Dynamic early warning system for snow-melting flood in arid area based on multi-source data fusion

Abstract

The invention discloses an arid region snow melting flood dynamic early warning system based on multi-source data fusion. The system comprises a data acquisition module, a calculation module and an early warning monitoring and issuing module. According to a mathematical model of multiple linear regression, main factors, including regional temperature trend, rainfall trend and regional position, influencing snow-melting flood occurrence are subjected to quantitative fitting, related data of snow-melting flood occurrence of the arid region over the years are summarized, an early warning value calculation function model is designed, and the early warning value is calculated. Through the early warning value calculation function model, an early warning value used for judging whether to issue snow-melting flood early warning or not can be calculated according to geographical and meteorological information of different areas of the arid region, and the early warning precision of snow-melting flood is effectively improved; meanwhile, the early warning value warning values of different areas of the arid area are calculated, and the monitoring values of the meteorological information under different meteorological conditions are calculated according to the early warning value warning values, so that the meteorological information is further monitored, and the snow-melting flood monitoring capability is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a snowmelt flood dynamic early warning system in arid regions based on multi-source data fusion. BACKGROUND

[0002] Snowmelt floods generally occurring in arid regions in the northwest include two types of snowmelt floods, i.e., temperature rise snowmelt flood and rain-snow mixed flood. Generally, it is believed that the temperature rise snowmelt flood in arid regions is mainly caused by an extreme temperature rise process, i.e., related to the regional temperature trend, while the rain-snow mixed flood is formed by the runoff of rainfall in the middle and low mountainous areas superimposed on the snowmelt in the high mountainous areas in the snowmelt season, i.e., related to the rainfall trend. Since the snow in arid regions is mainly distributed in mountainous areas with high mountains and steep slopes, where the runoff is fast, i.e., related to the regional location, under the induction of extreme temperature rise and / or snow surface rain (rain falling on the snow surface), combined with weak water conservancy facilities, snowmelt flood disasters are easily caused, which pose a serious threat to local residents, roads and bridges, etc. Among them, the snow surface rain event often causes a disastrous flood, because it not only carries more heat to accelerate snowmelt, but also means that an extreme temperature rise event has occurred, which also accelerates the snowmelt process; at the same time, the snow surface rain intensity is generally large, which forms strong erosion on the snow, while the ground is frozen and the infiltration is blocked, which easily forms surface runoff, and in the mountainous areas with steep terrain and more snow, the rain-snow ice mixed flood is quickly formed, which carries a large amount of sand and stone to erode the snow in the plain area, forming a rain-snow mixed disastrous flood.

[0003] However, the existing monitoring capabilities of meteorology, snow and flood in arid regions are weak, the formation mechanism of snowmelt flood is relatively scarce, the simulation basis of flood evolution process is poor, and the accuracy of flood forecasting and early warning is low, which brings great difficulties to high-precision forecasting and early warning of flood disasters. In view of this, the present application proposes a snowmelt flood dynamic early warning system in arid regions based on multi-source data fusion, which uses mathematical modeling to quantitatively fit the main factors affecting snowmelt flood, including regional temperature trend, rainfall trend and regional location, and at the same time, uses the computing power of large models to design a warning calculation model, which can effectively improve the accuracy and precision of snowmelt flood simulation and forecasting.

[0004] The information disclosed in this BACKGROUND section is only for the purpose of increasing the understanding of the background of the present application and should not be taken as admitting that the information forms prior art that is already known to those of ordinary skill in the art. SUMMARY

[0005] In order to solve the problems in the background art, the technical scheme of the present application quantitatively fits the relevant factors affecting snowmelt flood through mathematical modeling, designs a calculation model for snowmelt flood monitoring and early warning, and can more accurately monitor and early warn snowmelt flood according to the actual situation of different snowmelt flood monitoring and early warning areas.

[0006] In order to achieve the above-mentioned purpose, the technical scheme of the present application proposes a drought area snowmelt flood dynamic early warning system based on multi-source data fusion, wherein the system comprises a data acquisition module, and the data acquisition module comprises a meteorological data acquisition unit and a geographic data acquisition unit; Further, the meteorological data acquisition unit is used to collect meteorological information in a calculation period, the calculation period refers to a data acquisition period for collecting meteorological information, and the meteorological information includes snow accumulation, rainfall change trend and regional temperature change trend affecting snowmelt flood; Further, the geographic data acquisition unit is used to collect geographic information of the snowmelt flood monitoring and early warning area, the geographic information refers to the regional position of the snowmelt flood monitoring and early warning area, and the geographic data acquisition unit further comprises a geographic information quantitative calculation model, which is used to quantitatively calculate the obtained geographic information into a weighted coefficient; In the technical scheme of the present application, the system further comprises a calculation module, and the calculation module comprises a warning value calculation unit and a monitoring value calculation unit; Further, the warning value calculation unit comprises a warning value calculation model, and the warning value calculation unit is used to calculate a warning value, which is a judgment value for judging whether to issue a snowmelt flood warning; Further, the monitoring value calculation unit comprises a monitoring value calculation model, and the monitoring value calculation unit is used to calculate a monitoring value, which is a monitoring value of each meteorological information in snowmelt flood monitoring; In the technical scheme of the present application, the system further comprises a warning monitoring and issuing module, and the warning monitoring and issuing module comprises a warning monitoring unit and a warning issuing unit; Further, the warning monitoring unit monitors each meteorological information according to the monitoring value of each meteorological information calculated by the monitoring value calculation unit; Further, the warning issuing unit is used to issue a warning.

