A method and device for risk assessment of a mariculture area

Abstract

The application provides a mariculture area risk assessment method and device, through constructing a multi-dimensional dynamic element coupling simulation model, the joint simulation of water depth topography, atmospheric forcing field, typhoon moving path, tide boundary and seawater temperature and salinity flow and other various dynamic elements is realized, and more real and fine marine environment characteristic information can be obtained. Through bidirectional data exchange between the wind wave model and the hydrodynamic model, the key parameters such as sea wave radiation stress, tidal current velocity and tidal water level can be updated synchronously, so that the simulation accuracy under extreme sea conditions is significantly improved. On this basis, the application combines the differentiated characteristics of the breeding organisms and the physical disaster-bearing entities, respectively determines the risk grades of the breeding organisms and the physical disaster-bearing entities, and further generates a comprehensive risk assessment result for the target area, so that the actual risk condition can be more accurately reflected, and the scientificity and reliability of disaster warning and management decision of the mariculture area are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mariculture, in particular to a mariculture area risk assessment method and device. BACKGROUND

[0002] Coastal areas are faced with marine dynamic disaster risks, which pose a severe challenge to the sustainable development of coastal economy, ecological environment and aquaculture industry. It is necessary to conduct risk assessment on mariculture areas, which is helpful for prevention and risk avoidance.

[0003] At present, quantitative risk assessment for a disaster process is mainly based on post-disaster risk assessment investigation or establishment of a single-angle linear dynamic disaster assessment model. The existing methods cannot effectively predict risks, or the models are difficult to truly reflect the actual marine environment, and the effect is poor. SUMMARY

[0004] Therefore, the present application provides a mariculture area risk assessment method and device to improve the accuracy of mariculture area risk assessment.

[0005] Specifically, the present application is realized by the following technical solutions:

[0006] In a first aspect, the present application provides a mariculture area risk assessment method, comprising:

[0007] obtaining dynamic element data of a target area in multiple dimensions; the multiple dimensions include multiple of water depth topography dimension, atmospheric forcing field dimension, typhoon pressure dimension, tidal boundary dimension, seawater temperature dimension, seawater salinity dimension, and seawater flow velocity dimension;

[0008] inputting the dynamic element data into a multi-dimensional dynamic element coupling simulation model to obtain marine feature information output by the multi-dimensional dynamic element coupling simulation model; the marine feature information includes characteristic information of sea waves, tidal level, flow velocity, temperature and salinity; the multi-dimensional dynamic element coupling simulation model includes a first sub-model and a second sub-model; the first sub-model is used for numerical simulation of the ocean and water power based on the dynamic element data; the second sub-model is used for numerical simulation of wind waves based on the dynamic element data; the radiation stress data used by the first sub-model is determined and provided by the second sub-model; the tidal flow velocity and tidal level used by the second sub-model are determined and provided by the first sub-model;

[0009] determining a first risk level of the current marine environment to a cultured biological disaster-bearing entity in the target area based on the marine feature information; and determining a second risk level of the current marine environment to a physical disaster-bearing entity in the target area based on the marine feature information;

[0010] determine a third risk level of the physical disaster-bearing entity based on the attribute information of the physical disaster-bearing entity itself and the second risk level;

[0011] generate a risk assessment result for the target area based on the first risk level and the third risk level.

[0012] Optionally, the dynamic element data under the atmospheric forcing field dimension is determined by the following steps:

[0013] construct a typhoon pressure model based on typhoon moving path data;

[0014] obtain a reanalysis pressure model provided by a database, and fuse the typhoon pressure model and the reanalysis pressure model to obtain a fused pressure model;

[0015] determine the dynamic element data under the typhoon pressure dimension based on the fused pressure model.

[0016] Optionally, based on the marine feature information, a first risk level of a current marine environment to a disaster-bearing entity of a target area is determined, including:

[0017] for any kind of disaster-bearing entity of the target area, based on the type of the disaster-bearing entity, at least one target marine feature information is determined from a plurality of marine feature information;

[0018] based on each target marine feature information and a first weight corresponding to the target marine feature information under the disaster-bearing entity, an individual first risk level of the disaster-bearing entity is determined;

[0019] based on a second weight corresponding to each disaster-bearing entity and the individual first risk level, a total first risk level of the disaster-bearing entity as a whole is determined.

[0020] Optionally, based on the marine feature information, a second risk level of a current marine environment to a physical disaster-bearing entity in the target area is determined, including:

[0021] for any kind of marine feature information, a corresponding danger level of the marine feature information is determined;

[0022] based on each marine feature information and a third weight corresponding thereto, the second risk level of the current marine environment to the physical disaster-bearing entity in the target area is determined.

[0023] Optionally, the third weight corresponding to the marine feature information is determined by the following steps:

[0024] The influence degree of the marine feature information on the physical disaster-bearing entity is analyzed by using an analytic hierarchy process, and a fourth weight is obtained.

