CN116703036B - High-temperature disaster prevention measure generation method, system and device and medium - Google Patents
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Abstract
The invention discloses a high-temperature disaster prevention measure generation method, a system, a device and a medium, which relate to the technical field of weather disaster countermeasures, and comprise the steps of firstly obtaining corresponding high-temperature evaluation indexes based on basic data, secondly obtaining high-temperature evaluation factors by classifying the high-temperature evaluation indexes, comprehensively evaluating high-temperature risk indexes of a target area according to the high-temperature evaluation factors, finally optimizing a comprehensive effect objective function by adopting a high Wen Fangzai optimization model, realizing quantitative prediction of a high-temperature decision effect, automatically generating optimal high-temperature disaster prevention measures according to a prediction result and actual high-temperature risk indexes of the target area, eliminating the influence of decision subjective factors, reducing the dependence on case data and improving the effectiveness of the high-temperature disaster prevention measures.
Description
Technical Field
The invention relates to the technical field of weather disaster countermeasures, in particular to a method, a system, a device and a medium for generating high-temperature disaster prevention measures.
Background
The high-temperature extreme event is one of causes of secondary weather disasters such as drought, fire and the like, has great influence on social economy, and scientific and effective high-temperature disaster prevention measure decision is helpful for reducing life and property loss of people under the high-temperature extreme event, so that the method is an important link of emergency disaster reduction. In the process of generating corresponding disaster prevention measures aiming at high-temperature extreme events in the prior art, firstly, dividing the high Wen Fengxian grade of a research area according to meteorological indexes, and then selecting the corresponding disaster prevention measures according to historical case data and expert experience judgment. However, the high-temperature extreme events have space-time difference, and the high-temperature risk level of the research area is evaluated inaccurately based on a single meteorological index, so that the selection of high-temperature disaster prevention measures is affected; meanwhile, the generation of disaster prevention measures in the prior art is greatly influenced by subjective experience of experts, and depends on historical case data, so that effective high-temperature disaster prevention measures in the production process are difficult to produce when similar historical cases are lacking, and the application scenes of the measures are limited; therefore, the high-temperature disaster prevention measures obtained by the prior art have the problems that the application scene is limited and the high-temperature extreme events cannot be effectively dealt with.
Disclosure of Invention
Aiming at the problems that in the prior art, high Wen Fangzai measures are generated based on historical case data and expert experience judgment, so that the generated high Wen Fangzai measures cannot be quantified, the influence of subjective experiences of the expert is large, and the application scene of the measures is limited, the invention provides a high-temperature disaster prevention measure generation method, which comprises the following steps:
determining a target area and collecting basic data of the target area;
analyzing the basic data to obtain a plurality of high-temperature evaluation indexes; classifying the plurality of high-temperature evaluation indexes to obtain a classification result, and calculating a high-temperature evaluation factor according to the classification result;
calculating a high-temperature risk index corresponding to the target area according to the high-temperature evaluation factor;
collecting first disaster prevention measures, and respectively calculating a comprehensive effect objective function corresponding to the first disaster prevention measures;
predicting the effect of the first disaster prevention measure according to the comprehensive effect objective function corresponding to the first disaster prevention measure to obtain a prediction result;
and processing the first disaster prevention measure according to the prediction result to obtain a second disaster prevention measure.
According to the method, corresponding high-temperature evaluation indexes are obtained firstly based on basic data, then the high-temperature evaluation indexes are classified to obtain high-temperature evaluation factors, finally the high-temperature risk indexes of the target area are comprehensively evaluated according to the high-temperature evaluation factors, meanwhile, the decision effect is quantitatively evaluated, the optimal high-temperature disaster prevention measures are automatically generated according to the evaluation result and the actual high-temperature risk indexes of the target area, influences of decision subjective factors are eliminated, meanwhile, dependence on historical case data when the high-temperature disaster prevention measures are generated is reduced, the optimal high-temperature disaster prevention measures are automatically generated based on the actual high-temperature risk indexes of the target area, the application scene of the high-temperature disaster prevention measures is enlarged, and the effectiveness of the high-temperature disaster prevention measures is improved.
Furthermore, the high-temperature extreme events have space-time difference, different regions possibly have different high-temperature risks under the same air temperature condition, the applicable high Wen Fangzai measures also have difference, a single weather, hydrology or economic index cannot accurately evaluate the high-temperature risk index, and in order to accurately evaluate the high-temperature risk index of a target region, thereby generating a proper high-temperature disaster prevention measure, wherein the plurality of high-temperature evaluation factors comprise a risk factor, a vulnerability factor and an exposure factor; the risk factor is used for expressing the capability of the meteorological environment to damage the bearing body, the vulnerability factor is used for representing the vulnerability of the environment under the high-temperature damage with different intensities, and the exposure factor is used for representing the possibility of the influence of the high-temperature damage on the characteristics of the bearing body such as population, economy, life and property and the like under the action of the risk factor.
The specific method for calculating the high-temperature risk index corresponding to the target area according to the high-temperature evaluation factor comprises the following steps:
wherein R is a high temperature risk index, R j (j=1, 2, 3) is the j-th high temperature evaluation factor of the plurality of high temperature evaluation factors, W j And (j=1, 2, 3) is the weight corresponding to the j-th high-temperature evaluation factor in the plurality of high-temperature evaluation factors.
