High mountain cloud sea landscape prediction method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of landscape prediction, and discloses a high mountain cloud sea landscape prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the prediction basic data of a to-be-predicted day of a target region, and the prediction basic data comprises a relative humidity factor, a temperature condition factor, an atmospheric stability factor, a wind speed factor and a terrain uplift factor; and inputting the prediction basic data to the cloud sea landscape prediction model to obtain the cloud sea occurrence probability of the to-be-predicted day. According to the method, the application area is not limited, the construction cost is low, the prediction basic data of the to-be-predicted day in the target area can be continuously updated, passengers and photography enthusiasts can conveniently check the cloud sea occurrence probability in real time and arrange the travel time, the experience degree of the passengers is improved, and the scenic spot service quality is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of landscape prediction, and is a method, device, electronic device and storage medium for predicting alpine cloud sea landscapes. Background Art

[0002] Alpine cloud seas are a unique natural phenomenon, which are deeply favored by tourists and photography enthusiasts for their magnificent landscapes and ornamental values. In alpine regions, the formation of cloud seas usually depends on specific meteorological conditions and topographical characteristics, and their occurrence time and scope are highly uncertain. Therefore, accurately predicting the occurrence time and duration of cloud seas in real time is of great significance for tourism development, ecological protection, meteorological services, etc.

[0003] Existing methods for predicting alpine cloud sea landscapes include:

[0004] (1) Traditional weather forecasting methods

[0005] Based on traditional statistics to judge whether an alpine cloud sea landscape will appear on the next day, the appearance of cloud seas is affected by various factors such as temperature, humidity, wind speed, and atmospheric stability. Moreover, the terrain and environment in alpine regions are complex and changeable. This method of using non-real-time data for prediction is difficult to achieve accurate prediction, and it is difficult for ordinary users to understand the prediction results, and it cannot provide intuitive phenological landscape information, resulting in poor user interactivity.

[0006] (2) Methods assisted by prediction tools

[0007] This method requires the setting of cloud sea landscape prediction tools in alpine scenic areas, but the development cost of this tool is high and it is not easy to promote.

[0008] (3) Methods for in-depth analysis of meteorological data

[0009] For example, the existing published patent document with the publication number CN118761596B specifically discloses a cloud sea landscape forecasting method and system. Cloud amount data on a meteorological grid is obtained based on relative humidity and geopotential height meteorological data to establish a meteorological grid. A geographical grid is established based on geographical height data, and the geographical height data on the geographical grid is matched to the meteorological grid to obtain the geographical height data on the meteorological grid. Each meteorological grid point can obtain corresponding vertical multi-layer cloud amount characteristic data through the geographical height data, geopotential height data, and cloud amount data corresponding to the grid point. Finally, based on the vertical multi-layer cloud amount characteristic data, a cloud sea forecasting result is obtained.

[0010] However, the above methods have problems such as high development difficulty, not being easy to promote, or the analysis and processing process being relatively complex. Therefore, there is an urgent need for a method for predicting alpine cloud sea landscapes with low construction costs, not restricted by regions, and suitable for promotion. Summary of the Invention

[0011] The present invention provides a method, device, electronic device and storage medium for predicting alpine cloud sea landscapes, overcoming the deficiencies of the above-mentioned prior art, and effectively solving the problem in the prior art that the alpine cloud sea landscapes cannot be effectively predicted.

[0012] One of the technical solutions of the present invention is achieved by the following measures: A method for predicting alpine cloud sea landscapes, comprising:

[0013] Obtaining the prediction basic data of the target area for the day to be predicted, wherein the prediction basic data includes relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor;

[0014] Inputting the prediction basic data into the cloud sea landscape prediction model to obtain the cloud sea occurrence probability of the day to be predicted, wherein the cloud sea landscape prediction model is as follows:

[0015] P cloud = α·H rel + β·T diff + γ·S stab + δ·W wind + ∈·G topo + ζ

[0016] Wherein, P cloud is the cloud sea occurrence probability; H rel , T diff , S stab , W wind , G topo are the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor respectively; α, β, γ, δ, ∈ are the weights of the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor respectively; ζ is the deviation value.

