A precise early warning system for coping with low-temperature rain, snow and freezing risk

Abstract

The present application relates to the technical field of snow and ice warning, in particular to a precise early warning system for coping with low temperature snow and ice risk, comprising: a multi-source data acquisition module, based on a target area, collecting real-time and historical data related to low temperature snow and ice; a dynamic threshold generation module, based on historical meteorological data, calculating the threshold of the target area, including the low temperature threshold and the precipitation threshold; a hazard index calculation module, based on the low temperature threshold and the precipitation threshold generated by the regional dynamic threshold, combining the real-time collected temperature and precipitation data of the target area, calculating the low temperature snow and ice hazard index of the target area; a hazard-bearing body risk early warning module, based on the low temperature snow and ice hazard index of the target area, conducting hazard-bearing body risk classification early warning. The present application combines exposure and risk assessment, and refines the risk assessment of different regions through exposure weighting mechanism, effectively improving the pertinence of early warning and the effectiveness of response measures.

Description

Technical Field

[0001] This invention relates to the field of rain, snow and ice warning technology, and in particular to a precise warning system for dealing with the risk of low temperature rain, snow and ice. Background Technology

[0002] With climate change, the frequency and intensity of extreme weather events are gradually increasing. Low temperatures, rain, snow, and freezing weather have become one of the major climate disasters that seriously affect many industries and social life. Especially in winter, low temperatures, rain, snow, and freezing phenomena occur frequently, threatening transportation, agricultural production, infrastructure operation, and causing property damage. Therefore, how to provide early warnings and effectively respond to the risks of low temperatures, rain, snow, and freezing has become an urgent problem for governments at all levels and relevant departments to solve.

[0003] Currently, most early warning systems for the risk of low-temperature rain, snow, and freezing rely on traditional meteorological monitoring data, such as temperature and precipitation, to determine weather conditions. However, these early warning systems have certain shortcomings in terms of accuracy and timeliness, mainly in the following aspects:

[0004] Static threshold problem: Most existing early warning systems rely on static thresholds (such as fixed temperature and precipitation standards) to determine whether an early warning needs to be issued. This method ignores the differences in the tolerance of different regions to low temperature rain, snow and ice under different climatic conditions, resulting in large deviations in prediction results.

[0005] Lack of exposure assessment: Existing systems rarely consider the specific exposure of target areas, especially the combined effects of factors such as population density, infrastructure layout and crop distribution. The lack of exposure assessment results in a lack of targeted and precise early warning information, which means that some high-risk areas fail to receive timely warnings, while some low-risk areas may receive excessive alerts. Summary of the Invention

[0006] This invention provides a precise early warning system for dealing with the risks of low-temperature rain, snow, and ice.

[0007] A precise early warning system for addressing the risks of low-temperature rain, snow, and freezing, comprising:

[0008] The multi-source data acquisition module collects real-time and historical data related to low-temperature rain, snow, and freezing based on the target area, including:

[0009] Meteorological data: hourly updated temperature and precipitation;

[0010] Disaster-bearing body data: population distribution, crop data, and infrastructure layout;

[0011] The dynamic threshold generation module calculates the threshold for the target area based on historical meteorological data, specifically including:

[0012] Low temperature threshold: The low temperature level threshold of the target area is determined by the quantile method;

[0013] Precipitation threshold: Extract precipitation level thresholds by analyzing historical average precipitation data and extreme precipitation events in the target area;

[0014] The generated low-temperature threshold and precipitation threshold are used to calculate the hazard index;

[0015] The hazard index calculation module, based on the low temperature threshold and precipitation threshold generated by regional dynamic thresholds, and combined with real-time collected temperature and precipitation data of the target area, calculates the low temperature rain, snow, and freezing hazard index (HI) of the target area, specifically including:

[0016] The real-time collected temperature and precipitation data are compared with dynamic thresholds to determine whether the temperature and precipitation meet the warning conditions.

[0017] When the warning conditions are met, a freezing risk warning is issued, and an index of spatially distributed low temperature rain, snow and freezing risk is generated by using exposure and duration of impact for index weighting.