Further, in practical application, the high-temperature risks of the target area have spatial heterogeneity, that is, the high-temperature risk indexes of different positions in the specified target area are not necessarily equal, in order to calculate the high Wen Kongjian distribution of the target area, the important areas with larger high-temperature risks in the target area are extracted according to the high-temperature spatial distribution of the target area, and the high-temperature disaster prevention measure generation method further comprises the following steps after calculating the high-temperature risk indexes corresponding to the target area according to the plurality of high-temperature evaluation factors:
grading the high-temperature risk index to obtain a grading result;
partitioning the geographic position corresponding to the target area according to the grading result to obtain a partitioning result;
determining a high-temperature threshold, and screening the partition result according to the high-temperature threshold to obtain a screening result;
updating the target area according to the screening result;
the updated target area is used for representing a high-temperature key area in the original target area, so that targeted disaster prevention measures are conveniently taken for the key area, and the damage caused by high-temperature extreme events is further reduced.
Furthermore, the high-temperature disaster prevention measures realize the aim of reducing the high-temperature risk index by changing the high-temperature evaluation index of the target area and then changing the high-temperature evaluation index. Therefore, the relation between the high Wen Fangzai measure and the high temperature evaluation factor can be determined by analyzing the correlation between the high Wen Fangzai measure and the high temperature evaluation index, so that the effect of reducing the high temperature risk by different disaster prevention measures is quantified. The method for generating the high-temperature disaster prevention measures comprises the steps of collecting first disaster prevention measures, and respectively calculating the comprehensive effect functions corresponding to the first disaster prevention measures, wherein the comprehensive effect functions specifically comprise:
Collecting first disaster prevention measures, and respectively carrying out correlation analysis on the first disaster prevention measures and the plurality of evaluation indexes to obtain a quantitative relation between the first disaster prevention measures and high-temperature evaluation indexes;
calculating the change quantity of the high-temperature risk index corresponding to the first disaster prevention measure according to the quantized relation between the first disaster prevention measure and the high-temperature evaluation index, and obtaining the comprehensive effect objective function of the first disaster prevention measure according to the change quantity of the high-temperature risk index.
Furthermore, the high-temperature disaster prevention measures generated in practical application are limited by the economic investment, the personnel number, the material number and the like of the target area, so that the calculated comprehensive effect objective function is required to be updated according to the limiting conditions, thereby generating the high-temperature disaster prevention measures applicable to the known limiting conditions, and the high-temperature disaster prevention measure generation method further comprises the following steps after obtaining the comprehensive effect objective function corresponding to the first disaster prevention measure:
determining a constraint condition, wherein the constraint condition is used for limiting the high-temperature evaluation factor;
establishing a high Wen Fangzai optimization model according to the constraint conditions, wherein the high-temperature optimization model is used for optimizing the first disaster prevention measures;
And updating the comprehensive effect objective function according to the high-temperature disaster prevention optimization model to obtain a second disaster prevention measure.
Further, since the high temperature is harmful to the carrier body through the meteorological environment, and the meteorological indexes represented by the temperature are important disaster causing factors causing the high temperature, the meteorological indexes such as the temperature, the wind speed, the precipitation and the air pressure in the environmental data are used as the high temperature evaluation indexes corresponding to the risk factors, and therefore, the high temperature evaluation indexes corresponding to the risk factors comprise the air temperature data, the ground surface temperature data, the precipitation data, the wind speed data and the air pressure data, and the specific method for calculating the risk factors is as follows:
and respectively carrying out normalization processing on the air temperature data and the surface temperature data according to the following formula to obtain corresponding normalization calculation results:
V′ 1，i ＝(V 1，i -Min(V 1，i ))÷(Max(V 1，i )-Min(V 1，i ))
and respectively carrying out normalization processing on the precipitation data, the wind speed data and the air pressure data according to the following formulas to obtain corresponding normalization calculation results:
V′ 1，i ＝(Max(V 1，i )-Min(V 1，i ))÷(V 1，i -Min(V 1，i ))
wherein V is 1，i An i-th high temperature evaluation index corresponding to the risk factor, max (V 1，i ) Represents the maximum value, min (V 1，i ) Represents the ith height Wen Pinggu Target minimum, V' 1，i The normalized calculation result of the ith high-temperature evaluation index in the risk factors is represented;
and calculating according to the normalized calculation result to obtain the risk factor.
Further, since the exposure factor is used for characterizing the possibility that the high-temperature hazard affects the carrier characteristics of population, economy, life and property and the like under the action of the risk factor, and the population characteristic is the most important factor in the carrier characteristics and is an important aspect for judging the high-temperature risk, in order to accurately calculate the exposure factor, the high-temperature evaluation index corresponding to the exposure factor comprises juvenile mouth data, senile population data and sex ratio data, and the specific method for calculating the exposure factor is as follows:
and respectively carrying out normalization processing on the juvenile mouth data, the senile population data and the gender proportion data according to the following formula to obtain corresponding normalization calculation results:
V′ 2，i ＝(V 2，i -Min(V 2，i ))÷(Max(V 2，i )-Min(V 2，i ))
wherein V is 2，i An i-th high temperature evaluation index corresponding to the exposure factor, max (V 2，i ) Represents the maximum value, min (V 2，i ) Representing the minimum value of the ith high temperature evaluation index, V' 2，i A normalized calculation result of an ith high-temperature evaluation index of the exposure factor is represented;
and calculating according to the normalized calculation result to obtain the exposure factor.
