CN116774317A - Low-temperature cold tide weather process identification method, system, equipment and medium - Google Patents
Low-temperature cold tide weather process identification method, system, equipment and medium Download PDFInfo
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Abstract
The invention provides a method, a system, equipment and a medium for identifying a low-temperature cold and tide weather process, which are characterized in that firstly, gridding numerical weather forecast data are processed by utilizing a plurality of preset cold and tide verification modes to determine gridding positions influenced by cold and tide and then are marked; secondly, determining the influence range of low-temperature cold weather based on the identified gridding position and the unit grid area of gridding air temperature data; the plurality of cold tide verification modes comprise: the invention obtains the influence range of the low-temperature cold tide weather process by adopting the mode of gridding to the cooling process identification, the temperature negative distance flat check and the daily average temperature minimum check, thereby realizing the identification of the influence range of the low-temperature cold tide weather process under different time sections, solving the problem of large wind power output prediction extreme deviation under the low-temperature cold tide weather, and supporting power supply and having engineering applicability.
Description
Technical Field
The invention belongs to the technical field of cold tide identification, and particularly relates to a method, a system, equipment and a medium for identifying a low-temperature cold tide weather process.
Background
In the climate change background, extreme weather such as low-temperature chill or high temperature is frequently sent and retransmitted. At present, with the further increase of the installation ratio of new energy sources such as wind power, photovoltaic and the like, the power generation capacity of a new energy source system is reduced due to the negative influence of wind power, low temperature, ice coating and the like in low-temperature cold and damp weather, and the load demand is increased along with the reduction of low temperature, so that the unbalance risk of the supply and demand of a power system is increased sharply.
In order to reduce unbalance of supply and demand of the power system as much as possible, the connection between the new energy system and the power system can be cut off in time before the low-temperature cold tide comes, and the connection between the traditional power generation system and the power system is conducted, so that the unbalance of supply and demand of the power system can be effectively reduced through the prediction of the weather of the low-temperature cold tide.
In the current academic, in the process of predicting low-temperature chill weather, the condition of wind power generation loss is predicted, and is one of effective measures corresponding to the risk of potential power supply shortage of a power system in the process of low-temperature chill weather. However, the current prediction of the low-temperature cold tide weather is mainly modeled for the normalized weather process, a proprietary prediction technology and model for the low-temperature cold tide weather process are not constructed, the influence range of the low-temperature cold tide weather cannot be predicted, and then the wind power generation loss condition under the influence of the low-temperature cold tide weather cannot be predicted.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method, a system, equipment and a medium for identifying the weather process of low-temperature chill.
The technical scheme provided by the invention is as follows:
the invention provides a method for identifying a low-temperature cold and damp weather process, which comprises the following steps:
processing the gridding numerical weather forecast data by utilizing a plurality of preset cold tide verification modes to determine gridding positions influenced by the cold tide and marking;
determining the influence range of low-temperature cold and damp weather based on the identified grid position and the unit grid area of the grid air temperature data;
the plurality of cold tide verification modes comprise: and (5) identifying the cooling process, checking the temperature negative distance level and the average daily air temperature minimum value.
Preferably, the processing the meshing numerical weather forecast data by using a plurality of preset cold tide verification modes to determine meshing positions affected by the cold tide and identify the positions includes:
based on the gridding numerical weather forecast data, identifying and determining a first gridding position in a cooling process by utilizing a cooling process identification matrix for identification;
determining a second grid position by utilizing temperature negative-distance flat verification based on grid numerical weather forecast data, and marking by utilizing a temperature negative-distance flat verification matrix;
And determining a third grid position by utilizing the minimum value check of the daily air temperature based on grid numerical weather forecast data, and marking by utilizing a minimum value check matrix of the daily air temperature.
Preferably, the determining the first gridding position in the cooling process based on the gridding numerical weather forecast data by using the cooling process identification, and identifying by using the cooling process identification matrix includes:
determining a daily average air temperature highest value and a daily average air temperature lowest value of each gridding position in the cold air influence process based on gridding numerical weather forecast data;
determining a first air temperature difference value of each grid position based on the highest value and the lowest value of the daily average air temperature of each grid position in the cold air influence process;
taking the grid position with the first air temperature difference value larger than the first temperature difference threshold value as a first grid position;
and respectively using different cooling process identification parameters to identify the first gridding position and the non-first gridding position to obtain a cooling process identification matrix.
Preferably, the expression of the cooling process identification matrix is as follows:
wherein ,
wherein ,
in the formula ,S1 In order to identify the matrix for the cooling process,is (x) i ,y i ) Identifying parameters of the cooling process at the location, +.>Is (x) i ,y i ) The cold air at the location influences the average air temperature on all days of the process,/->Is (x) i ,y i ) The cold air on day d at the location influences the mean daily air temperature of the process, +.>Is (x) i ,y i ) Air temperature at position t, d tc For the tc-th time of day d, c is the number of samples at the time of day d, +.>Maximum value of the daily mean air temperature for the cold air influencing process,/-for>B being the lowest value of the daily average air temperature of the cold air influencing process 1 Is the first temperature difference threshold.
Preferably, the determining the second grid position by using temperature negative-distance flat check based on the grid numerical weather forecast data and marking by using a temperature negative-distance flat check matrix includes:
determining the daily average air temperature and the daily average air temperature of each grid position in the cold air influence process based on the grid numerical weather forecast data, wherein the daily average air temperature is the same as the daily average air temperature;
determining a second air temperature difference value of each grid position based on the daily average air temperature of each grid position in the cold air influence process and the daily average air temperature of the last ten days of the lowest daily average air temperature value;
taking the grid position with the second air temperature difference value not smaller than the second temperature difference threshold value as a second grid position;
And respectively using different cooling process identification parameters to identify the second gridding position and the non-second gridding position to obtain a temperature negative-distance flat check matrix.
