Seabed mineral resource development risk assessment method based on AHP and cloud matter element model

Abstract

The invention discloses a seabed mineral resource development risk assessment method based on an AHP and a cloud matter element model, belongs to the field of assessment methods, and aims to solve the problem that risk assessment is not comprehensive and accurate enough due to the fact that existing research cannot systematically cover policy, demarcation, environment, economy and other multi-dimensional risks. The method comprises the following steps: step 1, establishing an international seabed region resource development risk assessment index system; 2, collecting index data of a mineral resource development area of an international seabed area, and performing assignment quantification on the index data; 3, according to the international seabed region mineral resource development risk assessment index system, constructing a hierarchical structure model, analyzing and assessing an object development risk assessment index, and determining an index weight; and step 4, analyzing the mineral resource development risk of the international seabed area by using the cloud matter element model in combination with the index weight so as to determine the risk level of the mineral resource development area of the international seabed area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of evaluation methods, in particular to a seabed mineral resource development risk assessment method based on AHP and cloud matter element model. BACKGROUND

[0002] The international seabed area, i.e. the seabed, ocean floor and subsoil beyond the national jurisdiction, contains key mineral resources such as polymetallic nodules and hydrothermal sulfide, and is an important strategic resource for energy transition.

[0003] For example, the EU's "Oceanside" plan focuses on ecological impact assessment, but does not involve quantitative analysis of development risks at the governance boundary. The analytic hierarchy process (AHP) and cloud matter element model are widely used evaluation methods, widely used in economic feasibility, disaster susceptibility, risk analysis, decision support and other fields.

[0004] However, deep-sea mining faces multiple risks. First, environmental risks, as there is insufficient research on the impact of the ocean environment and data gaps in environmental baselines. Second, governance risks, as the international regulatory framework is not perfect, and disputes over jurisdiction and the layout of the high seas protection zone have led to conflicts over jurisdiction. Finally, economic risks, as development costs, technical uncertainties and market fluctuations can all affect profits.

[0005] The existing risk assessment methods have some defects. On the one hand, the indicators are one-sided, and existing research does not systematically cover policy, delimitation, environment and economic risks; on the other hand, the quantification is insufficient, and there is a lack of comprehensive evaluation models that can integrate certainty and uncertainty, making it difficult to objectively reflect the risk level. In addition, existing research has not been able to comprehensively and systematically cover policy, delimitation, environmental and economic risks, resulting in incomplete and inaccurate risk assessment; and there is a lack of comprehensive evaluation models that integrate certainty and uncertainty, making it difficult to objectively present the risk level comparison, and unable to provide scientific basis for decision-making. SUMMARY

[0006] To overcome the deficiencies of the prior art, the present application provides a seabed mineral resource development risk assessment method based on AHP and cloud matter element model, comprising the following steps: Step 1: Establish an international seabed area resource development risk assessment index system; Step 2: Collect international seabed area mineral resource development area index data, and assign and quantify the index data; Step 3: According to the international seabed area mineral resource development risk assessment index system, construct a hierarchical structure model, analyze the development risk assessment index of the evaluation object, and determine the index weight; Step 4: combining the index weight, using the cloud matter element model to analyze the international seabed area mineral resource development risk, so as to determine the risk level of the international seabed area mineral resource development area.

[0007] As a further improvement of the application, the index system is divided into three levels, namely the target layer, the criterion layer and the index layer.

[0008] As a further improvement of the application, the index weight determined in step 3 specifically comprises the following steps: Step 3.1: according to the international seabed area mineral resource development risk assessment index system, a hierarchical structure model is constructed; Step 3.2: a judgment comparison matrix is constructed by expert scoring; Step 3.3: an integrated judgment matrix is generated by integrating multiple judgment comparison matrices; Step 3.4: the relative weight of the integrated judgment matrix is calculated by the square root method, that is, the index weight; Step 3.5: the consistency of the integrated judgment matrix is verified; if CR <0.1 indicates that the matrix meets the requirements and does not need to be modified, if CR ≥0.1 needs to return to modify the judgment matrix until the consistency requirement is met.

