A warning method and system for joint defense against disastrous weather

Abstract

The present invention relates to the field of disaster prediction technology, and discloses an early warning method and system for joint defense of disastrous weather, the method comprising: obtaining precipitation intensity data of several historical years in the area to be detected and collecting the number of continuous days, and establishing a data model; obtaining the precipitation time of the current year in the area to be detected, and predicting the precipitation data of the current year according to the data model and the precipitation time, precipitation intensity prediction data and continuous days prediction data of the current year; calculating the precipitation slope stability parameter according to the precipitation data of the current year; generating a precipitation alarm signal or a landslide alarm signal based on the relationship between the precipitation data and the target precipitation alarm threshold, and the relationship between the precipitation slope stability parameter and the stability threshold. The present invention makes a comprehensive and accurate prediction of the precipitation situation in the area to be detected, and generates a landslide alarm signal through the precipitation slope stability parameter, thereby realizing a rapid response and effective early warning to disastrous weather.

Description

Technical Field

[0001] The present invention relates to the field of disaster warning technology, and in particular to an early warning method and system for joint defense against disastrous weather. Background Art

[0002] As global urbanization accelerates, the frequency and intensity of heavy precipitation events have increased significantly in most regions. Monitoring, forecasting and early warning of this weather phenomenon have always been a top priority in meteorological research and operations, and it is also a challenging task. Heavy precipitation refers to a situation where precipitation reaches or exceeds a certain threshold in a short period of time. It is characterized by small scale, strong suddenness and short life cycle, so it is difficult to predict. In addition, heavy precipitation may trigger serious secondary disasters such as mudslides and landslides.

[0003] However, the current forecasting performance of heavy rainfall is not satisfactory, and the subjective analysis ability of forecasters is limited. Especially when predicting landslides caused by rainfall, forecasters need to have a high level of technical skills. Forecasters usually only rely on radar combination reflectivity products for prediction, and cannot fully consider the potential risks and development trends in actual situations. Therefore, it is often the case that the corresponding alarm device can only be responded to after short-term heavy rainfall has affected the local area, which easily leads to problems such as landslides.

[0004] Therefore, it is necessary to design an early warning method and system for joint defense against disastrous weather to solve the problems existing in current technology. Summary of the invention

[0005] In view of this, the present invention proposes an early warning method and system for joint defense against disastrous weather, aiming to solve the problem that the current heavy rainfall forecast has low accuracy and untimely landslide prediction can easily cause loss of life and property.

[0006] In one aspect, the present invention provides an early warning method for joint defense against disastrous weather, comprising:

[0007] Based on big data, the precipitation intensity data of several historical years in the area to be detected are obtained, and the duration of each precipitation intensity data is collected;

[0008] A data model is established based on the precipitation intensity data and duration of each of the historical years; wherein the precipitation intensity data and duration of each of the historical years correspond to the precipitation times of the historical years;

[0009] Obtain the precipitation time of the current year in the area to be detected, and predict the precipitation data of the current year based on the data model and the precipitation time of the current year, the precipitation intensity prediction data and the duration days prediction data; wherein,

[0010] When there are data identical to the precipitation intensity prediction data and duration prediction data of the current year at the same precipitation time, the precipitation data of the current year is calculated based on the historical precipitation intensity data and duration data;

[0011] When there is no data identical to the precipitation intensity forecast data and duration forecast data of the current year at the same precipitation time, forecasting the precipitation data of the current year based on big data technology and historical precipitation intensity data and duration data;

[0012] Calculating precipitation slope stability parameters based on the precipitation data for the current year;

[0013] Based on the relationship between the precipitation data of the current year and the target precipitation alarm threshold, and the relationship between the precipitation slope stability parameter and the stability threshold, a precipitation alarm signal or a landslide alarm signal is generated and respectively responded to the corresponding alarm device.

[0014] Furthermore, the prediction of the precipitation data of the current year based on the big data technology and the historical precipitation intensity data and duration days includes:

[0015] Obtain the precipitation at the precipitation time according to each of the historical precipitation intensity data and duration days, and establish a historical precipitation data set; obtain the initial predicted precipitation for the current year according to the current year's precipitation intensity prediction data and duration days prediction data;

[0016] Calculating the difference between each data in the historical precipitation data set and the predicted precipitation;

[0017]

[0018] Wherein, Ci represents the difference between the i-th data in the historical precipitation data set and the predicted precipitation; Si represents the i-th data in the historical precipitation data set, and S0 represents the initial predicted precipitation of the current year;

[0019] Calculate the weight of each data in the historical precipitation dataset:

[0020]

[0021] Wherein, ti represents the weight of the i-th data in the historical precipitation data set, and w is the standard deviation;

[0022] Calculate the forecast precipitation correction:

[0023]

[0024] Wherein, n represents the total number of data in the historical precipitation data set, and J represents the predicted precipitation correction value.

