CN115936443A - Risk assessment method and device for typhoon storm disaster and electronic equipment - Google Patents
Risk assessment method and device for typhoon storm disaster and electronic equipment Download PDFInfo
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Abstract
The present disclosure provides a method, an apparatus, an electronic device and a storage medium for risk assessment of typhoon storm disasters, wherein the method comprises: the method comprises the steps of obtaining rainfall observation data and rainfall prediction data of a target grid area, determining a disaster causing factor risk coefficient value of the target grid area according to the rainfall observation data and the rainfall prediction data, determining a target disaster bearing capacity coefficient value of the target grid area, wherein the target disaster bearing capacity coefficient value describes the capacity of the target grid area for bearing typhoon storm disasters, determining an emergency capacity coefficient value of the target grid area, and determining a target disaster risk level of the target grid area according to the disaster causing factor risk coefficient value, the target disaster bearing capacity coefficient value and the emergency capacity coefficient value. According to the method and the device, the disaster factor risk, the disaster bearing capacity of the disaster bearing body in the grid area and the emergency capacity of the grid area can be combined, the rapid typhoon rainstorm disaster risk assessment is realized, and the accuracy of the typhoon rainstorm disaster risk assessment in the grid area is improved.
Description
Technical Field
The present disclosure relates to the field of technologies, and in particular, to a method and an apparatus for risk assessment of typhoon storm disasters, an electronic device, and a storage medium.
Background
In the related art, natural disaster warning is generally analyzed based on risk of disaster-causing factors, for example, typhoon storm disasters generally issue warning at rainfall intensity (e.g., 3 hours of rainfall).
In this way, natural disaster early warning evaluation is performed by relying on a single factor, so that the accuracy of an evaluation result is low easily, and the risk distribution in a target grid area cannot be calculated quickly in a disaster emergency state.
Disclosure of Invention
The present disclosure is directed to solving, at least in part, one of the technical problems in the related art.
Therefore, an object of the present disclosure is to provide a method, an apparatus, an electronic device, a storage medium, and a computer program product for evaluating a risk of a typhoon storm disaster, which can combine a disaster-causing factor risk, a disaster-bearing capability of a disaster-bearing body in a grid area, and an emergency capability of the grid area, so as to implement rapid evaluation of the risk of the typhoon storm disaster, and improve accuracy of evaluation of the risk of the typhoon storm disaster in the grid area.
An embodiment of the first aspect of the present disclosure provides a risk assessment method for a typhoon storm disaster, including: acquiring rainfall observation data and rainfall prediction data of a target grid area; determining the disaster factor risk coefficient value of the target grid area according to the precipitation observation data and the precipitation prediction data; determining a target disaster tolerance coefficient value of a target grid area, wherein the target disaster tolerance coefficient value describes the capacity of the target grid area to bear typhoon storm disasters; determining an emergency capacity coefficient value of the target grid area; and determining the target disaster risk level of the target grid area according to the disaster factor risk coefficient value, the target disaster tolerance capacity coefficient value and the emergency capacity coefficient value.
According to the risk assessment method for the typhoon rainstorm disaster, provided by the embodiment of the first aspect of the disclosure, by obtaining precipitation observation data and precipitation prediction data of a target grid area, determining a disaster-causing factor risk coefficient value of the target grid area according to the precipitation observation data and the precipitation prediction data, and determining a target disaster-bearing capacity coefficient value of the target grid area, wherein the target disaster-bearing capacity coefficient value describes the capacity of the target grid area to bear the typhoon rainstorm disaster, determining an emergency capacity coefficient value of the target grid area, and determining a target disaster risk level of the target grid area according to the disaster-causing factor risk coefficient value, the target disaster-bearing capacity coefficient value and the emergency capacity coefficient value, the disaster-causing factor risk, the disaster-bearing capacity of a disaster-bearing body in the grid area and the emergency capacity of the grid area can be combined, so that rapid typhoon rainstorm disaster risk assessment is realized, and the accuracy of the typhoon rainstorm disaster risk assessment of the grid area is improved.
An embodiment of a second aspect of the present disclosure provides a risk assessment apparatus for a typhoon storm disaster, including: the acquisition module is used for acquiring precipitation observation data and precipitation prediction data of the target grid area; the first determining module is used for determining the disaster-causing factor risk coefficient value of the target grid area according to the precipitation observation data and the precipitation prediction data; the second determination module is used for determining a target disaster tolerance coefficient value of the target grid area, wherein the target disaster tolerance coefficient value describes the capacity of the target grid area to bear typhoon storm disasters; a third determining module for determining an emergency capacity coefficient value of the target grid area; and the fourth determining module is used for determining the target disaster risk level of the target grid area according to the disaster factor risk coefficient value, the target disaster bearing capacity coefficient value and the emergency capacity coefficient value.
