Method for fine examination of lightning warning effect

Abstract

The present application belongs to the technical field of thunder and lightning early warning, and particularly relates to a lightning fine early warning effect inspection method, aiming at the problem that the existing radar early warning effect cannot be inspected, always relying on the traditional mode for early warning, and being unable to detect the early warning effect to improve and affect the accuracy of lightning early warning, the following scheme is proposed, which comprises the following steps: S1: inputting radar data into a radar detection platform and marking the region, and transmitting early warning data to the radar detection platform; S2: classifying according to the accuracy and regionality of early warning; S3: analyzing the classified data, calculating the weight value of different data in the early warning effect, and obtaining an inspection result. The present application can conveniently and quickly compare and inspect the early warning effect; find out excellent early warning data for unified comparison, establish radar models of different regions, find out the optimal early warning data and scheme of different regions, and can inspect and adjust the early warning of different regions.

Description

Technical Field

[0001] This invention relates to the field of lightning warning technology, and in particular to a method for refining the verification of lightning warning effectiveness. Background Technology

[0002] In recent years, extreme weather events such as rain, snow, ice, typhoons, and severe convection have occurred frequently, causing serious damage and losses to power grids and local buildings. Meteorological disasters are highly localized and have short assessment timelines. Their intensity, duration, and scope of impact on power grid equipment and operations vary. To minimize damage and losses, accurate and timely lightning warnings are crucial. Current methods rely on meteorological radar monitoring data for assessment, but radar monitoring data differs across regions, making the assessment of warning effectiveness particularly important.

[0003] The effectiveness of existing radar early warning systems cannot be verified, and the reliance on traditional methods for early warning has prevented the detection and improvement of early warning effectiveness, thus affecting the accuracy of lightning early warnings. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing radar early warning systems, which cannot verify the effectiveness of early warnings and rely on traditional methods for early warning, thus affecting the accuracy of lightning early warnings. The invention proposes a refined method for verifying the effectiveness of lightning early warnings.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A refined method for verifying the effectiveness of lightning warnings includes the following steps:

[0007] S1: Connect the radar data to the radar detection platform and mark the area, then transmit the early warning data to the radar detection platform;

[0008] S2: Classify warnings according to their accuracy and regionality;

[0009] S3: Analyze the classified data, calculate the weight values ​​of different data in the early warning effect, and obtain the test results;

[0010] S4: Identify data with high accuracy in early warning;

[0011] S5: Identify and save the top 2-3 data points that show the best early warning effect;

[0012] S6: Compare the data of all regions with good early warning effects to find the optimal early warning data;

[0013] S7: Establish lightning models for all regions, apply the best early warning data to the established regional lightning models, conduct trials in sequence, statistically analyze the simulated early warning data of all regional radar models, and find the most accurate early warning data.

[0014] Preferably, in step S1, when radar data is accessed by the radar detection platform, the data is checked and filtered. The filtering criterion is the accuracy of the warning. Warning data with a deviation of more than or equal to 60% from the accuracy value is directly deleted. Then, the filtered data is transmitted to the radar detection platform.

[0015] Preferably, in step S2, all data are divided into 4-8 categories according to region as required, and then the data of each region is classified accurately one by one.

[0016] Preferably, in step S3, the categorized data is organized and analyzed, and the effectiveness of the warning data is analyzed through images, videos, or text.

[0017] Preferably, in step S3, for data from the same region, after analysis, the analysis results are weighted and compared to obtain the test results.

[0018] Preferably, in step S4, the data with high accuracy in early warning are identified and ranked, and the ranking of the accuracy data error time values ​​of the early warning is as follows: 1-60s, 1-2min, and 2-5min.

[0019] Preferably, in step S5, for different regions, the top data with good early warning effect in each region is identified and saved, and the remaining data values ​​are removed.

[0020] Preferably, in step S6, the data with good early warning effects in all regions are compared to find the optimal early warning data. The comparison method is to rank the data according to the size of the early warning error value. The smaller the early warning error value, the better the early warning effect.

