FY-4B stationary satellite and cloud phase cooperative convection inception intelligent identification method

Abstract

The invention relates to the technical field of convective inception identification, and discloses an FY-4B stationary satellite and cloud phase state collaborative convective inception intelligent identification method, which comprises the following steps: acquiring convective inception associated influence parameters at a plurality of parameter acquisition moments, obtaining a plurality of convective inception associated influence parameter groups, determining an associated influence parameter evaluation coefficient, and determining a cloud phase state associated influence parameter evaluation coefficient; calculating sub-convection primary identification factors; obtaining a sub-convection primary identification factor sequence according to a median sub-convection primary identification factor; calculating a comprehensive convection primary identification factor; judging whether to send out a convection primary intelligent identification prompt; adaptive adjustment is carried out through the MSE index, the daily change difference of data is eliminated, the intelligent recognition precision and recognition efficiency of convection inception are ensured, the missing report rate and the false report rate are reduced, the weather state prediction precision is ensured, and the method has great significance in improving the early warning timeliness of severe convection weather.

Description

Technical Field

[0001] The present invention relates to the technical field of convection inception identification, and in particular to an intelligent convection inception identification method in collaboration with an FY-4B geostationary satellite and cloud phases. Background Art

[0002] Monitoring and early warning of severe convective weather is a key component of meteorological disaster prevention and mitigation, and early identification of convective initiation (CI) is paramount. In meteorology, convective initiation is defined as the first detection by Doppler weather radar of radar echo pixels with a reflectivity factor exceeding 35dBZ, resulting from developing convective clouds.

[0003] Existing technologies mainly use traditional neural network architectures or simple threshold judgment methods to identify the initiation of convection. Among them, the traditional neural network architecture uses multi-layer perceptrons or convolutional neural networks to train historical meteorological data to establish a mapping relationship between the initiation of convection and meteorological elements. However, when processing complex meteorological data, these methods often face problems such as insufficient feature extraction and poor model generalization ability, and the recognition accuracy rate drops significantly. The threshold judgment method mainly relies on meteorological observation data and manual experience judgment, and is easily affected by factors such as personal experience, cognitive bias, and fatigue, resulting in difficulty in ensuring the accuracy and consistency of the recognition results. It often only focuses on a single or a few meteorological elements, ignoring the interactions between meteorological elements. Summary of the Invention

[0004] In view of this, the present invention proposes an intelligent identification method for the initiation of convection by coordinating the FY-4B geostationary satellite and cloud phases. The present invention can integrate multi-dimensional meteorological parameters and utilize the synergistic effect between meteorological parameters to ensure the intelligent identification accuracy and efficiency of the initiation of convection and reduce the missed alarm rate and false alarm rate.

[0005] The present invention proposes an intelligent recognition method for incipient convection using the FY-4B geostationary satellite in coordination with cloud phases, comprising: Preset multiple parameter acquisition moments, and acquire convection incipient correlation influencing parameters corresponding to the multiple parameter acquisition moments based on an FY-4B geostationary satellite and a preset radar device, wherein the convection incipient correlation influencing parameters include cloud top parameters and cloud phase parameters; Combining the same type of convection incipient correlation influencing parameters corresponding to each parameter acquisition moment to obtain multiple groups of convection incipient correlation influencing parameter groups, and determining the correlation influencing parameter evaluation coefficient of each convection incipient correlation influencing parameter in the convection incipient correlation influencing parameter groups; Extract the correlation influence parameter evaluation coefficient corresponding to each convection incipient correlation influence parameter, analyze all the correlation influence parameter evaluation coefficients, and calculate the sub-convection incipient identification factor of the region based on the analysis results; Determining the median sub-convection primary recognition factor corresponding to all sub-convection primary recognition factors, dividing all sub-convection primary recognition factors to obtain multiple sub-convection primary recognition factor sequences, and calculating the comprehensive convection primary recognition factor of the region based on the sub-convection primary recognition factor sequences; According to the relationship between the comprehensive convection incipient identification factor and the preset comprehensive convection incipient identification factor, it is determined whether to issue a convection incipient intelligent identification reminder to the area.

[0006] Furthermore, when the convection incipient correlation influencing parameters corresponding to each parameter acquisition moment are combined in the same type to obtain multiple groups of convection incipient correlation influencing parameter groups, the following are included: Determining a first parameter acquisition time, and randomly extracting a convection incipient correlation influencing parameter from the first parameter acquisition time as a benchmark convection incipient correlation influencing parameter; Determining a remaining parameter acquisition time, and extracting a convection incipient correlation influence parameter of the same type as the reference convection incipient correlation influence parameter from the remaining parameter acquisition time; The benchmark convection inception correlation influencing parameter and all extracted convection inception correlation influencing parameters of the same type are combined to obtain a convection inception correlation influencing parameter group.