The risk assessment device for typhoon storm disaster provided by the embodiment of the second aspect of the disclosure determines the disaster factor risk coefficient value of the target grid area and determines the target disaster tolerance coefficient value of the target grid area by acquiring precipitation observation data and precipitation prediction data of the target grid area according to the precipitation observation data and the precipitation prediction data, wherein the target disaster tolerance coefficient value describes the capability of the target grid area to bear the typhoon storm disaster, determines the emergency capacity coefficient value of the target grid area, and determines the target disaster risk level of the target grid area according to the disaster factor risk coefficient value, the target disaster tolerance coefficient value and the emergency capacity coefficient value, so that the rapid risk assessment for typhoon storm disaster can be realized, and the accuracy of the risk assessment for typhoon storm disaster in the grid area can be improved.
An embodiment of a third aspect of the present disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the method for risk assessment of typhoon storm disasters as set forth in the embodiment of the first aspect of the present disclosure.
A fourth aspect of the present disclosure provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method for risk assessment of a typhoon storm disaster as set forth in the first aspect of the present disclosure.
A fifth aspect of the present disclosure provides a computer program product, wherein when instructions in the computer program product are executed by a processor, the method for risk assessment of a typhoon storm disaster as provided in the first aspect of the present disclosure is performed.
Additional aspects and advantages of the 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 the disclosure.
Drawings
The above and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flow chart of a risk assessment method for a storm disaster according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of a risk assessment method for a typhoon storm disaster according to another embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a risk assessment apparatus for a typhoon storm disaster according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a risk assessment device for a typhoon storm disaster according to another embodiment of the present disclosure;
FIG. 5 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of illustrating the present disclosure and should not be construed as limiting the same. On the contrary, the embodiments of the disclosure include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.
Fig. 1 is a schematic flow chart of a risk assessment method for a typhoon storm disaster according to an embodiment of the present disclosure.
It should be noted that the main execution subject of the method for assessing the risk of a typhoon storm disaster of the present embodiment is a device for assessing the risk of a typhoon storm disaster, which can be implemented by software and/or hardware, and which can be configured in an electronic device, without limitation.
As shown in fig. 1, the method for assessing the risk of a typhoon storm disaster includes:
s101: and acquiring precipitation observation data and precipitation prediction data of the target grid area.
The target grid area refers to a grid area to be subjected to risk assessment of typhoon storm disasters.
The precipitation observation data refers to actual precipitation data observed in the target grid region, and precipitation observation of the target grid region can be performed by taking one hour as a time unit, so that an hourly precipitation observation value of the target grid region is obtained and used as precipitation observation data of the target grid region.
The rainfall prediction data refers to rainfall data obtained by predicting the rainfall amount of the target grid area in a prediction mode, the rainfall prediction of the target grid area can be carried out by taking one hour as a time unit, and the rainfall observation data and the rainfall prediction data can be used for determining the risk coefficient of the typhoon storm disaster of the target grid area.
In the embodiment of the disclosure, when precipitation observation data and precipitation prediction data of a target grid region are obtained, typhoon path observation and precipitation prediction data, precipitation observation data and precipitation prediction data can be determined in a typhoon storm risk space-time range of the target grid region, the typhoon path observation and prediction data and the precipitation observation data and the precipitation prediction data are extracted according to a certain precipitation radius, the typhoon path observation and prediction data can be interpolated to 1 hour, that is, precipitation observation and precipitation prediction can be performed by taking one hour as a time unit, then based on the interpolated 1 hour path data, each path point is taken as a circle center, precipitation observation and prediction data are extracted according to a certain radius to serve as cyclone precipitation data, according to the precipitation characteristics of typhoon, the extraction radius can be set to 800km, precipitation observation data and precipitation prediction data within 24 hours are obtained, and precipitation observation data and precipitation prediction data of the target grid region are obtained.
S102: and determining the disaster factor risk coefficient value of the target grid area according to the precipitation observation data and the precipitation prediction data.
The disaster factor risk coefficient value is a risk simulation value used for indicating that the target grid area has flood disasters in a typhoon and rainstorm scene.
After the precipitation observation data and the precipitation prediction data of the target grid area are obtained, the disaster factor risk coefficient value of the target grid area can be determined according to the precipitation observation data and the precipitation prediction data.
In the embodiment of the disclosure, when determining the disaster factor risk coefficient value of the target grid area according to the precipitation observation data and the precipitation prediction data, a preset typhoon rainstorm disaster risk level division table may be obtained, where risk levels and risk coefficient values corresponding to a plurality of different precipitation intervals are recorded in the typhoon rainstorm disaster risk level division table, the precipitation observation data and the precipitation prediction data of the target grid area are analyzed, a maximum precipitation value of the target grid area in the whole 24-hour process is used as risk index determination data of the target grid area, the index determination data is compared with the plurality of different precipitation intervals in the typhoon rainstorm disaster risk level division table to obtain a precipitation interval to which the index determination data belongs, and the risk coefficient value corresponding to the precipitation interval is used as the disaster factor risk coefficient value of the target grid area.
