Global carbon emissions, warming and disaster risk impact monitoring method and device

Abstract

The present invention relates to a method and device for monitoring the impact of global carbon emissions, warming and disaster risks. The method discretizes regional industrial cumulative per capita emissions, regional average warming rate and regional climate disaster data, characterizes the impact relationship between global carbon emissions, warming and disaster risks according to the processing results, and fixes the characterization form according to a preset mapping table, thereby providing a reasonable regional impact monitoring method.

Description

Technical Field

[0001] The present invention relates to the field of meteorological climate application technology, and in particular to a method and device for monitoring the impact of global carbon emissions, warming and disaster risks. Background Art

[0002] With the development of the industrial age, the use of fossil energy has brought impacts on the global climate environment. The most significant is the global temperature rise caused by the increase in carbon dioxide concentration. The global warming caused by the increase in carbon dioxide concentration has brought many problems to the global environment. At the same time, changes in carbon dioxide concentration will also have a certain impact on the environment. Among them, the most prominent problem of environmental change is the disaster impact caused by various extreme weather. Therefore, studying the relationship between carbon dioxide concentration, global temperature and climate disasters has become the focus of current environmental protection research.

[0003] Among them, the change in carbon dioxide concentration is mainly characterized by the change in carbon emissions. However, there are great regional differences in carbon emissions, and there are also great differences in warming and disaster risks in various countries and regions. Traditional research is a one-size-fits-all approach, which averages out global carbon emissions and climate change to distribute carbon emissions or allocate emission reduction responsibilities. This approach ignores regional historical development differences and makes it difficult to reasonably measure the impact of global carbon emissions, warming and disaster risks in various regions. Summary of the invention

[0004] Based on this, it is necessary to provide a global carbon emissions, warming and disaster risk impact monitoring method and device to reasonably measure the impact relationship between global carbon emissions, warming and disaster risks in various regions.

[0005] A method for monitoring the impact of global carbon emissions, warming and disaster risks, comprising the steps of:

[0006] Obtain regional industrialization cumulative per capita emissions, regional average warming rate and regional climate disaster data for each region;

[0007] Calculate the global industrialization cumulative per capita emissions based on the regional industrialization cumulative per capita emissions, and obtain the first monitoring value corresponding to the region by taking the difference between the regional industrialization cumulative per capita emissions and the global industrialization cumulative per capita emissions;

[0008] Calculate the global average warming rate based on the regional average warming rate, and obtain the second monitoring value corresponding to the region based on the difference between the regional average warming rate and the global average warming rate;

[0009] Perform standardization processing on regional climate disaster data to obtain the third monitoring value;

[0010] The first monitoring value, the second monitoring value and the third monitoring value are respectively discretized, and the corresponding impact results are searched in the preset mapping table according to the numerical results of the interval discretization processing; wherein the preset mapping table includes the corresponding relationship between the preset numerical values ​​and the impact results.

[0011] The above-mentioned global carbon emissions, warming and disaster risk impact monitoring method, after obtaining the regional industrial cumulative per capita emissions, regional average warming rate and regional climate disaster data of each region, calculates the global industrial cumulative per capita emissions according to the regional industrial cumulative per capita emissions, and obtains the first monitoring value corresponding to the region by the difference between the regional industrial cumulative per capita emissions and the global industrial cumulative per capita emissions, calculates the global average warming rate according to the regional average warming rate, and obtains the second monitoring value corresponding to the region according to the difference between the regional average warming rate and the global average warming rate, and performs standardization on the regional climate disaster data to obtain the third monitoring value. Finally, the first monitoring value, the second monitoring value and the third monitoring value are respectively discretized, and the corresponding impact results are searched in the preset mapping table according to the numerical results of the interval discretization processing. Based on this, by performing interval discretization processing on the regional industrial cumulative per capita emissions, the regional average warming rate and the regional climate disaster data, the impact relationship between global carbon emissions, warming and disaster risks is characterized according to the processing results, and the representation form is fixed according to the preset mapping table, providing a reasonable regional impact monitoring method.

[0012] In one embodiment, the process of calculating the global industrial cumulative per capita emissions based on the regional industrial cumulative per capita emissions, and obtaining the first monitoring value corresponding to the region by the difference between the regional industrial cumulative per capita emissions and the global industrial cumulative per capita emissions is as follows:

[0013] Cumulative per capita emissions from regional industrialization C i As follows:

[0014]

[0015] Among them, i represents region, represents the carbon emissions of region i in year t, and represents the population of region i in a specific base year t;

[0016] The first monitoring value X is as follows:

[0017]

[0018] Where n represents the total number of regions. represents the cumulative per capita emissions from global industrialization.