[0007] Furthermore, when determining the correlation influence parameter evaluation coefficient of each convection incipient correlation influence parameter in the convection incipient correlation influence parameter group, it includes: randomly extracting a convection incipient correlation influencing parameter from the convection incipient correlation influencing parameter group as the extracted convection incipient correlation influencing parameter; Presetting a first preset parameter extraction value and a second preset parameter extraction value, and determining a first constrained convection incipient association influence parameter and a second constrained convection incipient association influence parameter for extracting the convection incipient association influence parameter based on the first preset parameter extraction value and the second preset parameter extraction value; Extracting the minimum convective incipient correlation influence parameter from all first-constrained convective incipient correlation influence parameters, and extracting the maximum convective incipient correlation influence parameter from all second-constrained convective incipient correlation influence parameters; Determining a difference between the minimum convection incipient correlation influence parameter and the maximum convection incipient correlation influence parameter as a constrained variation value for extracting the convection incipient correlation influence parameter; Determining a change value of a group association influence parameter of the convection primary association influence parameter group; An association influence parameter evaluation coefficient of each convection primary association influence parameter in the convection primary association influence parameter group is determined according to the constraint change value and the group association influence parameter change value, wherein the association influence parameter evaluation coefficient is a ratio of the constraint change value to the group association influence parameter change value.

[0008] Furthermore, when determining the change value of the group association influencing parameter of the convection primary association influencing parameter group, the method includes: The change value of the group correlation influence parameter of the convection primary correlation influence parameter group is calculated according to the following formula: ; Where s is the change value of the group correlation influence parameter of the convection incipient correlation influence parameter group, a is the number of convection incipient correlation influence parameters in the convection incipient correlation influence parameter group, and f d+1 is the d+1th convective incipient correlation influencing parameter in the convective incipient correlation influencing parameter group, f d is the dth convection incipient correlation influencing parameter in the convection incipient correlation influencing parameter group. The change value of the group correlation influencing parameter in the convection incipient correlation influencing parameter group is the change value of the group correlation influencing parameter of the same type.

[0009] Furthermore, when analyzing the evaluation coefficients of all associated influencing parameters and calculating the sub-convection incipient identification factor of the region based on the analysis results, it includes: Randomly extract an associated influence parameter evaluation coefficient, and determine the absolute value of the evaluation coefficient difference between the remaining associated influence parameter evaluation coefficients and the extracted associated influence parameter evaluation coefficient, extract the maximum evaluation coefficient difference absolute value from all the evaluation coefficient difference absolute values, and generate a maximum difference identifier; Randomly extract the remaining correlation influence parameter evaluation coefficients and determine the corresponding maximum difference mark; Extract the maximum evaluation coefficient difference absolute value corresponding to each maximum difference identifier, and determine whether there is the same maximum evaluation coefficient difference absolute value. If so, calculate the same evaluation coefficient and value of all the same maximum evaluation coefficient difference absolute values; Calculate the remaining evaluation coefficients and values ​​of the absolute values ​​of the remaining maximum evaluation coefficient differences corresponding to the maximum difference identifier; The ratio of the same evaluation coefficient and value to the remaining evaluation coefficient and value is used as the sub-convection primary identification factor of the region; If not, determining the maximum evaluation coefficient difference absolute value and the minimum evaluation coefficient difference absolute value from the maximum evaluation coefficient difference absolute value corresponding to the maximum difference identifier, and determining the extreme evaluation coefficient difference of the maximum evaluation coefficient difference absolute value and the minimum evaluation coefficient difference absolute value; Determine the remaining evaluation coefficients and values ​​of the remaining maximum evaluation coefficient difference absolute values ​​corresponding to the maximum difference identifier; The ratio of the extreme evaluation coefficient difference to the residual evaluation coefficient sum is used as the sub-convection primary identification factor of the region.

[0010] Furthermore, when all sub-convection primary identification factors are divided to obtain multiple sub-convection primary identification factor sequences, the sequences include: All sub-convection nascent identification factors are sorted in ascending order, and when a sub-convection nascent identification factor is less than or equal to the median sub-convection nascent identification factor, the corresponding sub-convection nascent identification factor is classified into the first factor sequence; When the sub-convection primary recognition factor is greater than the median sub-convection primary recognition factor, the corresponding sub-convection primary recognition factor is classified into the second factor sequence.

[0011] Furthermore, when calculating the comprehensive convection nascent recognition factor of a region according to the sub-convection nascent recognition factor sequence, the method includes: Randomly matching the sub-convection nascent recognition factors in the first factor sequence and the second factor sequence in pairs to obtain a plurality of sub-convection nascent recognition factor matching pairs; Calculate the matching pair sum value of the two sub-convection primary identification factors in each sub-convection primary identification factor matching pair; Extracting a maximum sub-convection primary identification factor from all sub-convection primary identification factors, and determining a ratio of the maximum sub-convection primary identification factor to each matching pair sum value as a relative matching pair sum value; The comprehensive convective incipient identification factor of the region is calculated based on all relative matching pairs and values.

[0012] Furthermore, when calculating the comprehensive convection nascent recognition factor of the region based on all relative matching pairs and values, it includes: The comprehensive convection incipient identification factor of the region is calculated according to the following formula: ; Where p is the comprehensive convection primary identification factor of the region, n is the number of relative matching pairs and values, and v i is the sum value of the i-th relative matching pair, v min is the minimum relative matching pair sum value, v max is the maximum relative matching pair sum value, For all The maximum value of .

