Climate risk-oriented space-time non-stationary monitoring method and system

Abstract

The invention provides a climate risk-oriented space-time non-stationary monitoring method and system, and belongs to the field of climate prediction and user login systems, and the method comprises the steps: S1, obtaining data in historical time according to an existing climate region, carrying out the preprocessing of the data, and obtaining a non-stationary data set at a spatial position; s2, performing operation on the non-stationary data set, and calculating a risk time period according to the risk factors; s3, extracting space-time data of stationarity in the risk time period, and performing area span simulation and weather simulation on the space-time data; and S4, according to the simulation process, recording the weather transition trend in the region, predicting the future weather according to the weather transition trend, performing risk prediction on the future time period which does not conform to the weather transition trend, and correcting the non-stationary capability of the risk factor according to the accuracy of the risk prediction.

Description

Technical Field

[0001] This specification relates to the field of climate data analysis, and particularly to a spatio-temporal non-stationarity monitoring method and system for climate risk. Background Art

[0002] Climate is the synthesis of a large number of weather processes within a certain period, including the weather conditions that often occur in a place over the years and the extreme weather conditions that occasionally appear in some years. Climate is long-term and stable, reflecting the average weather conditions of a region over the years. In time series analysis, stationarity is an important assumption because the statistical characteristics of stationary time series are stable, which can simplify the establishment and prediction of models. However, in reality, many spatio-temporal data are non-stationary, which requires us to consider spatio-temporal non-stationarity when analyzing and modeling.

[0003] Chinese Patent CN111045117B, a climate monitoring and prediction platform. In view of the defects of the lagging quality control method, poor accuracy and timeliness of meteorological services in the existing monitoring system, the present invention provides a climate monitoring and prediction platform. In the present invention, the basic database stores current climate information; the intelligent prediction and recommendation module scores different climate information; the background diagnosis module analyzes and diagnoses historical climate data and current climate data, and outputs target climate data; the visualization module displays the target climate data; the grid forecast module and the station forecast module respectively process the target climate data into grid data and station data as needed; the intelligent prediction module draws a color patch map; the climate event module compares the grid data and the station data with thresholds and outputs corresponding climate events; the historical forecast verification module scores and verifies the data messages; the message production module generates corresponding messages.

[0004] The above technology aims to obtain historical climate information and current climate data for intelligent prediction and display through the visualization module. However, the invention focuses on the display of predicted climate, and the statistical characteristics (such as mean, variance, etc.) of spatio-temporal data will change with the evolution of time and the change of spatial position. This means that at different time or spatial points, the statistical characteristics of the data may no longer be consistent, and we cannot solely rely on the obtained climate data for processing and prediction.

[0005] Therefore, it is necessary to provide a spatio-temporal non-stationarity monitoring method and system for climate risk to perform accurate analysis of climate. Summary of the Invention

[0006] One embodiment of this specification provides a spatio-temporal non-stationarity monitoring method for climate risk. This application analyzes through existing regional climate data. Since the formation of climate requires a large amount of weather data, which accumulates over the years to form the local climate, if only the change law of climate is used to predict future weather, the accuracy of weather forecasting will be reduced. Therefore, this application can select risk weather periods from regular historical climate data for data analysis, combine with ground properties, altitude weather parameters, etc., calculate the change law of severe weather, and then predict the future short-term weather change law through the weather change trend.

[0007] In some embodiments, a spatio-temporal non-stationarity monitoring method for climate risk includes: S1: Obtain data within a historical time according to an existing climate region, preprocess the data, and obtain a non-stationary data set at a spatial position; S2: Perform operations on the non-stationary data set, and calculate risk periods according to risk factors; S3: Extract spatio-temporal data with stationarity in the risk periods, and perform regional span simulation and weather simulation on the spatio-temporal data; S4: According to the simulation process, record the weather transition trend in this region, predict future weather according to the weather transition trend, and perform risk prediction on future periods that do not conform to the weather transition trend.

[0008] Through the above technical features, it is possible to extract data of risk weather from a large amount of climate data, combine spatio-temporal data and time-series data, deduce the change trend of extreme risk weather, and provide important data support for weather prediction.

[0009] Further, in S1, the non-stationary data set is In the formula, t1 is the initial value of the historical time, t2 is the end value of the historical time, ti is the initial value of the non-stationary period, tn is the end value of the non-stationary period, x i is the longitude of the spatial position, y i is the latitude of the spatial position, h i is the height of the spatial position. Among them, the regression parameter values of each sampling point are calculated through a spatial weight matrix, and the size of the non-negative attenuation parameter is calculated by substituting into the Gauss function, and the non-stationary data set is statistically obtained.

[0010] Through the above technical features, the non-stationary data in the historical climate data is screened out. The weather parameters measured by this data in space and time are unstable, and the weather parameter change gap is large with the change of position. Therefore, this non-stationary data set is extracted for subsequent data analysis.