For example, expressions for precipitation may be used
Representing acquired precipitation data, wherein P i2j,t For 24 hours precipitation of target grid area i at time t, with each grid P i24 The maximum value of the whole process of (1) is used as the grid risk index, and the disaster factor risk coefficient value of the target grid area is determined according to the corresponding relation between the precipitation and the risk grade score in the typhoon storm disaster risk grade division table, wherein the typhoon storm disaster risk grade division is shown in the following table 1:
TABLE 1
As shown in table 1, if the precipitation amount of the target grid area is greater than 250 mm, the disaster factor risk coefficient value of the target grid area is determined to be 10, if the precipitation amount of the target grid area is between 100 mm and 250 mm, the disaster factor risk coefficient value of the target grid area is determined to be 7, if the precipitation amount of the target grid area is between 50 mm and 100 mm, the disaster factor risk coefficient value of the target grid area is determined to be 4, and if the precipitation amount of the target grid area is between 25 mm and 50 mm, the disaster factor risk coefficient value of the target grid area is determined to be 1.
S103: and determining a target disaster tolerance coefficient value of the target grid area, wherein the target disaster tolerance coefficient value describes the capacity of the target grid area to bear typhoon storm disasters.
The target disaster tolerance coefficient value is a grade numerical value used for describing the comprehensive tolerance of at least one disaster tolerance body in the target grid area to the typhoon storm disaster, and the disaster tolerance body may be, for example, a school, a train station, a highway, a power plant, an airport, a residential area, and the like in the target grid area, which is not limited thereto.
In the embodiment of the disclosure, when determining a target disaster tolerance coefficient value of a target grid area, the disaster tolerance of a plurality of disaster-bearing bodies in the target grid area may be analyzed, the analysis of the disaster tolerance of the disaster-bearing bodies mainly includes analyzing contents of two aspects, namely vulnerability and importance degree of the disaster-bearing bodies, the vulnerability of the disaster-bearing bodies mainly refers to a property that whether the disaster-bearing bodies are easily physically damaged when being affected by a disaster-causing factor, the importance degree of the disaster-bearing bodies includes contents of two aspects, namely cost of the disaster-bearing bodies and influence of secondary and derivative events which may be caused after being damaged, for a main infrastructure and a person-intensive place of a city, the main infrastructure and the person-intensive place are key objects for storm disaster risk prevention and control caused by typhoon, the vulnerability of each disaster-bearing body is divided into 4 grades according to the property of the disaster-bearing bodies, the importance degree is divided into 5 grades, corresponding calculation scores are respectively given, the vulnerability grades and the importance degree grades of the plurality of disaster-bearing bodies in the target grid area are respectively analyzed and determined, so as to obtain corresponding vulnerability score values and importance degree values, and then the importance degree of each target grid area are calculated, and each objective comprehensive disaster-bearing degree value is used as a final objective grid area.
S104: determining an emergency capacity coefficient value for the target grid area.
The emergency capacity coefficient value refers to a numerical value obtained by scoring the emergency capacity of a target grid area when a typhoon and rainstorm disaster occurs, the emergency capacity refers to the comprehensive capacity of the target grid area in the key links of prevention and emergency preparation, monitoring and early warning, emergency treatment and rescue, after-event recovery and reconstruction and the like when an emergency event is handled, the damage caused by the disaster with the same intensity occurring in different areas with different emergency capacities is greatly different, and the emergency capacity of different areas is quantitatively evaluated based on the emergency capacity coefficient value.
In the embodiment of the present disclosure, when determining the emergency capacity coefficient value of the target grid area, the emergency capacity of the area may be evaluated by constructing an emergency capacity index system, quantizing the index weights at each level by methods such as expert scoring, and formulating a corresponding area emergency capacity level and coefficient value table to obtain the emergency capacity coefficient value of the target grid area, where the area emergency capacity level and coefficient value table is shown in table 2 below.
TABLE 2
| Level of emergency capacity | Coefficient of emergency capacity |
| I | 1.2 |
| II | 1.1 |
| III | 0.9 |
| IV | 0.8 |
S105: and determining the target disaster risk level of the target grid area according to the disaster factor risk coefficient value, the target disaster bearing capacity coefficient value and the emergency capacity coefficient value.
The target disaster risk grade refers to a grade corresponding to a target grid area disaster risk score value determined by integrating the disaster factor risk coefficient value, the target disaster tolerance capability coefficient value and the emergency capability coefficient value.
After determining the disaster-causing factor risk coefficient value of the target grid area, determining the target disaster-bearing capacity coefficient value of the target grid area and determining the emergency capacity coefficient value of the target grid area according to the precipitation observation data and the precipitation prediction data, the embodiment of the disclosure may determine the target disaster risk level of the target grid area according to the disaster-causing factor risk coefficient value, the target disaster-bearing capacity coefficient value and the emergency capacity coefficient value.
