A micro-meteorological device calibration method based on meteorological rationality analysis

Abstract

The application discloses a micro-meteorological device verification method based on meteorological rationality analysis, and steps are as follows: obtaining provincial meteorological center data, micro-meteorological device collection data, corresponding regional elevation and line tower information, and preprocessing into comparable grid point data; comparing and analyzing the change trend of correlation coefficient, dynamic time warping distance and slope difference of each device data and grid point data; performing basic verification of physical range, zero value continuity and grid point data comparison; for the device with icing, additionally performing special verification of icing existing condition, thickness rationality and growth rate; and marking data abnormal type and outputting abnormal device list according to the foregoing results. The method fills the blank of the prior art, solves the problems of meteorological data limitation and lack of systematic verification, improves data accuracy, provides support for power grid meteorological data management, eliminates data difference through preprocessing, and guarantees the effectiveness of verification basis.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, specifically, it relates to a method for verifying micro-meteorological devices based on meteorological rationality analysis. Background Technology

[0002] Currently, with the rapid development of the power grid, the demand for real-time operation and maintenance response is increasing, and the requirements for the accuracy of meteorological data are also rising. Existing meteorological data collected by meteorological bureaus has the following limitations: the data collection height is fixed (10m above ground), failing to reflect the actual meteorological conditions of transmission lines; the time granularity is coarse (mostly updated hourly) and is mostly hourly averages; it cannot meet the construction needs of power grid scenarios such as wind deflection, icing, and fault diagnosis. To compensate for these shortcomings, micro-meteorological devices have been installed on the towers of lines in special terrains and important protection lines. However, current papers and patents lack a systematic verification method for the data collected by micro-meteorological devices to improve the accuracy and rationality of the data collected and promote data governance of the devices. Therefore, researching a more accurate and usable verification method for micro-meteorological devices based on meteorological rationality analysis is particularly important. Summary of the Invention

[0003] To overcome the shortcomings of existing technologies, this invention provides a micro-meteorological device verification method based on meteorological rationality analysis, which realizes systematic and accurate verification of micro-meteorological device data and effectively improves the accuracy and rationality of data collected by micro-meteorological devices.

[0004] This invention is achieved through the following technical solution: a method for verifying micro-meteorological devices based on meteorological rationality analysis, comprising the following steps:

[0005] Step S1: Obtain the data to be verified and perform data preprocessing. The data to be verified includes meteorological data files from the provincial meteorological center, meteorological element data collected by all micro-meteorological devices, elevation data of the corresponding area, and information on the line towers corresponding to the micro-meteorological devices.

[0006] Step S2: For each micro-meteorological device, combine the grid data preprocessed in Step S1 with the meteorological element data collected by the micro-meteorological device to conduct a comparative analysis of the changing trends of each meteorological element within a day. The comparative analysis of changing trends includes correlation coefficient analysis, dynamic time regularization distance analysis, and slope difference analysis.

[0007] Step S3: Perform basic verification on the micro-meteorological device data collected after the trend comparison analysis in step S2. The basic verification includes physical range verification, zero value continuity verification, and grid data comparison verification.

[0008] Step S4: If the micro-meteorological device includes an icing device for collecting the icing thickness of the line in the data after the basic verification in step S3, then perform a special verification on the icing device for the data collected by the icing device. The special verification on icing includes verification of the icing existence conditions, verification of the rationality of the icing thickness, and verification of the icing growth rate.

[0009] Step S5: Based on the trend comparison analysis results of Step S2, the basic verification results of Step S3, and the icing-specific verification results of Step S4, mark the abnormal types of data collected by each micro-meteorological device and output a list of abnormal micro-meteorological devices.

[0010] Furthermore, in step S1, the data preprocessing includes: converting the meteorological data file of the provincial meteorological center into computable and comparable grid data of the entire province; processing outliers in the meteorological element data collected by the micro-meteorological device; unifying the time dimension, format, and unit of the grid data of the provincial meteorological center and the meteorological element data collected by the micro-meteorological device; performing height correction on the grid data of the provincial meteorological center; and performing interpolation processing on the height-corrected grid data to match the location of the micro-meteorological device.

[0011] Furthermore, in step S1, the meteorological data file of the provincial meteorological center includes temperature, humidity, wind speed, pressure, and precipitation data; the meteorological element data collected by the micro-meteorological device includes temperature, humidity, wind speed, pressure, light radiation intensity, rainfall intensity, and icing thickness data, depending on the sensor; the line tower information includes the voltage level of the line to which the tower belongs.