[0017] The following is a further optimization and / or improvement of the above-mentioned technical solution of the invention:

[0018] The construction process of the above cloud sea landscape prediction model includes:

[0019] Obtaining a number of historical influencing factor data, wherein the historical influencing factor data includes meteorological factors and terrain factors when the cloud sea landscape appears in different regions, and meteorological factors and terrain factors when the cloud sea landscape does not appear;

[0020] Performing a correlation analysis on each meteorological factor and terrain factor in the historical influencing factor data with whether the cloud sea landscape appears, and screening out the top 5 factors with the highest correlation to form a key factor set, wherein the 5 factors include relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor;

[0021] Weights are assigned to five types of factors, and a prediction model for cloud sea landscapes is constructed through weighting as follows:

[0022] P cloud = α·H rel + β·T diff + γ·S stab + δ·W wind + ∈·G topo + ζ

[0023] Where P cloud is the probability of cloud sea occurrence; H rel , T diff , S stab , W wind , G topo are the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor respectively; α, β, γ, δ, ∈ are the weights of the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor respectively; ζ is the deviation value.

[0024] The above-mentioned Pearson correlation coefficient analysis method is used to assign weight values to the five types of factors.

[0025] The above also includes weight correction for the cloud sea landscape prediction model based on multiple prediction result data, including:

[0026] Collect all prediction result data within a set time interval, where the prediction result data includes the prediction basic data input for prediction and the corresponding actual cloud sea occurrence results;

[0027] Use the correlation analysis algorithm to analyze the correlation between the prediction basic data and the actual cloud sea occurrence results;

[0028] According to the correlation analysis results, correct the weight values of various factors in the cloud sea landscape prediction model.

[0029] The process of obtaining the above-mentioned relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor includes:

[0030] Obtain meteorological data and calculate the humidity factor, temperature condition factor, atmospheric stability factor, and wind speed factor as follows:

[0031] The humidity factor H rel is as follows:

[0032]

[0033] Where RH is the relative humidity;

[0034] The temperature condition factor T diff is as follows:

[0035]

[0036] Among them, T surface is the actual air temperature; T dew is the dew point temperature; T range is the temperature difference normalization coefficient;

[0037] The atmospheric stability S stab factor is as follows:

[0038]

[0039] Among them, γ is the actual temperature lapse rate; γ dry is the dry adiabatic lapse rate;

[0040] The wind speed factor W wind is as follows:

[0041]

[0042] Among them, V is the wind speed; V opt is the optimal wind speed; σ is the wind speed width parameter; e is the constant term;

[0043] Obtain terrain data and calculate the terrain lifting factor G topo ;

[0044]

[0045] Among them, H mountain is the mountain peak height; H cloud_base is the cloud base height; H range is the normalized height difference.

[0046] The second technical solution of the present invention is achieved by the following measures: A high mountain cloud sea landscape prediction device, including:

[0047] A prediction data acquisition unit that acquires the prediction basic data of the target area on the day to be predicted, where the prediction basic data includes a relative humidity factor, a temperature condition factor, an atmospheric stability factor, a wind speed factor, and a terrain lifting factor;

[0048] A prediction unit that inputs the prediction basic data into the cloud sea landscape prediction model to obtain the cloud sea occurrence probability on the day to be predicted, where the cloud sea landscape prediction model is as follows:

[0049] P cloud = α·H rel + β·T diff + γ·S stab + δ·W wind + ∈·G topo + ζ

[0050] Among them, P cloud is the probability of cloud sea occurrence; H rel , T diff , S stab , W wind , G topo are the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor respectively; α, β, γ, δ, ∈ are the weights of the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor respectively; ζ is the deviation value.