[0025] Further, after obtaining the predicted precipitation correction value J, when predicting the precipitation data of the current year based on big data technology and historical precipitation intensity data and duration days, it also includes:

[0026] Collecting the influence coefficients of the surrounding areas, adjusting the predicted precipitation correction value to predict the precipitation data of the current year;

[0027]

[0028] Among them, Qz represents the influence coefficient of the mth area on the area to be detected, Z represents the total number of areas around the area to be detected, and Sy represents the precipitation data of the current year.

[0029] Furthermore, the influence coefficient is calculated by the following formula:

[0030]

[0031] Among them, Q represents the influence coefficient, k represents the influence degree coefficient, D represents the precipitation intensity in the surrounding area, and D0 represents the critical value of precipitation intensity influence.

[0032] Furthermore, when obtaining the influence coefficient k, it includes:

[0033] Collecting weather charts of the surrounding area, the weather charts include a 500hPa high-altitude weather chart and an 850hPa low-altitude weather chart; drawing isobars in the weather charts, identifying the types of the isobars, and calculating the minimum interval distance Gmin in the isobars, and determining the influence degree coefficient k according to the minimum interval distance Gmin;

[0034] When the isobars show precipitation convergence, the influence coefficient k is proportional to the minimum interval distance Gmin, and 5≤k≤10;

[0035] When the isobars show precipitation divergence, the influence coefficient k is inversely proportional to the minimum interval distance Gmin, and 0<k<5.

[0036] Further, when calculating the precipitation slope stability parameter according to the precipitation data of the current year, it includes:

[0037]

[0038] Among them, X represents the precipitation slope stability parameter, c represents the adsorption force between soil particles, the unit is kPa, G represents the weight per unit volume of soil, Sh represents the precipitation depth, Gs represents the density of water, α represents the slope angle, and β represents the internal friction angle of soil.

[0039] Furthermore, when obtaining the precipitation depth Sh, it includes:

[0040] The precipitation data Sy is compared with the first preset precipitation data Sy1 and the second preset precipitation data Sy2 respectively, Sy1<Sy2, and the precipitation depth Sh is determined according to the comparison result;

[0041] When Sy≤Sy1, the precipitation depth Sh is determined to be the first preset precipitation depth, that is, Sh=Sh1;

[0042] When Sy1<Sy≤Sy2, the precipitation depth Sh is determined to be a second preset precipitation depth, that is, Sh=Sh2;

[0043] When Sy2<Sy, the precipitation depth Sh is determined to be the third preset precipitation depth, that is, Sh=Sh3;

[0044] Among them, 0<Sh1<Sh2<Sh3.

[0045] Further, when generating a precipitation alarm signal or a landslide alarm signal based on the relationship between the precipitation data of the current year and the target precipitation alarm threshold, and the relationship between the precipitation slope stability parameter and the stability threshold, it includes:

[0046] The precipitation data Sy is compared with the first preset target precipitation alarm threshold value P1 and the second preset target precipitation alarm threshold value P2 respectively, P1<P2, and a precipitation alarm signal is generated according to the comparison result;

[0047] When Sy≤P1, a precipitation level 3 warning signal is generated;

[0048] When P1<Sy≤P2, a precipitation level 2 alarm signal is generated;

[0049] When P2<Sy, a precipitation level 1 alarm signal is generated;

[0050] The precipitation slope stability parameter X is compared with the first preset stability threshold value X1 and the second preset stability threshold value X2 respectively, X1<X2, and a landslide alarm signal is generated according to the comparison result;

[0051] When X≤X1, a first-level landslide alarm signal is generated;

[0052] When X1<X≤X2, a secondary landslide alarm signal is generated;

[0053] When X2<X, a third-level landslide alarm signal is generated.

[0054] Furthermore, after generating the precipitation warning signal or the landslide warning signal, the method further includes:

[0055] Obtaining a precipitation difference ΔS according to the precipitation data Sy and a first preset target precipitation alarm threshold P1, ΔS=Sy-P1, comparing the precipitation difference ΔS with a precipitation difference threshold ΔSmax, ΔSmax>0, and determining a next alarm time interval according to the comparison result;

[0056] When ΔS<0, the third preset alarm time interval T3 is selected as the next alarm time interval;

[0057] When 0<ΔS<ΔSmax, the second preset alarm time interval T2 is selected as the next alarm time interval;

[0058] When ΔSmax<ΔS, the first preset alarm time interval T1 is selected as the next alarm time interval;

[0059] Among them, 0<T1<T2<T3.