[0012] Furthermore, in step S1, the grid data from the provincial meteorological center undergoes height correction, including pressure height correction, temperature height correction, and wind speed height correction, as detailed below:

[0013] The process of atmospheric pressure altitude correction is expressed as: Corrected atmospheric pressure = Original atmospheric pressure × Exponent (-Target correction altitude / 8500), where 8500 meters is the atmospheric elevation constant, and the target correction altitude is obtained based on the coordinates of the grid point where the micro-meteorological device is located, combined with elevation data and tower height;

[0014] The process of temperature and height correction is expressed as: Corrected temperature = Grid temperature - Dynamic lapse rate × Micro-meteorological device height above ground;

[0015] The process of wind speed height correction is expressed as: corrected wind speed = grid point wind speed × (actual device height / reference height) raised to the power of the correction exponent.

[0016] Furthermore, in step S2, before conducting the comparative analysis of the changing trends, a step of unifying the time resolution of the preprocessed grid data and the data collected by the micro-meteorological device is also included.

[0017] Furthermore, in step S2,

[0018] In the slope difference analysis, the slope difference ratio is calculated as follows: Slope difference ratio = |grid data sequence slope - micro-meteorological device data sequence slope| / maximum value (|grid data sequence slope|, |micro-meteorological device data sequence slope|, safety mechanism parameter), where the safety mechanism parameter is used to prevent division by zero errors when the slope is close to zero.

[0019] Furthermore, in step S3, the physical range verification adopts a three-layer verification mechanism, specifically including:

[0020] The first layer: Single element boundary verification, referring to the seasonal characteristics of the current time and the historical extreme values ​​of meteorological elements in the area where the micro-meteorological device is located, presets and determines the reasonable range of each meteorological element, and judges whether the meteorological element data collected by the micro-meteorological device is within the reasonable range.

[0021] The second layer: multi-element correlation verification, which verifies the physical correlation between meteorological elements, including:

[0022] Temperature and humidity correlation verification, wet rain correlation verification, and compressed air correlation verification;

[0023] The third layer: spatiotemporal consistency verification, including:

[0024] Time consistency verification determines whether an anomaly exists based on whether the temperature change exceeds a set threshold.

[0025] Spatial consistency verification determines whether an anomaly exists based on whether the pressure difference collected by adjacent micrometeorological devices exceeds a set threshold.

[0026] Furthermore, in step S3, the zero-value continuity check specifically includes:

[0027] Regarding wind speed, if it is during the windy season and the wind speed is 0 m / s for 1-2 consecutive hours within a set time period, it is considered abnormal.

[0028] Regarding precipitation, if it is the rainy season and the precipitation is 0 mm for 3 consecutive hours within a set time period, it is considered abnormal.

[0029] If two or more meteorological elements collected simultaneously by a micro-meteorological device have zero data, the data must be compared with the historical data collected by the micro-meteorological device or with the data collected by a nearby micro-meteorological device. If the comparison results show that the data deviation exceeds the normal range, it is determined to be an anomaly caused by device malfunction.

[0030] Furthermore, in step S3, the grid data comparison and verification specifically involves: calculating the difference between the grid data after interpolation in step one and the data collected by the micro-meteorological device during the same period for the same type of meteorological elements; if the temperature difference, humidity difference, or wind speed difference exceeds their respective set thresholds, the data collected by the micro-meteorological device is marked as abnormal.

[0031] Furthermore, in step S4, the icing growth rate verification specifically includes the following sub-steps:

[0032] Step S401: Calculate the actual growth rate of the data collected by the icing device. The calculation method is: actual growth rate = (ice thickness at the current moment - ice thickness at the previous moment) / time interval;

[0033] Step S402: Calculate the theoretical growth rate of icing using the McCormick model;

[0034] Step S403: Calculate the ratio of the actual growth rate to the theoretical growth rate. If the ratio is within the range of 0.3 to 3, the icing growth rate verification is deemed to have passed; if the ratio exceeds the range of 0.3 to 3, the icing growth rate verification is deemed to have failed and is marked as abnormal.