[0007] In the technical scheme of the present application, the calculation period represents a data acquisition period for collecting meteorological information, that is, a calculation period for the calculation module to calculate the warning value and the monitoring value, and the calculation period is determined by the period of extreme weather causing snowmelt flood, and the specific value of the calculation period is the number of days of the longest extreme weather experienced in the history of snowmelt flood .

[0008] Further, the calculation period includes a current day , and a first half calculation period located before the current day , and a second half calculation period located after the current day , the meteorological information of the current day and the first half calculation period obtained by the meteorological data acquisition unit is actual meteorological information, the meteorological information of the second half calculation period obtained by the meteorological data acquisition unit is prediction data of meteorological forecast published by a meteorological observation station, and the meteorological information includes snowfall amount, rainfall variation trend and regional air temperature variation trend.

[0009] In the technical scheme of the present application, the geographic data acquisition unit further includes a snowmelt flood monitoring and early warning information database, the snowmelt flood monitoring and early warning information database includes corresponding weighting coefficients of different regional positions, and the geographic information quantification calculation model includes: a geographic information quantification calculation input port: inputting geographic information of a snowmelt flood monitoring and early warning region collected by the geographic data acquisition unit, the geographic information referring to a regional position of the snowmelt flood monitoring and early warning region; a geographic information quantification calculation calling port: calling the snowmelt flood monitoring and early warning information database to find a weighting coefficient of a corresponding regional position according to the input regional position; a geographic information quantification calculation output port: outputting the corresponding weighting coefficient.

[0010] Further, in the technical scheme of the present application, the weighting coefficients corresponding to different regional positions in the snowmelt flood monitoring and early warning information database are calculated and recorded respectively by the following steps: S1, according to relevant records of snowmelt flood events in arid regions in previous years, sample data at time nodes of the snowmelt flood events in arid regions in previous years within a calculation period are collected, each group of sample data including geographic information and meteorological information, i.e. regional position, snowfall amount, rainfall variation trend and regional air temperature variation trend, and the sample data are grouped according to regional position, wherein sample data of the same regional position is a group, and the number of snowmelt floods occurring in different regional positions is counted respectively; S2, the regional position with the largest number of snowmelt floods is set as a reference region, i.e. the weighting coefficient corresponding to the regional position with the largest number of snowmelt floods is 1, and the sample data group of the regional position with the largest number of snowmelt floods is extracted: ; In the formula: S1 represents the sample data set of the reference area, S2 is the snowfall amount of the monitoring and early warning area , S3 is the rainfall change trend in the calculation period , S4 is the regional temperature change trend in the calculation period , S5 represents the common group sample; S3, in the calculation period of snowmelt flood occurrence , the rainfall and the regional temperature both show linear correlation with the occurrence of snowmelt flood, that is, the higher the rainfall or temperature in the calculation period , the higher the possibility of snowmelt flood occurrence, according to the multiple linear regression mathematical model, the mathematical modeling is carried out on , and there are , , three characteristic values in three dimensions: ; In the formula: S1 represents the function of calculating the occurrence of snowmelt flood based on the data set of the reference area, , , and are sample parameters to be solved, and is obtained: = , = = ; In the formula: S1 represents the function value matrix of the calculation function, wherein all represent the function value of the reference area when snowmelt flood occurs, represents the sample parameter matrix, represents the sample matrix; S4, according to the least square method, the following is obtained: = ; The derivative of is 0, and the optimal solution of is obtained: ;​​ That is, get , , and The parameter values ​​are used to obtain the function for calculating snowmelt floods in the baseline area: ; In the formula: This function represents the calculation of snowmelt flooding in the baseline area. S5. Extract sample data sets of snowmelt flood occurrence from other regions. Group the sample data from the same location within each region into a single set. Substitute each set of data into the function for calculating snowmelt flood occurrence in the baseline region. ; The calculated function value is expressed as Calculate the results of each data set separately. The data set of function values, and extract each set separately. Maximum value in the data set and minimum value This allows us to obtain the weighting coefficient ranges for other regions: ; ; ; In the formula: This represents the function value used to calculate snowmelt flooding in other areas. and These represent the maximum and minimum function values ​​for calculating snowmelt floods in other regions, respectively. Indicates the weighting coefficient. This represents the minimum weighting coefficient. Indicates the maximum weighting coefficient; S6. Calculate the weighting coefficients. Range, and minimum weighting coefficient and maximum weighting coefficient Records are stored separately in the snowmelt flood monitoring and early warning information database. When accessing the snowmelt flood monitoring and early warning information database, the output weighting coefficients include the minimum weighting coefficient. and maximum weighting coefficient .

[0011] In the technical solution of the present invention, the warning value calculation unit further includes a warning value calculation method, and the warning value calculation model includes: Early warning value calculation input port: Input the meteorological information and geographical information collected by the data acquisition module. The meteorological information refers to the changing trends of snow accumulation, rainfall and regional temperature. The geographical information refers to the weighting coefficient corresponding to the regional location of the snowmelt flood monitoring and early warning area. Warning value calculation port: Calculates the warning value according to the aforementioned warning value calculation method; Warning value calculation output port: Outputs the calculated warning value.

