Multi-disaster-bearing body marine disaster risk assessment method based on sensitive matrix

Abstract

The invention discloses a multi-disaster-bearing body marine disaster risk assessment method based on a sensitive matrix, and relates to the technical field of marine science, the method comprises the following steps: defining a marine disaster assessment area, and determining multiple disaster-inducing factor types in the assessment area; and sorting the distribution characteristics of various disaster-bearing bodies in the evaluation area, and determining an evaluation object. According to the multi-disaster-bearing body marine disaster risk assessment method based on the sensitive matrix, the sensitive matrix of the disaster-inducing factors and the disaster-bearing bodies is constructed, the sensitivity difference of different disaster-bearing bodies to the disaster-inducing factors is quantified, the loss difference of different disaster-bearing bodies under similar disasters can be accurately described, the limitation of a traditional single disaster assessment method is avoided, and the risk assessment accuracy is improved. The method comprehensively analyzes the potential threats of disasters to different disaster-bearing bodies, improves the overall precision of risk assessment, and guarantees that the vulnerability degrees of the different disaster-bearing bodies are accurately reflected when the different disaster-bearing bodies face specific disaster-inducing factors through sensitive matrix normalization.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of marine science and technology, in particular to a multi-hazard-bearing body marine disaster risk assessment method based on a sensitivity matrix. BACKGROUND

[0002] Marine disasters have a wide range of impacts, involving coastal infrastructure, ecosystems, population distribution, and economic activities, etc., and different hazard-bearing bodies have different response mechanisms to disasters. Marine disasters (typhoons, storm surges, waves, tsunamis, etc.) are often interrelated, forming a disaster chain. Therefore, as marine disasters occur frequently and their impact range is expanding, the traditional single disaster assessment method has been difficult to meet the needs in complex environments. By integrating information from multiple fields to assess the risk of multi-hazard-bearing body marine disasters, the potential threats of disasters to different hazard-bearing bodies can be comprehensively analyzed.

[0003] In the prior art, risk assessment is often performed on a regional basis, and various hazard-bearing bodies are regarded as homogeneous units, ignoring the differences in the resistance of different hazard-bearing bodies to the same disaster-causing factor, i.e., sensitivity, which leads to overestimation or underestimation of risk. Therefore, how to introduce a sensitivity matrix normalization algorithm, construct a sensitivity matrix of disaster-causing factors x hazard-bearing bodies, quantify the sensitivity differences of different hazard-bearing bodies to each disaster-causing factor, and improve the overall precision of marine disaster risk assessment to accurately depict the loss differences of the same disaster-different hazard-bearing bodies, is the problem to be solved by the present application. For this reason, a multi-hazard-bearing body marine disaster risk assessment method based on a sensitivity matrix is proposed. SUMMARY

[0004] To solve the above technical problems, the present application is implemented by the following technical scheme: a multi-hazard-bearing body marine disaster risk assessment method based on a sensitivity matrix, comprising the following steps: S1, defining the assessment area of marine disasters and determining the types of multiple disaster-causing factors in the assessment area, including storm surges, waves, and floods; S2, sorting out the distribution characteristics of various hazard-bearing bodies in the assessment area and clarifying the assessment objects, including breeding areas, fishing ports, chemical areas, pastures, salt fields, and disaster relief facilities; S3, establishing an m x n-dimensional initial sensitivity comparison matrix with disaster-causing factors as rows and hazard-bearing bodies as columns; S4, comparing the risks of the same hazard-bearing body under different disaster-causing factors, determining the relative risk weights of each disaster-causing factor, and comparing the differences in the ability of different hazard-bearing bodies to resist all risks to calculate the total risk proportion of each hazard-bearing body; S5, determining the corresponding hazard-bearing risk weight according to the characteristics of each hazard-bearing body, multiplying the hazard-bearing risk weight by the total risk proportion to calculate the total risk coefficient of the hazard-bearing body, and comprehensively assessing the marine disaster risk of multi-hazard-bearing bodies to form a disaster-causing factor-hazard-bearing body cross-sensitivity matrix; S6, based on the disaster-causing factor-disaster-bearing body cross-sensitivity matrix, quantifying the sensitivity difference of different disaster-bearing bodies to each disaster-causing factor, setting the risk level division standard, determining the corresponding risk level, and matching the disaster mitigation measures.

[0005] Preferably, the S1 specifically comprises: The geographical scope of the marine disaster risk assessment is determined, the administrative division and the natural geographical boundary are combined to demarcate the assessment area, the regional integrity is ensured, and the regional coordinates, area and coastline features are determined through the GIS tool; According to the historical disaster records and meteorological and hydrological data, the natural factors causing disasters in the assessment area are sorted out, and three types of disaster-causing factors including storm surge, sea wave and flood are selected, and the disaster properties of each disaster-causing factor are determined.

[0006] Preferably, the S2 specifically comprises: Based on the functional division of the assessment area, the evaluation objects in the assessment area are determined, including 7 types of disaster-bearing bodies including breeding area, fishing port, chemical industry area, pasture, salt field, disaster relief facility and tourist area; Through remote sensing image interpretation, administrative division data and on-site verification, the basic disaster-bearing body data of the spatial distribution range, area and functional type of various disaster-bearing bodies are collected; The collected basic disaster-bearing body data is analyzed to determine its distribution characteristics, the spatial layout of each disaster-bearing body in the assessment area is evaluated, the relative position relationship between the disaster-bearing body and the natural geographical elements such as coastline, river and gulf is identified, and the disaster-bearing body database is established.