[0021] Preferably, in step S7, different lightning models are established for different regions. First, the best early warning data among the available data is used to test the lightning model for each region and the data value is recorded. Then, the second best early warning data is used to test the model and the data value is recorded. Similarly, the model is tested until 3-5 digit data values ​​are obtained and the data is recorded.

[0022] Preferably, in step S7, the data values ​​used in the trial are compared uniformly to find the optimal early warning data for different regions.

[0023] Compared with the prior art, the advantages of the present invention are as follows:

[0024] This scheme first classifies the data according to different regions, and then classifies it according to the accuracy of the early warning data, which can facilitate a quick comparison and verification of the early warning effect.

[0025] This solution identifies excellent early warning data for unified comparison, establishes radar models for different regions, and then tests them one by one to find the optimal early warning data and solutions for different regions. It can verify and adjust early warning systems for different regions and meet the needs of early warning use. Attached Figure Description

[0026] Figure 1 is a flowchart of the refined verification method for lightning early warning effect proposed in this invention. Detailed Implementation

[0027] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Example 1

[0028] Referring to Figure 1, the refined verification method for lightning warning effectiveness includes the following steps:

[0029] S1: Connect the radar data to the radar detection platform and mark the area, then transmit the early warning data to the radar detection platform;

[0030] S2: Classify warnings according to their accuracy and regionality;

[0031] S3: Analyze the classified data, calculate the weight values ​​of different data in the early warning effect, and obtain the test results;

[0032] S4: Identify data with high accuracy in early warning;

[0033] S5: Find the top two data points with the best early warning effect and save them;

[0034] S6: Compare the data of all regions with good early warning effects to find the optimal early warning data;

[0035] S7: Establish lightning models for all regions, apply the best early warning data to the established regional lightning models, conduct trials in sequence, statistically analyze the simulated early warning data of all regional radar models, and find the most accurate early warning data.

[0036] In this embodiment, in S1, when radar data is accessed by the radar detection platform, the data is checked and filtered. The filtering criterion is the accuracy of the warning. Warning data with a deviation of more than or equal to 70% from the accuracy value is directly deleted. Then the filtered data is transmitted to the radar detection platform.

[0037] In this embodiment, in S2, all data are divided into 4 categories according to region as required, and then the data of each region is classified accurately one by one.

[0038] In this embodiment, in S3, the classified data is organized and analyzed, and the effect of the warning data is analyzed through images, videos or text.

[0039] In this embodiment, in S3, after analyzing the data from the same region, the analysis results are weighted and compared to obtain the test results.

[0040] In this embodiment, in S4, the data with high accuracy in early warning are identified and ranked. The ranking of the accuracy data error time values ​​of the early warning is as follows: 20s, 1.5min, and 2.5min.

[0041] Project Area Precise data error time for early warning Save? Area 1 20s yes Area 2 1.5min yes Area 3 2.5min no Area 4 4min no

[0042] In this embodiment, in step S5, for different regions, the top data with good early warning effect in each region is identified and saved, and the remaining data values ​​are removed.

[0043] In this embodiment, in S6, the data with good early warning effect in the area are compared in a unified manner to find the optimal early warning data. The comparison method is to rank them according to the size of the early warning error value. The smaller the early warning error value, the better the early warning effect.

[0044] In this embodiment, in S7, different lightning models are established for different regions. First, the best warning data among the available data is used to test the lightning model for each region and the data value is recorded. Then, the second best warning data is used to test the model and the data value is recorded. Similarly, the model is tested up to 5 digits and the data is recorded.

[0045] In this embodiment, in step S7, the data values ​​used in the trial are compared uniformly to find the optimal early warning data for different regions.

[0046] Example 2

[0047] Referring to Figure 1, the refined verification method for lightning warning effectiveness includes the following steps:

[0048] S1: Connect the radar data to the radar detection platform and mark the area, then transmit the early warning data to the radar detection platform;

[0049] S2: Classify warnings according to their accuracy and regionality;

[0050] S3: Analyze the classified data, calculate the weight values ​​of different data in the early warning effect, and obtain the test results;

[0051] S4: Identify data with high accuracy in early warning;

[0052] S5: Find the top two data points with the best early warning effect and save them;

[0053] S6: Compare the data of all regions with good early warning effects to find the optimal early warning data;

[0054] S7: Establish lightning models for all regions, apply the best early warning data to the established regional lightning models, conduct trials in sequence, statistically analyze the simulated early warning data of all regional radar models, and find the most accurate early warning data.