[0013] Furthermore, when determining whether to issue a convection incipient intelligent identification reminder to a region based on the relationship between the comprehensive convection incipient identification factor and a preset comprehensive convection incipient identification factor, the method includes: When the comprehensive convection incipient recognition factor is less than the preset comprehensive convection incipient recognition factor, no convection incipient intelligent recognition reminder is issued to the area; When the comprehensive convection incipient recognition factor is greater than or equal to the preset comprehensive convection incipient recognition factor, a convection incipient intelligent recognition reminder is issued to the area.

[0014] Furthermore, when issuing a convection initiation intelligent identification reminder to a region, it includes: Presetting a first preset integrated convection primary identification factor and a second preset integrated convection primary identification factor; A first preset recognition reminder level, a second preset recognition reminder level, and a third preset recognition reminder level are pre-set, and the first preset recognition reminder level is less than the second preset recognition reminder level and is less than the third preset recognition reminder level; When the comprehensive convection incipient recognition factor is less than the first preset comprehensive convection incipient recognition factor, the first preset recognition reminder level is used as the convection incipient intelligent recognition reminder; When the comprehensive convection incipient recognition factor is greater than or equal to the first preset comprehensive convection incipient recognition factor and less than the second preset comprehensive convection incipient recognition factor, the second preset recognition reminder level is used as the convection incipient intelligent recognition reminder; When the comprehensive convection incipient recognition factor is greater than or equal to the second preset comprehensive convection incipient recognition factor, the third preset recognition reminder level is used as the convection incipient intelligent recognition reminder.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention relates to the technical field of convection inception identification, and discloses a method for intelligent convection inception identification in coordination with an FY-4B geostationary satellite and cloud phases. The method comprises the following steps: obtaining convection inception correlation influence parameters at a plurality of parameter acquisition moments, obtaining a plurality of groups of convection inception correlation influence parameter groups, determining correlation influence parameter evaluation coefficients, calculating sub-convection inception identification factors, obtaining a sub-convection inception identification factor sequence according to a median sub-convection inception identification factor, calculating a comprehensive convection inception identification factor, judging whether to issue a convection inception intelligent identification reminder, performing standardization processing on all data, utilizing the synergy between multi-source data from meteorological satellites, performing adaptive adjustment through an MSE indicator, eliminating daily variation differences in data, ensuring the intelligent identification accuracy and efficiency of convection inception, reducing omission rate and false alarm rate, and ensuring the accuracy of weather state prediction. The method is of great significance for improving the timeliness of severe convective weather warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings: Figure 1 A flow chart of a method for intelligently identifying the incipient convection using the FY-4B geostationary satellite and cloud phases in collaboration with an embodiment of the present invention. DETAILED DESCRIPTION

[0017] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0018] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides a method for intelligently identifying the incipient convection by coordinating an FY-4B geostationary satellite with cloud phases, including: S110: Preset multiple parameter acquisition moments, and acquire convection initiation correlation influencing parameters corresponding to the multiple parameter acquisition moments based on an FY-4B geostationary satellite and a preset radar device, wherein the convection initiation correlation influencing parameters include cloud top parameters and cloud phase parameters; In this embodiment, the parameter acquisition time is pre-set, which refers to a specific collection time. Here, the number of parameter acquisition times is preferably 10, that is, there are 10 parameter acquisition times, such as the 20th second, the 40th second, the 60th second, etc., which can be set according to actual conditions.

[0019] In this embodiment, the preset radar device includes microwave radar, laser radar, light rain radar, etc., which are not shown here one by one.

[0020] In this embodiment, the parameters affecting the initiation of convection include cloud top parameters and cloud phase parameters, wherein the cloud top parameters include brightness temperature, visible light reflectance, and cloud top height; the cloud phase parameters include cloud temperature, cloud humidity, cloud pressure, cloud water content, and cloud particle growth rate. The brightness temperature is 180K, 185K, 190K, 195K, 200K, 205K, 210K, 215K, 220K, and 225K; the visible light reflectance is 0.75, 0.78, 0.80, 0.82, 0.85, 0.88, 0.90, 0.92, 0.95, and 0.98; and the cloud top height is 8.0km, 9.5km, 10.0km, 11.0km, 12.0km, 13.0km, 14.0km, 15.0km, 16.0km, 17.0km, cloud layer temperatures are 5℃, 10℃, 15℃, 7℃, 6℃, -10℃, -15℃, -20℃, -5℃, -18℃, cloud layer humidity is 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 100%, 100%, cloud layer pressure is 150hPa, 200hPa, 250hPa, 300hPa, 350hPa, 400hPa, 450hPa, 500hPa, 550hPa, 600hPa, cloud water content is 0.1g / m 3 , 0.3g / m 3 , 0.5g / m 3 , 0.8g / m 3 , 1.0g / m 3 ,1.2g / m 3 , 1.5g / m 3 , 1.8g / m 3 , 2.0g / m 3 , 2.5g / m 3 , the cloud particle growth rates are 1μm / s, 2μm / s, 3μm / s, 5μm / s, 7μm / s, 10μm / s, 12μm / s, 15μm / s, 18μm / s, and 20μm / s. In this embodiment, each parameter acquisition moment corresponds to multiple convection initiation-related influencing parameters.

