A method and system for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors

Abstract

This application relates to a method and system for analyzing wind field characteristics in mountainous areas based on micrometeorological factors. The method involves acquiring micrometeorological data of mountainous areas; smoothing wind speeds obtained in time-ordered data to obtain smoothed curves; differentiating the smoothed curves to obtain slope variation curves and determining the degree of strong wind variation; fitting the smoothed curves with a quadratic function to obtain quadratic function curves; determining the strong wind level based on the quadratic function curves; identifying the smoothed curves and wind speed curve images to obtain strong wind shape categories; performing correlation analysis on wind speeds obtained in time-ordered data, as well as wind direction, temperature, humidity, and solar radiation during the same period to obtain strong wind correlation parameters; and constructing a mountainous wind field characteristic database. This application addresses the problem in related technologies of lacking relevant data on wind field characteristics in complex high-altitude mountainous areas affected by micrometeorological factors, and the lack of data support for simulating wind fields on long-span railway bridges in deep canyon areas.

Description

Technical Field

[0001] This application relates to the field of atmospheric science and technology, and in particular to a method and system for analyzing the characteristics of mountain wind fields based on micrometeorological factors. Background Technology

[0002] Field observation is currently a relatively effective and widely used research method for studying wind field characteristics in complex mountainous areas. Many researchers have established wind observation stations at bridge sites in mountainous areas to conduct field measurements of wind field characteristics in complex terrain. They have found that in the high-altitude, high-temperature-difference mountainous areas of western China, regular fluctuations in strong winds occur every afternoon, with wind speeds reaching 10 m / s in some areas. However, relevant literature does not discuss the wind field characteristics of such strong winds. In particular, research on the testing and characteristics of wind fields in high-altitude, complex mountainous areas considering the influence of micrometeorological factors is relatively rare.

[0003] The regular fluctuations in strong winds in complex mountainous terrain may have a certain impact on the construction of long-span railway bridges. When constructing bridges, it is necessary to simulate the wind field to guide the subsequent bridge construction.

[0004] However, due to the lack of relevant data on the characteristics of wind fields in complex mountainous areas at high altitudes affected by micrometeorological factors, there is a lack of data support for simulating the wind field of long-span railway bridges in deep canyon areas. Summary of the Invention

[0005] This application provides a method and system for analyzing the characteristics of mountain wind fields based on micrometeorological factors, in order to solve the problem in related technologies that there is a lack of relevant data on the characteristics of wind fields in complex mountainous areas at high altitudes due to the influence of micrometeorological factors, and the lack of data support when simulating the wind field of long-span railway bridges in deep canyons.

[0006] Firstly, a method for analyzing mountain wind field characteristics based on micro-meteorological factors is provided, which includes:

[0007] Acquire micro-meteorological data at different locations in the mountainous area along the elevation direction. The micro-meteorological data includes wind speed, wind direction, temperature, humidity, and solar radiation, all sorted by time. The wind speed is greater than a preset strong wind speed.

[0008] The wind speeds obtained by time sorting are smoothed to obtain a smooth curve of wind speed with respect to acquisition time.

[0009] The derivative of the smoothed curve of wind speed with respect to acquisition time is obtained to obtain the slope change curve. Based on the slope change curve, the level of strong wind change is determined.

[0010] A quadratic function is fitted to the smooth curve of wind speed with respect to acquisition time to obtain a quadratic function curve of wind speed with respect to acquisition time.

[0011] Based on the quadratic function curve of wind speed with respect to acquisition time, the duration and intensity of strong winds are determined to obtain the strong wind level.

[0012] By using image recognition technology, the smooth curve of wind speed with respect to acquisition time is identified with multiple pre-set wind speed curve images of different shape categories to obtain the strong wind shape category;

[0013] Correlation analysis is performed on wind speeds ordered by time, as well as wind direction, temperature, humidity, and solar radiation during the same period to obtain strong wind correlation parameters, which include one or more of wind direction, temperature, humidity, and solar radiation.

[0014] A mountain wind field characteristics database is constructed, which includes the micrometeorological data, the degree of strong wind change, the level of strong wind, the type of strong wind shape, and the correlation parameters of strong wind.