[0055] In this embodiment, in S1, when radar data is accessed by the radar detection platform, the data is checked and filtered. The filtering criterion is the accuracy of the warning. Warning data with a deviation of more than or equal to 65% in accuracy is directly deleted. Then the filtered data is transmitted to the radar detection platform.

[0056] In this embodiment, in S2, all data are divided into 5 categories according to region as required, and then the data of each region is classified accurately one by one.

[0057] In this embodiment, in S3, the classified data is organized and analyzed, and the effect of the warning data is analyzed through images, videos or text.

[0058] In this embodiment, in S3, after analyzing the data from the same region, the analysis results are weighted and compared to obtain the test results.

[0059] In this embodiment, in S4, the data with high accuracy in early warning are identified and ranked. The ranking of the accuracy data error time values ​​of the early warning is as follows: 30s, 1.3min, and 3min.

[0060] Project Area Precise data error time for early warning Save? Area 1 30s yes Area 2 1.3min yes Area 3 3min no Area 4 3.6min no Area 5 3.8min no

[0061] In this embodiment, in step S5, for different regions, the top data with good early warning effect in each region is identified and saved, and the remaining data values ​​are removed.

[0062] In this embodiment, in S6, the data with good early warning effect in the area are compared in a unified manner to find the optimal early warning data. The comparison method is to rank them according to the size of the early warning error value. The smaller the early warning error value, the better the early warning effect.

[0063] In this embodiment, in S7, different lightning models are established for different regions. First, the best warning data among the available data is used to test the lightning model for each region and the data value is recorded. Then, the second best warning data is used to test the model and the data value is recorded. Similarly, the model is tested up to 4-digit data values ​​and the data is recorded.

[0064] In this embodiment, in step S7, the data values ​​used in the trial are compared uniformly to find the optimal early warning data for different regions.

[0065] Example 3

[0066] Referring to Figure 1, the refined verification method for lightning warning effectiveness includes the following steps:

[0067] S1: Connect the radar data to the radar detection platform and mark the area, then transmit the early warning data to the radar detection platform;

[0068] S2: Classify warnings according to their accuracy and regionality;

[0069] S3: Analyze the classified data, calculate the weight values ​​of different data in the early warning effect, and obtain the test results;

[0070] S4: Identify data with high accuracy in early warning;

[0071] S5: Identify and save the top 3 data points with the best early warning effect;

[0072] S6: Compare the data of all regions with good early warning effects to find the optimal early warning data;

[0073] S7: Establish lightning models for all regions, apply the best early warning data to the established regional lightning models, conduct trials in sequence, statistically analyze the simulated early warning data of all regional radar models, and find the most accurate early warning data.

[0074] In this embodiment, in S1, when radar data is accessed by the radar detection platform, the data is checked and filtered. The filtering criterion is the accuracy of the warning. Warning data with a deviation of more than or equal to 60% from the accuracy value is directly deleted. Then the filtered data is transmitted to the radar detection platform.

[0075] In this embodiment, in S2, all data are divided into 6 categories according to region as required, and then the data of each region is classified accurately one by one.

[0076] In this embodiment, in S3, the classified data is organized and analyzed, and the effect of the warning data is analyzed through images, videos or text.

[0077] In this embodiment, in S3, after analyzing the data from the same region, the analysis results are weighted and compared to obtain the test results.

[0078] In this embodiment, in S4, the data with high accuracy in early warning are identified and ranked. The ranking of the accuracy data error time values ​​of the early warning is as follows: 40s, 1.8min, and 3.5min.

[0079]

[0080] In this embodiment, in step S5, for different regions, the top data with good early warning effect in each region is identified and saved, and the remaining data values ​​are removed.