[0019] In one embodiment, the global average warming rate is calculated according to the regional average warming rate, and the second monitoring value corresponding to the region is obtained according to the difference between the regional average warming rate and the global average warming rate, as shown in the following formula:

[0020]

[0021] Where Z represents the second monitoring value, a i represents the regional average warming rate of region i, represents the global average warming rate.

[0022] In one embodiment, the process of obtaining the average temperature increase rate of a region includes the steps of:

[0023] Obtain the original series value, time regression coefficient and residual of regional temperature;

[0024] The climate tendency rate is calculated based on the original temperature series values, time regression coefficients and residuals, and the regional average warming rate is determined based on the average value of the climate tendency rate.

[0025] In one embodiment, the regional climate disaster data includes regional disaster-related population mortality rate and regional disaster-related economic loss rate;

[0026] The process of performing standardization processing on regional climate disaster data to obtain the third monitoring value includes the following steps:

[0027] Performing standardization processing on the regional disaster-related population mortality rate and the regional disaster-related economic loss rate to obtain the standardized regional disaster-related population mortality rate and the standardized regional disaster-related economic loss rate;

[0028] Modify the coefficients of the standardized regional disaster-related population mortality rate and the standardized regional disaster-related economic loss rate;

[0029] The third monitoring value is determined based on the sum of the standardized regional disaster-related population mortality rate and the standardized regional disaster-related economic loss rate after coefficient correction.

[0030] In one embodiment, the process of performing interval discretization processing on the first monitoring value, the second monitoring value and the third monitoring value respectively includes the steps of:

[0031] The first monitoring value is discretized according to the first interval; the characterization interval corresponding to the first interval discretization includes a high emission area and a low emission area;

[0032] The second monitoring value is discretized according to the second interval; the second interval discretization corresponds to the high temperature increase area and the low temperature increase area of ​​the characterization interval;

[0033] The third monitoring value is discretized according to the third interval; the third interval discretization corresponds to the characterization interval high disaster loss rate level area and low high disaster loss rate level area.

[0034] In one embodiment, the steps are also included:

[0035] The numerical result of the interval discretization processing is corrected by the loss function; the loss function loss is as follows:

[0036]

[0037] Among them, n represents the area, N represents the total number of areas, s represents the numerical result, and S represents the corrected numerical result.

[0038] A device for monitoring the impact of global carbon emissions, warming and disaster risks, comprising:

[0039] The information acquisition module is used to obtain the regional industrialization cumulative per capita emissions, regional average warming rate and regional climate disaster data of each region;

[0040] A first calculation module is used to calculate the global industrial cumulative per capita emissions based on the regional industrial cumulative per capita emissions, and obtain a first monitoring value corresponding to the region by taking the difference between the regional industrial cumulative per capita emissions and the global industrial cumulative per capita emissions;

[0041] A second calculation module is used to calculate the global average warming rate according to the regional average warming rate, and obtain a second monitoring value corresponding to the region according to the difference between the regional average warming rate and the global average warming rate;

[0042] A third calculation module is used to perform standardization processing on the regional climate disaster data to obtain a third monitoring value;

[0043] The data search module is used to perform interval discretization processing on the first monitoring value, the second monitoring value and the third monitoring value respectively, and search the corresponding impact result in the preset mapping table according to the numerical result of the interval discretization processing; wherein the preset mapping table includes the corresponding relationship between the preset numerical value and the impact result.

[0044] The above-mentioned global carbon emission, warming and disaster risk impact monitoring device, after obtaining the regional industrial cumulative per capita emissions, regional average warming rate and regional climate disaster data of each region, calculates the global industrial cumulative per capita emissions according to the regional industrial cumulative per capita emissions, and obtains the first monitoring value corresponding to the region by the difference between the regional industrial cumulative per capita emissions and the global industrial cumulative per capita emissions, calculates the global average warming rate according to the regional average warming rate, and obtains the second monitoring value corresponding to the region according to the difference between the regional average warming rate and the global average warming rate, and performs standardization processing on the regional climate disaster data to obtain the third monitoring value. Finally, the first monitoring value, the second monitoring value and the third monitoring value are respectively subjected to interval discretization processing, and the corresponding impact results are searched in the preset mapping table according to the numerical results of the interval discretization processing. Based on this, by performing interval discretization processing on the regional industrial cumulative per capita emissions, the regional average warming rate and the regional climate disaster data, the impact relationship between global carbon emissions, warming and disaster risks is characterized according to the processing results, and the representation form is fixed according to the preset mapping table, providing a reasonable regional impact monitoring method.

[0045] A computer storage medium stores computer instructions, which, when executed by a processor, implement the method for monitoring the impact of global carbon emissions, warming and disaster risks of any of the above embodiments.