[0018] The disaster-bearing body risk early warning module conducts risk classification and early warning for disaster-bearing bodies based on the low-temperature rain, snow and ice hazard index of the target area.

[0019] Optionally, the multi-source data acquisition module specifically includes:

[0020] The meteorological data acquisition unit is used to establish an interface with the meteorological center in the target area to obtain real-time meteorological data, including:

[0021] Temperature data collection: Updates temperature data for the target area hourly;

[0022] Precipitation data collection: Obtaining the amount of precipitation per unit time and the regional precipitation distribution;

[0023] The disaster-bearing body data acquisition unit is used to collect characteristic data of disaster-bearing bodies through remote sensing technology and regional statistical data, including:

[0024] Population distribution data: Obtain the population density of the target area through regional statistical data, and identify the proportion of highly vulnerable groups (such as the elderly and children);

[0025] Crop data: Utilizing multispectral remote sensing technology to monitor crop distribution in target areas;

[0026] Infrastructure layout data: Information on the distribution of roads and power facilities in the target area.

[0027] Optionally, the dynamic threshold generation module specifically includes:

[0028] Low temperature threshold calculation: Collect historical temperature data of the target area, and determine the low temperature level threshold of the target area by means of quantile method according to the temperature distribution. This includes calculating each quantile of temperature according to the distribution of historical temperature data, and selecting the low temperature quantile as the low temperature threshold according to the climate characteristics of the target area.

[0029] Precipitation threshold calculation: Collect historical precipitation data for the target area, and extract precipitation level thresholds based on the analysis of historical average precipitation and extreme precipitation events. Specifically, this includes: calculating the average and standard deviation of historical precipitation in the target area, and determining the precipitation level thresholds based on statistical analysis of historical extreme precipitation events.

[0030] Optionally, the historical temperature data are represented as T1, T2, ..., T n Where T represents historical temperature data and n represents the total number of historical temperature data, the calculation of the low temperature threshold specifically includes:

[0031] Sort historical temperature data: T (1) ≤T (2) ≤…≤T (n) ;

[0032] By using the quantile method, the 25th quantile was selected as the low-temperature threshold T. low This means that the target area experienced temperatures equal to or lower than this value 25% of the time in historical temperature data.

[0033] in, This indicates rounding up to the nearest integer, ensuring that the temperature value corresponding to the quantile is obtained.

[0034] Optionally, the historical precipitation data of the target area are represented as P1, P2, ..., P m Where P represents historical precipitation data and m represents the total number of historical precipitation data, the precipitation threshold calculation specifically includes:

[0035] Calculate the average precipitation P avg : Among them, P i This is the i-th historical precipitation data point;

[0036] Calculate the standard deviation σ of precipitation P :

[0037] Extreme precipitation event analysis: To determine the precipitation threshold, the standard deviation of historical precipitation data is used to consider the occurrence of extreme events, and a precipitation threshold P is set. thresh For: P thresh =P avg +k·σ P, where k is the standard deviation coefficient, representing a multiple of the standard deviation.

[0038] Optionally, the current real-time temperature of the target area is T. current The current real-time precipitation data is P. current The real-time temperature data T current and precipitation data P current Compared with the low temperature threshold T of the target region respectively low and precipitation threshold P thresh Comparison:

[0039] If T current ≤T low This indicates that the temperature has reached the low-temperature warning condition;

[0040] If P current ≥P thresh If the precipitation reaches the warning level, it indicates that the precipitation has reached the warning level.

[0041] Optionally, the calculation of the low-temperature rain, snow, and freezing hazard index specifically includes:

[0042] Exposure: Exposure indicates the degree to which a target area is affected by low temperature, rain, snow and ice. Let the exposure be E, which is the degree to which the target area is exposed to the risk of low temperature, rain, snow and ice. E∈[0,1], and the higher the value, the greater the degree of exposure.

[0043] Duration of impact: Duration of impact represents the duration of low temperature rain, snow and freezing conditions, denoted as D, where D≥0. The longer the duration of impact, the higher the risk index.

