Typhoon storm surge disaster information prediction method, device and equipment based on ARMA model

Abstract

The present disclosure provides a typhoon storm surge disaster information prediction method, device and equipment based on an ARMA model, which comprises the following steps: obtaining the historical frequency of typhoon storm surges in a preset time period in a to-be-predicted area, the historical warning level of each typhoon storm surge, weighting and summing the historical warning levels of the historical typhoon storm surges in the preset time period according to corresponding weight coefficients to determine the total historical disaster intensity of the typhoon storm surges in a first time period, constructing a first autoregressive moving average (ARMA) model for disaster intensity prediction according to the total historical disaster intensity of the typhoon storm surges in the preset time period, constructing a second ARMA model for disaster frequency prediction according to the historical frequency of the typhoon storm surges in multiple preset time periods, and predicting the target disaster information of the typhoon storm surges in the first time period based on the first ARMA model and the second ARMA model, so that the typhoon storm surge disaster information prediction cost can be saved, and the target disaster information of the typhoon storm surges can be efficiently predicted.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a method, apparatus and equipment for predicting typhoon storm surge disaster information based on the ARMA model. Background Technology

[0002] Typhoon storm surge disasters refer to the abnormal rise in sea level caused by typhoon systems, which causes the tide level in the affected sea areas to greatly exceed the normal tide level, resulting in loss of life and property. Accurate prediction of typhoon storm surge disaster information can provide effective scientific basis for disaster prevention and mitigation in coastal areas.

[0003] Among related technologies, the prediction cost of typhoon storm surge disaster information is relatively high, and the prediction efficiency is relatively low. Summary of the Invention

[0004] This disclosure proposes a method, device, electronic equipment, storage medium, and computer program product for predicting typhoon storm surge disaster information based on the ARMA model, aiming to at least partially solve the technical problems in related technologies.

[0005] The first aspect of this disclosure proposes a method for predicting typhoon storm surge disaster information based on an ARMA model, comprising: acquiring the historical frequency of typhoon storm surges in the area to be predicted within a preset time period, and the historical warning level of each typhoon storm surge, wherein different historical warning levels have corresponding weight coefficients; performing a weighted summation of the historical warning levels of historical typhoon storm surges within the preset time period according to the weight coefficients to determine the total historical disaster severity of typhoon storm surges in the first time period; constructing a first autoregressive moving average (ARMA) model for disaster severity prediction based on the total historical disaster severity of typhoon storm surges within the preset time period, and constructing a second ARMA model for disaster frequency prediction based on the historical frequency of typhoon storm surges in multiple preset time periods; and predicting target disaster information of typhoon storm surges in the area to be predicted within the first time period based on the first ARMA model and the second ARMA model.

[0006] A second aspect of this disclosure provides a typhoon storm surge disaster information prediction device based on an ARMA model, comprising: a first acquisition module, configured to acquire the historical frequency of typhoon storm surges in a region to be predicted within a preset time period, and the historical warning level of each typhoon storm surge, wherein different historical warning levels have corresponding weight coefficients; a determination module, configured to perform a weighted summation of the historical warning levels of historical typhoon storm surges within the preset time period according to the weight coefficients, to determine the historical warning level of typhoon storm surges within the first time period; a construction module, configured to construct a first autoregressive moving average (ARMA) model for disaster severity prediction based on the historical warning levels of typhoon storm surges within the preset time period, and construct a second ARMA model for disaster frequency prediction based on the historical frequency of typhoon storm surges within multiple preset time periods; and a prediction module, configured to predict target disaster information of typhoon storm surges within the first time period based on the first ARMA model and the second ARMA model.

[0007] A third aspect of this disclosure provides an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement a typhoon storm surge disaster information prediction method based on an ARMA model.

[0008] The fourth aspect of this disclosure provides a computer-readable storage medium that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to execute a typhoon storm surge disaster information prediction method based on an ARMA model.

[0009] The fifth aspect of this disclosure provides a computer program product, including a computer program, characterized in that the computer program is executed by a processor as a method for predicting typhoon storm surge disaster information based on an ARMA model.

