A sea ice disaster mariculture risk assessment method, device and equipment

Abstract

The present application provides a kind of sea ice disaster mariculture risk assessment method, device and equipment, it is related to geographic environment technical field, method includes: obtaining the hazard factor risk criterion of sea ice disaster to mariculture disaster-causing, hazard-accepting body vulnerability criterion and hazard-accepting body exposure criterion correspond respectively multiple index factors;Determine the target weight of hazard factor risk criterion, hazard-accepting body vulnerability criterion and hazard-accepting body exposure criterion correspond respectively multiple index factors correspond respectively;Respectively determine hazard factor risk index, hazard-accepting body vulnerability index and hazard-accepting body exposure index;According to the hazard factor risk index, hazard-accepting body vulnerability index and hazard-accepting body exposure index, determine the risk assessment index of target area sea ice disaster to mariculture.The scheme of the present application can provide targeted solution to mariculture in combination with sea ice disaster risk assessment index, and early warning is carried out to sea ice disaster.

Description

Technical Field

[0001] This invention relates to the field of geographical environment technology, and in particular to a method, apparatus and equipment for risk assessment of marine aquaculture caused by sea ice disasters. Background Technology

[0002] Sea ice is most prevalent in polar and high-latitude waters, but it can also form intermittently in mid-latitude nearshore waters, causing catastrophic impacts on shore-based facilities and ships. Compared to other marine disasters, sea ice disasters are characterized by their suddenness, long duration, and wide range of impact, posing a significant threat to offshore engineering facilities, socio-economic development, and people's lives and property. Marine aquaculture is particularly vulnerable to severe impacts from sea ice disasters during years of heavy ice, posing significant risks to the industry's development.

[0003] Overall, existing sea ice disaster prevention and early warning systems mainly focus on ice data acquisition, ice condition monitoring, and ice disaster prevention recommendations, while sea ice disaster risk assessment for the aquaculture industry is relatively insufficient. Summary of the Invention

[0004] This invention provides a method, apparatus, and equipment for assessing the risks of sea ice disasters to marine aquaculture, which can assess the risks of sea ice disasters to marine aquaculture.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] A method for assessing marine aquaculture risks caused by sea ice disasters, comprising:

[0007] To obtain multiple indicator factors corresponding to the hazard criterion, vulnerability criterion, and exposure criterion of sea ice disasters to marine aquaculture.

[0008] Determine the target weights for the multiple indicator factors corresponding to the hazard criterion of disaster-causing factors, the vulnerability criterion of disaster-bearing bodies, and the exposure criterion of disaster-bearing bodies, respectively;

[0009] The disaster-causing factor risk index is determined based on the first number of indicator factors corresponding to the disaster-causing factor risk criterion and the target weights corresponding to the first number of indicator factors.

[0010] The vulnerability index of the disaster-bearing body is determined based on the second number of indicator factors corresponding to the vulnerability criterion of the disaster-bearing body and the target weights corresponding to the second number of indicator factors.

[0011] The disaster-bearing body exposure index is determined based on the third number of indicator factors corresponding to the disaster-bearing body exposure criterion and the target weights corresponding to the third number of indicator factors.

[0012] Based on the hazard index of the disaster-causing factor, the vulnerability index of the disaster-bearing body, and the exposure index of the disaster-bearing body, the risk assessment index of sea ice disasters in the target area for marine aquaculture is determined.

[0013] Optionally, obtain multiple indicator factors corresponding to the hazard hazard criteria, vulnerability criteria, and exposure criteria of sea ice disasters to marine aquaculture, including:

[0014] Based on the disaster-causing mechanism of sea ice disasters, the criteria for the risk of disaster-causing factors, the vulnerability of disaster-bearing bodies, and the exposure of disaster-bearing bodies to sea ice disasters were determined.

[0015] Obtain the sea ice thickness index factor, station temperature index factor, ice period days index factor, sea ice concentration index factor, empirical first ice day anomaly index factor, and station wind speed index factor corresponding to the disaster-causing factor hazard criterion.

[0016] Obtain the cable tensile strength index factor and marine cold resistance index factor corresponding to the aforementioned disaster-bearing body vulnerability criterion;

[0017] Obtain the economic loss index factor and marine personnel index factor of sea ice disaster under the aforementioned exposure criterion for disaster-bearing bodies.

[0018] Optionally, determine the target weights for each of the multiple indicator factors corresponding to the hazard causative factor criterion, the vulnerability criterion of the disaster-bearing body, and the exposure criterion of the disaster-bearing body, including:

[0019] The importance data corresponding to the first number of indicator factors corresponding to the hazard criterion of the disaster-causing factor are used as elements of the first weight matrix. The elements of the first weight matrix are operated on to obtain the initial weight components corresponding to the first number of indicator factors. The initial weight components corresponding to the first number of indicator factors are normalized to obtain the final weights corresponding to the first number of indicator factors.

[0020] The importance data corresponding to the second number of indicator factors corresponding to the vulnerability criterion of the disaster-bearing body are used as elements of the second weight matrix. The elements of the second weight matrix are operated on to obtain the initial weight components corresponding to the second number of indicator factors. The initial weight components corresponding to the second number of indicator factors are normalized to obtain the final weights corresponding to the second number of indicator factors.

[0021] The importance data corresponding to the third number of indicator factors corresponding to the disaster-bearing body exposure criterion are used as elements of the third weight matrix. The elements of the third weight matrix are operated on to obtain the initial weight components corresponding to the third number of indicator factors. The initial weight components corresponding to the third number of indicator factors are then normalized to obtain the final weights corresponding to the third number of indicator factors.

[0022] Optionally, operations are performed on the elements of the first weight matrix, including:

[0023] Calculate the first product of the elements in each row of the first weight matrix, and perform a square root operation on the first product;

[0024] The elements of the second weight matrix are operated on, including:

[0025] Calculate the second product of the elements in each row of the second weight matrix, and perform a square root operation on the second product;

[0026] The operations performed on the elements of the third weight matrix include:

[0027] Calculate the third product of each row of elements in the third weight matrix, and perform a square root operation on the third product.

[0028] Optionally, based on the first number of indicator factors corresponding to the hazard causative factor criterion and the target weights corresponding to the first number of indicator factors, a hazard causative factor hazard index is determined, including:

[0029] pass Determine the hazard index of disaster-causing factors;

[0030] in, For disaster-causing factor hazard index, For the standardized first Individual disaster-causing factors and their risk indicators. Here, f represents the target weight corresponding to the first number of indicator factors, and f represents the nearshore station temperature indicator factor. b represents the target weight corresponding to the nearshore station temperature index factor, and b represents the nearshore station wind speed index factor. c represents the target weight corresponding to the nearshore station wind speed index factor, and c represents the sea ice concentration index factor. The target weights for the sea ice concentration index factors are represented by h, and the sea ice thickness index factor is represented by h. The target weights for the sea ice thickness index factor are represented by q, and the number of ice-covered days index factor is represented by q. The target weight corresponding to the number of days in the glacial period index factor is represented by u, which represents the empirical first glacial day anomaly index factor. This represents the target weight corresponding to the empirical first glacial anomaly index factor.