Furthermore, as the vulnerability factors are used for representing the vulnerability under the high-temperature disaster-causing factors with different intensities, the types of the underlying surfaces such as vegetation, buildings, water bodies and the like can directly influence the easiness of the high temperature to the damage of the supporting body, and therefore the vulnerability factors are obtained by calculating according to the vegetation index, the water body index and the building index.
In order to achieve the above object, the present invention further provides a high temperature disaster prevention measure generating system, the system comprising:
the data acquisition module is used for determining a target area and acquiring basic data of the target area;
the preprocessing module is used for analyzing the basic data to obtain a plurality of high-temperature evaluation indexes; classifying the plurality of high-temperature evaluation indexes to obtain a classification result, and calculating a high-temperature evaluation factor according to the classification result;
the data analysis module is used for calculating a high-temperature risk index corresponding to the target area according to the high-temperature evaluation factor; collecting first disaster prevention measures, and respectively calculating comprehensive effect objective functions of the first disaster prevention measures; predicting the effect of the first disaster prevention measure according to the comprehensive effect objective function to obtain a prediction result;
And the measure generating module is used for processing the first disaster prevention measure according to the prediction result to obtain a second disaster prevention measure.
In order to achieve the above object, the present invention further provides a device for generating a high-temperature disaster prevention measure, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the steps of the method for generating a high-temperature disaster prevention measure according to any one of the above steps when executing the computer program.
To achieve the above object, the present invention also provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of a high-temperature disaster prevention measure generation method according to any one of the above.
The one or more technical schemes provided by the invention at least comprise the following advantages:
1. according to the invention, a plurality of high-temperature evaluation indexes are obtained and classified based on basic data, and high-temperature evaluation factors are obtained; the high-temperature risk index of the target area is comprehensively and accurately evaluated according to the high-temperature evaluation factors, and the influence caused by the space-time difference of the high-temperature extreme events is eliminated;
2. The method carries out quantitative evaluation on the effect of the cooling measures, provides data support for the generated high-temperature disaster prevention measures, eliminates the influence of subjective factors when specific disaster prevention measures are generated, and depends on case data;
3. according to the invention, the high-temperature disaster prevention measures which are suitable for the actual demands of the target area are generated by updating and optimizing the comprehensive effect objective function corresponding to the high-temperature disaster prevention measures, and the requirements of the limiting conditions such as economic investment, personnel quantity and material quantity of the target area are met.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention;
FIG. 1 is a schematic flow chart of a method for generating high-temperature disaster prevention measures in the invention;
FIG. 2 is a schematic diagram of a high temperature disaster prevention measure generation system architecture according to the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. In addition, the embodiments of the present invention and the features in the embodiments may be combined with each other without collision.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than within the scope of the description, and the scope of the invention is therefore not limited to the specific embodiments disclosed below.
Example 1
Referring to fig. 1, a first embodiment of the present invention provides a method for generating a high-temperature disaster prevention measure, where the method includes the following steps:
determining a target area and collecting basic data of the target area;
analyzing the basic data to obtain a plurality of high-temperature evaluation indexes;
classifying the plurality of high-temperature evaluation indexes to obtain a classification result, and calculating a high-temperature evaluation factor according to the classification result;
calculating a high-temperature risk index corresponding to the target area according to the high-temperature evaluation factor;
collecting a first disaster prevention measure, and calculating a comprehensive effect objective function of the measure;
predicting the effect of the first disaster prevention measure according to the comprehensive effect objective function to obtain a prediction result;
and processing the first disaster prevention measure according to the prediction result to obtain a second disaster prevention measure.
The basic data of the target area includes, but is not limited to, all or part of meteorological, hydrological and economic data such as surface temperature, air temperature, wind speed, land data, population proportion data, vegetation index, water index and building index, and the basic data is analyzed, that is, correlation analysis is performed on the data and the high temperature event of the target area, a plurality of data included in the environmental data are screened, a data item related to the high temperature event in the environmental data is used as a high temperature evaluation index of the target area, specifically, correlation analysis between the environmental data and the high temperature event of the target area can be completed through a Spearman correlation analysis method, a Pearson correlation analysis method or a Gamma correlation analysis method, and the specific correlation analysis method is determined according to actual needs.
The first disaster prevention measures include, but are not limited to, various measures for reducing high temperature risk, reducing exposure and reducing vulnerability, such as environmental greening measures, ecological irrigation measures, artificial precipitation measures, medical level improvement measures, personnel regulation and control, etc., and the first disaster prevention measures are based on high temperature countermeasure data collected during actual application, and the embodiment is not limited specifically herein.