[0010] The typhoon storm surge disaster information prediction method, device, electronic equipment, storage medium, and computer program product based on the ARMA model proposed in this embodiment have at least the following beneficial effects: By acquiring the historical frequency of typhoon storm surges in the area to be predicted within a preset time period, and the historical warning level of each typhoon storm surge, wherein different historical warning levels have corresponding weight coefficients, the historical warning levels of historical typhoon storm surges in the preset time period are weighted and summed according to the weight coefficients to determine the total historical disaster intensity of typhoon storm surges in the first time period. Based on the total historical disaster intensity of typhoon storm surges in the preset time period, a first autoregressive moving average (ARMA) model for disaster intensity prediction is constructed, and a second ARMA model for disaster frequency prediction is constructed based on the historical frequency of typhoon storm surges in multiple preset time periods. Based on the first ARMA model and the second ARMA model, the target disaster information of typhoon storm surges in the area to be predicted in the first time period is predicted, thereby enabling efficient prediction of target disaster information of typhoon storm surges while saving the cost of typhoon storm surge disaster information prediction. Furthermore, this solves the technical problems of high prediction costs and low efficiency in predicting typhoon storm surge disaster information in existing technologies.

[0011] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0012] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which: Figure 1 This is a flowchart illustrating a method for predicting typhoon storm surge disaster information based on an ARMA model, according to the first embodiment of this disclosure. Figure 2 This is a flowchart illustrating a method for predicting typhoon storm surge disaster information based on an ARMA model, according to the second embodiment of this disclosure. Figure 3 This is a block diagram of a typhoon storm surge disaster information prediction device based on the ARMA model, as disclosed in this disclosure; Figure 4 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Detailed Implementation

[0013] Embodiments of this disclosure are described in detail below, with examples of embodiments illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are used only to explain this disclosure, and should not be construed as limiting this disclosure. Rather, embodiments of this disclosure include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0014] It should be noted that the execution subject of the typhoon storm surge disaster information prediction method based on the ARMA model in this embodiment can be a typhoon storm surge disaster information prediction device based on the ARMA model. This device can be implemented by software and / or hardware. The device can be configured in an electronic device, which may include, but is not limited to, a terminal, a server, etc.

[0015] It should be noted that the acquisition, storage, use, and processing of information in this disclosed technical solution comply with the relevant provisions of national laws and regulations and do not violate public order and good morals.

[0016] Figure 1 This is a flowchart illustrating a typhoon storm surge disaster information prediction method based on the ARMA model, as shown in the first embodiment of this disclosure. Figure 1 As shown, the method includes: S101: Obtain the historical frequency of typhoon storm surges in the area to be predicted within a preset time period, and the historical warning level for each typhoon storm surge. Different historical warning levels have corresponding weighting coefficients.

[0017] The area where typhoon storm surge disaster information is currently to be predicted is the area to be predicted. This area to be predicted may be a specific province, city, district, etc., divided according to administrative divisions, or it may be an area corresponding to a pre-set range of latitude and longitude. There are no restrictions on this.

[0018] The preset time period can be a pre-defined time period, such as a year or a quarter. There is no limit to the number of preset time periods.

[0019] Among them, typhoon storm surge disaster refers to the abnormal rise in sea level caused by typhoon system, which causes the tide level in the affected sea area to greatly exceed the normal tide level, resulting in loss of life and property.

[0020] The historical frequency is the number of times a typhoon storm surge occurs in the area to be predicted within a preset time period. The warning level corresponding to each typhoon storm surge is the historical warning level. This warning level is determined based on a comprehensive assessment of factors such as the typhoon's intensity, path, and expected impact area. It is typically divided into different levels, such as blue, yellow, orange, and red warnings.

[0021] In this embodiment of the disclosure, different historical warning levels have corresponding weighting coefficients, which can be used to describe the severity of typhoon storm surge disasters caused by historical levels of typhoon storm surge.

[0022] In this embodiment of the disclosure, the historical warning level and the weight coefficient corresponding to the historical warning level are positively correlated. That is to say, the higher the historical warning level, the greater the weight coefficient corresponding to the historical warning level. In other words, the higher the historical warning level, the greater the severity of the typhoon storm surge disaster.