[0011] Further, in S2, the calculation process of the risk period is as follows: In the formula: is the risk factor under the risk period, W(x i , y i , h i ) is the attenuation function, tq is the initial value of the risk period, tp is the end value of the risk period, X is the explanatory variable, and Y is the dependent variable.

[0012] Through the above technical features, by using the spatial position and the geographically weighted regression attenuation function, analyze the time period when extreme weather occurs, and obtain the risk period and the corresponding weather parameters and spatial position parameters of the risk period.

[0013] Further, in S3, the span simulation includes statistically analyzing the weather duration at each location based on the obtained position coordinate information, simulating continuous time series data in different regions, where the time series data includes the weather parameters at that location, selecting the transition parameter between the stationary weather parameters as the weather transition trend value, and recording the underlying surface properties of the region.

[0014] Through the above technical features, further process the weather parameters within the risk period, divide them into weather parameters within multiple small time periods, divide the spatial positions according to different underlying surface properties within the region, obtain the weather change rules within small time periods at different positions, after selecting the stationary weather periods, study the change trend of the weather parameters between the two stationary weather periods at both ends, and obtain the weather transition trend value.

[0015] Further, in S4, combine the underlying surface properties within the region to obtain the weather time series parameters at the spatial position, obtain the weather parameters within the limited region and limited time as the comparison parameters, and predict the risk weather in the future time period.

[0016] It also includes a spatio-temporal non-stationarity monitoring system for climate risk, including a preprocessing module, a risk period module, a weather simulation module, a transition trend module, and a risk prediction module; Process the historical climate data through the preprocessing module to obtain a non-stationary climate data set; Calculate the risk period in the non-stationary climate data through the risk period module; Simulate the weather within the risk period through the weather simulation module to obtain the weather transition trend between the stationary weathers in the region; Calculate the underlying surface properties and weather parameters of the region under the weather transition trend through the transition trend module, and use this weather parameter as the comparison parameter; Through the risk prediction module, analogize based on the predicted weather parameters and the comparison parameters to obtain the change trend of the weather state in the future time period.

[0017] Further, the regression parameter values of each sampling point are calculated through a spatial weight matrix, and the values are substituted into the Gauss function to calculate the magnitude of the non-negative attenuation parameter, and a non-stationary data set is statistically obtained.

[0018] Further, the comparison with the comparison parameter includes analogizing the time-series weather parameters at the same position. When the proportion of the overlapping duration of the parameters exceeds the extreme weather threshold, a warning is issued. The extreme weather threshold is calculated through the transition trend.

[0019] The beneficial effects of the present invention are as follows:

[0020] 1. Optimize the prediction of weather parameters in a conventional large area into the prediction of weather trends in small areas and multiple time periods. During the risk weather period, further decompose the change trend of weather parameters to obtain the front-end trend of extreme risk weather changes;

[0021] 2. According to the differences in spatial positions and the changes in weather parameters, calculate the risk period, which is more accurate, rather than the extreme risk and severe weather defined conventionally by humans. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] This specification will be further described in the form of exemplary embodiments, and these exemplary embodiments will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0023] Figure 1 is a schematic diagram of the weather transition trend shown in some embodiments of this specification;

[0024] Figure 2 is a schematic diagram of the working principle shown in some embodiments of this specification;

[0025] Figure 3 is a schematic diagram of weather parameters shown in some embodiments of this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To more clearly illustrate the technical solutions of the embodiments of this specification, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios according to these drawings. Unless it is obvious from the language environment or otherwise stated, the same reference numerals in the drawings represent the same structure or operation.

[0027] It should be understood that the "system", "device", "unit" and / or "module" used herein is a way to distinguish different components, elements, parts, sections or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0028] As shown in this specification and the claims, unless the context clearly indicates otherwise, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0029] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the previous or subsequent operations do not necessarily need to be executed precisely in sequence. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more steps can be removed from these processes. Embodiment:

[0030] Please refer to Figure 2 , titled A Spatiotemporal Non-Stationarity Monitoring Method for Climate Risk: including S1: Obtain data within the historical time according to the existing climate regions, preprocess the data, and obtain a set of non-stationary data at the spatial positions; S2: Perform operations on the set of non-stationary data, and calculate the risk period according to the risk factors; S3: Extract the spatiotemporal data with stationarity in the risk period, and perform regional span simulation and weather simulation on the spatiotemporal data; S4: According to the simulation process, record the weather transition trend in this region, predict the future weather according to the weather transition trend, and perform risk prediction on the future periods that do not conform to the weather transition trend.

