Climate risk response method based on measure response function and co-optimization network

Abstract

The application discloses a climate risk response method based on a measure response function and a collaborative optimization network, relates to the technical field of climate governance, and comprises the following steps: determining the risk value reduction amount corresponding to each response measure according to the first risk value of each risk element, the measure response function and the measure implementation parameter of each response measure, and determining the collaborative reduction coefficient between each two response measures; determining the reduced risk value of a target region according to the first risk value of each risk element, the measure selection result of each response measure, each collaborative reduction coefficient and the risk value reduction amount of each response measure; determining the total implementation time length according to the measure selection result of each response measure and the implementation time length of each response measure; and taking the minimum reduced risk value and the shortest total implementation time length as the target, solving the measure selection result of each response measure to obtain the final selection result of each response measure, so that the climate disaster can be avoided to the greatest extent and the response efficiency can be improved.

Description

Technical Field

[0001] This invention relates to the field of climate governance technology, and in particular to a climate risk response method based on measure response functions and collaborative optimization networks. Background Technology

[0002] As global warming continues, extreme events such as extreme precipitation, high temperatures, strong winds, floods, and droughts are becoming increasingly common. Taking measures to address climate risks and carrying out climate governance has become an important direction for disaster prevention and mitigation.

[0003] In existing technologies, the mainstream approach involves conducting risk assessments on each climate response measure under fixed scenarios, and then determining the appropriate measures to be implemented based on the assessment results. However, the effectiveness of these climate response measures assessed by this existing technology is fragmented. In complex climate scenarios, this can easily lead to a mismatch between the determined climate response measures and the actual climate risks, resulting in poor response outcomes. Summary of the Invention

[0004] This invention provides a climate risk response method based on measure response functions and collaborative optimization networks to improve the efficiency and effectiveness of climate risk response.

[0005] In a first aspect, embodiments of the present invention provide a climate risk response method based on a measure response function and a collaborative optimization network, including:

[0006] The system obtains the first risk value of risk factors in the target area, the response function and implementation duration of the corresponding countermeasures for each risk factor, and the implementation parameters of each response function; the risk factors include hazard, exposure, and vulnerability; the response function is used to correct the first risk value of the corresponding risk factor.

[0007] Based on the first risk value of each risk element, the response function of each response measure, and the implementation parameters of the measures, determine the risk value reduction amount corresponding to each response measure, and determine the synergistic reduction coefficient between each pair of response measures;

[0008] Using the selection results of each of the aforementioned countermeasures as the parameters to be solved, the reduced risk value of the target area is determined based on the first risk value of each risk element, the selection results of each of the aforementioned countermeasures, the collaborative reduction coefficients, and the risk value reduction amount of each of the aforementioned countermeasures.

[0009] The total implementation time is determined based on the selection results of each of the aforementioned countermeasures and the implementation duration of each of the aforementioned countermeasures;

[0010] With the goal of minimizing the reduced risk value and the shortest total implementation time, the selection results of each of the aforementioned countermeasures are solved to obtain the final selection results of each of the aforementioned countermeasures.

[0011] Secondly, embodiments of the present invention also provide a climate risk response device based on a measure response function and a collaborative optimization network, comprising:

[0012] The acquisition module is used to acquire the first risk value of risk elements in the target area, the response function and implementation duration of the corresponding countermeasures for each risk element, and the implementation parameters of each response function; the risk elements include hazard, exposure, and vulnerability; the response function is used to correct the first risk value of the corresponding risk element;

[0013] The reduction coefficient determination module is used to determine the risk value reduction amount corresponding to each response measure and the synergistic reduction coefficient between each pair of response measures based on the first risk value of each risk element, the response function of each response measure and the implementation parameters of the response measures.

[0014] The reduction value determination module is used to determine the reduced risk value of the target area by taking the selection result of each of the aforementioned countermeasures as the parameter to be solved, based on the first risk value of each risk element, the selection result of each of the aforementioned countermeasures, the collaborative reduction coefficient, and the risk value reduction amount of each of the aforementioned countermeasures.

[0015] The implementation duration determination module is used to determine the total implementation duration based on the selection results of each of the aforementioned countermeasures and the implementation duration of each of the aforementioned countermeasures;

[0016] The measure selection solution module is used to solve the measure selection results of each of the above-mentioned countermeasures with the goal of minimizing the risk value after reduction and minimizing the total implementation time, so as to obtain the final selection result of each of the above-mentioned countermeasures.

[0017] Thirdly, embodiments of the present invention also provide an electronic device, comprising:

[0018] At least one processor; and

[0019] A memory that is communicatively connected to at least one processor; wherein

[0020] The memory stores instructions that can be executed by at least one processor, which enables the at least one processor to perform the climate risk response method based on the action response function and the cooperative optimization network provided in any embodiment of the present invention.

[0021] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions for causing a processor to execute the climate risk response method based on a measure response function and a cooperative optimization network according to any embodiment of the present invention.

[0022] Fifthly, embodiments of the present invention also provide a computer program product, characterized in that the computer program product includes a computer program, which, when executed by a processor, implements the climate risk response method based on a measure response function and a collaborative optimization network according to any embodiment of the present invention.