[0044] Combining the exposure level E and the duration of impact D, the weighted low-temperature rain, snow, and freezing hazard index HI is calculated:

[0045] HI = w1·E + w2·D, where w1 and w2 are weighting coefficients, representing the degree of contribution of exposure and duration of influence to the hazard index, and w1 + w2 = 1.

[0046] Optionally, the exposure level E considers population exposure, infrastructure exposure, and crop exposure, and is expressed as: E = E p +E i +E a E p Population exposure, reflecting the degree to which a population is affected, E i E represents infrastructure exposure, indicating the degree to which infrastructure such as roads and power facilities are affected. a Agricultural exposure reflects the degree to which crops are affected by factors such as low temperature, freezing, and precipitation.

[0047] Optionally, the duration of influence D represents the duration of the effects of low temperature, rain, snow, and freezing on the target area. The duration of influence D is calculated based on the following factors:

[0048] The duration of actual weather events is measured using real-time meteorological data;

[0049] The duration of historical extreme events can be used as a reference to estimate the duration of similar events;

[0050] Let T be the actual duration of the extreme weather events of low temperature and precipitation in the target area. actual The duration of the influence, D, is calculated as: D = max(T) actual T avg ), where T actual T is the actual duration of the event derived from real-time meteorological data. avg It is the average duration of similar historical events within the target area.

[0051] Optionally, the disaster-bearing body risk classification early warning specifically includes:

[0052] The Low Temperature Rain, Snow, and Ice Risk Index (HI) is classified into levels, and corresponding thresholds are set for graded early warning.

[0053] The beneficial effects of this invention are:

[0054] This invention, by integrating historical meteorological data and disaster-bearing body data (such as population, agriculture, infrastructure layout, etc.), can calculate the low-temperature rain, snow, and freezing hazard index of a target area. By calculating the dynamic thresholds of low temperature and precipitation, and combining real-time data with historical data, it can accurately determine whether the area has reached the warning conditions. Based on the combination of the dynamic threshold generation module and real-time data, the accuracy of the warning is greatly improved, avoiding the "lag" and "false alarm" problems common in traditional warning systems.

[0055] This invention, through exposure assessment, comprehensively evaluates the potential impact of low-temperature rain, snow, and freezing disasters from multiple dimensions (such as population distribution, infrastructure layout, and agricultural crop cultivation). In exposure calculation, particular attention is paid to vulnerable groups (such as the elderly and children). By combining exposure with risk assessment and refining risk assessments for different regions through an exposure-weighted mechanism, it effectively improves the targeting of early warnings and the effectiveness of response measures.

[0056] This invention generates a specific risk index for a region and uses this data to provide precise regional early warnings. It dynamically and in real-time offers customized regional warning information to local emergency departments and the public, and, combined with specific spatial distribution data, optimizes resource allocation and emergency response. For example, it allows for the timely deployment of repair teams and rescue resources in high-risk areas, avoiding resource waste and minimizing the impact of disasters. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a schematic diagram of the system functional modules according to an embodiment of the present invention;

[0059] Figure 2 This is a schematic diagram illustrating the calculation method of the low-temperature rain, snow, and freezing hazard index according to an embodiment of the present invention. Detailed Implementation

[0060] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0061] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0062] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0063] like Figures 1-2 As shown, a precise early warning system for addressing the risks of low-temperature rain, snow, and freezing includes:

[0064] The multi-source data acquisition module collects real-time and historical data related to low-temperature rain, snow, and freezing based on the target area, including:

[0065] Meteorological data: hourly updated temperature and precipitation;

[0066] Disaster-bearing body data: population distribution, crop data, and infrastructure layout;

[0067] The dynamic threshold generation module calculates the threshold for the target area based on historical meteorological data, specifically including:

[0068] Low temperature threshold: The low temperature level threshold of the target area is determined by the quantile method;

[0069] Precipitation threshold: Extract precipitation level thresholds by analyzing historical average precipitation data and extreme precipitation events in the target area;

[0070] As historical data is continuously updated, the thresholds are also updated regularly.