[0007] Preferably, the S3 specifically comprises: Based on the determined disaster-causing factors and disaster-bearing body types in the assessment area, the disaster characteristics and functional properties of the disaster-bearing body are analyzed, the corresponding relationship between the disaster-causing factors and the disaster-bearing body is determined, and the structured list of the disaster-causing factor row vector and the disaster-bearing body column vector is formed; Taking the disaster-causing factor as the row and the disaster-bearing body as the column, an m*n dimensional empty matrix is constructed, wherein the number of rows m corresponds to the number of disaster-causing factors, the number of columns n corresponds to the number of disaster-bearing bodies, the matrix element is defined as the sensitivity value of the disaster-causing factor to the disaster-bearing body, the assignment rule of the matrix element is determined, and the matrix structure is matched with the subsequent quantitative analysis requirements; According to the action intensity of the disaster-causing factor, the vulnerability of the disaster-bearing body and the spatial exposure relationship, the sensitivity value of the matrix element is filled item by item through expert scoring to form an initial sensitivity comparison matrix.

[0008] Preferably, the S4 specifically comprises: Based on the sensitivity matrix, the sensitivity values of the same type of disaster-bearing body under each disaster-causing factor are extracted, the relative risk weight of each disaster-causing factor is quantified through the entropy weight method combined with the historical disaster frequency, and the weight is ensured to reflect the comprehensive influence of the disaster occurrence probability and the vulnerability of the disaster-bearing body; For different disaster-bearing bodies, their sensitivity values ​​to all disaster-causing factors are summarized. Based on the relative risk weight of each disaster-causing factor and the sensitivity values ​​of each disaster-bearing body under different disaster-causing factors, the total risk value of each disaster-bearing body is calculated by weighted summation. The total risk value of each disaster-bearing body is normalized, and the total risk ratio of each disaster-bearing body is calculated. Using the sum of the total risk values ​​of a group of disaster-bearing bodies as a benchmark, the total risk value of each disaster-bearing body is converted into a relative ratio, so as to achieve standardized comparison of the risk levels of different disaster-bearing bodies and clarify the relative position of each disaster-bearing body in the overall risk.

[0009] Preferably, the analysis process for the total risk value of each disaster-bearing entity is as follows: For different disaster-bearing bodies within the assessment area, their sensitivity values ​​to all disaster-causing factors are summarized. The data are then organized by disaster-bearing body type as rows and disaster-causing factor type as columns to form a disaster-bearing body-disaster-causing factor sensitivity matrix. The matrix elements are standardized sensitivity values ​​to eliminate differences in dimensions. By combining the established relative risk weights of disaster-causing factors, the sensitivity values ​​of each disaster-bearing entity are weighted. The relative risk weight of each disaster-causing factor is multiplied by the corresponding sensitivity value of the disaster-bearing entity to obtain the weighted sensitivity value of each disaster-causing factor-disaster-bearing entity combination. All weighted sensitivity values ​​of the same disaster-bearing entity are summed to obtain the total risk value of each disaster-bearing entity, which reflects its risk level under comprehensive exposure to multiple disaster-causing factors.

[0010] Preferably, S5 specifically includes: Based on the physical properties and exposure characteristics of the disaster-bearing bodies, combined with expert experience and historical disaster data, the analytic hierarchy process (AHP) is used to quantify the differences in vulnerability of each disaster-bearing body to marine disasters, determine its disaster risk weight, and reflect the degree of contribution of different disaster-bearing bodies to the risk. By weighting and coupling the disaster risk weight with the total risk ratio, the total risk coefficient of the disaster-bearing body is obtained. The dual effects of the disaster-bearing body's own characteristics and the degree of disaster exposure are analyzed to ensure that the assessment results take into account both subjective risk and subject differences. The total risk coefficients of all disaster-bearing bodies are summarized, and a cross-sensitivity matrix of disaster-causing factors and disaster-bearing bodies is constructed. With disaster-bearing bodies as rows and the total risk coefficients of disaster-bearing bodies as columns, the comprehensive risk level of each disaster-bearing body in marine disaster risk assessment is displayed.

[0011] Preferably, the formula for calculating the total risk coefficient of the disaster-bearing body is as follows: ; ; ; In the formula: Let i be the total risk coefficient of the i-th disaster-bearing body; Let be the disaster risk weight of the i-th disaster-bearing body, reflecting the contribution of the disaster-bearing body's own physical attributes and exposure characteristics to the risk. The overall vulnerability score for the i-th disaster-bearing entity is calculated using the Analytic Hierarchy Process (AHP), where n is the total number of disaster-bearing entities and i is the index of the disaster-bearing entity. The weights of physical properties, Let i be the score of the i-th disaster-bearing entity under physical attributes. Weights for exposing features, This represents the score of the i-th disaster-bearing entity under the exposure characteristics; denoted as the total risk ratio of the i-th disaster-bearing entity.

[0012] Preferably, S6 specifically includes: The historical disaster records of the region are analyzed and evaluated, and the total risk coefficient of the disaster-bearing body based on the historical disaster records is calculated. Then, according to the total risk coefficient of the disaster-bearing body under the historical context, different risk levels are divided, including Level 1, Level 2 and Level 3 risk levels. The severity of the risk level increases from Level 1 to Level 3. At the same time, corresponding risk thresholds are set for each risk level in combination with the total risk coefficient of the disaster-bearing body to determine the risk level classification standard. Differentiated disaster reduction measures should be formulated for disaster-bearing bodies with different risk levels to ensure the disaster resistance capacity of high-risk disaster-bearing bodies is improved, and to achieve precise resource allocation and dynamic risk management. Based on the established risk level classification standards, analyze the risk level corresponding to the total risk coefficient of the disaster-bearing body under the target scenario, match appropriate disaster reduction measures, allocate disaster relief materials, and ensure the disaster-bearing body's disaster resistance capacity.