In the embodiment of the disclosure, when determining a target disaster risk level of a target grid area according to a disaster-causing factor risk coefficient value, a target disaster-bearing capacity coefficient value and an emergency capacity coefficient value, an emergency-oriented risk assessment is performed by integrating three elements, namely, the disaster-causing factor risk coefficient value, the target disaster-bearing capacity coefficient value and the emergency capacity coefficient value, based on a natural disaster system theory and a public safety triangle theory, and establishing an emergency-oriented risk assessment concept formula R = f (H, V, C), wherein the target disaster risk level (R) is a function of the disaster-causing factor risk coefficient value (H), the target disaster-bearing capacity coefficient value (V) and the emergency capacity coefficient value (C), the corresponding coefficient values are substituted into the risk assessment concept formula R = f (H, V, C) for calculation processing to obtain a risk level score value of the target grid area, the risk level score value is compared with pre-set score interval thresholds corresponding to each risk level to obtain a risk interval to which the risk level score value belongs, and the risk level corresponding to the target disaster level corresponding to the target grid area is used as a target disaster level of the target grid area.
In the embodiment, by acquiring precipitation observation data and precipitation prediction data of a target grid area, determining a disaster-causing factor risk coefficient value of the target grid area according to the precipitation observation data and the precipitation prediction data, and determining a target disaster-bearing capacity coefficient value of the target grid area, wherein the target disaster-bearing capacity coefficient value describes the capacity of the target grid area to bear typhoon storm disasters, determining an emergency capacity coefficient value of the target grid area, and determining a target disaster risk level of the target grid area according to the disaster-causing factor risk coefficient value, the target disaster-bearing capacity coefficient value and the emergency capacity coefficient value, the disaster-causing factor risk, the disaster-bearing capacity of a disaster-bearing body in the grid area, and the emergency capacity of the grid area can be combined, so that rapid typhoon storm disaster risk assessment is realized, and the accuracy of grid area typhoon storm disaster risk assessment is improved.
Fig. 2 is a schematic flow chart of a method for risk assessment of a typhoon storm disaster according to another embodiment of the present disclosure.
As shown in fig. 2, the method for assessing the risk of a typhoon storm disaster includes:
s201: and acquiring precipitation observation data and precipitation prediction data of the target grid area.
S202: and determining the disaster factor risk coefficient value of the target grid area according to the precipitation observation data and the precipitation prediction data.
For the description of S201 and S202, reference may be made to the above embodiments specifically, and details are not repeated here.
S203: and determining the vulnerability coefficient value and the importance coefficient value corresponding to at least one disaster-bearing body in the target grid area.
The vulnerability coefficient value is a point value obtained by evaluating the property of whether physical damage is easy to occur when the disaster-bearing body is affected by the disaster-causing factor.
The importance degree coefficient value is a score value obtained by evaluating the construction cost of the disaster bearing body and the influence of secondary and derivative events possibly caused by damage.
In the embodiment of the disclosure, when determining the vulnerability coefficient value and the importance degree coefficient value corresponding to at least one disaster-bearing body in the target grid area, the vulnerability and importance degree grade values of different disaster-bearing bodies such as a power plant, an airport, a train station, a hospital, a school, a main road, a railway, a residential area and the like in the target grid area can be divided in advance to obtain the typhoon storm disaster sensitive disaster-bearing body and the vulnerability and importance degree grade value table thereof, each disaster-bearing body in the target grid area is matched with the typhoon storm disaster sensitive disaster-bearing body and the type of the disaster-bearing body in the vulnerability and importance degree grade value table thereof to obtain the vulnerability coefficient value and the importance degree coefficient value corresponding to each disaster-bearing body, and the vulnerability and importance degree grade value table thereof are shown in the following table 3.
TABLE 2
S204: and determining the disaster bearing capacity coefficient value of the disaster bearing body according to the vulnerability coefficient value and the importance coefficient value.
The disaster-bearing capacity coefficient value refers to a disaster-bearing capacity coefficient value of one disaster-bearing body in the target grid area, and each disaster-bearing body has a corresponding disaster-bearing capacity coefficient value.
After determining the vulnerability coefficient value and the importance coefficient value corresponding to at least one disaster-bearing body in the target grid area, the embodiment of the disclosure can determine the disaster-bearing capacity coefficient value of the disaster-bearing body according to the vulnerability coefficient value and the importance coefficient value.
In the implementation of the present disclosure, when determining the disaster tolerance coefficient value of the disaster-bearing body according to the vulnerability coefficient value and the importance coefficient value, a calculation formula between the disaster tolerance coefficient value of the disaster-bearing body and the vulnerability coefficient value and the importance coefficient value can be introduced
Wherein, V ij The disaster tolerance coefficient value, F, of the disaster tolerance body j in the target grid mesh i j For the value of the vulnerability coefficient of the disaster-bearing body j, j and (3) for the important degree coefficient value of the disaster bearing body j, the damageable coefficient value and the important degree coefficient value are put into a formula to be processed so as to obtain a calculation result of the formula, and the calculation result is used as the disaster bearing capacity coefficient value of the disaster bearing body.
Optionally, in some embodiments, when the disaster tolerance coefficient value of the disaster-bearing body is determined according to the vulnerability coefficient value and the importance degree coefficient value, the vulnerability coefficient value and the importance degree coefficient value may be multiplied to obtain a third result value, the third result value is squared to obtain a fourth result value, and the fourth result value is used as the disaster tolerance coefficient value.