[0023] The embodiments of the present invention can determine the risk reduction amount of a single response measure on the risk value of a risk element through the response function of the measure, and calculate the synergistic reduction coefficient between every two response measures. Thus, using the result of measure selection as a parameter, the accurate risk value after the synergistic implementation of multiple response measures is determined, and the optimal combination of application measures is solved with the dual objectives of minimizing the risk value after reduction and minimizing the duration. This improves the risk reduction effect and response efficiency at the same time.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a climate risk response method based on a measure response function and a collaborative optimization network provided in Embodiment 1 of the present invention;

[0027] Figure 2 This is a flowchart of a climate risk response method based on a measure response function and a collaborative optimization network provided in Embodiment 2 of the present invention;

[0028] Figure 3 This is a schematic diagram of a climate risk response device based on a measure response function and a collaborative optimization network according to Embodiment 3 of the present invention;

[0029] Figure 4 This is a structural diagram of an electronic device that implements the climate risk response method based on the measure response function and the collaborative optimization network of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first" and "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] In the technical solutions of the embodiments of the present invention, the acquisition, storage and application of the first risk value and implementation time, etc., all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0033] Example 1

[0034] Figure 1 This is a flowchart of a climate risk response method based on a measure response function and a collaborative optimization network provided in Embodiment 1 of the present invention. This embodiment can be applied to the situation of determining measures to address climate risks. The method can be executed by a climate risk response device based on a measure response function and a collaborative optimization network. The climate risk response device based on a measure response function and a collaborative optimization network can be implemented in hardware and / or software and specifically configured in an electronic device.

[0035] See Figure 1 The climate risk response methods shown include those based on measure response functions and collaborative optimization networks:

[0036] S101. Obtain the first risk value of the risk elements in the target area, the response function and implementation duration of the corresponding countermeasures for each risk element, and the implementation parameters of each response function.

[0037] S102. Based on the first risk value of each risk element, the response function of each response measure, and the implementation parameters of the measures, determine the risk value reduction amount corresponding to each response measure, and determine the synergistic reduction coefficient between each pair of response measures.

[0038] S103. Using the selection results of each of the aforementioned countermeasures as the parameters to be solved, and based on the first risk value of each risk element, the selection results of each of the aforementioned countermeasures, the collaborative reduction coefficients, and the risk value reduction amount of each of the aforementioned countermeasures, determine the reduced risk value of the target area.

[0039] S104. Determine the total implementation time based on the selection results of each of the aforementioned countermeasures and the implementation duration of each of the aforementioned countermeasures.

[0040] S105. With the goal of minimizing the reduced risk value and the shortest total implementation time, the selection results of each of the aforementioned countermeasures are solved to obtain the final selection results of each of the aforementioned countermeasures.

[0041] In this embodiment, the target area can be a region where countermeasures are to be taken to address climate risks; risk factors can include, but are not limited to, hazard, exposure, and vulnerability; wherein, hazard can be used to characterize the intensity of climate risk in the target area; exposure can be used to characterize the scale of exposure of disaster-bearing bodies in the target area; vulnerability can be used to characterize the degree of fragility of disaster-bearing bodies in the target area. The first risk value can be the original risk value. In one specific implementation, the first risk value of hazard, the first risk value of exposure, and the first risk value of vulnerability can be determined using any existing climate risk assessment framework.

[0042] Response measures can be measures to address climate risks, including but not limited to engineering control measures, ecological buffer measures, spatial control measures, early warning and response measures, resource allocation measures, and emergency recovery measures. Among them, engineering control measures can be measures to address climate risks by implementing engineering projects, including but not limited to at least one of the following: dike reinforcement and drainage pipe networks; ecological buffer measures can be measures to address climate risks by regulating the ecology, including but not limited to one of the following: vegetation restoration and wetland restoration; spatial control measures can be measures to address climate risks by controlling space, including but not limited to one of the following: relocation for disaster avoidance, establishment of no-construction zones, and control of risk zones; early warning and response measures can be measures to address climate risks by issuing early warnings, including but not limited to one of the following: monitoring and early warning, emergency response, evacuation and disaster avoidance, and stockpiling emergency supplies; resource allocation measures can be measures to address climate risks by allocating resources, including but not limited to one of the following: inter-regional water transfer, stockpiling drought-resistant water sources, and scheduling water supply; and emergency recovery measures can be measures to address climate risks by conducting emergency recovery after a disaster, including but not limited to one of the following: pre-positioning emergency supplies, rapid repair facilities, construction of emergency dispatch platforms, and standardized shelters.

[0043] The measure response function is used to correct the first risk value of the corresponding risk element. Accordingly, the measure response function may include, but is not limited to, engineering measure response functions corresponding to engineering control measures, ecological measure response functions corresponding to ecological buffer measures, spatial control measure response functions corresponding to spatial control measures, early warning response measure response functions corresponding to early warning response measures, resource allocation measure response functions corresponding to resource allocation measures, and emergency recovery measure response functions corresponding to emergency recovery measures.

[0044] Implementation duration can be the time from the start to the end of implementation of a response measure. This duration can be a calibration value, which can be determined from the implementation design data of the response measure. Implementation parameters can be parameters describing how the response measure is specifically implemented. Risk reduction can be the change in the risk value of climate risk caused by the implementation of the response measure. Synergistic reduction coefficient can be used to characterize the ability of two response measures to further reduce the risk value due to the synergistic effect between them. Response selection result is a binary result, including selection and non-selection. Reduced risk value can be the risk value of the target area after implementing the response measure. Total implementation duration can be the sum of the implementation durations required to implement the selected response measures.