[0051] The following is a further optimization or / and improvement of the above-mentioned technical solution of the invention:

[0052] The above also includes a model construction unit, including:

[0053] A historical data acquisition module that acquires a number of historical influencing factor data, where the historical influencing factor data includes meteorological factors and terrain factors when the cloud sea landscape appears in different regions, and meteorological factors and terrain factors when the cloud sea landscape does not appear;

[0054] A factor screening module that performs a correlation analysis on each meteorological factor and terrain factor in the historical influencing factor data with whether the cloud sea landscape appears, and screens out the top 5 factors with the highest correlation to form a key factor set, where the 5 factors include the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor;

[0055] A weight distribution module that assigns weight values to the 5 factors and constructs a cloud sea landscape prediction model through weighting, specifically as follows:

[0056] P cloud = α·H rel + β·T diff + γ·S stab + δ·W wind + ∈·G topo + ζ

[0057] Among them, P cloud is the probability of cloud sea occurrence; H rel , T diff , S stab , W wind , G topo are the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor respectively; α, β, γ, δ, ∈ are the weights of the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor respectively; ζ is the deviation value.

[0058] The above also includes a weight correction unit, including:

[0059] A data acquisition module that acquires all prediction result data within a set time interval, where the prediction result data includes the prediction basic data of the prediction input and the corresponding actual cloud sea occurrence result;

[0060] An analysis module that analyzes the correlation between the prediction basic data and the actual cloud sea occurrence result by using a correlation analysis algorithm;

[0061] A calibration module that calibrates the weight values of various factors in the cloud sea landscape prediction model according to the correlation analysis result.

[0062] The third technical solution of the present invention is achieved by the following measures: An electronic device includes a processor and a memory, and a computer program is stored in the memory. The computer program is loaded and executed by the processor to implement the steps in the alpine cloud sea landscape prediction method.

[0063] The fourth technical solution of the present invention is achieved by the following measures: A storage medium, characterized in that a computer program readable by a computer is stored on the storage medium, and the computer program is set to execute the steps in the alpine cloud sea landscape prediction method when running.

[0064] The present invention establishes a cloud sea landscape prediction model, and combines the cloud sea landscape prediction model with the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplift factor of the day to be predicted, and obtains the cloud sea occurrence probability. The greater the cloud sea occurrence probability, the greater the probability of the alpine cloud sea landscape occurring. This method does not limit the application area, has a low construction cost, and can continuously update the prediction basic data of the day to be predicted in the target area, facilitating tourists and photography enthusiasts to view the cloud sea occurrence probability in real time, facilitating their travel time arrangement, and thus improving the tourist experience and the scenic area service quality. Description of the Drawings

[0065] Att Figure 1 It is a schematic flowchart of the alpine cloud sea landscape prediction method provided by the present invention.

[0066] Att Figure 2 It is a schematic flowchart of the cloud sea landscape prediction model construction method provided by the present invention.

[0067] Att Figure 3 It is a schematic flowchart of the cloud sea landscape prediction model weight calibration method provided by the present invention.

[0068] Att Figure 4 It is a map of the actual occurrence of the alpine cloud sea landscape provided by the present invention.

[0069] Att Figure 5 It is a schematic structural diagram of an alpine cloud sea landscape prediction device provided by the present invention.

[0070] AttFigure 6 Another structural schematic diagram of the alpine cloud sea landscape prediction device provided by the present invention.

[0071] Appendix Figure 7 An implementation environment schematic diagram provided by the present invention. Specific implementation manners

[0072] The present invention is not limited by the following embodiments, and specific implementation manners can be determined according to the technical solutions of the present invention and actual situations.

[0073] Those skilled in the art of the present technology can understand that, unless specifically stated, the "module" or "unit" in the embodiments of the present invention refers to a computer program with a predetermined function or a part of a computer program, which works together with other related parts to achieve a predetermined goal, and can be fully or partially implemented by using software, hardware (such as a processing circuit or a memory), or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be a part of an overall module or unit including the functions of the module or unit.

[0074] In addition, "a plurality of" in the embodiments of the present invention refers to two or more, and "first" and "second" etc. are used for distinguishing descriptions, and cannot be understood as implying relative importance.

[0075] The embodiments of the present invention provide an alpine cloud sea landscape prediction method, device, electronic device and storage medium. The alpine cloud sea landscape prediction device can be integrated in a computer device, and the computer device can be a server or a terminal device, etc.; it can also be jointly executed by a terminal and a server. The above examples should not be understood as a limitation to the present invention.

[0076] As shown in the appendix Figure 7 Taking the case where the alpine cloud sea landscape prediction method is jointly executed by a terminal and a server as an example, the terminal and the server are connected through a network, which can be a wired network or a wireless network connection, etc. Among them, the ancient city wall data restoration method can be integrated in the terminal.