[0081] In this embodiment, in S6, the data with good early warning effect in the area are compared in a unified manner to find the optimal early warning data. The comparison method is to rank them according to the size of the early warning error value. The smaller the early warning error value, the better the early warning effect.

[0082] In this embodiment, in S7, different lightning models are established for different regions. First, the best warning data among the available data is used to test the lightning model for each region and the data value is recorded. Then, the second best warning data is used to test the model and the data value is recorded. Similarly, the model is tested until 3-digit data values ​​are obtained and the data is recorded.

[0083] In this embodiment, in step S7, the data values ​​used in the trial are compared uniformly to find the optimal early warning data for different regions.

[0084] Example 4

[0085] Referring to Figure 1, the refined verification method for lightning warning effectiveness includes the following steps:

[0086] S1: Connect the radar data to the radar detection platform and mark the area, then transmit the early warning data to the radar detection platform;

[0087] S2: Classify warnings according to their accuracy and regionality;

[0088] S3: Analyze the categorized data and weight the different data in the early warning effect.

[0089] The repetition value is calculated to obtain the test result;

[0090] S4: Identify data with high accuracy in early warning;

[0091] S5: Identify and save the top 3 data points with the best early warning effect;

[0092] S6: Compare the data of all regions with good early warning effects to find the optimal early warning data;

[0093] S7: Establish lightning models for all regions, apply the best early warning data to the established regional lightning models, conduct trials in sequence, statistically analyze the simulated early warning data of all regional radar models, and find the most accurate early warning data.

[0094] In this embodiment, in S1, when radar data is accessed by the radar detection platform, the data is checked and filtered. The filtering criterion is the accuracy of the warning. Warning data with a deviation of more than or equal to 50% from the accuracy value is directly deleted. Then the filtered data is transmitted to the radar detection platform.

[0095] In this embodiment, in S2, all data are divided into 7 categories according to region as required, and then the data of each region is classified accurately one by one.

[0096] In this embodiment, in S3, the classified data is organized and analyzed, and the effect of the warning data is analyzed through images, videos or text.

[0097] In this embodiment, in S3, after analyzing the data from the same region, the analysis results are weighted and compared to obtain the test results.

[0098] In this embodiment, in S4, the data with high accuracy in early warning are identified and ranked. The ranking of the accuracy data error time values ​​of the early warning is as follows: 50s, 1.8min, and 4min.

[0099]

[0100] In this embodiment, in step S5, for different regions, the top data with good early warning effect in each region is identified and saved, and the remaining data values ​​are removed.

[0101] In this embodiment, in S6, the data with good early warning effect in the area are compared in a unified manner to find the optimal early warning data. The comparison method is to rank them according to the size of the early warning error value. The smaller the early warning error value, the better the early warning effect.

[0102] In this embodiment, in S7, different lightning models are established for different regions. First, the best warning data among the available data is used to test the lightning model for each region and the data value is recorded. Then, the second best warning data is used to test the model and the data value is recorded. Similarly, the model is tested up to 4-digit data values ​​and the data is recorded.

[0103] In this embodiment, in step S7, the data values ​​used in the trial are compared uniformly to find the optimal early warning data for different regions.

[0104] Example 5

[0105] Referring to Figure 1, the refined verification method for lightning warning effectiveness includes the following steps:

[0106] S1: Connect the radar data to the radar detection platform and mark the area, then transmit the early warning data to the radar detection platform;

[0107] S2: Classify warnings according to their accuracy and regionality;

[0108] S3: Analyze the categorized data and weight the different data in the early warning effect.

[0109] The repetition value is calculated to obtain the test result;

[0110] S4: Identify data with high accuracy in early warning;

[0111] S5: Identify and save the top 3 data points with the best early warning effect;

[0112] S6: Compare the data of all regions with good early warning effects to find the optimal early warning data;

[0113] S7: Establish lightning models for all regions, apply the best early warning data to the established regional lightning models, conduct trials in sequence, statistically analyze the simulated early warning data of all regional radar models, and find the most accurate early warning data.