[0046] The above-mentioned computer storage medium, after obtaining the regional industrial cumulative per capita emissions, regional average warming rate and regional climate disaster data of each region, calculates the global industrial cumulative per capita emissions according to the regional industrial cumulative per capita emissions, and obtains the first monitoring value corresponding to the region by the difference between the regional industrial cumulative per capita emissions and the global industrial cumulative per capita emissions, calculates the global average warming rate according to the regional average warming rate, and obtains the second monitoring value corresponding to the region according to the difference between the regional average warming rate and the global average warming rate, and performs standardization processing on the regional climate disaster data to obtain the third monitoring value. Finally, the first monitoring value, the second monitoring value and the third monitoring value are respectively subjected to interval discretization processing, and the corresponding impact results are searched in the preset mapping table according to the numerical results of the interval discretization processing. Based on this, by performing interval discretization processing on the regional industrial cumulative per capita emissions, the regional average warming rate and the regional climate disaster data, the impact relationship between global carbon emissions, warming and disaster risks is characterized according to the processing results, and the representation form is fixed according to the preset mapping table, providing a reasonable regional impact monitoring method.

[0047] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for monitoring the impact of global carbon emissions, warming, and disaster risks of any of the above-mentioned embodiments is implemented.

[0048] The above-mentioned computer device, after obtaining the regional industrial cumulative per capita emissions, regional average warming rate and regional climate disaster data of each region, calculates the global industrial cumulative per capita emissions according to the regional industrial cumulative per capita emissions, and obtains the first monitoring value corresponding to the region by the difference between the regional industrial cumulative per capita emissions and the global industrial cumulative per capita emissions, calculates the global average warming rate according to the regional average warming rate, and obtains the second monitoring value corresponding to the region according to the difference between the regional average warming rate and the global average warming rate, and performs standardization processing on the regional climate disaster data to obtain the third monitoring value. Finally, the first monitoring value, the second monitoring value and the third monitoring value are respectively subjected to interval discretization processing, and the corresponding impact results are searched in the preset mapping table according to the numerical results of the interval discretization processing. Based on this, by performing interval discretization processing on the regional industrial cumulative per capita emissions, the regional average warming rate and the regional climate disaster data, the impact relationship between global carbon emissions, warming and disaster risks is characterized according to the processing results, and the representation form is fixed according to the preset mapping table, providing a reasonable regional impact monitoring method. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flow chart of a method for monitoring the impact of global carbon emissions, warming and disaster risks in one implementation mode;

[0050] Figure 2 A flow chart of a method for monitoring the impact of global carbon emissions, warming and disaster risks according to another embodiment;

[0051] Figure 3 A flow chart of a method for monitoring the impact of global carbon emissions, warming and disaster risks according to yet another embodiment;

[0052] Figure 4 A module structure diagram of a device for monitoring the impact of global carbon emissions, warming and disaster risks according to one embodiment;

[0053] Figure 5 A schematic diagram of the internal structure of a computer according to an embodiment. DETAILED DESCRIPTION

[0054] In order to better understand the purpose, technical solution and technical effect of the present invention, the present invention is further explained below in conjunction with the accompanying drawings and embodiments. At the same time, it is stated that the embodiments described below are only used to explain the present invention and are not used to limit the present invention.

[0055] An embodiment of the present invention provides a method for monitoring the impact of global carbon emissions, warming and disaster risks.

[0056] Figure 1 A flow chart of a method for monitoring the impact of global carbon emissions, warming and disaster risks according to an implementation method is shown in FIG. Figure 1As shown, a method for monitoring the impact of global carbon emissions, warming and disaster risks in one embodiment includes steps S100 to S104:

[0057] S100, obtain the regional industrialization cumulative per capita emissions, regional average warming rate and regional climate disaster data of each region;

[0058] S101, calculating the global industrial cumulative per capita emissions based on the regional industrial cumulative per capita emissions, and obtaining the first monitoring value corresponding to the region by taking the difference between the regional industrial cumulative per capita emissions and the global industrial cumulative per capita emissions;

[0059] S102, calculating the global average warming rate according to the regional average warming rate, and obtaining a second monitoring value corresponding to the region according to the difference between the regional average warming rate and the global average warming rate;

[0060] S103, performing standardization processing on the regional climate disaster data to obtain a third monitoring value;

[0061] S104, performing interval discretization processing on the first monitoring value, the second monitoring value and the third monitoring value respectively, and searching for corresponding impact results in a preset mapping table according to numerical results of the interval discretization processing; wherein the preset mapping table includes a correspondence between preset numerical values ​​and impact results.