[0077] Among them, the terminal provides an interactive interface for alpine cloud sea landscape prediction, which is used to select a date and view the alpine cloud sea landscape prediction result of that day. The terminal here can include a mobile phone, a wearable intelligent device, a tablet computer, a notebook computer, a personal computer (PC), a vehicle-mounted computer, etc., and the present invention does not limit this. The present invention does not limit the number of terminal devices.

[0078] Among them, the server provides various data processing and analysis processes in the alpine cloud sea landscape prediction method, specifically including constructing a cloud sea landscape prediction model; obtaining the prediction basic data of the target area on the day to be predicted, where the prediction basic data includes relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplift factor; inputting the prediction basic data into the cloud sea landscape prediction model to obtain the cloud sea occurrence probability on the day to be predicted. The server here can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The present invention does not limit this.

[0079] Based on this, the technical solutions of the present invention will be introduced and described below in combination with several examples.

[0080] Example 1: As shown in the appendix Figure 1 The embodiment of the present invention discloses an alpine cloud sea landscape prediction method, including:

[0081] Step S110, obtaining the prediction basic data of the target area on the day to be predicted, where the prediction basic data includes relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplift factor;

[0082] In this step, the five types of factors, namely relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplift factor, are selected to predict the cloud sea occurrence probability, which is obtained through comprehensive analysis of the meteorological factors and terrain factors when the cloud sea landscape appears and the meteorological factors and terrain factors when the cloud sea landscape does not appear in the historical cloud sea landscape data of different regions.

[0083] In this step, the prediction basic data of the target area on the day to be predicted is obtained through future meteorological data.

[0084] Step S120, inputting the prediction basic data into the cloud sea landscape prediction model to obtain the cloud sea occurrence probability on the day to be predicted, where the cloud sea landscape prediction model is as follows:

[0085] P cloud =α·H rel +β·T diff +γ·S stab +δ·W wind +∈·G topo +ζ

[0086] Among them, P cloud is the cloud sea occurrence probability; H rel , T diff , S stab, W wind , G topo are the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor respectively; α, β, γ, δ, and ∈ are the weights of the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor respectively; ζ is the deviation value (usually taken as 0).

[0087] An embodiment of the present invention discloses a method for predicting alpine cloud sea landscapes, establishing a cloud sea landscape prediction model, and combining the cloud sea landscape prediction model with the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor of the day to be predicted to obtain the probability of cloud sea occurrence. The greater the probability of cloud sea occurrence, the greater the probability of the occurrence of alpine cloud sea landscapes. This method does not limit the application area, has a low construction cost, and can continuously update the prediction basic data of the day to be predicted in the target area, facilitating tourists and photography enthusiasts to view the probability of cloud sea occurrence in real time, facilitating their travel time arrangements, and thus improving the tourist experience and the service quality of the scenic area.

[0088] Embodiment 2: An embodiment of the present invention discloses a method for predicting alpine cloud sea landscapes, which is a further optimization of the above embodiment. The process of obtaining the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor includes:

[0089] (1) Obtain meteorological data and calculate the humidity factor, temperature condition factor, atmospheric stability factor, and wind speed factor as follows:

[0090] (a) Humidity factor H rel (representing the humidity degree of the atmosphere, ranging from 0 to 1) is as follows:

[0091]

[0092] where RH is the relative humidity (%).

[0093] When the above humidity is close to 100%, cloud sea is more likely to appear.

[0094] (b) Temperature condition factor T diff (representing the proximity of the air temperature to the dew point temperature, ranging from 0 to 1) is as follows:

[0095]

[0096] where T surface is the actual air temperature (°C); T dew is the dew point temperature (°C); T range is the temperature difference normalization coefficient (which can be set to 10°C);

[0097] When the air temperature is close to the dew point as described above, T diff is close to 1.

[0098] (c) Atmospheric stability S stab The factor (reflecting the stability of the atmospheric stratification, ranging from 0 to 1) is as follows:

[0099]

[0100] where γ is the actual temperature lapse rate (℃ / km); γ dry is the dry adiabatic lapse rate, which can be set to 9.8℃ / km;

[0101] When the above stratification is stable (the temperature lapse rate is low), S stab is close to 1.