Preferably, the expression of the temperature negative range flat check matrix is as follows:
wherein ,
wherein ,
wherein ,
in the formula ,S2 For checking temperature negative distance levelThe matrix is formed by a matrix of,is (x) i ,y i ) The temperature at the location is negative from the flat check parameter,is (x) i ,y i ) The cold air at the location influences the average air temperature on all days of the process,/->Is (x) i ,y i ) Average air temperature in the last years of the day of the lowest value of all the day average air temperatures of the cold air influencing processes at the location, +.>Is (x) i ,y i ) Average air temperature of all days of cold air influence process of the (q) th day of the (q) th year of the position tN For the nth year, N is the number of samples in the last year, ++>Is (x) i ,y i ) Air temperature at position t, d tc For the tc-th time of day d, c is the number of samples at the time of day d, +.>B being the lowest value of the daily average air temperature of the cold air influencing process 2 Is the second temperature difference threshold.
Preferably, the determining the third grid position by using the minimum value check of the average daily air temperature based on the grid numerical weather forecast data and the identifying by using the minimum value check matrix of the average daily air temperature includes:
Determining a daily average air temperature minimum value of each gridding position in the cold air influence process based on gridding numerical weather forecast data;
taking the grid position with the lowest daily average air temperature value smaller than the third temperature threshold value as a third grid position;
and respectively marking the third grid position and the non-third grid position by using different daily average air temperature minimum value parameters to obtain a daily average air temperature minimum value check matrix.
Preferably, the expression of the daily average air temperature minimum check matrix is as follows:
wherein ,
in the formula ,S3 Is a check matrix with the lowest value of the average daily air temperature,is (x) i ,y i ) Checking the minimum value of the average daily air temperature at the position,/-)>Is (x) i ,y i ) The cold air at the location influences the average air temperature on all days of the process,/->B being the lowest value of the daily average air temperature of the cold air influencing process 3 Is a third temperature threshold.
Preferably, the determining the influence range of the low-temperature cold weather based on the identified grid position and the unit grid area of the grid air temperature data includes:
determining a cold tide identification result for the identified grid position of the cooling process identification, the temperature negative distance flat check and the daily average air temperature minimum value check based on the cold tide check mode;
And determining the influence range of the low-temperature cold weather according to the cold weather identification result and the unit grid area of the grid air temperature data.
Preferably, the determining the cold weather identification result for the identified grid position based on the cold weather verification mode for cooling process identification, temperature negative distance flat verification and daily average temperature minimum value verification includes:
sequentially determining a cooling process identification matrix, a temperature negative distance flat check matrix and a daily average air temperature minimum check matrix based on the identified grid position of the cooling process identification, the temperature negative distance flat check matrix and the daily average air temperature minimum check based on the cold-damp check mode;
determining a chill identification result matrix by using an intersection of the cooling process identification matrix, the temperature negative-distance flat check matrix and the average daily temperature minimum check matrix;
the chill identification result matrix is used for representing the chill identification result.
Preferably, the calculation formula of the cold-tide recognition result matrix is as follows:
S＝S 1 ∩S 2 ∩S 3
wherein S is a cold tide recognition result matrix, S 1 Identifying a matrix of parameters for the temperature reduction process of each position meshing, S 2 Matrix of temperature negative range flat check parameters gridded for each position, S 3 A matrix of the minimum daily air temperature check parameters for each position meshing;
The calculation formula of the influence range of the low-temperature cold tide weather is as follows:
wherein ,
where, alpha is the unit grid area of the grid air temperature data,is the influence of low-temperature cold and damp weatherSurrounding, A is the range threshold, S is the identification result of chill, and->Is (x) i ,y i ) The cold tide identification parameter at the position is m is the maximum value of the abscissa of the position, and n is the maximum value of the ordinate of the position.
Based on the same inventive concept, the invention also provides a low-temperature cold weather process identification system, which comprises:
the grid position identification module is used for processing the grid numerical weather forecast data by utilizing a plurality of preset cold tide verification modes to determine grid positions affected by the cold tide and identifying the grid positions;
the chill influence range determining module is used for determining the influence range of low-temperature chill weather based on the identified grid position and the unit grid area of the grid air temperature data;
the plurality of cold tide verification modes comprise: and (5) identifying the cooling process, checking the temperature negative distance level and the average daily air temperature minimum value.
Preferably, the cold tide grid position identification module is specifically configured to:
based on the gridding numerical weather forecast data, identifying and determining a first gridding position in a cooling process by utilizing a cooling process identification matrix for identification;
Determining a second grid position by utilizing temperature negative-distance flat verification based on grid numerical weather forecast data, and marking by utilizing a temperature negative-distance flat verification matrix;
and determining a third grid position by utilizing the minimum value check of the daily air temperature based on grid numerical weather forecast data, and marking by utilizing a minimum value check matrix of the daily air temperature.