[0013] Preferably, the multiple risk levels correspond one-to-one with the multiple risk thresholds, and the correspondence is as follows: The risk threshold for the first-level risk level is: ; The risk threshold for the secondary risk level is: ; The risk threshold for the three-level risk level is: ; in, The total risk coefficient of the disaster-bearing body. These are the upper threshold for Level 1 risk and the lower threshold for Level 2 risk. These are the upper threshold for Level 2 risk and the lower threshold for Level 3 risk.

[0014] This invention provides a method for assessing marine hazard risk in multiple disaster-bearing bodies based on a sensitivity matrix. It has the following beneficial effects: (I) The marine disaster risk assessment method based on the sensitivity matrix for multiple disaster-bearing bodies quantifies the sensitivity differences of different disaster-bearing bodies to various disaster-bearing bodies by constructing a sensitivity matrix between disaster-causing factors and disaster-bearing bodies. It can accurately characterize the loss differences of different disaster-bearing bodies under the same type of disaster, avoid the limitations of traditional single disaster assessment methods, comprehensively analyze the potential threats of disasters to different disaster-bearing bodies, and improve the overall accuracy of risk assessment. At the same time, the sensitivity matrix normalization ensures that the vulnerability of different disaster-bearing bodies to specific disaster-causing factors is accurately reflected.

[0015] (II) This marine disaster risk assessment method based on the sensitivity matrix combines historical disaster records, functional positioning and spatial distribution characteristics of disaster-bearing bodies, and determines the relative risk weights of each disaster-causing factor and the sensitivity values ​​of disaster-bearing bodies through expert scoring and entropy weighting methods. This makes the risk assessment results more objective and can more accurately reflect the risk level of different disaster-bearing bodies in marine disasters. Through normalization and information entropy calculation, the rationality of weight allocation is further ensured.