[0012] Furthermore, in the technical solution of the present invention, the method for calculating the warning value specifically includes the following steps: S1. Based on relevant records of snowmelt flood events in arid regions over the years, the time points of these events during the calculation period are collected. The sample data within the data set includes geographic and meteorological information, namely, regional location, snow cover, precipitation trends, and regional temperature trends. S2. Obtain the sample data set: ; In the formula: This indicates the sample data set for calculating the warning value. This represents the weighting coefficient corresponding to the region's location. To monitor and provide early warning of snow accumulation in the region , For the calculation period Rainfall variation trend within , For the calculation period Regional temperature change trend , Indicates shared ownership Group samples; S3, Calculation period for snowmelt floods Internally, based on the multiple linear regression mathematical model, a mathematical model is constructed for the warning value: ; In the formula: This indicates the calculated warning value for the sample data set based on the warning value. , , and All parameters are sample parameters to be solved. This formula is used to calculate the sample data set based on the warning value. The warning value calculation function, substituted into get: = ; = = ; In the formula: This represents the calculation of the early warning value matrix, where These all represent warning values ​​for snowmelt floods. Represents the sample parameter matrix. Represents the sample matrix; S4. Solving using the least squares method, we get: = ; right By taking the derivative, the solution can be found when the derivative is 0. The optimal solution: ; That is, get , , and The parameter values ​​are used to obtain the function for calculating the warning value: ; In the formula: To calculate the warning value, sample data from historical snowmelt flood events in different regions of the arid area are substituted into the data. This allows for the calculation of warning values ​​for different regions during snowmelt flood events, i.e., warning and alert values ​​for different regions. This indicates that the warning values ​​and alert values ​​for different regions are... The records are stored separately in the snowmelt flood monitoring and early warning information database, which includes the early warning value and the alert value. .

[0013] S5. Based on the input meteorological and geographical information, including the weighting coefficients corresponding to the regional location. Snow accumulation Rainfall variation trend and regional temperature change trends The function used to calculate the warning value: ; Calculate the warning value to be output The weighting coefficients Including minimum weighting coefficients and maximum weighting coefficient : ; ; In the formula: This represents the minimum warning value. This represents the maximum warning value, the calculated warning value. Including minimum warning value and maximum warning value .

[0014] In the technical solution of the present invention, the monitoring value calculation unit further includes a monitoring value calculation method, and the monitoring value calculation model includes: The monitoring value calculation input port is used to input the warning value calculated by the warning value calculation unit and the meteorological and geographical information obtained by the data acquisition module. The meteorological information refers to the changing trends of snow accumulation, rainfall and regional temperature. The geographical information refers to the weighting coefficient corresponding to the regional location of the snowmelt flood monitoring and warning area. Monitoring value calculation call port: Calls the snowmelt flood monitoring and early warning information database, and finds the early warning value and alert value of the corresponding area based on the input area location information; Monitoring value calculation port: Calculates the monitoring values ​​of various meteorological information according to the monitoring value calculation method described above; Monitoring value calculation output port: Outputs the calculated monitoring values ​​of various meteorological information.

[0015] Furthermore, in the technical solution of the present invention, the method for calculating the monitored value specifically includes the following steps: S1. Based on the input warning value, meteorological information, and geographic information, use the warning value and alert value for the corresponding area location. Calculate the monitoring values ​​of various meteorological information and substitute them into... get: ; In the formula: This indicates the warning or alert value corresponding to the location of the area. This represents the weighting coefficient corresponding to the region's location. To monitor and provide early warning of snow accumulation in the region , For the calculation period Rainfall variation trend within , For the calculation period Regional temperature change trend ; S2. Based on the input snow accumulation... Rainfall variation trend Calculate the regional temperature change trend Maximum value: ; In the formula: Indicates regional temperature change trend The maximum value, i.e., the monitored value of the regional temperature change trend; S2. Based on the input snow accumulation... Regional temperature change trend Calculate the trend of rainfall change Maximum value: ; In the formula: Indicates the trend of rainfall change The maximum value, i.e. the monitored value of the rainfall change trend.

[0016] In the technical solution of the present invention, the early warning issuing unit is used to issue early warnings, including snow accumulation warnings, snowmelt flood warnings, emergency snowmelt flood warnings, and extreme weather warnings; Furthermore, the snow accumulation warning refers to the snow accumulation in the snowmelt flood monitoring and warning area exceeding the minimum snow accumulation that can cause snowmelt floods, i.e.: ; In the formula: To monitor and provide early warning of snow accumulation in the region, This data represents the minimum snow accumulation during snowmelt flood events in the monitoring and early warning area over the years. Furthermore, the snowmelt flood warning refers to the minimum warning value calculated by the warning value calculation unit. Less than the warning value or alert value of the monitoring and early warning area And the maximum warning value The warning value is greater than the alert value of the monitoring and warning area. : ; In the formula: This refers to the warning and alert values ​​for the monitored and early warning area. The minimum warning value calculated by the warning value calculation unit. The maximum warning value calculated by the warning value calculation unit; Furthermore, the emergency snowmelt flood warning refers to the minimum warning value calculated by the warning value calculation unit. and maximum warning value All are greater than the warning and alert values ​​for the monitoring and early warning area. : ; In the formula: This refers to the warning and alert values ​​for the monitored and early warning area. The minimum warning value calculated by the warning value calculation unit. The maximum warning value calculated by the warning value calculation unit; Furthermore, the extreme weather warning refers to the trend of rainfall changes in the snowmelt flood monitoring and warning area. and regional temperature change trends There are cases where the monitored value is greater than the value calculated by the monitoring value calculation unit: ; In the formula: This section describes the rainfall variation trend in the snowmelt flood monitoring and early warning area. This section describes the regional temperature change trend in the snowmelt flood monitoring and early warning area. The monitoring value represents the trend of rainfall change calculated by the monitoring value calculation unit. The monitoring value represents the regional temperature change trend calculated by the monitoring value calculation unit.