[0021] In this embodiment, since there are diurnal variations in the parameters influencing the incipient association of convection, the present invention performs adaptive adjustment through the MSE indicator to eliminate the diurnal variation differences in the data. Among them, the mean square error (MSE) is a commonly used evaluation indicator used to measure the difference between the predicted value and the actual value. Adaptive adjustment through the MSE indicator can effectively eliminate the diurnal variation differences in the data. When adaptively adjusting according to the MSE indicator to eliminate the diurnal variation differences in the data: calculate the MSE for different parameters influencing the incipient association of convection, and assign weights according to the size of the MSE. The date parameters with smaller MSE have higher weights, and the date parameters with larger MSE have lower weights. Eliminate the diurnal variation differences by weighted averaging or other methods.

[0022] S120: Combining the same type of convection incipient correlation influencing parameters corresponding to each parameter acquisition moment to obtain multiple groups of convection incipient correlation influencing parameter groups, and determining a correlation influencing parameter evaluation coefficient of each convection incipient correlation influencing parameter in the convection incipient correlation influencing parameter groups; In some embodiments of the present application, when the convection incipient correlation influencing parameters corresponding to each parameter acquisition moment are combined in the same type to obtain multiple groups of convection incipient correlation influencing parameter groups, the following steps are included: Determining a first parameter acquisition time, and randomly extracting a convection incipient correlation influencing parameter from the first parameter acquisition time as a benchmark convection incipient correlation influencing parameter; Determining a remaining parameter acquisition time, and extracting a convection incipient correlation influence parameter of the same type as the reference convection incipient correlation influence parameter from the remaining parameter acquisition time; The benchmark convection inception correlation influencing parameter and all extracted convection inception correlation influencing parameters of the same type are combined to obtain a convection inception correlation influencing parameter group.

[0023] In this embodiment, a convection incipient correlation influencing parameter is randomly extracted from the first parameter acquisition moment, such as the visible light reflectance mentioned above, and the corresponding visible light reflectance is extracted from the remaining parameter acquisition moments. There are a total of 10 parameter acquisition moments, and there are a total of 10 visible light reflectances. The visible light reflectances corresponding to each parameter acquisition moment are combined in the same type to obtain a convection incipient correlation influencing parameter group, and the corresponding convection incipient correlation influencing parameter group is generated according to the remaining convection incipient correlation influencing parameters.

[0024] The beneficial effect of the above technical solution is that the present invention combines the baseline convection primary correlation influence parameters and all extracted convection primary correlation influence parameters of the same type to obtain a convection primary correlation influence parameter group, which can provide a basis for subsequent processing and ensure the same-dimensional analysis of the convection primary correlation influence parameters.

[0025] In some embodiments of the present application, when determining the correlation influence parameter evaluation coefficient of each convection primary correlation influence parameter in the convection primary correlation influence parameter group, the process includes: randomly extracting a convection incipient correlation influencing parameter from the convection incipient correlation influencing parameter group as the extracted convection incipient correlation influencing parameter; Presetting a first preset parameter extraction value and a second preset parameter extraction value, and determining a first constrained convection incipient association influence parameter and a second constrained convection incipient association influence parameter for extracting the convection incipient association influence parameter based on the first preset parameter extraction value and the second preset parameter extraction value; Extracting the minimum convective incipient correlation influence parameter from all first-constrained convective incipient correlation influence parameters, and extracting the maximum convective incipient correlation influence parameter from all second-constrained convective incipient correlation influence parameters; Determining a difference between the minimum convection incipient correlation influence parameter and the maximum convection incipient correlation influence parameter as a constrained variation value for extracting the convection incipient correlation influence parameter; Determining a change value of a group association influence parameter of the convection primary association influence parameter group; An association influence parameter evaluation coefficient of each convection primary association influence parameter in the convection primary association influence parameter group is determined according to the constraint change value and the group association influence parameter change value, wherein the association influence parameter evaluation coefficient is a ratio of the constraint change value to the group association influence parameter change value.

[0026] In this embodiment, the first preset parameter extraction value and the second preset parameter extraction value are set in advance. Here, the first preset parameter extraction value is preferably 3, and the second preset parameter extraction value is preferably 4.

[0027] In this embodiment, three left-neighboring convection primary association influence parameters of the convection primary association influence parameters are extracted based on the first preset parameter extraction value as the first constrained convection primary association influence parameters, and four right-neighboring convection primary association influence parameters of the convection primary association influence parameters are extracted based on the second preset parameter extraction value as the second constrained convection primary association influence parameters. It should be noted that if the convection primary association influence parameters of the left or right neighbor are less than the preset parameter extraction value, the actual number shall be extracted.

[0028] In this embodiment, the constraint change value refers to the absolute value of the difference between the minimum convection primary association influence parameter and the maximum convection primary association influence parameter.

[0029] The beneficial effect of the above technical solution is: the present invention determines the association influence parameter evaluation coefficient of each convection primary association influence parameter in the convection primary association influence parameter group according to the constraint change value and the group association influence parameter change value. The constraint change value can feedback the degree of change of the extracted convection primary association influence parameter within a period of time, and the group association influence parameter change value can feedback the degree of transformation of all convection primary association influence parameters. Through the ratio of the constraint change value and the group association influence parameter change value, the change of each convection primary association influence parameter relative to all convection primary association influence parameters is determined, thereby ensuring the comprehensiveness and accuracy of convection primary intelligent identification.

