Mountain area wind field characteristic analysis method and system based on micrometeorological factors

Abstract

The invention relates to a mountainous area wind field characteristic analysis method and system based on micro-meteorological factors. The method comprises the following steps: acquiring micro-meteorological data of a mountainous area; smoothing the wind speed sorted according to the acquisition time to obtain a smooth curve; deriving the smooth curve to obtain a slope change curve, and determining a strong wind change degree level; performing quadratic function fitting on the smooth curve to obtain a quadratic function curve; determining a strong wind level based on a quadratic function curve; identifying the smooth curve and the wind speed curve image to obtain a strong wind shape category; performing correlation analysis on the wind speed sorted according to the acquisition time and the wind direction, the temperature, the humidity and the solar radiation in the same period as the wind speed to obtain strong wind correlation parameters; and constructing a mountainous area wind field characteristic database. The problem that data support is lacked when the wind field of the railway long-span bridge in the deep canyon region is subjected to analog simulation due to the lack of related data for researching the wind field characteristics of the high-altitude complex mountainous area influenced by micrometeorological factors in the related technology can be solved.

Description

Technical Field

[0001] The present application relates to the field of atmospheric science and technology, and in particular to a method and system for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors. Background Art

[0002] Field observation is currently a relatively effective and widely used research method for studying the characteristics of wind fields in complex mountainous areas. Many researchers have measured the characteristics of wind fields in complex mountainous terrain by establishing wind observation stations at bridge locations in mountainous areas. It was found that in the high-altitude and high-temperature-difference mountainous areas in the west, there are regular and fluctuating strong and windy winds every afternoon, and the wind speed in some areas can reach 10m / s, but the wind field characteristics of such strong and windy winds are not discussed in the relevant literature. In particular, the test research on wind field characteristics in high-altitude complex mountainous areas considering the influence of micro-meteorological factors is relatively rare.

[0003] Regularly fluctuating 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 subsequent bridge construction.

[0004] However, due to the lack of relevant research data on the characteristics of wind fields in high-altitude complex mountainous areas affected by micro-meteorological factors, there is a lack of data support when simulating the wind fields of large-span railway bridges in deep canyon areas. Summary of the invention

[0005] The embodiments of the present application provide a method and system for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors, so as to solve the problem in the related art of lack of relevant data on the research characteristics of wind fields in high-altitude complex mountainous areas affected by micro-meteorological factors, and lack of data support when simulating the wind fields of large-span railway bridges in deep canyon areas.

[0006] In a first aspect, a method for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors is provided, which includes:

[0007] Acquire micro-meteorological data at different positions in the mountainous area in the elevation direction, wherein the micro-meteorological data includes wind speed, wind direction, temperature, humidity and solar radiation, all in order of acquisition time, wherein the wind speed is greater than a preset strong wind speed;

[0008] Smoothing the wind speeds sorted by acquisition time to obtain a smooth curve of wind speed with respect to acquisition time;

[0009] Derivative the smooth curve of wind speed with respect to acquisition time to obtain a slope change curve, and determine the level of strong wind change based on the slope change curve;

[0010] Performing 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;

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

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

[0013] Performing correlation analysis on the wind speeds sorted in time, and the wind direction, temperature, humidity and solar radiation in the same period as the wind speeds, to obtain strong wind correlation parameters, wherein the strong wind correlation parameters include one or more of wind direction, temperature, humidity and solar radiation;

[0014] A mountainous area wind field characteristic database is constructed, wherein the database includes the micro-meteorological data, the level of strong wind variation, the level of strong wind, the category of strong wind shape and the strong wind correlation parameters.

[0015] In some embodiments, based on the slope change curve, determining the level of strong wind change specifically includes:

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

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

[0018] The mutation value is compared with a plurality of preset mutation value ranges corresponding to different strong wind change degree levels to obtain the corresponding strong wind change degree level.

[0019] In some embodiments, based on the quadratic function curve of wind speed with respect to acquisition time, the duration and intensity of strong wind are determined to obtain the strong wind level, specifically including:

[0020] Obtain the time period between the first point and the last point 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 wind is classified into different levels according to the duration and intensity of the strong wind to obtain a strong wind level.