[0031] Optionally, based on the second number of indicator factors corresponding to the vulnerability criterion of the disaster-bearing body and the target weights corresponding to the second number of indicator factors, a vulnerability index for the disaster-bearing body is determined, including:

[0032] pass Determine the vulnerability index of the disaster-bearing body;

[0033] in, For the vulnerability index of disaster-bearing bodies, For the standardized first Individual vulnerability index factors of disaster-bearing bodies The target weights corresponding to the second number of indicator factors, where t represents the marine cold resistance indicator factor. This represents the target weight corresponding to the marine cold resistance index factor, and l represents the cable tensile strength index factor. This indicates the target weight corresponding to the cable tensile strength index factor.

[0034] Optionally, based on the third number of indicator factors corresponding to the disaster-bearing body exposure criterion and the target weights corresponding to the third number of indicator factors, a disaster-bearing body exposure index is determined, including:

[0035] pass Determine the exposure index of the disaster-bearing body;

[0036] in, Exposure index of disaster-bearing bodies, For the standardized first Individual disaster-bearing body exposure index factors The target weights corresponding to the third number of indicator factors, where p represents the economic loss indicator factor for sea ice disasters. The target weights of the economic loss indicators for sea ice disasters are represented by s, and the employment figures for people working in the marine sector are represented by s. This indicates the target weight corresponding to the indicator factor for marine-related employment personnel.

[0037] Optionally, based on the hazard index of the disaster-causing factor, the vulnerability index of the disaster-bearing body, and the exposure index of the disaster-bearing body, a risk assessment index for the sea ice disaster in the target area on marine aquaculture is determined, including:

[0038] pass Determine the risk assessment index of sea ice disasters to marine aquaculture in the target area;

[0039] in, For risk assessment index, For disaster-causing factor hazard index, Exposure index of disaster-bearing bodies, It is the vulnerability index of the disaster-bearing body.

[0040] The present invention also provides a marine aquaculture risk assessment device for sea ice disasters, comprising:

[0041] The acquisition module is used to acquire multiple indicator factors corresponding to the hazard criterion, vulnerability criterion, and exposure criterion of the disaster-causing factors of sea ice disasters to marine aquaculture.

[0042] The processing module is used to determine the target weights of multiple indicator factors corresponding to the hazard criterion for disaster-causing factors, the vulnerability criterion for disaster-bearing bodies, and the exposure criterion for disaster-bearing bodies, respectively; to determine the hazard index for disaster-causing factors based on a first number of indicator factors corresponding to the hazard criterion for disaster-causing factors and their corresponding target weights; to determine the vulnerability index for disaster-bearing bodies based on a second number of indicator factors corresponding to the vulnerability criterion for disaster-bearing bodies and their corresponding target weights; to determine the exposure index for disaster-bearing bodies based on a third number of indicator factors corresponding to the exposure criterion for disaster-bearing bodies and their corresponding target weights; and to determine the risk assessment index of sea ice disasters in the target area for marine aquaculture based on the hazard index for disaster-causing factors, the vulnerability index for disaster-bearing bodies, and the exposure index for disaster-bearing bodies.

[0043] The present invention also provides a computing device, comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above.

[0044] The above-described solution of the present invention has at least the following beneficial effects:

[0045] The above-described solution of the present invention obtains multiple indicator factors corresponding to the hazard criterion, vulnerability criterion, and exposure criterion of sea ice disasters to marine aquaculture; determines the target weights corresponding to the multiple indicator factors corresponding to the hazard criterion, vulnerability criterion, and exposure criterion; determines the hazard index of the hazard factor based on a first number of indicator factors and their corresponding target weights; determines the vulnerability index of the affected body based on a second number of indicator factors and their corresponding target weights; determines the exposure index of the affected body based on a third number of indicator factors and their corresponding target weights; and determines the risk assessment index of sea ice disasters to marine aquaculture in the target area based on the hazard index, vulnerability index, and exposure index. This allows for the assessment of the risk of sea ice disasters to marine aquaculture. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating the sea ice disaster risk assessment method for aquaculture according to an embodiment of the present invention.

[0047] Figure 2 This is a technical roadmap of the marine aquaculture risk assessment method for sea ice disasters according to an embodiment of the present invention;

[0048] Figure 3 This is a structural diagram of the marine aquaculture risk assessment device for sea ice disasters according to an embodiment of the present invention. Detailed Implementation

[0049] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0050] like Figure 1 As shown, an embodiment of the present invention proposes a method for risk assessment of marine aquaculture caused by sea ice disasters, comprising:

[0051] Step 11: Obtain multiple indicator factors corresponding to the hazard criterion, vulnerability criterion, and exposure criterion of the disaster-causing factors of sea ice disasters to marine aquaculture.

[0052] Here, based on the mechanism of sea ice disasters, three criteria are identified that affect marine aquaculture: the hazard level of the disaster-causing factor, the vulnerability of the disaster-bearing organism, and the exposure level of the disaster-bearing organism. Each criterion corresponds to several specific sub-indicators; the marine products mentioned include scallops.

[0053] Step 12: Determine the target weights of the multiple indicator factors corresponding to the hazard criterion of disaster-causing factors, the vulnerability criterion of disaster-bearing bodies, and the exposure criterion of disaster-bearing bodies, respectively.

[0054] Here, weights are assigned based on the importance of each indicator factor under each criterion, resulting in target weights for each indicator factor.

[0055] Step 13: Determine the disaster-causing factor risk index based on the first number of index factors corresponding to the disaster-causing factor risk criterion and the target weights corresponding to the first number of index factors.

[0056] Based on the weights of each indicator factor under the hazard hazard criterion and the standardized values ​​of each indicator factor collected by monitoring, the hazard hazard index is calculated. Preferably, the first number is 6.

[0057] Step 14: Determine the vulnerability index of the disaster-bearing body based on the second number of index factors corresponding to the vulnerability criterion of the disaster-bearing body and the target weights corresponding to the second number of index factors.

[0058] Based on the target weights and standardized values ​​of each indicator factor under the vulnerability criterion for disaster-bearing bodies, the vulnerability index for disaster-bearing bodies is calculated. Preferably, the second number is two.

[0059] Step 15: Determine the disaster-bearing body exposure index based on the third number of index factors corresponding to the disaster-bearing body exposure criterion and the target weights corresponding to the third number of index factors.

[0060] Based on the target weights of each indicator factor and the standardized values ​​of each indicator factor under the disaster-bearing body exposure criterion, the disaster-bearing body exposure index is calculated. Preferably, the third factor is two.

[0061] Step 16: Determine the risk assessment index of sea ice disasters in the target area for marine aquaculture based on the disaster-causing factor hazard index, disaster-bearing body vulnerability index, and disaster-bearing body exposure index.