[0062] Among them, global carbon emissions include various types of carbon emissions, such as per capita carbon emissions, total carbon emissions, etc. Through various carbon emission calculation indicators, global carbon emissions are quantified for subsequent impact monitoring. Similarly, warming is used to characterize the rise in global temperatures, which can be quantified by warming rate, warming value, etc.; disaster risks include quantitative representations such as disaster types, disaster frequencies, and disaster losses.

[0063] Based on this, in step S100, regional impact monitoring is performed by selecting the regional industrial cumulative per capita emissions in the per capita carbon emissions, the regional average warming rate in the warming rate, and the regional climate disaster data.

[0064] In one embodiment, the cumulative per capita emissions of regional industrialization, the regional average warming rate and the regional climate disaster data are obtained by obtaining various types of climate observation data. As a preferred implementation method, the cumulative per capita emissions of regional industrialization, the regional average warming rate and the regional climate disaster data are obtained by obtaining carbon emission data CDIAC, the Global Historical Climate Network Monthly Dataset GHCN-V4, and the Global Climate Disaster and Risk Loss Database (EM-DAT).

[0065] The region includes countries, provinces, urban areas or custom geographical areas. As a preferred implementation, the region includes countries. By acquiring data in carbon emission data CDIAC, the Global Historical Climate Network Monthly Dataset GHCN-V4, and the Global Climate Disaster and Risk Loss Database (EM-DAT) on a country basis, the regional industrialization cumulative per capita emissions, regional average warming rate and regional climate disaster data are determined.

[0066] The geographical composition of each region can constitute a global region. Based on this, the average data in the global range is determined by accumulating and averaging the regional data.

[0067] According to the regional cumulative per capita emissions of industrialization and the regional composition of the world, the global cumulative per capita emissions of industrialization are calculated. In one embodiment, the process of calculating the global cumulative per capita emissions of industrialization according to the regional cumulative per capita emissions of industrialization in step S101, and obtaining the first monitoring value corresponding to the region by the difference between the regional cumulative per capita emissions of industrialization and the global cumulative per capita emissions of industrialization, is as follows:

[0068] Cumulative per capita emissions from regional industrialization C i As follows:

[0069]

[0070] Among them, i represents region, represents the carbon emissions of region i in year t, and represents the population of region i in a specific base year t;

[0071] The first monitoring value X is as follows:

[0072]

[0073] Where n represents the total number of regions. represents the cumulative per capita emissions from global industrialization.

[0074] In one of the embodiments, the difference between the cumulative per capita emissions of regional industrialization and the cumulative per capita emissions of global industrialization is multiplied by a correction coefficient to determine the first monitoring value. It should be noted that the first monitoring value is determined by using the difference between the cumulative per capita emissions of regional industrialization and the cumulative per capita emissions of global industrialization. In the subsequent interval discretization processing, the discrete results presented by the difference are more dispersed compared to other data such as ratios, highlighting the differences between regions. At the same time, the configuration of the subsequent loss function is more effective in correcting the discrete results of the difference. Compared with complex data processing forms such as standard deviation, the difference method is convenient for reducing the amount of data calculation, and the gap in effect verification is smaller than the ratio. Similarly, the subsequent second monitoring values ​​are all calculated in the form of differences.

[0075] Based on this, the global average warming rate is calculated according to the regional average warming rate and the regional composition of the world. Generally, when obtaining climate data, the climate data needs to be further processed to determine the global average warming rate.

[0076] In one embodiment, the regional average warming rate is determined by performing anomaly processing on the temperature data in the multi-year climate data, wherein the regional average warming rate represents the degree Celsius of the temperature change in the region every ten years (℃ / 10a).

[0077] In one embodiment, Figure 2 A flow chart of a method for monitoring the impact of global carbon emissions, warming and disaster risks according to another embodiment of the present invention is shown in FIG. Figure 2 As shown, the process of obtaining the regional average temperature increase rate in step S100 includes steps S200 and S201:

[0078] S200, obtaining the original series value, time regression coefficient and residual of the regional temperature;

[0079] S201, calculating the climate tendency rate based on the original temperature series value, time regression coefficient and residual, and determining the regional average warming rate based on the average value of the climate tendency rate.

[0080] Wherein, step S200 and step S201 are as follows:

[0081] y=at+b

[0082] Among them, y represents the original temperature series value, a×10 represents the climate tendency rate, whose unit is ℃ / 10a, t represents the time regression coefficient, and b represents the residual. The regional average warming rate is determined based on the climate tendency rate.

[0083] In one embodiment, in step S102, the global average warming rate is calculated according to the regional average warming rate, and the second monitoring value corresponding to the region is obtained according to the difference between the regional average warming rate and the global average warming rate, as shown in the following formula:

[0084]

[0085] Where Z represents the second monitoring value, a i represents the regional average warming rate of region i, represents the global average warming rate.