[0025] The influence degree of the marine feature information on the physical disaster-bearing entity is analyzed by using an entropy value method, and a fifth weight is obtained.

[0026] Based on the fourth weight and the fifth weight, a third weight corresponding to the marine feature information is determined.

[0027] Optionally, the third risk level of the physical disaster-bearing entity is determined based on the attribute information of the physical disaster-bearing entity itself and the second risk level, and the third risk level of the physical disaster-bearing entity is determined based on the aging degree and the wear degree of the physical disaster-bearing entity.

[0028] For any kind of physical disaster-bearing entity in the target area, the fourth risk level of the physical disaster-bearing entity is determined based on the aging degree and the wear degree of the physical disaster-bearing entity.

[0029] Based on the fourth risk level of each kind of physical disaster-bearing entity and the second risk level, the third risk level of the physical disaster-bearing entity is determined.

[0030] Optionally, the risk assessment result for the target area is generated based on the first risk level and the third risk level, and the risk assessment result for the target area is generated based on the first risk level and the third risk level.

[0031] Based on the first risk level, the damage rate of the aquaculture biological disaster-bearing entity in the target area is determined, and based on the third risk level, the damage probability of the physical disaster-bearing entity in the target area is determined.

[0032] Based on the damage rate and the damage probability, the expected loss of the target area is determined.

[0033] The method further comprises:

[0034] Based on the first risk level, the third risk level and the risk assessment result, a visual risk assessment map of the target area is generated; the visual risk assessment map includes the risk degree and the expected loss corresponding to a plurality of positions in the target area.

[0035] In a second aspect, the application also provides a seawater aquaculture area risk assessment device, comprising:

[0036] An acquisition module is configured to acquire dynamic element data of a target area in multiple dimensions; the multiple dimensions include multiple dimensions selected from the group consisting of water depth and topography dimension, atmospheric forcing field dimension, typhoon pressure dimension, tidal boundary dimension, seawater temperature dimension, seawater salinity dimension and seawater flow velocity dimension.

[0037] simulate the marine environment and hydrodynamic force based on the dynamic element data; the second sub-model is configured to simulate the wind wave based on the dynamic element data; the radiation stress data used by the first sub-model is determined and provided by the second sub-model; the tidal current velocity and the tidal level used by the second sub-model are determined and provided by the first sub-model;

[0038] The first determining module is configured to determine a first risk level of the current marine environment to a breeding biological disaster-bearing entity in the target region based on the marine feature information, and determine a second risk level of the current marine environment to a physical disaster-bearing entity in the target region based on the marine feature information.

[0039] The second determining module is configured to determine a third risk level of the physical disaster-bearing entity based on attribute information of the physical disaster-bearing entity itself and the second risk level.

[0040] The evaluating module is configured to generate a risk evaluation result for the target region based on the first risk level and the third risk level.

[0041] Optionally, the simulation module is further configured to:

[0042] construct a typhoon pressure model based on typhoon moving path data;

[0043] obtain a reanalysis pressure model provided by a database, and fuse the typhoon pressure model and the reanalysis pressure model to obtain a fused pressure model;

[0044] determine the dynamic element data under the typhoon pressure dimension based on the fused pressure model.

[0045] Optionally, the first determining module is configured to:

[0046] for any kind of breeding biological disaster-bearing entity in the target region, determine at least one target marine feature information from a plurality of marine feature information based on the type of the breeding biological disaster-bearing entity;

[0047] determine an individual first risk level of the breeding biological disaster-bearing entity based on each target marine feature information and a first weight corresponding to the target marine feature information under the breeding biological disaster-bearing entity;

[0048] Based on the second weight corresponding to each aquaculture disaster-bearing body and the individual first risk level, a total first risk level for the aquaculture disaster-bearing body as a whole is determined.

[0049] Optionally, the first determining module is configured to:

[0050] For any kind of marine feature information, a danger level corresponding to the marine feature information is determined.

[0051] Based on each marine feature information and the third weight corresponding thereto, a second risk level of the current marine environment to the physical disaster-bearing entity in the target region is determined.

[0052] Optionally, the first determining module is further configured to:

[0053] The influence degree of the marine feature information on the physical disaster-bearing entity is analyzed by using the analytic hierarchy process to obtain a fourth weight.

[0054] The influence degree of the marine feature information on the physical disaster-bearing entity is analyzed by using the entropy method to obtain a fifth weight.

[0055] Based on the fourth weight and the fifth weight, the third weight corresponding to the marine feature information is determined.

[0056] Optionally, the second determining module is configured to:

[0057] For any kind of physical disaster-bearing entity in the target region, based on the aging degree and the wear degree of the physical disaster-bearing entity, a fourth risk level of the physical disaster-bearing entity is determined.

