-hour forecast meteorological data, and recording the coordinates, the current meteorological data and the n-hour forecast meteorological data into the system in real time;
further, the value of n is 1h, 3h, 12h and 24 h.
Step 3): forming a vectorization grid according to the province GIS map, and respectively associating the meteorological data and the grid facility coordinates of each monitoring point to the vectorization grid;
further, forming a vectorization grid according to the provincial GIS map specifically includes: and carrying out grid division on the province by taking m by m as a unit area, wherein the value of m is 1KM, 2KM or 5 KM.
Step 4): computing the meteorological data by using an interpolation algorithm to form a meteorological data vectorization grid;
further, the interpolation algorithm is preferably an inverse distance weight method;
further, the computing the meteorological data by using an interpolation algorithm to form a meteorological data vectorization grid specifically includes:
setting grids corresponding to the meteorological monitoring points as reference points, and setting the rest grids as estimation points;
calculating the meteorological data of each type of facility of the estimation point where the power grid facility is located by using an inverse distance weight method according to the current meteorological data of the reference point in the grid to obtain the meteorological data related to each type of facility in the power grid facility at present;
and calculating the meteorological data of each type of facility of the estimation point where the power grid facility is located by using an inverse distance weighting method according to each predicted meteorological data of each reference point in the grid within n hours to obtain each predicted meteorological data related to each type of facility in each power grid facility.
Further, the calculating of the meteorological data of the estimation point where the power grid facility is located by using the inverse distance weighting method specifically includes:
wherein p is0Meteorological data of an estimation point where a power grid facility is located; p is a radical ofiMeteorological data for a reference point; lambda [ alpha ]iAre weights of the reference points.
Step 5): inputting current meteorological data and n-hour predicted meteorological data of each monitoring point into a trainer to obtain meteorological data of a grid where a power grid facility is located in a vectorized grid;
further, before inputting the current meteorological data and the n-hour predicted meteorological data of each monitoring point into the trainer, the method further comprises:
obtaining historical meteorological data to form a historical meteorological database;
training by taking meteorological data pairs of positions of meteorological monitoring points and power grid facilities in historical meteorological data as samples to obtain a trainer;
further, the meteorological data pair specifically includes: and (3) taking the meteorological data of each meteorological monitoring point and the meteorological data of the position of a power grid facility as a meteorological data pair.
Step 6): and calculating the current meteorological data and the n-hour forecast meteorological data of each power grid facility site according to each meteorological data calculated by the interpolation algorithm and each meteorological data obtained by the trainer, and performing early warning.
Further, the calculating of the current meteorological data and the n-hour predicted meteorological data of each power grid facility site according to each meteorological data calculated by the interpolation algorithm and each meteorological data obtained by the trainer specifically includes:
setting each meteorological data weighted value W calculated by interpolation algorithm1And setting the weighted value W of each meteorological data obtained by the trainer2；
And calculating the current meteorological data and the n-hour predicted meteorological data of the power grid facility by using a weighted average method.
Further, before calculating the n-hour forecast meteorological data of the power grid facility, the method further comprises:
obtaining sensor data for each grid facility site, comprising: temperature, wind speed, wind direction, air humidity;
comparing the data of each sensor on the site of the power grid facility with the meteorological data of the power grid facility calculated in the step 6), and adjusting the weight value W according to the comparison result1And W2The size of (2).
Step 7): judging whether the current meteorological data of each power grid facility exceeds a meteorological data early warning threshold, if so, alarming, starting a field camera of the power grid facility, and displaying disaster data and field video data in a GIS display interface, otherwise, executing a step 8);
step 8): and judging whether the meteorological data of each power grid facility in n hours exceeds a meteorological data early warning threshold, if so, giving an alarm, starting a field camera of the power grid facility, and displaying disaster data and field video data in a GIS display interface.