[0035] The present invention has the following beneficial effects:

[0036] 1. Filling the gaps in existing technologies and solving the problem of micro-meteorological data verification: Addressing the limitations of existing meteorological bureau data collection, such as fixed data levels and coarse time granularity, and the lack of a systematic verification method for micro-meteorological device data, this method combines high-resolution meteorological data from provincial meteorological centers with physical principles to construct a complete process from data preprocessing to specialized verification. This is the first time that systematic and accurate verification of micro-meteorological device data has been achieved, effectively improving the accuracy and rationality of micro-meteorological device data and providing key technical support for power grid meteorological data governance.

[0037] 2. Data preprocessing mechanism improves data reliability and ensures the validity of the verification basis: By converting binary data from the provincial meteorological center into gridded data, processing outliers in micro-meteorological device data, and performing height corrections for air pressure, temperature, and wind speed, and by using spatial bilinear interpolation to match the location of micro-meteorological devices, the differences in format, unit, collection height, and spatial location of data from different sources are effectively eliminated. This makes the meteorological center data and micro-meteorological device data comparable, laying a solid foundation for the accuracy of subsequent verification processes. Attached Figure Description

[0038] Figure 1 This is a flowchart of the method of the present invention;

[0039] Figure 2 This is a flowchart illustrating the specific steps of S1 in this invention.

[0040] Figure 3 This is a flowchart illustrating step S2 of the present invention.

[0041] Figure 4 This is a line graph comparing the temperature trend in step S2 of the present invention (6.28).

[0042] Figure 5 This is a line graph comparing the temperature trend in step S2 of the present invention (7.2).

[0043] Figure 6 This is a correlation diagram between grid point 6.28 and temperature M4 in step S2 of the present invention;

[0044] Figure 7 This is a correlation diagram between grid point 7.2 and temperature M4 in step S2 of the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Reference Figure 1 A method for verifying micro-meteorological devices based on meteorological rationality analysis includes the following steps:

[0047] Step S1: Obtain the data to be verified and perform data preprocessing. The data to be verified includes meteorological data files from the provincial meteorological center, meteorological element data collected by all micro-meteorological devices, elevation data of the corresponding area, and information on the line towers corresponding to the micro-meteorological devices.

[0048] Step S2: For each micro-meteorological device, combine the grid data preprocessed in Step S1 with the meteorological element data collected by the micro-meteorological device to conduct a comparative analysis of the changing trends of each meteorological element within a day. The comparative analysis of changing trends includes correlation coefficient analysis, dynamic time regularization distance analysis, and slope difference analysis.

[0049] Step S3: Perform basic verification on the micro-meteorological device data collected after the trend comparison analysis in step S2. The basic verification includes physical range verification, zero value continuity verification, and grid data comparison verification.

[0050] Step S4: If the micro-meteorological device includes an icing device for collecting the icing thickness of the line in the data after the basic verification in step S3, then perform a special verification on the icing device for the data collected by the icing device. The special verification on icing includes verification of the icing existence conditions, verification of the rationality of the icing thickness, and verification of the icing growth rate.

[0051] Step S5: Based on the trend comparison analysis results of Step S2, the basic verification results of Step S3, and the icing-specific verification results of Step S4, mark the abnormal types of data collected by each micro-meteorological device and output a list of abnormal micro-meteorological devices.

[0052] Furthermore, in step S1, the data preprocessing includes: converting the meteorological data file from the provincial meteorological center into computable and comparable gridded data for the entire province; processing outliers in the meteorological element data collected by the micro-meteorological device; unifying the time dimension, format, and units of the gridded data from the provincial meteorological center and the meteorological element data collected by the micro-meteorological device; performing height correction on the gridded data from the provincial meteorological center; and interpolating the height-corrected gridded data to match the location of the micro-meteorological device. The meteorological data file from the provincial meteorological center includes temperature, humidity, wind speed, pressure, and precipitation data; the meteorological element data collected by the micro-meteorological device, depending on the sensor, includes temperature, humidity, wind speed, pressure, solar radiation intensity, rainfall intensity, and icing thickness data; the line tower information includes the voltage level of the line to which the tower belongs.

[0053] Furthermore, in step S1, the grid data from the provincial meteorological center undergoes height correction, including pressure height correction, temperature height correction, and wind speed height correction, as detailed below:

[0054] The process of atmospheric pressure altitude correction is expressed as: Corrected atmospheric pressure = Original atmospheric pressure × Exponent (-Target correction altitude / 8500), where the unit of corrected atmospheric pressure is hectopascals, the unit of original atmospheric pressure is hectopascals, 8500 meters is the atmospheric elevation constant, the unit of target correction altitude is meters, and the target correction altitude is obtained based on the coordinates of the grid point where the micro-meteorological device is located, combined with elevation data and tower height.