[0023] In this embodiment of the disclosure, obtaining the historical frequency of typhoon storm surges in the area to be predicted within a preset time period, and the historical warning level of each typhoon storm surge, can be achieved by real-time monitoring of the frequency of typhoon storm surges in the area to be predicted within a preset time period (e.g., each year in history), and by incrementing the historical frequency by one when a typhoon storm surge is detected, and recording the warning level of the typhoon storm surge, thereby obtaining the historical frequency of typhoon storm surges in the area to be predicted within the preset time period, and the historical warning level of each typhoon storm surge.

[0024] Alternatively, one can obtain the historical frequency of typhoon storm surges in the area to be predicted within a preset time period, as well as the historical warning level for each typhoon storm surge. Another option is to obtain all historical meteorological data and determine the historical frequency of typhoon storm surges in the area to be predicted within a preset time period, as well as the historical warning level for each typhoon storm surge, from all historical meteorological data.

[0025] S102: Based on the weighting coefficients, the historical warning levels of historical typhoon storm surges in the preset time period are weighted and summed to determine the total historical disaster intensity of typhoon storm surges in the first time period.

[0026] Historical disaster severity can be used to describe the impact and severity of a typhoon storm surge, while total historical disaster severity refers to the sum of the historical disaster severity of all typhoon storm surges in the first time period.

[0027] In this embodiment of the disclosure, the historical disaster severity of a typhoon storm surge can be determined by: determining the historical warning level of the typhoon storm surge, determining the weight coefficient corresponding to the historical warning level, then determining the product between the weight coefficient and the historical prediction level, and using the product between the weight coefficient and the historical prediction level as the historical disaster severity of the typhoon storm surge.

[0028] In this embodiment of the disclosure, the total historical disaster severity of typhoon storm surges in the first time period can be determined by weighting and summing the historical warning levels of historical typhoon storm surges in a preset time period according to the weighting coefficient.

[0029] S103: Based on the total historical disaster severity of typhoon storm surges in a preset time period, construct a first autoregressive moving average (ARMA) model for disaster severity prediction, and based on the historical frequency of typhoon storm surges in multiple preset time periods, construct a second ARMA model for disaster frequency prediction.

[0030] In this embodiment of the present disclosure, after weighting and summing the historical warning levels of historical typhoon storm surges in a preset time period according to the weighting coefficients to determine the total historical disaster severity of typhoon storm surges in the first time period, a first autoregressive moving average (ARMA) model for disaster severity prediction can be constructed based on the total historical disaster severity of typhoon storm surges in the preset time period.

[0031] The first autoregressive moving average (ARMA) model can be used for disaster severity prediction. This involves determining model parameters based on the total historical disaster severity of typhoons and storm surges over multiple preset time periods, and then constructing the first ARMA model based on these parameters. Specifically, this can be achieved by plotting the autocorrelation function (ACF) graph of the data series based on the total historical disaster severity of typhoons and storm surges over multiple preset time periods. This graph displays the autocorrelation coefficients for different lag orders. The ACF represents the correlation between the current observation and the lagged observations. In the ACF graph, the correlation coefficient varies from -1 to 1 on the vertical axis. Generally, if there is a significant correlation coefficient at one or more lag orders, it may be necessary to consider the order of the AR or MA model and plot the partial autocorrelation function (PACF) graph of the data series. This graph displays the partial autocorrelation coefficients for different lag orders. The PACF eliminates the influence of other lags and more directly reflects the correlation between the current observation and the lagged observations. Similar to the ACF plot, in the PACF plot, the correlation coefficient varies from -1 to 1 on the vertical axis. Generally, if there is a significant correlation coefficient at one or more lag orders, the order of the AR or MA model may need to be considered. In the ACF plot, if the autocorrelation coefficient decays rapidly at the lag order and becomes insignificant at subsequent lag orders, the order of the AR model may be present. The largest insignificant lag order can be selected as the order of the AR model based on the ACF plot. Similarly, in the PACF plot, if the partial autocorrelation coefficient decays rapidly at the lag order and becomes insignificant at subsequent lag orders, the order of the MA model may be present. The largest insignificant lag order can be selected as the order of the MA model based on the PACF plot.