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

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

[0025] Taking the wind speed greater than the preset strong wind speed as a screening condition, the original micro-meteorological data is screened to obtain the screened micro-meteorological data;

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

[0027] In some embodiments, the filtered micrometeorological data is processed for missing values, 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 interpolation method is used to fill the missing value;

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

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

[0031] Setting confidence levels and confidence intervals;

[0032] Points outside the confidence interval are regarded as outliers and replaced with the average of the two adjacent points before and after the point.

[0033] In some embodiments, the filtered micrometeorological data is averaged, specifically including:

[0034] Averages multiple points over a preset time period.

[0035] In a second aspect, a mountainous area wind field characteristic analysis system based on micro-meteorological factors is provided, which comprises:

[0036] The first module is used to obtain micro-meteorological data at different positions in the mountainous area in the elevation direction, wherein the micro-meteorological data includes wind speed, wind direction, temperature, humidity and solar radiation, all of which are sorted by acquisition time, wherein the wind speed is greater than a preset strong wind speed;

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

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

[0039] A fourth module is used to: perform quadratic function fitting on a 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] A fifth module is used to: determine the duration and intensity of the strong wind based on a quadratic function curve of the wind speed with respect to the 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 with a plurality of pre-set wind speed curve images of different shape categories through image recognition technology to obtain the strong wind shape category;

[0042] The seventh module is used to: perform correlation analysis on the wind speed sorted by time, and the wind direction, temperature, humidity and solar radiation in the same period as the wind speed 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 characteristic database, which includes the micro-meteorological data, strong wind change degree level, strong wind level, strong wind shape category and strong wind correlation parameters.

[0044] In a third aspect, a storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, any of the above-mentioned methods for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors is implemented.

[0045] In a fourth aspect, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program running on the processor, and when the processor executes the computer program, any of the above-mentioned methods for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors is implemented.

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

[0047] The embodiments of the present application provide a method and system for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors. Based on real-time observation of wind fields and meteorology, the spatiotemporal distribution data of wind fields and meteorology in high-altitude complex mountainous areas are obtained, the distribution patterns of wind fields, temperature, humidity, solar radiation, etc. in high-altitude mountainous areas are studied, the coupling evolution patterns of wind fields and micro-meteorological characteristics in high-altitude complex mountainous areas are revealed, and a wind field characteristic database that takes into account micro-meteorology in high-altitude complex mountainous areas is formed, providing a data basis for wind field simulation and forecasting of large-span railway bridges in deep canyon areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A flow chart of a method for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors provided in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of the arrangement of instruments and equipment provided in the embodiment of the present application;

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

[0052] Figure 4 A plurality of wind speed curve images of different shape categories are pre-set in the embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0054] See also Figure 1 As shown, a method for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors comprises the following steps:

[0055] 101: Acquire micro-meteorological data at different positions in the elevation direction in a mountainous area, wherein the micro-meteorological data include wind speed, wind direction, temperature, humidity and solar radiation, all sorted by acquisition time, wherein the wind speed is greater than a preset strong wind speed.

[0056] It is understandable that, since the above wind speed is greater than the preset strong wind speed, the above micro-meteorological data represents strong wind data. The preset strong wind speed can be set manually according to actual needs, and is not specifically limited in this application.

[0057] See also Figure 2 As shown, using the tower cranes required for the construction of existing long bridges, three-dimensional anemometers, temperature and humidity sensors, solar radiation sensors and other instruments and equipment are arranged at equal intervals along the tower height; the instruments can be fixed with bracket clamps, and one bracket clamp can fix multiple instruments and equipment. Using the tower cranes required for the construction of existing long bridges as the base for meteorological data measurement can effectively reduce the cost of instruments and equipment and improve economic efficiency.

[0058] Figure 2In the A layer, a three-dimensional anemometer and temperature, humidity and solar radiation sensors are arranged. In addition to the instruments on the A layer, a rainfall sensor can be added to the B layer to avoid using radar anemometers and save costs. The three-dimensional anemometer collects wind speed, wind direction and wind attack angle, the temperature and humidity sensor collects temperature and humidity changes, and the solar radiation sensor collects solar radiation changes.