[0017] Effective Gain: In summary, this invention proposes a dynamic early warning system for snowmelt floods in arid regions based on multi-source data fusion. Utilizing the linear correlation between rainfall and regional temperature on the occurrence of snowmelt floods within a certain time period, and based on a multiple linear regression mathematical model, the main factors influencing snowmelt flood occurrence, including regional temperature trends, rainfall trends, and regional location, are quantitatively fitted. By summarizing relevant data on snowmelt flood occurrences in arid regions over the years, an early warning value calculation function model is designed. This model can calculate early warning values ​​for determining whether to issue a snowmelt flood warning based on geographical and meteorological information from different regions of the arid area, effectively improving the accuracy of snowmelt flood early warnings. On the other hand, the present invention further utilizes a quantitative fitting calculation model based on the main factors affecting the occurrence of snowmelt floods to calculate the warning values ​​and alert values ​​for different regions in arid areas. At the same time, it can calculate the monitoring values ​​of various meteorological information under different meteorological conditions based on the warning values ​​and alert values, so as to further monitor various meteorological information. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall process of the dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion, as described in this invention. Figure 2 This is a schematic diagram of the module of the dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion according to the present invention; Figure 3 This is a schematic diagram illustrating the calculation cycle of the early warning value for the dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion, as described in this invention. Detailed Implementation

[0019] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0020] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0021] In the description of the present invention, it should be understood that the terms "upper", "lower", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0022] Furthermore, in the description of this invention, "a number" means two or more, unless otherwise explicitly specified.

[0023] In order to solve the problems mentioned in the background art, such as Figure 1 As shown, this embodiment of the invention proposes a dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion, such as... Figure 2 As shown, in this embodiment, the dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion includes a data acquisition module, which includes a meteorological data acquisition unit and a geographic data acquisition unit. Specifically, the meteorological data acquisition unit is used to collect meteorological information within a calculation cycle, where the calculation cycle refers to the data collection cycle for collecting meteorological information. Specifically, the geographic data acquisition unit is used to collect geographic information of the snowmelt flood monitoring and early warning area. The geographic data acquisition unit also includes a geographic information quantification calculation model, which is used to quantify the acquired geographic information into weighted coefficients. In this embodiment, the dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion also includes a calculation module, which includes an early warning value calculation unit and a monitoring value calculation unit. Specifically, the warning value calculation unit includes a warning value calculation model. The warning value calculation unit is used to calculate the warning value, which refers to the judgment value used to determine whether to issue a snowmelt flood warning. Specifically, the monitoring value calculation unit includes a monitoring value calculation model. The monitoring value calculation unit is used to calculate the monitoring value, which refers to the monitoring values ​​of various meteorological information in snowmelt flood monitoring. In this embodiment, the dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion also includes an early warning monitoring and dissemination module, wherein the early warning monitoring and dissemination module includes an early warning monitoring unit and an early warning dissemination unit; Specifically, the early warning monitoring unit monitors various meteorological information based on the monitoring values ​​calculated by the monitoring value calculation unit; Specifically, the early warning issuing unit is used to issue early warnings.

[0024] In this embodiment, the calculation period refers to the data acquisition period for collecting meteorological information, that is, the calculation period used by the calculation module to calculate the warning value and the monitoring value. The calculation period is determined by the cycle of extreme weather that causes snowmelt floods, and the specific value of the calculation period is taken as the number of days of the longest extreme weather that has occurred in the history of snowmelt floods. .

[0025] Specifically, such as Figure 3 As shown, the calculation period Including the current day Located on the current day The first half of the previous calculation cycle and located on the current day The second half of the calculation cycle The meteorological data acquisition unit acquires the current daily data. and the first half of the calculation cycle The meteorological information is the actual meteorological information, and the meteorological data acquisition unit acquires the latter half of the calculation cycle. The meteorological information refers to the forecast data released by meteorological observation stations, which includes trends in snow cover, rainfall, and regional temperature.

[0026] In this embodiment, the geographic data acquisition unit further includes a snowmelt flood monitoring and early warning information database, which includes corresponding weighting coefficients for different regional locations. The geographic information quantification calculation model includes: Geographic information quantification calculation input port: Input the geographic information of the snowmelt flood monitoring and early warning area collected by the geographic data acquisition unit. The geographic information refers to the regional location of the snowmelt flood monitoring and early warning area. Geographic information quantification calculation call port: calls the snowmelt flood monitoring and early warning information database, and finds the weighting coefficient of the corresponding regional location based on the input regional location; Geographic information quantification calculation output port: outputs the corresponding weighting coefficients.