[0058] Based on the fourth risk level of each physical disaster-bearing entity and the second risk level, a third risk level of the physical disaster-bearing entity is determined.

[0059] Optionally, the evaluation module is configured to:

[0060] Based on the first risk level, a damage rate of the aquaculture disaster-bearing entity in the target region is determined, and based on the third risk level, a damage probability of the physical disaster-bearing entity in the target region is determined.

[0061] Based on the damage rate and the damage probability, a predicted loss of the target region is determined.

[0062] The device further comprises a visualization module configured to:

[0063] generate a visual risk assessment map of the target area based on the first risk level, the third risk level, and the risk assessment result; the visual risk assessment map includes risk levels and expected losses corresponding to multiple positions in the target area.

[0064] In a third aspect, an embodiment of the present application further provides a computer device, which comprises a processor and a memory, the memory stores machine readable instructions executable by the processor, and the processor is configured to execute the machine readable instructions stored in the memory, and the machine readable instructions are configured to perform the steps of the first aspect or any possible implementation manner of the first aspect.

[0065] In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is configured to perform the steps of the first aspect or any possible implementation manner of the first aspect when the computer program is executed.

[0066] The method and device for risk assessment of a mariculture area provided by the embodiments of the present application can realize joint simulation of a plurality of dynamic factors such as water depth topography, atmospheric forcing field, typhoon moving path, tidal boundary, and seawater temperature and salinity flow by constructing a multi-dimensional dynamic factor coupling simulation model, and can obtain more real and fine marine environment characteristic information. By performing bidirectional data exchange between the wind wave model and the hydrodynamic model, key parameters such as sea wave radiation stress, tidal current velocity, and tidal water level can be updated synchronously, thereby significantly improving the simulation accuracy under extreme sea conditions. On this basis, the embodiments of the present application determine the risk levels of the mariculture organisms and the physical disaster-bearing entities respectively according to their differentiated characteristics, and further generate a comprehensive risk assessment result for the target area, which can more accurately reflect the actual risk situation and improve the scientificity and reliability of disaster warning and management decision of the mariculture area. BRIEF DESCRIPTION OF DRAWINGS

[0067] Figure 1 FIG. 1 is a flowchart of a method for risk assessment of a mariculture area according to an example embodiment of the present application;

[0068] Figure 2 FIG. 2 is a schematic diagram of a multi-dimensional dynamic factor coupling simulation model according to an example embodiment of the present application;

[0069] Figure 3 FIG. 3 is a schematic diagram of a risk assessment model according to an example embodiment of the present application;

[0070] Figure 4 FIG. 4 is a flowchart of a step of determining a loss according to an example embodiment of the present application;

[0071] Figure 5is a flow chart of another method for risk assessment of a mariculture zone according to an example embodiment of the present application;

[0072] Figure 6 is a schematic diagram of a device for risk assessment of a mariculture zone according to an example embodiment of the present application;

[0073] Figure 7 is a schematic diagram of a computer device according to an example embodiment of the present application. DETAILED DESCRIPTION

[0074] The example embodiments will be described in detail herein with reference to the attached drawings. The description of the example embodiments is intended to apply to any example embodiment, unless specifically noted otherwise. It is noted that the description of the example embodiments is not meant to limit the application. The following description of the example embodiments is provided as an enabling teaching of the example embodiments. Studies have found that at present, quantitative risk assessment for a disaster process is mainly based on post-disaster risk assessment investigation or establishment of a single linear dynamic disaster assessment model. The existing methods cannot effectively predict risks, or the models are difficult to truly reflect the actual marine environment, and the effect is poor.

[0075] The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in the present application and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0076] It should be understood that although the terms first, second, third, etc. can be used herein to describe various information, these terms are not intended to denote a particular order. These terms are used only to distinguish one piece of information from another. For example, a first piece of information can be termed a second piece of information without departing from the scope of the present application. Similarly, a second piece of information can be termed a first piece of information. The word "if" as used herein means "when" or "upon" or "in response to the determination" depending on the context.

[0077] Studies have found that at present, quantitative risk assessment for a disaster process is mainly based on post-disaster risk assessment investigation, or establishment of a single linear dynamic disaster assessment model. The existing methods cannot effectively predict risks, or the models are difficult to truly reflect the actual marine environment, and the effect is poor.

[0078] Therefore, the application provides a seawater breeding area risk assessment method and device. By constructing a multi-dimensional dynamic element coupling simulation model, the joint simulation of water depth topography, atmospheric forcing field, typhoon moving path, tide boundary, and seawater temperature, salinity, and flow is realized, and more realistic and detailed marine environment characteristic information can be obtained. Through bidirectional data exchange between the wind wave model and the hydrodynamic model, the key parameters such as wave radiation stress, tidal current velocity, and tidal water level can be updated synchronously, thereby significantly improving the simulation accuracy under extreme sea conditions. On this basis, the application determines the risk level of the breeding organisms and physical disaster-bearing entities respectively according to the differentiated characteristics of the breeding organisms and physical disaster-bearing entities, and further generates a comprehensive risk assessment result for the target area, which can more accurately reflect the actual risk situation and improve the scientificity and reliability of disaster warning and management decision of the seawater breeding area.