[0086] Among them, regional climate disaster data includes multiple types of data. Therefore, it is necessary to standardize the regional climate disaster data so that it falls into a specific interval for interval discretization. Among them, the standardization of regional climate disaster data includes function conversion or normalization.

[0087] In one embodiment, the regional climate disaster data includes the regional disaster-related population mortality rate and the regional disaster-related economic loss rate.

[0088] In one embodiment, if Figure 2 As shown, the process of performing standardization processing on the regional climate disaster data in step S103 to obtain the third monitoring value includes steps S300 to S302:

[0089] S300, performing standardization processing on the regional disaster-related population mortality rate and the regional disaster-related economic loss rate to obtain a standardized regional disaster-related population mortality rate and a standardized regional disaster-related economic loss rate;

[0090] S301, coefficient correction is made to the standardized regional disaster-related population mortality rate and the standardized regional disaster-related economic loss rate;

[0091] S302, determining a third monitoring value according to the sum of the standardized regional disaster-related population mortality rate and the standardized regional disaster-related economic loss rate after coefficient correction.

[0092] In one embodiment, the regional disaster population mortality rate H is as follows:

[0093]

[0094] Among them, d i represents the number of deaths due to disasters in the region in year i, p i represents the regional population in year i.

[0095] At the same time, combined with the GDP data of each country, the economic loss rate V due to disasters in each country during this period is calculated as follows:

[0096]

[0097] Among them, D i represents the amount of economic loss caused by disasters in the region in year i, G i Represents the regional GDP value in the i-th year.

[0098] The regional disaster-related population mortality rate and regional disaster-related economic loss rate are normalized to achieve standardization. The standardized value is between 0 and 1.

[0099] A weight value of 0.5 is assigned as the correction coefficient for the regional disaster-related population mortality rate and the regional disaster-related economic loss rate. It should be noted that the configuration of the weight value is not unique.

[0100] According to the sum of the standardized regional disaster-related population mortality rate and the standardized regional disaster-related economic loss rate after coefficient correction, the third monitoring value E is determined as follows:

[0101]

[0102] in, represents the standardized regional disaster population mortality rate, represents the standardized regional disaster-caused economic loss rate, Hmax represents the maximum regional disaster-caused population mortality rate, Hmin represents the minimum regional disaster-caused population mortality rate, Vmax represents the maximum regional disaster-caused economic loss rate, and Vmin represents the minimum regional disaster-caused economic loss rate.

[0103] Among them, the first monitoring value corresponds to the carbon emission, which characterizes the emission level of the region. The second monitoring value corresponds to the warming rate, which characterizes the warming level of the region. The third monitoring value corresponds to the climate disaster, which characterizes the disaster level of the region. Based on this, according to the preset result definition of the emission level, warming level and disaster level, the preset mapping table determined will distinguish the emission level, warming level and disaster level horizontally, determine each discrete interval, and determine the first monitoring value, second monitoring value and third monitoring value corresponding to each discrete interval. Based on this, the correspondence between the preset numerical value and the impact result is realized.

[0104] In one implementation, uniform interval discretization processing is performed on the first monitoring value, the second monitoring value, and the third monitoring value.

[0105] In one embodiment, if Figure 2 As shown, the process of performing interval discretization processing on the first monitoring value, the second monitoring value and the third monitoring value in step S104 includes steps S400 to S402:

[0106] S400, performing a first interval discretization process on the first monitoring value; the representation interval corresponding to the first interval discretization process includes a high emission area and a low emission area;

[0107] S401, performing a second interval discretization process on the second monitoring value; the second interval discretization process corresponds to a high temperature increase area and a low temperature increase area of ​​the characterization interval;

[0108] S402, discretizing the third monitoring value according to the third interval; the third interval discretizing corresponds to the characterization interval high disaster loss rate level area and low high disaster loss rate level area.

[0109] According to the size of the first monitoring value, the characterization interval is distinguished to complete the discretization process. As a preferred implementation, when the first monitoring value is greater than 0, the corresponding characterization interval is a high emission area; when the first monitoring value is less than 0, the corresponding characterization interval is a low emission area.

[0110] According to the size of the second monitoring value, the characterization interval is distinguished to complete the discretization process. As a preferred implementation, when the second monitoring value is greater than 0, the corresponding characterization interval is a high temperature increase area; when the second monitoring value is less than 0, the corresponding characterization interval is a low temperature increase area.

[0111] According to the size of the third monitoring value, the characterization interval is distinguished to complete the discretization process. As a preferred implementation, the characterization interval greater than the median of the third monitoring value is a high disaster loss rate level area, and the characterization interval less than the median of the third monitoring value is a low disaster loss rate level area.