[0015] In some embodiments, the degree of strong wind change is determined based on the slope change curve, specifically including:

[0016] Calculate the slope value at each point in the slope change curve;

[0017] The maximum value among all slope values ​​is determined as the abrupt change value of strong wind;

[0018] The mutation value is compared with the mutation value range corresponding to several different levels of strong wind change to obtain the corresponding level of strong wind change.

[0019] In some embodiments, the duration and intensity of strong winds are determined based on a quadratic function curve of wind speed with respect to acquisition time, in order to obtain the strong wind level. Specifically, this includes:

[0020] Obtain the time interval between the first and last points on the quadratic function curve and determine it as the duration of the strong wind.

[0021] Obtain the maximum wind speed of the quadratic function curve and determine it as the intensity of the strong wind;

[0022] The strong winds are classified into different levels based on their duration and intensity to obtain a strong wind level.

[0023] In some embodiments, acquiring micrometeorological data at different locations along the elevation direction in mountainous areas specifically includes:

[0024] Collect raw micro-meteorological data at different locations along the elevation direction in the mountainous area;

[0025] Using wind speeds greater than a preset strong wind speed as a filtering condition, the original micrometeorological data is filtered to obtain filtered micrometeorological data.

[0026] The screened micrometeorological data were processed by handling missing values, outliers, and averages to obtain micrometeorological data at different locations in the mountainous area along the elevation direction.

[0027] In some embodiments, missing value processing is performed on the screened micrometeorological data, specifically including:

[0028] When there is a missing value between two adjacent points and the missing time is lower than the missing time threshold, the missing value is filled by interpolation.

[0029] When there is a missing value between two adjacent points and the missing time is higher than the missing time threshold, the missing value is filled with 0.

[0030] In some embodiments, outlier processing is performed on the screened micrometeorological data, specifically including:

[0031] Set the confidence level and confidence interval;

[0032] Points that exceed the confidence interval are considered outliers and replaced with the average of the two points immediately preceding and following that point.

[0033] In some embodiments, the screened micrometeorological data are averaged, specifically including:

[0034] Average multiple points within a preset time period.

[0035] Secondly, a mountain wind field characteristic analysis system based on micro-meteorological factors is provided, which includes:

[0036] The first module is used to: acquire micro-meteorological data at different locations in the mountainous area in the elevation direction, wherein the micro-meteorological data includes wind speed, wind direction, temperature, humidity and solar radiation acquired in time order, wherein the wind speed is greater than a preset strong wind speed;

[0037] The second module is used to: smooth the wind speeds obtained by time sorting to obtain a smooth curve of wind speed with respect to acquisition time.

[0038] The third module is used to: differentiate the smooth curve of wind speed with respect to acquisition time to obtain the slope change curve, and determine the level of strong wind change based on the slope change curve;

[0039] The fourth module is used to: perform quadratic function fitting on the smooth curve of wind speed with respect to acquisition time to obtain a quadratic function curve of wind speed with respect to acquisition time;

[0040] The fifth module is used to determine the duration and intensity of strong winds based on the quadratic function curve of wind speed with respect to acquisition time, so as to obtain the strong wind level.

[0041] The sixth module is used to: identify the smooth curve of wind speed with respect to acquisition time and the wind speed curve images of multiple different shape categories by using image recognition technology, so as to obtain the strong wind shape category;

[0042] The seventh module is used to: perform correlation analysis on wind speeds obtained in time order, as well as wind direction, temperature, humidity and solar radiation during the same period to obtain strong wind correlation parameters, wherein the strong wind correlation parameters include one or more of wind direction, temperature, humidity and solar radiation.

[0043] The eighth module is used to construct a mountain wind field characteristics database, which includes the micrometeorological data, strong wind variation level, strong wind level, strong wind shape category and strong wind correlation parameters.

[0044] Thirdly, a storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements any of the above-described methods for analyzing the characteristics of mountain wind fields based on micrometeorological factors.

[0045] Fourthly, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program running on the processor, and the processor executes the computer program to implement any of the above-described methods for analyzing the characteristics of mountain wind fields based on micrometeorological factors.