[0055] The process of correcting the temperature and height is expressed as follows: Corrected temperature = grid point temperature - dynamic lapse rate × height of micro-meteorological device above ground, where the unit of corrected temperature is degrees Celsius, the unit of grid point temperature is degrees Celsius, the unit of height of micro-meteorological device above ground is meters, and the dynamic lapse rate is 1.08 degrees Celsius / 100 meters.

[0056] The process of wind speed height correction is expressed as: corrected wind speed = grid wind speed × (actual device height / reference height) raised to the power of correction, where the unit of corrected wind speed is meters per second, the unit of grid wind speed is meters per second, the reference height is set to 10 meters, and the actual device height is determined according to the voltage level of the line to which the micro-meteorological device is installed.

[0057] Furthermore, in step S1, the interpolation processing of the height-corrected grid data employs a spatial bilinear interpolation method, specifically including the following sub-steps:

[0058] Step S101: Based on the latitude and longitude coordinates of the micro-meteorological device, locate the grid cell where the micro-meteorological device is located. The coordinates of the four corner points of the grid cell are the lower left corner, the lower right corner, the upper left corner, and the upper right corner, respectively.

[0059] Step S102: Calculate the normalized offset of the micro-meteorological device relative to the lower left corner of the grid cell, where the longitude offset = (longitude of micro-meteorological device - longitude of lower left corner) / (longitude of lower right corner - longitude of lower left corner), and the latitude offset = (latitude of micro-meteorological device - latitude of lower left corner) / (latitude of upper left corner - latitude of lower left corner), and the values ​​of both the longitude offset and the latitude offset are in the range of 0 to 1;

[0060] Step S103: Based on the grid data values ​​of the four corner points and the normalized offset, the interpolated data value of the micro-meteorological device location is obtained through bilinear weighted calculation. The calculation method is as follows: Interpolated data value = (1 - longitude offset) × (1 - latitude offset) × grid data value of the lower left corner point + longitude offset × (1 - latitude offset) × grid data value of the lower right corner point + (1 - longitude offset) × latitude offset × grid data value of the upper left corner point + longitude offset × latitude offset × grid data value of the upper right corner point.

[0061] Furthermore, in step S2, before conducting the comparative analysis of changing trends, a step of unifying the time resolution of the preprocessed grid data and the data collected by the micro-meteorological device is also included. Specifically, the time bucket principle is adopted to retain 24 time buckets corresponding to the hourly update frequency of the grid data of the provincial meteorological center. The time bucket represents the time range from 00:00 to 23:00. The meteorological element data collected by the micro-meteorological device is aggregated according to the average value of the 24 time buckets.

[0062] Furthermore, in step S2, the specific threshold settings for the trend comparison analysis are as follows:

[0063] In the correlation coefficient analysis, the threshold for the Pearson correlation coefficient is 0.8, and the threshold for the Spearman correlation coefficient is 0.75.

[0064] In the dynamic time warping distance analysis, the threshold for dynamic time warping distance is set to 15;

[0065] In the slope difference analysis, the threshold for the slope difference ratio is set at 30%.

[0066] The slope difference ratio = |grid data sequence slope - micro-meteorological device data sequence slope| / maximum value (|grid data sequence slope|, |micro-meteorological device data sequence slope|, safety mechanism parameter), where the safety mechanism parameter is used to prevent division by zero errors when the slope is close to zero.

[0067] Furthermore, in step S3, the physical range verification adopts a three-layer verification mechanism, specifically including:

[0068] The first layer: Single element boundary verification, referring to the seasonal characteristics of the current time and the historical extreme values ​​of meteorological elements in the area where the micro-meteorological device is located, presets and determines the reasonable range of each meteorological element, and judges whether the meteorological element data collected by the micro-meteorological device is within the reasonable range.

[0069] The second layer is multi-element correlation verification, which is based on the physical correlation between meteorological elements. This includes temperature and humidity correlation verification. If the temperature is greater than 25 degrees Celsius and the humidity is greater than 85%, it is judged as abnormal.