[0112] Based on this, the characterization intervals of the first monitoring value, the second monitoring value, and the third monitoring value are determined, and each characterization interval is assigned a value to complete the interval discretization process. For example, a value of 1 is assigned to low emission areas, low temperature increase areas, and low disaster loss rate areas, and a value of 2 is assigned to high emission areas, high temperature increase areas, and high disaster loss rate areas.

[0113] In one embodiment, the preset value of the preset mapping table includes a combination of the first monitoring value, the second monitoring value and the third monitoring value according to the order of assigning values ​​to the characterization intervals, such as 121 / 122, etc. Based on the above assignments, the preset values ​​include eight values ​​such as 111 / 112 / 121 / 211 / 122 / 212 / 222 / 221. The corresponding impact result is a combination of characterization intervals. Based on this, according to the predetermined preset mapping table, the corresponding characterization interval combination can be obtained by calculating any area.

[0114] In one embodiment, Figure 3 A flow chart of a method for monitoring the impact of global carbon emissions, warming and disaster risks according to another embodiment of the present invention is shown in FIG. Figure 3 As shown, the method for monitoring the impact of global carbon emissions, warming and disaster risks in another embodiment further includes step S500:

[0115] S500, the numerical result of the interval discretization processing is corrected by the loss function; the loss function loss is as follows:

[0116]

[0117] Where n represents the region, N represents the total number of regions, s represents the numerical result, S represents the corrected numerical result, and δ represents the error term.

[0118] When performing interval discretization processing on multiple representation intervals (representation intervals much larger than 2), the numerical results tend to be averaged, and it is difficult to reflect the differences between regions. Based on this, the loss function is optimized through the output numerical results to reflect the differences between regions. It should be noted that the representation interval corrected by the loss function is optimal within 8-15.

[0119] In one embodiment, the correction result of the loss function is approximated to an integer as the final corrected numerical result.

[0120] In one embodiment, after the process of correcting the numerical result of the interval discretization processing by the loss function in step S500, the method further includes the following steps:

[0121] Perform health algorithm correction on the corrected numerical results.

[0122] Since the results after discretization processing will have precision fluctuations due to the influence of the interval range, the discretization processing results are corrected through the health algorithm to ensure the precision of the numerical results and reduce the fluctuation of the numerical results.

[0123] In one embodiment, the process of performing health algorithm correction on the corrected numerical result is as follows:

[0124]

[0125] Wherein, τ represents the mapping value of the first monitoring value, the second monitoring value or the third monitoring value within the characterization interval, Δτ represents the correction amount of the mapping value to the maximum or minimum range of the characterization interval, S Δ represents the numerical result after the health algorithm is corrected, γ and χ represent the correction coefficients, and θ new Indicates health value.

[0126] The health value, correction coefficient and correction amount can be assigned according to the monitoring results to adjust the reference of the health algorithm correction. In one embodiment, the rounded discretization result is determined by rounding the numerical result after the health algorithm correction.

[0127] The impact monitoring method of global carbon emissions, warming and disaster risks of any of the above embodiments, after obtaining the regional industrial cumulative per capita emissions, regional average warming rate and regional climate disaster data of each region, calculates the global industrial cumulative per capita emissions according to the regional industrial cumulative per capita emissions, and obtains the first monitoring value corresponding to the region by the difference between the regional industrial cumulative per capita emissions and the global industrial cumulative per capita emissions, calculates the global average warming rate according to the regional average warming rate, and obtains the second monitoring value corresponding to the region according to the difference between the regional average warming rate and the global average warming rate, and performs standardization processing on the regional climate disaster data to obtain the third monitoring value. Finally, the first monitoring value, the second monitoring value and the third monitoring value are respectively subjected to interval discretization processing, and the corresponding impact results are searched in the preset mapping table according to the numerical results of the interval discretization processing. Based on this, by performing interval discretization processing on the regional industrial cumulative per capita emissions, the regional average warming rate and the regional climate disaster data, the impact relationship between global carbon emissions, warming and disaster risks is characterized according to the processing results, and the representation form is fixed according to the preset mapping table, providing a reasonable regional impact monitoring method.

[0128] The embodiment of the present invention also provides a device for monitoring the impact of global carbon emissions, warming and disaster risks.