In this embodiment, the risk factor is used to express the ability of the weather environment to harm the carrier; vulnerability factors are used to characterize the vulnerability of the environment to high temperature hazards of varying intensity; the exposure factor is used for representing the possibility of the influence of high-temperature damage to the characteristics of the bearing bodies such as population, economy, life and property and the like under the action of the risk factor; therefore, in order to accurately calculate the high-temperature risk index of the target area, the plurality of high-temperature evaluation factors include a risk factor, a vulnerability factor and an exposure factor, and the specific method for calculating the high-temperature risk index corresponding to the target area according to the high-temperature evaluation factors is as follows:
wherein R is a high temperature risk index, R j (j=1, 2, 3) is the j-th high temperature evaluation factor of the plurality of high temperature evaluation factors, W j And (j=1, 2, 3) is the weight corresponding to the j-th high-temperature evaluation factor in the plurality of high-temperature evaluation factors.
The weight corresponding to the high temperature evaluation factor may be obtained according to an analytic hierarchy process, a priority diagram process or a CRITIC algorithm, and the specific calculation method is determined according to actual needs, which is not limited herein.
In this embodiment, the high temperature evaluation index corresponding to the risk factor includes air temperature data, ground surface temperature data, precipitation data, wind speed data and air pressure data, in order to collect the corresponding high temperature evaluation index conveniently, and make the collected data have the same calculation standard, so that the risk factor obtained by calculation is reliable, the air temperature data is air temperature data at a position 2 meters away from the ground surface, the ground surface temperature data is soil temperature data of the ground surface, the precipitation data is average precipitation, the wind speed data is average wind speed data at a position 2 meters away from the ground surface, the air pressure data is ground air pressure data, and the specific method for calculating the risk factor is as follows:
And respectively carrying out normalization processing on the air temperature data and the surface temperature data according to the following formula to obtain corresponding normalization calculation results:
V′ 1，i ＝(V 1.i -Min(V 1，i ))÷(Max(V 1，i )-Min(V 1，i ))
and respectively carrying out normalization processing on the precipitation data and the air pressure data according to the following formula to obtain corresponding normalization calculation results:
V′ 1，i ＝(Max(V 1，i )-Min(V 1，i ))÷(V 1，i -Min(V 1，i ))
wherein V is 1，i An i-th high temperature evaluation index corresponding to the risk factor, max (V 1，i ) Represents the maximum value, min (V 1，i ) Representing the minimum value of the ith high temperature evaluation index, V' 1，i The normalized calculation result of the ith high-temperature evaluation index in the risk factors is represented;
the collection standard of the high-temperature evaluation index corresponding to the risk factor can be adjusted according to actual needs, and the embodiment is not particularly limited herein;
calculating according to the normalized calculation result to obtain the risk factor, specifically:
wherein R is 1 Representing risk factors, V' 1，i Normalized calculation result representing ith high temperature evaluation index in risk factors, W 1，i And the weight data corresponding to the ith high-temperature evaluation index in the risk factors is represented.
Wherein in the embodiment, the high-temperature evaluation index corresponding to the exposure factor comprises juveniles mouth data, old population data and sex ratio data,
In order to collect corresponding high-temperature evaluation indexes conveniently, and meanwhile, the collected data have the same calculation standard, so that the exposure factor obtained by calculation is reliable, the population of the young and the young is 14 years old and below, the population of the old is 65 years old and above, and the specific method for calculating the exposure factor is as follows:
and respectively carrying out normalization processing on the juvenile mouth data, the senile population data and the gender proportion data according to the following formula to obtain corresponding normalization calculation results:
V′ 2，i ＝(V 2，i -Min(V 2，i ))÷(Max(V 2，i )-Min(V 2，i ))
wherein V is 2，i An i-th high temperature evaluation index corresponding to the exposure factor, max (V 2，i ) Represents the maximum value of the ith high temperature evaluation index in the exposure factor, min (V) 2，i ) Representing the minimum value, V ', of the ith high temperature assessment index in the exposure factor' 2，i Representing a normalized calculation result of an ith high-temperature evaluation index in the exposure factor;
the collection standard of the high-temperature evaluation index corresponding to the exposure factor can be adjusted according to actual needs, and the embodiment is not particularly limited herein;
calculating according to the normalized calculation result to obtain the exposure factor, specifically:
wherein R is 2 Represents the exposure factor, V' 2，i Normalized calculation result of ith high temperature evaluation index in exposure factor, W 2，i And the weight data corresponding to the ith high-temperature evaluation index in the exposure factor is represented. In this embodiment, the high-temperature evaluation index corresponding to the vulnerability factor includes a vegetation index, a water body index and a building index, and the specific method for calculating the vulnerability factor is as follows:
wherein R is 3 Representing vulnerability factors, V' 3，i Indicating the ith high-temperature evaluation index in the vulnerability factors, W 3，i And the weight data corresponding to the ith high-temperature evaluation index in the vulnerability factors is represented.
In this embodiment, in the method for generating high-temperature disaster prevention measures, first disaster prevention measures are collected, and the comprehensive effect objective functions of the first disaster prevention measures are calculated specifically as follows:
collecting a first disaster prevention measure, and carrying out correlation analysis on the first disaster prevention measure and the plurality of evaluation indexes to obtain a quantitative relation between the first disaster prevention measure and the high-temperature evaluation indexes;
and respectively calculating the variation of the high-temperature risk index under each first disaster prevention measure according to the quantitative relation, and obtaining the comprehensive effect objective function of the first disaster prevention measure according to the variation of the high-temperature risk index.