[0016] (III) The marine disaster risk assessment method based on the sensitivity matrix dynamically analyzes the corresponding risk level of the disaster-bearing body according to the total risk coefficient of the disaster-bearing body under the target scenario, and matches the corresponding disaster reduction measures. Through real-time monitoring and evaluation, the risk level is updated in a timely manner to ensure that the disaster reduction measures match the current risk status. It can effectively cope with the uncertainty and complexity of marine disasters and improve the adaptability and effectiveness of disaster reduction measures. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the workflow of a multi-hazard-bearing marine disaster risk assessment method based on a sensitivity matrix according to the present invention. Figure 2 This is a flowchart of a method for assessing marine disaster risk in multiple disaster-bearing bodies based on a sensitivity matrix, according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0019] Example 1, please refer to Figure 1 , Figure 2 This invention provides a technical solution: a method for assessing marine disaster risk in multiple disaster-bearing bodies based on a sensitivity matrix, comprising the following steps: S1. Define the assessment area for marine disasters and identify the types of multiple disaster-causing factors within the assessment area, including storm surges, waves, and floods. Clarify the geographical scope of the marine disaster risk assessment, delineate the assessment area in conjunction with administrative divisions and natural geographical boundaries to ensure regional integrity, and determine the regional coordinates, area, and coastline characteristics using GIS tools. Based on historical disaster records and meteorological and hydrological data, sort out the natural factors that cause disasters within the assessment area, screen out three types of disaster-causing factors including storm surges, waves, and floods, and clarify the disaster attributes of each disaster-causing factor. The specific work includes: clearly defining the geographical boundaries of the assessment area to ensure regional integrity; delineating the assessment scope based on administrative divisions and natural geographical features (distribution of estuaries, bays, and islands) to avoid fragmentation of risk assessment due to artificial divisions; extracting regional coordinates, area, and coastline characteristics (length, tortuosity, and slope) using GIS tools to quantify the amplification or buffering effect of coastal spatial characteristics on disasters; and identifying natural factors that cause marine disasters within the assessment area based on historical disaster records (typhoon paths, tide observation data) and meteorological and hydrological data (wind speed, wave height, and flow rate). Through statistical analysis of the frequency, intensity, and disaster-causing mechanisms of disaster-causing factors, identifying storm surges and waves that have a significant impact on the region. The three categories of disaster-causing factors—storm surge, storm surge, and flood—are identified, and their disaster attributes are clearly defined, including triggering conditions, modes of action (inundation, impact, and erosion), and spatial impact range. Specifically, storm surge's disaster attributes involve tidal level fluctuations and rate of water increase; ocean waves' disaster attributes include wave height, period, and wave propagation direction; flood's disaster attributes encompass flood discharge, inundation depth, and inundation range. Storm surge's triggering condition is the passage of a typhoon, its modes of action are inundation and impact, and its spatial impact range is coastal areas; ocean waves' triggering condition is strong winds, its modes of action are impact and erosion, and its spatial impact range is nearshore waters; flood's triggering condition is heavy rainfall, its mode of action is inundation, and its spatial impact range is downstream rivers and low-lying coastal areas. S2. Analyze the distribution characteristics of various disaster-bearing bodies within the assessment area, and identify the assessment objects, including aquaculture areas, fishing ports, chemical industrial zones, pastures, salt fields, and disaster relief facilities. Based on the functional division of the assessment area, identify the assessment objects within the assessment area, namely, seven types of disaster-bearing bodies including aquaculture areas, fishing ports, chemical industrial zones, pastures, salt fields, disaster relief facilities, and tourist areas. Through remote sensing image interpretation, administrative division data, and on-site verification, collect basic disaster-bearing body data on the spatial distribution range, area, and functional type of various disaster-bearing bodies. Analyze the collected basic disaster-bearing body data to clarify their distribution characteristics, assess the spatial layout of each disaster-bearing body within the assessment area, identify their relative positional relationship with natural geographical elements such as coastlines, rivers, and bays, and integrate and establish a disaster-bearing body database. The specific work content is as follows: Based on the functional positioning of the assessment area, seven types of disaster-bearing entities are identified as assessment objects: aquaculture areas, fishing ports, chemical industrial zones, pastures, salt fields, disaster relief facilities, and tourist areas. By reviewing regional development plans and current land use, the functional attributes of each disaster-bearing entity are defined: aquaculture areas and salt fields focus on marine resource utilization; fishing ports and tourist areas are associated with coastal economic activities; chemical industrial zones represent high-risk industrial facilities; pastures embody ecological and agricultural value; and disaster relief facilities undertake disaster emergency response functions. After identifying the various types of disaster-bearing entities within the assessment area, basic disaster-bearing entity data is collected through multiple means, including remote sensing image interpretation, administrative division data lookup, and on-site verification. Remote sensing image interpretation provides large-scale spatial data, enabling rapid acquisition of disaster-bearing entity data. The data collection process combines three methods: spatial distribution and area information of disaster-bearing bodies; administrative division data to clarify the administrative affiliation and functional type of disaster-bearing bodies; and on-site verification to supplement and validate the accuracy of remote sensing and administrative division data, ensuring data reliability. This comprehensive approach gathers basic disaster-bearing body data for all types of disaster-bearing bodies, including their spatial distribution, area, and functional type. After collecting this basic data, analysis clarifies the spatial layout characteristics of each disaster-bearing body within the assessment area, identifying its relative positional relationship with natural geographical elements such as coastlines, rivers, and bays. Based on this, the collected basic disaster-bearing body data and analysis results are integrated to establish a comprehensive disaster-bearing body database, containing information on the spatial distribution, area, and functional type of disaster-bearing bodies. Information on energy types and their relative positions with natural geographical elements is provided. Specifically, for aquaculture areas, the spatial distribution (coastal mudflats), area (50 square kilometers), functional type (marine resource utilization), and relative position with natural geographical elements (close to the coastline and river estuaries) are specified. For fishing ports, the spatial distribution (within the bay), area (10 square kilometers), functional type (coastal economic activities), and relative position with natural geographical elements (close to tourist areas) are specified. For chemical industrial zones, the spatial distribution (coastal industrial zone), area (20 square kilometers), functional type (high-risk industrial facilities), and relative position with natural geographical elements (located on the north side of the bay, adjacent to the downstream of a river) are specified. For pastures, the spatial distribution (inland low-lying areas), area, and relative position with natural geographical elements are specified. For salt fields, spatial distribution (coastal saline-alkali land), area (40 square kilometers), functional type (marine resource utilization), and relative location with natural geographical elements (located on the south side of the bay, parallel to the coastline); for disaster relief facilities, spatial distribution (urban center area), area (5 square kilometers), functional type (disaster emergency function), and relative location with natural geographical elements (connected to major transportation routes); for tourist areas, spatial distribution (around the bay), area (15 square kilometers), functional type (coastal economic activities), and relative location with natural geographical elements (adjacent to the coastline). S3. Establish an m×n dimensional initial sensitivity comparison matrix with disaster-causing factors as rows and disaster-bearing bodies as columns. Based on the disaster-causing factors and disaster-bearing body types within the determined assessment area, analyze the disaster characteristics and functional attributes of the disaster-bearing bodies, determine the correspondence between disaster-causing factors and disaster-bearing bodies, and form a structured list of disaster-causing factor row vectors and disaster-bearing body column vectors. Construct an m×n dimensional empty matrix with disaster-causing factors as rows and disaster-bearing bodies as columns, where the number of rows m corresponds to the number of disaster-causing factors and the number of columns n corresponds to the number of disaster-bearing bodies. Define the matrix elements as the sensitivity values ​​of disaster-causing factors to disaster-bearing bodies, clarify the assignment rules of matrix elements, and ensure that the matrix structure matches the needs of subsequent quantitative analysis. Based on the intensity of the disaster-causing factors, the vulnerability of disaster-bearing