[0046] The beneficial effects of the technical solution provided in this application include:

[0047] This application provides a method and system for analyzing wind field characteristics in mountainous areas based on micrometeorological factors. Based on real-time observation of wind fields and meteorology, it acquires spatiotemporal distribution data of wind fields and meteorology in complex high-altitude mountainous areas, studies the distribution patterns of wind fields, temperature, humidity, and solar radiation in high-altitude mountainous areas, reveals the coupling evolution law of wind fields and micrometeorological characteristics in complex high-altitude mountainous areas, and forms a wind field characteristic database that considers micrometeorology in complex high-altitude mountainous areas. This provides data basis for the simulation and forecasting of wind fields on long-span railway bridges in deep canyon areas. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 Flowchart of the method for analyzing mountain wind field characteristics based on micrometeorological factors provided in this application embodiment;

[0050] Figure 2 This is a schematic diagram showing the arrangement of instruments and equipment in an embodiment of this application;

[0051] Figure 3 A schematic diagram of a quadratic function curve provided in an embodiment of this application;

[0052] Figure 4 The embodiments of this application provide pre-defined wind speed curve images of multiple different shape categories. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0054] See Figure 1 As shown, a method for analyzing the characteristics of mountain wind fields based on micrometeorological factors includes the following steps:

[0055] 101: Obtain micro-meteorological data at different locations in the mountainous area along the elevation direction. The micro-meteorological data includes wind speed, wind direction, temperature, humidity, and solar radiation, all obtained in time order, wherein the wind speed is greater than a preset strong wind speed.

[0056] It is understandable that, since the wind speeds mentioned above are greater than the preset strong wind speeds, the aforementioned micrometeorological data represent strong wind data. The preset strong wind speeds can be set manually according to actual needs, and this application does not impose specific limitations on them.

[0057] See Figure 2 As shown, existing tower cranes necessary for the construction of long bridges can be used to arrange instruments and equipment such as three-dimensional anemometers, temperature and humidity sensors, and solar radiation sensors at equal intervals along the tower height. Support clamps can be used to fix the instruments, with one clamp capable of holding multiple instruments. Using existing tower cranes necessary for the construction of long bridges as a meteorological data measurement base effectively reduces the cost of instruments and equipment and improves economic efficiency.

[0058] Figure 2In the middle, a 3D anemometer and temperature, humidity, and solar radiation sensors are installed on layer A. In addition to the instruments in layer A, layer B can also add a rain sensor, thus avoiding the need for a radar anemometer and saving costs. The 3D anemometer collects wind speed, direction, and angle of attack; the temperature and humidity sensors collect changes in temperature and humidity; and the solar radiation sensor collects changes in solar radiation.

[0059] It is powered by solar cells and transmitted to the backend server via 4G signal.

[0060] 102: Smooth the wind speeds obtained by time sorting to obtain a smooth curve of wind speed with respect to acquisition time.

[0061] Smoothing is achieved by using a moving average filter to smooth the wind speed values ​​in the vector. The default window width of the moving average filter is 5.

[0062] 103: Differentiate the smoothed curve of wind speed with respect to acquisition time to obtain the slope change curve, and determine the level of strong wind change based on the slope change curve;

[0063] 104: Perform quadratic function fitting on the smooth curve of wind speed with respect to acquisition time to obtain the quadratic function curve of wind speed with respect to acquisition time.

[0064] 105: Based on the quadratic function curve of wind speed with respect to acquisition time, determine the duration and intensity of strong winds to obtain the strong wind level.

[0065] 106: By using image recognition technology, the smooth curve of wind speed with respect to acquisition time is identified with multiple pre-set wind speed curve images of different shape categories to obtain the strong wind shape category.

[0066] 107: Perform correlation analysis on wind speeds ordered by time, as well as wind direction, temperature, humidity and solar radiation during the same period to obtain strong wind correlation parameters, which include one or more of wind direction, temperature, humidity and solar radiation.

[0067] By using wind direction, temperature, humidity, and solar radiation data from the same period, correlation analysis is performed on wind speed to identify the factors affecting wind speed.

[0068] Regarding wind direction, we can analyze wind direction changes at different altitudes, as well as vertical and horizontal wind angles, to determine wind speed changes under atmospheric influence, including three-dimensional shear and mesocyclone phenomena; and the regular wind direction changes along the canyon, including local uplift and downlift of airflow caused by uneven solar radiation.

[0069] For temperature, the correlation analysis of strong winds under the influence of temperature changes and temperature differences, whether it is accompanied by humidity changes, if there is obvious surface wind shear or a significant temperature, pressure and humidity gradient, gusts may occur.