[0032] In this embodiment of the disclosure, after determining the model order of the ARMA model, the parameters of the ARMA model can be fitted using maximum likelihood estimation or other methods based on the determined order, thereby obtaining a first ARMA model.

[0033] The second autoregressive moving average (ARMA) model can be used for disaster frequency prediction. This involves determining model parameters based on the historical frequency of typhoons and storm surges over multiple preset time periods, and then constructing a second ARMA model based on these parameters. Specifically, this can be achieved by plotting the autocorrelation function (ACF) graph of the data series based on the historical frequency of typhoons and storm surges over multiple preset time periods. This graph displays the autocorrelation coefficients for different lag orders. The ACF represents the correlation between the current observation and the lagged observations. In the ACF graph, the correlation coefficient varies from -1 to 1 on the vertical axis. Generally, if there is a significant correlation coefficient at one or more lag orders, it may be necessary to consider the order of the AR or MA model and plot the partial autocorrelation function (PACF) graph of the data series. This graph displays the partial autocorrelation coefficients for different lag orders. The PACF eliminates the influence of other lags and more directly reflects the correlation between the current observation and the lagged observations. Similar to the ACF plot, in the PACF plot, the correlation coefficient varies from -1 to 1 on the vertical axis. Generally, if there is a significant correlation coefficient at one or more lag orders, it may be necessary to consider the order of the AR or MA model. In the ACF plot, if the autocorrelation coefficient decays rapidly at the lag order and becomes insignificant at subsequent lag orders, an AR model may be present. The largest insignificant lag order can be selected as the order of the AR model based on the ACF plot. Similarly, in the PACF plot, if the partial autocorrelation coefficient decays rapidly at the lag order and becomes insignificant at subsequent lag orders, an MA model may be present. The largest insignificant lag order can be selected as the order of the MA model based on the PACF plot.

[0034] In this embodiment of the disclosure, after determining the model order of the ARMA model, the parameters of the ARMA model can be fitted using maximum likelihood estimation or other methods based on the determined order, thereby obtaining a second ARMA model.

[0035] S104: Based on the first ARMA model and the second ARMA model, predict the target disaster information of typhoon storm surge in the area to be predicted during the first time period.

[0036] The current time period to be predicted in the future can be referred to as the first time period.

[0037] Among them, the target disaster information can be used to describe the occurrence of typhoon storm surge disasters in the area to be predicted during the first time period.

[0038] In this embodiment of the disclosure, after constructing a first autoregressive moving average (ARMA) model for disaster severity prediction based on the total historical disaster severity of typhoon storm surges in a preset time period, and constructing a second ARMA model for disaster frequency prediction based on the historical frequency of typhoon storm surges in multiple preset time periods, the target disaster information of the area to be predicted in the first time period can be predicted based on the first ARMA model and the second ARMA model.

[0039] In other words, in this embodiment of the present disclosure, the total disaster severity of the typhoon storm surge in the area to be predicted in the first time period can be predicted based on the first ARMA model, and the frequency of typhoon storm surge disasters in the area to be predicted in the first time period can be predicted based on the second ARMA model. The total disaster severity and frequency of typhoon storm surge in the area to be predicted in the first time period are used as target disaster information, thereby obtaining more comprehensive target disaster information and providing comprehensive reference opinions such as the frequency of disasters and the total disaster severity.

[0040] In this embodiment, the historical frequency of typhoon storm surges in the area to be predicted within a preset time period and the historical warning level of each typhoon storm surge are obtained. Different historical warning levels have corresponding weight coefficients. The historical warning levels of historical typhoon storm surges within the preset time period are weighted and summed according to the weight coefficients to determine the total historical disaster intensity of typhoon storm surges in the first time period. Based on the total historical disaster intensity of typhoon storm surges within the preset time period, a first autoregressive moving average (ARMA) model for disaster intensity prediction is constructed. Based on the historical frequency of typhoon storm surges in multiple preset time periods, a second ARMA model for disaster frequency prediction is constructed. Based on the first ARMA model and the second ARMA model, the target disaster information of typhoon storm surges in the area to be predicted within the first time period is predicted. This allows for efficient prediction of target disaster information of typhoon storm surges while saving the cost of predicting typhoon storm surge disaster information.