The method comprises the steps of carrying out correlation analysis on a first disaster prevention measure and a plurality of evaluation indexes through a data statistics method and a data multi-item fitting method to obtain a quantification relation between the first disaster prevention measure and the high-temperature evaluation indexes, wherein the quantification relation between the first disaster prevention measure and the high-temperature evaluation indexes is expressed as follows:
wherein DeltaV' j，i To evaluate index variation at high temperature, m k，j，i For the first disaster prevention measure, f (m k，j，i ) For the quantitative relation between the first disaster prevention measure and the high temperature evaluation index, j represents the j-th high temperature evaluation factor in the plurality of high temperature evaluation factors, i represents the i-th high temperature evaluation index contained in the j-th high temperature evaluation factor, k represents the k-th measure number for changing the i-th high temperature evaluation index of the j-th high Wen Pinggu factor, and M represents the total measure number for changing the high temperature evaluation index.
The first disaster prevention measure changes the high temperature evaluation index, the high temperature evaluation index is used for classifying and calculating the high temperature evaluation factor, and finally, the high temperature risk index of the target area is changed, so that the comprehensive effect objective function corresponding to the first disaster prevention measure is used for describing the sum of the change amounts of the first disaster prevention measure to the high temperature risk index, and the comprehensive effect objective function is expressed as:
Wherein DeltaR is the total quantity of high-temperature risk index change, w of the target area j，i The weight corresponding to the ith high temperature evaluation index in the jth high temperature evaluation factor, W j (j=1, 2, 3) is the weight corresponding to the j-th high temperature evaluation factor in the plurality of high temperature evaluation factors, f (m) k，j，i ) The quantitative relation between the first disaster prevention measure and the high-temperature evaluation index is adopted.
In this embodiment, the method for generating a high-temperature disaster prevention measure further includes the following steps after obtaining the comprehensive effect objective function of the first disaster prevention measure:
determining a constraint condition, wherein the constraint condition is used for limiting the high-temperature evaluation factor;
establishing a high Wen Fangzai optimization model according to the constraint conditions, wherein the high-temperature disaster prevention optimization model is used for optimizing the first disaster prevention measures;
and updating the comprehensive effect objective function according to the high-temperature disaster prevention optimization model to obtain a second disaster prevention measure.
Wherein the constraint is expressed as A min ≤h(X)≤A max H (X) represents constraint, A min Represents constraint lower bound, A max And representing the upper bound of a constraint condition, updating the comprehensive effect objective function according to the high-temperature optimization measure, namely changing the action range of the comprehensive effect objective function according to the constraint condition, predicting the change amount of the high-temperature risk index under the optimized first cooling measure by the value range of the comprehensive effect objective function, and selecting the high-temperature measure combination corresponding to the value optimal point corresponding to the high-temperature risk index change amount in the calculated comprehensive effect objective function to obtain the second disaster prevention measure.
Example two
Referring to fig. 1, a second embodiment of the present invention provides a method for generating a high-temperature disaster prevention measure, which further includes, based on the first embodiment, the following steps after calculating a high-temperature risk index corresponding to the target area according to the plurality of high-temperature evaluation factors:
grading the high-temperature risk index to obtain a grading result;
partitioning the geographic position corresponding to the target area according to the grading result to obtain a partitioning result;
determining a high-temperature threshold, and screening the partition result according to the high-temperature threshold to obtain a screening result;
and updating the target area according to the screening result.
The high-temperature risk index may be classified according to a quantile classification method or a natural breakpoint method, where the quantile classification method defines a classification number limit, and the natural breakpoint method can select the classification number according to needs to obtain a flexible classification result, so that the embodiment prefers the natural breakpoint method to classify the high-temperature risk index, and the specific classification number is determined according to actual needs, and the embodiment is not specifically limited herein.
The high temperature threshold is used for dividing a key region with a larger high temperature risk in the target region, and a specific value range of the high temperature threshold is determined according to actual needs, which is not particularly limited in this embodiment.
After the classification result is obtained, the target area is divided into a plurality of blocks according to the classification result, and for each block, the high-temperature risk level is the same, in this embodiment, after the partition result is obtained in the high-temperature disaster prevention measure generating method, a first thematic map corresponding to the target area is generated according to the calculation result, where the first thematic map is used to describe the high-temperature risk levels corresponding to different areas in the target area.
In this embodiment, the first thematic map is marked on the blocks by numerical values, colors and the like, describes the high Wen Fengxian grades corresponding to different blocks, and can intuitively represent the high-temperature distribution condition of the target area.
The first thematic map is first created to include a spatial target layer corresponding to the grading result corresponding to the high-temperature risk index, then the spatial target layer is corresponding to a map corresponding to a target area, the first thematic map can be generated through a function provided by MapInfo or ArcGIS, a specific generation method is determined according to actual needs, and the embodiment is not limited specifically herein.