[0062] In this embodiment, sea ice disasters exhibit an interannual persistence, exacerbating the risks to fisheries economic operations. The impact of different sea ice year types on aquaculture varies. In heavy ice years, sea ice disasters are characterized by their sudden onset and rapid development, with limited time windows for early warning and emergency evacuation. Large quantities of aquatic products may remain stranded in high-risk aquaculture areas, directly exposed to the ice-affected environment. This exposure is amplified step-by-step through multiple disaster-causing mechanisms, from physical impact to environmental stress, ultimately leading to biological damage and economic losses, forming a multi-stage, progressive disaster chain.

[0063] From a physical perspective, when sea ice erupts earlier in heavy glaciation years, the sea ice damages aquaculture cages through impact, compression, and abrasion. Wind stress drives nearshore fixed ice to move slowly as a whole, and the massive ice masses pose an impact threat to aquaculture facilities. The expansion of seawater when it freezes causes the ice surface to rise, which in turn causes compression damage to the aquaculture raft structure. In open waters, floating ice drifting with the tides, currents, and winds directly impacts and abrades the cage cables, causing them to break.

[0064] Besides indirectly threatening marine life by destroying the physical barriers of aquaculture, sea ice also directly threatens the marine life itself. The early formation and persistence of sea ice in heavy ice years impact aquaculture through both direct lethality and indirect growth inhibition. If marine life is not relocated in time, it will be directly exposed to sea ice and face multiple lethal threats. Furthermore, the dense, continuous ice cover in aquaculture areas obstructs the exchange of substances between seawater and air, leading to oxygen deprivation and death in marine life. While timely relocation to warmer waters avoids the direct impact of sea ice disasters, the continued presence of sea ice indirectly affects marine life by shortening its effective growth period.

[0065] Based on the aforementioned sea ice disaster-causing mechanism, this invention identifies 10 key indices with significant impact. The values ​​of each indices are obtained, and weights are assigned to each indices. Based on the obtained values ​​and corresponding weights, the hazard index of the disaster-causing factor, the vulnerability index of the disaster-bearing body, and the exposure index of the disaster-bearing body are calculated. These three indices are then combined to calculate the risk index of sea ice disasters on aquaculture in the target area. This invention constructs a three-dimensional index system for sea ice disaster risk, analyzes the feedback mechanism between sea ice disasters and aquaculture, and provides spatial decision support for regional disaster early warning and prevention.

[0066] In an optional embodiment of the present invention, step 11, obtaining multiple indicator factors corresponding to the hazard criterion, vulnerability criterion, and exposure criterion of sea ice disasters to marine aquaculture, respectively, may include:

[0067] Step 111: Based on the disaster-causing mechanism of sea ice disasters, determine the hazard criterion of disaster-causing factors, the vulnerability criterion of disaster-bearing bodies, and the exposure criterion of disaster-bearing bodies for the disaster-causing effects of sea ice disasters on marine aquaculture.

[0068] Step 112: Obtain the sea ice thickness index factor, station temperature index factor, ice period days index factor, sea ice concentration index factor, empirical first ice day anomaly index factor, and station wind speed index factor corresponding to the disaster-causing factor hazard criterion.

[0069] Step 113: Obtain the cable tensile strength index factor and marine cold resistance index factor corresponding to the vulnerability criterion of the disaster-bearing body;

[0070] Step 114: Obtain the economic loss index factor and marine personnel index factor of sea ice disaster under the disaster exposure criterion.

[0071] In this embodiment, as Figure 2As shown, this invention selects indicators from three aspects—sea ice physical properties, meteorological driving conditions, and time dimension—when constructing the sea ice disaster risk index. Winter cold air intrusion leads to rapid cooling of the sea surface, resulting in a corresponding increase in sea ice thickness. Low temperatures foster sea ice formation, while wind drives its movement. Specifically, station temperature reflects the stress of the winter low-temperature environment on marine life, while station wind speed reflects the dynamic driving conditions of sea ice. Sea ice concentration describes the proportion of ice on the sea surface; its increase indicates that scattered floating ice floes are aggregated by external forces to form continuous ice floes, which collectively cover the marine aquaculture area. The empirical first-ice daily anomaly quantifies the abnormal characteristics of sea ice arrival; a larger value corresponds to a smaller warning response window and a higher difficulty in marine life relocation. The number of ice-covered days characterizes the degree of compression of the suitable long-term cycle for marine life and directly affects the development process of marine life. Therefore, six indicators—sea ice thickness, station temperature, station wind speed, sea ice concentration, empirical first-ice daily anomaly, and the number of ice-covered days—are selected to calculate the sea ice disaster hazard index.

[0072] Exposure to hazardous elements measures the quantity or value of hazardous elements (population, property, infrastructure, ecosystems, etc.) within the impact range of hazardous factors in a given area. Under conditions of consistent hazardous factor hazard, the greater the number of people engaged in marine aquaculture, the greater the potential losses. Simultaneously, the economic benefits of fisheries production will also be impacted. Therefore, an exposure index to hazardous elements is constructed by selecting the number of people engaged in aquaculture and the economic losses caused by sea ice disasters.

[0073] The vulnerability of disaster-bearing structures is related to natural, economic, social, and environmental factors, determining the degree of disaster a region will suffer under given hazardous conditions caused by disaster-causing factors. For marine aquaculture, aquaculture facilities are the physical disaster-bearing structures, acting as physical barriers against sea ice disasters, directly and passively bearing the impact of sea ice. Marine products, as the biological disaster-bearing structures within the aquaculture system, are directly subjected to environmental stresses such as low temperatures and low salinity. The cold resistance of marine products determines their ability to survive in low temperatures; species with poor cold resistance suffer greater losses and higher repair costs under sea ice conditions. Therefore, vulnerability is comprised of two core dimensions: an engineering dimension—the tensile strength of cables; and a biological dimension—the cold resistance of marine products.

[0074] As shown in Table 1 below, the criteria and corresponding indicators, as well as the evaluation standards for the indicators, are as follows.

[0075] Table 1. Framework for Sea Ice Disaster Risk Assessment in Aquaculture

[0076]

[0077] In an optional embodiment of the present invention, step 12, determining the target weights corresponding to the multiple indicator factors for the hazard causative factor criterion, the vulnerability criterion for the disaster-bearing body, and the exposure criterion for the disaster-bearing body, may include:

[0078] Step 121: Take the importance data corresponding to the first number of indicator factors corresponding to the hazard criterion of the disaster-causing factor as the elements of the first weight matrix, perform operations on the elements of the first weight matrix to obtain the initial weight components corresponding to the first number of indicator factors, and normalize the initial weight components corresponding to the first number of indicator factors to obtain the final weights corresponding to the first number of indicator factors.