In the embodiment of the disclosure, when the disaster tolerance coefficient value of the disaster tolerance body is determined according to the damage coefficient value and the importance coefficient value, the damage coefficient value and the importance coefficient value may be multiplied to obtain a corresponding product value as a third result value, and then the third result value is squared to obtain a fourth result value, and the fourth result value is used as the disaster tolerance coefficient value.
S205: and determining the corresponding set parameters of the disaster bearing body.
The set parameters refer to preset set weight values for comprehensively calculating the disaster bearing capacity coefficient values of the disaster bearing bodies.
In the embodiment of the present disclosure, when determining the setting parameter corresponding to the disaster-bearing body, the importance of the multiple disaster-bearing bodies in the target grid area may be sorted in a descending order, the setting parameter corresponding to the disaster-bearing body is determined according to the sequence number obtained after the descending order, and the numerical power corresponding to the sequence number may be processed for 0.5, that is, if the sequence number of the disaster-bearing body is j, the setting parameter corresponding to the disaster-bearing body is 0.5 j 。
S206: and determining the target disaster tolerance coefficient value according to the disaster tolerance coefficient value and the set parameter.
After determining the disaster-bearing capacity coefficient value of the disaster-bearing body according to the vulnerability coefficient value and the importance coefficient value and determining the setting parameter corresponding to the disaster-bearing body, the embodiment of the disclosure can determine the target disaster-bearing capacity coefficient value according to the disaster-bearing capacity coefficient value and the setting parameter.
In the embodiment of the disclosure, when the target disaster-bearing capacity coefficient value is determined according to the disaster-bearing capacity coefficient value and the set parameter, the target disaster-bearing capacity coefficient value and the calculation formula for introducing the disaster-bearing capacity coefficient value and the set parameter can be used
Wherein, V i Target disaster tolerance coefficient value, V, for target grid area ij Is a target netThe disaster bearing capacity coefficient value of the disaster bearing body j in the grid i is compared with the disaster bearing capacity coefficient value V of the disaster bearing body j ij And corresponding setting parameter 0.5 j And substituting the calculated value into a formula to perform calculation processing so as to obtain a calculation result, and taking the calculation result as a target disaster bearing capacity coefficient value of the target grid area.
Optionally, in some embodiments, when the target disaster tolerance coefficient value is determined according to the disaster tolerance coefficient value and the setting parameter, the disaster tolerance coefficient value and the setting parameter may be multiplied to obtain a fifth result value, and an accumulated value of at least one fifth result value is used as the target disaster tolerance coefficient value.
In the embodiment of the disclosure, when the target disaster tolerance coefficient value is determined according to the disaster tolerance coefficient value and the setting parameter, the disaster tolerance coefficient value corresponding to each disaster tolerance body may be multiplied by the setting parameter, an obtained calculation result is used as a fifth result value, the fifth result value corresponding to each disaster tolerance body in the target grid area is accumulated, and the accumulated value is used as the target disaster tolerance coefficient value of the target grid area.
S207: an emergency capability coefficient value for the target grid area is determined.
For description of S207, reference may be made to the foregoing embodiments specifically, and details are not repeated here.
S208: and determining the disaster risk coefficient value of the target grid area according to the disaster factor risk coefficient value, the target disaster-bearing capacity coefficient value and the emergency capacity coefficient value.
In the embodiment of the disclosure, when determining the target disaster risk level of the target grid area according to the disaster causing factor risk coefficient value, the target disaster tolerance capability coefficient value and the emergency capability coefficient value, the disaster risk coefficient value of the target grid area may be determined according to the disaster causing factor risk coefficient value, the target disaster tolerance capability coefficient value and the emergency capability coefficient value, and the calculation formulas of the disaster risk coefficient value and the disaster causing factor risk coefficient value, the target disaster tolerance capability coefficient value and the emergency capability coefficient value may be introduced
Wherein R is i Disaster risk coefficient value, H, for target grid area i i Disaster factor risk factor value, V, for target grid area i i A target disaster tolerance coefficient value, C, for a target grid area i k And (3) for the emergency capacity coefficient value of the area k, the corresponding disaster factor risk coefficient value, the target disaster bearing capacity coefficient value and the emergency capacity coefficient value are put into a formula for calculation processing to obtain a calculation result, and the calculation result is used as the disaster risk coefficient value of the target grid area.
Optionally, in some embodiments, when determining the disaster risk coefficient value of the target grid area according to the disaster factor risk coefficient value, the target disaster tolerance coefficient value, and the emergency tolerance coefficient value, the disaster factor risk coefficient value and the target disaster tolerance coefficient value may be multiplied to obtain a first result value, and the disaster risk coefficient value may be determined according to the first result value and the emergency tolerance coefficient value.
Optionally, in some embodiments, when determining the disaster risk coefficient value according to the first result value and the emergency capacity coefficient value, the first result value may be squared to obtain a second result value, a ratio result of the second result value to the emergency capacity coefficient value is determined, and the ratio result is used as the disaster risk coefficient value.
S209: and acquiring a disaster risk grade division threshold value.