[0009] As a further improvement of the application, the risk level of the international seabed area mineral resource development area determined in step 4 specifically comprises the following steps: Step 4.1: determine the risk level standard cloud; Step 4.2: construct the to-be-evaluated matter element of the international seabed area mineral resource development area; Step 4.3: calculate the membership degree of each secondary risk index value to each risk level; Step 4.4: aggregate the membership degrees of the secondary indexes to obtain the membership degree of the primary index; Step 4.5: calculate the membership degree of the to-be-evaluated matter element to each risk level ; Step 4.6: determine the final risk level of each to-be-evaluated matter element according to the maximum membership degree principle.

[0010] The application has the following beneficial effects: 1. The international seabed area mineral resource development risk assessment index system constructed is more comprehensive, covering policy, delimitation, environment and economy and other multidimensional risks, especially the delimitation risk of outer continental shelf and the risk of creating a high seas protection zone. This makes up for the deficiency of the prior art in the quantitative analysis of the boundary risk of marine governance, and makes the risk assessment more systematic and complete.

[0011] 2. The combination of AHP and cloud matter-element model realizes dynamic weighting and fuzzy evaluation of risk indicators. AHP is used to determine the indicator weight, which can more scientifically reflect the importance of each risk factor; the cloud matter-element model can more accurately quantify the risk level by simulating uncertainty. This combination overcomes the problems of existing technologies in the fuzziness of indicator weighting and the randomness of grade determination, making the risk assessment results more scientific and reliable.

[0012] 3. Special attention is paid to the continental shelf delimitation risk and the open sea protected area creation risk, which are important components of risk assessment, making up for the shortcomings of existing research in the quantitative analysis of boundary risks in marine governance. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 is a flowchart of the seabed mineral resource development risk assessment method based on AHP and cloud matter-element model in this embodiment; Figure 2 is the quantitative result of the index data assignment of each indicator in this embodiment; Figure 3 is a flowchart of the method for determining the weight of the indicators in this embodiment; Figure 4 is the weight of each indicator in this embodiment; Figure 5 is a flowchart of the method for determining the final risk level of each matter-element in this embodiment; Figure 6 is the risk level interval and the corresponding standard cloud model in this embodiment; Figure 7 is the membership degree of the risk indicators with respect to the standard cloud of each risk level in this embodiment; Figure 8 is the basis for the quantitative assignment of the indicators in this embodiment. DETAILED DESCRIPTION

[0014] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. The embodiments described are shown in the drawings. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0015] In this embodiment, an international seabed area mineral resource development project is taken as the evaluation object for risk level evaluation.

[0016] As shown in Figure 1 , a seabed mineral resource development risk assessment method based on AHP and cloud matter-element model includes the following steps: Step 1: Based on the comprehensive analysis of each risk factor affecting the development cost, benefit and progress, an international seabed area resource development risk assessment indicator system is established; In specific applications, the indicator system is divided into three levels, namely the target level, the criterion level and the indicator level; The objective of the target-level assessment is to analyze the risks faced by international seabed mining contractors in the new marine zoning governance context and to promote the sustainable development of international seabed mineral resources; As shown in Table 1, the criteria level is based on the target assessment and further divides the development risk into the first-level indicators of outer continental shelf delimitation risk, potential high seas protected area risk and mining area economic value risk; The indicator layer is the basic assessment work of risk assessment, which is a subdivision based on the criterion layer and includes several secondary indicators.

[0017] Table 1 Preferred indicator system

[0018] Step 2: Collect indicator data of the assessment area and quantify the indicator data; Collect information on the assessment area and quantify the indicators based on the principles of scientificity, systematicity, measurability and complementarity; Different indicator data have different units and dimensions and cannot be directly compared and calculated. Therefore, the indicator data are subjected to standard quantification processing to form a unified attribute feature value database to facilitate comprehensive calculations between indicators.

[0019] In addition, the regional differences in risk conditions determine that the selection of evaluation indicators and quantitative classification have strong regional characteristics. Based on this, the quantitative classification process must fully consider the actual situation of the study area.

[0020] The information in the indicator data, including information on politics, economy, technology, science, environment, society, industry, and law, will be combined with expert advice to assign values ​​to each assessment indicator according to the risk level. A higher score indicates a higher corresponding risk. In the specific application process, a unified indicator value quantification standard will be established to assist in the indicator quantification work. The quantitative results of the indicator data assignment for each indicator are shown in Figure 2 As shown, the quantitative assignment of indicator certainty is based on Figure 8 shown.