[0060] Compared with the prior art, the beneficial effect of the present invention is that: by analyzing the precipitation intensity data and duration of several historical years, a data model is established, which can predict the precipitation data of the current year based on historical data, thereby providing early warning of possible precipitation conditions. In addition, by using big data technology, the prediction of the current year's precipitation data not only takes into account the situation of historical data, but also can flexibly respond to new situations that may arise in the current year, ensuring the accuracy and timeliness of the prediction. By calculating the precipitation slope stability parameters and comparing them with the stability threshold, the corresponding precipitation or landslide alarm signal is generated, and in response to the corresponding alarm device, a rapid response and effective warning to disastrous weather is achieved, thereby ensuring the safety of people's lives and property.

[0061] On the other hand, the present application also provides an early warning system for joint prevention of disastrous weather, which is used to apply the above-mentioned early warning method for joint prevention of disastrous weather, including:

[0062] A collection unit is configured to obtain precipitation intensity data of several historical years in the area to be detected based on big data, and collect the duration of each precipitation intensity data;

[0063] A calculation unit is configured to establish a data model according to the precipitation intensity data and the duration of each of the historical years; wherein the precipitation intensity data and the duration of each of the historical years correspond to the precipitation time of the historical years;

[0064] The prediction unit is configured to obtain the precipitation time of the current year in the area to be detected, and predict the precipitation data of the current year based on the data model and the precipitation time of the current year, the precipitation intensity prediction data and the duration days prediction data; wherein,

[0065] When there are data identical to the precipitation intensity prediction data and duration prediction data of the current year at the same precipitation time, the precipitation data of the current year is calculated based on the historical precipitation intensity data and duration data;

[0066] When there is no data identical to the precipitation intensity forecast data and duration forecast data of the current year at the same precipitation time, forecasting the precipitation data of the current year based on big data technology and historical precipitation intensity data and duration data;

[0067] A processing unit configured to calculate a precipitation slope stability parameter based on the precipitation data of the current year;

[0068] The early warning unit is configured to generate a precipitation alarm signal or a landslide alarm signal based on the relationship between the precipitation data of the current year and the target precipitation alarm threshold, and the relationship between the precipitation slope stability parameter and the stability threshold, and respond to the corresponding alarm devices respectively.

[0069] It is understandable that the above-mentioned early warning method and system for joint defense against disastrous weather have the same beneficial effects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0071] Figure 1 A flow chart of an early warning method for joint prevention of disastrous weather provided by an embodiment of the present invention;

[0072] Figure 2 A functional block diagram of an early warning system for joint defense against disastrous weather provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0073] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0074] As the impact of climate change on all walks of life becomes increasingly significant, the importance of weather warnings has become increasingly prominent. Weather warnings can predict possible disastrous weather in advance, giving the public enough time to respond. Weather warning information can provide decision-making support for the government and relevant departments, helping them to rationally arrange resources, carry out disaster response and rescue work, and thus minimize the impact of disasters on life, work and transportation. Therefore, weather warnings play an important role in reducing disaster losses, ensuring life safety, assisting decision-making, and improving the accuracy of warnings.

[0075] Forecasters can usually only rely on limited radar combination reflectivity products for prediction, and this method cannot fully consider the potential risks and development trends in actual situations. Especially when predicting landslides caused by rainfall, forecasters with higher technical levels are needed. Due to the limited subjective analysis ability of forecasters, it is often the case that heavy rainfall has already affected the area in a short period of time before they can respond to the corresponding alarm device in time, which may lead to serious problems such as landslides. Therefore, we urgently need to design a new joint warning method and system for severe weather to solve these problems existing in current technologies and improve the warning and response capabilities for heavy rainfall events.

[0076] In some embodiments of the present application, see Figure 1 As shown, an early warning method for joint prevention of disastrous weather includes:

[0077] S100: Obtain precipitation intensity data of several historical years in the area to be detected based on big data, and collect the duration of each precipitation intensity data.

[0078] S200: Establishing a data model based on the precipitation intensity data and duration of each historical year, wherein the precipitation intensity data and duration of each historical year correspond to the precipitation time.

[0079] S300: Obtain the precipitation time of the current year in the area to be detected, and predict the precipitation data of the current year based on the data model and the precipitation time, precipitation intensity prediction data and duration prediction data of the current year. When there is data that is the same as the precipitation intensity prediction data and duration prediction data of the current year at the same precipitation time, the precipitation data of the current year is calculated based on the historical precipitation intensity data and duration. When there is no data that is the same as the precipitation intensity prediction data and duration prediction data of the current year at the same precipitation time, the precipitation data of the current year is predicted based on big data technology and historical precipitation intensity data and duration.

[0080] S400: Calculate precipitation slope stability parameters according to the precipitation data of the current year.

[0081] S500: Generate a precipitation alarm signal or a landslide alarm signal based on the relationship between the current year's precipitation data and the target precipitation alarm threshold, and the relationship between the precipitation slope stability parameter and the stability threshold, and respond to the corresponding alarm devices respectively.