[0070] Regarding humidity, the changes in humidity caused by the downwind winds from glaciers in canyon areas, as well as the different wind direction changes of glacier winds and valley winds, can all serve as a basis for classifying strong winds.

[0071] Record changes in wind direction, temperature, humidity, and solar radiation data, perform correlation analysis with strong wind speed data, link various micrometeorological conditions with strong winds, obtain coupled evolution patterns, and identify whether strong winds are primarily due to temperature changes or atmospheric influences.

[0072] 108: Construct a mountain wind field characteristics database, which includes the micrometeorological data, strong wind variation level, strong wind level, strong wind shape category and strong wind correlation parameters.

[0073] A wind field measurement database that takes into account the complex micro-meteorology of high-altitude mountainous areas will be established to provide data support for CFD simulation in canyon mountainous areas.

[0074] The method for analyzing mountain wind field characteristics based on micrometeorological factors provided in this application provides a method for analyzing mountain wind field characteristics based on real-time observation of wind field and meteorology. It obtains spatiotemporal distribution data of wind field and meteorology in complex high-altitude mountainous areas, studies the distribution patterns of wind field, temperature, humidity and solar radiation in high-altitude mountainous areas, reveals the coupling evolution law of wind field and micrometeorological characteristics in complex high-altitude mountainous areas, and forms a wind field characteristic database that considers micrometeorology in complex high-altitude mountainous areas. This provides a data basis for the simulation and forecasting of wind field for long-span railway bridges in deep canyon areas.

[0075] In step 103 above, the degree of strong wind change is determined based on the slope change curve, which specifically includes the following steps:

[0076] 401: Calculate the slope value at each point in the slope change curve.

[0077] The positive and negative values ​​on the slope curve represent whether the wind speed value is increasing or decreasing. If the slope is positive, it means that the wind speed value is increasing; if the slope is negative, it means that the wind speed value is decreasing.

[0078] 402: The maximum value among all slope values ​​is determined as the abrupt change value of strong wind;

[0079] Obtain the magnitude of the slope change curve, starting from the first point of the slope change curve, determine whether the two points before and after are both positive, and record the slope value when they are both positive. Then this segment is continuously increasing.

[0080] Since the averaged wind speed data have the same time interval, it can be assumed that the slope change value of the same time interval reflects the severity of the sudden change in strong wind. The value of the slope change curve is obtained, and the maximum value of the slope value is determined as the sudden change value of the strong wind in that segment.

[0081] 403: Compare the mutation value with the mutation value range corresponding to multiple pre-set different strong wind change levels to obtain the corresponding strong wind change level.

[0082] See Figure 3 The image shows a quadratic function curve of wind speed obtained in step 104 above, expressed as a function of acquisition time. It can be understood that the wind speed, ordered by acquisition time, is actually represented by multiple points, each determined by the acquisition time and the corresponding wind speed. For example... Figure 3 The horizontal axis represents the acquisition time, and the vertical axis represents the wind speed. Therefore, the wind speed sorted by acquisition time is actually a curve, which has several peaks and / or several troughs.

[0083] By fitting a quadratic function, we can obtain its general direction, which is a peak curve, i.e., a quadratic function curve.

[0084] Based on the quadratic function curve, the duration and intensity of strong winds can be determined, thus obtaining the strong wind level. Specifically, obtaining the strong wind level includes the following steps:

[0085] 201: Obtain the time interval between the first and last points on the quadratic function curve and determine it as the duration of the strong wind.

[0086] Since a quadratic function curve is a peak curve, and it is obtained by fitting multiple points, the time interval between the acquisition times corresponding to the first and last points on the quadratic function curve represents the duration of the corresponding strong wind.

[0087] 202: Obtain the maximum wind speed of the quadratic function curve and determine it as the intensity of the strong wind.

[0088] Since the quadratic function curve is a peak curve, it has a maximum wind speed. Therefore, the maximum wind speed is determined as the intensity of the strong wind.

[0089] 203: Based on the duration and intensity of the strong winds, classify the strong winds into levels to obtain strong wind levels.

[0090] Duration and intensity can be used as two classification indicators. For example, if there are n levels for duration and m levels for intensity, then there are n×m levels for strong wind. When classifying strong winds, find the strong wind level that includes both the duration and intensity of the strong wind.

[0091] In step 106 above, multiple wind speed curve images of different shapes can be preset. For example, such as... Figure 4 As shown, (a) is defined as the first shape, (b) as the second shape, (c) as the third shape, and (d) as the fourth shape.