[0045] Specifically, the assessment values ​​of hazard, exposure, and vulnerability in the target area assessed by the climate risk assessment framework are obtained and respectively determined as the first risk value for hazard, the first risk value for exposure, and the first risk value for vulnerability. The response function and implementation duration of the corresponding countermeasures for each risk element are obtained, as well as the implementation parameters corresponding to each response function. Using a specific algorithm, based on the first risk value of each risk element, the response function of each countermeasure, and the implementation parameters, the risk reduction amount corresponding to each countermeasure is determined, as well as the synergistic reduction coefficient between any two countermeasures. Finally, using a specific algorithm, the selection results of each countermeasure are used to determine the risk reduction amount. For the parameters to be solved, the reduced risk value of the target area is determined based on the first risk value of each risk element, the selection result of each response measure, the synergistic reduction coefficient, and the risk value reduction amount of each response measure. When response measures are implemented sequentially, the implementation time of the selected response measures is accumulated to obtain the total implementation time. When response measures are implemented in parallel, the longest implementation time among the selected response measures is determined as the total implementation time. Using a certain algorithm, with the goal of minimizing the reduced risk value and the shortest total implementation time, the selection result of each response measure is solved to obtain the final selection result of each response measure.

[0046] Optionally, the step of using the selection results of each of the aforementioned countermeasures as the parameters to be solved, and determining the reduced risk value of the target region based on the first risk value of each risk element, the selection results of each of the aforementioned countermeasures, the collaborative reduction coefficients, and the risk value reduction amount of each of the aforementioned countermeasures, includes: fusing the first risk values ​​of each of the aforementioned risk elements to obtain the first total risk value of the target region; determining the collaborative measure reduction amount based on the selection results of each of the aforementioned countermeasures, the collaborative reduction coefficient between every two countermeasures, and the risk value reduction amount of each of the aforementioned countermeasures; determining the sum of the single risk value reduction amounts based on the selection results of each of the aforementioned countermeasures and the risk value reduction amounts of each of the aforementioned countermeasures; adding the collaborative measure reduction amounts and the sum of the single risk value reduction amounts to obtain the total risk reduction amount; and determining the difference between the first total risk value and the total risk reduction amount as the reduced risk value.

[0047] Specifically, the first risk value of each of the aforementioned risk elements is multiplied to obtain the first total risk value of the target region; the reduction amount of coordinated measures is determined using the following formula:

[0048] ;

[0049] in, This indicates the amount of reduction achieved through coordinated measures; Indicates the number of types of response measures; This indicates the result of the selection of the response measures for the i-th type. This indicates the result of the selection of the response measures for the j-th type. This represents the synergistic reduction coefficient between the i-th type of response and the j-th type of response; This represents the risk reduction amount of the i-th type of response measure; This represents the risk reduction amount of the j-th type of response measure.

[0050] The total reduction amount for each risk value is determined using the following formula:

[0051] ;

[0052] in, This represents the total reduction in a single risk value.

[0053] The sum of the reduction amount of the coordinated measures and the reduction amount of the single risk value is added together to obtain the total risk reduction; the difference between the first total risk value and the total risk reduction is determined as the risk value after reduction.

[0054] Understandably, by adopting the above technical solution, the risk reduction amount of each response measure is determined independently, and the reduction amount of the coordinated measures is determined separately. The total risk reduction amount is obtained by summing them up. Then, the risk value after reduction is determined by the reduction amount and the first total risk value, which improves the accuracy of the risk value after reduction.

[0055] Optionally, the step of solving the selection results of each of the response measures with the goal of minimizing the reduced risk value and the shortest total implementation time to obtain the final selection result of each of the response measures includes: weighting and fusing the reduced risk value and the total implementation time to obtain a target value; and solving the selection results of each of the response measures with the goal of minimizing the target value to obtain the final selection result of each of the response measures.

[0056] Specifically, the reduced risk value and the total implementation time are weighted and fused to obtain a target value. With the goal of minimizing the target value, the selection results of each countermeasure are updated successively until the target value converges or the number of updates reaches an update threshold. The selection results of each countermeasure when the target value converges or the number of updates reaches the update threshold are taken as the final selection results of each countermeasure. It should be noted that the weighting of the reduced risk value and the total implementation time can be set independently by technical personnel based on the desired reduction of the risk value or the desired minimization of the total implementation time; this invention does not limit this. Similarly, the update threshold can be set independently by technical personnel based on actual needs or practical experience; this invention does not limit this.

[0057] Understandably, by adopting the above technical solution, the dual objectives of risk value and implementation time can be transformed into a single objective value for solution through weighted fusion, thereby reducing the solution complexity of multi-objective optimization. At the same time, different decision preferences can be flexibly matched by the weights of risk value and implementation time, ensuring the effectiveness of risk response while also taking into account implementation efficiency. This not only improves the solution efficiency of the optimal measure selection result, but also enhances the adaptability to actual engineering scenarios.

[0058] The embodiments of the present invention can determine the risk reduction amount of a single response measure on the risk value of a risk element through the response function of the measure, and calculate the synergistic reduction coefficient between every two response measures. Thus, using the result of measure selection as a parameter, the accurate risk value after the synergistic implementation of multiple response measures is determined, and the optimal combination of application measures is solved with the dual objectives of minimizing the risk value after reduction and minimizing the duration. This improves the risk reduction effect and response efficiency at the same time.

[0059] Example 2

[0060] Figure 2 This is a flowchart of a climate risk response method based on a measure response function and a collaborative optimization network provided in Embodiment 2 of the present invention. The present invention has been optimized and improved based on the technical solutions of the above embodiments.