Preferably, the cold tide grid position identification module identifies and determines a first grid position in a cooling process by using cooling process identification based on grid numerical weather forecast data, and identifies by using a cooling process identification matrix, and includes:
determining a daily average air temperature highest value and a daily average air temperature lowest value of each gridding position in the cold air influence process based on gridding numerical weather forecast data;
determining a first air temperature difference value of each grid position based on the highest value and the lowest value of the daily average air temperature of each grid position in the cold air influence process;
taking the grid position with the first air temperature difference value larger than the first temperature difference threshold value as a first grid position;
and respectively using different cooling process identification parameters to identify the first gridding position and the non-first gridding position to obtain a cooling process identification matrix.
Preferably, the expression of the cooling process identification matrix is as follows:
wherein ,
wherein ,
in the formula ,S1 In order to identify the matrix for the cooling process,is (x) i ,y i ) Identifying parameters of the cooling process at the location, +.>Is (x) i ,y i ) The cold air at the location influences the average air temperature on all days of the process,/->Is (x) i ,y i ) The cold air on day d at the location influences the mean daily air temperature of the process, +.>Is (x) i ,y i ) Air temperature at position t, d tc For the tc-th time of day d, c is the number of samples at the time of day d, +.>Maximum value of the daily mean air temperature for the cold air influencing process,/-for>B being the lowest value of the daily average air temperature of the cold air influencing process 1 Is the first temperature difference threshold.
Preferably, the cold tide grid position identification module determines a second grid position by using temperature negative distance flat check based on grid numerical weather forecast data, and identifies the second grid position by using a temperature negative distance flat check matrix, and the cold tide grid position identification module comprises:
determining the daily average air temperature and the daily average air temperature of each grid position in the cold air influence process based on the grid numerical weather forecast data, wherein the daily average air temperature is the same as the daily average air temperature;
determining a second air temperature difference value of each grid position based on the daily average air temperature of each grid position in the cold air influence process and the daily average air temperature of the last ten days of the lowest daily average air temperature value;
Taking the grid position with the second air temperature difference value not smaller than the second temperature difference threshold value as a second grid position;
and respectively using different cooling process identification parameters to identify the second gridding position and the non-second gridding position to obtain a temperature negative-distance flat check matrix.
Preferably, the expression of the temperature negative range flat check matrix is as follows:
wherein ,
wherein ,
wherein ,
in the formula ,S2 Is a temperature negative-distance flat check matrix,is (x) i ,y i ) The temperature at the location is negative from the flat check parameter,is (x) i ,y i ) The cold air at the location influences the average air temperature on all days of the process,/->Is (x) i ,y i ) Average air temperature in the last years of the day of the lowest value of all the day average air temperatures of the cold air influencing processes at the location, +.>Is (x) i ,y i ) Average air temperature of all days of cold air influence process of the (q) th day of the (q) th year of the position tN For the nth year, N is the number of samples in the last year, ++>Is (x) i ,y i ) Air temperature at position t, d tc For the tc-th time of day d, c is the number of samples at the time of day d, +.>B being the lowest value of the daily average air temperature of the cold air influencing process 2 Is the second temperature difference threshold.
Preferably, the cold tide grid position identification module determines a third grid position by using a daily average air temperature minimum value check based on grid numerical weather forecast data, and identifies the third grid position by using a daily average air temperature minimum value check matrix, and the cold tide grid position identification module comprises:
Determining a daily average air temperature minimum value of each gridding position in the cold air influence process based on gridding numerical weather forecast data;
taking the grid position with the lowest daily average air temperature value smaller than the third temperature threshold value as a third grid position;
and respectively marking the third grid position and the non-third grid position by using different daily average air temperature minimum value parameters to obtain a daily average air temperature minimum value check matrix.
Preferably, the expression of the daily average air temperature minimum check matrix is as follows:
wherein ,
in the formula ,S3 Is a check matrix with the lowest value of the average daily air temperature,is (x) i ,y i ) Checking the minimum value of the average daily air temperature at the position,/-)>Is (x) i ,y i ) The cold air at the location influences the average air temperature on all days of the process,/->B being the lowest value of the daily average air temperature of the cold air influencing process 3 Is a third temperature threshold.
Preferably, the cold wave influence range determining module is specifically configured to:
determining a cold tide identification result for the identified grid position of the cooling process identification, the temperature negative distance flat check and the daily average air temperature minimum value check based on the cold tide check mode;
and determining the influence range of the low-temperature cold weather according to the cold weather identification result and the unit grid area of the grid air temperature data.
Preferably, the determining of the influence range of the cold weather determines a cold weather identification result for the identified grid position of the cooling process identification, the temperature negative distance flat check and the daily average air temperature minimum check based on the cold weather check mode, and includes:
sequentially determining a cooling process identification matrix, a temperature negative distance flat check matrix and a daily average air temperature minimum check matrix based on the identified grid position of the cooling process identification, the temperature negative distance flat check matrix and the daily average air temperature minimum check based on the cold-damp check mode;
determining a chill identification result matrix by using an intersection of the cooling process identification matrix, the temperature negative-distance flat check matrix and the average daily temperature minimum check matrix;
the chill identification result matrix is used for representing the chill identification result.
Preferably, the calculation formula of the cold-tide recognition result matrix is as follows:
S＝S 1 ∩S 2 ∩S 3
wherein S is a cold tide recognition result matrix, S 1 Identifying a matrix of parameters for the temperature reduction process of each position meshing, S 2 Matrix of temperature negative range flat check parameters gridded for each position, S 3 Matrix of minimum value check parameters of average daily air temperature for each position meshing；
The calculation formula of the influence range of the low-temperature cold tide weather is as follows:
wherein ,
where, alpha is the unit grid area of the grid air temperature data,is the influence range of low-temperature cold and damp weather, A is a range threshold, S is the cold and damp identification result, and ++>Is (x) i ,y i ) The cold tide identification parameter at the position is m is the maximum value of the abscissa of the position, and n is the maximum value of the ordinate of the position.