And the disaster risk grade division threshold is used for describing disaster risk early warning grades to which disaster risk coefficient values with different values belong.
In the embodiment of the present disclosure, when the disaster risk classification threshold is obtained, a typhoon rainstorm disaster risk classification standard table may be formulated according to different score thresholds that classify risk values into 4 classes and correspond to four classes of risk early warning, where the typhoon rainstorm disaster risk classification standard table is shown in table 4 below.
TABLE 4
S210: and dividing a threshold value according to the disaster risk coefficient value and the disaster risk grade, and determining the target disaster risk grade.
In the embodiment of the present disclosure, after determining the disaster risk coefficient value of the target grid area according to the disaster causing factor risk coefficient value, the target disaster tolerance coefficient value, and the emergency capacity coefficient value, and acquiring the disaster risk classification threshold value, the target disaster risk grade may be determined by classifying the threshold value according to the disaster risk coefficient value and the disaster risk grade.
In the embodiment of the present disclosure, when determining the target disaster risk level according to the disaster risk coefficient value and the disaster risk level classification threshold, the disaster risk coefficient value may be compared with a plurality of disaster risk level classification thresholds in the typhoon storm disaster risk level classification standard table to obtain a disaster risk level classification threshold to which the disaster risk coefficient value of the target grid area belongs, and a risk level corresponding to the disaster risk level classification threshold is used as a corresponding target disaster risk level of the target grid area.
In the embodiment, for serving accurate risk prevention and control of disaster events, the grid areas are used as evaluation units to carry out risk calculation, the disaster risk of each grid is calculated based on the disaster-causing factor risk, the disaster-bearing capacity and the emergency capacity of the grid areas, the natural disaster system theory and the public safety triangle theory are used as the basis, the three factors of the disaster-causing factor, the disaster-bearing body and the area emergency capacity are integrated, the emergency rapid risk evaluation is oriented, the accuracy of risk grade evaluation is effectively guaranteed, and meanwhile, the rapid risk evaluation is realized.
In the embodiment, three disaster risk factors of each grid are calculated based on a natural system theory and a public safety triangle theory, the rapid risk evaluation is performed facing the emergency risk factor, the emergency capacity and the emergency capacity of each grid, and the three effective risk factors of each grid are ensured.
Fig. 3 is a schematic structural diagram of a risk assessment apparatus for a typhoon storm disaster according to an embodiment of the present disclosure.
As shown in fig. 3, the apparatus 30 for evaluating a risk of a typhoon storm disaster includes:
an obtaining module 301, configured to obtain precipitation observation data and precipitation prediction data of a target grid region;
a first determining module 302, configured to determine a disaster factor risk coefficient value of the target grid area according to the precipitation observation data and the precipitation prediction data;
a second determining module 303, configured to determine a target disaster tolerance coefficient value of the target grid area, where the target disaster tolerance coefficient value describes a capability of the target grid area to withstand a typhoon storm disaster;
a third determining module 304 for determining an emergency capacity coefficient value of the target grid area;
a fourth determining module 305, configured to determine a target disaster risk level of the target grid area according to the disaster-causing factor risk coefficient value, the target disaster tolerance coefficient value, and the emergency capability coefficient value.
In some embodiments of the present disclosure, as shown in fig. 4, fig. 4 is a schematic structural diagram of a risk assessment apparatus for a typhoon storm disaster according to another embodiment of the present disclosure, wherein the fourth determination module 305 includes:
the first determining submodule 3051 is configured to determine a disaster risk coefficient value of the target grid area according to the disaster factor risk coefficient value, the target disaster tolerance coefficient value, and the emergency capacity coefficient value;
an obtaining submodule 3052, configured to obtain a disaster risk classification threshold;
the second determination sub-module 3053 is configured to divide the threshold value according to the disaster risk coefficient value and the disaster risk level, and determine a target disaster risk level.
In some embodiments of the disclosure, the first determining sub-module 3051 is specifically configured to:
multiplying the disaster factor risk coefficient value and the target disaster-bearing capacity coefficient value to obtain a first result value;
and determining a disaster risk coefficient value according to the first result value and the emergency capacity coefficient value.
In some embodiments of the disclosure, wherein the first determination sub-module 3051 is further configured to:
squaring the first result value to obtain a second result value;
determining a ratio result of the second result value and the emergency capacity coefficient value;
the ratio results are used as disaster risk coefficient values.
In some embodiments of the present disclosure, the second determining module 303 is specifically configured to:
determining a vulnerability coefficient value and an importance degree coefficient value corresponding to at least one disaster-bearing body in a target grid area;
determining the disaster bearing capacity coefficient value of the disaster bearing body according to the vulnerability coefficient value and the importance coefficient value;
determining a set parameter corresponding to a disaster bearing body;
and determining the target disaster tolerance coefficient value according to the disaster tolerance coefficient value and the set parameter.
In some embodiments of the present disclosure, the second determining module 303 is further configured to:
multiplying the vulnerability coefficient value and the importance degree coefficient value to obtain a third result value;
squaring the third result value to obtain a fourth result value;
and taking the fourth result value as a disaster tolerance coefficient value.