[0082] It can be understood that in the S100 stage, a complete database is established by obtaining precipitation intensity data and duration days of multiple historical years. In the S200 stage, a data model is established using these historical data to predict future precipitation conditions. In the S300 stage, by comparing with the precipitation data of the current year, the precipitation is calculated based on historical data or predicted using big data technology, thereby providing more accurate data support in the early warning stage. Then, in the S400 stage, the precipitation slope stability parameters are calculated based on the precipitation data of the current year, which is an important basis for predicting catastrophic events such as landslides. Finally, in the S500 stage, according to the relationship between the precipitation data and the alarm threshold, and the relationship between the precipitation slope stability parameters and the stability threshold, the corresponding precipitation or landslide alarm signal is generated, and the corresponding alarm device is responded to in a timely manner.

[0083] In some embodiments of the present application, when predicting the precipitation data of the current year based on big data technology and historical precipitation intensity data and duration days, it includes:

[0084] The precipitation at the time of precipitation is obtained based on the historical precipitation intensity data and duration data, and a historical precipitation data set is established. The initial predicted precipitation for the current year is obtained based on the current year's precipitation intensity forecast data and duration forecast data.

[0085] Calculate the gap between each data in the historical precipitation dataset and the predicted precipitation.

[0086]

[0087] Among them, Ci represents the difference between the i-th data in the historical precipitation data set and the predicted precipitation. Si represents the i-th data in the historical precipitation data set, and S0 represents the initial predicted precipitation of the current year.

[0088] Calculate the weight of each data in the historical precipitation dataset:

[0089]

[0090] Among them, ti represents the weight of the i-th data in the historical precipitation dataset, and w is the standard deviation.

[0091] Calculate the forecast precipitation correction:

[0092]

[0093] Among them, n represents the total number of data in the historical precipitation dataset, and J represents the correction value of predicted precipitation.

[0094] It can be understood that by establishing a historical precipitation data set, combined with the current year's precipitation intensity forecast data and duration forecast data, the initial forecast precipitation for the current year is obtained. Then, the gap between each data in the historical precipitation data set and the forecast precipitation, as well as the weight of each data, are calculated, and then the correction value of the forecast precipitation is calculated. Through such calculations and analysis, the current year's precipitation can be predicted more accurately, and possible heavy precipitation events can be warned in advance, thereby effectively reducing disaster losses. This method not only takes into account the impact of historical data, but also the weights and deviations between the data, making the prediction results more reliable and practical.

[0095] In some embodiments of the present application, after obtaining the predicted precipitation correction value J, when predicting the precipitation data of the current year based on big data technology and historical precipitation intensity data and duration days, it also includes:

[0096] The influence coefficients of surrounding areas are collected and the correction value of predicted precipitation is adjusted to predict the precipitation data of the current year.

[0097]

[0098] Among them, Qz represents the influence coefficient of the mth region on the area to be detected, Z represents the total number of areas around the area to be detected, and Sy represents the precipitation data of the current year.

[0099] It can be understood that by collecting the influence coefficients of the surrounding areas and applying them to the predicted precipitation correction value, the precipitation data of the current year is further adjusted, thereby improving the accuracy and precision of the prediction. First, the influence coefficients of the surrounding areas are obtained, which reflect the degree of influence of the precipitation in the surrounding areas on the precipitation in the area to be detected. Then, these influence coefficients are applied to the predicted precipitation correction value to adjust the precipitation data of the current year. By considering the influence of the surrounding areas, the prediction results are closer to the actual situation, and the precipitation of the current year can be predicted more accurately, providing more reliable data support for the early warning of disastrous weather.

[0100] In some embodiments of the present application, the influence coefficient is calculated by the following formula:

[0101]

[0102] Among them, Q represents the influence coefficient, k represents the influence degree coefficient, D represents the precipitation intensity in the surrounding area, and D0 represents the critical value of precipitation intensity influence.

[0103] It is understandable that the calculation of the influence coefficient takes into account the relationship between the precipitation intensity in the surrounding area and the preset precipitation intensity impact threshold value, and the influence degree is adjusted by parameter k, so that the influence coefficient can more accurately reflect the impact of the precipitation in the surrounding area on the precipitation in the area to be detected. The influence degree is adjusted by parameter k, making the influence coefficient more flexible and adjustable, so that the current year's precipitation can be predicted more accurately, providing more reliable data support for the early warning of disastrous weather.

[0104] In some embodiments of the present application, when obtaining the influence degree coefficient k, it includes: collecting weather charts of the surrounding area, the weather charts include 500hPa high-altitude weather charts and 850hPa low-altitude weather charts. Draw isobars in the weather charts, identify the type of isobars, and calculate the minimum interval distance Gmin in the isobars, and determine the influence degree coefficient k according to the minimum interval distance Gmin.