bodies, and the spatial exposure relationship, fill in the sensitivity values ​​of matrix elements item by item through expert scoring to form the initial sensitivity comparison matrix. The specific work involves: based on the identified disaster-causing factors and types of disaster-bearing bodies within the assessment area, analyzing the correlation between disaster characteristics and the functional attributes of disaster-bearing bodies, clarifying the correspondence between disaster-causing factors and disaster-bearing bodies, and combining regional historical disaster data, functional positioning of disaster-bearing bodies, and spatial distribution characteristics to form a structured list, presented in the form of row vectors (disaster-causing factors) and column vectors (disaster-bearing bodies), ensuring that the correspondence covers the entire process of the disaster chain and the differentiated responses of disaster-bearing bodies; constructing an m×n dimensional empty matrix with identified disaster-causing factors as rows and disaster-bearing bodies as columns, where m represents the number of disaster-causing factors and n represents the number of disaster-bearing bodies, and defining the matrix elements as the sensitivity values ​​of disaster-causing factors to disaster-bearing bodies, used to reflect the vulnerability of different disaster-bearing bodies to specific disaster-causing factors, and designing the assignment rules for matrix elements so that the matrix reflects the differences in sensitivity of different disaster-bearing bodies to each disaster-causing factor, and classifying each type... The disaster-bearing bodies under the causative factors are divided into different levels of risk: no risk, low risk, medium risk, high risk, and extremely high risk. Risk values ​​are assigned to different levels: no risk is assigned 0, low risk 0.25, medium risk 0.5, high risk 0.75, and extremely high risk 1. Based on the intensity of the causative factor, the vulnerability of the disaster-bearing body, and spatial exposure relationships, sensitivity values ​​are filled into matrix elements one by one using an expert scoring method. A sensitivity score is given for each causative factor-disaster-bearing body combination to form an initial sensitivity comparison matrix. Five to seven experts in marine disasters, coastal engineering, and chemical safety are invited to independently read the correlation list and assignment rules, understand the disaster chain and the response logic of the disaster-bearing body, and score the risk level of each causative factor-disaster-bearing body combination, noting the basis for their scores. The expert scores are summarized, and the average value is calculated as the sensitivity value. S4. Compare the risks of similar disaster-bearing entities under different disaster-causing factors, determine the relative risk weight of each disaster-causing factor, compare the differences in the ability of different disaster-bearing entities to resist all risks, calculate the total risk ratio of each disaster-bearing entity, extract the sensitivity values ​​of similar disaster-bearing entities under each disaster-causing factor based on the sensitivity matrix, combine historical disaster frequency, quantify the relative risk weight of each disaster-causing factor through the entropy weight method, ensure that the weight reflects the comprehensive impact of the probability of disaster occurrence and the vulnerability of the disaster-bearing entity, summarize the sensitivity values ​​of different disaster-bearing entities facing all disaster-causing factors, calculate the total risk value of each disaster-bearing entity through weighted summation based on the relative risk weight of each disaster-causing factor, normalize the total risk value of each disaster-bearing entity, calculate the total risk ratio of each disaster-bearing entity, and convert the total risk value of each disaster-bearing entity into a relative ratio based on the sum of the total risk values ​​of a group of disaster-bearing entities, realize the standardized comparison of the risk levels of different disaster-bearing entities, and clarify the relative position of each disaster-bearing entity in the overall risk. Furthermore, the analysis process for the total risk value of each disaster-bearing entity is as follows: For different disaster-bearing entities within the assessment area, their sensitivity values ​​to all disaster-causing factors are summarized. The data is organized by disaster-bearing entity type as rows and disaster-causing factor type as columns to form a disaster-bearing entity-disaster-causing factor sensitivity matrix. To ensure that the matrix elements are standardized sensitivity values ​​and eliminate differences in dimensions, the sensitivity values ​​of each disaster-bearing entity are weighted in combination with the determined relative risk weights of disaster-causing factors. The relative risk weight of each disaster-causing factor is multiplied by its corresponding disaster-bearing entity sensitivity value to obtain the weighted sensitivity value of each disaster-causing factor-disaster-bearing entity combination. All weighted sensitivity values ​​of the same disaster-bearing entity are summed to obtain the total risk value of each disaster-bearing entity, reflecting its risk level under comprehensive exposure to multiple disaster-causing factors. The specific work involves: extracting sensitivity values ​​of similar disaster-bearing bodies under various disaster-causing factors based on the sensitivity matrix, forming sensitivity vectors for single disaster-bearing body types; constructing a sensitivity-frequency composite index system by combining regional historical disaster frequency data, where historical frequency reflects the probability of disaster-causing factors occurring, and sensitivity values ​​reflect the vulnerability of disaster-bearing bodies; and calculating the relative risk weights of each disaster-causing factor using the entropy weight method. Specifically, the sensitivity values ​​and historical frequencies are standardized to eliminate dimensional differences, the information entropy of each disaster-causing factor is calculated to reflect the degree of impact of its uncertainty on risk, and the relative risk weights are determined based on the information entropy, ensuring that the weight allocation considers both the probability of disaster occurrence (high-frequency disaster-causing factors have higher weights) and the vulnerability of disaster-bearing bodies (the impact on highly sensitive disaster-bearing bodies is more significant). (The hazard-causing factors have higher weights). For different hazard-bearing bodies, the sensitivity values ​​of each body to all hazard-causing factors are summarized to form a hazard-bearing body-hazard-causing factor sensitivity matrix. Combined with the determined relative risk weights of hazard-causing factors, the sensitivity values ​​of each hazard-bearing body are weighted and summed: the weight of each hazard-causing factor is multiplied by its corresponding hazard-bearing body sensitivity value to obtain a weighted sensitivity value. All weighted sensitivity values ​​of the same hazard-bearing body are summed to obtain the total risk value of that hazard-bearing body. The total risk values ​​of each hazard-bearing body are normalized to eliminate the influence of dimensions. The sum of the total risk values ​​of all hazard-bearing bodies is calculated as a benchmark value. The total risk value of each hazard-bearing body is divided by the benchmark value to obtain its risk ratio, clarifying the relative position of the two in the overall risk and realizing a standardized comparison of the risk levels of different hazard-bearing bodies. The formula for calculating relative risk weights is as follows: ; In the formula: Let j be the relative risk weight of the hazard-causing factor. Let m be the information entropy of the j-th disaster-causing factor, m be the total number of disaster-causing factors, and j be the index of the disaster-causing factor. The smaller the value, the lower the uncertainty of the hazard-causing factor, and the lower the relative risk weight. The higher the information entropy, the higher the information entropy. The larger the value, the higher the uncertainty of the hazard-causing factor, and the higher its relative risk weight. The lower; The expression for calculating the weighted sensitivity value is as follows: ; In the formula: Let be the weighted sensitivity value of the i-th disaster-bearing body to the j-th disaster-causing factor. Let be the sensitivity value of the i-th disaster-bearing body to the j-th disaster-causing factor, where i is the index of the disaster-bearing body, and is the index of the disaster-bearing body. The higher, The higher the value, the higher the relative risk weight. The higher; The formula for calculating the total risk value of each disaster-bearing entity is as follows: ; In the formula: Let be the total risk value of the i-th disaster-bearing body, and be the weighted sensitivity value of the disaster-bearing body to each disaster-causing factor. The higher the value, the higher the total risk value; the more disaster-causing factors m there are, the wider the range of values ​​for the total risk value. The formula for calculating the total risk proportion of each disaster-bearing entity is as follows: ; In the formula: Let n be the total risk proportion of the i-th disaster-bearing body, and n be the total number of disaster-bearing bodies. The higher the overall risk ratio, the more disaster-bearing entities there are, and the smaller the overall risk ratio of each disaster-bearing entity. S5. Based on the characteristics of each disaster-bearing body, determine the corresponding disaster-bearing risk weight, multiply the disaster-bearing risk weight by the total risk ratio, calculate the total risk coefficient of the disaster-bearing body, comprehensively assess the marine disaster risk of multiple disaster-bearing bodies, and form a disaster-causing factor-disaster-bearing body cross-sensitivity matrix. S6. Based on the cross-sensitivity matrix of disaster-causing factors and disaster-bearing bodies, quantify the differences in sensitivity of different disaster-bearing bodies to each disaster-causing factor, set risk level classification standards, determine the corresponding risk levels, and match disaster reduction measures.