In some embodiments of the present disclosure, the second determining module 303 is further configured to:
multiplying the disaster tolerance coefficient value by the set coefficient to obtain a fifth result value;
and taking the accumulated value of the at least one fifth result value as a target disaster tolerance coefficient value.
Corresponding to the method for evaluating the risk of a typhoon rainstorm disaster provided in the embodiment of fig. 1 to 2, the present disclosure also provides a device for evaluating the risk of a typhoon rainstorm disaster, and since the device for evaluating the risk of a typhoon rainstorm disaster provided in the embodiment of the present disclosure corresponds to the method for evaluating the risk of a typhoon rainstorm disaster provided in the embodiment of fig. 1 to 2, the embodiment of the method for evaluating the risk of a typhoon rainstorm disaster provided in the embodiment of the present disclosure is also applicable to the device for evaluating the risk of a typhoon rainstorm disaster provided in the embodiment of the present disclosure, and will not be described in detail in the embodiment of the present disclosure.
In the embodiment, by obtaining precipitation observation data and precipitation prediction data of a target grid area, determining a disaster-causing factor risk coefficient value of the target grid area according to the precipitation observation data and the precipitation prediction data, and determining a target disaster tolerance coefficient value of the target grid area, wherein the target disaster tolerance coefficient value describes the capacity of the target grid area to bear the typhoon storm disaster, determining an emergency capacity coefficient value of the target grid area, and determining a target disaster risk grade of the target grid area according to the disaster-causing factor risk coefficient value, the target disaster tolerance coefficient value and the emergency capacity coefficient value, the disaster tolerance of a disaster-bearing body in the grid area and the emergency capacity of the grid area can be combined, so that rapid typhoon storm disaster risk assessment is realized, and the accuracy of the typhoon storm disaster risk assessment of the grid area is improved.
In order to achieve the above embodiments, the present disclosure also proposes a non-transitory computer readable storage medium on which a computer program is stored, which when executed by a processor, implements a risk assessment method for a typhoon storm disaster as proposed in the previous embodiments of the present disclosure.
In order to implement the foregoing embodiments, the present disclosure also provides a computer program product, which when executed by an instruction processor in the computer program product, performs the risk assessment method for a typhoon storm disaster as set forth in the foregoing embodiments of the present disclosure.
FIG. 5 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure.
The computer device 12 shown in fig. 5 is only one example and should not bring any limitations to the functionality or scope of use of the embodiments of the present disclosure.
As shown in FIG. 5, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Although not shown in FIG. 5, a disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk Read Only Memory (CD-ROM), a Digital versatile disk Read Only Memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by 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 that are configured to carry out the functions of embodiments of the disclosure.
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 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination of which may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the embodiments described in this disclosure.
The processing unit 16 executes various functional applications and parameter information determination by running a program stored in the system memory 28, for example, implementing the risk assessment method for a typhoon storm disaster mentioned in the foregoing embodiments.
It should be noted that, in the description of the present disclosure, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present disclosure, the meaning of "a plurality" is two or more unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present disclosure includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present disclosure.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried out in the method of implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
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, and that changes, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present disclosure.
Claims (17)
1. A risk assessment method for a typhoon storm disaster is characterized by comprising the following steps:
acquiring rainfall observation data and rainfall prediction data of a target grid area;
determining the disaster causing factor risk coefficient value of the target grid area according to the precipitation observation data and the precipitation prediction data;
determining a target disaster tolerance coefficient value of the target grid area, wherein the target disaster tolerance coefficient value describes the capacity of the target grid area to bear typhoon storm disasters;
determining an emergency capacity coefficient value for the target grid area;
and determining the target disaster risk level of the target grid area according to the disaster factor risk coefficient value, the target disaster-bearing capacity coefficient value and the emergency capacity coefficient value.
2. The method of claim 1, wherein determining the target disaster risk level for the target grid area based on the disaster factor risk factor value, the target disaster tolerance capacity coefficient value, and the emergency capacity coefficient value comprises:
determining a disaster risk coefficient value of the target grid area according to the disaster factor risk coefficient value, the target disaster-bearing capacity coefficient value and the emergency capacity coefficient value;
acquiring a disaster risk grade division threshold value;
and determining the target disaster risk level according to the disaster risk coefficient value and the disaster risk level division threshold value.
3. The method of claim 2, wherein determining the disaster risk coefficient value for the target grid area based on the disaster factor risk coefficient value, the target disaster tolerance coefficient value, and the emergency capacity coefficient value comprises:
multiplying the disaster factor risk coefficient value and the target disaster-bearing capacity coefficient value to obtain a first result value;
and determining the disaster risk coefficient value according to the first result value and the emergency capacity coefficient value.
4. The method of claim 3, wherein the determining the disaster risk coefficient value based on the first result value and the emergency capacity coefficient value comprises:
squaring the first result value to obtain a second result value;
determining a ratio result of the second result value to the emergency capacity coefficient value;
and taking the ratio result as the disaster risk coefficient value.