[0031] It should be noted that in S1, the set of non-stationary data is In the formula, t1 is the initial value of the historical time, t2 is the end value of the historical time, ti is the initial value of the non-stationary period, tn is the end value of the non-stationary period, x i is the longitude of the spatial position, y i is the dimension of the spatial position, h i is the height of the spatial position. Among them, the regression parameter values of each sampling point are calculated through the spatial weight matrix, and the size of the non-negative attenuation parameter is calculated by substituting into the Gauss function, and the set of non-stationary data is statistically obtained.

[0032] It should be noted that in S2, the calculation process of the risk period is as follows: In the formula, is the risk factor under the risk period, W(x i , y i , h i ) is the attenuation function, tq is the initial value of the risk period, tp is the end value of the risk period, X is the explanatory variable, and Y is the dependent variable. It should be noted that in S3, the span simulation includes, based on the obtained position coordinate information, statistically calculating the weather duration at each position, simulating continuous time series data in different regions, where the time series data includes the weather parameters at that position, selecting the transition parameter between stable weather parameters as the weather transition trend value, and recording the underlying surface properties of the region. Among them, the weather parameters in this application include the temperature model, humidity, wind speed, and rainfall. The above characteristic quantities are also related to the spatial geographical location and time. In order to make full use of weather data, in the non-stable data set, the stability of the weather parameters after calculating and dividing the underlying surface properties is calculated, and the weather change trend is simulated between two stable weather periods.

[0033] Among them, please refer to Figure 3 , the underlying surface property refers to the characteristics of the earth's surface in direct contact with the lower layer of the atmosphere, including terrain, geology, soil, and vegetation, etc. The underlying surface is the main heat source and water vapor source of the atmosphere, and has a significant impact on the physical state and chemical composition of the atmosphere. It is not only the boundary surface of the low-level atmospheric movement, but also has an important impact on the climate through heat and moisture exchange. According to the different underlying surface properties, the traditionally divided climate regions can be geographically segmented. In the above risk period, for each weather parameter corresponding to the underlying surface property, based on the weather parameter values at the spatial position coordinates with a certain height after geographical segmentation, the weather parameter values at different times, different underlying surface properties, and different heights are obtained, and the change trend of the dependent variable weather parameter in the short time series is analyzed when the independent variables are the underlying surface property and the spatial coordinate position.

[0034] It should be noted that in S4, the weather time series parameters at the spatial position are obtained by combining the underlying surface properties in the region, and the weather parameters within the limited region and limited time are used as comparison parameters to predict the risk weather in the future period.

[0035] It should be noted that please refer to Figure 1 , including a preprocessing module, a risk period module, a weather simulation module, a transition trend module, and a risk prediction module; The historical climate data is processed by the preprocessing module to obtain a non-stationary climate data set; Calculate the risk period in non-stationary climate data through the risk period module; Simulate the weather during the risk period through the weather simulation module to obtain the weather transition trend between stationary weathers in the region; Calculate the surface properties and weather parameters of the region under the weather transition trend through the transition trend module, and use the weather parameters as comparison parameters; Based on the predicted weather parameters and the comparison parameters, the risk prediction module analogizes to obtain the weather state change trend in the future time period. It should be noted that the regression parameter values of each sampling point are calculated through the spatial weight matrix, and the non-negative attenuation parameter size is calculated by substituting into the Gauss function, and the non-stationary data set is statistically obtained.

[0036] It should be noted that the analogy with the comparison parameter includes analogizing the temporal weather parameters at the same position. When the proportion of the overlapping duration of the parameters exceeds the extreme weather threshold, a warning is issued. The extreme weather threshold is calculated through the past trend module. By clustering and calculating a large number of comparison parameters, a preliminary coincidence value is artificially defined and trained to obtain the extreme weather threshold.

[0037] It should be noted that the risk factors include one or more combinations of temperature, air pressure, visibility, humidity, the number of extremely low temperature days in the cold season, the maximum precipitation index, etc. Correcting the non-stationary ability of the risk factors according to the accuracy of the risk prediction includes, when there is a deviation in the risk prediction, calculating the ability of the corresponding risk factor according to the degree of the deviation, and determining that the climate impact degree of the risk factor and the location of the region is a positive response or a negative response, that is, a positive impact or a negative impact. By substituting historical data into the calculation, the impact of natural weather parameters on the climate at the corresponding regional spatial position is obtained, which is conducive to subsequent weather-climate research.

[0038] In summary, through the data analysis and calculation of the existing climate in the region, the data of non-stationary data at different time series, different spatial positions, and different surface properties are obtained. In the existing weather budget, non-stationary data is discarded because it is not stable, has little reference significance, and has no regularity. However, this application disassembles historical data to obtain the factors affecting its stationarity, reuses the above data, reorganizes the data in the non-stationary data set, splits it to obtain the risk extreme weather parameter set, and calculates the regular weather threshold by simulating the weather change trend, providing support for subsequent weather prediction.

[0039] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this specification.