[0041] Figure 2 This is a flowchart illustrating a typhoon storm surge disaster information prediction method based on the ARMA model, according to the second embodiment of this disclosure. Figure 2 As shown, the method includes: S201: Obtain the historical frequency of typhoon storm surges in the area to be predicted within a preset time period, and the historical warning level for each typhoon storm surge. Different historical warning levels have corresponding weighting coefficients.

[0042] S202: Based on the weighting coefficients, the historical warning levels of historical typhoon storm surges in the preset time period are weighted and summed to determine the total historical disaster intensity of typhoon storm surges in the first time period.

[0043] S203: Based on the total historical disaster severity of typhoon storm surges in a preset time period, construct a first autoregressive moving average (ARMA) model for disaster severity prediction, and based on the historical frequency of typhoon storm surges in multiple preset time periods, construct a second ARMA model for disaster frequency prediction.

[0044] For a detailed description of S201-S203, please refer to the above embodiments, which will not be repeated here.

[0045] S204: Based on the first ARMA model, predict the first disaster severity of the typhoon storm surge in the first time period, where the first disaster severity is used as the target disaster information.

[0046] The total disaster severity of typhoon storm surge during the first time period is the first disaster severity.

[0047] In other words, in this embodiment of the disclosure, the first disaster severity of the typhoon storm surge in the first time period can be predicted based on the first ARMA model, and the first disaster severity can be used as the target disaster information.

[0048] S205: Based on the second ARMA model, predict the first disaster frequency of typhoon storm surge in the first time period, where the first disaster frequency is used as the target disaster information.

[0049] The frequency of typhoon storm surge disasters during the first time period is the first disaster frequency.

[0050] In other words, in this embodiment of the disclosure, the frequency of the first typhoon storm surge in the first time period can be predicted based on the second ARMA model, and the frequency of the first typhoon storm surge can be used as the target typhoon information.

[0051] In this embodiment of the disclosure, the first disaster severity of the typhoon storm surge in the first time period is predicted based on the first ARMA model, and the first disaster frequency of the typhoon storm surge in the first time period is predicted based on the second ARMA model. The first disaster severity and the first disaster frequency are used together as target disaster information, thereby obtaining more comprehensive target disaster information.

[0052] S206: Determine the disaster change trend data of typhoon storm surge based on at least one of the first disaster severity and the first disaster frequency, wherein the disaster change trend data is used as the target disaster information.

[0053] Among them, disaster change trend data can be used to describe the changing trend of typhoon storm surge disasters. Specifically, it can be used to describe the changes in typhoon storm surge disasters in the first time period of this forecast compared to the typhoon storm surge disasters in the previous time period.

[0054] In this embodiment of the disclosure, the first disaster severity level can be compared with the disaster severity level in the previous time period, and the overall trend of the disaster severity level can be determined based on the comparison result. For example, when the comparison result shows that the disaster severity level is increasing, it can be determined that the disaster severity level is showing an overall upward trend. Alternatively, the first disaster frequency can be compared with the disaster frequency in the previous time period, and the overall trend of the disaster frequency can be determined based on the comparison result. For example, when the comparison result shows that the disaster frequency is increasing, it can be determined that the disaster frequency is showing a frequent occurrence trend. Thus, when it is determined that the disaster severity level is showing an overall upward trend or that the disaster frequency is showing a frequent occurrence trend, preventive measures can be taken in advance to effectively reduce the impact of typhoon storm surge disasters.

[0055] Optionally, in some embodiments, the disaster change trend data of typhoon storm surge is determined based on at least one of the first disaster severity and the first disaster frequency. This can be achieved by determining the frequency change trend of typhoon storm surge based on the first disaster frequency and the second disaster frequency of typhoon storm surge in the second time period, and using the frequency change trend as the disaster change trend data.

[0056] The second time period is the time period preceding the first time period.

[0057] Among them, the frequency change trend data can be used to describe the frequency of typhoon storm surges in the first time period, compared with the change in the frequency of typhoon storm surges in the second time period.