[0129] Figure 4 The module structure diagram of the global carbon emission, warming and disaster risk impact monitoring device in one embodiment is as follows: Figure 4 As shown, a global carbon emission, warming and disaster risk impact monitoring device according to one embodiment includes an information acquisition module 100, a first calculation module 101, a second calculation module 102, a third calculation module 103 and a data search module 104:

[0130] The information acquisition module 100 is used to obtain the regional industrial cumulative per capita emissions, regional average temperature increase rate and regional climate disaster data of each region;

[0131] The first calculation module 101 is used to calculate the global industrial cumulative per capita emissions according to the regional industrial cumulative per capita emissions, and obtain the first monitoring value corresponding to the region by the difference between the regional industrial cumulative per capita emissions and the global industrial cumulative per capita emissions;

[0132] A second calculation module 102 is used to calculate the global average warming rate according to the regional average warming rate, and obtain a second monitoring value corresponding to the region according to the difference between the regional average warming rate and the global average warming rate;

[0133] The third calculation module 103 is used to perform standardization processing on the regional climate disaster data to obtain a third monitoring value;

[0134] The data search module 104 is used to perform interval discretization processing on the first monitoring value, the second monitoring value and the third monitoring value respectively, and search the corresponding impact result in the preset mapping table according to the numerical result of the interval discretization processing; wherein the preset mapping table includes the correspondence between the preset numerical value and the impact result.

[0135] The above-mentioned global carbon emission, warming and disaster risk impact monitoring device, after obtaining the regional industrial cumulative per capita emissions, regional average warming rate and regional climate disaster data of each region, calculates the global industrial cumulative per capita emissions according to the regional industrial cumulative per capita emissions, and obtains the first monitoring value corresponding to the region by the difference between the regional industrial cumulative per capita emissions and the global industrial cumulative per capita emissions, calculates the global average warming rate according to the regional average warming rate, and obtains the second monitoring value corresponding to the region according to the difference between the regional average warming rate and the global average warming rate, and performs standardization processing on the regional climate disaster data to obtain the third monitoring value. Finally, the first monitoring value, the second monitoring value and the third monitoring value are respectively subjected to interval discretization processing, and the corresponding impact results are searched in the preset mapping table according to the numerical results of the interval discretization processing. Based on this, by performing interval discretization processing on the regional industrial cumulative per capita emissions, the regional average warming rate and the regional climate disaster data, the impact relationship between global carbon emissions, warming and disaster risks is characterized according to the processing results, and the representation form is fixed according to the preset mapping table, providing a reasonable regional impact monitoring method.

[0136] An embodiment of the present invention further provides a computer storage medium on which computer instructions are stored. When the instructions are executed by a processor, the surface temperature monitoring method based on the conduction path of any of the above embodiments is implemented.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0138] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the relevant technology. The computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, RAM, ROM, magnetic disks or optical disks.

[0139] Corresponding to the above-mentioned computer storage medium, in one embodiment, a computer device is also provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for monitoring the impact of global carbon emissions, warming and disaster risks as described in any one of the above-mentioned embodiments is implemented.

[0140] The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 5As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for monitoring the impact of global carbon emissions, warming and disaster risks is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0141] The above-mentioned computer device, after obtaining the regional industrial cumulative per capita emissions, regional average warming rate and regional climate disaster data of each region, calculates the global industrial cumulative per capita emissions according to the regional industrial cumulative per capita emissions, and obtains the first monitoring value corresponding to the region by the difference between the regional industrial cumulative per capita emissions and the global industrial cumulative per capita emissions, calculates the global average warming rate according to the regional average warming rate, and obtains the second monitoring value corresponding to the region according to the difference between the regional average warming rate and the global average warming rate, and performs standardization processing on the regional climate disaster data to obtain the third monitoring value. Finally, the first monitoring value, the second monitoring value and the third monitoring value are respectively subjected to interval discretization processing, and the corresponding impact results are searched in the preset mapping table according to the numerical results of the interval discretization processing. Based on this, by performing interval discretization processing on the regional industrial cumulative per capita emissions, the regional average warming rate and the regional climate disaster data, the impact relationship between global carbon emissions, warming and disaster risks is characterized according to the processing results, and the representation form is fixed according to the preset mapping table, providing a reasonable regional impact monitoring method.

[0142] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0143] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for monitoring the impact of global carbon emissions, warming and disaster risks, characterized in that: Includes steps: Obtain the regional industrialization cumulative per capita emissions, regional average warming rate and regional climate disaster data of each region; the regional climate disaster data include regional disaster-related population mortality rate and regional disaster-related economic loss rate; Calculate the global industrial cumulative per capita emissions based on the regional industrial cumulative per capita emissions, and obtain the first monitoring value corresponding to the region by taking the difference between the regional industrial cumulative per capita emissions and the global industrial cumulative per capita emissions; Calculating the global average warming rate according to the regional average warming rate, and obtaining a second monitoring value corresponding to the region according to the difference between the regional average warming rate and the global average warming rate; Performing standardization processing on the regional climate disaster data to obtain a third monitoring value; The first monitoring value, the second monitoring value and the third monitoring value are respectively discretized into intervals, and the corresponding impact results are searched in a preset mapping table according to the numerical results of the interval discretization processing; wherein the preset mapping table includes a correspondence between preset numerical values ​​and impact results.