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

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

[0061] Smoothing is to use a moving average filter to smooth the wind speed value in the vector. The default window width of the moving average filter is 5.

[0062] 103: Deriving a smooth curve of wind speed with respect to acquisition time to obtain a slope change curve, and determining a 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 a 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 the strong wind to obtain the strong wind level.

[0065] 106: Using image recognition technology, identify the smooth curve of wind speed with respect to acquisition time with a plurality of pre-set wind speed curve images of different shape categories to obtain a strong wind shape category.

[0066] 107: performing correlation analysis on the time-sequentially acquired wind speed, and the wind direction, temperature, humidity, and solar radiation in the same period as the wind speed, to obtain strong wind correlation parameters, wherein the strong wind correlation parameters include one or more of wind direction, temperature, humidity, and solar radiation.

[0067] Using the wind direction, temperature, humidity and solar radiation data of the same period, the wind speed is analyzed and judged for correlation to determine the factors affecting the wind speed.

[0068] For wind direction, we can analyze the wind direction changes at different heights, as well as the vertical wind angle and horizontal wind angle, to identify the wind speed changes under the influence of the atmosphere, including three-dimensional shear and mesocyclone phenomena; the regular wind direction changes along the canyon, including the local rise and fall of airflow caused by uneven solar radiation;

[0069] For temperature, temperature changes and strong wind correlation analysis under the influence of temperature difference, whether it is accompanied by humidity changes. If there is obvious ground wind shear, or a more obvious temperature, pressure and humidity gradient, gusts may occur.

[0070] As for humidity, the changes in humidity caused by the downdrafts from glaciers in canyon areas, as well as the different wind direction changes between glacier winds and valley winds, can all be used as a basis for classifying strong winds.

[0071] Record changes in wind direction, temperature, humidity and solar radiation data, conduct correlation analysis with strong wind speed data, link various types of micrometeorological data with strong winds, obtain the coupled evolution law, and identify whether strong winds are mostly caused by temperature changes or atmospheric influences.

[0072] 108: Construct a mountain wind field characteristic database, wherein the database includes the micro-meteorological data, the level of strong wind change, the strong wind level, the strong wind shape category and the strong wind correlation parameter.

[0073] A wind field measurement database that takes into account the micrometeorology of complex mountainous areas at high altitudes is formed to provide data support for CFD simulations in canyon and mountainous areas.

[0074] The method for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors provided in the embodiment of the present application obtains the spatiotemporal distribution data of wind field and meteorology in high-altitude complex mountainous areas based on real-time observation of wind field and meteorology, studies the distribution patterns of wind field, temperature, humidity and solar radiation in high-altitude mountainous areas, reveals the coupling evolution patterns of wind field and micro-meteorological characteristics in high-altitude complex mountainous areas, forms a wind field characteristic database that takes into account micro-meteorology in high-altitude complex mountainous areas, and provides data basis for simulation and forecasting of wind fields of large-span railway bridges in deep canyon areas.

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

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

[0077] The positive and negative values ​​on the slope change 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 mutation value of strong wind;

[0079] Get the value on the slope change curve, start recording 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. If so, this section is continuously increasing.

[0080] Since the wind speed data after average processing have the same time interval, it can be considered that the slope change value at the same time interval reflects the severity of the sudden change of strong wind. The value of the slope change curve is obtained, and the maximum value of the slope value is considered to be the sudden change value of the strong wind in this section.

[0081] 403: Compare the mutation value with a plurality of preset mutation value ranges corresponding to different strong wind change degree levels to obtain a corresponding strong wind change degree level.

[0082] See also Figure 3 As shown, it is a quadratic function curve of the wind speed obtained in the above step 104 with respect to the acquisition time. It can be understood that for the wind speeds sorted by the acquisition time, it is actually a plurality of points, each of which is determined by the acquisition time and the corresponding wind speed, such as Figure 3 The horizontal axis is the acquisition time, and the vertical axis is the wind speed, so the wind speed sorted by the acquisition time is actually also a curve, and the curve has several peaks and / or several troughs.

[0083] By fitting a quadratic function, we can obtain its general direction, and obtain a peak curve, that is, a quadratic function curve.