[0027] In this embodiment, the weighting coefficients corresponding to different regional locations in the snowmelt flood monitoring and early warning information database are calculated and recorded through the following steps: S1. Based on relevant records of snowmelt flood events in arid regions over the years, the time points of these events during the calculation period are collected. The sample data includes geographical and meteorological information, namely regional location, snow cover, precipitation trend and regional temperature trend. The sample data is grouped according to regional location, with sample data in the same regional location forming one group. The number of snowmelt floods occurring in different regional locations is counted separately. S2. Set the area with the highest frequency of snowmelt floods as the baseline area, meaning the weighting coefficient corresponding to the area with the highest frequency of snowmelt floods is 1, and extract the sample data group of the area with the highest frequency of snowmelt floods: ; In the formula: This represents a sample data set representing the baseline region. To monitor and provide early warning of snow accumulation in the region , For the calculation period Rainfall variation trend within , For the calculation period Regional temperature change trend , Indicates shared ownership Group samples; S3, Calculation period for snowmelt floods Within the calculation period, the effects of rainfall and regional temperature on the occurrence of snowmelt floods both showed a linear correlation, meaning that within the calculation period... Within the region, higher rainfall or temperatures indicate a higher likelihood of snowmelt flooding. According to a multiple linear regression mathematical model, for... To conduct mathematical modeling, there are a total of , , Three dimensions of feature values: ; In the formula: This represents a function used to calculate snowmelt flooding based on a dataset from a baseline region. , , and These are all parameters of the sample to be solved, substituted into get: = , = = ; In the formula: Represents the matrix for calculating function values, where All represent function values ​​at the time of snowmelt flooding in the baseline area. Represents the sample parameter matrix. Represents the sample matrix; S4. Solving using the least squares method, we get: = ; right By taking the derivative, the solution can be found when the derivative is 0. The optimal solution: ; That is, get , , and The parameter values ​​are used to obtain the function for calculating snowmelt floods in the baseline area: ; In the formula: This function represents the calculation of snowmelt flooding in the baseline area. S5. Extract sample data sets of snowmelt flood occurrence from other regions. Group the sample data from the same location within each region into a single set. Substitute each set of data into the function for calculating snowmelt flood occurrence in the baseline region. ; The calculated function value is expressed as Calculate the results of each data set separately. The data set of function values, and extract each set separately. Maximum value in the data set and minimum value This allows us to obtain the weighting coefficient ranges for other regions: ; ; ; In the formula: This represents the function value used to calculate snowmelt flooding in other areas. and These represent the maximum and minimum function values ​​for calculating snowmelt floods in other regions, respectively. Indicates the weighting coefficient. This represents the minimum weighting coefficient. Indicates the maximum weighting coefficient; S6. Calculate the weighting coefficients. Range, and minimum weighting coefficient and maximum weighting coefficient Records are stored separately in the snowmelt flood monitoring and early warning information database. When accessing the snowmelt flood monitoring and early warning information database, the output weighting coefficients include the minimum weighting coefficient. and maximum weighting coefficient .

[0028] In this embodiment, the warning value calculation unit further includes a warning value calculation method, and the warning value calculation model includes: Early warning value calculation input port: Input the meteorological and geographical information collected by the data acquisition module. The meteorological information refers to the changing trends of snow accumulation, rainfall and regional temperature. The geographical information refers to the weighting coefficient corresponding to the regional location of the snowmelt flood monitoring and early warning area. Warning value calculation port: Calculates warning values ​​according to the warning value calculation method; Warning value calculation output port: Outputs the calculated warning value.

[0029] In this embodiment, the method for calculating the warning value specifically includes the following steps: S1. Based on relevant records of snowmelt flood events in arid regions over the years, the time points of these events during the calculation period are collected. The sample data within the data set includes geographic and meteorological information, namely, regional location, snow cover, precipitation trends, and regional temperature trends. S2. Obtain the sample data set: ; In the formula: This indicates the sample data set for calculating the warning value. This represents the weighting coefficient corresponding to the region's location. To monitor and provide early warning of snow accumulation in the region , For the calculation period Rainfall variation trend within , For the calculation period Regional temperature change trend , Indicates shared ownership Group samples; S3, Calculation period for snowmelt floods Internally, based on the multiple linear regression mathematical model, a mathematical model is constructed for the warning value: ; In the formula: This indicates the calculated warning value for the sample data set based on the warning value. , , and All parameters are sample parameters to be solved. This formula is used to calculate the sample data set based on the warning value. The warning value calculation function, substituted into get: = ; = = ; In the formula: This represents the calculation of the early warning value matrix, where These all represent warning values ​​for snowmelt floods. Represents the sample parameter matrix. Represents the sample matrix; S4. Solving using the least squares method, we get: = ; right By taking the derivative, the solution can be found when the derivative is 0. The optimal solution: ; That is, get , , and The parameter values ​​are used to obtain the function for calculating the warning value: ; In the formula: To calculate the warning value, sample data from historical snowmelt flood events in different regions of the arid area are substituted into the data. This allows for the calculation of warning values ​​for different regions during snowmelt flood events, i.e., warning and alert values ​​for different regions. This indicates the warning and alert values ​​for different regions. Records are stored separately in the snowmelt flood monitoring and early warning information database, which includes warning values ​​and alert values. .

[0030] S5. Based on the input meteorological and geographical information, including the weighting coefficients corresponding to the regional location. Snow accumulation Rainfall variation trend and regional temperature change trends The function used to calculate the warning value: ; Calculate the warning value to be output The weighting coefficients Including minimum weighting coefficients and maximum weighting coefficient : ; ; In the formula: This represents the minimum warning value. This represents the maximum warning value, the calculated warning value. Including minimum warning value and maximum warning value .