[0070] The wet rain correlation check indicates that if the precipitation is greater than 5 mm and the humidity is less than 75%, it is considered abnormal.

[0071] Compressed air correlation verification: if the pressure drop exceeds 3 hPa and the wind speed is < 3 m / s, it is judged as abnormal.

[0072] The third layer: spatiotemporal consistency verification, including:

[0073] Time consistency check: if the temperature changes by more than 10 degrees Celsius within 1 hour, it is judged as abnormal;

[0074] Spatial consistency verification: if the pressure difference collected by adjacent micrometeorological devices exceeds 2 hPa, it is judged as abnormal.

[0075] Furthermore, in step S3, the zero-value continuity check specifically includes:

[0076] Regarding wind speed, if it is during the windy season and the wind speed is 0 m / s for 1-2 consecutive hours, it is considered abnormal.

[0077] Regarding precipitation, if it is the rainy season and the precipitation is 0 mm for 3 consecutive hours, it is considered abnormal.

[0078] If two or more meteorological elements collected simultaneously by a micro-meteorological device have zero data, the data must be compared with the historical data collected by the micro-meteorological device or with the data collected by a nearby micro-meteorological device. If the comparison results show that the data deviation exceeds the normal range, it is determined to be an anomaly caused by device malfunction.

[0079] Furthermore, in step S3, the grid data comparison and verification specifically involves: calculating the difference between the grid data after interpolation in step one and the data collected by the micro-meteorological device during the same period for the same type of meteorological elements. If the temperature difference is 10 degrees Celsius or above, the humidity difference is 20% or above, or the wind speed difference is 5 meters per second or above, then the data collected by the micro-meteorological device is marked as abnormal.

[0080] Furthermore, in step S4, the icing growth rate verification specifically includes the following sub-steps:

[0081] Step S401: Calculate the actual growth rate of the data collected by the icing device. The calculation method is: actual growth rate = (ice thickness at the current moment - ice thickness at the previous moment) / time interval, where the ice thickness at the current moment and the ice thickness at the previous moment are both in millimeters, and the time interval is in hours.

[0082] Step S402: Calculate the theoretical growth rate of icing using the McCormick model. The McCormick model is calculated as follows: Theoretical growth rate = empirical parameter a × empirical parameter b (wind speed) × empirical parameter c (liquid water content), where empirical parameter a is 0.067, empirical parameter b is 0.846, and empirical parameter c is 0.519. Wind speed is in meters per second, and liquid water content is in grams per cubic meter. Liquid water content is estimated empirically based on humidity and temperature, and is calculated as follows: Liquid water content = 0.1 × (relative humidity / 100) × exp(0.1 × temperature), where relative humidity is in percent, temperature is in degrees Celsius, and exp is an exponential function.

[0083] Step S403: Calculate the ratio of the actual growth rate to the theoretical growth rate. If the ratio is within the range of 0.3 to 3, the icing growth rate verification is deemed to have passed; if the ratio exceeds the range of 0.3 to 3, the icing growth rate verification is deemed to have failed and is marked as abnormal.

[0084] In one example, a micro-meteorological device verification method based on meteorological rationality analysis includes step S1: acquiring data and preprocessing it. The specific process is as follows: Figure 2 As shown.

[0085] Step S101: Obtain the text data of various meteorological elements from the provincial meteorological center, convert it into a gridded data file covering the entire region, and simultaneously merge the various meteorological elements into a single data file containing meteorological elements such as wind speed, temperature, humidity, pressure, and precipitation, along with gridded surface coordinates, based on a unified time point principle. Additionally, according to the principle of spatial correlation, it is necessary to locate each grid point where the micro-meteorological device is located using the coordinates of the transmission towers. Combining elevation data and tower information, the required height correction for that grid point can be determined. Taking Jiangxi Province as an example, the gridded data provided by the meteorological center should cover the entire province. Taking the grid data updated at 8:00 AM on July 2, 2025 as an example, the meteorological information for grid point G1 is as follows: wind speed -3 (unit: m / s, field: Wind), precipitation -0 (unit: mm, field: Pre), temperature -30 (unit: ℃, field: Temp), humidity -63 (unit: %, field: RHU), and air pressure -1004 (unit: hPa, field: Vis). The micro-meteorological device M1, located at grid point G1, has an elevation data of 100m. Since M1 is installed on the 220kV line L1, the final corrected elevation is 120m. According to the correction formula provided in the technical solution, the corrected elements are: wind speed -4.93m / s, temperature -28.7℃, precipitation -0mm, humidity -63%, and air pressure -986.2hPa. The corresponding surface coordinates of this grid point are P1 (geometry, polygon type).