[0102] (d) Wind speed factor W wind (indicating the influence of wind speed on the stability of the cloud sea, ranging from 0 to 1) is as follows:

[0103]

[0104] where V is the wind speed (m / s); V opt is the optimal wind speed, generally taken as 2 to 5 m / s; σ is the wind speed width parameter; e is a constant term, which can be taken as 1.

[0105] (2) Obtain terrain data and calculate the terrain uplifting factor G topo (reflecting the effect of terrain on air ascent, ranging from 0 to 1);

[0106]

[0107] where H mountain is the mountain peak height (m); H cloud_base is the cloud base height (m), which can be estimated through the moist adiabatic model; H range is the standardized height difference, that is, the difference between the maximum height and the minimum height of historical data.

[0108] Example 3: As attached Figure 2 described, the embodiment of the present invention discloses a method for predicting the alpine cloud sea landscape, which is a further optimization of the above embodiment. The construction process of the cloud sea landscape prediction model includes:

[0109] Step S210, obtain a number of historical influence factor data, where the historical influence factor data includes meteorological factors and terrain factors when the cloud sea landscape appears in different regions, and meteorological factors and terrain factors when the cloud sea landscape does not appear;

[0110] Step S220: Perform a correlation analysis between each meteorological factor and terrain factor in the historical impact factor data and the occurrence of the sea of clouds landscape respectively, and screen out the top 5 factors with the highest correlation to form a key factor set. The 5 factors include the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor.

[0111] In this embodiment, there is no limitation on the method of correlation analysis. It can be screened manually based on a table, or a correlation analysis algorithm can be introduced for correlation analysis.

[0112] Step S230: Assign weight values to the 5 factors and construct a prediction model for the sea of clouds landscape through weighting, specifically as follows:

[0113] P cloud = α·H rel + β·T diff + γ·S stab + δ·W wind + ∈·G topo + ζ

[0114] Among them, P cloud is the probability of the occurrence of the sea of clouds; H rel , T diff , S stab , W wind , G topo are the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor respectively; α, β, γ, δ, ∈ are the weights of the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor respectively; ζ is the deviation value.

[0115] In this embodiment, the weight values of the 5 factors can be assigned by, but not limited to, using the Pearson correlation coefficient analysis method. The specific process of implementing using the Pearson correlation coefficient analysis method includes:

[0116] (1) Calculate the correlation coefficient r between the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, terrain uplifting factor and the occurrence of the sea of clouds landscape respectively based on multiple historical impact factor data. The corresponding calculation formula is as follows:

[0117]

[0118] Among them, X i is the value of a certain factor in the i-th historical impact factor data; Y i is the sea of clouds occurrence result mark corresponding to the i-th historical impact factor data (1 indicates the occurrence of the sea of clouds, 0 indicates non-occurrence); n is the number of historical impact factor data; is the mean value of variables X and Y.

[0119] (2) Normalize the absolute value of the correlation coefficient r of the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor to ensure that the sum of all weights is 1.

[0120]

[0121] Among them, X 影响因子 includes the weight α of the relative humidity factor, the weight β of the temperature factor, the weight γ of the atmospheric stability factor, the weight δ of the wind speed factor, and the weight ∈ of the terrain uplifting factor; x1, x2, x3, x4, and x5 are the correlation coefficients r of the humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor respectively.

[0122] Furthermore, after the cloud sea landscape prediction model, multiple historical impact factor data can be used for verification. If the verification is unsuccessful, the adjustment factor type or weight is returned.

[0123] Example 4: As attached Figure 3 described, the embodiment of the present invention discloses a method for predicting the alpine cloud sea landscape, which is a further optimization of the above embodiment, and also includes weight correction of the cloud sea landscape prediction model based on multiple prediction result data, including:

[0124] Step S310, collect all prediction result data within a set time interval, where the prediction result data includes the prediction basic data input for prediction and the corresponding actual cloud sea occurrence result;

[0125] Step S320, use the correlation analysis algorithm to analyze the correlation between the prediction basic data and the actual cloud sea occurrence result; this step of the correlation process can be completed by, but not limited to, the Pearson correlation coefficient analysis method

[0126] Step S330, according to the correlation analysis result, correct the weight values of various factors in the cloud sea landscape prediction model.