[0071] The generated low-temperature threshold and precipitation threshold are used to calculate the hazard index;

[0072] The hazard index calculation module, based on the low temperature threshold and precipitation threshold generated by regional dynamic thresholds, and combined with real-time collected temperature and precipitation data of the target area, calculates the low temperature rain, snow, and freezing hazard index (HI) of the target area, specifically including:

[0073] The real-time collected temperature and precipitation data are compared with dynamic thresholds to determine whether the temperature and precipitation meet the warning conditions.

[0074] When the warning conditions are met, a freezing risk warning is issued, and an index of spatially distributed low temperature rain, snow and freezing risk is generated by using exposure and duration of impact for index weighting.

[0075] The disaster-bearing body risk early warning module conducts risk classification and early warning for disaster-bearing bodies based on the low-temperature rain, snow and ice hazard index of the target area.

[0076] The multi-source data acquisition module specifically includes:

[0077] The meteorological data acquisition unit is used to establish an interface with the meteorological center in the target area to obtain real-time meteorological data, including:

[0078] Temperature data collection: Updates temperature data for the target area hourly;

[0079] Precipitation data collection: Obtaining the amount of precipitation per unit time and the regional precipitation distribution;

[0080] The disaster-bearing body data acquisition unit is used to collect characteristic data of disaster-bearing bodies through remote sensing technology and regional statistical data, including:

[0081] Population distribution data: Obtain the population density of the target area through regional statistical data, and identify the proportion of highly vulnerable groups (such as the elderly and children);

[0082] Crop data: Utilizing multispectral remote sensing technology to monitor crop distribution in target areas;

[0083] Infrastructure layout data: Information on the distribution of roads and power facilities in the target area.

[0084] The dynamic threshold generation module specifically includes:

[0085] Low temperature threshold calculation: Collect historical temperature data of the target area, and determine the low temperature level threshold of the target area by means of quantile method according to the temperature distribution. This includes calculating each quantile of temperature (such as 25th quantile, 50th quantile and 75th quantile) according to the distribution of historical temperature data, and selecting appropriate low temperature quantile as low temperature threshold according to the climate characteristics of the target area.

[0086] Precipitation threshold calculation: Collect historical precipitation data for the target area, and extract precipitation level thresholds based on the analysis of historical average precipitation and extreme precipitation events. Specifically, this includes: calculating the average and standard deviation of historical precipitation in the target area, and determining the precipitation level thresholds based on statistical analysis of historical extreme precipitation events (such as extreme precipitation frequency, precipitation intensity, etc.).

[0087] Historical temperature data are represented as T1, T2, ..., T n Where T represents historical temperature data and n represents the total number of historical temperature data points, the calculation of the low temperature threshold specifically includes:

[0088] Sort historical temperature data: T (1) ≤T (2) ≤…≤T (n) ;

[0089] By selecting the quantile method, the 25th quantile (lower quartile) is taken as the low temperature threshold T. low This means that the target area experienced temperatures equal to or lower than this value 25% of the time in historical temperature data.

[0090] in, This indicates rounding up to the nearest integer, ensuring that the temperature value corresponds to the appropriate quantile.

[0091] Historical precipitation data for the target area are represented as P1, P2, ..., Pm Where P represents historical precipitation data, m represents the total number of historical precipitation data, and the precipitation threshold calculation specifically includes:

[0092] Calculate the average precipitation P avg : Among them, P i This is the i-th historical precipitation data point;

[0093] Calculate the standard deviation σ of precipitation P :

[0094] Extreme precipitation event analysis: To determine the precipitation threshold, the standard deviation of historical precipitation data is used to consider the occurrence of extreme events, and a precipitation threshold P is set. thresh For: P thresh =P avg +k·σ P Where k is the standard deviation coefficient, representing the multiple of the standard deviation, and is set between 1.5 and 3. k is usually set to 2, which represents the probability of extreme precipitation events based on the average precipitation.

[0095] The current real-time temperature of the target area is T. current The current real-time precipitation data is P. current The real-time temperature data T current and precipitation data P current Compared with the low temperature threshold T of the target region respectively low and precipitation threshold P thresh Comparison:

[0096] If T current ≤T low This indicates that the temperature has reached the low-temperature warning condition;

[0097] If P current ≥P thresh If the precipitation reaches the warning level, it indicates that the precipitation has reached the warning level.