2. The method for monitoring the impact of global carbon emissions, warming and disaster risks according to claim 1, characterized in that: The process of calculating the global industrial cumulative per capita emissions according to the regional industrial cumulative per capita emissions, and obtaining the first monitoring value corresponding to the region by the difference between the regional industrial cumulative per capita emissions and the global industrial cumulative per capita emissions is as follows: Cumulative per capita emissions from industrialization in the region C i As follows: Wherein, i represents the region, represents the carbon emissions of region i in year t, and represents the population of region i in a specific base year t; The first monitoring value X is as follows: Where n represents the total number of regions. represents the cumulative per capita emissions from global industrialization.

3. The method for monitoring the impact of global carbon emissions, warming and disaster risks according to claim 1, characterized in that: The global average warming rate is calculated according to the regional average warming rate, and the second monitoring value corresponding to the region is obtained according to the difference between the regional average warming rate and the global average warming rate, as shown in the following formula: Wherein, Z represents the second monitoring value, a i represents the regional average warming rate of region i, denotes the stated global average warming rate.

4. The method for monitoring the impact of global carbon emissions, warming and disaster risks according to claim 1, characterized in that: The process of obtaining the regional average temperature increase rate comprises the steps of: Obtain the original series value, time regression coefficient and residual of the temperature in the area; The climate tendency rate is calculated based on the original temperature sequence values, time regression coefficients and residuals, and the average warming rate of the region is determined based on the average value of the climate tendency rate.

5. The method for monitoring the impact of global carbon emissions, warming and disaster risks according to claim 1, characterized in that: The process of performing standardization processing on the regional climate disaster data to obtain the third monitoring value comprises the steps of: Performing standardization processing on the regional disaster-related population mortality rate and the regional disaster-related economic loss rate to obtain a standardized regional disaster-related population mortality rate and a standardized regional disaster-related economic loss rate; Perform coefficient correction on the standardized regional disaster-induced population mortality rate and the standardized regional disaster-induced economic loss rate; The third monitoring value is determined according to the sum of the standardized regional disaster-induced population mortality rate and the standardized regional disaster-induced economic loss rate after correction by the coefficient.

6. The method for monitoring the impact of global carbon emissions, warming and disaster risks according to claim 1, characterized in that: The process of performing interval discretization processing on the first monitoring value, the second monitoring value and the third monitoring value respectively comprises the steps of: The first monitoring value is discretized according to a first interval; the characterization interval corresponding to the first interval discretization includes a high emission area and a low emission area; The second monitoring value is discretized according to the second interval; the second interval discretization corresponds to the high temperature increase area and the low temperature increase area of ​​the characterization interval; The third monitoring value is discretized according to a third interval; the third interval discretization corresponds to a characterization interval high disaster loss rate level area and a low-high disaster loss rate level area.

7. The method for monitoring the impact of global carbon emissions, warming and disaster risks according to any one of claims 1 to 6, characterized in that: Also includes the steps: The numerical result of the interval discretization process is corrected by a loss function; the loss function loss is as follows: Wherein, n represents the region, N represents the total number of the regions, s represents the numerical result, S represents the corrected numerical result, and δ represents the error term.

8. A device for monitoring the impact of global carbon emissions, warming and disaster risks, characterized in that: include: The information acquisition module is used to obtain the regional industrialization cumulative per capita emissions, regional average warming rate and regional climate disaster data of each region; The regional climate disaster data include regional disaster-related population mortality rate and regional disaster-related economic loss rate; A first calculation module is used to calculate the global industrial cumulative per capita emissions according to the regional industrial cumulative per capita emissions, and obtain a first monitoring value corresponding to the region by taking the difference between the regional industrial cumulative per capita emissions and the global industrial cumulative per capita emissions; a second calculation module, configured to calculate a global average warming rate according to the regional average warming rate, and obtain a second monitoring value corresponding to the region according to a difference between the regional average warming rate and the global average warming rate; A third calculation module is used to perform standardization processing on the regional climate disaster data to obtain a third monitoring value; A data search module is used to perform interval discretization processing on the first monitoring value, the second monitoring value and the third monitoring value respectively, and search for corresponding impact results in a preset mapping table according to the numerical results of the interval discretization processing; wherein the preset mapping table includes a correspondence between preset numerical values ​​and impact results.

9. A computer storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by the processor, the method for monitoring the impact of global carbon emissions, warming and disaster risks as described in any one of claims 1 to 7 is implemented.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method for monitoring the impact of global carbon emissions, warming and disaster risks as described in any one of claims 1 to 7 is implemented.

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