[0058] Specifically, when the frequency of typhoon storm surges in the first time period is greater than that in the second time period, it can be determined that the frequency change trend of typhoon storm surges is a trend of frequent occurrence. Conversely, when the frequency of typhoon storm surges in the first time period is less than that in the second time period, it can be determined that the frequency change trend of typhoon storm surges is a trend of infrequent occurrence.

[0059] Optionally, in other embodiments, the disaster change trend data of typhoon storm surge is determined based on at least one of the first disaster severity and the first disaster frequency. Alternatively, the first average disaster severity of typhoon storm surge in a first time period can be determined based on the first disaster frequency and the first disaster severity, and the second average disaster severity of typhoon storm surge in a second time period can be determined based on the second disaster frequency and the second disaster severity. Then, the disaster severity change trend data of typhoon storm surge is determined based on the first average disaster severity and the second average disaster severity, and the disaster severity change trend data is used as the disaster change trend data.

[0060] In other words, in this embodiment of the disclosure, the first average disaster severity of the typhoon storm surge in the first time period can be determined, and the second average disaster severity of the typhoon storm surge in the second time period can be determined based on the second disaster frequency and the second disaster severity. Then, the disaster severity trend data of the typhoon storm surge can be determined based on the first average disaster severity and the second average disaster severity.

[0061] In this embodiment of the disclosure, the trend data of typhoon storm surge disaster severity is determined based on the first average disaster severity and the second average disaster severity. When the first average disaster severity is greater than the second average disaster severity, it can be determined that the disaster severity is showing a deteriorating trend, that is, the degree of impact of the disaster is becoming more serious. In this case, preventive measures can be taken in advance, thereby effectively reducing the impact of typhoon storm surge disaster.

[0062] In this embodiment, the historical frequency of typhoon storm surges in the area to be predicted within a preset time period, and the historical warning level of each typhoon storm surge, are obtained. The historical warning levels of historical typhoon storm surges within the preset time period are weighted and summed according to weighting coefficients to determine the total historical disaster severity of typhoon storm surges in the first time period. Then, based on the total historical disaster severity of typhoon storm surges within the preset time period, a first autoregressive moving average (ARMA) model for disaster severity prediction is constructed. Finally, based on the historical frequency of typhoon storm surges across multiple preset time periods, a second ARMA model for disaster frequency prediction is constructed. Finally, based on the first ARMA model… Model A predicts the first hazard intensity of typhoon storm surge in the first time period, where the first hazard intensity is used as the target hazard information. Then, based on the second ARMA model, the first hazard frequency of typhoon storm surge in the first time period is predicted, where the first hazard frequency is used as the target hazard information. Then, based on at least one of the first hazard intensity and the first hazard frequency, the hazard change trend data of typhoon storm surge is determined, where the hazard change trend data is used as the target hazard information. Thus, comprehensive target hazard information can be determined with less prediction cost, thereby providing comprehensive target hazard information for typhoon storm surge disaster prevention.

[0063] Figure 3 This is a block diagram of a typhoon storm surge disaster information prediction device based on the ARMA model disclosed in this disclosure, such as... Figure 3 As shown, the typhoon storm surge disaster information prediction device 30 based on the ARMA model includes: The first acquisition module 301 is used to acquire the historical frequency of typhoon storm surges in the area to be predicted within a preset time period, and the historical warning level of each typhoon storm surge. Different historical warning levels have corresponding weight coefficients, which can be used to describe the severity of typhoon storm surge disasters caused by historical levels of typhoon storm surges. The determination module 302 is used to perform a weighted summation of the historical warning levels of historical typhoon storm surges in a preset time period based on the weighting coefficient, so as to determine the historical warning level of typhoon storm surges in the first time period. Module 303 is used to construct a first autoregressive moving average (ARMA) model for disaster severity prediction based on the historical warning levels of typhoon storm surges in a preset time period, and to construct a second ARMA model for disaster frequency prediction based on the historical frequency of typhoon storm surges in multiple preset time periods. The prediction module 304 is used to predict the target disaster information of typhoon storm surge in the first time period based on the first ARMA model and the second ARMA model.