[0079] The above-mentioned defects of the prior art are the results of the inventors after practice and careful research, and therefore, the discovery process of the above-mentioned problems and the solutions proposed by the application to solve the above-mentioned problems in the following should be the contributions of the inventors to the application.

[0080] To facilitate the understanding of the present embodiment, first, the application scenario of the seawater breeding area risk assessment method disclosed by the present embodiment is introduced. The execution subject of the seawater breeding area risk assessment method provided by the present embodiment can be a computer device. In some possible implementation manners, the seawater breeding area risk assessment method can be realized by a processor calling computer readable instructions stored in a memory.

[0081] Referring to Figure 1 Fig. 1 is a flowchart of a seawater breeding area risk assessment method according to an example embodiment of the present application. The method comprises S101-S105, wherein:

[0082] S101, acquiring dynamic element data of a target area in multiple dimensions; the multiple dimensions include multiple dimensions of water depth topography, atmospheric forcing field, typhoon pressure, tide boundary, seawater temperature, seawater salinity, and seawater flow velocity.

[0083] In this step, the dynamic element data required for constructing a multi-dimensional dynamic element coupling simulation model can be acquired. The target area can refer to a region containing a seawater breeding area, and the size of the region can be set according to actual needs, and can contain the seawater breeding area and the surrounding sea and land areas.

[0084] The dynamic element data of multiple related dimensions can be collected for modeling. There is a certain mutual influence relationship between the data of these dimensions, which can be linear or nonlinear.

[0085] The water depth topography dimension is used to describe the topographic features of the target sea area, including water depth distribution, seabed slope, seabed morphology and other information, which determines the spatial structure of water dynamic processes such as tidal flow distribution, wave propagation and energy attenuation. The atmospheric forcing field dimension is used to represent the meteorological conditions acting on the sea surface, including wind field, pressure field, precipitation, radiation and other atmospheric elements, which are important external inputs to drive sea waves, wind-driven current and sea surface response. The typhoon pressure dimension is used to describe the real-time or predicted position, moving direction, central pressure and maximum wind speed of the typhoon, which can be used to simulate extreme wind waves, storm surges and strong current field changes caused by typhoons. The tidal boundary dimension is used to reflect the tidal forcing from the open sea, including the amplitude, phase and main tidal component parameters of the astronomical tide, which is one of the main driving factors of tidal level and tidal current evolution. The seawater temperature dimension is used to describe the temperature distribution and its variation characteristics at different depths of the sea area, which affects the water density, ocean stratification structure and ocean dynamic process, and has a direct impact on the physiological state of the cultured organisms. The seawater salinity dimension is used to represent the variation of seawater salinity with time and space, which determines the seawater density structure together with temperature, and is an important control variable for tidal current and ocean current distribution and mixing process. The seawater flow velocity dimension is used to describe the flow velocity and direction information of the sea area, including tidal current, residual current, density current and other flows of different origins, which is a key parameter for evaluating seawater exchange capacity, material transport characteristics and facility structure stability.

[0086] For example, the dynamic element data of the water depth topography dimension can be obtained from the publicly available database to obtain the water depth data, which is interpolated to a high-resolution model grid. For the dynamic element data of the atmospheric forcing field dimension, future wind field prediction data can be obtained, and the wind speed, air pressure, long and short wave radiation and other data in the wind field prediction data can be processed into grid data suitable for numerical models. For the dynamic element data of the typhoon pressure dimension, the typhoon moving path can be obtained from the data published by the meteorological bureau data center, and the typhoon pressure model can be established by using the typhoon moving path to determine. For the dynamic element data of the tidal boundary dimension, the harmonic parameters of the tidal boundary can be determined by a global high-precision tidal model, which is used as an open boundary to force. The seawater temperature, salinity and flow velocity dimensions can be obtained from a global high-resolution ocean reanalysis dataset to drive the model on the open boundary.

[0087] In some embodiments, a typhoon pressure model can be constructed based on typhoon moving path data; then, a reanalysis pressure model provided by a database is obtained, and the typhoon pressure model and the reanalysis pressure model are fused to obtain a fused pressure model; finally, based on the fused pressure model, the dynamic element data under the typhoon pressure dimension is determined.

[0088] Specifically, typhoon movement path data can include the typhoon's maximum wind speed, central pressure, and movement path. This data can be used to build a typhoon Holland model, and the Willoughby wind speed radius formula and Vickery's Holland parameter B formula can be used to construct the typhoon pressure model parameter calculation formula. The specific formula is as follows:

[0089] ;

[0090] ;

[0091] ;

[0092] .