[0061] Furthermore, the step of "determining the risk reduction amount corresponding to each response measure and the synergistic reduction coefficient between each pair of response measures based on the first risk value of each risk element, the response function of each response measure, and the implementation parameters of the response measures" is refined to "integrating the first risk values ​​of each risk element to obtain the first total risk value of the target area; for each response measure, extracting the implementation parameters corresponding to the response function of the response measure from the implementation information; correcting the first risk value of the risk element corresponding to the response measure based on the response function of the response measure and the implementation parameters corresponding to the response function to obtain the second risk value of the risk element corresponding to the response measure; and correcting the first risk value of the risk element corresponding to the response measure based on the response function of the response measure and the implementation parameters of the response function." The second risk value of the corresponding risk element and the first risk value of the first auxiliary risk element are fused to obtain the second total risk value corresponding to the response measure; the first auxiliary risk element is a risk element other than the risk element corresponding to the response measure; the difference between the first total risk value and the second total risk value corresponding to the response measure is determined as the risk value reduction amount corresponding to the response measure; for every two response measures, a synergistic reduction coefficient between the two response measures is determined based on the first total risk value, the second risk value of the two response measures, the first risk value of the second auxiliary risk element, and the risk value reduction amount of the two response measures; the second auxiliary risk element is a risk element other than the risk element corresponding to the two response measures.

[0062] It should be noted that for any parts not described in detail in the embodiments of the present invention, please refer to the description in the foregoing embodiments.

[0063] See Figure 2 The climate risk response methods shown include those based on measure response functions and collaborative optimization networks:

[0064] S201. Obtain the first risk value of risk elements in the target area, the response function and implementation duration of the corresponding countermeasures for each risk element, and the implementation parameters of each response function; the risk elements include hazard, exposure and vulnerability; the response function is used to correct the first risk value of the corresponding risk element.

[0065] S202. The first risk values ​​of each of the aforementioned risk elements are integrated to obtain the first total risk value of the target region.

[0066] S203. For each response measure, extract the response parameters corresponding to the response function of the response measure from the implementation information of the response measure.

[0067] S204. Based on the response function of the response measures and the implementation parameters of the response function, the risk element corresponding to the response measures is modified to obtain the second risk value of the risk element corresponding to the response measures.

[0068] S205. The second risk value of the risk element corresponding to the response measure and the first risk value of the first auxiliary risk element are fused to obtain the second total risk value corresponding to the response measure; the first auxiliary risk element is a risk element other than the risk element corresponding to the response measure.

[0069] S206. The difference between the first total risk value and the second total risk value corresponding to the countermeasure is determined as the risk value reduction amount corresponding to the countermeasure.

[0070] S207. For each pair of response measures, a synergistic reduction coefficient between the two response measures is determined based on the first total risk value, the second risk value of the two response measures, the first risk value of the second auxiliary risk element, and the risk value reduction amount of the two response measures; the second auxiliary risk element is a risk element other than the risk element corresponding to the two response measures.

[0071] S208. Using the selection results of each of the aforementioned countermeasures as the parameters to be solved, and based on the first risk value of each risk element, the selection results of each of the aforementioned countermeasures, the collaborative reduction coefficients, and the risk value reduction amount of each of the aforementioned countermeasures, determine the reduced risk value of the target area.

[0072] S209. Determine the total implementation time based on the selection results of each of the aforementioned countermeasures and the implementation duration of each of the aforementioned countermeasures.

[0073] S210. With the goal of minimizing the reduced risk value and the shortest total implementation time, the selection results of each of the aforementioned countermeasures are solved to obtain the final selection results of each of the aforementioned countermeasures.

[0074] The second risk value can be the risk value of the risk element after correction by the response function. Specifically, the first risk values ​​of each risk element are multiplied together to obtain the first total risk value of the target area.

[0075] For each response measure, the implementation parameters corresponding to the response function of the response measure are extracted from the implementation information. The first risk value of the risk element corresponding to the response measure is corrected based on the response function and the implementation parameters, resulting in a second risk value. The second risk value and the first risk value of the first auxiliary risk element are multiplied to obtain a second total risk value. The difference between the first total risk value and the second total risk value is determined as the risk reduction amount. Using a specific algorithm, for every two response measures, a synergistic reduction coefficient is determined based on the first total risk value, the second risk values ​​of the two response measures, the first risk value of the second auxiliary risk element, and the risk reduction amount of the two response measures. The second auxiliary risk element is any risk element other than the risk elements corresponding to the two response measures.

[0076] Optionally, determining the synergistic reduction coefficient between the two response measures based on the first total risk value, the second risk values ​​of the two response measures, the first risk value of the second auxiliary risk element, and the risk value reduction amount of the two response measures includes: fusing the second risk values ​​of the two response measures and the first risk value of the second auxiliary risk element to obtain a third total risk value; calculating the risk value difference between the first total risk value and the second total risk value corresponding to the response measures; and determining the synergistic reduction coefficient between the two response measures based on the risk value difference and the risk value reduction amount of the two response measures.

[0077] Specifically, the second risk value of the two response measures and the first risk value of the second auxiliary risk element are multiplied to obtain a third total risk value; the risk value difference between the first total risk value and the second total risk value corresponding to the response measures is calculated; and a certain algorithm is used to determine the synergistic reduction coefficient between the two response measures based on the risk value difference and the risk value reduction amount of the two response measures.