Based on the same inventive concept, the invention further provides a computer device, comprising: one or more processors; a memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, a low-temperature cold weather process identification method is implemented.
Based on the same inventive concept, the invention further provides a computer readable storage medium, on which a computer program is stored, which when executed, implements the above-mentioned method for identifying the weather process of low-temperature chill.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention provides a method, a system, equipment and a medium for identifying a low-temperature cold and tide weather process, which are characterized in that firstly, gridding numerical weather forecast data are processed by utilizing a plurality of preset cold and tide verification modes to determine gridding positions influenced by cold and tide and then are marked; secondly, determining the influence range of low-temperature cold weather based on the identified gridding position and the unit grid area of gridding air temperature data; the plurality of cold tide verification modes comprise: the invention acquires the average daily air temperature in the beginning and the end of the cold air influence process at each position, and adopts a gridding mode to obtain the influence range of the low-temperature chill weather process by adopting the cooling process identification, the temperature negative distance flat check and the average daily air temperature minimum value check, thereby realizing the identification of the influence range of the low-temperature chill weather process, solving the problem of large wind power output prediction extreme deviation in the low-temperature chill weather, and further supporting power conservation and engineering applicability.
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FIG. 1 is a schematic flow chart of a method for identifying a low-temperature cold weather process according to the present invention;
fig. 2 is a schematic connection diagram of a low-temperature cold weather process identification system according to the present invention.
Detailed Description
The following describes the embodiments of the present invention in further detail with reference to the drawings.
Example 1:
the invention provides a method for identifying a low-temperature cold and damp weather process, which is shown in figure 1 and comprises the following steps:
step 1: processing the gridding numerical weather forecast data by utilizing a plurality of preset cold tide verification modes to determine gridding positions influenced by the cold tide and marking;
step 2: determining the influence range of low-temperature cold and damp weather based on the identified grid position and the unit grid area of the grid air temperature data;
the plurality of cold tide verification modes comprise: and (5) identifying the cooling process, checking the temperature negative distance level and the average daily air temperature minimum value.
The invention identifies the weather process of low-temperature cold and damp based on grid air temperature forecast data, namely that the longitude is x i Latitude is y i At the time, the data is obtained from an external input. The method comprises the steps of process cooling identification, temperature range average verification, area affected verification, average daily minimum temperature identification and the like.
Processing the gridding numerical weather forecast data by utilizing a plurality of preset cold tide verification modes in the step 1 to determine gridding positions influenced by the cold tide and marking, wherein the method comprises the following three aspects:
(1) based on the gridding numerical weather forecast data, identifying and determining a first gridding position in a cooling process by utilizing a cooling process identification matrix for identification;
(2) determining a second grid position by utilizing temperature negative-distance flat verification based on grid numerical weather forecast data, and marking by utilizing a temperature negative-distance flat verification matrix;
(3) and determining a third grid position by utilizing the minimum value check of the daily air temperature based on grid numerical weather forecast data, and marking by utilizing a minimum value check matrix of the daily air temperature.
In the present invention, the meshing corresponds to a matrix, that is, the present invention predicts the influence range of the weather of the chill by using a matrix method.
For (1), the first gridding position in the cooling process is determined by using cooling process identification based on gridding numerical weather forecast data, and the first gridding position is identified by using a cooling process identification matrix, and the method comprises the following steps:
Determining the highest daily average air temperature value and the lowest daily average air temperature value of each grid position in the cold air influence process based on the grid numerical weather forecast data;
determining a first air temperature difference value of each grid position based on the highest value and the lowest value of the daily average air temperature of each grid position in the cold air influence process;
the method comprises the following steps of using a grid position with a first air temperature difference value larger than a first temperature difference threshold value as a first grid position;
and identifying the first grid position and the non-first grid position by using different cooling process identification parameters respectively to obtain a cooling process identification matrix.
The expression of the cooling process identification matrix is as follows:
wherein ,
wherein ,
in the formula ,S1 In order to identify the matrix for the cooling process,is (x) i ,y i ) Identifying parameters of the cooling process at the location, +.>Is (x) i ,y i ) The cold air at the location influences the average air temperature on all days of the process,/->Is (x) i ,y i ) The cold air on day d at the location influences the mean daily air temperature of the process, +.>Is (x) i ,y i ) Air temperature at position t, d tc For the tc-th time of day d, c is the number of samples at the time of day d, +.>For the highest value of the daily average air temperature of the cold air influencing process,b being the lowest value of the daily average air temperature of the cold air influencing process 1 Is a first temperature difference threshold (generally, B 1 ＝10)。
For (2), the second meshing position is determined by using temperature negative-distance flat verification based on meshing numerical weather forecast data, and the identification is performed by using a temperature negative-distance flat verification matrix, and the method comprises the following steps:
determining the daily average air temperature and the daily average air temperature of each grid position in the cold air influence process based on the weather forecast data of the grid values, wherein the daily average air temperature is the last ten days of the lowest value of the daily average air temperature;
determining a second air temperature difference value of each grid position based on the daily average air temperature of each grid position in the cold air influence process and the daily average air temperature of the day of the last ten years of the day of the lowest daily average air temperature value;
the grid position with the second air temperature difference value not smaller than the second temperature difference threshold value is taken as a second grid position;
and identifying the second grid position and the non-second grid position by using different cooling process identification parameters respectively to obtain a temperature negative-distance flat check matrix.