In this embodiment, the high-temperature risk indicators included in the geographic range included in the target area have spatial heterogeneity, and for the first block corresponding to the wetland, the coastal river, and other areas in the target area, the vegetation coverage is high, the personnel access amount is small, and the air humidity is high, so that the high-temperature risk level corresponding to the first block is low, and the second block adjacent to the first block may still have higher high-temperature risk. If the second areas adjacent to the first area have higher high-temperature risks, the first area is easily affected by surrounding areas, the possibility of being affected by the secondary weather disasters is higher, in order to analyze according to the above conditions, an accurate high-temperature early warning special drawing is obtained, so that disaster prevention and reduction measures are pertinently implemented, and after the first special drawing corresponding to the target area is generated, the high-temperature disaster prevention measure generating method further comprises the following steps:
traversing the blocks, and obtaining a corresponding first high-temperature risk index for each block and obtaining adjacent blocks of the blocks;
calculating the number of the adjacent blocks and a second high-temperature risk index corresponding to the adjacent blocks to obtain first data;
Calculating the number of adjacent blocks of which the second high-temperature risk index is larger than the first high-temperature risk index to obtain second data;
calculating the ratio of the second data to the first data to obtain third data;
determining a threshold value, calculating the size relation between the third data and the threshold value, and if the third data is larger than the threshold value, updating the first high-temperature risk level according to the second high-temperature risk level corresponding to the adjacent block, and updating the first thematic map to obtain a second thematic map.
The threshold value is used for determining the possibility that the current block is affected by the weather secondary disaster of the adjacent block, if the third data is larger than the threshold value, the adjacent block of the current block is considered to have higher high-temperature risk, and the possibility that the current block is affected by the weather secondary disaster of the adjacent block is considered to be higher; if the third data is smaller than or equal to the threshold, the high-temperature risk of the adjacent block of the current block is considered to be in a receivable range, the possibility that the current block is affected by the weather secondary disaster of the adjacent block is small, the specific value of the threshold is determined according to actual needs, and the embodiment is not limited specifically.
The specific calculation method may be determined according to actual needs, and the embodiment is not limited herein specifically, where the first high-temperature risk index may be updated according to the average value and the median of the second high-temperature risk index, or the convolution of the first high-temperature risk index and the second risk index.
Example III
Referring to fig. 2, a third embodiment of the present invention provides a high-temperature disaster prevention measure generating system, which includes:
the data acquisition module is used for determining a target area and acquiring basic data of the target area;
the preprocessing module is used for analyzing the basic data to obtain a plurality of high-temperature evaluation indexes; classifying the plurality of high-temperature evaluation indexes to obtain a classification result, and calculating a high-temperature evaluation factor according to the classification result;
the data analysis module is used for calculating a high-temperature risk index corresponding to the target area according to the high-temperature evaluation factor; collecting first disaster prevention measures, analyzing the quantitative relation of the first disaster prevention measures to high-temperature evaluation indexes, and respectively calculating comprehensive effect objective functions corresponding to the first disaster prevention measures; predicting the effect of the first disaster prevention measure according to the comprehensive effect objective function to obtain a prediction result;
And the measure generating module is used for processing the first disaster prevention measure according to the prediction result to obtain a second disaster prevention measure.
Example IV
The fourth embodiment of the invention provides a high-temperature disaster prevention measure generating device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the high-temperature disaster prevention measure generating method when executing the computer program.
Example five
A fifth embodiment of the present invention provides a computer-readable storage medium storing a computer program, which when executed by a processor, implements the steps of the high-temperature disaster prevention measure generation method.
The processor may be a central processing unit (CPU, central Processing Unit), other general purpose processors, digital signal processors (digital signal processor), application specific integrated circuits (Application Specific Integrated Circuit), off-the-shelf programmable gate arrays (Field programmable gate array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or any conventional processor.
The memory may be used to store the computer program and/or the module, and the processor may implement various functions of the inventive high-temperature disaster prevention measure generation device by running or executing the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, application programs required for at least one function (such as a sound playing function, an image playing function, etc.), and the like. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card, secure digital card, flash memory card, at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The high temperature disaster prevention measure generating device may be stored in a computer readable storage medium if implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, the present invention may implement all or part of the flow of the method of the above-described embodiments, or may be stored in a computer readable storage medium by a computer program, which when executed by a processor, implements the steps of the method embodiments described above. Wherein the computer program comprises computer program code, object code forms, executable files, or some intermediate forms, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, a point carrier signal, a telecommunication signal, a software distribution medium, and the like. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the legislation and the patent practice in the jurisdiction.
Having described the basic concept of the invention, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, those skilled in the art will appreciate that the various aspects of the specification can be illustrated and described in terms of several patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the present description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the specification may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.
The computer storage medium may contain a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer storage medium may be any computer readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.