[0021] Step 3: Based on the international seabed area mineral resource development risk assessment indicator system, construct a hierarchical model, analyze the development risk assessment indicators of the assessment object, and determine the indicator weights; like Figure 3 As shown, the specific steps include: Step 3.1: Construct a hierarchical model based on the risk assessment indicator system for mineral resource development in the international seabed area; Step 3.2: Constructing judgment comparison matrix by expert scoring; The multi-expert decision-making method is used to compare the scoring matrix formed by k k =1,2,.., m elements in the same level, and assign corresponding values. In this process, 1-9 scales are selected as the judgment scale of elements in the judgment matrix, and the comparison scale of elements in the judgment matrix is shown in Table 2.

[0022] Table 2 Comparison scale of elements in judgment matrix

[0023] Each two elements in the scoring matrix are judged according to the scale in Table 2, and n a judgment comparison matrix of order A is obtained.

[0024]

[0025] Step 3.3: Generating integrated judgment matrix by integrating multiple judgment comparison matrices; The judgment comparison matrices formed by k experts are multiplied by position, and then raised to the power of m to obtain the judgment integrated matrix , The elements in the matrix are the geometric mean values of m judgment comparison matrices, and the formula is as follows:

[0026] The integrated judgment matrix about each level obtained by the geometric mean method is as follows:

[0027]

[0028]

[0029]

[0030] Step 3.4: Calculating the relative weight of the judgment integrated matrix by the square root method; The judgment integrated matrix is taken by the geometric mean method, that is, the square root method is used to calculate the weight of each secondary index relative to the upper level primary index , and the formula is as follows:

[0031] The weight of each index is shown in Table 4: Figure 4 ​​ Step 3.5: Verify the consistency of the judgment integration matrix; if CR <0.1 indicates that the matrix meets the requirements and does not need to be modified. CR ≥0.1, the judgment matrix needs to be modified until the consistency requirement is met; In practice, experts may reach inconsistent conclusions when comparing indicators pairwise, so it is necessary to perform consistency checks on the existing judgment matrix to ensure the rationality of the indicator weights. CR As a criterion for judging the consistency of the matrix, CR Consistency index CI and the average random consistency index RI If CR <0.1 indicates that the matrix meets the requirements and does not need to be modified; otherwise, experts should be asked to revise the judgment matrix to make the calculation result CR 0.1.

[0032] CR The calculation formula is shown in formula (4):

[0033] Where, is the maximum eigenvalue of the judgment matrix; CI The calculation formula is shown in formula (5):

[0034]

[0035] Where, for and The weight vector The cross product of is a matrix No. i A portion.

[0036] RI The specific values ​​related to the matrix order are shown in Table 3 below.

[0037] Table 3 Average random consistency index of judgment matrix RI value

[0038] Step 4: Analyze the risks of mineral resource development in the international seabed area and the indicator weights based on the cloud-matter-element model to determine the risk level of the assessment object; like Figure 5 As shown, the specific steps include: Step 4.1: Determine the risk level standard cloud; According to the evaluation needs, divide into s Risk levels, for the evaluation level exists upper and lower limit interval , the cloud parameters of the standard cloud are calculated as follows:

[0039]

[0040]

[0041] In the formula, is the expectation; is the entropy; is the hyper-entropy; s is a constant, which is determined according to the fuzziness and randomness of the evaluation index.

[0042] Figure 6 The risk level division interval and the corresponding standard cloud model are as follows: the risk level is divided into low, lower, medium, higher, and high in sequence, and the corresponding level intervals are (0, 1), (1, 3), (3, 6), (6, 8), and (8, 9).

[0043] Step 4.2: Construct the to-be-evaluated matter element of the evaluation object, including multiple evaluation indexes, is a secondary evaluation index, represents the value corresponding to the secondary index , q is the international seabed area resource development risk. Therefore, the to-be-evaluated matter element can be expressed as: (10) Step 4.3: Calculate the membership degree of each secondary risk index value to each risk level; Treat each secondary index evaluation value as a cloud drop, and calculate its membership degree to each risk level cloud. The calculation process is as follows: Step 4.3.1: The numerical characteristics of the level cloud are ; Step 4.3.2: Generate a normal random number , which is subject to a distribution with an expectation value of and a standard deviation of ; Step 4.3.3: Let the determined value be a cloud drop, and calculate the membership degree of the cloud drop to its level cloud:

[0044] The membership degree of the risk index to each risk level standard cloud is as follows Figure 7as shown.