[0031] In this embodiment, the monitoring value calculation unit further includes a monitoring value calculation method, and the monitoring value calculation model includes: Input port for monitoring value calculation: Input the warning value calculated by the warning value calculation unit and the meteorological and geographical information obtained by the data acquisition module. The meteorological information refers to the changing trends of snow accumulation, rainfall and regional temperature. The geographical information refers to the weighting coefficient corresponding to the regional location of the snowmelt flood monitoring and warning area. Monitoring value calculation call port: Calls the snowmelt flood monitoring and early warning information database, and finds the corresponding early warning value and alert value based on the input regional location information; Monitoring value calculation port: Calculates the monitoring values ​​of various meteorological information according to the monitoring value calculation method; Monitoring value calculation output port: Outputs the calculated monitoring values ​​of various meteorological information.

[0032] In this embodiment, the method for calculating the monitored value specifically includes the following steps: S1. Based on the input warning value, meteorological information, and geographic information, use the warning value and alert value for the corresponding area location. Calculate the monitoring values ​​of various meteorological information and substitute them into... get: ; In the formula: This indicates the warning or alert value corresponding to the location of the area. This represents the weighting coefficient corresponding to the region's location. To monitor and provide early warning of snow accumulation in the region , For the calculation period Rainfall variation trend within , For the calculation period Regional temperature change trend ; S2. Based on the input snow accumulation... Rainfall variation trend Calculate the regional temperature change trend Maximum value: ; In the formula: Indicates regional temperature change trend The maximum value, i.e., the monitored value of the regional temperature change trend; S2. Based on the input snow accumulation... Regional temperature change trend Calculate the trend of rainfall change Maximum value: ; In the formula: Indicates the trend of rainfall change The maximum value, i.e. the monitored value of the rainfall change trend.

[0033] In this embodiment, the early warning issuing unit is used to issue early warnings, including snow accumulation warnings, snowmelt flood warnings, emergency snowmelt flood warnings, and extreme weather warnings; Specifically, a snow accumulation warning refers to a situation where the snow accumulation in the monitored and warning area exceeds the minimum snow accumulation required to cause a snowmelt flood. ; In the formula: To monitor and provide early warning of snow accumulation in the region, This data represents the minimum snow accumulation during snowmelt flood events in the monitoring and early warning area over the years. Specifically, snowmelt flood warning refers to the minimum warning value calculated by the warning value calculation unit. Less than the warning value or alert value of the monitoring and early warning area And the maximum warning value The warning value is greater than the alert value of the monitoring and warning area. : ; In the formula: This refers to the warning and alert values ​​for the monitored and early warning area. The minimum warning value calculated by the warning value calculation unit. The maximum warning value calculated by the warning value calculation unit; Specifically, an emergency snowmelt flood warning refers to the minimum warning value calculated by the warning value calculation unit. and maximum warning value All are greater than the warning and alert values ​​for the monitoring and early warning area. : ; In the formula: This refers to the warning and alert values ​​for the monitored and early warning area. The minimum warning value calculated by the warning value calculation unit. The maximum warning value calculated by the warning value calculation unit; Specifically, extreme weather warnings refer to the monitoring and warning of rainfall trends in snowmelt flood areas. and regional temperature change trends The monitoring value is calculated by the unit that calculates the monitoring value if it is greater than the monitoring value: ; In the formula: This section describes the rainfall variation trend in the snowmelt flood monitoring and early warning area. This section describes the regional temperature change trend in the snowmelt flood monitoring and early warning area. The monitoring value represents the trend of rainfall change calculated by the monitoring value calculation unit. The monitoring value represents the regional temperature change trend calculated by the monitoring value calculation unit.

[0034] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion, characterized in that, include: The data acquisition module includes a meteorological data acquisition unit and a geographic data acquisition unit; The meteorological data acquisition unit is used to collect meteorological information within a calculation cycle, where the calculation cycle refers to the data collection cycle for collecting meteorological information. The geographic data acquisition unit is used to collect geographic information of the snowmelt flood monitoring and early warning area. The geographic data acquisition unit also includes a geographic information quantification calculation model, which is used to quantify the acquired geographic information into weighted coefficients. The calculation module includes a warning value calculation unit and a monitoring value calculation unit; The warning value calculation unit includes a warning value calculation model. The warning value calculation unit is used to calculate the warning value, which refers to the judgment value used to determine whether to issue a snowmelt flood warning. The monitoring value calculation unit includes a monitoring value calculation model. The monitoring value calculation unit is used to calculate the monitoring value, which refers to the monitoring value of specific meteorological information in snowmelt flood monitoring. The early warning monitoring and dissemination module includes an early warning monitoring unit and an early warning dissemination unit; The early warning monitoring unit monitors various meteorological information based on the monitoring values ​​calculated by the monitoring value calculation unit; The warning release unit is used to release warnings.

2. The dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion according to claim 1, characterized in that, The calculation period refers to the data acquisition period for collecting meteorological information, that is, the calculation period used by the calculation module to calculate the warning value and the monitoring value. The calculation period is determined by the cycle of extreme weather events that cause snowmelt floods, and the specific value of the calculation period is taken as the number of days of the longest extreme weather event experienced in previous years when snowmelt floods occurred. .