[0093] in, The air pressure of the typhoon model. To calculate the distance between the point and the center of the typhoon, The air pressure at the center of the typhoon. The outer sea surface pressure of the typhoon can be taken in this application. The value is 1013.25 hPa. This is the maximum wind speed of the typhoon. This represents the latitude of the calculation point. Next, the reanalysis pressure field from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data (i.e., the reanalysis pressure model provided by the database) can be fused with the Holland pressure model. The following formula is obtained:

[0094] ;

[0095] ;

[0096] .

[0097] in, This is the composite pressure field (i.e., the fused pressure model). Reanalyze the pressure field for ERA5. The influence factor represents the weight of the background wind field in the wind field fusion process. The effect of the background wind field will increase as the distance of the calculation point from the typhoon center increases, and n can be 9.

[0098] S102, input the dynamic element data into a multi-dimensional dynamic element coupling simulation model to obtain marine feature information output by the multi-dimensional dynamic element coupling simulation model; the marine feature information includes feature information of sea waves, tidal levels, flow velocities, temperatures, and salinities; the multi-dimensional dynamic element coupling simulation model includes a first sub-model and a second sub-model; the first sub-model is used for numerical simulation of oceans and hydrodynamics based on the dynamic element data; the second sub-model is used for numerical simulation of wind waves based on the dynamic element data; radiation stress data used by the first sub-model is determined and provided by the second sub-model; tidal flow velocities and tidal levels used by the second sub-model are determined and provided by the first sub-model.

[0099] In this step, the multi-dimensional dynamic element coupling simulation model can include a first sub-model and a second sub-model, the first sub-model can be a non-structured grid numerical model, and the second sub-model can be a wind wave numerical model, and the two sub-models are coupled with each other. For example, the first sub-model and the second sub-model can exchange information at a certain time interval, the first sub-model can transmit tidal flow velocities and tidal levels to the second sub-model, and the second sub-model can transmit radiation stress data to the first sub-model, which can adapt to large-scale high-resolution multi-scale requirements, has high accuracy, high efficiency, and is more stable.

[0100] The first sub-model is a core calculation module of ocean and hydrodynamic processes, and is used for numerical simulation of tides, tidal flows, and three-dimensional hydrodynamic fields of a regional sea area based on externally input dynamic element data. For example, the first model can use an unstructured network (such as a triangular grid) to construct a calculation domain, which can effectively adapt to complex coastlines, reefs, shoals, and other terrains, and improve simulation accuracy. The first sub-model can solve shallow water equations or three-dimensional hydrodynamic equation sets, and output marine elements such as tidal levels, tidal flow velocities, temperature and salinity structures. In addition, it can support high-resolution local encryption and be suitable for multi-level nested simulation requirements from large scales to small scales. At the same time, the first sub-model can receive radiation stress data provided by the second sub-model and update the hydrodynamic equation, so that the hydrodynamic field can reflect the influence of wind waves on ocean flow and tidal levels.

[0101] The second sub-model is used to simulate the wind-generated wave process of the target sea area. Based on the atmospheric forcing field data, the typhoon pressure model data and the tidal current field information, the dynamic reconstruction of the sea wave elements can be realized. For example, the second sub-model can use the spectral method or the grid method to simulate the wind wave evolution, which can depict the wave generation, propagation, refraction, breaking and other processes; based on the input wind field data, pressure field data and the tidal current velocity and water level information provided by the first sub-model, the wave energy spectrum can be updated in real time to generate sea wave characteristic information including significant wave height, mean wave direction, mean period, wave energy distribution and the like. At the same time, the second sub-model can also output radiation stress data and transmit it to the first sub-model to reflect the coupling effect of wind wave on seawater movement (such as wind wave set-up, sea current enhancement, etc.).

[0102] In this way, through the bidirectional coupling mechanism, the integrated simulation process of ocean dynamics and wind wave dynamics can be realized, various physical fields are coupled in a nonlinear way through mutual influence and feedback, so that the finally obtained ocean characteristic information is more consistent with the real sea state.

[0103] Referring to Figure 2 Fig. 1 shows a schematic diagram of a multi-dimensional dynamic element coupling simulation model according to an example embodiment of the present application. The multi-dimensional dynamic element coupling simulation model includes an input module, a calculation module and a storage module. The calculation module includes a first sub-model and a second sub-model. The input module can input the dynamic element data of each dimension to the corresponding sub-model. The first sub-model and the second sub-model are coupled with each other and can exchange data. The storage module can store the calculation results output by the first sub-model and the second sub-model. When storing, the calculation results can be stored in a time tag. The first sub-model can be a Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM), and the second sub-model can be a Wind Wave Model (WWM).