The expression of the temperature negative-distance flat check matrix is as follows:
wherein ,
wherein ,
wherein ,
in the formula ,S2 Is a temperature negative-distance flat check matrix,is (x) i ,y i ) The temperature at the location is negative from the flat check parameter, Is (x) i ,y i ) The cold air at the location influences the average air temperature on all days of the process,/->Is (x) i ,y i ) Average air temperature in the last years of the day of the lowest value of all the day average air temperatures of the cold air influencing processes at the location, +.>Is (x) i ,y i ) Average air temperature of all days of cold air influence process of the (q) th day of the (q) th year of the position tN For the nth year, N is the number of samples in the last year, ++>Is (x) i ,y i ) Air temperature at position t, d tc For the tc-th time of day d, c is the number of samples at the time of day d, +.>B being the lowest value of the daily average air temperature of the cold air influencing process 2 Is a second temperature difference threshold (generally, B 2 ＝5)。
For the purposes of the present invention, reference is made toIs (x) i ,y i ) The cold air at the location affects the average air temperature over the years of the day of the process, the day at which the lowest value of all the daily air temperatures is located, as exemplified herein below: for example, the average air temperature of 4 months 5 is lowest, and since 4 months 5 is the last ten days of 4 months, this last ten days represents the last ten days of 4 months of many years, and the average air temperature of many years is the average air temperature of last ten days of 4 months of many years.
For (3), the third grid position is determined by using the minimum value check of the average daily air temperature based on the grid numerical weather forecast data, and the identification is performed by using the minimum value check matrix of the average daily air temperature, and the method comprises the following steps:
Determining the daily average air temperature minimum value of each gridding position in the cold air influence process based on gridding numerical weather forecast data;
the grid position with the lowest daily average air temperature value smaller than a third temperature threshold value is taken as a third grid position;
and identifying the third grid position and the non-third grid position by using different daily average air temperature minimum value parameters respectively to obtain a daily average air temperature minimum value check matrix.
The expression of the daily air temperature minimum check matrix is as follows:
wherein ,
in the formula ,S3 Is a check matrix with the lowest value of the average daily air temperature,is (x) i ,y i ) Checking the minimum value of the average daily air temperature at the position,/-)>Is (x) i ,y i ) The cold air at the location influences the average air temperature on all days of the process,/->B being the lowest value of the daily average air temperature of the cold air influencing process 3 Is a third temperature threshold (typically, B 3 ＝5)。
In the invention, the identification parameter and the verification parameter are 0 or 1, which indicates that the identification parameter and the verification parameter are none or exist.
For the step 2 of the present invention, the determining the influence range of the weather of the low-temperature chill based on the identified grid position and the unit grid area of the grid air temperature data includes the following steps:
determining a cold and damp identification result for the identified grid position after identifying the cooling process based on the cold and damp verification mode, the temperature negative distance flat verification and the daily average air temperature minimum value verification;
And determining the influence range of low-temperature cold-damp weather according to the cold-damp identification result and the unit grid area of the grid air temperature data.
Wherein, based on the cold tide verification mode, the cold tide recognition result is determined for the identified grid position of the cooling process recognition, the temperature negative distance flat verification and the day average temperature minimum value verification, and the method comprises the following steps:
determining a cooling process identification matrix, a temperature negative-distance flat check matrix and a daily average air temperature minimum check matrix in sequence for the identified grid position of cooling process identification, temperature negative-distance flat check matrix and daily average air temperature minimum check based on a cold-damp check mode;
determining a cold tide recognition result matrix by using the intersection of the recognition matrix in the cooling process, the temperature negative-distance flat check matrix and the daily average temperature minimum check matrix;
the chill identification result matrix is used for representing the chill identification result.
The calculation formula of the chill identification result matrix is as follows:
S＝S 1 ∩S 2 ∩S 3
in the method, in the process of the invention,s is a cold and damp identification result matrix, S 1 Identifying a matrix of parameters for the temperature reduction process of each position meshing, S 2 Matrix of temperature negative range flat check parameters gridded for each position, S 3 A matrix of the minimum daily air temperature check parameters for each position meshing;
The calculation formula of the influence range of the low-temperature cold tide weather is as follows:
/>
wherein ,
wherein α is a unit grid area of the grid air temperature data (is an auxiliary information parameter for inputting the grid air temperature data), and α×For the influence range of low-temperature cold and damp weather, A is a range threshold (which is set according to engineering requirements, such as 10 ten thousand square kilometers), S is a cold and damp identification result, and ++>Is (x) i ,y i ) The cold tide identification parameter (taking 1 to indicate cold tide and 0 to indicate no cold tide) at the position, m is the maximum value of the abscissa of the position, and n is the maximum value of the ordinate of the position.
In conclusion, the invention focuses on the problem of proprietary prediction in the turning weather process and provides a method for identifying the weather process of low-temperature chill. The method provided by the invention can identify the influence range (the range refers to the range consisting of physical coordinates, namely the latitude and longitude range) of the low-temperature cold weather process under different time sections, so that the problem of large extreme deviation of wind power output prediction under the low-temperature cold weather is solved, and the power supply is supported, so that the method has engineering applicability.
Example 2:
based on the same inventive concept, the invention provides a low-temperature chill weather process identification system, as shown in fig. 2, comprising: the system comprises a chill grid position identification module and a chill influence range determination module.