[0064] In some embodiments of this disclosure, the prediction module 304 is further configured to: The first disaster severity of the typhoon storm surge is predicted based on the first ARMA model in the first time period, where the first disaster severity is used as the target disaster information. The frequency of the first typhoon storm surge is predicted based on the second ARMA model in the first time period, where the frequency of the first typhoon storm surge is used as the target typhoon information. Based on at least one of the first disaster severity and the first disaster frequency, determine the disaster change trend data of typhoon storm surge, wherein the disaster change trend data is used as the target disaster information.

[0065] In some embodiments of this disclosure, the prediction module 304 is further configured to: Based on the frequency of the first disaster and the frequency of the second disaster of the typhoon storm surge in the second time period, the frequency trend of the typhoon storm surge is determined. The second time period is the time period preceding the first time period. The frequency trend is used as data on the trend of disaster change.

[0066] In some embodiments of this disclosure, the prediction module 304 is further configured to: Based on the frequency and severity of the first disaster, the first average disaster severity of typhoon storm surge in the first time period is determined. Based on the second disaster frequency and the second disaster severity, determine the second average disaster severity of typhoon storm surge in the second time period; Based on the first average disaster severity and the second average disaster severity, determine the trend data of typhoon storm surge disaster severity. The disaster severity trend data is used as the disaster change trend data.

[0067] In some embodiments of this disclosure, the historical warning level and the weighting coefficient corresponding to the historical warning level are positively correlated.

[0068] In this embodiment, the historical frequency of typhoon storm surges in the area to be predicted within a preset time period and the historical warning level of each typhoon storm surge are obtained. Different historical warning levels have corresponding weight coefficients. The historical warning levels of historical typhoon storm surges within the preset time period are weighted and summed according to the weight coefficients to determine the total historical disaster intensity of typhoon storm surges in the first time period. Based on the total historical disaster intensity of typhoon storm surges within the preset time period, a first autoregressive moving average (ARMA) model for disaster intensity prediction is constructed. Based on the historical frequency of typhoon storm surges in multiple preset time periods, a second ARMA model for disaster frequency prediction is constructed. Based on the first ARMA model and the second ARMA model, the target disaster information of typhoon storm surges in the area to be predicted within the first time period is predicted. This allows for efficient prediction of target disaster information of typhoon storm surges while saving the cost of predicting typhoon storm surge disaster information.

[0069] According to embodiments of this disclosure, this disclosure also provides an electronic device, a computer-readable storage medium, and a computer program product.

[0070] Figure 4 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown.

[0071] Figure 4 The electronic device 12 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0072] like Figure 4 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, memory 28, and bus 18 connecting different system components (including memory 28 and processing unit 16).

[0073] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0074] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0075] Memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 4 Not shown; usually referred to as a "hard drive".

[0076] although Figure 4 As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a compact disc read-only memory (CD-ROM), a digital video disc read-only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0077] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0078] Electronic device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable human interaction with electronic device 12, and / or with any device that enables electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, electronic device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0079] The processing unit 16 executes various functional applications and parameter information determination by running programs stored in the memory 28, such as implementing the business data storage method mentioned in the foregoing embodiments, or implementing the business data acquisition method mentioned in the foregoing embodiments.

[0080] It should be noted that in the description of this disclosure, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this disclosure, unless otherwise stated, "a plurality of" means two or more.

[0081] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0082] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0083] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0084] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0085] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0086] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0087] Although embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method for predicting typhoon storm surge disaster information based on the ARMA model, characterized in that, The method includes: The historical frequency of typhoon storm surges in the area to be predicted within a preset time period is obtained, as well as the historical warning level of each typhoon storm surge. Different historical warning levels have corresponding weight coefficients, which are used to describe the severity of typhoon storm surge disasters caused by typhoon storm surges at the historical warning levels. The historical warning levels of historical typhoon storm surges in the preset time period are weighted and summed according to the weighting coefficients to determine the total historical disaster intensity of typhoon storm surges in the first time period. Based on the total historical disaster severity of typhoon storm surges in the preset time period, a first autoregressive moving average model for disaster severity prediction is constructed, and based on the historical frequency of typhoon storm surges in multiple preset time periods, a second autoregressive moving average model for disaster frequency prediction is constructed. Based on the first autoregressive moving average model and the second autoregressive moving average model, the target disaster information of typhoon storm surge in the area to be predicted during the first time period is predicted.