[0092] Image recognition technology is used to identify the smooth curve of wind speed with respect to acquisition time and the wind speed curve images of multiple pre-set different shape categories to obtain the strong wind shape category.

[0093] The image recognition techniques described above can be directly applied to existing methods. For example, the mean hash algorithm can be used. First, the original image is reduced to a fixed-size pixel image. Then, the pixel image is converted to a grayscale image. By comparing each pixel of the reduced image with the average grayscale value, a set of hash values ​​is generated. Finally, the Hamming distance between the hash values ​​of the two sets of images is used to evaluate the similarity between the images, thus completing the image recognition.

[0094] In practical applications, when determining whether a wind speed curve fits a category, the minimum Hamming distance is used for image classification.

[0095] In step 101 above, obtaining micrometeorological data at different locations along the elevation direction in the mountainous area specifically includes the following steps:

[0096] 301: Collect raw micro-meteorological data at different locations along the elevation direction in mountainous areas.

[0097] The raw micrometeorological data includes wind speed, wind direction, temperature, humidity, and solar radiation.

[0098] It is understandable that the frequency of data collection for wind speed, wind direction, temperature, humidity, and solar radiation varies depending on the instruments and equipment used.

[0099] For example, wind speed and direction are collected by a three-dimensional anemometer, temperature and humidity are collected by a temperature and humidity sensor, and solar radiation is collected by a solar radiation sensor. The collection frequencies of each instrument are not the same.

[0100] Therefore, although the original micrometeorological data, including wind speed, wind direction, temperature, humidity, and solar radiation, are time-related and sorted based on the collection time, the collection times for different types of data may not be the same. Moreover, the original micrometeorological data consists of multiple points, each composed of the collection time and wind speed, wind direction, temperature, humidity, or solar radiation.

[0101] For example, the time for collecting wind speed and wind direction data can be the same, and the time for collecting temperature and humidity data can be the same. However, the time for collecting wind speed and wind direction data may not be the same as the time for collecting temperature and humidity data, nor may it be the same as the time for collecting solar radiation data.

[0102] 302: Use wind speeds greater than the preset strong wind speed as a screening condition to filter the original micrometeorological data to obtain the filtered micrometeorological data.

[0103] Because the regular fluctuations in strong winds occurring in the high-altitude, high-temperature-difference mountainous areas of western China can affect bridge construction, it is only necessary to focus on strong wind data. Moreover, the original micro-meteorological data is very large and complex to process. Therefore, wind speeds greater than the preset strong wind speed are used as a screening criterion to filter the original micro-meteorological data and remove data that are not strong winds. This not only reduces the amount of data processing, but also treats data that is not strong wind as noise data and removes it, thus avoiding unnecessary impact from this part of the data on subsequent data processing.

[0104] 303: The screened micrometeorological data were processed for missing values, outliers, and averages to obtain micrometeorological data at different locations in the mountainous area along the elevation direction.

[0105] Because instruments and equipment may experience abnormal conditions during operation, such as power outages or instrument failures followed by automatic recovery, resulting in missing or abnormal values, it is necessary to handle missing and abnormal values.

[0106] In addition, although the original micrometeorological data was screened in step 302, due to the high frequency of data collection by the instruments and equipment and the large amount of data, it is still necessary to perform data averaging to further reduce the processing volume.

[0107] It is understandable that the above averaging process can be performed before or after missing value processing and outlier processing.

[0108] In step 303 above, missing value processing is performed on the screened micrometeorological data, specifically including:

[0109] When there is a missing value between two adjacent points and the missing time is lower than the missing time threshold, the missing value is filled by interpolation.

[0110] When there is a missing value between two adjacent points and the missing time is higher than the missing time threshold, the missing value is filled with 0.

[0111] The vacancy time threshold can be set according to actual needs. For example, it can be set to 3 hours to distinguish between short-term vacancies and long-term vacancies.

[0112] If there are one or more missing values ​​between two adjacent points, and the missing value is short-term, interpolation can be used to fill in the missing value between the two adjacent points by interpolating the ordinate values ​​of the two adjacent points.

[0113] If there are one or more missing values ​​between two adjacent points, and the missing value is for a long time, then the ordinate value of the missing position is directly assigned to 0.