[0114] In this embodiment, in S1, when radar data is accessed by the radar detection platform, the data is checked and filtered. The filtering criterion is the accuracy of the warning. Warning data with a deviation of more than or equal to 60% from the accuracy value is directly deleted. Then the filtered data is transmitted to the radar detection platform.

[0115] In this embodiment, in S2, all data are divided into 8 categories according to region as required, and then the data of each region is classified accurately one by one.

[0116] In this embodiment, in S3, the classified data is organized and analyzed, and the effect of the warning data is analyzed through images, videos or text.

[0117] In this embodiment, in S3, after analyzing the data from the same region, the analysis results are weighted and compared to obtain the test results.

[0118] In this embodiment, in S4, the data with high accuracy in early warning are identified and ranked. The ranking of the accuracy data error time values ​​of the early warning is as follows: 60s, 2min, and 5min.

[0119]

[0120] In this embodiment, in step S5, for different regions, the top data with good early warning effect in each region is identified and saved, and the remaining data values ​​are removed.

[0121] In this embodiment, in S6, the data with good early warning effect in the area are compared in a unified manner to find the optimal early warning data. The comparison method is to rank them according to the size of the early warning error value. The smaller the early warning error value, the better the early warning effect.

[0122] In this embodiment, in S7, different lightning models are established for different regions. First, the best warning data among the available data is used to test the lightning model for each region and the data value is recorded. Then, the second best warning data is used to test the model and the data value is recorded. Similarly, the model is tested up to 5 digits and the data is recorded.

[0123] In this embodiment, in step S7, the data values ​​used in the trial are compared uniformly to find the optimal early warning data for different regions.

[0124] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for fine inspection of lightning warning effect, characterized in that, The fine verification method comprises the following steps: S1: access radar data to the radar detection platform and mark the region, and transmit early warning data to the radar detection platform; S2: classify according to the accuracy and regionality of early warning; S3: analyze the classified data, calculate the weight value of different data in the early warning effect, and obtain the verification result; S4: find data with high early warning accuracy; S5: find the top 2-3 data with good early warning effect and save them; S6: compare all the data with good early warning effect in all regions, find the optimal early warning data, and rank them according to the early warning error value, the smaller the early warning error value, the better the early warning effect; S7: establish all regional lightning models, apply the optimal early warning data to the established regional lightning models, and sequentially apply them, count all regional radar model simulation early warning data, and find the most accurate early warning data.

2. The method of claim 1, wherein the method is characterized by, In S1, when the radar data is accessed to the radar detection platform, the data is checked and screened, the screening standard is the accuracy of early warning, and the early warning data with a deviation of greater than or equal to 60% of the early warning accuracy value is directly deleted, and then the screened data is transmitted to the radar detection platform.

3. The method of claim 1, wherein the method is characterized by, In S2, all data are classified into 4-8 categories according to regionality, and then the data of each region are classified according to accuracy.

4. The lightning pre-warning effect refinement verification method according to claim 1, characterized in that, In S3, the classified data are analyzed and arranged, and the effect of the early warning data is analyzed through images, videos or texts.

5. The lightning pre-warning effect refinement verification method according to claim 1, characterized in that, In S3, the data in the same region are analyzed, the analysis results are calculated and compared, and the verification result is obtained.

6. The lightning pre-warning effect refinement verification method according to claim 1, characterized in that, In S4, the data with high early warning accuracy are ranked, and the early warning accuracy error time value ranking is 1-60s, 1-2min and 2-5min.

7. The method of claim 1, wherein the method is used for lightning warning effect refinement test. In S5, for different regions, the top data with good early warning effect in each region are saved, and the remaining data values are removed.

8. The lightning pre-warning effect refinement verification method according to claim 1, characterized in that, In S7, different lightning models are established for different regions, the optimal early warning data in the used data is used to test each regional lightning model, and the data value is recorded, then the second optimal early warning data is used for testing, and the data value is recorded, similarly, the 3-5 data values are tested, and the data is recorded.

9. The lightning pre-warning effect refinement verification method according to claim 1, characterized in that, In S7, the used test data values are compared, and the optimal early warning data of different regions are found.

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