[0078] Optionally, determining the synergistic reduction coefficient between the two response measures based on the risk value difference and the risk value reduction amount of the two response measures includes: calculating the sum of the risk value reduction amounts of the two response measures; calculating the difference between the risk value difference and the sum; and determining the ratio between the difference and the sum as the synergistic reduction coefficient between the two response measures.

[0079] For example, the synergistic reduction factor can be determined by the following formula:

[0080] ;

[0081] in, This represents the difference in risk values.

[0082] In an optional embodiment, for each response measure, the response function corresponding to the response measure is extracted from the response measure implementation information; the first risk value of the risk element corresponding to the response measure is corrected according to the response function of the response measure and the response function corresponding to the response function to obtain the second risk value of the risk element corresponding to the response measure, including:

[0083] For engineering control measures, the construction ratio and construction efficiency are extracted from the implementation information. The construction ratio can be the proportion of construction volume to the original building volume, such as the proportion of embankment reinforcement height to the original embankment height, or the proportion of pipeline expansion. The construction efficiency can be the reciprocal of the expected construction time. The first risk value of the hazard is corrected using the response function of the engineering control measure, the construction ratio, and the construction efficiency to obtain a second risk value corresponding to the engineering control measure. For example, the second risk value corresponding to the engineering control measure can be determined by the following formula:

[0084] ;

[0085] in, The second risk value indicates the level of danger corresponding to engineering control measures; Indicates the proportion of the project under construction; This indicates the efficiency of the engineering construction.

[0086] For ecological buffering measures, ecological restoration intensity and ecological measure impact coefficient are extracted from the implementation information. Ecological restoration intensity characterizes the restoration effect, such as the percentage increase in vegetation cover or the proportion of wetland restoration area. The ecological measure impact coefficient characterizes the degree of influence of unit ecological restoration intensity on rainfall runoff coefficient. The first risk value of the hazard is corrected using the measure response function, ecological restoration intensity, and ecological measure impact coefficient to obtain a second risk value corresponding to the ecological buffering measures. For example, the second risk value corresponding to the ecological buffering measures can be determined using the following formula:

[0087] ;

[0088] in, The second risk value indicates the level of danger corresponding to engineering control measures; Intensity of ecological restoration; This represents the impact coefficient of ecological measures.

[0089] For spatial control measures, the proportion of land prohibited from development, the proportion of population relocation, and the proportion of important facilities are extracted from the implementation information of the measures. The proportion of land prohibited from development can be the percentage of land area prohibited from development within the target area; the proportion of population relocation can be the percentage of the population needing to be relocated within the target area; the proportion of important facilities can be the percentage of a specified type of important facilities within the target area. The specified type can be determined by technical personnel, such as cultural heritage buildings, etc., and this invention does not limit this. The first risk value of exposure is corrected using the measure response function of the spatial control measures, the proportion of land prohibited from development, the proportion of population relocation, and the proportion of important facilities to obtain a second risk value of exposure corresponding to the spatial control measures. For example, the second risk value of exposure corresponding to the spatial control measures can be determined by the following formula:

[0090] ;

[0091] in, The second risk value representing the exposure associated with space control measures; The first risk value indicates the level of exposure; This indicates the proportion of land prohibited from development; Indicates the proportion of population relocation; Indicates the proportion of important facilities; Indicates land weight. Indicates population weight. The ownership of facilities can be determined independently by technical personnel.

[0092] For early warning response measures, the early warning lead time, standard value of early warning lead time, early warning area coverage rate, and early warning success avoidance rate are extracted from the measure implementation information. The early warning lead time can be the time during which an early warning is issued in advance within the early warning response measures; the standard value of early warning lead time can be the lower limit of the early warning lead time; the early warning success avoidance rate can be determined based on the total number of historical early warning events and the number of historical early warnings that resulted in successful avoidance. The first risk value of vulnerability is corrected using the measure response function of the early warning response measures, the early warning lead time, the early warning area coverage rate, the early warning success avoidance rate, and the standard value of early warning lead time to obtain the second risk value of vulnerability corresponding to the early warning response measures. For example, the second risk value of vulnerability corresponding to early warning response measures can be determined using the following formula:

[0093] ;

[0094] in, The second risk value indicating the vulnerability corresponding to early warning and response measures; The first risk value representing vulnerability; Indicates the advance warning time, Early warning area coverage and Success rate of early warning and risk avoidance; This indicates the standard value for the advance warning time.

[0095] This invention improves the accuracy of the collaborative reduction coefficient by determining the response function corresponding to each response measure, determining the second risk value of the risk element after the implementation of the response measure, and then determining the total risk value after the implementation of the response measure based on the first risk value of the remaining risk elements. The difference between the total risk value before the implementation of the response measure and the total risk value is calculated to determine the risk value reduction amount. Based on the risk value reduction amount between every two response measures, a collaborative reduction coefficient is determined.

[0096] Example 3

[0097] Figure 3 This is a schematic diagram of a climate risk response device based on a measure response function and a collaborative optimization network, provided in Embodiment 3 of the present invention. This embodiment of the invention is applicable to situations where measures to address climate risks are determined. The device can execute a climate risk response method based on a measure response function and a collaborative optimization network. This climate risk response device based on a measure response function and a collaborative optimization network can be implemented in hardware and / or software, and can be configured in an electronic device.

[0098] See Figure 3 The climate risk response device based on the measure response function and collaborative optimization network shown includes an acquisition module 301, a reduction coefficient determination module 302, a reduction value determination module 303, an implementation duration determination module 304, and a measure selection solution module 305.