The computer program code necessary for operation of portions of the present description may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, c#, vb net, python and the like, a conventional programming language such as C language, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, ruby and Groovy, or other programming languages and the like. The program code may execute entirely on the user's computer or as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or the use of services such as software as a service (SaaS) in a cloud computing environment.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (4)
1. The high-temperature disaster prevention measure generation method is characterized by comprising the following steps of:
determining a target area and collecting basic data of the target area;
analyzing the basic data, carrying out correlation analysis on the basic data and a high-temperature event of a target area, and taking a data item related to the high-temperature event as a high-temperature evaluation index of the target area;
classifying the high-temperature evaluation indexes to obtain classification results, and calculating high-temperature evaluation factors according to the classification results;
calculating a high-temperature risk index corresponding to the target area according to the high-temperature evaluation factor;
collecting first disaster prevention measures, and respectively calculating a comprehensive effect objective function corresponding to the first disaster prevention measures;
predicting the effect of the first disaster prevention measure according to the comprehensive effect objective function of the first disaster prevention measure to obtain a prediction result; processing the first disaster prevention measures according to the prediction result to obtain second disaster prevention measures; the high temperature assessment factors comprise risk factors, vulnerability factors and exposure factors, wherein the risk factors are used for expressing the capability of a meteorological environment to damage a bearing body, the vulnerability factors are used for representing the vulnerability of the environment under high temperature damage of different intensities, and the exposure factors are used for representing the possibility of the influence of the high temperature damage under the action of the risk factors on the characteristics of the population, economy and life and property bearing body; the high-temperature evaluation indexes corresponding to the vulnerability factors comprise vegetation indexes, water indexes and building indexes; the specific method for calculating the high-temperature risk index corresponding to the target area according to the high-temperature evaluation factor comprises the following steps:
；
Wherein R is a high temperature risk index, R j (j=1, 2, 3) is the jth high temperature evaluation factor, W, of the high temperature evaluation factors j (j=1, 2, 3) is the weight corresponding to the j-th high-temperature evaluation factor in the high-temperature evaluation factors; the high-temperature risk index corresponding to the target area is calculated according to the high-temperature evaluation factor in the high-temperature disaster prevention measure generation method, and then the method further comprises the following steps:
grading the high-temperature risk index to obtain a grading result;
partitioning the geographic position corresponding to the target area according to the grading result to obtain a partitioning result;
determining a high-temperature threshold, and screening the partition result according to the high-temperature threshold to obtain a screening result;
updating the target area according to the screening result; the method for generating the high-temperature disaster prevention measures comprises the steps of collecting first disaster prevention measures, and respectively calculating a comprehensive effect objective function corresponding to the first disaster prevention measures, wherein the comprehensive effect objective function specifically comprises the following steps:
collecting first disaster prevention measures, and respectively carrying out correlation analysis on the first disaster prevention measures and the evaluation indexes to obtain the quantitative relation between the first disaster prevention measures and the high-temperature evaluation indexes;
respectively calculating the high-temperature risk index variation corresponding to the first disaster prevention measure according to the quantized relation, and obtaining a comprehensive effect objective function of the first disaster prevention measure according to the high-temperature risk index variation; the method for generating the high-temperature disaster prevention measures further comprises the following steps after obtaining the comprehensive effect objective function corresponding to the first disaster prevention measure:
Determining a constraint condition, wherein the constraint condition is used for limiting the high-temperature evaluation factor; establishing a high Wen Fangzai optimization model according to the constraint conditions, wherein the high-temperature disaster prevention optimization model is used for optimizing the first disaster prevention measures;
updating the comprehensive effect objective function according to the high-temperature disaster prevention optimization model; the high-temperature evaluation index corresponding to the risk factor comprises air temperature data, ground surface temperature data, precipitation data, wind speed data and air pressure data, and the specific method for calculating the risk factor comprises the following steps:
and respectively carrying out normalization processing on the air temperature data and the surface temperature data according to the following formula to obtain corresponding normalization calculation results:
；
and respectively carrying out normalization processing on the precipitation data, the wind speed data and the air pressure data according to the following formulas to obtain corresponding normalization calculation results:
；
wherein,an i-th high temperature evaluation index indicating the risk factor correspondence,/for>Representation ofMaximum value of i-th high temperature evaluation index, < ->Minimum value representing i-th high temperature evaluation index,/->The normalized calculation result of the ith high-temperature evaluation index in the risk factors is represented;
calculating according to the normalized calculation result to obtain the risk factor; the high-temperature evaluation index corresponding to the exposure factor comprises juvenile oral data, senile population data and sex ratio data, and the specific method for calculating the exposure factor comprises the following steps:
And respectively carrying out normalization processing on the juvenile mouth data, the senile population data and the gender proportion data according to the following formula to obtain corresponding normalization calculation results:
；
wherein,an i-th high temperature evaluation index corresponding to the exposure factor,/th high temperature evaluation index>Represents the maximum value of the i-th high temperature evaluation index,/->Minimum value representing i-th high temperature evaluation index,/->Representing the normalized calculation result of the ith high temperature evaluation index;
and calculating according to the normalized calculation result to obtain the exposure factor.