[0105] When isobars show precipitation convergence, the influence coefficient k is proportional to the minimum separation distance Gmin, and 5≤k≤10. On a meteorological map, the area where isobars converge, i.e., precipitation convergence, is usually shown as dense isobars, with the distance between isobars narrowing, forming an area convex to the ground. The convergence of isobars causes the air to rise in the area, thus forming clouds and precipitation.

[0106] When the isobars show precipitation divergence, the influence coefficient k is inversely proportional to the minimum interval distance Gmin, and 0<k<5. On the meteorological map, the area where the isobars diverge, that is, the precipitation diverges, usually shows that the isobars are sparse, the distance between the isobars becomes wider, and an area concave to the ground is formed. The divergence of isobars will cause the air to sink in the area, resulting in clear weather and less precipitation.

[0107] It can be understood that the influence coefficient k is determined by analyzing the shape of the isobars in the weather chart and combining the interval distance between the isobars. This processing method is more intuitive and flexible, and can dynamically adjust the value of the influence coefficient k according to the changes in the weather conditions in the surrounding areas, so as to more accurately evaluate the influence of the surrounding areas on the precipitation in the area to be detected, thereby improving the precision and accuracy of the prediction.

[0108] In some embodiments of the present application, when calculating the precipitation slope stability parameter according to the precipitation data of the current year, it includes:

[0109]

[0110] Among them, X represents the precipitation slope stability parameter, c represents the adsorption force between soil particles, the unit is kPa, G represents the weight per unit volume of soil, Sh represents the precipitation depth, Gs represents the density of water, α represents the slope angle, and β represents the internal friction angle of soil.

[0111] It is understandable that the influence of multiple factors on soil stability is comprehensively considered, including not only the properties of the soil itself, but also external factors such as the depth and slope of precipitation. By calculating the precipitation slope stability parameters, the stability of the soil can be more comprehensively assessed, and possible landslides and other catastrophic events can be warned in advance.

[0112] In some embodiments of the present application, when obtaining the precipitation depth Sh, it includes: comparing the precipitation data Sy with the first preset precipitation data Sy1 and the second preset precipitation data Sy2 respectively, Sy1<Sy2, and determining the precipitation depth Sh according to the comparison result.

[0113] Specifically, when Sy≤Sy1, the precipitation depth Sh is determined to be the first preset precipitation depth, that is, Sh=Sh1. When Sy1<Sy≤Sy2, the precipitation depth Sh is determined to be the second preset precipitation depth, that is, Sh=Sh2. When Sy2<Sy, the precipitation depth Sh is determined to be the third preset precipitation depth, that is, Sh=Sh3. Among them, 0<Sh1<Sh2<Sh3.

[0114] It is understandable that the precipitation depth is determined to be one of the preset different depths according to the relationship between the precipitation data and the preset precipitation data, that is, the precipitation depth is dynamically determined according to the actual situation. The relationship between the actual precipitation and the preset value is fully considered, and the precipitation depth is dynamically adjusted according to the actual situation, so that the precipitation depth is closer to the actual situation, thereby improving the accuracy and reliability of the precipitation depth. This dynamic adjustment method can better adapt to different precipitation conditions and provide more accurate data support for landslides.

[0115] In some embodiments of the present application, when generating a precipitation alarm signal or a landslide alarm signal based on the relationship between the current year's precipitation data and the target precipitation alarm threshold, and the relationship between the precipitation slope stability parameter and the stability threshold, it includes: comparing the precipitation data Sy with the pre-set first preset target precipitation alarm threshold P1 and the second preset target precipitation alarm threshold P2, P1<P2, and generating a precipitation alarm signal according to the comparison result.

[0116] Specifically, when Sy≤P1, a precipitation level 3 alarm signal is generated. When P1<Sy≤P2, a precipitation level 2 alarm signal is generated. When P2<Sy, a precipitation level 1 alarm signal is generated.

[0117] Specifically, the precipitation slope stability parameter X is compared with the first preset stability threshold value X1 and the second preset stability threshold value X2 respectively, X1<X2, and a landslide alarm signal is generated according to the comparison result. When X≤X1, a first-level landslide alarm signal is generated. When X1<X≤X2, a second-level landslide alarm signal is generated. When X2<X, a third-level landslide alarm signal is generated.