[0020] Example 2, as Figure 1 , Figure 2 As shown, based on Example 1, the present invention provides a technical solution: S5 specifically includes: based on the physical properties and exposure characteristics of the disaster-bearing body, combined with expert experience and historical disaster data, using the analytic hierarchy process (AHP) to quantify the differences in vulnerability of each disaster-bearing body to marine disasters, determine its disaster-bearing risk weight, reflect the degree of contribution of different disaster-bearing bodies to the risk, weightedly couple the disaster-bearing risk weight with the total risk ratio to obtain the total risk coefficient of the disaster-bearing body, analyze the dual role of the disaster-bearing body's own characteristics and the degree of disaster exposure, ensure that the assessment results take into account both objective risk and subject differences, summarize the total risk coefficients of all disaster-bearing bodies, construct a disaster-causing factor-disaster-bearing body cross-sensitivity matrix, with disaster-bearing bodies as rows and the total risk coefficients of disaster-bearing bodies as columns, to show the comprehensive risk level of each disaster-bearing body in marine disaster risk assessment; The specific work involves: based on the physical properties (structural strength, material durability) and exposure characteristics (geographical location, spatial distribution) of the disaster-bearing bodies, combined with expert experience and historical disaster data, using the Analytic Hierarchy Process (AHP) to quantify the differences in vulnerability of each disaster-bearing body to marine disasters, and constructing a hierarchical model. The target layer is set as the vulnerability assessment of the disaster-bearing bodies; the criteria layer includes two main categories: physical properties and exposure characteristics; and the alternative layer represents specific disaster-bearing body types. The weight of each criterion layer relative to the target layer is determined through expert scoring, as well as the score of each disaster-bearing body under the criteria layer. The comprehensive vulnerability score of each disaster-bearing body is calculated to determine its disaster-bearing risk weight; and the disaster-bearing risk weight is then weighted by the total risk ratio. Coupling involves multiplying the disaster risk weight of each disaster-bearing body by its total risk ratio to obtain the total risk coefficient of the disaster-bearing body. The disaster risk weight reflects the contribution of the disaster-bearing body's own physical attributes and exposure characteristics to the risk, while the total risk ratio reflects the relative position of the disaster-bearing body in the overall risk. Through weighted coupling, the dual effects of the disaster-bearing body's own characteristics and the degree of disaster exposure are combined to ensure that the assessment results consider both objective risks and the differences between the subjects. Then, the total risk coefficients of all disaster-bearing bodies are summarized to construct a disaster-causing factor-disaster-bearing body cross-sensitivity matrix. This matrix has disaster-bearing bodies as rows and the total risk coefficients of disaster-bearing bodies as columns, which intuitively shows the comprehensive risk level of each disaster-bearing body in marine disaster risk assessment. Furthermore, the formula for calculating the total risk coefficient of the disaster-bearing body is as follows: ; ; ; In the formula: Let i be the total risk coefficient of the i-th disaster-bearing body; Let be the disaster risk weight of the i-th disaster-bearing body, reflecting the contribution of the disaster-bearing body's own physical attributes and exposure characteristics to the risk. The overall vulnerability score for the i-th disaster-bearing body is calculated using the Analytic Hierarchy Process (AHP), where n is the total number of disaster-bearing bodies and i is the total number of disaster-bearing bodies. The weights of physical properties, Let i be the score of the i-th disaster-bearing entity under physical attributes. Weights for exposing features, This represents the score of the i-th disaster-bearing entity under the exposure characteristics; The total risk proportion of the i-th disaster-bearing body reflects its relative position in the overall risk, and the disaster-bearing risk weight. The higher the coefficient, the greater the overall risk of the disaster-bearing body. The higher the overall risk ratio, the higher the risk ratio. The higher the coefficient, the greater the overall risk of the disaster-bearing body. The higher the overall vulnerability score, the better. The higher the value, the greater the disaster risk weight. The higher; S6 specifically includes: analyzing and assessing the region's historical disaster records, calculating the total risk coefficient of disaster-bearing bodies based on historical disaster records, and then classifying different risk levels according to the total risk coefficient of disaster-bearing bodies under historical scenarios, including Level 1, Level 2, and Level 3 risk levels, with the severity of risk levels increasing progressively from Level 1 to Level 3. At the same time, in conjunction with the total risk coefficient of disaster-bearing bodies, corresponding risk thresholds are set for each risk level, and risk level classification standards are determined. Differentiated disaster reduction measures are formulated for disaster-bearing bodies with different risk levels to ensure the disaster resistance capacity of high-risk disaster-bearing bodies is improved, and to achieve precise resource allocation and dynamic risk management. Based on the determined risk level classification standards, the risk level corresponding to the total risk coefficient of disaster-bearing bodies under the target scenario is analyzed, and corresponding disaster reduction measures are matched to allocate disaster relief materials and ensure the disaster resistance capacity of disaster-bearing bodies. The specific work includes: analyzing and assessing the region's historical disaster records, calculating the total risk coefficient of disaster-bearing bodies based on these records, quantifying the damage to each disaster-bearing body in past disasters through statistical analysis of historical disaster data, classifying different risk levels according to the total risk coefficient of disaster-bearing bodies under historical scenarios, covering Level 1, Level 2, and Level 3 risk levels, and setting corresponding risk thresholds for each risk level based on the total risk coefficient of disaster-bearing bodies to determine the risk level classification standards, ensuring the objectivity of risk assessment and providing a basis for the formulation of disaster reduction measures; developing differentiated disaster reduction measures for disaster-bearing bodies at different risk levels, prioritizing resource allocation for Level 3 risk-bearing bodies, strengthening the construction of physical protection facilities, and improving... Enhance structural strength and material durability; for disaster-bearing bodies at level II risk, optimize spatial layout to reduce exposure risks; for disaster-bearing bodies at level I risk, focus on improving monitoring and early warning systems to ensure timely response, and improve overall disaster reduction efficiency through differentiated measures to ensure rational resource utilization and effective risk control; based on the determined risk level classification standards, analyze the risk level corresponding to the total risk coefficient of the disaster-bearing body under the target scenario (i.e., current or future), match corresponding disaster reduction measures, and update the risk level through real-time monitoring and evaluation under the target scenario to ensure that disaster reduction measures match the current risk status, achieve dynamic risk management and continuous improvement of the disaster-bearing body's disaster resistance capacity, and provide a guarantee for the sustainable development of the region; In addition, multiple risk levels correspond one-to-one with multiple risk thresholds, as follows: The risk threshold for Level 1 risk is: Emphasis should be placed on improving the monitoring and early warning system to ensure timely response; The risk threshold for Level 2 risk is: Optimize spatial layout to reduce exposure risks; The risk threshold for Level 3 risk is: Prioritize resource allocation, strengthen the construction of physical protection facilities, and improve structural strength and material durability; in, The total risk coefficient of the disaster-bearing body. These are the upper threshold for Level 1 risk and the lower threshold for Level 2 risk. These are the upper threshold for Level 2 risk and the lower threshold for Level 3 risk.