[0030] In some embodiments of the present application, determining the group association influencing parameter change value of the convection primary association influencing parameter group includes: The change value of the group correlation influence parameter of the convection primary correlation influence parameter group is calculated according to the following formula: ; Where s is the change value of the group correlation influence parameter of the convection incipient correlation influence parameter group, a is the number of convection incipient correlation influence parameters in the convection incipient correlation influence parameter group, and f d+1 is the d+1th convective incipient correlation influencing parameter in the convective incipient correlation influencing parameter group, f d is the dth convection incipient correlation influencing parameter in the convection incipient correlation influencing parameter group. The change value of the group correlation influencing parameter in the convection incipient correlation influencing parameter group is the change value of the group correlation influencing parameter of the same type.

[0031] S130: extracting the correlation influence parameter evaluation coefficient corresponding to each convection incipient correlation influence parameter, analyzing all the correlation influence parameter evaluation coefficients, and calculating the sub-convection incipient identification factor of the region based on the analysis results; In some embodiments of the present application, when analyzing the evaluation coefficients of all associated influencing parameters and calculating the sub-convection primary identification factor of the region based on the analysis results, the following steps are included: Randomly extract an associated influence parameter evaluation coefficient, and determine the absolute value of the evaluation coefficient difference between the remaining associated influence parameter evaluation coefficients and the extracted associated influence parameter evaluation coefficient, extract the maximum evaluation coefficient difference absolute value from all the evaluation coefficient difference absolute values, and generate a maximum difference identifier; Randomly extract the remaining correlation influence parameter evaluation coefficients and determine the corresponding maximum difference mark; Extract the maximum evaluation coefficient difference absolute value corresponding to each maximum difference identifier, and determine whether there is the same maximum evaluation coefficient difference absolute value. If so, calculate the same evaluation coefficient and value of all the same maximum evaluation coefficient difference absolute values; Calculate the remaining evaluation coefficients and values ​​of the absolute values ​​of the remaining maximum evaluation coefficient differences corresponding to the maximum difference identifier; The ratio of the same evaluation coefficient and value to the remaining evaluation coefficient and value is used as the sub-convection primary identification factor of the region; If not, determining the maximum evaluation coefficient difference absolute value and the minimum evaluation coefficient difference absolute value from the maximum evaluation coefficient difference absolute value corresponding to the maximum difference identifier, and determining the extreme evaluation coefficient difference of the maximum evaluation coefficient difference absolute value and the minimum evaluation coefficient difference absolute value; Determine the remaining evaluation coefficients and values ​​of the remaining maximum evaluation coefficient difference absolute values ​​corresponding to the maximum difference identifier; The ratio of the extreme evaluation coefficient difference to the residual evaluation coefficient sum is used as the sub-convection primary identification factor of the region.

[0032] In this embodiment, a maximum difference identifier can be determined based on each associated influencing parameter evaluation coefficient. It should be noted here that if the number of the maximum evaluation coefficient difference absolute values ​​is not unique, a maximum evaluation coefficient difference absolute value is randomly extracted to generate the maximum difference identifier.

[0033] In this embodiment, the absolute value of the maximum evaluation coefficient difference corresponding to each maximum difference identifier is extracted to determine whether there is a same absolute value of the maximum evaluation coefficient difference.

[0034] In this embodiment, the maximum absolute value of the evaluation coefficient difference refers to extracting a maximum value from the absolute values ​​of the maximum evaluation coefficient differences corresponding to all the maximum difference identifiers. If the number is not unique, then one can be randomly selected. The minimum absolute value of the evaluation coefficient difference refers to extracting a minimum value from the absolute values ​​of the maximum evaluation coefficient differences corresponding to all the maximum difference identifiers. If the number is not unique, then one can be randomly selected.

[0035] The beneficial effect of the above technical solution is: the present invention uses the ratio of the same evaluation coefficient sum value to the remaining evaluation coefficient sum value as the sub-convection initiation identification factor of the region, or uses the ratio of the extreme evaluation coefficient difference to the remaining evaluation coefficient sum value as the sub-convection initiation identification factor of the region. The present invention provides two different methods for determining the sub-convection initiation identification factor according to two different situations, thereby ensuring the accuracy of determining the sub-convection initiation identification factor. The sub-convection initiation identification factor can feedback the degree of influence of the convection initiation-related influencing parameters on the regional convection initiation, thereby ensuring the intelligent identification accuracy of convection initiation.

[0036] S140: determining a median sub-convection primary identification factor corresponding to all sub-convection primary identification factors, dividing all sub-convection primary identification factors to obtain a plurality of sub-convection primary identification factor sequences, and calculating a comprehensive convection primary identification factor of the region according to the sub-convection primary identification factor sequences; In some embodiments of the present application, when all sub-convection nascent identification factors are divided to obtain multiple sub-convection nascent identification factor sequences, the following steps are included: All sub-convection nascent identification factors are sorted in ascending order, and when a sub-convection nascent identification factor is less than or equal to the median sub-convection nascent identification factor, the corresponding sub-convection nascent identification factor is classified into the first factor sequence; When the sub-convection primary recognition factor is greater than the median sub-convection primary recognition factor, the corresponding sub-convection primary recognition factor is classified into the second factor sequence.