[0118] It can be understood that by comparing the current year's precipitation data with the preset target precipitation alarm threshold, dynamically generating a precipitation alarm signal, and by comparing the precipitation slope stability parameter with the preset stability threshold, dynamically generating a landslide alarm signal, timely warning of disastrous weather is achieved. According to the relationship between the precipitation data and the target precipitation alarm threshold, precipitation alarm signals of different levels are generated so that timely response measures can be taken. At the same time, according to the relationship between the precipitation slope stability parameter and the stability threshold, landslide alarm signals of different levels are generated, providing important warning information for possible landslide events. The method of dynamically generating alarm signals can more accurately issue warnings based on actual conditions, thereby improving the efficiency and accuracy of responding to disastrous weather events.

[0119] In some embodiments of the present application, after generating a precipitation alarm signal or a landslide alarm signal, it also includes: obtaining a precipitation difference ΔS according to the precipitation data Sy and the first preset target precipitation alarm threshold P1, ΔS=Sy-P1, comparing the precipitation difference ΔS with the precipitation difference threshold ΔSmax, ΔSmax>0, and determining the next alarm time interval according to the comparison result.

[0120] Specifically, when ΔS<0, the third preset alarm time interval T3 is selected as the next alarm time interval. When 0<ΔS<ΔSmax, the second preset alarm time interval T2 is selected as the next alarm time interval. When ΔSmax<ΔS, the first preset alarm time interval T1 is selected as the next alarm time interval. Wherein, 0<T1<T2<T3.

[0121] It is understandable that, according to the relationship between the precipitation data and the target precipitation alarm threshold, the difference in precipitation is determined, and the difference is compared with the pre-set precipitation difference threshold to determine the time interval for the next alarm. By dynamically adjusting the alarm time interval, the alarm frequency can be flexibly adjusted according to the changes in the actual precipitation situation, making the alarm information more accurate and timely. By dynamically adjusting the alarm time interval according to the actual situation, the situation of false alarms or missed alarms can be effectively reduced, and the accuracy and reliability of the alarm can be improved.

[0122] It is understandable that in order to better cope with the challenges brought by climate change, a subjective and objective early warning integrated alarm method and system can be set up based on the above content. This method combines subjective and objective early warnings to improve the accuracy and timeliness of disastrous weather warnings. Subjective early warnings mainly rely on the experience and judgment of meteorological experts, while objective early warnings are based on the above schemes. The alarm device can be called and alarmed by multiple units and departments. Through this subjective and objective early warning integrated alarm method, the possibility and scope of impact of disastrous weather events can be more accurately predicted and evaluated, the accuracy and timeliness of early warnings can be improved, and strong support can be provided for disaster prevention and response.

[0123] In the above embodiment, by analyzing the precipitation intensity data and duration of several historical years, a data model is established, which can predict the precipitation data of the current year based on historical data, thereby providing early warning of possible precipitation conditions. In addition, by using big data technology, the prediction of the current year's precipitation data not only takes into account the historical data, but also can flexibly respond to new situations that may arise in the current year, ensuring the accuracy and timeliness of the prediction. By calculating the precipitation slope stability parameters and comparing them with the stability threshold, a corresponding precipitation or landslide alarm signal is generated, and in response to the corresponding alarm device, a rapid response and effective warning to disastrous weather is achieved, thereby ensuring the safety of people's lives and property.

[0124] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides an early warning system for joint defense against disastrous weather, which is used to apply the above-mentioned early warning method for joint defense against disastrous weather, including:

[0125] The collection unit is configured to obtain precipitation intensity data of several historical years in the area to be detected based on big data, and collect the duration of each precipitation intensity data;

[0126] The calculation unit is configured to establish a data model according to the precipitation intensity data and the duration of each historical year; wherein the precipitation intensity data and the duration of each historical year correspond to the precipitation time;

[0127] The prediction unit is configured to obtain the precipitation time of the current year in the area to be detected, and predict the precipitation data of the current year based on the data model and the precipitation time of the current year, the precipitation intensity prediction data and the duration days prediction data; wherein,

[0128] When there are data identical to the current year's precipitation intensity forecast data and duration forecast data at the same precipitation time, the current year's precipitation data is calculated based on the historical precipitation intensity data and duration data;

[0129] When there is no data identical to the current year's precipitation intensity forecast data and duration forecast data at the same precipitation time, the current year's precipitation data is forecasted based on big data technology and historical precipitation intensity data and duration data;

[0130] a processing unit configured to calculate a precipitation slope stability parameter based on precipitation data of a current year;

[0131] The early warning unit is configured to generate a precipitation alarm signal or a landslide alarm signal based on the relationship between the current year's precipitation data and the target precipitation alarm threshold, and the relationship between the precipitation slope stability parameter and the stability threshold, and respond to the corresponding alarm devices respectively.