3. The dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion according to claim 2, characterized in that, The calculation cycle Including the current day Located on the current day The first half of the previous calculation cycle and located on the current day The second half of the calculation cycle The meteorological data acquisition unit acquires the current day and the first half of the calculation cycle The meteorological information is the actual meteorological information, and the meteorological data acquisition unit acquires the latter half of the calculation cycle. The meteorological information refers to the forecast data released by meteorological observation stations, which includes the trends in snow accumulation, rainfall, and regional temperature.

4. The dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion according to claim 2, characterized in that, The geographic data acquisition unit also includes a snowmelt flood monitoring and early warning information database, which includes corresponding weighting coefficients for different regional locations. The geographic information quantification calculation model includes: Geographic information quantification calculation input port: Input the geographic information of the snowmelt flood monitoring and early warning area collected by the geographic data acquisition unit, wherein the geographic information refers to the regional location of the snowmelt flood monitoring and early warning area; Geographic information quantification calculation call port: calls the snowmelt flood monitoring and early warning information database, and finds the weighting coefficient of the corresponding regional location based on the input regional location; Geographic information quantification calculation output port: outputs the corresponding weighting coefficients.

5. The dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion according to claim 4, characterized in that, In the snowmelt flood monitoring and early warning information database, the weighting coefficients corresponding to different regional locations are calculated and recorded through the following steps: S1. Based on relevant records of snowmelt flood events in arid regions over the years, the time points of these events during the calculation period are collected. The sample data includes geographical and meteorological information, namely regional location, snow cover, precipitation trend and regional temperature trend. The sample data is grouped according to regional location, with sample data in the same regional location forming one group. The number of snowmelt floods occurring in different regional locations is counted separately. S2. Set the area with the highest frequency of snowmelt floods as the baseline area, meaning the weighting coefficient corresponding to the area with the highest frequency of snowmelt floods is 1, and extract the sample data group of the area with the highest frequency of snowmelt floods: ； In the formula: This represents a sample data set representing the baseline region. To monitor and provide early warning of snow accumulation in the region , For the calculation period Rainfall variation trend within , For the calculation period Regional temperature change trend , Indicates shared ownership Group samples; S3, Calculation period for snowmelt floods Within the calculation period, the effects of rainfall and regional temperature on the occurrence of snowmelt floods both showed a linear correlation, meaning that within the calculation period... Within the region, higher rainfall or temperatures indicate a higher likelihood of snowmelt flooding. According to a multiple linear regression mathematical model, for... To conduct mathematical modeling, there are a total of , , Three dimensions of feature values: ； In the formula: This represents a function used to calculate snowmelt flooding based on a dataset from a baseline region. , , and These are all parameters of the sample to be solved, substituted into get: = , = = ； In the formula: Represents the matrix for calculating function values, where All represent function values ​​at the time of snowmelt flooding in the baseline area. Represents the sample parameter matrix. Represents the sample matrix; S4. Solving using the least squares method, we get: = ； right By taking the derivative, the solution can be found when the derivative is 0. The optimal solution: ； That is, get , , and The parameter values ​​are used to obtain the function for calculating snowmelt floods in the baseline area: ； In the formula: This function represents the calculation of snowmelt flooding in the baseline area. S5. Extract sample data sets of snowmelt flood occurrence from other regions. Group the sample data from the same location within each region into a single set. Substitute each set of data into the function for calculating snowmelt flood occurrence in the baseline region. ； The calculated function value is expressed as Calculate the results of each data set separately. The data set of function values, and extract each set separately. Maximum value in the data set and minimum value This allows us to obtain the weighting coefficient ranges for other regions: ； ； ； In the formula: This represents the function value used to calculate snowmelt flooding in other areas. and These represent the maximum and minimum function values ​​for calculating snowmelt floods in other regions, respectively. Indicates the weighting coefficient. This represents the minimum weighting coefficient. Indicates the maximum weighting coefficient; S6. Calculate the weighting coefficients. Range, and minimum weighting coefficient and maximum weighting coefficient Records are stored separately in the snowmelt flood monitoring and early warning information database. When accessing the snowmelt flood monitoring and early warning information database, the output weighting coefficients include the minimum weighting coefficient. and maximum weighting coefficient .

6. The dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion according to claim 5, characterized in that, The warning value calculation unit further includes a warning value calculation method, and the warning value calculation model includes: Early warning value calculation input port: Input the meteorological information and geographical information collected by the data acquisition module. The meteorological information refers to the changing trends of snow accumulation, rainfall and regional temperature. The geographical information refers to the weighting coefficient corresponding to the regional location of the snowmelt flood monitoring and early warning area. Warning value calculation port: Calculates the warning value according to the aforementioned warning value calculation method; Warning value calculation output port: Outputs the calculated warning value.