The embodiment provides a power grid engineering project weather early warning method, which combines an inverse distance weight method and a trainer to comprehensively obtain current weather data and n-hour forecast weather data of power grid facilities, and improves the accuracy of the forecast. Meanwhile, the weighting weight ratio is adjusted in real time by using the sensor data of the power grid facility site, so that the prediction result changes along with the change of actual weather, and the prediction accuracy is further improved. The method also reduces the integral calculation amount and data transmission amount of the system and improves the working efficiency of the system by only carrying out meteorological data calculation aiming at the position of the power grid site and starting the power grid site camera when the meteorological data reaches a threshold value.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (7)
1. A power grid engineering project weather early warning method is characterized by comprising the following steps:
step 1): aiming at the province covered by the system, inputting the established power grid facility basic data and the power grid facility basic data in construction of the province into the system, and inputting the early warning threshold value of each meteorological data of each power grid facility;
step 2): acquiring coordinates of each meteorological monitoring point of the province, current meteorological data and n-hour forecast meteorological data, and recording the coordinates, the current meteorological data and the n-hour forecast meteorological data into the system in real time;
step 3): forming a vectorization grid according to the province GIS map, and respectively associating the meteorological data and the grid facility coordinates of each monitoring point to the vectorization grid;
step 4): computing the meteorological data by using an interpolation algorithm to form a meteorological data vectorization grid;
the computing of the meteorological data by using the interpolation algorithm to form the meteorological data vectorization grid specifically includes:
setting grids corresponding to the meteorological monitoring points as reference points, and setting the rest grids as estimation points;
calculating the meteorological data of each type of facility of the estimation point where the power grid facility is located by using an inverse distance weight method according to the current meteorological data of the reference point in the grid to obtain the meteorological data related to each type of facility in the power grid facility at present;
according to each predicted meteorological data of each reference point in the grid within n hours, calculating the meteorological data of each type of facility of the estimation point where the power grid facility is located by using an inverse distance weight method to obtain each predicted meteorological data related to each type of facility in each power grid facility;
step 5): inputting current meteorological data and n-hour predicted meteorological data of each monitoring point into a trainer to obtain meteorological data of a grid where a power grid facility is located in a vectorized grid;
before the current meteorological data and the n-hour predicted meteorological data of each monitoring point are input into the trainer, the method further comprises the following steps:
obtaining historical meteorological data to form a historical meteorological database;
training by taking meteorological data pairs of positions of meteorological monitoring points and power grid facilities in historical meteorological data as samples to obtain a trainer;
the method comprises the following steps that meteorological data of each meteorological monitoring point and meteorological data of a position where a power grid facility is located are used as meteorological data pairs;
step 6): calculating current meteorological data and n-hour forecast meteorological data of each power grid facility site according to each meteorological data calculated by an interpolation algorithm and each meteorological data obtained by a trainer, and performing early warning;
the method for calculating the current meteorological data and the n-hour forecast meteorological data of each power grid facility site according to the meteorological data calculated by the interpolation algorithm and the meteorological data obtained by the trainer specifically comprises the following steps:
setting each meteorological data calculated by interpolation algorithmWeighted value W1And setting the weighted value W of each meteorological data obtained by the trainer2；
Calculating the current meteorological data and the n-hour predicted meteorological data of the power grid facility by using a weighted average method;
comparing the data of each sensor on the site of the power grid facility with the meteorological data of the power grid facility calculated in the step 6), and adjusting the weight value W according to the comparison result1And W2The size of (2).
2. The power grid engineering project weather early warning method of claim 1, the early warning comprising:
step 7): judging whether the current meteorological data of each power grid facility exceeds a meteorological data early warning threshold, if so, alarming, starting a field camera of the power grid facility, and displaying disaster data and field video data in a GIS display interface, otherwise, executing a step 8);
step 8): and judging whether the meteorological data of each power grid facility in n hours exceeds a meteorological data early warning threshold, if so, giving an alarm, starting a field camera of the power grid facility, and displaying disaster data and field video data in a GIS display interface.
3. The power grid engineering project weather early warning method of claim 2, the power grid infrastructure data comprising: name, GIS three-dimensional map coordinate, altitude, jurisdiction area, start time, completion time, facility type, three-dimensional space data of each type of facility, personnel type and personnel number.
4. The power grid engineering project weather early warning method according to claim 3, wherein the three-dimensional spatial data of each type of facility specifically includes: length, width and height of each type of facility;
the meteorological data includes: fire, rainfall, snow, temperature, wind speed.
5. The power grid engineering project weather early warning method according to claim 4, wherein the interpolation algorithm is specifically an inverse distance weight method.
6. The power grid engineering project weather early warning method according to claim 5, wherein the forming of the vectoring grid according to the provincial GIS map specifically includes:
and carrying out grid division on the province by taking m by m as a unit area, wherein the value of m is 1KM, 2KM or 5 KM.
7. The power grid engineering project weather early warning method according to claim 1, wherein the calculation of the weather data of the estimation point where the power grid facility is located by using an inverse distance weighting method is specifically as follows:
wherein p is0Meteorological data of an estimation point where a power grid facility is located; p is a radical ofiMeteorological data for a reference point; lambda [ alpha ]iAre weights of the reference points.
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