[0132] It is understandable that the alarm device displays monitoring and warning information based on WEBG IS, displays the warning type, warning level, warning site, warning time through a list, supports one-click confirmation of alarm information, and terminates the alarm after confirmation. For short-term heavy rainfall or rainstorm warnings, the module combines the site data of actual precipitation with the short-term extrapolation fusion product data to set different rainstorm warning thresholds. By setting thresholds for different levels of precipitation, when the data reaches the threshold, it is displayed on the forecast and warning page. At the same time, based on this warning system, it can also be used for warnings of factors such as strong winds, low visibility, and cold waves. According to the grid data of factor forecasts and high-resolution multivariate data fusion analysis data, the threshold for future factor warnings can be set. By setting the effective period and the thresholds of different levels of each factor, when a certain proportion of automatic station data reaches the threshold, it will be displayed on the forecast and warning page.

[0133] It is understandable that by analyzing the precipitation intensity data and duration of several historical years, a data model has been established, which can predict the precipitation data of the current year based on historical data, thereby providing early warning of possible precipitation conditions. In addition, the use of big data technology not only takes into account the historical data, but also can flexibly respond to new situations that may arise in the current year, ensuring the accuracy and timeliness of the prediction. By calculating the precipitation slope stability parameters and comparing them with the stability threshold, the corresponding precipitation or landslide alarm signal is generated, and the corresponding alarm device is responded to, a rapid response and effective warning to disastrous weather is achieved, thereby ensuring the safety of people's lives and property.

[0134] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0135] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0136] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A warning method for joint defense of disastrous weather, characterized in that: include: Based on big data, the precipitation intensity data of several historical years in the area to be detected are obtained, and the duration of each precipitation intensity data is collected; A data model is established based on the precipitation intensity data and duration of each of the historical years; wherein the precipitation intensity data and duration of each of the historical years correspond to the precipitation times of the historical years; Obtain the precipitation time of the current year in the area to be detected, and predict the precipitation data of the current year based on the data model and the precipitation time of the current year, the precipitation intensity prediction data and the duration days prediction data; wherein, When there is data identical to the precipitation intensity prediction data and duration prediction data of the current year at the same precipitation time in the historical year as the precipitation time in the current year, the precipitation data of the current year is calculated based on the historical precipitation intensity data and duration data; When there is no data identical to the precipitation intensity forecast data and duration forecast data of the current year at the same precipitation time in the historical year as the precipitation time in the current year, forecasting the precipitation data of the current year based on big data technology and the historical precipitation intensity data and duration data; Calculating precipitation slope stability parameters based on the precipitation data for the current year; Generate a precipitation alarm signal or a landslide alarm signal based on the relationship between the current year's precipitation data and the target precipitation alarm threshold, and the relationship between the precipitation slope stability parameter and the stability threshold, and respond to the corresponding alarm devices respectively; The prediction of the precipitation data for the current year based on big data technology and historical precipitation intensity data and duration days includes: According to each of the historical precipitation intensity data and duration days, the precipitation at the precipitation time of the historical year is obtained, and a historical precipitation data set is established; according to the current year's precipitation intensity forecast data and duration days forecast data, the initial forecast precipitation of the current year is obtained; Calculating the difference between each data in the historical precipitation data set and the initial predicted precipitation; Wherein, Ci represents the difference between the i-th data in the historical precipitation data set and the initial predicted precipitation; Si represents the i-th data in the historical precipitation data set, and S0 represents the initial predicted precipitation of the current year; Calculate the weight of each data in the historical precipitation dataset: Wherein, ti represents the weight of the i-th data in the historical precipitation data set, and w is the standard deviation; Calculate the forecast precipitation correction: Wherein, n represents the total number of data in the historical precipitation data set, and J represents the predicted precipitation correction value; After obtaining the predicted precipitation correction value J, when predicting the precipitation data of the current year based on big data technology and historical precipitation intensity data and duration days, it also includes: Collecting the influence coefficients of the surrounding areas, adjusting the predicted precipitation correction value to predict the precipitation data of the current year; in, represents the influence coefficient of the mth region on the region to be detected, Z represents the total number of regions surrounding the region to be detected, and Sy represents the precipitation data of the current year.

2. The early warning method for joint defense against disastrous weather according to claim 1, characterized in that: The influence coefficient is calculated by the following formula: in, represents the influence coefficient of the mth region on the region to be detected, km represents the influence degree coefficient, Dm represents the precipitation intensity in the surrounding area, and D0 represents the critical value of precipitation intensity influence.

3. The early warning method for joint defense against disastrous weather according to claim 2, characterized in that: When obtaining the impact coefficient km, it includes: Collect weather charts of the surrounding area, wherein the weather charts include a 500 hPa high-altitude weather chart and an 850 hPa low-altitude weather chart; draw isobars in the weather charts, identify the types of the isobars, and calculate the minimum interval distance Gmin in the isobars, and determine the influence degree coefficient km according to the minimum interval distance Gmin; When the isobars show precipitation convergence, the influence coefficient km is proportional to the minimum interval distance Gmin, and 5≤km≤10; When the isobars show precipitation divergence, the influence coefficient km is inversely proportional to the minimum interval distance Gmin, and 0<km<5.