[0114] In step 303 above, outlier processing is performed on the screened micrometeorological data, specifically including:

[0115] Set the confidence level and confidence interval;

[0116] Points that exceed the confidence interval are considered outliers and replaced with the average of the two points immediately preceding and following that point.

[0117] Set the confidence level and confidence interval according to actual needs, and judge the ordinate of each point. If it is within the confidence interval, the point is a normal value. If it is not within the confidence interval, whether it is lower than the minimum value of the confidence interval or higher than the maximum value of the confidence interval, the point is judged as an outlier. Calculate the average of the ordinates of the two adjacent points before and after the point, and use the average value as the ordinate of the point.

[0118] In step 303 above, the filtered micrometeorological data are averaged, specifically including:

[0119] Average multiple points within a preset time period.

[0120] As mentioned earlier, the frequency of data collection for wind speed, wind direction, temperature, humidity, and solar radiation varies depending on the instruments and equipment used.

[0121] By averaging the ordinate data of all points within a certain period of time for each type of data, data on wind speed, wind direction, temperature, humidity, and solar radiation for the same period can be generated.

[0122] Understandably, the preset time length can be set according to actual needs, such as averaging over one minute or ten minutes.

[0123] For example, wind speed is collected periodically at a frequency of 10 seconds, and temperature is collected periodically at a frequency of 20 seconds.

[0124] Regarding wind speed, the following points apply:

[0125] C1(12:00:10s, c1), C2(12:00:20s, c2), C3(12:00:30s, c3),

[0126] C4 (12:00:40s, c4), C5 (12:00:50s, c5), C6 (12:01:00s, c6),

[0127] C7 (12:01:10s, c7), C8 (12:01:20s, c8), C9 (12:01:30s, c9),

[0128] C 10 (12:01:40s, c) 10 ), C 11 (12:01:50s, c) 11 ), C 12 (12:02:00s, c 12 ).

[0129] In the above points, the former represents the collection time, and the latter represents the wind speed. Averaging the 12 wind speed points over one minute yields two points:

[0130] The wind speed at the first point is: c'1 = (c1 + c2 + c3 + c4 + c5 + c6) / 6.

[0131] The wind speed at the second point is: c'2 = (c7 + c8 + c9 + c) 10 +c 11 +c 12 ) / 6.

[0132] The first point is obtained from several points between 12:00:10s and 12:01:00s, so the corresponding acquisition time can be set to 12:01:00s. Similarly, the acquisition time of the second point can be set to 12:02:00s.

[0133] The two points are C'1 (12:01:00s, c'1) and C'2 (12:02:00s, c'2).

[0134] In the above points, the former refers to the acquisition time, and the latter refers to the average wind speed after processing.

[0135] Similarly, for temperature, the following point holds:

[0136] D1(12:00:20s, d1), D2(12:00:40s, d2), D3(12:01:00s, d3),

[0137] D4 (12:01:20s, d4), D5 (12:01:40s, d5), D6 (12:02:00s, d6),

[0138] D7 (12:02:20s, d7), D8 (12:02:40s, d8), D9 (12:03:00s, d9),

[0139] D 10 (12:03:20s, d) 10 ), D 11 (12:03:40s, d) 11 ), D 12 (12:04:00s, d) 12 ).

[0140] In the above points, the former represents the collection time, and the latter represents the temperature rate. Averaging the 12 temperature points over 1 minute yields four points:

[0141] The temperature of the first point is: d'1 = (d1 + d2 + d3) / 3.

[0142] The temperature at the second point is: d'2 = (d4 + d5 + d6) / 3.

[0143] The temperature at the third point is: d'3 = (d7 + d8 + d9) / 3.

[0144] The temperature at the fourth point is: d'4 = (d 10 +d 11 +d 12 ) / 3.

[0145] The first point is obtained from several points between 12:00:20s and 12:01:00s, so the corresponding acquisition time can be set to 12:01:00s. Similarly, the acquisition time of the second point can be set to 12:02:00s, the acquisition time of the third point can be set to 12:03:00s, and the acquisition time of the fourth point can be set to 12:04:00s.

[0146] The four points are D'1 (12:01:00s, d'1), D'2 (12:02:00s, d'2), D'3 (12:03:00s, d'3), and D'4 (12:04:00s, d'4).

[0147] In the above points, the former refers to the acquisition time, and the latter refers to the average temperature after processing.