5. The method of claim 1, wherein said determining a target disaster tolerance coefficient value for said target grid area comprises:
determining a vulnerability coefficient value and an importance degree coefficient value corresponding to at least one disaster-bearing body in the target grid area;
determining the disaster bearing capacity coefficient value of the disaster bearing body according to the vulnerability coefficient value and the importance coefficient value;
determining a set parameter corresponding to the disaster bearing body;
and determining the target disaster tolerance coefficient value according to the disaster tolerance coefficient value and the set parameter.
6. The method of claim 5, wherein said determining a disaster tolerance coefficient value for said disaster-bearing body based on said vulnerability coefficient value and said importance coefficient value comprises:
multiplying the vulnerability coefficient value and the importance coefficient value to obtain a third result value;
squaring the third result value to obtain a fourth result value;
and taking the fourth result value as the disaster-bearing capacity coefficient value.
7. The method according to claim 5, wherein said determining the target disaster tolerance coefficient value based on the disaster tolerance coefficient value and the set parameter comprises:
multiplying the disaster tolerance coefficient value by the set coefficient to obtain a fifth result value;
and taking the accumulated value of the at least one fifth result value as the target disaster tolerance coefficient value.
8. A risk assessment device for a typhoon storm disaster, comprising:
the acquisition module is used for acquiring precipitation observation data and precipitation prediction data of the target grid area;
the first determination module is used for determining the disaster factor risk coefficient value of the target grid area according to the precipitation observation data and the precipitation prediction data;
a second determining module, configured to determine a target disaster tolerance coefficient value of the target grid area, where the target disaster tolerance coefficient value describes an ability of the target grid area to withstand a typhoon storm disaster;
a third determining module for determining an emergency capacity coefficient value for the target grid area;
and the fourth determining module is used for determining the target disaster risk level of the target grid area according to the disaster factor risk coefficient value, the target disaster tolerance capacity coefficient value and the emergency capacity coefficient value.
9. The apparatus of claim 8, wherein the fourth determination module comprises:
the first determining submodule is used for determining a disaster risk coefficient value of the target grid area according to the disaster factor risk coefficient value, the target disaster-bearing capacity coefficient value and the emergency capacity coefficient value;
the obtaining submodule is used for obtaining a disaster risk grade division threshold;
and the second determining submodule is used for determining the target disaster risk level according to the disaster risk coefficient value and the disaster risk level division threshold value.
10. The apparatus of claim 9, wherein the first determination submodule is specifically configured to:
multiplying the disaster factor risk coefficient value and the target disaster-bearing capacity coefficient value to obtain a first result value;
and determining the disaster risk coefficient value according to the first result value and the emergency capacity coefficient value.
11. The apparatus of claim 10, wherein the first determination submodule is further configured to:
squaring the first result value to obtain a second result value;
determining a ratio result of the second result value to the emergency capacity coefficient value;
and taking the ratio result as the disaster risk coefficient value.
12. The apparatus of claim 8, wherein the second determining module is specifically configured to:
determining a vulnerability coefficient value and an importance degree coefficient value corresponding to at least one disaster-bearing body in the target grid area;
determining the disaster bearing capacity coefficient value of the disaster bearing body according to the vulnerability coefficient value and the importance coefficient value;
determining a set parameter corresponding to the disaster bearing body;
and determining the target disaster bearing capacity coefficient value according to the disaster bearing capacity coefficient value and the set parameter.
13. The apparatus of claim 12, wherein the second determining module is further configured to:
multiplying the vulnerability coefficient value and the importance coefficient value to obtain a third result value;
squaring the third result value to obtain a fourth result value;
and taking the fourth result value as the disaster tolerance coefficient value.
14. The apparatus of claim 12, wherein the second determining module is further configured to:
multiplying the disaster tolerance coefficient value by the set coefficient to obtain a fifth result value;
and taking the accumulated value of the at least one fifth result value as the target disaster tolerance coefficient value.
15. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-7.
17. A computer program product, characterized in that it comprises a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116562621A (en) * | 2023-05-04 | 2023-08-08 | 北京师范大学 | Sea ice disaster shipping risk assessment method and device and computing equipment |
| CN116911620A (en) * | 2023-09-12 | 2023-10-20 | 航天宏图信息技术股份有限公司 | Typhoon full life cycle risk assessment and early warning method and device and electronic equipment |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116562621A (en) * | 2023-05-04 | 2023-08-08 | 北京师范大学 | Sea ice disaster shipping risk assessment method and device and computing equipment |
| CN116562621B (en) * | 2023-05-04 | 2023-12-08 | 北京师范大学 | Sea ice disaster shipping risk assessment method and device and computing equipment |
| CN116911620A (en) * | 2023-09-12 | 2023-10-20 | 航天宏图信息技术股份有限公司 | Typhoon full life cycle risk assessment and early warning method and device and electronic equipment |
| CN116911620B (en) * | 2023-09-12 | 2023-12-15 | 航天宏图信息技术股份有限公司 | Typhoon full life cycle risk assessment and early warning method and device and electronic equipment |
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