[0021] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0022] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for assessing marine hazard risk in multiple disaster-bearing bodies based on a sensitivity matrix, characterized in that, Includes the following steps: S1. Define the assessment area for marine disasters and identify the types of multiple disaster-causing factors within the assessment area; S2. Analyze the distribution characteristics of various disaster-bearing bodies within the assessment area and clarify the assessment objects; S3. Establish an m×n-dimensional initial sensitivity comparison matrix with disaster-causing factors as rows and disaster-bearing bodies as columns; S4. Compare the risks of similar disaster-bearing bodies under different disaster-causing factors, determine the relative risk weight of each disaster-causing factor, compare the differences in the ability of different disaster-bearing bodies to resist all risks, and calculate the total risk ratio of each disaster-bearing body. S5. Based on the characteristics of each disaster-bearing body, determine the corresponding disaster risk weight, multiply the disaster risk weight by the total risk ratio, calculate the total risk coefficient of the disaster-bearing body, and form a disaster-causing factor-disaster-bearing body cross-sensitivity matrix. S6. Based on the cross-sensitivity matrix of disaster-causing factors and disaster-bearing bodies, set risk level classification standards, determine the corresponding risk levels, and match disaster reduction measures.

2. The method for assessing marine hazard risk of multiple disaster-bearing bodies based on a sensitivity matrix according to claim 1, characterized in that: S1 specifically includes: Define the geographical scope of marine disaster risk assessment, delineate the assessment area by combining administrative divisions and natural geographical boundaries, and determine the regional coordinates, area and coastline characteristics by using GIS tools; Based on historical disaster records and meteorological and hydrological data, the natural factors that cause disasters in the region are sorted out and assessed, and three types of disaster-causing factors, including storm surge, ocean waves and floods, are selected, and the disaster attributes of each disaster-causing factor are clarified.

3. The method for assessing marine hazard risk of multiple disaster-bearing bodies based on a sensitivity matrix according to claim 1, characterized in that: S2 specifically includes: Based on the functional division of the assessment area, the assessment objects within the assessment area are clearly defined, namely, seven types of disaster-bearing entities including aquaculture areas, fishing ports, chemical industrial zones, pastures, salt fields, disaster relief facilities, and tourist areas. By interpreting remote sensing images, using administrative division data, and conducting on-site verification, we collect basic disaster-bearing body data on the spatial distribution range, area, and functional type of various disaster-bearing bodies. The collected basic disaster-bearing body data are analyzed to clarify their distribution characteristics, assess the spatial layout of each disaster-bearing body in the assessment area, identify their relative positional relationship with natural geographical elements, and integrate them to establish a disaster-bearing body database.

4. The method for assessing marine hazard risk of multiple disaster-bearing bodies based on a sensitivity matrix according to claim 1, characterized in that: S3 specifically includes: Based on the identified disaster-causing factors and disaster-bearing body types within the assessment area, the disaster characteristics and functional attributes of the disaster-bearing bodies are analyzed to determine the correspondence between disaster-causing factors and disaster-bearing bodies, forming a structured list of row vectors for disaster-causing factors and column vectors for disaster-bearing bodies. Construct an m×n dimensional empty matrix with disaster-causing factors as rows and disaster-bearing bodies as columns. The number of rows m corresponds to the number of disaster-causing factors and the number of columns n corresponds to the number of disaster-bearing bodies. Define the matrix elements as the sensitivity values ​​of disaster-causing factors to disaster-bearing bodies and clarify the assignment rules for matrix elements. Based on the intensity of the disaster-causing factors, the vulnerability of the disaster-bearing body, and the spatial exposure relationship, the sensitivity values ​​of matrix elements are filled in one by one through expert scoring to form an initial sensitivity comparison matrix.