[0148] Although the temperature and wind speed were collected at different frequencies and at different times, the acquisition time for temperature and wind speed was unified after the above averaging process.

[0149] For data such as humidity, wind direction, and solar radiation, the above averaging process can also be used to ensure that the acquisition time is consistent. Then, the correlation analysis of wind speed can be performed using wind direction, temperature, humidity, and solar radiation data from the same period.

[0150] Corresponding to the above-mentioned method for analyzing mountain wind field characteristics based on micrometeorological factors, this application also provides a system for analyzing mountain wind field characteristics based on micrometeorological factors, which includes:

[0151] The first module is used to: acquire micro-meteorological data at different locations in the mountainous area in the elevation direction, wherein the micro-meteorological data includes wind speed, wind direction, temperature, humidity and solar radiation acquired in time order, wherein the wind speed is greater than a preset strong wind speed;

[0152] The second module is used to: smooth the wind speeds obtained by time sorting to obtain a smooth curve of wind speed with respect to acquisition time.

[0153] The third module is used to: differentiate the smooth curve of wind speed with respect to acquisition time to obtain the slope change curve, and determine the level of strong wind change based on the slope change curve;

[0154] The fourth module is used to: perform quadratic function fitting on the smooth curve of wind speed with respect to acquisition time to obtain a quadratic function curve of wind speed with respect to acquisition time;

[0155] The fifth module is used to determine the duration and intensity of strong winds based on the quadratic function curve of wind speed with respect to acquisition time, so as to obtain the strong wind level.

[0156] The sixth module is used to: identify the smooth curve of wind speed with respect to acquisition time and the wind speed curve images of multiple different shape categories by using image recognition technology, so as to obtain the strong wind shape category;

[0157] The seventh module is used to: perform correlation analysis on wind speeds obtained in time order, as well as wind direction, temperature, humidity and solar radiation during the same period to obtain strong wind correlation parameters, wherein the strong wind correlation parameters include one or more of wind direction, temperature, humidity and solar radiation.

[0158] The eighth module is used to construct a mountain wind field characteristics database, which includes the micrometeorological data, strong wind variation level, strong wind level, strong wind shape category and strong wind correlation parameters.

[0159] Corresponding to the above-described method for analyzing mountain wind field characteristics based on micrometeorological factors, this application also provides a storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the above embodiments. It should be noted that the storage medium in this application embodiment can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. Computer-readable storage media can be, for example, but not limited to: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or apparatus.

[0160] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0161] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0162] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0163] Corresponding to the above-mentioned method for analyzing the characteristics of mountain wind fields based on micrometeorological factors, this application also provides an electronic device, including a memory and a processor. The memory stores a computer program that runs on the processor, and the processor executes the computer program to implement the steps of the above embodiments.

[0164] In the description of this application, it should be noted that the terms "upper," "lower," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0165] In the description of this application, it should be noted that the terms "upper," "lower," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0166] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0167] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for analyzing the characteristics of mountain wind fields based on micrometeorological factors, characterized in that, It includes: Acquire micro-meteorological data at different locations in the mountainous area along the elevation direction. The micro-meteorological data includes wind speed, wind direction, temperature, humidity, and solar radiation, all sorted by time. The wind speed is greater than a preset strong wind speed. The wind speeds obtained by time sorting are smoothed to obtain a smooth curve of wind speed with respect to acquisition time. The derivative of the smoothed curve of wind speed with respect to acquisition time is obtained to obtain the slope change curve. Based on the slope change curve, the level of strong wind change is determined. A quadratic function is fitted to the smooth curve of wind speed with respect to acquisition time to obtain a quadratic function curve of wind speed with respect to acquisition time. Based on the quadratic function curve of wind speed with respect to acquisition time, the duration and intensity of strong winds are determined to obtain the strong wind level. By using image recognition technology, the smooth curve of wind speed with respect to acquisition time is identified with multiple pre-set wind speed curve images of different shape categories to obtain the strong wind shape category; Correlation analysis is performed on wind speeds ordered by time, as well as wind direction, temperature, humidity, and solar radiation during the same period to obtain strong wind correlation parameters, which include one or more of wind direction, temperature, humidity, and solar radiation. A mountain wind field characteristics database is constructed, which includes the micrometeorological data, the degree of strong wind change, the level of strong wind, the type of strong wind shape, and the correlation parameters of strong wind.