2. A high temperature disaster prevention measure generation system, the system comprising:
the data acquisition module is used for determining a target area and acquiring basic data of the target area;
the preprocessing module is used for analyzing the basic data, carrying out correlation analysis on the basic data and a high-temperature event of a target area, and taking a data item related to the high-temperature event as a high-temperature evaluation index of the target area; classifying the high-temperature evaluation indexes to obtain classification results, and calculating high-temperature evaluation factors according to the classification results;
the data analysis module is used for calculating a high-temperature risk index corresponding to the target area according to the high-temperature evaluation factor; collecting first disaster prevention measures, and respectively calculating a comprehensive effect objective function corresponding to the first disaster prevention measures; predicting the effect of the first disaster prevention measure according to the comprehensive effect objective function to obtain a prediction result;
The measure generating module is used for processing the first disaster prevention measure according to the prediction result to obtain a second disaster prevention measure;
the high temperature assessment factors comprise risk factors, vulnerability factors and exposure factors, wherein the risk factors are used for expressing the capability of a meteorological environment to damage a bearing body, the vulnerability factors are used for representing the vulnerability of the environment under high temperature damage of different intensities, and the exposure factors are used for representing the possibility of the influence of the high temperature damage under the action of the risk factors on the characteristics of the population, economy and life and property bearing body; the high-temperature evaluation indexes corresponding to the vulnerability factors comprise vegetation indexes, water indexes and building indexes; the specific method for calculating the high-temperature risk index corresponding to the target area according to the high-temperature evaluation factor comprises the following steps:
；
wherein R is a high temperature risk index, R j (j=1, 2, 3) is the jth high temperature evaluation factor, W, of the high temperature evaluation factors j (j=1, 2, 3) is the weight corresponding to the j-th high-temperature evaluation factor in the high-temperature evaluation factors; the data analysis module is further configured to:
grading the high-temperature risk index to obtain a grading result;
partitioning the geographic position corresponding to the target area according to the grading result to obtain a partitioning result;
Determining a high-temperature threshold, and screening the partition result according to the high-temperature threshold to obtain a screening result;
updating the target area according to the screening result;
the data analysis module is specifically used for:
collecting first disaster prevention measures, and respectively carrying out correlation analysis on the first disaster prevention measures and the evaluation indexes to obtain the quantitative relation between the first disaster prevention measures and the high-temperature evaluation indexes;
respectively calculating the high-temperature risk index variation corresponding to the first disaster prevention measure according to the quantized relation, and obtaining a comprehensive effect objective function of the first disaster prevention measure according to the high-temperature risk index variation;
the data analysis module is further configured to:
determining a constraint condition, wherein the constraint condition is used for limiting the high-temperature evaluation factor; establishing a high Wen Fangzai optimization model according to the constraint conditions, wherein the high-temperature disaster prevention optimization model is used for optimizing the first disaster prevention measures;
updating the comprehensive effect objective function according to the high-temperature disaster prevention optimization model;
the high-temperature evaluation index corresponding to the risk factor comprises air temperature data, ground surface temperature data, precipitation data, wind speed data and air pressure data, and the specific method for calculating the risk factor comprises the following steps:
And respectively carrying out normalization processing on the air temperature data and the surface temperature data according to the following formula to obtain corresponding normalization calculation results:
；
and respectively carrying out normalization processing on the precipitation data, the wind speed data and the air pressure data according to the following formulas to obtain corresponding normalization calculation results:
；
wherein,an i-th high temperature evaluation index indicating the risk factor correspondence,/for>Represents the maximum value of the i-th high temperature evaluation index,/->Minimum value representing i-th high temperature evaluation index,/->The normalized calculation result of the ith high-temperature evaluation index in the risk factors is represented;
calculating according to the normalized calculation result to obtain the risk factor; the high-temperature evaluation index corresponding to the exposure factor comprises juvenile oral data, senile population data and sex ratio data, and the specific method for calculating the exposure factor comprises the following steps:
and respectively carrying out normalization processing on the juvenile mouth data, the senile population data and the gender proportion data according to the following formula to obtain corresponding normalization calculation results:
；
wherein,representation ofThe ith high temperature evaluation index corresponding to the exposure factor, < > >Represents the maximum value of the i-th high temperature evaluation index,/->Minimum value representing i-th high temperature evaluation index,/->Representing the normalized calculation result of the ith high temperature evaluation index;
and calculating according to the normalized calculation result to obtain the exposure factor.
3. A high temperature disaster prevention measure generating device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor implements the steps of a high temperature disaster prevention measure generating method as claimed in claim 1 when said computer program is executed by said processor.
4. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of a high temperature disaster prevention measure generation method as claimed in claim 1.
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| CN114580979A (en) * | 2022-05-07 | 2022-06-03 | 中国科学院地理科学与资源研究所 | High-temperature disaster prevention index detection method, device, equipment, storage medium and product |
| CN114611942A (en) * | 2022-03-14 | 2022-06-10 | 山东睿源公共安全及应急技术服务有限公司 | Building fire risk assessment method |
| CN114897350A (en) * | 2022-05-09 | 2022-08-12 | 中国城市规划设计研究院 | Comprehensive disaster prevention capability assessment method, device, equipment and medium |
| KR102476821B1 (en) * | 2022-05-19 | 2022-12-13 | 한국소방안전원 | hazard assessment methods for fire safety diagnosis of facility |
| CN116187752A (en) * | 2022-12-27 | 2023-05-30 | 福建省气候中心 | Refined risk assessment method in typhoon disaster process |
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| CN114611942A (en) * | 2022-03-14 | 2022-06-10 | 山东睿源公共安全及应急技术服务有限公司 | Building fire risk assessment method |
| CN114580979A (en) * | 2022-05-07 | 2022-06-03 | 中国科学院地理科学与资源研究所 | High-temperature disaster prevention index detection method, device, equipment, storage medium and product |
| CN114897350A (en) * | 2022-05-09 | 2022-08-12 | 中国城市规划设计研究院 | Comprehensive disaster prevention capability assessment method, device, equipment and medium |
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