2. The method as described in claim 1, characterized in that, The prediction of target disaster information of typhoon storm surge in the area to be predicted during the first time period based on the first autoregressive moving average model and the second autoregressive moving average model includes: The first disaster severity of typhoon storm surge is predicted based on the first autoregressive moving average model in the first time period, wherein the first disaster severity is used as the target disaster information. The frequency of typhoon storm surge is predicted based on the second autoregressive moving average model in the first time period, wherein the frequency of the first disaster is used as the target disaster information. Based on at least one of the first disaster severity and the first disaster frequency, disaster change trend data of typhoon storm surge is determined, wherein the disaster change trend data is used as the target disaster information.

3. The method as described in claim 2, characterized in that, The step of determining the typhoon storm surge disaster change trend data based on at least one of the first disaster severity and the first disaster frequency includes: Based on the frequency of the first disaster and the frequency of the second disaster of the typhoon storm surge in the second time period, the frequency change trend of the typhoon storm surge is determined, wherein the second time period is the time period preceding the first time period. The frequency change trend is used as the disaster change trend data.

4. The method as described in claim 2, characterized in that, The step of determining the typhoon storm surge disaster change trend data based on at least one of the first disaster severity and the first disaster frequency includes: Based on the first disaster frequency and the first disaster severity, determine the first average disaster severity of typhoon storm surge in the first time period; Based on the second disaster frequency and the second disaster severity, the second average disaster severity of typhoon storm surge in the second time period is determined, wherein the second time period is the time period preceding the first time period; Based on the first average disaster severity and the second average disaster severity, determine the disaster severity trend data of typhoon storm surge; The disaster severity change trend data is used as the disaster change trend data.

5. The method according to any one of claims 1-4, characterized in that, The historical warning levels and the weighting coefficients corresponding to the historical warning levels are positively correlated.

6. A typhoon storm surge disaster information prediction device based on the ARMA model, characterized in that, The device includes: The first acquisition module is used to acquire the historical frequency of typhoon storm surges in the area to be predicted within a preset time period, and the historical warning level of each typhoon storm surge. Different historical warning levels have corresponding weight coefficients, which are used to describe the severity of typhoon storm surge disasters caused by typhoon storm surges at the historical warning levels. The determination module is used to perform a weighted summation of the historical warning levels of historical typhoon storm surges in the preset time period based on the weighting coefficients, so as to determine the total historical disaster intensity of typhoon storm surges in the first time period; The construction module is used to construct a first autoregressive moving average model for disaster severity prediction based on the total historical disaster severity of typhoon storm surges in the preset time period, and to construct a second autoregressive moving average model for disaster frequency prediction based on the historical frequency of typhoon storm surges in multiple preset time periods. The prediction module is used to predict the target disaster information of typhoon storm surge in the first time period based on the first autoregressive moving average model and the second autoregressive moving average model.

7. The apparatus as claimed in claim 6, characterized in that, The prediction module is also used for: The first disaster severity of typhoon storm surge is predicted based on the first autoregressive moving average model in the first time period, wherein the first disaster severity is used as the target disaster information. The frequency of typhoon storm surge is predicted based on the second autoregressive moving average model in the first time period, wherein the frequency of the first disaster is used as the target disaster information. Based on at least one of the first disaster severity and the first disaster frequency, disaster change trend data of typhoon storm surge is determined, wherein the disaster change trend data is used as the target disaster information.

8. The apparatus as claimed in claim 7, characterized in that, The prediction module is also used for: Based on the frequency of the first disaster and the frequency of the second disaster of the typhoon storm surge in the second time period, the frequency change trend of the typhoon storm surge is determined, wherein the second time period is the time period preceding the first time period. The frequency change trend is used as the disaster change trend data.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method as described in any one of claims 1-5.

10. A computer-readable storage medium, wherein instructions in the computer-readable storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method as described in any one of claims 1-5.

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