2. The method for analyzing mountain wind field characteristics based on micrometeorological factors as described in claim 1, characterized in that, Based on the slope change curve, the severity level of strong winds is determined, specifically including: Calculate the slope value at each point in the slope change curve; The maximum value among all slope values ​​is determined as the abrupt change value of strong wind; The mutation value is compared with the mutation value range corresponding to several different levels of strong wind change to obtain the corresponding level of strong wind change.

3. The method for analyzing mountain wind field characteristics based on micrometeorological factors as described in claim 1, characterized in that, Based on the quadratic function curve of wind speed with respect to acquisition time, the duration and intensity of strong winds are determined to obtain the strong wind level, specifically including: Obtain the time interval between the first and last points on the quadratic function curve and determine it as the duration of the strong wind. Obtain the maximum wind speed of the quadratic function curve and determine it as the intensity of the strong wind; The strong winds are classified into different levels based on their duration and intensity to obtain a strong wind level.

4. The method for analyzing mountain wind field characteristics based on micrometeorological factors as described in claim 1, characterized in that, Acquiring micrometeorological data at different locations along the elevation direction in mountainous areas, specifically including: Collect raw micro-meteorological data at different locations along the elevation direction in the mountainous area; Using wind speeds greater than a preset strong wind speed as a filtering condition, the original micrometeorological data is filtered to obtain filtered micrometeorological data. The screened micrometeorological data were processed by handling missing values, outliers, and averages to obtain micrometeorological data at different locations in the mountainous area along the elevation direction.

5. The method for analyzing mountain wind field characteristics based on micrometeorological factors as described in claim 4, characterized in that, Missing values ​​were processed in the filtered micrometeorological data, specifically including: When there is a missing value between two adjacent points and the missing time is lower than the missing time threshold, the missing value is filled by interpolation. When there is a missing value between two adjacent points and the missing time is higher than the missing time threshold, the missing value is filled with 0.

6. The method for analyzing mountain wind field characteristics based on micrometeorological factors as described in claim 4, characterized in that, Outlier handling was performed on the filtered micrometeorological data, specifically including: Set the confidence level and confidence interval; Points that exceed the confidence interval are considered outliers and replaced with the average of the two points immediately preceding and following that point.

7. The method for analyzing mountain wind field characteristics based on micrometeorological factors as described in claim 4, characterized in that, The selected micrometeorological data were averaged, specifically including: Average multiple points within a preset time period.

8. A mountain wind field characteristic analysis system based on micrometeorological factors, characterized in that, It includes: The first module is used to: acquire micro-meteorological data at different locations in the mountainous area in the elevation direction, wherein the micro-meteorological data includes wind speed, wind direction, temperature, humidity and solar radiation acquired in time order, wherein the wind speed is greater than a preset strong wind speed; The second module is used to: smooth the wind speeds obtained by time sorting to obtain a smooth curve of wind speed with respect to acquisition time. The third module is used to: differentiate the smooth curve of wind speed with respect to acquisition time to obtain the slope change curve, and determine the level of strong wind change based on the slope change curve; The fourth module is used to: perform quadratic function fitting on the smooth curve of wind speed with respect to acquisition time to obtain a quadratic function curve of wind speed with respect to acquisition time; The fifth module is used to determine the duration and intensity of strong winds based on the quadratic function curve of wind speed with respect to acquisition time, so as to obtain the strong wind level. The sixth module is used to: identify the smooth curve of wind speed with respect to acquisition time and the wind speed curve images of multiple different shape categories by using image recognition technology, so as to obtain the strong wind shape category; The seventh module is used to: perform correlation analysis on wind speeds obtained in time order, as well as wind direction, temperature, humidity and solar radiation during the same period to obtain strong wind correlation parameters, wherein the strong wind correlation parameters include one or more of wind direction, temperature, humidity and solar radiation. The eighth module is used to construct a mountain wind field characteristics database, which includes the micrometeorological data, strong wind variation level, strong wind level, strong wind shape category and strong wind correlation parameters.

9. A storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, it implements the mountain wind field characteristic analysis method based on micrometeorological factors as described in any one of claims 1 to 7.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that runs on the processor, characterized in that: When the processor executes the computer program, it implements the mountain wind field characteristic analysis method based on micrometeorological factors as described in any one of claims 1 to 7.

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