[0099] The acquisition module 301 is used to acquire the first risk value of risk elements in the target area, the response function and implementation duration of the corresponding countermeasures for each risk element, and the implementation parameters of each response function; the risk elements include hazard, exposure and vulnerability; the response function is used to correct the first risk value of the corresponding risk element;

[0100] The reduction coefficient determination module 302 is used to determine the risk value reduction amount corresponding to each response measure and to determine the synergistic reduction coefficient between each pair of response measures based on the first risk value of each risk element, the response function of each response measure and the implementation parameters of the response measures.

[0101] The reduction value determination module 303 is used to determine the reduced risk value of the target area by taking the selection result of each of the countermeasures as the parameter to be solved, based on the first risk value of each risk element, the selection result of each of the countermeasures, the collaborative reduction coefficient, and the risk value reduction amount of each of the countermeasures.

[0102] The implementation duration determination module 304 is used to determine the total implementation duration based on the selection results of each of the aforementioned countermeasures and the implementation duration of each of the aforementioned countermeasures;

[0103] The measure selection module 305 is used to solve the measure selection results of each of the above-mentioned countermeasures with the goal of minimizing the risk value after reduction and minimizing the total implementation time, so as to obtain the final selection result of each of the above-mentioned countermeasures.

[0104] The embodiments of the present invention can determine the risk reduction amount of a single response measure on the risk value of a risk element through the response function of the measure, and calculate the synergistic reduction coefficient between every two response measures. Thus, using the result of measure selection as a parameter, the accurate risk value after the synergistic implementation of multiple response measures is determined, and the optimal combination of application measures is solved with the dual objectives of minimizing the risk value after reduction and minimizing the duration. This improves the risk reduction effect and response efficiency at the same time.

[0105] Optionally, the reduction coefficient determination module 302 includes:

[0106] The first fusion unit is used to fuse the first risk values ​​of each of the aforementioned risk elements to obtain the first total risk value of the target region;

[0107] An extraction unit is used to extract the measure implementation parameters corresponding to the measure response function of each response measure from the measure implementation information;

[0108] The risk value determination unit is used to perform correction processing on the risk element corresponding to the response measure according to the response function of the response measure and the implementation parameters of the response function, so as to obtain the second risk value of the risk element corresponding to the response measure;

[0109] The second fusion unit is used to fuse the second risk value of the risk element corresponding to the response measure and the first risk value of the first auxiliary risk element to obtain the second total risk value corresponding to the response measure; the first auxiliary risk element is a risk element other than the risk element corresponding to the response measure.

[0110] The reduction amount determination unit is used to determine the difference between the first total risk value and the second total risk value corresponding to the response measure as the risk value reduction amount corresponding to the response measure;

[0111] The reduction coefficient determination unit is used to determine the synergistic reduction coefficient between two response measures for each pair of response measures, based on a first total risk value, a second risk value of the two response measures, a first risk value of a second auxiliary risk element, and the risk value reduction amount of the two response measures; the second auxiliary risk element is a risk element other than the risk element corresponding to the two response measures.

[0112] Optionally, the reduction coefficient determination unit includes:

[0113] The fusion subunit is used to fuse the second risk value of the two countermeasures and the first risk value of the second auxiliary risk element to obtain a third total risk value;

[0114] A calculation subunit is used to calculate the risk value difference between the first total risk value and the second total risk value corresponding to the countermeasure;

[0115] The reduction coefficient determination subunit is used to determine the synergistic reduction coefficient between the two response measures based on the risk value difference and the risk value reduction amount of the two response measures.

[0116] Optionally, the reduction factor determines the sub-unit, specifically for:

[0117] Calculate the sum of the risk reduction amounts of the two response measures;

[0118] Calculate the difference between the risk value difference and the summed value;

[0119] The ratio between the difference and the sum is determined as the synergistic reduction coefficient between the two responses.

[0120] Optionally, the reduction value determination module 303 is specifically used for:

[0121] The first risk values ​​of each of the aforementioned risk elements are merged to obtain the first total risk value of the target region.

[0122] The reduction amount of the coordinated measures is determined based on the selection results of each of the aforementioned response measures, the synergistic reduction coefficient between every two response measures, and the risk value reduction amount of each of the aforementioned response measures;

[0123] Based on the selection results of each of the aforementioned countermeasures and the risk value reduction amount of each of the aforementioned countermeasures, determine the total amount of single risk value reduction;

[0124] The total risk reduction is obtained by adding the reduction amount of the coordinated measures and the reduction amount of the single risk value.

[0125] The difference between the first total risk value and the total amount of risk reduction is determined as the risk value after reduction.

[0126] Optionally, the solution module 305 is selected, specifically for:

[0127] The reduced risk value and the total implementation time are weighted and fused to obtain the target value;

[0128] With the goal of minimizing the target value, the selection results of each of the aforementioned countermeasures are solved to obtain the final selection results of each of the aforementioned countermeasures.

[0129] The climate risk response device based on the measure response function and the collaborative optimization network provided in the embodiments of the present invention can execute the climate risk response method based on the measure response function and the collaborative optimization network provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the climate risk response method based on the measure response function and the collaborative optimization network.