[0098] The calculation of the low-temperature rain, snow, and freezing hazard index specifically includes:

[0099] Exposure: Exposure indicates the degree to which a target area is affected by low temperature, rain, snow and ice. It is usually related to factors such as population density, important infrastructure and crop distribution. Let the exposure be E, which is the degree to which the target area is exposed to the risk of low temperature, rain, snow and ice. E∈[0,1], and the higher the value, the greater the degree of exposure.

[0100] Duration of impact: Duration of impact represents the duration of low temperature rain, snow and freezing conditions, denoted as D, in hours. D≥0, the longer the duration of impact, the higher the risk index.

[0101] Combining the exposure level E and the duration of impact D, the weighted low-temperature rain, snow, and freezing hazard index HI is calculated:

[0102] HI = w1·E + w2·D, where w1 and w2 are weighting coefficients, representing the degree of contribution of exposure and duration of influence to the hazard index. w1 + w2 = 1, and w1 = 0.6 and w2 = 0.4 are set. The specific weights can also be adjusted according to actual needs.

[0103] Exposure E considers population exposure, infrastructure exposure, and crop exposure, and is expressed as: E = E p +E i +E a E p Population exposure, reflecting the degree to which a population is affected, E i E represents infrastructure exposure, indicating the degree to which infrastructure such as roads and power facilities are affected. a Agricultural exposure reflects the degree to which crops are affected by factors such as low temperature, freezing, and precipitation;

[0104] Population Exposure E p The calculation is based on the population density of the target area and the proportion of vulnerable groups (such as the elderly and children). Let the population density of the target area be ρ and the proportion of vulnerable groups be ρ_v. v Then, population exposure is expressed as: Where, ρ max and ρ vmax These represent the maximum values ​​for population density and the proportion of vulnerable populations in the target area, respectively.

[0105] Infrastructure Exposure E i Considering the distribution of critical infrastructure such as roads, power facilities, and communication facilities in the target area, the impact range of these facilities is closely related to their distribution. This impact range is determined by calculating the proportion of facilities within the area. Let the density of infrastructure in the target area be I, and the maximum overall distribution value of facilities be I0. max The infrastructure exposure is then:

[0106] Agricultural Exposure E a Agricultural exposure, reflecting the impact of extreme weather events such as low temperatures and precipitation, can be calculated by the distribution and cold resistance of major crops within a target area. Let A be the crop coverage area within the target area, and C be the average cold resistance of the crops. avg (This can be defined as a standardized coefficient), then the agricultural exposure is: Among them, A max C represents the total crop area of ​​the target region. max E represents the maximum cold resistance of crops. p Ei E a All data were normalized.

[0107] The cold resistance (or frost tolerance) of crops can be found in agricultural research, botanical literature, or climate adaptability databases, which typically contain the lowest temperatures that each crop can withstand.

[0108] Consult data on the cold resistance of various crops. This data is categorized according to the type of crop, variety, local climate conditions, and other factors.

[0109] Collect this data for the main crops in the target area. You can also refer to standardized agricultural planting manuals, many of which list the cold tolerance thresholds (minimum growth temperatures) for common crops.

[0110] For example:

[0111] Wheat's cold tolerance is approximately -10℃;

[0112] Rice has low cold resistance, with a maximum temperature of around 0℃;

[0113] Corn is severely affected when the temperature is below -4°C;

[0114] From this data, you can deduce the average cold resistance of the main crops in the target area.

[0115] The duration of influence, D, represents the duration of the effects of low temperature, rain, snow, and freezing on the target area. The calculation of the duration of influence, D, is based on the following factors:

[0116] The duration of actual weather events is measured using real-time meteorological data;

[0117] The duration of historical extreme events can be used as a reference to estimate the duration of similar events;

[0118] Let T be the actual duration of the extreme weather events of low temperature and precipitation in the target area. actual The duration of the influence, D, is calculated as: D = max(T) actual T avg ), where T actual T is the actual duration of the event derived from real-time meteorological data. avg T is the average duration of similar historical events within the target area. As a benchmark, it is typically calculated based on historical data. If historical data indicates that a certain extreme weather event (such as low temperatures, snowfall, or freezing) usually lasts for a certain period, then T is used. avg It can be used as a comparison value to ensure the reasonableness of the duration.