The grid position identification module is used for processing the grid numerical weather forecast data by utilizing a plurality of preset cold tide verification modes to determine grid positions affected by the cold tide and identifying the grid positions;
the chill influence range determining module is used for determining the influence range of low-temperature chill weather based on the identified grid position and the unit grid area of the grid air temperature data;
the plurality of cold tide verification modes comprise: and (5) identifying the cooling process, checking the temperature negative distance level and the average daily air temperature minimum value.
The cold tide grid position identification module is specifically used for:
based on the gridding numerical weather forecast data, identifying and determining a first gridding position in a cooling process by utilizing a cooling process identification matrix for identification;
determining a second grid position by utilizing temperature negative-distance flat verification based on grid numerical weather forecast data, and marking by utilizing a temperature negative-distance flat verification matrix;
and determining a third grid position by utilizing the minimum value check of the daily air temperature based on grid numerical weather forecast data, and marking by utilizing a minimum value check matrix of the daily air temperature.
The cold tide grid position identification module identifies a first grid position in a cooling process by utilizing cooling process identification based on grid numerical weather forecast data and identifies the first grid position by using a cooling process identification matrix, and comprises the following steps:
Determining a daily average air temperature highest value and a daily average air temperature lowest value of each gridding position in the cold air influence process based on gridding numerical weather forecast data;
determining a first air temperature difference value of each grid position based on the highest value and the lowest value of the daily average air temperature of each grid position in the cold air influence process;
taking the grid position with the first air temperature difference value larger than the first temperature difference threshold value as a first grid position;
and respectively using different cooling process identification parameters to identify the first gridding position and the non-first gridding position to obtain a cooling process identification matrix.
The expression of the cooling process identification matrix is as follows:
wherein ,
wherein ,
in the formula ,S1 In order to identify the matrix for the cooling process,is (x) i ,y i ) Identifying parameters of the cooling process at the location, +.>Is (x) i ,y i ) The cold air at the location influences the average air temperature on all days of the process,/->Is (x) i ,y i ) The cold air on day d at the location influences the mean daily air temperature of the process, +.>Is (x) i ,y i ) At the position at time tAir temperature, d tc For the tc-th time of day d, c is the number of samples at the time of day d, +.>Maximum value of the daily mean air temperature for the cold air influencing process,/-for>B being the lowest value of the daily average air temperature of the cold air influencing process 1 Is the first temperature difference threshold.
The cold tide grid position identification module determines a second grid position by utilizing temperature negative distance flat check based on grid numerical weather forecast data and identifies the second grid position by using a temperature negative distance flat check matrix, and the cold tide grid position identification module comprises the following steps:
determining the daily average air temperature and the daily average air temperature of each grid position in the cold air influence process based on the grid numerical weather forecast data, wherein the daily average air temperature is the same as the daily average air temperature;
determining a second air temperature difference value of each grid position based on the daily average air temperature of each grid position in the cold air influence process and the daily average air temperature of the last ten days of the lowest daily average air temperature value;
taking the grid position with the second air temperature difference value not smaller than the second temperature difference threshold value as a second grid position;
and respectively using different cooling process identification parameters to identify the second gridding position and the non-second gridding position to obtain a temperature negative-distance flat check matrix.
The expression of the temperature negative-distance flat check matrix is as follows:
wherein ,
wherein ,
wherein ,
in the formula ,S2 Is a temperature negative-distance flat check matrix,is (x) i ,y i ) The temperature at the location is negative from the flat check parameter,is (x) i ,y i ) The cold air at the location influences the average air temperature on all days of the process,/- >Is (x) i ,y i ) Average air temperature in the last years of the day of the lowest value of all the day average air temperatures of the cold air influencing processes at the location, +.>Is (x) i ,y i ) Average air temperature of all days of cold air influence process of the (q) th day of the (q) th year of the position tN For the nth year, N is the number of samples in the last year, ++>Is (x) i ,y i ) Air temperature at position t, d tc For the tc-th time of day d, c is the number of samples at the time of day d, +.>B being the lowest value of the daily average air temperature of the cold air influencing process 2 Is the second temperature difference threshold.
The cold tide grid position identification module is used for determining a third grid position by utilizing a daily average air temperature minimum value check based on grid numerical weather forecast data and identifying by using a daily average air temperature minimum value check matrix, and comprises the following steps:
determining a daily average air temperature minimum value of each gridding position in the cold air influence process based on gridding numerical weather forecast data;
taking the grid position with the lowest daily average air temperature value smaller than the third temperature threshold value as a third grid position;
and respectively marking the third grid position and the non-third grid position by using different daily average air temperature minimum value parameters to obtain a daily average air temperature minimum value check matrix.
The expression of the daily air temperature minimum check matrix is as follows:
wherein ,
in the formula ,S3 Is a check matrix with the lowest value of the average daily air temperature,is (x) i ,y i ) Checking the minimum value of the average daily air temperature at the position,/-)>Is (x) i ,y i ) The cold air at the location influences the average air temperature on all days of the process,/->B being the lowest value of the daily average air temperature of the cold air influencing process 3 Is a third temperature threshold.
The cold tide influence range determining module is specifically configured to:
determining a cold tide identification result for the identified grid position of the cooling process identification, the temperature negative distance flat check and the daily average air temperature minimum value check based on the cold tide check mode;
and determining the influence range of the low-temperature cold weather according to the cold weather identification result and the unit grid area of the grid air temperature data.