[0045] Step 4.4: Aggregating the membership degrees of the secondary indicators to obtain the membership degree of the primary indicator; The membership degree of each primary indicator to the risk level can be directly obtained by weighting the membership degrees of its secondary indicators to each risk level:

[0046] wherein, represents the to-be-evaluated matter in the sub-project layer; represents the corresponding secondary indicator; represents the value corresponding to the secondary indicator ; and is the membership degree of the jth primary indicator to the risk level ; is the membership degree of the jth secondary indicator corresponding to the ith primary indicator to the risk level ; is the weight of the secondary indicator relative to the ith primary indicator, which is calculated based on each by the AHP method; and represents the cyclic indication, i.e., the cyclic calculation of the secondary indicators. Step 4.5: Calculating the membership degree of the to-be-evaluated matter to each risk level , which is obtained by weighting the membership degrees of its primary indicators to each level: i p wherein,

[0047] is the weight of the primary indicator relative to the target layer, which is calculated based on each by the AHP method.

[0048] Step 4.6: Determining the final risk level of each matter according to the maximum membership degree principle; The cloud matter model combines the membership degree of each indicator to the risk level, i.e., the degree to which the indicator meets the characteristics of a certain risk level, with the corresponding weight, to obtain the risk level to which each matter belongs. If:

[0049] then the to-be-evaluated matter belongs to the risk level .

[0050]

[0051] ​​​​​​​The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be noted that for ordinary skilled persons in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A risk assessment method for seabed mineral resource development based on AHP and cloud matter-element model, characterized by: The following steps are involved: Step 1: Establish an indicator system for risk assessment of resource development in the international seabed area; Step 2: Collect regional indicator data on mineral resource development in the international seabed area and quantify the indicator data; Step 3: Based on the international seabed area mineral resource development risk assessment indicator system, construct a hierarchical model, analyze the development risk assessment indicators of the assessment object, and determine the indicator weights; Step 4: Combined with the indicator weights, the cloud-matter-element model is used to analyze the risks of mineral resource development in the international seabed area, thereby determining the risk level of the mineral resource development area in the international seabed area.

2. The method for risk assessment of seabed mineral resource development based on AHP and cloud matter-element model according to claim 1 is characterized in that: The indicator system is divided into three levels, namely the target level, the criterion level and the indicator level.

3. The method for risk assessment of seabed mineral resource development based on AHP and cloud matter-element model according to claim 1 is characterized in that: Determining the indicator weights in step 3 specifically includes the following steps: Step 3.1: Construct a hierarchical model based on the risk assessment indicator system for mineral resource development in the international seabed area; Step 3.2: Construct a judgment comparison matrix based on expert scoring; Step 3.3: Combine multiple judgment comparison matrices to generate an integrated judgment matrix; Step 3.4: Calculate the relative weight of the integrated matrix by the square root method, which is the indicator weight; Step 3.5: Verify the consistency of the judgment integration matrix; if CR <0.1 indicates that the matrix meets the requirements and does not need to be modified. CR If the value is ≥0.1, the judgment matrix needs to be modified until the consistency requirement is met.

4. The method for risk assessment of seabed mineral resource development based on AHP and cloud matter-element model according to claim 1 is characterized in that: Determining the risk level of the international seabed mineral resource development area in step 4 specifically includes the following steps: Step 4.1: Determine the risk level standard cloud; Step 4.2: Construct the entity element of the mineral resource development area in the international seabed area to be evaluated; Step 4.3: Calculate the membership of each secondary risk indicator value to each risk level; Step 4.4: Aggregate the membership of the secondary indicators to obtain the membership of the primary indicators; Step 4.5: Calculate the membership degree of the entity to be evaluated to each risk level ; Step 4.6: Determine the final risk level of each entity to be evaluated based on the maximum membership principle.

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