7. The dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion according to claim 6, characterized in that, The method for calculating the warning value specifically includes the following steps: S1. Based on relevant records of snowmelt flood events in arid regions over the years, the time points of these events during the calculation period are collected. The sample data within the data set includes geographic and meteorological information, namely, regional location, snow cover, precipitation trends, and regional temperature trends. S2. Obtain the sample data set: ； In the formula: This indicates the sample data set for calculating the warning value. This represents the weighting coefficient corresponding to the region's location. To monitor and provide early warning of snow accumulation in the region , For the calculation period Rainfall variation trend within , For the calculation period Regional temperature change trend , Indicates shared ownership Group samples; S3, Calculation period for snowmelt floods Internally, based on the multiple linear regression mathematical model, a mathematical model is constructed for the warning value: ； In the formula: This indicates the calculated warning value for the sample data set based on the warning value. , , and All parameters are sample parameters to be solved. This formula is used to calculate the sample data set based on the warning value. The warning value calculation function, substituted into get: = ； = = ； In the formula: This represents the calculation of the early warning value matrix, where These all represent warning values ​​for snowmelt floods. Represents the sample parameter matrix. Represents the sample matrix; S4. Solving using the least squares method, we get: = ； right By taking the derivative, the solution can be found when the derivative is 0. The optimal solution: ； That is, get , , and The parameter values ​​are used to obtain the function for calculating the warning value: ； In the formula: To calculate the warning value, sample data from historical snowmelt flood events in different regions of the arid area are substituted into the data. This allows for the calculation of warning values ​​for different regions during snowmelt flood events, i.e., warning and alert values ​​for different regions. This indicates that the warning values ​​and alert values ​​for different regions are... The records are stored separately in the snowmelt flood monitoring and early warning information database, which includes the early warning value and the alert value. ; S5. Based on the input meteorological and geographical information, including the weighting coefficients corresponding to the regional location. Snow accumulation Rainfall variation trend and regional temperature change trends The function used to calculate the warning value: ； Calculate the warning value to be output The weighting coefficients Including minimum weighting coefficients and maximum weighting coefficient : ； ； In the formula: This represents the minimum warning value. This represents the maximum warning value, the calculated warning value. Including minimum warning value and maximum warning value .

8. The dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion according to claim 7, characterized in that, The monitoring value calculation unit further includes a monitoring value calculation method, and the monitoring value calculation model includes: The monitoring value calculation input port is used to input the warning value calculated by the warning value calculation unit and the meteorological and geographical information obtained by the data acquisition module. The meteorological information refers to the changing trends of snow accumulation, rainfall and regional temperature. The geographical information refers to the weighting coefficient corresponding to the regional location of the snowmelt flood monitoring and warning area. Monitoring value calculation call port: Calls the snowmelt flood monitoring and early warning information database, and finds the early warning value and alert value of the corresponding area based on the input area location information; Monitoring value calculation port: Calculates the monitoring values ​​of various meteorological information according to the monitoring value calculation method described above; Monitoring value calculation output port: Outputs the calculated monitoring values ​​of various meteorological information.

9. The dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion according to claim 8, characterized in that, The method for calculating the monitored values ​​specifically includes the following steps: S1. Based on the input warning value, meteorological information, and geographic information, use the warning value and alert value for the corresponding area location. Calculate the monitoring values ​​of various meteorological information and substitute them into... get: ； In the formula: This indicates the warning or alert value corresponding to the location of the area. This represents the weighting coefficient corresponding to the region's location. To monitor and provide early warning of snow accumulation in the region , For the calculation period Rainfall variation trend within , For the calculation period Regional temperature change trend ; S2. Based on the input snow accumulation... Rainfall variation trend Calculate the regional temperature change trend Maximum value: ； In the formula: Indicates regional temperature change trend The maximum value, i.e., the monitored value of the regional temperature change trend; S2. Based on the input snow accumulation... Regional temperature change trend Calculate the trend of rainfall change Maximum value: ； In the formula: Indicates the trend of rainfall change The maximum value, i.e. the monitored value of the rainfall change trend.

10. The dynamic early warning system for snowmelt floods in arid areas based on multi-source data fusion according to claim 9, characterized in that, The warning issuing unit is used to issue warnings, including snow accumulation warnings, snowmelt flood warnings, emergency snowmelt flood warnings, and extreme weather warnings; The snow accumulation warning refers to the snow accumulation in the snowmelt flood monitoring and warning area exceeding the minimum snow accumulation that can cause snowmelt floods, that is: ； In the formula: To monitor and provide early warning of snow accumulation in the region, This data represents the minimum snow accumulation during snowmelt flood events in the monitoring and early warning area over the years. The snowmelt flood warning refers to the minimum warning value calculated by the warning value calculation unit. Less than the warning value or alert value of the monitoring and early warning area And the maximum warning value The warning value is greater than the alert value of the monitoring and warning area. : ； In the formula: This refers to the warning and alert values ​​for the monitored and early warning area. The minimum warning value calculated by the warning value calculation unit. The maximum warning value calculated by the warning value calculation unit; The emergency snowmelt flood warning refers to the minimum warning value calculated by the warning value calculation unit. and maximum warning value All are greater than the warning and alert values ​​for the monitoring and early warning area. : ； In the formula: This refers to the warning and alert values ​​for the monitored and early warning area. The minimum warning value calculated by the warning value calculation unit. The maximum warning value calculated by the warning value calculation unit; The extreme weather warning refers to the trend of rainfall changes in the snowmelt flood monitoring and warning area. and regional temperature change trends There exists a value greater than the value calculated by the monitoring value calculation unit: ； In the formula: This section describes the rainfall variation trend in the snowmelt flood monitoring and early warning area. This section describes the regional temperature change trend in the snowmelt flood monitoring and early warning area. The monitoring value represents the trend of rainfall change calculated by the monitoring value calculation unit. The monitoring value represents the regional temperature change trend calculated by the monitoring value calculation unit.

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