[0119] The specific components of the disaster-bearing body risk classification and early warning system include:

[0120] The hazard index HI for low-temperature rain, snow, and freezing is classified into levels, and corresponding thresholds are set for graded early warning:

[0121] HI∈[0, 0.3] indicates low risk and no warning is needed;

[0122] HI∈(0.3, 0.6] indicates a medium risk, and a blue alert is issued.

[0123] HI∈(0.6,0.7] indicates high risk, and a yellow alert is issued;

[0124] HI∈(0.9,1.0] indicates a severe risk, and a red alert is issued.

[0125] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0126] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A precise early warning system for addressing the risks of low-temperature rain, snow, and freezing, characterized in that, include: The multi-source data acquisition module collects real-time and historical data related to low-temperature rain, snow, and freezing based on the target area, including: Meteorological data: hourly updated temperature and precipitation; Disaster-bearing body data: population distribution, crop data, and infrastructure layout; The dynamic threshold generation module calculates the threshold for the target area based on historical meteorological data, specifically including: Low temperature threshold: The low temperature level threshold of the target area is determined by the quantile method; Precipitation threshold: Extract precipitation level thresholds by analyzing historical average precipitation data and extreme precipitation events in the target area; The generated low-temperature threshold and precipitation threshold are used to calculate the hazard index; The hazard index calculation module, based on the low-temperature threshold and precipitation threshold generated by regional dynamic thresholds, and combined with real-time collected temperature and precipitation data of the target area, calculates the low-temperature rain, snow, and freezing hazard index of the target area, specifically including: The real-time collected temperature and precipitation data are compared with dynamic thresholds to determine whether the temperature and precipitation meet the warning conditions. When the warning conditions are met, a freezing risk warning is issued, and an index of spatially distributed low temperature rain, snow and freezing risk is generated by using exposure and duration of impact for index weighting. The disaster-bearing body risk early warning module conducts risk classification and early warning for disaster-bearing bodies based on the low-temperature rain, snow and ice hazard index of the target area.

2. The precise early warning system for addressing the risk of low-temperature rain, snow, and freezing according to claim 1, characterized in that, The multi-source data acquisition module specifically includes: The meteorological data acquisition unit is used to establish an interface with the meteorological center in the target area to obtain real-time meteorological data, including: Temperature data collection: Updates temperature data for the target area hourly; Precipitation data collection: Obtaining the amount of precipitation per unit time and the regional precipitation distribution; The disaster-bearing body data acquisition unit is used to collect characteristic data of disaster-bearing bodies through remote sensing technology and regional statistical data, including: Population distribution data: Obtain the population density of the target area through regional statistical data and identify the proportion of highly vulnerable people; Crop data: Utilizing multispectral remote sensing technology to monitor crop distribution in target areas; Infrastructure layout data: Information on the distribution of roads and power facilities in the target area.

3. The precise early warning system for addressing the risk of low-temperature rain, snow, and freezing according to claim 1, characterized in that, The dynamic threshold generation module specifically includes: Low temperature threshold calculation: Collect historical temperature data of the target area, and determine the low temperature level threshold of the target area by means of quantile method according to the temperature distribution. This includes calculating each quantile of temperature according to the distribution of historical temperature data, and selecting the low temperature quantile as the low temperature threshold according to the climate characteristics of the target area. Precipitation threshold calculation: Collect historical precipitation data for the target area, and extract precipitation level thresholds based on the analysis of historical average precipitation and extreme precipitation events. Specifically, this includes: calculating the average and standard deviation of historical precipitation in the target area, and determining the precipitation level thresholds based on statistical analysis of historical extreme precipitation events.