[0079] The first weight matrix is ​​shown in Table 2:

[0080] Table 2, First Weight Matrix

[0081] Weights ( ) 1 1.33 2 2.5 3 5 0.312 0.75 1 1.5 1.8 2.2 3.5 0.234 0.5 0.67 1 1.25 1.5 2.5 0.156 0.4 0.56 0.8 1 1.2 2 0.125 0.33 0.45 0.67 0.83 1 1.7 0.109 0.2 0.29 0.4 0.5 0.59 1 0.064

[0082] in, to These represent the sea ice thickness index factor, the station temperature index factor, the ice period days index factor, the sea ice concentration index factor, the empirical first glaciation daily anomaly index factor, and the station wind speed index factor, respectively. This represents the hazard assessment criteria for disaster-causing factors. Each element in the table represents importance data. For example, using... OK Taking the value "1.33" as an example, it represents... Compared to The importance level is 1.33, which is obtained through the following calculation process:

[0083] First, pre-determine the importance level: 1: Equally important; 3: Slightly important; 5: Significantly important; 7: Strongly important; 9: Extremely important. Then, initially score the item 1.33. For example, score it 1 the first time, 3 the second time, and repeat this scoring process five times. Summarize the initial scores from the five rounds using a formula. Calculated Compared to The final importance value, for example , , .in, To determine the importance values ​​in the matrix, to These are the initial scores for 5 rounds of scoring.

[0084] Through the above process, the value of each importance element in the first weight matrix can be obtained.

[0085] Step 121, performing operations on the elements of the first weight matrix, may include:

[0086] Step 1211: Calculate the first product of the elements in each row of the first weight matrix, and perform a square root operation on the first product;

[0087] Through formula Calculate the product of the elements in each row of the first weight matrix, where i represents the row and j represents the column. When calculating the first weight matrix, The first product is denoted as n, where n is the number of elements in the weight matrix of the i-th row, i.e., the first quantity.

[0088] The square root operation of the first product is as follows: ,in, Let be the initial weight component corresponding to the i-th index factor.

[0089] The initial weight components are normalized, i.e. The final weights corresponding to the first number of indicator factors are obtained. : .

[0090] Furthermore, a consistency check can be performed on the final weights to obtain the target weights for each indicator. The consistency check process includes:

[0091] Calculate the maximum eigenvalue based on the final weights and the first weight matrix. :

[0092]

[0093] in Represents the first weight matrix With final weight The product of the first product There are one component, and n is the first quantity. The consistency index is calculated based on the largest eigenvalue:

[0094]

[0095] in, As a consistency indicator. When At that time, the matrices are completely identical; The larger the value, the worse the consistency. The consistency ratio is then calculated based on the aforementioned consistency index. :

[0096]

[0097] in The average random consistency index (obtained from a standard table, such as a 6th order matrix) ).

[0098] The consistency ratio is compared with a preset threshold to obtain the consistency verification result; preferably, the preset threshold is 0.1. The consistency check passed.

[0099] Step 122: Take the importance data corresponding to the second number of indicator factors corresponding to the vulnerability criterion of the disaster-bearing body as the elements of the second weight matrix, perform operations on the elements of the second weight matrix to obtain the initial weight components corresponding to the second number of indicator factors, and normalize the initial weight components corresponding to the second number of indicator factors to obtain the final weights corresponding to the second number of indicator factors.

[0100] The second weight matrix is ​​shown in Table 3:

[0101] Table 3, Second Weight Matrix

[0102] Weights ( ) 1 2.03 0.670 0.49 1 0.330

[0103] in, Represents the tensile strength of the cable, Represents the cold resistance of seafood This represents the vulnerability criterion of the disaster-bearing body. The calculation method for the elements in the second weight matrix is ​​the same as that for the elements in the first weight matrix, which is obtained by multiplying the scores of the five assessments and then taking the square root. This will not be elaborated further here.

[0104] Step 122, performing operations on the elements of the second weight matrix, may include:

[0105] Step 1221: Calculate the second product of the elements in each row of the second weight matrix, and perform a square root operation on the second product;

[0106] Through formula Calculate the product of the elements in each row of the second weight matrix, where i represents the row and j represents the column. When calculating the second weight matrix, The second product is denoted by n, where n is the number of elements in the weight matrix of the i-th row, i.e., the second quantity.

[0107] The square root operation of the second product is as follows: ,in, Let be the initial weight component corresponding to the i-th index factor.

[0108] The initial weight components are normalized, i.e. The final weights corresponding to the second number of indicator factors are obtained. : .

[0109] Furthermore, a consistency check can be performed on the final weights to obtain the target weights for each indicator. The consistency check process includes:

[0110] Calculate the maximum eigenvalue based on the final weights and the second weight matrix. :

[0111]

[0112] in Represents the second weight matrix With final weight The product of the first product There are one component, and n is the second quantity. The consistency index is calculated based on the largest eigenvalue:

[0113]

[0114] in, As a consistency indicator. When At that time, the matrices are completely identical; The larger the value, the worse the consistency. The consistency ratio is then calculated based on the aforementioned consistency index. :

[0115]

[0116] in The average random consistency index (obtained from a standard table, such as a 6th order matrix) ).

[0117] The consistency ratio is compared with a preset threshold to obtain the consistency verification result; preferably, the preset threshold is 0.1. The consistency check passed.

[0118] Step 123: Use the importance data corresponding to the third number of indicator factors corresponding to the disaster-bearing body exposure criterion as elements of the third weight matrix, perform calculations on the elements of the third weight matrix to obtain the initial weight components corresponding to the third number of indicator factors, and normalize the initial weight components corresponding to the third number of indicator factors to obtain the final weights corresponding to the third number of indicator factors.

[0119] The third weight matrix is ​​shown in Table 4:

[0120] Table 4, Third Weight Matrix

[0121] Weights ( ) 1 3 0.750 0.33 1 0.250

[0122] in, Represents the economic losses from sea ice disasters, Representatives of maritime personnel This represents the exposure criterion for disaster-bearing bodies. The calculation method for the elements in the third weight matrix is ​​the same as that for the elements in the first and second weight matrices, which is obtained by multiplying the scores of the five assessments and then taking the square root. This will not be elaborated further here.

[0123] Step 123, performing operations on the elements of the third weight matrix, may include:

[0124] Step 1231: Calculate the third product of each row of elements in the third weight matrix, and perform a square root operation on the third product.

[0125] Through formula Calculate the product of elements in each row of the third weight matrix, where i represents the row and j represents the column. When calculating the third weight matrix, This is the third product, where n is the number of elements in the weight matrix of the i-th row, i.e., the third quantity. The root operation of the third product is: ,in, Let be the initial weight component corresponding to the i-th index factor.

[0126] The initial weight components are normalized, i.e. The final weights corresponding to the third number of indicator factors are obtained. : .

[0127] Furthermore, a consistency check can be performed on the final weights to obtain the target weights for each indicator. The consistency check process includes:

[0128] Calculate the maximum eigenvalue based on the final weights and the third weight matrix. :

[0129]

[0130] in Represents the third weight matrix With final weight The product of the first product There are one component, and n is the third quantity. The consistency index is calculated based on the largest eigenvalue:

[0131]

[0132] in, As a consistency indicator. When At that time, the matrices are completely identical; The larger the value, the worse the consistency. The consistency ratio is then calculated based on the aforementioned consistency index. :

[0133]

[0134] in The average random consistency index (obtained from a standard table, such as a 6th order matrix) ).

[0135] The consistency ratio is compared with a preset threshold to obtain the consistency verification result; preferably, the preset threshold is 0.1. The consistency check passed.