[0127] Example 5: Use the method disclosed in the above embodiment to predict the cloud sea occurrence probability on a certain day and verify the method disclosed in the present invention, specifically as follows:

[0128] (1) Construct a cloud sea landscape prediction model

[0129] (a) Obtain 5 groups of meteorological monitoring data values of the target area described in Table 1.

[0130] Table 1 Meteorological Monitoring Data Table

[0131]

[0132] (b) Assign weight values to 5 types of factors, and the assignment results are shown in Table 2.

[0133] Table 2 Weight Analysis Table

[0134]

[0135] (2) Obtain the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor on a certain summer day in the target area, specifically as follows:

[0136] The predicted humidity RH is about 90%.

[0137] The predicted air temperature T surface is about 21°C, and the dew point temperature T of this place dew is about 20°C

[0138] The predicted actual temperature lapse rate is about γ about 3°C / km, and the dry adiabatic lapse rate takes the constant γ dry about 9.8°C / km

[0139] The predicted wind speed V is about 3 m / s, and the optimal wind speed V opt is about 3 m / s, σ = 2

[0140] The height of the mountain peak H mountain is about 1350 m, and the cloud base height H cloud_base is about 1000 m, and the standardized height difference H range is about 500 m

[0141] The corresponding relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor are as follows:

[0142] Humidity factor (H rel ): 0.9

[0143] Temperature condition factor (T diff ): 0.9

[0144] Atmospheric stability factor (S stab ): 0.69

[0145] Wind speed factor (W wind ): 1

[0146] Terrain uplifting factor (G topo ): 0.7

[0147] Bias term (ζ): Take 0

[0148] (3) Substitute the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplifting factor into the cloud sea landscape prediction model, and the cloud sea occurrence probability on the day to be predicted is 82.09%, and this cloud sea occurrence probability is relatively high.

[0149] (4) The actual alpine cloud sea landscape on the same day is as shown in the appendix Figure 4 shown in the appendix Figure 4 There is a cloud sea landscape in the appendix, indicating that the method disclosed in the present invention is effective.

[0150] Example 6: As shown in the appendix Figure 5 shown, the embodiment of the present invention discloses an alpine cloud sea landscape prediction device, including:

[0151] A prediction data acquisition unit that acquires the prediction basic data of the target area for the day to be predicted, where the prediction basic data includes a relative humidity factor, a temperature condition factor, an atmospheric stability factor, a wind speed factor, and a terrain uplift factor;

[0152] A prediction unit that inputs the prediction basic data into the cloud sea landscape prediction model to obtain the cloud sea occurrence probability of the day to be predicted, where the cloud sea landscape prediction model is as follows:

[0153] P cloud =α·H rel +β·T diff +γ·S stab +δ·W wind +∈·G topo +ζ

[0154] Where, P cloud is the cloud sea occurrence probability; H rel , T diff , S stab , W wind , G topo are the relative humidity factor, the temperature condition factor, the atmospheric stability factor, the wind speed factor, and the terrain uplift factor respectively; α, β, γ, δ, ∈ are the weights of the relative humidity factor, the temperature condition factor, the atmospheric stability factor, the wind speed factor, and the terrain uplift factor respectively; ζ is the deviation value.

[0155] Example 7: As shown in the appendix Figure 6 shown, the embodiment of the present invention discloses an alpine cloud sea landscape prediction device, which is a further optimization of the above embodiment, and further includes:

[0156] A model construction unit, including:

[0157] A historical data acquisition module that acquires a number of historical influence factor data, where the historical influence factor data includes the meteorological factors and terrain factors when the cloud sea landscape appears in different regions, and the meteorological factors and terrain factors when the cloud sea landscape does not appear;

[0158] Factor screening module: perform correlation analysis between each meteorological factor and terrain factor in the historical impact factor data and the occurrence of cloud sea landscapes respectively, and screen out the top 5 factors with the highest correlation to form a key factor set. The 5 factors include relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplift factor.