[0086] Step S102: Perform outlier processing on the data collected by the micrometeorological device. The normal value ranges for each element are as follows: wind speed—0~40 (m / s), temperature—25~45 (°C), humidity—0~100 (%), air pressure—950~1050 (hPa), precipitation—less than 70 (mm). Data exceeding the normal value range are marked as outliers.

[0087] Step S103: Interpolate the grid data to find the meteorological element values ​​of the grid points corresponding to the micro-meteorological device. Take the four grid points in Table 1 as an example.

[0088] Table 1

[0089]

[0090] Using the bilinear interpolation formula in the technical solution, the interpolation results and corrections (device height 50m, grid reference height 10m) of the calculation device M1 (location: 30.25°N, 120.33°E) are shown in Table 2.

[0091] Table 2

[0092]

[0093] Step S2: Perform basic verification on the data collected by the micrometeorological device. The specific process is as follows: Figure 3 As shown.

[0094] Step S201: Unify the temporal resolution of gridded data and micro-meteorological data. Since micro-meteorological data is updated more frequently, it is aggregated according to the time bucket principle, that is, the average value is used for aggregation, and 24 complete hourly buckets (00:00 to 23:00) are retained.

[0095] Taking the temperature data below as an example, its original half-hourly update data is shown in Table 3.

[0096] Table 3

[0097]

[0098] The results after aggregation are shown in Table 4.

[0099] Table 4

[0100]

[0101] Step S202: Perform a consistency analysis on the trends of various meteorological elements at the grid points and the micro-meteorological device. Taking the grid point temperatures from the provincial meteorological center and the data collected by the micro-meteorological device M4 on June 28, 2025 and July 2, 2025 as examples. The micro-meteorological device data is shown in Table 5; the meteorological center data is shown in Table 6.

[0102] Table 5

[0103]

[0104] Table 6

[0105]

[0106] The trend judgment results are shown in Table 7.

[0107] Table 7

[0108]

[0109] To verify the results, the data at grid points and M4 can be visualized, such as... Figure 4 , Figure 5 , Figure 6 , Figure 7 These are, respectively, a line graph comparing the temperature trend on June 28, a line graph comparing the temperature trend on July 2, a graph showing the correlation between the grid points on June 28 and the M4 temperature, and a graph showing the correlation between the grid points on July 2 and the M4 temperature.

[0110] Step S3: Perform basic verification on the data collected by the micrometeorological device. The specific process includes:

[0111] Step S301: Perform physical range verification on the data collected by the micrometeorological device. Taking a micrometeorological device in an 800m high-altitude area in Jiangxi Province as an example, the data collected by the device in mid-March (13:00 on the 13th) were: temperature -28℃, humidity -88%, wind speed -1.5m / s, air pressure -995hPa, and precipitation -6mm / h. Simultaneously, based on the data from the previous moment (12:00 on the 13th), the temperature change was calculated as 15℃→28℃ (ΔT=13℃>10℃), and the air pressure at the adjacent station at 13:00 was 1005hPa→|995-1005|=10hPa>2hPa. After consulting historical meteorological information, the upper limit of temperature was 25℃ and the upper limit of humidity was 90%. The results of the physical range verification are shown in Table 8.

[0112] Table 8

[0113]

[0114] Step S302: Perform zero-value continuity verification on the data collected by the micro-meteorological device. This requires obtaining continuous data from the device to be evaluated at different times, and determining the meteorological background (rainy season, dry season, etc.) to which the time of evaluation belongs. Here, we take an icing device during a rainy season as an example; its continuous 3-hour data is shown in Table 9.

[0115] Table 9

[0116]

[0117] Step S303: Perform grid point data comparison and verification on the data collected by the micro-meteorological device. Taking micro-meteorological device M2 as an example, it is located in a high-altitude area with coordinates of 30.25°N, 120.33°E. The meteorological element values ​​are shown in Table 10.

[0118] Table 10

[0119]

[0120] Based on the principles provided in S3, the verification results are shown in Table 11 below.

[0121] Table 11

[0122]

[0123] Step S4: Perform a specific icing calibration on the icing device in the micro-meteorological device. The specific procedure is as follows:

[0124] Step S401: Filter out icing devices by analyzing the values ​​of the icing thickness field in the data.