[0136] In an optional embodiment of the present invention, step 13, determining the hazard index of a disaster-causing factor based on a first number of index factors corresponding to the hazard criterion of the disaster-causing factor and the target weights corresponding to the first number of index factors, may include:

[0137] Step 131, through Determine the hazard index of disaster-causing factors;

[0138] in, For disaster-causing factor hazard index, For the standardized first Individual disaster-causing factors and their risk indicators. Here, f represents the target weight corresponding to the first number of indicator factors, and f represents the nearshore station temperature indicator factor. b represents the target weight corresponding to the nearshore station temperature index factor, and b represents the nearshore station wind speed index factor. c represents the target weight corresponding to the nearshore station wind speed index factor, and c represents the sea ice concentration index factor. The target weights for the sea ice concentration index factors are represented by h, and the sea ice thickness index factor is represented by h. The target weights for the sea ice thickness index factor are represented by q, and the number of ice-covered days index factor is represented by q. The target weight corresponding to the number of days in the glacial period index factor is represented by u, which represents the empirical first glacial day anomaly index factor. This represents the target weight corresponding to the empirical first glacial anomaly index factor.

[0139] Specifically, based on the calculation process of steps 121 to 123 above, it can be obtained that, preferably, , , , , , .

[0140] In an optional embodiment of the present invention, step 14, determining the vulnerability index of the disaster-bearing body based on the second number of index factors corresponding to the vulnerability criterion of the disaster-bearing body and the target weights corresponding to the second number of index factors, may include:

[0141] Step 141, through Determine the vulnerability index of the disaster-bearing body;

[0142] in, For the vulnerability index of disaster-bearing bodies, For the standardized first Individual vulnerability index factors of disaster-bearing bodies The target weights corresponding to the second number of indicator factors, where t represents the marine cold resistance indicator factor. This represents the target weight corresponding to the marine cold resistance index factor, and l represents the cable tensile strength index factor. This indicates the target weight corresponding to the cable tensile strength index factor.

[0143] Among them, the preferred ones are , .

[0144] In an optional embodiment of the present invention, step 15, determining the disaster-bearing body exposure index based on the third number of index factors corresponding to the disaster-bearing body exposure criterion and the target weights corresponding to the third number of index factors, may include:

[0145] Step 151, through Determine the exposure index of the disaster-bearing body;

[0146] in, Exposure index of disaster-bearing bodies, For the standardized first Individual disaster-bearing body exposure index factors The target weights corresponding to the third number of indicator factors, where p represents the economic loss indicator factor for sea ice disasters. The target weights of the economic loss indicators for sea ice disasters are represented by s, and the employment figures for people working in the marine sector are represented by s. This indicates the target weight corresponding to the indicator factor for marine-related employment personnel.

[0147] Among them, the preferred ones are , .

[0148] In an optional embodiment of the present invention, step 16, determining the risk assessment index of sea ice disasters to marine aquaculture in the target area based on the hazard index of the disaster-causing factor, the vulnerability index of the disaster-bearing body, and the exposure index of the disaster-bearing body, may include:

[0149] Step 161, through Determine the risk assessment index of sea ice disasters to marine aquaculture in the target area;

[0150] in, For risk assessment index, For disaster-causing factor hazard index, Exposure index of disaster-bearing bodies, It is the vulnerability index of the disaster-bearing body.

[0151]

[0152] In an optional embodiment of the present invention, the method further includes: step 17, comparing the risk assessment index with a preset threshold to obtain a risk level.

[0153] In this embodiment, the natural discontinuity method is used to classify the risk level in order to scientifically assess the impact of sea ice disaster risks. This invention classifies sea ice disaster risk factors into five levels: high, relatively high, medium, relatively low, and low, as shown in Table 5 below:

[0154] Table 5. Classification of Sea Ice Disaster Risk Factors

[0155] grade Level 1 Level 2 Level 3 Level 4 Level 5 Risks (including hazards, exposure levels, and vulnerabilities) high higher middle lower Low

[0156] In an optional embodiment of the present invention, the risk assessment method for sea ice disasters on marine aquaculture further includes:

[0157] Step 18: Adjust the value of the vulnerability index factor of the disaster-bearing body in the target area according to the risk level.

[0158] Taking the tensile strength index factor of cable as an example, through the formula Determine the redundancy coefficient of tensile strength for aquaculture cables in the target area, whereby... For the cable tensile strength redundancy coefficient, For risk level, This represents the weighting of the cable tensile strength index factor. The corrected value of the cable tensile strength index factor is determined based on the cable tensile strength redundancy coefficient.

[0159] In this embodiment, by establishing a nonlinear mapping relationship between the risk index and the physical parameters of aquaculture facilities, the quantitative calibration of the intensity of disaster prevention facility investment is achieved, which can solve the problem of blind investment in disaster prevention facilities in different sea areas. For example, in a certain Class 1 risk area, dominated by the danger of high disaster-causing factors, the aquaculture system faces extremely strong kinetic energy impact. To reduce the vulnerability of the disaster-bearing body, the tensile strength weight of the cable (0.670) is used as the core correction coefficient, and it is calculated that the tensile strength of the aquaculture cable in this area needs to be increased by more than 45.3% compared with the industry standard. In a certain Class 4 and Class 5 low-risk area, the facility redundancy only needs to be maintained within 1.1 times the standard value. By determining the facility design strength redundancy according to the risk level, quantitative support is provided for the differentiated configuration of aquaculture facilities. High-strength polyethylene composite cables are mandatory in Class 1 risk grids, while conventional configurations are used in low-risk grids. This not only implements disaster reduction measures from an engineering perspective, but also significantly reduces the comprehensive disaster prevention cost of marine aquaculture in the entire sea area by setting up defenses as needed.

[0160] In an optional embodiment of the present invention, the risk assessment method for sea ice disasters on marine aquaculture further includes:

[0161] Step 19: Based on the historical sea ice disaster data and the standardized values ​​of the currently collected indicator factors, issue an early warning for sea ice disasters in the target area. Specifically, step 19 may include:

[0162] Step 191: Based on the historical sea ice disaster data and the standardized values ​​of the currently collected indicator factors, use the formula... Calculate the similarity between the current scenario and historical scenarios, where, Let represent the similarity between the current scenario and the historical scenario, i be the i-th indicator factor, and n be the number of indicator factors. This refers to the standardized value of the currently collected i-th indicator factor, such as the standardized value of the sea ice thickness indicator factor. This represents the historical data for the i-th indicator factor;

[0163] Step 192: Issue an early warning for sea ice disasters in the target area based on the similarity.

[0164] In this embodiment, to further enhance the real-time early warning capability of risk assessment, a dynamic early warning model based on scenario similarity matching is constructed. The distance similarity between current meteorological monitoring indicators (sea ice thickness, first ice anomaly, wind speed, etc.) and feature vectors of historical severe disaster scenarios is calculated. When the similarity between the environmental indicator and the historical scenario exceeds 0.82, the system automatically triggers a severe disaster recurrence warning. At this time, based on the differences in dominant factors in different regions, the early warning system will output differentiated response strategies: in areas dominated by risk, the monitoring weight of the first ice anomaly indicator is automatically increased by 20%, guiding aquaculture farmers to reinforce facilities in advance; while in areas dominated by exposure, asset risk avoidance prompts are prioritized, achieving precise disaster reduction through the connection of emergency dispatch resources within the region. This dynamic matching mechanism based on historical scenario playback effectively shortens the time lag from ice condition identification to response decision-making, providing physically meaningful early warning support for nearshore fixed aquaculture areas.