[0159] Weight assignment module: assign weight values to the 5 factors and construct a prediction model for cloud sea landscapes through weighting, as follows:

[0160] P cloud = α·H rel + β·T diff + γ·S stab + δ·W wind + ∈·G topo + ζ

[0161] Where P cloud is the probability of cloud sea occurrence; H rel , T diff , S stab , W wind , G topo are the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplift factor respectively; α, β, γ, δ, ∈ are the weights of the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain uplift factor respectively; ζ is the deviation value.

[0162] Weight correction unit, including:

[0163] Data acquisition module: collect all prediction result data within a set time interval, where the prediction result data includes the prediction basic data input for prediction and the corresponding actual cloud sea occurrence results;

[0164] Analysis module: analyze the correlation between the prediction basic data and the actual cloud sea occurrence results using the correlation analysis algorithm;

[0165] Correction module: correct the weight values of various factors in the cloud sea landscape prediction model according to the correlation analysis results.

[0166] Example 8: The embodiment of the present invention discloses a storage medium, on which a computer program readable by a computer is stored, and the computer program is set to execute the prediction method for alpine cloud sea landscapes when running.

[0167] The above storage medium may include, but is not limited to: various media such as USB flash drives, read-only memories, mobile hard disks, magnetic disks, or optical discs that can store computer programs.

[0168] Example 9: An embodiment of the present invention discloses an electronic device, including a processor and a memory. A computer program is stored in the memory and is loaded and executed by the processor to implement the alpine cloud and sea landscape prediction method.

[0169] The above-mentioned processor may be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in connection with the disclosure of the present invention. It can also be a combination that realizes computing functions, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, and so on. The memory may include, but is not limited to: various media that can store computer programs, such as USB flash drives, read-only memories, mobile hard disks, magnetic disks, or optical discs.

[0170] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) that contain computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0171] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or multiple blocks.

[0172] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one or more of the flows Figure 1The functions specified in one or more boxes.

[0173] The above content is only a specific implementation of the present invention, which has strong adaptability and implementation effects. However, the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. A method for predicting high mountain sea of ​​clouds, characterized in that: include: Obtain the forecast basic data for the target area on the forecast day, where the forecast basic data includes relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain lift factor; Input the prediction basic data into the sea of ​​clouds landscape prediction model to obtain the probability of sea of ​​clouds on the predicted day, where the sea of ​​clouds landscape prediction model is as follows: P cloud =α·H rel +β·T diff +γ·S stab +δ·W wind +∈·G topo +g Among them, P cloud is the probability of occurrence of sea of ​​clouds; H rel , T diff , S stab , W wind , G topo are the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain lift factor respectively; α, β, γ, δ, ∈ are the weights of the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain lift factor respectively; ζ is the deviation value.

2. The method for predicting high mountain sea of ​​clouds according to claim 1, characterized in that: The process of constructing the sea of ​​clouds landscape prediction model includes: Acquire a number of historical influencing factor data, wherein the historical influencing factor data include meteorological factors and topographic factors when the sea of ​​clouds landscape appears in different regions, and meteorological factors and topographic factors when the sea of ​​clouds landscape does not appear; The correlation between each meteorological factor and terrain factor in the historical influencing factor data and the occurrence of sea of ​​clouds was analyzed, and the five factors with the highest correlation were selected to form a key factor set, including relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain lift factor. Assign weight values ​​to the five factors and construct a weighted sea of ​​clouds landscape prediction model, as follows: P cloud =α·H rel +β·T diff +γ·S stab +δ·W wind +∈·G topo +g Among them, P cloud is the probability of occurrence of sea of ​​clouds; H rel , T diff , S stab , W wind , G topo are the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain lift factor respectively; α, β, γ, δ, ∈ are the weights of the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain lift factor respectively; ζ is the deviation value.

3. The method for predicting high mountain sea of ​​clouds according to claim 2, characterized in that: The Pearson correlation coefficient analysis method was used to assign weight values ​​to the five factors.