[0130] Example 4

[0131] Figure 4 A schematic diagram of a climate risk response device 410 based on a measure response function and a cooperative optimization network, which can be used to implement embodiments of the present invention, is shown. The climate risk response device based on a measure response function and a cooperative optimization network is intended to represent various forms of digital computers, such as laptops, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The climate risk response device based on a measure response function and a cooperative optimization network can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0132] like Figure 4As shown, the climate risk response device 410 based on the action response function and the cooperative optimization network includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412 and a random access memory (RAM) 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 may also store various programs and data required for the operation of the climate risk response device 410 based on the action response function and the cooperative optimization network. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0133] The climate risk response device 410 based on action response functions and cooperative optimization networks has multiple components connected to an I / O interface 415, including: an input unit 416, such as a keyboard, mouse, etc.; an output unit 417, such as various types of displays, speakers, etc.; a storage unit 418, such as a disk, optical disk, etc.; and a communication unit 419, such as a network interface card, modem, wireless transceiver, etc. The communication unit 419 allows the climate risk response device 410 based on action response functions and cooperative optimization networks to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0134] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as climate risk response methods based on action response functions and collaborative optimization networks.

[0135] In some embodiments, the climate risk response method based on the action response function and the cooperative optimization network can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program can be loaded and / or installed on the climate risk response device 410 based on the action response function and the cooperative optimization network via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the climate risk response method based on the action response function and the cooperative optimization network described above can be performed. Alternatively, in other embodiments, processor 411 can be configured to perform the climate risk response method based on the action response function and the cooperative optimization network by any other suitable means (e.g., by means of firmware).

[0136] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0137] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable climate risk response apparatus based on action response functions and cooperative optimization networks, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0138] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0139] To provide user interaction, the systems and techniques described herein can be implemented on a climate risk response device based on action response functions and cooperative optimization networks, which includes: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the climate risk response device based on action response functions and cooperative optimization networks. Other types of devices can also be used to provide user interaction; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0140] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0141] A computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system. It addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability.

[0142] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0143] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A climate risk response method based on measure response function and collaborative optimization network, characterized in that, The method includes: The system obtains the first risk value of risk factors in the target area, the response function and implementation duration of the corresponding countermeasures for each risk factor, and the implementation parameters of each response function; the risk factors include hazard, exposure, and vulnerability; the response function is used to correct the first risk value of the corresponding risk factor. Based on the first risk value of each risk element, the response function of each response measure, and the implementation parameters of the measures, determine the risk value reduction amount corresponding to each response measure, and determine the synergistic reduction coefficient between each pair of response measures; Using the selection results of each of the aforementioned countermeasures as the parameters to be solved, the reduced risk value of the target area is determined based on the first risk value of each risk element, the selection results of each of the aforementioned countermeasures, the collaborative reduction coefficients, and the risk value reduction amount of each of the aforementioned countermeasures. The total implementation time is determined based on the selection results of each of the aforementioned countermeasures and the implementation duration of each of the aforementioned countermeasures; With the goal of minimizing the reduced risk value and the shortest total implementation time, the selection results of each of the aforementioned countermeasures are solved to obtain the final selection results of each of the aforementioned countermeasures.

2. The method according to claim 1, characterized in that, The step of determining the risk value reduction amount corresponding to each response measure and the synergistic reduction coefficient between every two response measures, based on the first risk value of each risk element, the response function of each response measure, and the implementation parameters of the response measures, includes: The first risk values ​​of each of the aforementioned risk elements are merged to obtain the first total risk value of the target region. For each response measure, extract the response function corresponding to the response measure from the implementation information; Based on the response function of the response measures and the corresponding implementation parameters of the response function, the risk elements corresponding to the response measures are modified to obtain the second risk value of the risk elements corresponding to the response measures. The second risk value of the risk element corresponding to the response measure and the first risk value of the first auxiliary risk element are fused together to obtain the second total risk value corresponding to the response measure; the first auxiliary risk element is a risk element other than the risk element corresponding to the response measure. The difference between the first total risk value and the second total risk value corresponding to the response measure is determined as the risk value reduction amount corresponding to the response measure; For each pair of response measures, a synergistic reduction coefficient between the two response measures is determined based on the first total risk value, the second risk value of the two response measures, the first risk value of the second auxiliary risk element, and the risk value reduction amount of the two response measures; the second auxiliary risk element is a risk element other than the risk element corresponding to the two response measures.

3. The method according to claim 2, characterized in that, The determination of the synergistic reduction coefficient between the two response measures based on the first total risk value, the second risk values ​​of the two response measures, the first risk value of the second auxiliary risk element, and the risk value reduction amount of the two response measures includes: The second risk value of the two countermeasures and the first risk value of the second auxiliary risk element are combined to obtain a third total risk value. Calculate the risk value difference between the first total risk value and the second total risk value corresponding to the countermeasures; Based on the risk value difference and the risk value reduction amount of the two response measures, a synergistic reduction coefficient between the two response measures is determined; The synergistic reduction coefficient is determined by the following formula: ； in, This represents the synergistic reduction coefficient between the i-th type of response and the j-th type of response; This represents the difference in risk values; This represents the risk reduction amount of the i-th type of response measure; This represents the risk reduction amount of the j-th type of response measure.