[0165] The risk assessment method for sea ice disasters on marine aquaculture, as described in the above embodiments of the present invention, further includes:

[0166] Step 110: Based on the calculated hazard index of the disaster-causing factor, the vulnerability index of the disaster-bearing body, the exposure index of the disaster-bearing body, and the risk level, determine the spatial distribution of the hazard index of the disaster-causing factor, the vulnerability index of the disaster-bearing body, the exposure index of the disaster-bearing body, and the risk level in different regions.

[0167] In this embodiment, a certain sea area is divided into different levels based on the hazard index of disaster-causing factors, corresponding to the hazard of sea ice disasters in different regions. According to the spatial distribution of disaster-causing factor hazards, a certain sea area has a relatively high hazard, constituting a Level 1 hazard zone, accounting for 16.97% of the coastal buffer zone area, approximately 518.72 kha. This area is located in a high-latitude zone, is most strongly affected by Siberian cold waves in winter, and is surrounded by land on three sides, resulting in slow heat exchange with the external sea areas, causing cold air to accumulate over the sea and along the coast. The hazard values ​​show clear boundaries between different regions. The distribution of disaster-causing factor hazards indicates that the impact of coastal sea ice accumulation on aquaculture is higher than that of surface ice in the same sea area. This is because marine life, driven by its habits, is mainly distributed in near-shore areas, while sea ice, under external forces, generates kinetic energy that impacts coastal aquaculture facilities.

[0168] Based on the exposure index of disaster-bearing bodies, a certain sea area is divided into different levels, corresponding to different degrees of exposure of disaster-bearing bodies in different regions. According to the spatial distribution of disaster-bearing body exposure, the area of ​​the Level 1 exposure zone is approximately 640.82 kha. This is mainly because this area suffers from economic losses due to sea ice disasters and has a large number of people engaged in aquaculture, resulting in a much higher exposure index than other areas.

[0169] Based on the vulnerability index of sea ice disaster-bearing bodies, a region is divided into different levels according to the vulnerability index. The physical vulnerability of marine aquaculture is the aquaculture facilities, while the biological vulnerability is organisms, such as scallops. Looking at the entire study area, wherever aquaculture areas exist, there is a risk of disaster damage, while non-aquaculture areas are not. For a certain marine aquaculture area, the vulnerability index is relatively high, between 0.6 and 0.9, classifying it as a level 1 or 2 vulnerable area. The root cause is the large-scale farming of Yesso scallops in this area. This scallop species thrives in higher temperatures than bay scallops; growth slows below 5 degrees Celsius and ceases below 0 degrees Celsius. However, the winter temperatures in this aquaculture area are below the freezing point, lower than the optimal temperature for Yesso scallop survival, thus limiting their survival.

[0170] The embodiments of this invention, based on historical scenarios, use the significantly earlier first glaciation date and extended sea ice coverage time in heavy glaciation years as key scenarios to assess marine aquaculture disasters in target areas. In this scenario, sudden and prolonged sea ice events lead to the failure of experienced management practices and insufficient early warning responses by aquaculture personnel, posing a severe threat to the aquaculture system. This invention comprehensively assesses sea ice disasters from three levels, selecting multiple indicator factors for analysis based on the disaster-causing mechanisms of sea ice disasters to obtain the risk distribution of sea ice disasters in the target area. Furthermore, it combines the sea ice disaster risk assessment index to provide targeted solutions for marine aquaculture and to provide early warnings for sea ice disasters.

[0171] like Figure 3As shown, an embodiment of the present invention also provides a marine aquaculture risk assessment device 30 for sea ice disasters, comprising:

[0172] The acquisition module 31 is used to acquire multiple indicator factors corresponding to the hazard criterion, vulnerability criterion, and exposure criterion of the disaster-causing factors of sea ice disasters to marine aquaculture.

[0173] Processing module 32 is used to determine the target weights of multiple indicator factors corresponding to the hazard criterion of disaster-causing factors, the vulnerability criterion of disaster-bearing bodies, and the exposure criterion of disaster-bearing bodies, respectively; determine the hazard index of disaster-causing factors based on a first number of indicator factors corresponding to the hazard criterion of disaster-causing factors and their corresponding target weights; determine the vulnerability index of disaster-bearing bodies based on a second number of indicator factors corresponding to the vulnerability criterion of disaster-bearing bodies and their corresponding target weights; determine the exposure index of disaster-bearing bodies based on a third number of indicator factors corresponding to the exposure criterion of disaster-bearing bodies and their corresponding target weights; and determine the risk assessment index of sea ice disasters in the target area for marine aquaculture based on the hazard index of disaster-causing factors, the vulnerability index of disaster-bearing bodies, and the exposure index of disaster-bearing bodies.

[0174] Optionally, obtain multiple indicator factors corresponding to the hazard hazard criteria, vulnerability criteria, and exposure criteria of sea ice disasters to marine aquaculture, including:

[0175] Based on the disaster-causing mechanism of sea ice disasters, the criteria for the risk of disaster-causing factors, the vulnerability of disaster-bearing bodies, and the exposure of disaster-bearing bodies to sea ice disasters were determined.

[0176] Obtain the sea ice thickness index factor, station temperature index factor, ice period days index factor, sea ice concentration index factor, empirical first ice day anomaly index factor, and station wind speed index factor corresponding to the disaster-causing factor hazard criterion.

[0177] Obtain the cable tensile strength index factor and marine cold resistance index factor corresponding to the aforementioned disaster-bearing body vulnerability criterion;

[0178] Obtain the economic loss index factor and marine personnel index factor of sea ice disaster under the aforementioned exposure criterion for disaster-bearing bodies.

[0179] Optionally, determine the target weights for each of the multiple indicator factors corresponding to the hazard causative factor criterion, the vulnerability criterion of the disaster-bearing body, and the exposure criterion of the disaster-bearing body, including:

[0180] The importance data corresponding to the first number of indicator factors corresponding to the hazard criterion of the disaster-causing factor are used as elements of the first weight matrix. The elements of the first weight matrix are operated on to obtain the initial weight components corresponding to the first number of indicator factors. The initial weight components corresponding to the first number of indicator factors are normalized to obtain the final weights corresponding to the first number of indicator factors.

[0181] The importance data corresponding to the second number of indicator factors corresponding to the vulnerability criterion of the disaster-bearing body are used as elements of the second weight matrix. The elements of the second weight matrix are operated on to obtain the initial weight components corresponding to the second number of indicator factors. The initial weight components corresponding to the second number of indicator factors are normalized to obtain the final weights corresponding to the second number of indicator factors.