[0040] Meanwhile, this specification uses specific terms to describe the embodiments of this specification. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0041] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences, the use of numerical letters, or the use of other names in this specification is not used to limit the order of the processes and methods of this specification. Although some currently considered useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details only serve the purpose of illustration. The appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0042] Similarly, it should be noted that, in order to simplify the expression of the disclosure of this specification and thus help the understanding of one or more embodiments of the invention, in the previous description of the embodiments of this specification, sometimes multiple features are merged into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the features required by the object of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are less than all the features of the individual embodiments disclosed above.

[0043] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification can be regarded as consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for monitoring spatiotemporal non-stationarity of climate risk, characterized in that: include, S1: Obtain data in historical time according to the existing climate regions, pre-process the data, and obtain a non-stationary data set at the spatial location; S2: Operate on the non-stationary data set and calculate the risk period based on the risk factor; S3: Extract the spatial and temporal data of stability during the risk period, and conduct regional span simulation and weather simulation on the spatial and temporal data; S4: According to the simulation process, the weather transition trend in the area is recorded, and the future weather is predicted based on the weather transition trend. Risk prediction is performed for future time periods that do not conform to the weather transition trend, wherein the non-stationary ability of the risk factor is corrected according to the accuracy of the risk prediction.

2. A method for monitoring spatiotemporal non-stationarity of climate risk according to claim 1, characterized in that: In S1, the non-stationary data set is, In the formula, t1 is the initial value of the historical time, t2 is the end value of the historical time, ti is the initial value of the non-stationary period, tn is the end value of the non-stationary period, and x i is the longitude of the spatial position, y i is the dimension of the spatial position, h i is the height of the spatial position, where the regression parameter value of each sampling point is calculated through the spatial weight matrix, and the Gauss function is used to calculate the size of the non-negative attenuation parameter, and the non-stationary data set is obtained by statistics.

3. A method for monitoring spatiotemporal non-stationarity of climate risk according to claim 2, characterized in that: In S2, the calculation process of the risk period is: In the formula, is the risk factor in the risk period, W(x i ,y i ,h i ) is the decay function, tq is the initial value of the risk period, tp is the end value of the risk period, X is the explanatory variable, and Y is the dependent variable.

4. A method for monitoring spatiotemporal non-stationarity of climate risk according to claim 3, characterized in that: In S3, the span simulation includes counting the weather duration at each location based on the acquired location coordinate information, simulating continuous time series data in different areas, the time series data includes the weather parameters at the location, selecting the transition parameters between the stable weather parameters as the weather transition trend value, and recording the underlying surface properties of the area.

5. A method for monitoring spatiotemporal non-stationarity of climate risk according to claim 4, characterized in that: In S4, the weather time series parameters at the spatial position are obtained in combination with the properties of the underlying surface in the area, and the weather parameters in a limited area and a limited time are obtained as comparison parameters to predict the risk weather in the future period.

6. A spatiotemporal non-stationarity monitoring system for climate risk, applied to the spatiotemporal non-stationarity monitoring method for climate risk according to claim 5, characterized in that: It includes pre-processing module, risk period module, weather simulation module, transition trend module and risk prediction module; The historical climate data is processed through the preprocessing module to obtain a non-stationary climate data set; Calculate the risk period in non-stationary climate data through the risk period module; The weather simulation module is used to simulate the weather during the risk period and obtain the weather transition trend between stable weather in the region; The regional underlying surface properties and weather parameters under the weather transition trend are calculated through the transition trend module, and the weather parameters are used as comparison parameters; The risk prediction module compares the predicted weather parameters with the comparison parameters to derive the changing trend of weather conditions in the future period.

7. A spatiotemporal non-stationarity monitoring system for climate risk according to claim 6, characterized in that: The regression parameter value of each sampling point is calculated through the spatial weight matrix, and the Gauss function is used to calculate the size of the non-negative attenuation parameter, and the non-stationary data set is obtained by statistics.

8. A spatiotemporal non-stationarity monitoring system for climate risk according to claim 7, characterized in that: The analogy with the comparison parameters includes analogy with the time-series weather parameters at the same location. When the proportion of the overlapping time of the parameters exceeds the extreme weather threshold, an early warning is issued. The extreme weather threshold is calculated by the transition trend module.

9. A spatiotemporal non-stationarity monitoring system for climate risk according to claim 8, characterized in that: Risk factors include one or more combinations of temperature, air pressure, visibility, and humidity.

10. A spatiotemporal non-stationarity monitoring system for climate risk according to claim 9, characterized in that: The non-stationary ability of the risk factor is corrected according to the accuracy of the risk prediction. When there is a deviation in the risk prediction, the ability of the corresponding risk factor is calculated according to the degree of deviation to determine whether the risk factor has a positive or negative response to the climate impact of the regional location.

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