4. The method according to claim 2, characterized in that, The response measures include engineering control measures, ecological buffer measures, spatial control measures, and early warning response measures; the risk element corresponding to the engineering control measures is hazard; the risk element corresponding to the ecological buffer measures is hazard; and the risk element corresponding to the spatial control measures is exposure. The risk element corresponding to the aforementioned early warning and response measures is vulnerability. Based on the response function of the response measures and the corresponding implementation parameters of the response function, the risk elements corresponding to the response measures are modified to obtain a second risk value for the risk elements corresponding to the response measures, including: The first risk value of the hazard is corrected based on the response function of the engineering control measures, the engineering construction ratio, and the engineering construction efficiency to obtain the second risk value of the hazard corresponding to the engineering control measures. The second risk value of the hazard corresponding to the engineering control measures is determined by the following formula: ； in, The second risk value indicates the level of danger corresponding to engineering control measures; Indicates the proportion of the project under construction; Indicates the efficiency of engineering construction; The first risk value of the hazard is corrected based on the response function, ecological restoration intensity, and ecological measure impact coefficient of the ecological buffer measures to obtain the second risk value of the hazard corresponding to the ecological buffer measures; the second risk value of the hazard corresponding to the ecological buffer measures is determined by the following formula: ； in, The second risk value indicates the level of danger corresponding to engineering control measures; Intensity of ecological restoration; Indicates the impact coefficient of ecological measures; The first risk value of exposure is adjusted based on the response function of spatial control measures, the proportion of land prohibited from development, the proportion of population relocation, and the proportion of important facilities to obtain the second risk value of exposure corresponding to spatial control measures. The second risk value of exposure corresponding to spatial control measures is determined by the following formula: ； in, The second risk value representing the exposure associated with space control measures; The first risk value indicates the level of exposure; This indicates the proportion of land prohibited from development; Indicates the proportion of population relocation; Indicates the proportion of important facilities; Indicates land weight. Indicates population weight. Indicates facility rights; The first risk value of vulnerability is corrected based on the response function of the aforementioned early warning response measures, the early warning lead time, the early warning area coverage rate, the early warning success avoidance rate, and the standard value of the early warning lead time, to obtain the second risk value of vulnerability corresponding to the early warning response measures; the second risk value of vulnerability corresponding to early warning response measures is determined by the following formula: ； in, The second risk value indicating the vulnerability corresponding to early warning and response measures; The first risk value representing vulnerability; Indicates the advance warning time, Early warning area coverage and Success rate of early warning and risk avoidance; This indicates the standard value for the advance warning time.

5. The method according to claim 1, characterized in that, The step of using the selection results of each of the aforementioned countermeasures as the parameters to be solved, and determining the reduced risk value of the target area based on the first risk value of each risk element, the selection results of each of the aforementioned countermeasures, the synergistic reduction coefficients, and the risk value reduction amount of each of the aforementioned countermeasures, includes: The first risk values ​​of each of the aforementioned risk elements are merged to obtain the first total risk value of the target region. The reduction amount of the coordinated measures is determined based on the selection results of each of the aforementioned response measures, the synergistic reduction coefficient between every two response measures, and the risk value reduction amount of each of the aforementioned response measures; Based on the selection results of each of the aforementioned countermeasures and the risk value reduction amount of each of the aforementioned countermeasures, determine the total amount of single risk value reduction; The total risk reduction is obtained by adding the reduction amount of the coordinated measures and the reduction amount of the single risk value. The difference between the first total risk value and the total risk reduction amount is determined as the risk value after reduction; The reduction amount achieved through coordinated measures is determined using the following formula: ； in, This indicates the amount of reduction achieved through coordinated measures; Indicates the number of types of response measures; This indicates the result of the selection of the response measures for the i-th type. This indicates the result of the selection of the response measures for the j-th type. This represents the synergistic reduction coefficient between the i-th type of response and the j-th type of response; This represents the risk reduction amount of the i-th type of response measure; This represents the risk reduction amount of the j-th type of response measure; The total reduction in single risk amount is determined by the following formula: The total reduction amount for each risk value is determined using the following formula: ； in, This represents the total reduction in a single risk value.

6. The method according to claim 1, characterized in that, The goal is to minimize the reduced risk value and the total implementation time. The selection results for each of the aforementioned countermeasures are then calculated to obtain the final selection results for each countermeasure, including: The reduced risk value and the total implementation time are weighted and fused to obtain the target value; With the goal of minimizing the target value, the selection results of each of the aforementioned countermeasures are solved to obtain the final selection results of each of the aforementioned countermeasures.

7. A climate risk response device based on a measure response function and a collaborative optimization network, characterized in that, The device includes: The acquisition module is used to acquire the first risk value of risk elements in the target area, the response function and implementation duration of the corresponding countermeasures for each risk element, and the implementation parameters of each response function; the risk elements include hazard, exposure, and vulnerability; the response function is used to correct the first risk value of the corresponding risk element; The reduction coefficient determination module is used to determine the risk value reduction amount corresponding to each response measure and the synergistic reduction coefficient between each pair of response measures based on the first risk value of each risk element, the response function of each response measure and the implementation parameters of the response measures. The reduction value determination module is used to determine the reduced risk value of the target area by taking the selection result of each of the aforementioned countermeasures as the parameter to be solved, based on the first risk value of each risk element, the selection result of each of the aforementioned countermeasures, the collaborative reduction coefficient, and the risk value reduction amount of each of the aforementioned countermeasures. The implementation duration determination module is used to determine the total implementation duration based on the selection results of each of the aforementioned countermeasures and the implementation duration of each of the aforementioned countermeasures; The measure selection solution module is used to solve the measure selection results of each of the above-mentioned countermeasures with the goal of minimizing the risk value after reduction and minimizing the total implementation time, so as to obtain the final selection result of each of the above-mentioned countermeasures.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the climate risk response method based on the action response function and the cooperative optimization network as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the climate risk response method based on the measure response function and the cooperative optimization network as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the climate risk response method based on the action response function and the collaborative optimization network as described in any one of claims 1-6.

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