[0182] The importance data corresponding to the third number of indicator factors corresponding to the disaster-bearing body exposure criterion are used as elements of the third weight matrix. The elements of the third weight matrix are operated on to obtain the initial weight components corresponding to the third number of indicator factors. The initial weight components corresponding to the third number of indicator factors are then normalized to obtain the final weights corresponding to the third number of indicator factors.

[0183] Optionally, operations are performed on the elements of the first weight matrix, including:

[0184] Calculate the first product of the elements in each row of the first weight matrix, and perform a square root operation on the first product;

[0185] The elements of the second weight matrix are operated on, including:

[0186] Calculate the second product of the elements in each row of the second weight matrix, and perform a square root operation on the second product;

[0187] The operations performed on the elements of the third weight matrix include:

[0188] Calculate the third product of each row of elements in the third weight matrix, and perform a square root operation on the third product.

[0189] Optionally, based on the first number of indicator factors corresponding to the hazard causative factor criterion and the target weights corresponding to the first number of indicator factors, a hazard causative factor hazard index is determined, including:

[0190] pass Determine the hazard index of disaster-causing factors;

[0191] in, For disaster-causing factor hazard index, For the standardized first Individual disaster-causing factors and their risk indicators. Here, f represents the target weight corresponding to the first number of indicator factors, and f represents the nearshore station temperature indicator factor. b represents the target weight corresponding to the nearshore station temperature index factor, and b represents the nearshore station wind speed index factor. c represents the target weight corresponding to the nearshore station wind speed index factor, and c represents the sea ice concentration index factor. The target weights for the sea ice concentration index factors are represented by h, and the sea ice thickness index factor is represented by h. The target weights for the sea ice thickness index factor are represented by q, and the number of ice-covered days index factor is represented by q. The target weight corresponding to the number of days in the glacial period index factor is represented by u, which represents the empirical first glacial day anomaly index factor. This represents the target weight corresponding to the empirical first glacial anomaly index factor.

[0192] Optionally, based on the second number of indicator factors corresponding to the vulnerability criterion of the disaster-bearing body and the target weights corresponding to the second number of indicator factors, a vulnerability index for the disaster-bearing body is determined, including:

[0193] pass Determine the vulnerability index of the disaster-bearing body;

[0194] in, For the vulnerability index of disaster-bearing bodies, For the standardized first Individual vulnerability index factors of disaster-bearing bodies The target weights corresponding to the second number of indicator factors, where t represents the marine cold resistance indicator factor. This represents the target weight corresponding to the marine cold resistance index factor, and l represents the cable tensile strength index factor. This indicates the target weight corresponding to the cable tensile strength index factor.

[0195] Optionally, based on the third number of indicator factors corresponding to the disaster-bearing body exposure criterion and the target weights corresponding to the third number of indicator factors, a disaster-bearing body exposure index is determined, including:

[0196] pass Determine the exposure index of the disaster-bearing body;

[0197] in, Exposure index of disaster-bearing bodies, For the standardized first Individual disaster-bearing body exposure index factors The target weights corresponding to the third number of indicator factors, where p represents the economic loss indicator factor for sea ice disasters. The target weights of the economic loss indicators for sea ice disasters are represented by s, and the employment figures for people working in the marine sector are represented by s. This indicates the target weight corresponding to the indicator factor for marine-related employment personnel.

[0198] Optionally, based on the hazard index of the disaster-causing factor, the vulnerability index of the disaster-bearing body, and the exposure index of the disaster-bearing body, a risk assessment index for the sea ice disaster in the target area on marine aquaculture is determined, including:

[0199] pass Determine the risk assessment index of sea ice disasters to marine aquaculture in the target area;

[0200] in, For risk assessment index, For disaster-causing factor hazard index, Exposure index of disaster-bearing bodies, It is the vulnerability index of the disaster-bearing body.

[0201] It should be noted that this device is the same as the method described above. All implementations in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effect.

[0202] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method as described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0203] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0204] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0205] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0206] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0207] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0208] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0209] Furthermore, it should be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent solutions of the present invention. Moreover, the steps performing the above series of processes can naturally be executed in the order described, but are not necessarily required to be executed in chronological order; some steps can be executed in parallel or independently of each other. Those skilled in the art will understand that all or any step or component of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or network of computing devices, in hardware, firmware, software, or a combination thereof. This is something that those skilled in the art can achieve using their basic programming skills after reading the description of the present invention.

[0210] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing device. The computing device can be a known general-purpose device. Therefore, the object of the present invention can also be achieved simply by providing a program product containing program code implementing the method or apparatus. That is, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent to the present invention. Furthermore, the steps performing the above series of processes can naturally be performed in the order described, but are not necessarily required to be performed in chronological order. Some steps can be performed in parallel or independently of each other.

[0211] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for assessing the risk of sea ice disasters in aquaculture, characterized in that, include: To obtain multiple indicator factors corresponding to the hazard criterion, vulnerability criterion, and exposure criterion of sea ice disasters to marine aquaculture. Determine the target weights for the multiple indicator factors corresponding to the hazard criterion of disaster-causing factors, the vulnerability criterion of disaster-bearing bodies, and the exposure criterion of disaster-bearing bodies, respectively; The disaster-causing factor risk index is determined based on the first number of indicator factors corresponding to the disaster-causing factor risk criterion and the target weights corresponding to the first number of indicator factors. The vulnerability index of the disaster-bearing body is determined based on the second number of indicator factors corresponding to the vulnerability criterion of the disaster-bearing body and the target weights corresponding to the second number of indicator factors. The disaster-bearing body exposure index is determined based on the third number of indicator factors corresponding to the disaster-bearing body exposure criterion and the target weights corresponding to the third number of indicator factors. Based on the aforementioned hazard index of disaster-causing factors, vulnerability index of disaster-bearing bodies, and exposure index of disaster-bearing bodies, the risk assessment index of sea ice disasters on marine aquaculture in the target area is determined; Specifically, the disaster-causing factor risk index is determined based on a first number of indicator factors corresponding to the disaster-causing factor risk criterion and the target weights corresponding to the first number of indicator factors, including: pass Determine the hazard index of disaster-causing factors; in, For disaster-causing factor hazard index, For the standardized first Individual disaster-causing factors and their risk indicators. The target weights corresponding to the first number of indicator factors. f Indicates the temperature index factor of nearshore stations, This indicates the target weight corresponding to the nearshore station temperature index factor. b Indicates the wind speed index factor at nearshore stations, This indicates the target weight corresponding to the wind speed index factor at nearshore stations. c Indicators of sea ice concentration, This indicates the target weights corresponding to the sea ice concentration index factors. h Indicators of sea ice thickness, This indicates the target weight corresponding to the sea ice thickness index factor. q Indicator factors representing the number of days in an ice age This indicates the target weight corresponding to the glacial period days index factor. u Indicators of empirical first glacier anomaly index factor This represents the target weight corresponding to the empirical first glacial anomaly index factor.