[0037] In this embodiment, the median sub-convection primary identification factor refers to the median corresponding to all sub-convection primary identification factors.

[0038] The beneficial effect of the above technical solution is that when the sub-convection incipient identification factor is less than or equal to the median sub-convection incipient identification factor, the impact on the regional convection incipient is relatively weak; when the sub-convection incipient identification factor is greater than the median sub-convection incipient identification factor, the impact on the regional convection incipient is relatively strong. Therefore, the first factor sequence and the second factor sequence are obtained by division, thereby ensuring the recognition accuracy of convection incipient.

[0039] In some embodiments of the present application, when calculating the comprehensive convection nascent recognition factor of a region according to the sub-convection nascent recognition factor sequence, the method includes: Randomly matching the sub-convection nascent recognition factors in the first factor sequence and the second factor sequence in pairs to obtain a plurality of sub-convection nascent recognition factor matching pairs; Calculate the matching pair sum value of the two sub-convection primary identification factors in each sub-convection primary identification factor matching pair; Extracting a maximum sub-convection primary identification factor from all sub-convection primary identification factors, and determining a ratio of the maximum sub-convection primary identification factor to each matching pair sum value as a relative matching pair sum value; The comprehensive convective incipient identification factor of the region is calculated based on all relative matching pairs and values.

[0040] In this embodiment, the sub-convection nascent identification factors in the first factor sequence and the second factor sequence are randomly matched in pairs. If there is a single unmatched sub-convection nascent identification factor, it is deleted.

[0041] The beneficial effects of the above technical solution are: the present invention determines the ratio of the maximum sub-convection inception identification factor to each matching pair sum value as the relative matching pair sum value, and calculates the comprehensive convection inception identification factor of the area based on all the relative matching pair sum values. The present invention does not require manual participation in calculation and identification, effectively eliminating the identification error and subjectivity of convection inception. The present invention fully considers the multi-dimensional convection inception correlation influencing parameters, thereby avoiding the singleness and limitations brought by threshold comparison, and realizes intelligent identification of convection inception through the comprehensive convection inception identification factor.

[0042] In some embodiments of the present application, when calculating the comprehensive convection nascent recognition factor of a region based on all relative matching pairs and values, the method includes: The comprehensive convection incipient identification factor of the region is calculated according to the following formula: ; Where p is the comprehensive convection primary identification factor of the region, n is the number of relative matching pairs and values, and v i is the sum value of the i-th relative matching pair, v min is the minimum relative matching pair sum value, v max is the maximum relative matching pair sum value, For all The maximum value of .

[0043] S150: Determine whether to issue a convection incipient intelligent identification reminder to the area based on the relationship between the comprehensive convection incipient identification factor and a preset comprehensive convection incipient identification factor.

[0044] In some embodiments of the present application, when determining whether to issue a convection incipient intelligent identification reminder for a region based on the relationship between the comprehensive convection incipient identification factor and a preset comprehensive convection incipient identification factor, the process includes: When the comprehensive convection incipient recognition factor is less than the preset comprehensive convection incipient recognition factor, no convection incipient intelligent recognition reminder is issued to the area; When the comprehensive convection incipient recognition factor is greater than or equal to the preset comprehensive convection incipient recognition factor, a convection incipient intelligent recognition reminder is issued to the area.

[0045] In this embodiment, the preset comprehensive convection primary identification factor is preferably 4, and can be adjusted according to actual conditions.

[0046] The beneficial effect of the above technical solution is: the present invention determines whether to issue a convection initiation intelligent recognition reminder to the region based on the relationship between the comprehensive convection initiation recognition factor and the preset comprehensive convection initiation recognition factor, thereby ensuring the intelligent recognition accuracy and efficiency of convection initiation, reducing the omission rate and false alarm rate, and ensuring the accuracy of weather condition prediction, which is of great significance for improving the timeliness of severe convective weather warnings.

[0047] In some embodiments of the present application, when issuing a convection incipient intelligent identification reminder to a region, the process includes: Presetting a first preset integrated convection primary identification factor and a second preset integrated convection primary identification factor; A first preset recognition reminder level, a second preset recognition reminder level, and a third preset recognition reminder level are pre-set, and the first preset recognition reminder level is less than the second preset recognition reminder level and is less than the third preset recognition reminder level; When the comprehensive convection incipient recognition factor is less than the first preset comprehensive convection incipient recognition factor, the first preset recognition reminder level is used as the convection incipient intelligent recognition reminder; When the comprehensive convection incipient recognition factor is greater than or equal to the first preset comprehensive convection incipient recognition factor and less than the second preset comprehensive convection incipient recognition factor, the second preset recognition reminder level is used as the convection incipient intelligent recognition reminder; When the comprehensive convection incipient recognition factor is greater than or equal to the second preset comprehensive convection incipient recognition factor, the third preset recognition reminder level is used as the convection incipient intelligent recognition reminder.

[0048] In this embodiment, the first preset comprehensive convection nascent identification factor is smaller than the second preset comprehensive convection nascent identification factor. The first preset comprehensive convection nascent identification factor is preferably 6, and the second preset comprehensive convection nascent identification factor is preferably 9.