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

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

[0086] Since the quadratic function curve is a peak curve and is obtained by fitting multiple points, the time period between the acquisition times corresponding to the first point and the last point 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 crest curve, it has a maximum wind speed, so the maximum wind speed is determined as the intensity of the strong wind.

[0089] 203: Classify the strong wind according to the duration and intensity of the strong wind to obtain a strong wind level.

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

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

[0092] By using image recognition technology, a smooth curve of wind speed with respect to acquisition time is identified with a plurality of pre-set wind speed curve images of different shape categories to obtain a strong wind shape category.

[0093] The above-mentioned image recognition technology can directly use existing ones. For example, as an example, the mean hash algorithm can be used to first reduce the original image to a pixel image of a fixed size, and then convert the pixel image to a grayscale image, and generate a set of hash values ​​by comparing each pixel of the reduced image with the average grayscale value. Finally, the Hamming distance of the hash values ​​of the two groups of images is used to evaluate the similarity of the images, thereby completing image recognition.

[0094] In actual applications, when determining whether a wind speed curve meets a category, the minimum Hamming distance is used to classify the image.

[0095] In the above step 101, obtaining micro-meteorological data at different positions in the mountainous area in the elevation direction specifically includes the following steps:

[0096] 301: Collect original micro-meteorological data at different locations in the mountainous area in the elevation direction.

[0097] As for the original micrometeorological data, it includes wind speed, wind direction, temperature, humidity and solar radiation.

[0098] It is understandable that the frequency of collecting wind speed, wind direction, temperature, humidity and solar radiation is different due to the different instruments and equipment used.

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

[0100] Therefore, although the wind speed, wind direction, temperature, humidity and solar radiation in the original micro-meteorological data are also related to time and are sorted based on the collection time, the collection time of different types of data is not necessarily the same. In addition, the original micro-meteorological data is also composed of multiple points, each of which is composed of the collection time and wind speed, wind direction, temperature, humidity or solar radiation.

[0101] For example, the collection time of wind speed and wind direction can be the same, and the collection time of temperature and humidity can be the same, but the collection time of wind speed and wind direction is not necessarily the same as the collection time of temperature and humidity, and is not necessarily the same as the collection time of solar radiation.

[0102] 302: Using the wind speed greater than the preset strong wind speed as a screening condition, the original micro-meteorological data is screened to obtain screened micro-meteorological data.

[0103] Since the regularly fluctuating strong winds in the high-altitude and high-temperature mountainous areas in the west will have an impact on bridge construction, we only need to pay attention to the strong wind data. In addition, there is a lot of original micro-meteorological data, which is complicated to process. Therefore, the wind speed greater than the preset strong wind speed is used as a screening condition to screen the original micro-meteorological data and eliminate the data that is not strong wind. This can not only reduce the amount of data processing, but also eliminate the data that is not strong wind as noise data, and avoid this part of the data from causing unnecessary influence on the subsequent data processing process.

[0104] 303: Perform missing value processing, outlier processing and average processing on the screened micro-meteorological data to obtain micro-meteorological data at different positions in the mountainous area in the elevation direction.

[0105] Since abnormal conditions may occur during the operation of instruments and equipment, such as power outages, or the instrument automatically recovers to normal after a short failure, etc., missing values ​​or abnormal values ​​may appear, so missing value processing and abnormal value processing are required.

[0106] In addition, although the original micro-meteorological data is screened once in step 302, due to the high frequency of instrument collection and the large amount of data, it is necessary to perform average processing on the data to further reduce the processing volume.

[0107] It can be understood that the above-mentioned averaging process can be performed before the missing value process and the outlier value process, or can be performed after the missing value process and the outlier value process.

[0108] In the above step 303, the missing value processing is performed on the filtered micro-meteorological 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 interpolation method is used to fill the missing value;

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

[0111] The vacancy time threshold may be set according to actual needs, for example, it may 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 values ​​are short-term, the interpolation method can be used to interpolate the vertical coordinate values ​​of the two adjacent points to fill in the missing values ​​between the two adjacent points.

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

[0114] In the above step 303, outlier processing is performed on the filtered micro-meteorological data, specifically including:

[0115] Setting confidence levels and confidence intervals;

[0116] Points outside the confidence interval are regarded as outliers and replaced with the average of the two adjacent points before and after the point.