[0125] Step S402: Verify the icing conditions of the icing device data. The verification rules are: temperature < 0℃, humidity ≥ 80%RH, and wind speed ≥ 1.0m / s. Taking icing device C1 as an example, its ID is LM00000000JXJA163, and the data acquisition time is 2025-03-15 02:00:00. Based on the coordinate information of the tower where it is installed, it is determined to be located in a high-altitude area. Data details are as follows.

[0126] Table 12

[0127]

[0128] The verification result is: normal.

[0129] Step S403: Verify the reasonableness of the icing thickness data from the icing device. The verification principle is to exclude negative / excessively large values ​​and compare with historical icing data for the region. Query the historical icing data for this region: the maximum historical icing thickness during the same period (March) was 50.0 mm; the minimum historical icing thickness was 0.5 mm. Therefore, the normal icing thickness value for this device should be within the range of 0.5-50.0 mm. Taking the S302 icing device C1 and its data collected on 2025-03-15 02:00:00 as an example, its icing thickness is reasonable, the icing thickness reasonableness verification passes, and the verification result is: normal.

[0130] Step S404: Verify the growth rate of the icing device data. First, calculate the actual growth rate of the device based on the actual data collected. Then, calculate its theoretical growth rate using the Makkonen model. Finally, calculate the ratio of the actual growth rate to the theoretical growth rate to determine whether the icing is reasonable. Taking icing device C1 (S302) and its data collected at 02:00:00 and 01:00:00 on March 15, 2025 as an example, referring to the formula in technical solution S3, the actual growth rate is 2.5 mm / h. The theoretical growth rate is calculated using wind, temperature, humidity, and empirical parameters: a=0.067, b=0.846, c=0.519, with a liquid water content of 0.0696. Therefore, its theoretical growth rate is 0.0688 mm / h, and the ratio of actual to theoretical growth is 36.34. The verification result is: abnormal.

[0131] Based on the above-mentioned ice-specific verification steps, the data for this ice-repelling device was ultimately determined to be abnormal.

[0132] Step S5: Combine all verification sub-processes, label each data entry, distinguish between labels for non-icing devices and labels for icing devices, and store questionable, abnormal, and other types of data or devices in separate tables to obtain a list of abnormal micro-meteorological devices for subsequent data management and device fault investigation.

[0133] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A microclimate device verification method based on meteorological plausibility analysis, characterized by, The method comprises the following steps: Step S1: obtaining to-be-verified data and performing data preprocessing, wherein the to-be-verified data comprises meteorological data files of a provincial meteorological center, meteorological element data collected by all micro meteorological devices, elevation data of a corresponding region, and line tower information corresponding to the micro meteorological devices; The data preprocessing comprises: converting the meteorological data files of the provincial meteorological center into full-province grid point data that can be calculated and compared, performing outlier processing on the meteorological element data collected by the micro meteorological devices, unifying time dimensions, formats and units of the grid point data of the provincial meteorological center and the meteorological element data collected by the micro meteorological devices, performing height correction on the grid point data of the provincial meteorological center, and performing interpolation processing on the height-corrected grid point data to match the positions of the micro meteorological devices; Step S2: for each micro meteorological device, combining the grid point data after the preprocessing in step S1 and the meteorological element data collected by the micro meteorological device, performing change trend comparison and analysis on each meteorological element within a day, wherein the change trend comparison and analysis comprises correlation coefficient analysis, dynamic time warping distance analysis and slope difference analysis; Step S3: performing basic verification on the micro meteorological device collected data after the trend comparison and analysis in step S2, wherein the basic verification comprises physical range verification, zero value continuous verification and grid point data comparison verification; the physical range verification adopts a three-layer verification mechanism, specifically comprising: The first layer: single-element boundary verification, referring to the seasonal characteristics and historical meteorological element extreme values of the current time of the region where the micro meteorological device is located, predefining the reasonable range of each meteorological element, and judging whether the meteorological element data collected by the micro meteorological device is within the reasonable range; The second layer: multi-element correlation verification, which is based on the physical correlation between meteorological elements, including: temperature and humidity correlation verification, humidity and rain correlation verification, and pressure and wind correlation verification; The third layer: time and space consistency verification, comprising: Time consistency verification, which determines whether it is abnormal according to whether the temperature change exceeds a set threshold; Space consistency verification, which determines whether it is abnormal according to whether the pressure difference value collected by adjacent micro meteorological devices exceeds a set threshold; Step S4: among the data after the basic verification in step S3, if the micro meteorological device contains an icing device for collecting line icing thickness, performing icing special verification on the data collected by the icing device, wherein the icing special verification comprises icing existence condition verification, icing thickness reasonableness verification and icing growth rate verification; Step S5: combining the trend comparison and analysis results in step S2, the basic verification results in step S3 and the icing special verification results in step S4, marking the abnormal types of the micro meteorological device collected data, and outputting an abnormal micro meteorological device list.