[0049] The beneficial effect of the above technical solution is: the present invention selects the corresponding preset identification reminder level according to the relationship between the comprehensive convection initiation identification factor, the first preset comprehensive convection initiation identification factor and the second preset comprehensive convection initiation identification factor, thereby realizing refined early warning reminders for convection initiation, significantly improving the accuracy and timeliness of the warning, and providing reliable technical support for early warning and emergency response of meteorological disasters.

[0050] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0051] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0052] These computer program instructions may 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, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0053] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for intelligently identifying the incipient convection using the FY-4B geostationary satellite and cloud phases, characterized in that: include: Preset multiple parameter acquisition moments, and acquire convection incipient correlation influencing parameters corresponding to the multiple parameter acquisition moments based on an FY-4B geostationary satellite and a preset radar device, wherein the convection incipient correlation influencing parameters include cloud top parameters and cloud phase parameters; Combining the same type of convection incipient correlation influencing parameters corresponding to each parameter acquisition moment to obtain multiple groups of convection incipient correlation influencing parameter groups, and determining the correlation influencing parameter evaluation coefficient of each convection incipient correlation influencing parameter in the convection incipient correlation influencing parameter groups; Extract the correlation influence parameter evaluation coefficient corresponding to each convection incipient correlation influence parameter, analyze all the correlation influence parameter evaluation coefficients, and calculate the sub-convection incipient identification factor of the region based on the analysis results; Determining the median sub-convection primary recognition factor corresponding to all sub-convection primary recognition factors, dividing all sub-convection primary recognition factors to obtain multiple sub-convection primary recognition factor sequences, and calculating the comprehensive convection primary recognition factor of the region based on the sub-convection primary recognition factor sequences; According to the relationship between the comprehensive convection incipient identification factor and the preset comprehensive convection incipient identification factor, it is determined whether to issue a convection incipient intelligent identification reminder to the area.

2. The method for intelligently identifying the incipient convection using the FY-4B geostationary satellite and cloud phase coordination according to claim 1 is characterized in that: When the same type of convection initiation correlation influencing parameters corresponding to each parameter acquisition moment are combined to obtain multiple groups of convection initiation correlation influencing parameter groups, the following are included: Determining a first parameter acquisition time, and randomly extracting a convection incipient correlation influencing parameter from the first parameter acquisition time as a benchmark convection incipient correlation influencing parameter; Determining a remaining parameter acquisition time, and extracting a convection incipient correlation influence parameter of the same type as the reference convection incipient correlation influence parameter from the remaining parameter acquisition time; The benchmark convection inception correlation influencing parameter and all extracted convection inception correlation influencing parameters of the same type are combined to obtain a convection inception correlation influencing parameter group.

3. The method for intelligently identifying the incipient convection using the FY-4B geostationary satellite and cloud phase coordination according to claim 1 is characterized in that: When determining the correlation influence parameter evaluation coefficient of each convection incipient correlation influence parameter in the convection incipient correlation influence parameter group, it includes: randomly extracting a convection incipient correlation influencing parameter from the convection incipient correlation influencing parameter group as the extracted convection incipient correlation influencing parameter; Presetting a first preset parameter extraction value and a second preset parameter extraction value, and determining a first constrained convection incipient association influence parameter and a second constrained convection incipient association influence parameter for extracting the convection incipient association influence parameter based on the first preset parameter extraction value and the second preset parameter extraction value; Extracting the minimum convective incipient correlation influence parameter from all first-constrained convective incipient correlation influence parameters, and extracting the maximum convective incipient correlation influence parameter from all second-constrained convective incipient correlation influence parameters; Determining a difference between the minimum convection incipient correlation influence parameter and the maximum convection incipient correlation influence parameter as a constrained variation value for extracting the convection incipient correlation influence parameter; Determining a change value of a group association influence parameter of the convection primary association influence parameter group; An association influence parameter evaluation coefficient of each convection primary association influence parameter in the convection primary association influence parameter group is determined according to the constraint change value and the group association influence parameter change value, wherein the association influence parameter evaluation coefficient is a ratio of the constraint change value to the group association influence parameter change value.

4. The method for intelligently identifying the incipient convection using the FY-4B geostationary satellite and cloud phase coordination according to claim 3 is characterized in that: When determining the change value of the group association influencing parameter of the convection primary association influencing parameter group, it includes: The change value of the group correlation influence parameter of the convection primary correlation influence parameter group is calculated according to the following formula: ； Where s is the change value of the group correlation influence parameter of the convection incipient correlation influence parameter group, a is the number of convection incipient correlation influence parameters in the convection incipient correlation influence parameter group, and f d+1 is the d+1th convective incipient correlation influencing parameter in the convective incipient correlation influencing parameter group, f d is the dth convection incipient correlation influencing parameter in the convection incipient correlation influencing parameter group. The change value of the group correlation influencing parameter in the convection incipient correlation influencing parameter group is the change value of the group correlation influencing parameter of the same type.