4. The precise early warning system for addressing the risk of low-temperature rain, snow, and freezing according to claim 3, characterized in that, The historical temperature data are represented as T1, T2, ..., T n Where T represents historical temperature data and n represents the total number of historical temperature data, the calculation of the low temperature threshold specifically includes: Sort historical temperature data: T (1) ≤T (2) ≤…≤T (n) ; By using the quantile method, the 25th quantile was selected as the low-temperature threshold T. low This means that the target area experienced temperatures equal to or lower than this value 25% of the time in historical temperature data. in, This indicates rounding up to the nearest integer, ensuring that the temperature value corresponding to the quantile is obtained.

5. A precise early warning system for addressing the risk of low-temperature rain, snow, and freezing according to claim 4, characterized in that, The historical precipitation data for the target area are represented as P1, P2, ..., P... m Where P represents historical precipitation data and m represents the total number of historical precipitation data, the precipitation threshold calculation specifically includes: Calculate the average precipitation P avg : Among them, P i This is the i-th historical precipitation data point; Calculate the standard deviation σ of precipitation P : Extreme precipitation event analysis: To determine the precipitation threshold, the standard deviation of historical precipitation data is used to consider the occurrence of extreme events, and a precipitation threshold P is set. thresh For: P thresh =P avg +k·σ P , where k is the standard deviation coefficient, representing a multiple of the standard deviation.

6. A precise early warning system for addressing the risk of low-temperature rain, snow, and freezing according to claim 1, characterized in that, The current real-time temperature in the target area is T. current The current real-time precipitation data is P. current The real-time temperature data T current and precipitation data P current Compared with the low temperature threshold T of the target region respectively low and precipitation threshold P thresh Comparison: If T current ≤T low This indicates that the temperature has reached the low-temperature warning condition; If P current ≥P thresh If the precipitation reaches the warning level, it indicates that the precipitation has reached the warning level.

7. A precise early warning system for addressing the risk of low-temperature rain, snow, and freezing according to claim 1, characterized in that, The calculation of the low-temperature rain, snow, and freezing hazard index specifically includes: Exposure: Exposure indicates the degree to which a target area is affected by low temperature, rain, snow and ice. Let the exposure be E, which is the degree to which the target area is exposed to the risk of low temperature, rain, snow and ice. E∈[0,1], and the higher the value, the greater the degree of exposure. Duration of impact: Duration of impact represents the duration of low temperature rain, snow and freezing conditions, denoted as D, where D≥0. The longer the duration of impact, the higher the risk index. Combining the exposure level E and the duration of impact D, the weighted low-temperature rain, snow, and freezing hazard index HI is calculated: HI = w1·E + w2·D, where w1 and w2 are weighting coefficients, representing the degree of contribution of exposure and duration of influence to the hazard index, and w1 + w2 = 1.

8. A precise early warning system for addressing the risk of low-temperature rain, snow, and freezing according to claim 7, characterized in that, The exposure level E considers population exposure, infrastructure exposure, and crop exposure, and is expressed as: E = E p +E i +E a E p Population exposure, reflecting the degree to which a population is affected, E i E represents infrastructure exposure, indicating the degree to which infrastructure such as roads and power facilities are affected. a Agricultural exposure reflects the degree to which crops are affected by factors such as low temperature, freezing, and precipitation.

9. A precise early warning system for addressing the risk of low-temperature rain, snow, and freezing according to claim 8, characterized in that, The duration of influence D represents the duration of the effects of low temperature, rain, snow, and freezing on the target area. The duration of influence D is calculated based on the following factors: The duration of actual weather events is measured using real-time meteorological data; The duration of historical extreme events can be used as a reference to estimate the duration of similar events; Let T be the actual duration of the extreme weather events of low temperature and precipitation in the target area. actual The duration of the influence, D, is calculated as: D = max(T) actual ,T avg ), where T actual T is the actual duration of the event derived from real-time meteorological data. avg It is the average duration of similar historical events within the target area.

10. A precise early warning system for addressing the risk of low-temperature rain, snow, and freezing according to claim 1, characterized in that, The disaster-bearing body risk classification and early warning specifically includes: The Low Temperature Rain, Snow, and Ice Risk Index (HI) is classified into levels, and corresponding thresholds are set for graded early warning.

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