4. The early warning method for joint defense against disastrous weather according to claim 1, characterized in that: When calculating the precipitation slope stability parameters based on the precipitation data for the current year, include: Among them, X represents the precipitation slope stability parameter, c represents the adsorption force between soil particles, the unit is kPa, G represents the weight per unit volume of soil, Sh represents the precipitation depth, Gs represents the density of water, α represents the slope angle, and β represents the internal friction angle of soil.

5. The early warning method for joint defense against disastrous weather according to claim 4, characterized in that: When obtaining precipitation depth Sh, include: The precipitation data Sy is compared with the first preset precipitation data Sy1 and the second preset precipitation data Sy2 respectively, Sy1<Sy2, and the precipitation depth Sh is determined according to the comparison result; When Sy≤Sy1, the precipitation depth Sh is determined to be the first preset precipitation depth, that is, Sh=Sh1; When Sy1<Sy≤Sy2, the precipitation depth Sh is determined to be a second preset precipitation depth, that is, Sh=Sh2; When Sy2<Sy, the precipitation depth Sh is determined to be the third preset precipitation depth, that is, Sh=Sh3; Among them, 0<Sh1<Sh2<Sh3.

6. The early warning method for joint defense against disastrous weather according to claim 5, characterized in that: When generating a precipitation alarm signal or a landslide alarm signal based on the relationship between the precipitation data of the current year and the target precipitation alarm threshold, and the relationship between the precipitation slope stability parameter and the stability threshold, it includes: The precipitation data Sy is compared with the first preset target precipitation alarm threshold value P1 and the second preset target precipitation alarm threshold value P2 respectively, P1<P2, and a precipitation alarm signal is generated according to the comparison result; When Sy≤P1, a precipitation level 3 warning signal is generated; When P1<Sy≤P2, a precipitation level 2 alarm signal is generated; When P2<Sy, a precipitation level 1 alarm signal is generated; The precipitation slope stability parameter X is compared with the first preset stability threshold value X1 and the second preset stability threshold value X2 respectively, X1<X2, and a landslide alarm signal is generated according to the comparison result; When X≤X1, a first-level landslide alarm signal is generated; When X1<X≤X2, a secondary landslide alarm signal is generated; When X2<X, a third-level landslide alarm signal is generated.

7. The early warning method for joint defense against disastrous weather according to claim 6, characterized in that: After generating a precipitation alarm signal or a landslide alarm signal, it also includes: Obtaining a precipitation difference ΔS according to the precipitation data Sy and a first preset target precipitation alarm threshold P1, ΔS=Sy-P1, comparing the precipitation difference ΔS with a precipitation difference threshold ΔSmax, ΔSmax>0, and determining a next alarm time interval according to the comparison result; When ΔS<0, the third preset alarm time interval T3 is selected as the next alarm time interval; When 0<ΔS<ΔSmax, the second preset alarm time interval T2 is selected as the next alarm time interval; When ΔSmax<ΔS, the first preset alarm time interval T1 is selected as the next alarm time interval; Among them, 0<T1<T2<T3.

8. An early warning system for joint defense against disastrous weather, used for applying the early warning method for joint defense against disastrous weather as claimed in any one of claims 1 to 7, characterized in that: A collection unit is configured to obtain precipitation intensity data of several historical years in the area to be detected based on big data, and collect the duration of each precipitation intensity data; A calculation unit is configured to establish a data model according to the precipitation intensity data and the duration of each of the historical years; wherein the precipitation intensity data and the duration of each of the historical years correspond to the precipitation time of the historical years; The prediction unit is configured to obtain the precipitation time of the current year in the area to be detected, and predict the precipitation data of the current year based on the data model and the precipitation time of the current year, the precipitation intensity prediction data and the duration days prediction data; wherein, When there is data identical to the precipitation intensity prediction data and duration prediction data of the current year at the same precipitation time in the historical year as the precipitation time in the current year, the precipitation data of the current year is calculated based on the historical precipitation intensity data and duration data; When there is no data identical to the precipitation intensity forecast data and duration forecast data of the current year at the same precipitation time in the historical year as the precipitation time in the current year, forecasting the precipitation data of the current year based on big data technology and the historical precipitation intensity data and duration data; A processing unit configured to calculate a precipitation slope stability parameter based on the precipitation data of the current year; The early warning unit is configured to generate a precipitation alarm signal or a landslide alarm signal based on the relationship between the precipitation data of the current year and the target precipitation alarm threshold, and the relationship between the precipitation slope stability parameter and the stability threshold, and respond to the corresponding alarm devices respectively.

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