5. The method for intelligently identifying the incipient convection using the FY-4B geostationary satellite and cloud phase coordination according to claim 1 is characterized in that: When analyzing the evaluation coefficients of all associated influencing parameters and calculating the regional sub-convection incipient identification factor based on the analysis results, it includes: Randomly extract an associated influence parameter evaluation coefficient, and determine the absolute value of the evaluation coefficient difference between the remaining associated influence parameter evaluation coefficients and the extracted associated influence parameter evaluation coefficient, extract the maximum evaluation coefficient difference absolute value from all the evaluation coefficient difference absolute values, and generate a maximum difference identifier; Randomly extract the remaining correlation influence parameter evaluation coefficients and determine the corresponding maximum difference mark; Extract the maximum evaluation coefficient difference absolute value corresponding to each maximum difference identifier, and determine whether there is the same maximum evaluation coefficient difference absolute value. If so, calculate the same evaluation coefficient and value of all the same maximum evaluation coefficient difference absolute values; Calculate the remaining evaluation coefficients and values ​​of the absolute values ​​of the remaining maximum evaluation coefficient differences corresponding to the maximum difference identifier; The ratio of the same evaluation coefficient and value to the remaining evaluation coefficient and value is used as the sub-convection primary identification factor of the region; If not, determining the maximum evaluation coefficient difference absolute value and the minimum evaluation coefficient difference absolute value from the maximum evaluation coefficient difference absolute value corresponding to the maximum difference identifier, and determining the extreme evaluation coefficient difference of the maximum evaluation coefficient difference absolute value and the minimum evaluation coefficient difference absolute value; Determine the remaining evaluation coefficients and values ​​of the remaining maximum evaluation coefficient difference absolute values ​​corresponding to the maximum difference identifier; The ratio of the extreme evaluation coefficient difference to the residual evaluation coefficient sum is used as the sub-convection primary identification factor of the region.

6. The method for intelligently identifying the incipient convection using the FY-4B geostationary satellite and cloud phase coordination according to claim 1 is characterized in that: When all sub-convection primary identification factors are divided to obtain multiple sub-convection primary identification factor sequences, the sequences include: All sub-convection nascent identification factors are sorted in ascending order, and when a sub-convection nascent identification factor is less than or equal to the median sub-convection nascent identification factor, the corresponding sub-convection nascent identification factor is classified into the first factor sequence; When the sub-convection primary recognition factor is greater than the median sub-convection primary recognition factor, the corresponding sub-convection primary recognition factor is classified into the second factor sequence.

7. The method for intelligently identifying the incipient convection using the FY-4B geostationary satellite and cloud phase coordination according to claim 6 is characterized in that: When calculating the comprehensive convection nascent recognition factor of a region according to the sub-convection nascent recognition factor sequence, the method includes: Randomly matching the sub-convection nascent recognition factors in the first factor sequence and the second factor sequence in pairs to obtain a plurality of sub-convection nascent recognition factor matching pairs; Calculate the matching pair sum value of the two sub-convection primary identification factors in each sub-convection primary identification factor matching pair; Extracting a maximum sub-convection primary identification factor from all sub-convection primary identification factors, and determining a ratio of the maximum sub-convection primary identification factor to each matching pair sum value as a relative matching pair sum value; The comprehensive convective incipient identification factor of the region is calculated based on all relative matching pairs and values.

8. The method for intelligently identifying the incipient convection using the FY-4B geostationary satellite and cloud phase coordination according to claim 7 is characterized in that: When calculating the comprehensive convective nascent identification factor for a region based on all relative matching pairs and values, including: The comprehensive convection incipient identification factor of the region is calculated according to the following formula: ； Where p is the comprehensive convection primary identification factor of the region, n is the number of relative matching pairs and values, and v i is the sum value of the i-th relative matching pair, v min is the minimum relative matching pair sum value, v max is the maximum relative matching pair sum value, For all The maximum value of .

9. The method for intelligently identifying the incipient convection using the FY-4B geostationary satellite and cloud phase coordination according to claim 1 is characterized in that: When determining whether to issue a convection incipient intelligent identification reminder for a region based on the relationship between the comprehensive convection incipient identification factor and a preset comprehensive convection incipient identification factor, the method includes: When the comprehensive convection incipient recognition factor is less than the preset comprehensive convection incipient recognition factor, no convection incipient intelligent recognition reminder is issued to the area; When the comprehensive convection incipient recognition factor is greater than or equal to the preset comprehensive convection incipient recognition factor, a convection incipient intelligent recognition reminder is issued to the area.

10. The method for intelligently identifying the incipient convection using the FY-4B geostationary satellite and cloud phases according to claim 9 is characterized in that: When issuing a convection initiation intelligent identification alert for a region, it includes: Presetting a first preset integrated convection primary identification factor and a second preset integrated convection primary identification factor; A first preset recognition reminder level, a second preset recognition reminder level, and a third preset recognition reminder level are pre-set, and the first preset recognition reminder level is less than the second preset recognition reminder level and is less than the third preset recognition reminder level; When the comprehensive convection incipient recognition factor is less than the first preset comprehensive convection incipient recognition factor, the first preset recognition reminder level is used as the convection incipient intelligent recognition reminder; When the comprehensive convection incipient recognition factor is greater than or equal to the first preset comprehensive convection incipient recognition factor and less than the second preset comprehensive convection incipient recognition factor, the second preset recognition reminder level is used as the convection incipient intelligent recognition reminder; When the comprehensive convection incipient recognition factor is greater than or equal to the second preset comprehensive convection incipient recognition factor, the third preset recognition reminder level is used as the convection incipient intelligent recognition reminder.

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