[0104] S103, based on the ocean characteristic information, determining a first risk level of a current marine environment to a cultured biological disaster-bearing entity in the target area; and based on the ocean characteristic information, determining a second risk level of the current marine environment to a physical disaster-bearing entity in the target area.

[0105] In this step, the risk levels of the cultured biological disaster-bearing entity and the physical disaster-bearing entity can be determined. The cultured biological disaster-bearing entity can refer to animals or plants for cultivation in the marine aquaculture area, such as fish, shellfish, crabs, algae, etc. The physical disaster-bearing entity can refer to equipment, buildings, tools, etc. in the marine aquaculture area.

[0106] In this embodiment, the analytic hierarchy process and the entropy method can be respectively used to weight the risk indicators (i.e., marine feature information), and a comprehensive weight is constructed on this basis to improve the objectivity and reliability of the evaluation model.

[0107] The analytic hierarchy process (AHP) is a subjective weighting method, which is used to calculate the relative importance of each index by constructing a judgment matrix and introducing expert experience for pairwise comparison. This method is suitable for situations where there is a strong hierarchical structure between indexes, and it more subjectively reflects the importance differences of different risk factors.

[0108] The entropy method is an objective weighting method, which allocates weights according to the information entropy of each index in the sample. The smaller the information entropy, the greater the difference between the indexes and the more information, and the higher the weight. Therefore, the entropy method can effectively overcome the deviation caused by subjective judgment, making the evaluation results more objective.

[0109] After determining the weights, the disaster risk and the vulnerability of the aquaculture area can be calculated based on the marine feature information and the aquaculture area data.

[0110] The first risk level can be used to represent the hazard of the biological disaster-bearing entity. This first risk level can represent the threat of the disaster-causing factor (i.e., marine feature information) to the organism, and can be graded according to the biological characteristics of the biological disaster-bearing entity.

[0111] In some embodiments, suitable marine feature information can be selected for a specific biological disaster-bearing entity to calculate the risk level. Specifically, for any kind of biological disaster-bearing entity in the target area, at least one target marine feature information can be determined from a plurality of marine feature information based on the type of the biological disaster-bearing entity. Then, the individual first risk level of the biological disaster-bearing entity can be determined based on each target marine feature information and the first weight of the target marine feature information corresponding to the biological disaster-bearing entity. Finally, the overall first risk level for the biological disaster-bearing entity as a whole can be determined based on the second weight corresponding to each biological disaster-bearing entity and the individual first risk level.

[0112] For example, for the farmed large yellow croaker in the aquaculture area, the target marine feature information can include temperature variation amplitude and salinity variation amplitude. For the Sargassum, the target marine feature information can include maximum wind speed, maximum tide height exceeding extreme high water level value, maximum water level increase, maximum wave height, and maximum flow rate. In this embodiment, the correspondence between the biological type and the marine feature information can be established according to the sensitivity differences of different farmed organisms to environmental factors.

[0113] The first weight is used to represent the different degrees of influence of different marine feature information on the risk of the cultured organism. For example, for fish with high temperature sensitivity, the first weight corresponding to temperature can be relatively large, and for shellfish with strong salt tolerance, the first weight corresponding to salinity can be appropriately reduced. By combining the risk index of the target marine feature information with the first weight corresponding to the feature information, the individual risk level of the cultured organism hazard body in the current environment can be quantitatively evaluated.

[0114] For example, the individual first risk level can be as follows:

[0115] ;

[0116] wherein S is the individual first risk level, which can be expressed in percentage, is the threat index corresponding to the i-th marine feature information, is the first weight of the i-th marine feature information, and n is the number of marine feature information considered.

[0117] The second weight is used to represent the proportion, importance or economic value of the cultured organism in the entire culture area. For example, when a certain organism has a high proportion in the culture area, a large economic value, or a high ecological vulnerability, it can be assigned a high second weight. The individual first risk level of each cultured organism hazard body and the corresponding second weight are weighted and summarized to generate the overall first risk level for the entire cultured organism hazard entity.

[0118] For example, the correspondence between different target marine feature information of large yellow croaker and threat index (I to V, from high to low) can be as follows:

[0119] Temperature change (℃): <±3℃—V; ±3℃~±6℃—Ⅳ; ±6℃~±9℃—Ⅲ; ±9℃~±12℃—Ⅱ; ±12℃~±15℃—I;

[0120] Salinity drop (%): [0,3)—V; [3,3.5)—Ⅳ; [3.5,4)—Ⅲ; [4,5)—Ⅱ; [5,+∞)—I.

[0121] The correspondence between different target marine feature information of large yellow croaker and the first weight can be as follows:

[0122] Temperature—0.5; salinity 0.5.