[0117] Set the confidence level and confidence interval according to actual needs, and judge the ordinate of each point. If it is in the confidence interval, the point is a normal value. If it is not in 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 to be an outlier. Calculate the average of the ordinates of the two points before and after the point, and use the average as the ordinate of the point.

[0118] In the above step 303, the filtered micro-meteorological data is averaged, specifically including:

[0119] Averages multiple points over a preset time period.

[0120] As mentioned above, the frequency of collecting wind speed, wind direction, temperature, humidity and solar radiation varies due to the different instruments and equipment used.

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

[0122] It is understandable that the above-mentioned preset time length can be set according to actual needs, for example, it can be a one-minute average, or a ten-minute average.

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

[0124] For wind speed, we have the following points:

[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] Among the above points, the former is the collection time, and the latter is the wind speed. The above 12 wind speed points are averaged according to 1 minute, and two points can be obtained:

[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] Among the above points, the former is the acquisition time, and the latter is the wind speed after average processing.

[0135] Similarly, for temperature, we have the following points:

[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] Among the above points, the former is the acquisition time, and the latter is the temperature speed. The above 12 temperature points are averaged according to 1 minute, and four points can be obtained:

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

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

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

[0144] The temperature of 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] Among the above points, the former is the acquisition time, and the latter is the average temperature after processing.

[0148] Although the acquisition frequencies and times of temperature and wind speed are different, the acquisition times of temperature and wind speed are unified after the above averaging process.

[0149] For data such as humidity, wind direction, and solar radiation, the above-mentioned averaging process can also be used to unify the acquisition time, and then the wind direction, temperature, humidity, and solar radiation data of the same period can be used to perform correlation analysis on wind speed.

[0150] Corresponding to the above-mentioned method for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors, the embodiment of the present application further provides a system for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors, which includes:

[0151] The first module is used to obtain micro-meteorological data at different positions in the mountainous area in the elevation direction, wherein the micro-meteorological data includes wind speed, wind direction, temperature, humidity and solar radiation, all of which are sorted by acquisition time, wherein the wind speed is greater than a preset strong wind speed;

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

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

[0154] A fourth module is used to: perform quadratic function fitting on a 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] A fifth module is used to: determine the duration and intensity of the strong wind based on a quadratic function curve of the wind speed with respect to the 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 with a plurality of pre-set wind speed curve images of different shape categories through image recognition technology to obtain the strong wind shape category;

[0157] The seventh module is used to: perform correlation analysis on the wind speed sorted by time, and the wind direction, temperature, humidity and solar radiation in the same period as the wind speed 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 characteristic database, which includes the micro-meteorological data, strong wind change degree level, strong wind level, strong wind shape category and strong wind correlation parameters.

[0159] Corresponding to the above-mentioned method for analyzing the characteristics of mountainous wind fields based on micro-meteorological factors, the embodiment of the present application also provides a storage medium, on which a computer program is stored, and the steps of the above-mentioned embodiments are implemented when the computer program is executed by the processor. It should be noted that the storage medium of the embodiment of the present application can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0160] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0161] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

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

[0163] Corresponding to the above-mentioned method for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors, an embodiment of the present application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program running on the processor, and the processor implements the steps of the above-mentioned embodiments when executing the computer program.

[0164] In the description of the present application, it should be noted that the terms "upper", "lower", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0165] In the description of the present application, it should be noted that the terms "upper", "lower", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present 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 only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0167] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A method for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors, characterized in that: It includes: Acquire micro-meteorological data at different positions in the mountainous area in the elevation direction, wherein the micro-meteorological data includes wind speed, wind direction, temperature, humidity and solar radiation, all in order of acquisition time, wherein the wind speed is greater than a preset strong wind speed; Smoothing the wind speeds sorted by acquisition time to obtain a smooth curve of wind speed with respect to acquisition time; Derivative the smooth curve of wind speed with respect to acquisition time to obtain a slope change curve, and determine the level of strong wind change based on the slope change curve; Performing 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; Based on the quadratic function curve of wind speed with respect to acquisition time, the duration and intensity of the strong wind are determined to obtain the strong wind level; By using image recognition technology, a smooth curve of wind speed with respect to acquisition time is identified with a plurality of pre-set wind speed curve images of different shape categories to obtain a strong wind shape category; Performing correlation analysis on the wind speeds sorted in time, and the wind direction, temperature, humidity and solar radiation in the same period as the wind speeds, to obtain strong wind correlation parameters, wherein the strong wind correlation parameters include one or more of wind direction, temperature, humidity and solar radiation; A mountainous area wind field characteristic database is constructed, wherein the database includes the micro-meteorological data, the level of strong wind variation, the level of strong wind, the category of strong wind shape and the strong wind correlation parameters.