5. The method for assessing marine hazard risk of multiple disaster-bearing bodies based on a sensitivity matrix according to claim 1, characterized in that: S4 specifically includes: Based on the sensitivity matrix, the sensitivity values ​​of similar disaster-bearing bodies under various disaster-causing factors are extracted. Combined with the historical disaster frequency, the relative risk weight of each disaster-causing factor is quantified by the entropy weight method. For different disaster-bearing bodies, their sensitivity values ​​to all disaster-causing factors are summarized. Based on the relative risk weight of each disaster-causing factor and the sensitivity values ​​of each disaster-bearing body under different disaster-causing factors, the total risk value of each disaster-bearing body is calculated by weighted summation. The total risk value of each disaster-bearing body is normalized, and the total risk ratio of each disaster-bearing body is calculated. Based on the sum of the total risk values ​​of a group of disaster-bearing bodies, the total risk value of each disaster-bearing body is converted into a relative ratio, thus clarifying the relative position of each disaster-bearing body in the overall risk.

6. The method for assessing marine disaster risk of multiple disaster-bearing bodies based on a sensitivity matrix according to claim 5, characterized in that: The analysis process for the total risk value of each disaster-bearing entity is as follows: For different disaster-bearing bodies within the assessment area, their sensitivity values ​​to all disaster-causing factors are summarized. The data are then organized by disaster-bearing body type as rows and disaster-causing factor type as columns to form a disaster-bearing body-disaster-causing factor sensitivity matrix. By combining the established relative risk weights of disaster-causing factors, the sensitivity values ​​of each disaster-bearing entity are weighted. The relative risk weight of each disaster-causing factor is multiplied by the corresponding sensitivity value of the disaster-bearing entity to obtain the weighted sensitivity value of each disaster-causing factor-disaster-bearing entity combination. All weighted sensitivity values ​​of the same disaster-bearing entity are summed to obtain the total risk value of each disaster-bearing entity, which reflects its risk level under comprehensive exposure to multiple disaster-causing factors.

7. The method for assessing marine hazard risk of multiple disaster-bearing bodies based on a sensitivity matrix according to claim 1, characterized in that: S5 specifically includes: Based on the physical properties and exposure characteristics of the disaster-bearing bodies, combined with expert experience and historical disaster data, the analytic hierarchy process (AHP) was used to quantify the differences in vulnerability of each disaster-bearing body to marine disasters and determine its disaster risk weight. By weighting and coupling the disaster risk weight with the total risk ratio, the total risk coefficient of the disaster-bearing body is obtained, and the dual effects of the disaster-bearing body's own characteristics and the degree of disaster exposure are analyzed. The total risk coefficients of all disaster-bearing bodies are summarized, and a cross-sensitivity matrix of disaster-causing factors and disaster-bearing bodies is constructed. With disaster-bearing bodies as rows and the total risk coefficients of disaster-bearing bodies as columns, the comprehensive risk level of each disaster-bearing body in marine disaster risk assessment is displayed.

8. The method for assessing marine disaster risk of multiple disaster-bearing bodies based on a sensitivity matrix according to claim 7, characterized in that: The formula for calculating the total risk coefficient of the disaster-bearing body is as follows: ； ； ； In the formula: Let i be the total risk coefficient of the i-th disaster-bearing body; Let the disaster risk weight be the weight of the i-th disaster-bearing body. Let be the overall vulnerability score of the i-th disaster-bearing entity, where n is the total number of disaster-bearing entities, and i is the index of the disaster-bearing entity. The weights of physical properties, Let i be the score of the i-th disaster-bearing entity under physical attributes. Weights for exposing features, This represents the score of the i-th disaster-bearing entity under the exposure characteristics; denoted as the total risk ratio of the i-th disaster-bearing entity.

9. The method for assessing marine disaster risk of multiple disaster-bearing bodies based on a sensitivity matrix according to claim 8, characterized in that: S6 specifically includes: The historical disaster records of the region are analyzed and evaluated, and the total risk coefficient of the disaster-bearing body based on the historical disaster records is calculated. Then, according to the total risk coefficient of the disaster-bearing body under the historical context, different risk levels are divided, including Level 1, Level 2 and Level 3 risk levels. The severity of the risk level increases from Level 1 to Level 3. At the same time, corresponding risk thresholds are set for each risk level in combination with the total risk coefficient of the disaster-bearing body to determine the risk level classification standard. Differentiated disaster reduction measures should be formulated for disaster-bearing bodies with different risk levels to ensure the improvement of disaster resistance capabilities of high-risk disaster-bearing bodies; Based on the established risk level classification standards, analyze the risk level corresponding to the total risk coefficient of the disaster-bearing body under the target scenario, match the corresponding disaster reduction measures, and allocate disaster relief materials.

10. The method for assessing marine hazard risk of multiple disaster-bearing bodies based on a sensitivity matrix according to claim 9, characterized in that: The various risk levels correspond one-to-one with the various risk thresholds, and the correspondence is as follows: The risk threshold for the first-level risk level is: ; The risk threshold for the secondary risk level is: ; The risk threshold for the three-level risk level is: ; in, The total risk coefficient of the disaster-bearing body. These are the upper threshold for Level 1 risk and the lower threshold for Level 2 risk. These are the upper threshold for Level 2 risk and the lower threshold for Level 3 risk.

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