[0123] The correspondence between different target marine feature information of Sargassum fusiforme and threat index (I to V) can be as follows:

[0124] Maximum wind speed (m / s): <10—V; 10~20—Ⅳ; 20~30—Ⅲ; 30~40—Ⅱ; >40—I;

[0125] Maximum tidal height exceeding extreme high water level (cm): <30—V; 30~60—Ⅳ; 60~90—Ⅲ; 90~120—Ⅱ; >120—I;

[0126] Maximum water increase (cm): <30—V; 30~50—Ⅳ; 50~100—Ⅲ; 100~150—Ⅱ; 150~200—I;

[0127] Maximum wave height (m): <2—V; 2~4—Ⅳ; 4~6—Ⅲ; 6~9—Ⅱ; 9~12—I;

[0128] Maximum flow velocity (m / s): <0.3—V; 0.3~0.6—Ⅳ; 0.6~0.9—Ⅲ; 0.9~1.2—Ⅱ; >1.2—I.

[0129] The correspondence between different target marine feature information of Sargassum fusiforme and the first weight can be shown as follows:

[0130] Maximum wind speed: 0.278; Maximum tidal height exceeding extreme high water level: 0.122; Maximum water level rise: 0.133; Maximum wave height: 0.278; Maximum current velocity: 0.269.

[0131] Among them, Sargassum fusiforme is more sensitive to wind speed, seawater flow speed, and wave height in the aquaculture area. Therefore, the weight of maximum wind speed, maximum flow speed, and maximum wave height in the Sargassum fusiforme aquaculture assessment can be appropriately increased.

[0132] The aforementioned second risk level can be used to characterize the danger posed by hazard-causing factors to physically hazardous entities. In some implementations, for any type of marine feature information, the risk level corresponding to that marine feature information can be determined; then, based on each type of marine feature information and its corresponding third weight, the second risk level of the current marine environment to physically hazardous entities in the target area can be determined. When determining the second risk level, calculations can be performed for each spatial grid point in the unstructured grid, followed by normalization to obtain the overall second risk level.

[0133] The correspondence between marine characteristic information and hazard levels (I to V, from high to low) can be seen as follows:

[0134] Maximum wind speed (m / s): <10—V; 10~20—Ⅳ; 20~30—Ⅲ; 30~40—Ⅱ; >40—I;

[0135] Maximum tidal range over extreme high water level value (cm): <30—V; 30~60—Ⅳ; 60~90—Ⅲ; 90~120—Ⅱ; >120—I;

[0136] Maximum tidal range over extreme high water level value (cm): <30—V; 30~60—Ⅳ; 60~90—Ⅲ; 90~120—Ⅱ; >120—I;

[0137] Maximum tidal range over extreme high water level value (cm): <30—V; 30~60—Ⅳ; 60~90—Ⅲ; 90~120—Ⅱ; >120—I;

[0138] Maximum tidal range over extreme high water level value (cm): <30—V; 30~60—Ⅳ; 60~90—Ⅲ; 90~120—Ⅱ; >120—I.

[0139] Since the analytic hierarchy process is greatly influenced by subjective experience, and the entropy method completely depends on sample data, and cannot be combined with actual conditions, therefore, the single weighting has certain limitations, therefore, the application can combine the analytic hierarchy process and the entropy method by using the subjective and objective balanced weighting method, and the comprehensive weight model is constructed by averaging the two calculation results, the expert experience and the objective data facts are taken into account, and the organic combination of the subjective and objective weighting is realized.

[0140] In some embodiments, the third weight can be determined by the following way:

[0141] The influence degree of the marine feature information on the physical disaster-bearing entity is analyzed by using the analytic hierarchy process, and the fourth weight is obtained;

[0142] The influence degree of the marine feature information on the physical disaster-bearing entity is analyzed by using the entropy method, and the fifth weight is obtained;

[0143] Based on the fourth weight and the fifth weight, the third weight corresponding to the marine feature information is determined.

[0144] For example, the third weight can be as follows:

[0145] ;

[0146] Wherein, is the fusion weight (i.e., the third weight), is the analytic hierarchy process weight (i.e., the fourth weight), is the entropy method weight (i.e., the fifth weight), 0.5 can be taken.

[0147] In some embodiments, the corresponding relationship between the marine feature information and the third weight can be as follows:

[0148] Maximum wind speed (m / s): 0.188;

[0149] The maximum tidal height exceeded the extreme high water level value (cm): 0.139;

[0150] Maximum water level increase (cm): 0.152;

[0151] Maximum wave height (m): 0.265;

[0152] Maximum flow velocity (m / s): 0.256.

[0153] S104. Based on the attribute information of the physical disaster-bearing entity itself and the second risk level, determine the third risk level of the physical disaster-bearing entity.

[0154] In this step, the vulnerability index of the physical disaster-bearing entity can be determined based on its own attribute information, and then the third risk level of the physical disaster-bearing entity can be determined based on the vulnerability index and the second risk level.