2. The method for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors according to claim 1, characterized in that: Based on the slope change curve, the level of strong wind change is determined, including: Calculate the slope value of each point in the slope change curve; The maximum value among all slope values ​​is determined as the sudden change value of strong wind; The mutation value is compared with a plurality of preset mutation value ranges corresponding to different strong wind change degree levels to obtain the corresponding strong wind change degree level.

3. The method for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors according to 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 wind are determined to obtain the strong wind level, including: Obtain the time period between the first point and the last point 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 wind is classified into different levels according to the duration and intensity of the strong wind to obtain a strong wind level.

4. The method for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors according to claim 1, characterized in that: Obtain micro-meteorological data at different locations in the mountainous area in the elevation direction, including: Collect original micro-meteorological data at different locations in the mountainous area in the elevation direction; Taking the wind speed greater than the preset strong wind speed as a screening condition, the original micro-meteorological data is screened to obtain the screened micro-meteorological data; The screened micrometeorological data are processed with missing values, outliers and averages to obtain micrometeorological data at different locations in the mountainous area in the elevation direction.

5. The method for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors according to claim 4, characterized in that: The missing value processing of the screened micrometeorological data includes: When there is a missing value between two adjacent points and the missing time is lower than the missing time threshold, the interpolation method is used to fill the missing value; When there is a missing value between two adjacent points and the missing time is higher than the missing time threshold, 0 is used to fill the missing value.

6. The method for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors according to claim 4, characterized in that: The outlier processing of the screened micrometeorological data includes: Setting confidence levels and confidence intervals; Points outside the confidence interval are regarded as outliers and replaced with the average of the two adjacent points before and after the point.

7. The method for analyzing wind field characteristics in mountainous areas based on micro-meteorological factors according to claim 4, characterized in that: The filtered micro-meteorological data are averaged, including: Averages multiple points over a preset time period.

8. A mountainous area wind field characteristic analysis system based on micro-meteorological factors, characterized in that: It includes: The first module is used to obtain micro-meteorological data at different positions in the mountainous area in the elevation direction, wherein the micro-meteorological data includes wind speed, wind direction, temperature, humidity and solar radiation, all of which are sorted by acquisition time, wherein the wind speed is greater than a preset strong wind speed; The second module is used to: smooth the wind speeds sorted by acquisition time to obtain a smooth curve of wind speed with respect to acquisition time; The third module is used to: derive a smooth curve of wind speed with respect to acquisition time to obtain a slope change curve, and determine the level of strong wind change based on the slope change curve; A fourth module is used to: perform quadratic function fitting on a smooth curve of wind speed with respect to acquisition time to obtain a quadratic function curve of wind speed with respect to acquisition time; A fifth module is used to: determine the duration and intensity of the strong wind based on a quadratic function curve of the wind speed with respect to the 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 with a plurality of pre-set wind speed curve images of different shape categories through image recognition technology to obtain the strong wind shape category; The seventh module is used to: perform correlation analysis on the wind speed sorted by time, and the wind direction, temperature, humidity and solar radiation in the same period as the wind speed 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 characteristic database, which includes the micro-meteorological data, strong wind change degree level, strong wind level, strong wind shape category and strong wind correlation parameters.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for analyzing wind field characteristics in mountainous area based on micro-meteorological factors as described in any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program running on the processor, wherein: When the processor executes the computer program, the method for analyzing wind field characteristics in mountainous area based on micro-meteorological factors as described in any one of claims 1 to 7 is implemented.

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