The method for determining the influence range of the cold tide is characterized in that the method for determining the influence range of the cold tide is based on the identified grid position after the cold tide is identified in the cooling process, the temperature negative distance flat checksum and the daily average air temperature minimum value verification, and the method comprises the following steps:
sequentially determining a cooling process identification matrix, a temperature negative distance flat check matrix and a daily average air temperature minimum check matrix based on the identified grid position of the cooling process identification, the temperature negative distance flat check matrix and the daily average air temperature minimum check based on the cold-damp check mode;
Determining a chill identification result matrix by using an intersection of the cooling process identification matrix, the temperature negative-distance flat check matrix and the average daily temperature minimum check matrix;
the chill identification result matrix is used for representing the chill identification result.
The calculation formula of the chill identification result matrix is as follows:
S＝S 1 ∩S 2 ∩S 3
wherein S is a cold tide recognition result matrix, S 1 Identifying a matrix of parameters for the temperature reduction process of each position meshing, S 2 Matrix of temperature negative range flat check parameters gridded for each position, S 3 A matrix of the minimum daily air temperature check parameters for each position meshing;
the calculation formula of the influence range of the low-temperature cold tide weather is as follows:
wherein ,
where, alpha is the unit grid area of the grid air temperature data,is the influence range of low-temperature cold and damp weather, A is a range threshold, S is the cold and damp identification result, and ++>Is (x) i ,y i ) The cold tide identification parameter at the position is m is the maximum value of the abscissa of the position, and n is the maximum value of the ordinate of the position.
In conclusion, the method provided by the invention can identify the influence range of the low-temperature cold weather process under different time sections, and solves the problem of large extreme deviation of wind power output prediction under the low-temperature cold weather, so that the power supply is supported, and the method has engineering applicability.
Example 3:
based on the same inventive concept, the invention also provides a computer device comprising a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application SpecificIntegrated Circuit, ASIC), off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular to load and execute one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding functions, to implement the steps of a low temperature and cold weather process identification method in the above embodiments.
Example 4:
based on the same inventive concept, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a computer device, for storing programs and data. It is understood that the computer readable storage medium herein may include both built-in storage media in a computer device and extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the steps of a low-temperature cold weather process identification method in the above embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the scope of protection thereof, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that various changes, modifications or equivalents may be made to the specific embodiments of the application after reading the present invention, and these changes, modifications or equivalents are within the scope of protection of the claims appended hereto.
Claims (16)
1. The method for identifying the low-temperature cold tide weather process is characterized by comprising the following steps of:
processing the gridding numerical weather forecast data by utilizing a plurality of preset cold tide verification modes, determining gridding positions affected by the cold tide and marking;
determining the influence range of low-temperature cold and damp weather based on the identified grid position and the unit grid area of the grid air temperature data;
the plurality of cold tide verification modes comprise: and (5) identifying the cooling process, checking the temperature negative distance level and the average daily air temperature minimum value.
2. The method of claim 1, wherein the processing the gridding numerical weather forecast data using a plurality of predetermined cold weather verification methods, determining and identifying gridding locations affected by cold weather, comprises:
based on the gridding numerical weather forecast data, identifying and determining a first gridding position in a cooling process by utilizing a cooling process identification matrix for identification;
determining a second grid position by utilizing temperature negative-distance flat verification based on grid numerical weather forecast data, and marking by utilizing a temperature negative-distance flat verification matrix;
and determining a third grid position by utilizing the minimum value check of the daily air temperature based on grid numerical weather forecast data, and marking by utilizing a minimum value check matrix of the daily air temperature.
3. The method of claim 2, wherein determining a first meshing location during a cooling process using cooling process identification based on meshing numerical weather forecast data and identifying with a cooling process identification matrix comprises:
determining a daily average air temperature highest value and a daily average air temperature lowest value of each gridding position in the cold air influence process based on gridding numerical weather forecast data;
determining a first air temperature difference value of each grid position based on the highest value and the lowest value of the daily average air temperature of each grid position in the cold air influence process;
taking the grid position with the first air temperature difference value larger than the first temperature difference threshold value as a first grid position;
and respectively using different cooling process identification parameters to identify the first gridding position and the non-first gridding position to obtain a cooling process identification matrix.
4. The method of claim 3, wherein the cooling process identification matrix is expressed as follows:
wherein ,
wherein ,
in the formula ,S1 In order to identify the matrix for the cooling process,is (x) i ,y i ) Identifying parameters of the cooling process at the location, +.>Is (x) i ,y i ) The cold air at the location influences the average air temperature on all days of the process,/- >Is (x) i ,y i ) The cold air on day d at the location influences the mean daily air temperature of the process, +.>Is (x) i ,y i ) Air temperature at position t, d tc The tc-th time on day d, c is the number of samples at day d，/>For the highest value of the daily average air temperature of the cold air influencing process,b being the lowest value of the daily average air temperature of the cold air influencing process 1 Is the first temperature difference threshold.
5. The method of claim 2, wherein determining the second meshing location using temperature negative range flat check based on meshing numerical weather forecast data and identifying with a temperature negative range flat check matrix comprises:
determining the daily average air temperature and the daily average air temperature of each grid position in the cold air influence process based on the grid numerical weather forecast data, wherein the daily average air temperature is the same as the daily average air temperature;
determining a second air temperature difference value of each grid position based on the daily average air temperature of each grid position in the cold air influence process and the daily average air temperature of the last ten days of the lowest daily average air temperature value;
taking the grid position with the second air temperature difference value not smaller than the second temperature difference threshold value as a second grid position;
and respectively using different cooling process identification parameters to identify the second gridding position and the non-second gridding position to obtain a temperature negative-distance flat check matrix.