2. The microclimate device verification method based on meteorological reasonableness analysis of claim 1, wherein, In step S1, the meteorological data files of the provincial meteorological center contain temperature, humidity, wind speed, pressure and precipitation data; the meteorological element data collected by the micro meteorological devices include temperature, humidity, wind speed, pressure, light radiation intensity, rain intensity and icing thickness data according to different sensors; and the line tower information comprises the voltage grade of the line to which the tower belongs. 3.The microclimate device verification method based on meteorological reasonableness analysis of claim 1, wherein, In step S1, the grid data of the provincial meteorological center is corrected in height, including pressure height correction, temperature height correction and wind speed height correction, which are as follows: The process of the pressure height correction is represented as: corrected pressure = original pressure × exponential (-target correction height / 8500), wherein 8500 meters is an atmospheric constant height, and the target correction height is obtained according to the coordinates of the grid point where the micro-meteorological device is located in combination with the elevation data and the tower height; The process of the temperature height correction is represented as: corrected temperature = grid temperature - dynamic decrement rate × height of the micro-meteorological device from the ground; The process of the wind speed height correction is represented as: corrected wind speed = grid wind speed × (correction exponent of the actual height of the device / reference height).

4. The microclimate device verification method based on meteorological plausibility analysis of claim 3, wherein, In step S2, before the trend comparison and analysis, the time resolution of the preprocessed grid data and the micro-meteorological device collected data is unified.

5. The microclimate device verification method based on meteorological plausibility analysis of claim 4, wherein, In step S2, in the slope difference analysis, the slope difference ratio is calculated; the slope difference ratio = |grid data sequence slope - micro-meteorological device data sequence slope| / max(|grid data sequence slope|, |micro-meteorological device data sequence slope|, safety mechanism parameter), wherein the safety mechanism parameter is used to prevent division by zero error when the slope is close to zero.

6. The microclimate device verification method based on meteorological plausibility analysis as claimed in claim 5, wherein, In step S3, the zero value continuous verification specifically includes: For the wind speed element, if it is in the wind season and the wind speed is continuously 0 in the set time period, it is determined as abnormal; For the precipitation element, if it is in the rain season and the precipitation is continuously 0 in the set time period, it is determined as abnormal; For multiple meteorological elements collected by the micro-meteorological device simultaneously, if the collected data of 2 or more elements are all zero values, the historical collected data of the micro-meteorological device or the collected data of the adjacent micro-meteorological device needs to be compared, and if the comparison result shows that the data deviation exceeds the normal range, it is determined as abnormal caused by device failure.

7. The microclimate device verification method based on meteorological plausibility analysis of claim 6, wherein, In step S3, the grid data comparison verification is specifically: the difference values of the same type of meteorological elements between the grid data after the interpolation processing in step 1 and the micro-meteorological device collected data in the same period are calculated, and if the temperature difference value, humidity difference value or wind speed difference value exceeds the respective set threshold value, the collected data of the micro-meteorological device is marked as abnormal. 8.The microclimate device verification method based on meteorological reasonableness analysis of claim 7, wherein, In step S4, the ice accretion rate verification specifically includes the following sub-steps: Step S401: the actual growth rate of the ice accretion device collected data is calculated, and the calculation method is: actual growth rate = (current time ice thickness - previous time ice thickness) / time interval; Step S402: the theoretical growth rate of the ice accretion is calculated by using the McAdams model; Step S403: the ratio of the actual growth rate to the theoretical growth rate is calculated, if the ratio is within the range of 0.3-3, it is determined that the ice accretion rate verification is passed; if the ratio exceeds the range of 0.3-3, it is determined that the ice accretion rate verification is not passed and is marked as abnormal.

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