4. The method for predicting high mountain sea of ​​clouds landscape according to any one of claims 1 to 3, characterized in that: It also includes weight correction of the cloud sea landscape prediction model based on multiple prediction result data, including: Collect all forecast result data within a set time interval, where the forecast result data includes the forecast basic data inputted by the forecast and the corresponding actual sea of ​​clouds occurrence results; The correlation analysis algorithm is used to analyze the correlation between the prediction basic data and the actual sea of ​​clouds occurrence results; According to the results of correlation analysis, the weight values ​​of various factors in the sea of ​​clouds landscape prediction model are corrected.

5. The method for predicting high mountain sea of ​​clouds landscape according to any one of claims 1 to 4, characterized in that: The process of obtaining the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain lift factor includes: Obtain meteorological data and calculate humidity factor, temperature condition factor, atmospheric stability factor, and wind speed factor, as follows: Humidity factor H rel As shown below: Where RH is relative humidity; Temperature Condition Factor T diff As shown below: Among them, T surface is the actual temperature; T dew is the dew point temperature; T range is the temperature difference normalization coefficient; Atmospheric stability S stab The factors are as follows: Where γ is the actual temperature decrease rate; γ dry is the dry adiabatic lapse rate; Wind speed factor W wind As shown below: Where, V is the wind speed; V opt is the optimal wind speed; σ is the wind speed width parameter; e is a constant term; Obtain terrain data and calculate terrain lift factor G topo ; Among them, H mountain is the peak height; H cloud_base is the cloud base height; H range is the normalized height difference.

6. A device for predicting high mountain sea of ​​clouds landscape using the method as claimed in any one of claims 1 to 5, characterized in that: include: A forecast data acquisition unit is used to acquire forecast basic data of the target area on the forecast day, wherein the forecast basic data includes relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain lift factor; The prediction unit inputs the prediction basic data into the sea of ​​clouds landscape prediction model to obtain the probability of sea of ​​clouds on the day to be predicted, wherein the sea of ​​clouds landscape prediction model is as follows: P cloud =α·H rel +β·T diff +γ·S stab +δ·W wind +∈·G topo +g Among them, P cloud is the probability of occurrence of sea of ​​clouds; H rel , T diff , S stab , W wind , G topo are the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain lift factor respectively; α, β, γ, δ, ∈ are the weights of the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain lift factor respectively; ζ is the deviation value.

7. The high mountain sea of ​​clouds landscape prediction device according to claim 6, characterized in that: Also included are model building units, including: A historical data acquisition module is used to acquire a number of historical influencing factor data, wherein the historical influencing factor data include meteorological factors and topographic factors when the sea of ​​clouds appears in different regions, and meteorological factors and topographic factors when the sea of ​​clouds does not appear; The factor screening module conducts correlation analysis between each meteorological factor and terrain factor in the historical influencing factor data and the occurrence of sea of ​​clouds, and screens out the five factors with the highest correlation to form a key factor set. The five factors include relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain lift factor. The weight allocation module assigns weight values ​​to the five factors and constructs a cloud sea landscape prediction model by weighting, as follows: P cloud =α·H rel +β·T diff +γ·S stab +δ·W wind +∈·G topo +g Among them, P cloud is the probability of occurrence of sea of ​​clouds; H rel , T diff , S stab , W wind , G topo are the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain lift factor respectively; α, β, γ, δ, ∈ are the weights of the relative humidity factor, temperature condition factor, atmospheric stability factor, wind speed factor, and terrain lift factor respectively; ζ is the deviation value.

8. The high mountain sea of ​​clouds landscape prediction device according to claim 6 or 7, characterized in that: Also included is a weight correction unit, including: The data collection module collects all the forecast result data within the set time interval, wherein the forecast result data includes the forecast basic data inputted by the forecast and the corresponding actual sea of ​​clouds occurrence results; The analysis module uses a correlation analysis algorithm to analyze the correlation between the prediction basic data and the actual cloud sea occurrence results; The correction module corrects the weight values ​​of various factors in the sea of ​​clouds landscape prediction model according to the correlation analysis results.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the steps in the method according to any one of claims 1 to 5.

10. A storage medium, characterized in that: The storage medium stores a computer program that can be read by a computer, and the computer program is configured to execute the steps of the method according to any one of claims 1 to 5 when running.

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