6. The method of claim 5, wherein the temperature negative-pitch flat check matrix is expressed as follows:
wherein ,
wherein ,
wherein ,
in the formula ,S2 Is a temperature negative-distance flat check matrix,is (x) i ,y i ) Temperature negative range level check parameter at the location, < ->Is (x) i ,y i ) The cold air at the location influences the average air temperature on all days of the process,/->Is (x) i ,y i ) Average air temperature in the last years of the day of the lowest value of all the day average air temperatures of the cold air influencing processes at the location, +.>Is (x) i ,y i ) Average air temperature of all days of cold air influence process of the (q) th day of the (q) th year of the position tN For the nth year, N is the number of samples in the last year, ++>Is (x) i ,y i ) Air temperature at position t, d tc The tc-th time on day d, c is the number of samples at day d，/>B being the lowest value of the daily average air temperature of the cold air influencing process 2 Is the second temperature difference threshold.
7. The method of claim 2, wherein determining a third grid location using a daily average air temperature minimum check based on the grid numerical weather forecast data and identifying with a daily average air temperature minimum check matrix comprises:
determining a daily average air temperature minimum value of each gridding position in the cold air influence process based on gridding numerical weather forecast data;
Taking the grid position with the lowest daily average air temperature value smaller than the third temperature threshold value as a third grid position;
and respectively marking the third grid position and the non-third grid position by using different daily average air temperature minimum value parameters to obtain a daily average air temperature minimum value check matrix.
8. The method of claim 7, wherein the average daily air temperature minimum check matrix is expressed as follows:
wherein ,
in the formula ,S3 Is a check matrix with the lowest value of the average daily air temperature,is (x) i ,y i ) Checking the minimum value of the average daily air temperature at the position,/-)>Is (x) i ,y i ) The cold air at the location influences the average air temperature on all days of the process,/->B being the lowest value of the daily average air temperature of the cold air influencing process 3 Is a third temperature threshold.
9. The method of claim 1, wherein determining the range of influence of low temperature cold weather based on the identified grid location and the unit grid area of the grid air temperature data comprises:
determining a cold tide identification result for the identified grid position of the cooling process identification, the temperature negative distance flat check and the daily average air temperature minimum value check based on the cold tide check mode;
and determining the influence range of the low-temperature cold weather according to the cold weather identification result and the unit grid area of the grid air temperature data.
10. The method of claim 9, wherein the determining the cold weather identification result for the identified grid-like location based on the cold weather verification method for the cooling process identification, the temperature negative range flat verification, and the daily average air temperature minimum verification comprises:
sequentially determining a cooling process identification matrix, a temperature negative distance flat check matrix and a daily average air temperature minimum check matrix based on the identified grid position of the cooling process identification, the temperature negative distance flat check matrix and the daily average air temperature minimum check based on the cold-damp check mode;
determining a chill identification result matrix by using an intersection of the cooling process identification matrix, the temperature negative-distance flat check matrix and the average daily temperature minimum check matrix;
the chill identification result matrix is used for representing the chill identification result.
11. The method of claim 10, wherein the chill identification result matrix is calculated as follows:
S＝S 1 ∩S 2 ∩S 3
wherein S is a cold tide recognition result matrix, S 1 Identifying a matrix of parameters for the temperature reduction process of each position meshing, S 2 Matrix of temperature negative range flat check parameters gridded for each position, S 3 A matrix of the minimum daily air temperature check parameters for each position meshing;
the calculation formula of the influence range of the low-temperature cold tide weather is as follows:
wherein ,
where, alpha is the unit grid area of the grid air temperature data,is the influence range of low-temperature cold and damp weather, A is a range threshold, S is the cold and damp identification result, and ++>Is (x) i ,y i ) The cold tide identification parameter at the position is m is the maximum value of the abscissa of the position, and n is the maximum value of the ordinate of the position.
12. A low temperature cold weather process identification system, comprising:
the grid position identification module is used for processing the grid numerical weather forecast data by utilizing a plurality of preset cold tide verification modes to determine grid positions affected by the cold tide and identifying the grid positions;
the chill influence range determining module is used for determining the influence range of low-temperature chill weather based on the identified grid position and the unit grid area of the grid air temperature data;
the plurality of cold tide verification modes comprise: and (5) identifying the cooling process, checking the temperature negative distance level and the average daily air temperature minimum value.
13. The system of claim 12, wherein the cold grid location identification module is specifically configured to:
based on the gridding numerical weather forecast data, identifying and determining a first gridding position in a cooling process by utilizing a cooling process identification matrix for identification;
Determining a second grid position by utilizing temperature negative-distance flat verification based on grid numerical weather forecast data, and marking by utilizing a temperature negative-distance flat verification matrix;
and determining a third grid position by utilizing the minimum value check of the daily air temperature based on grid numerical weather forecast data, and marking by utilizing a minimum value check matrix of the daily air temperature.
14. The system of claim 12, wherein the chill range determination module is configured to:
determining a cold tide identification result for the identified grid position of the cooling process identification, the temperature negative distance flat check and the daily average air temperature minimum value check based on the cold tide check mode;
and determining the influence range of the low-temperature cold weather according to the cold weather identification result and the unit grid area of the grid air temperature data.
15. A computer device, comprising: one or more processors; a memory for storing one or more programs;
a low temperature cold weather process identification method according to any one of claims 1 to 11, when the one or more programs are executed by the one or more processors.
16. A computer-readable storage medium, on which a computer program is stored, which computer program, when executed, implements a method of identifying a low-temperature cold weather process according to any one of claims 1 to 11.
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