2. The method for assessing marine aquaculture risks caused by sea ice disasters according to claim 1, characterized in that, To obtain multiple indicator factors corresponding to the hazard criterion, vulnerability criterion, and exposure criterion of sea ice disasters to marine aquaculture, including: Based on the disaster-causing mechanism of sea ice disasters, the criteria for the risk of disaster-causing factors, the vulnerability of disaster-bearing bodies, and the exposure of disaster-bearing bodies to sea ice disasters were determined. Obtain the sea ice thickness index factor, station temperature index factor, ice period days index factor, sea ice concentration index factor, empirical first ice day anomaly index factor, and station wind speed index factor corresponding to the disaster-causing factor hazard criterion. Obtain the cable tensile strength index factor and marine cold resistance index factor corresponding to the aforementioned disaster-bearing body vulnerability criterion; Obtain the economic loss index factor and marine employment index factor corresponding to the exposure criterion of the disaster-bearing body.

3. The method for assessing marine aquaculture risks caused by sea ice disasters according to claim 1, characterized in that, The target weights for the multiple indicator factors corresponding to the hazard causative factor criterion, the vulnerability criterion of the disaster-bearing body, and the exposure criterion of the disaster-bearing body are determined, including: The importance data corresponding to the first number of indicator factors corresponding to the hazard criterion of the disaster-causing factor are used as elements of the first weight matrix. The elements of the first weight matrix are operated on to obtain the initial weight components corresponding to the first number of indicator factors. The initial weight components corresponding to the first number of indicator factors are normalized to obtain the final weights corresponding to the first number of indicator factors. The importance data corresponding to the second number of indicator factors corresponding to the vulnerability criterion of the disaster-bearing body are used as elements of the second weight matrix. The elements of the second weight matrix are operated on to obtain the initial weight components corresponding to the second number of indicator factors. The initial weight components corresponding to the second number of indicator factors are normalized to obtain the final weights corresponding to the second number of indicator factors. The importance data corresponding to the third number of indicator factors corresponding to the disaster-bearing body exposure criterion are used as elements of the third weight matrix. The elements of the third weight matrix are operated on to obtain the initial weight components corresponding to the third number of indicator factors. The initial weight components corresponding to the third number of indicator factors are then normalized to obtain the final weights corresponding to the third number of indicator factors.

4. The method for assessing marine aquaculture risks caused by sea ice disasters according to claim 3, characterized in that, Perform operations on the elements of the first weight matrix, including: Calculate the first product of the elements in each row of the first weight matrix, and perform a square root operation on the first product; The elements of the second weight matrix are operated on, including: Calculate the second product of the elements in each row of the second weight matrix, and perform a square root operation on the second product; The operations performed on the elements of the third weight matrix include: Calculate the third product of each row of elements in the third weight matrix, and perform a square root operation on the third product.

5. The method for assessing marine aquaculture risks caused by sea ice disasters according to claim 1, characterized in that, Based on the second number of indicator factors corresponding to the vulnerability criterion of the disaster-bearing body and the target weights corresponding to the second number of indicator factors, the vulnerability index of the disaster-bearing body is determined, including: pass Determine the vulnerability index of the disaster-bearing body; in, For the vulnerability index of disaster-bearing bodies, For the standardized first Individual vulnerability index factors of disaster-bearing bodies The target weights corresponding to the second number of indicator factors. t Indicators of cold resistance in marine organisms This indicates the target weights corresponding to the marine cold resistance index factors. l Indicates the tensile strength index factor of the cable. This indicates the target weight corresponding to the cable tensile strength index factor.

6. The method for assessing marine aquaculture risks caused by sea ice disasters according to claim 1, characterized in that, Based on the third number of indicator factors corresponding to the disaster-bearing body exposure criterion and the target weights corresponding to the third number of indicator factors, the disaster-bearing body exposure index is determined, including: pass Determine the exposure index of the disaster-bearing body; in, Exposure index of disaster-bearing bodies, For the standardized first Individual disaster-bearing body exposure index factors The target weights corresponding to the third number of indicator factors. p Indicators of economic losses from sea ice disasters This indicates the target weights corresponding to the economic loss index factors of sea ice disasters. s Indicators of marine-related employment factors This indicates the target weight corresponding to the indicator factor for marine-related employment personnel.

7. The method for assessing marine aquaculture risks caused by sea ice disasters according to claim 1, characterized in that, Based on the aforementioned hazard index of disaster-causing factors, vulnerability index of disaster-bearing bodies, and exposure index of disaster-bearing bodies, a risk assessment index for the impact of sea ice disasters on marine aquaculture in the target area is determined, including: pass Determine the risk assessment index of sea ice disasters to marine aquaculture in the target area; in, For risk assessment index, For disaster-causing factor hazard index, Exposure index of disaster-bearing bodies, It is the vulnerability index of the disaster-bearing body.

8. A risk assessment device for marine aquaculture affected by sea ice disasters, characterized in that, include: The acquisition module is used to acquire multiple indicator factors corresponding to the hazard criterion, vulnerability criterion, and exposure criterion of the disaster-causing factors of sea ice disasters to marine aquaculture. The processing module is used to determine the target weights of multiple indicator factors corresponding to the hazard criterion of disaster-causing factors, the vulnerability criterion of disaster-bearing bodies, and the exposure criterion of disaster-bearing bodies, respectively. The disaster-causing factor risk index is determined based on the first number of indicator factors corresponding to the disaster-causing factor risk criterion and the target weights corresponding to the first number of indicator factors. Based on the second number of indicator factors corresponding to the vulnerability criterion of the disaster-bearing body and the target weights corresponding to the second number of indicator factors, the vulnerability index of the disaster-bearing body is determined; based on the third number of indicator factors corresponding to the exposure criterion of the disaster-bearing body and the target weights corresponding to the third number of indicator factors, the exposure index of the disaster-bearing body is determined; based on the hazard index of the disaster-causing factor, the vulnerability index of the disaster-bearing body, and the exposure index of the disaster-bearing body, the risk assessment index of sea ice disasters in the target area for marine aquaculture is determined. Specifically, the disaster-causing factor risk index is determined based on a first number of indicator factors corresponding to the disaster-causing factor risk criterion and the target weights corresponding to the first number of indicator factors, including: pass Determine the hazard index of disaster-causing factors; in, For disaster-causing factor hazard index, For the standardized first Individual disaster-causing factors and their risk indicators. The target weights corresponding to the first number of indicator factors. f Indicates the temperature index factor of nearshore stations, This indicates the target weight corresponding to the nearshore station temperature index factor. b Indicates the wind speed index factor at nearshore stations, This indicates the target weight corresponding to the wind speed index factor at nearshore stations. c Indicators of sea ice concentration, This indicates the target weights corresponding to the sea ice concentration index factors. h Indicators of sea ice thickness, This indicates the target weight corresponding to the sea ice thickness index factor. q Indicator factors representing the number of days in an ice age This indicates the target weight corresponding to the glacial period days index factor. u Indicators of empirical first glacier anomaly index factor This represents the target weight corresponding to the empirical first glacial anomaly index factor.

9. A computing device, characterized in that, include: A processor, a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method as described in any one of claims 1 to 7.

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