[0155] In some embodiments, for any physical disaster-bearing entity in the target area, a fourth risk level of the physical disaster-bearing entity can be determined based on the aging and wear degree of the physical disaster-bearing entity; then, a third risk level of the physical disaster-bearing entity is determined based on the fourth risk level of each physical disaster-bearing entity and the second risk level.

[0156] For example, taking physical disaster-bearing structures such as rafts as an example, the degree of aging and wear can correspond to multiple evaluation indicators, ranging from light to severe, designated as Level V to Level I. The evaluation indicators for the degree of aging can be as follows:

[0157] Level I: Very serious, indicating severe corrosion and cracks that affect functionality;

[0158] Level II: Severe, indicating extensive aging of the material, with obvious cracks and corrosion;

[0159] Grade III: Moderate, indicating significant fading and moderate cracking, requiring regular inspection;

[0160] Grade IV: Slight, indicating slight fading and cracking, with no significant impact on strength and durability;

[0161] Grade V: In good condition, indicating no signs of aging and excellent corrosion resistance.

[0162] The evaluation indicators for wear and tear can be as follows:

[0163] Level I: Very serious, indicating severe wear and tear, reduced load-bearing capacity, and potential safety hazards; must be taken out of service immediately.

[0164] Grade II: Severe, indicating obvious wear or deformation, affecting usability;

[0165] Grade III: moderate, indicating significant wear and tear, and possible local weak points;

[0166] Grade IV: slight, indicating slight wear and tear, and no impact on use and function;

[0167] Grade V: perfect, indicating no wear and tear, and full function.

[0168] The weights of the aging degree and the wear degree under each evaluation index can be obtained by using the analytic hierarchy process, and the total physical vulnerability can be calculated based on the weights and the determined second risk grade, to obtain the total fourth risk grade of the physical hazard-bearing entity.

[0169] For example, the fourth risk grade can be represented by the following formula:

[0170] ;

[0171] Where R is the fourth risk grade, H is the second risk grade, V is the vulnerability index of the physical hazard-bearing entity (i.e., the fourth risk grade), and w is the weight coefficient, taking a value of [0, 1]. The second risk grade can be decomposed according to various relevant marine characteristic information, and can be represented as The fourth risk grade can be decomposed according to different physical hazard-bearing entities, and can be represented as , which can be obtained as:

[0172] ;

[0173] Where is the total weight of the second risk grade and the fourth risk grade , is the second risk grade of the jth marine characteristic information, is the weight of the jth marine characteristic information, is the fourth risk grade of the kth physical hazard-bearing entity, is the weight of the kth physical hazard-bearing entity, m is the number of marine characteristic information, and n is the number of physical hazard-bearing entities.

[0174] S105, based on the first risk grade and the third risk grade, generating a risk assessment result for the target area.

[0175] In some possible implementations, the damage rate of the aquaculture hazard-bearing entity in the target area can be determined based on the first risk grade, and the damage probability of the physical hazard-bearing entity in the target area can be determined based on the third risk grade; and the expected loss of the target area can be determined based on the damage rate and the damage probability.

[0176] Exemplarily, for a physical hazard-bearing body, when the fourth risk level R is less than 0.5, it can be determined that the marine environment in which the physical hazard-bearing body is located is safe; when R is greater than 0.7, it can be determined that the physical hazard-bearing body will be destroyed; and when R is between 0.5 and 0.7, it can be considered as partial damage, and the damage rate can be determined according to a linear interpolation method.

[0177] For a cultured biological hazard-bearing body, the risk assessment can be performed according to the first risk level to calculate the damage rate.

[0178] In some embodiments, the risk assessment result can include the loss rate of each hazard-bearing body and the predicted economic loss.

[0179] In some embodiments, the method can further generate a visual risk assessment map of the target area for reference by the user.

[0180] Exemplarily, a visual risk assessment map of the target area can be generated based on the first risk level, the third risk level, and the risk assessment result. The risk assessment map is used to display the risk degree and the predicted loss at different positions in the target area in a graphical manner, so that the management personnel can intuitively understand the risk distribution and the potential damage scale. Exemplarily, the target area can be divided into a plurality of grid units or spatial position points, and a corresponding risk index can be generated for each position point based on the local marine feature information, the first risk level, and the third risk level, and further converted into a color gradient, a symbol mark, or other expression forms for display.

[0181] In addition, the visual risk assessment map can also show the predicted loss corresponding to each spatial position, including the loss amount of cultured biological, the damage degree of physical structure, and the comprehensive economic loss, and the like. By superimposing the risk level and the loss degree, the user can intuitively identify the high-risk area, the potential high-damage area, and the area that needs to be protected or monitored.

[0182] In some embodiments, the above visual risk assessment map can be classified and stored according to time.

[0183] The visual risk assessment map in this embodiment can realize the spatial expression of risk information, make the risk assessment result more understandable and decision-making, improve the response ability of the aquaculture manager to the extreme marine environment, and can be used for disaster warning, production scheduling optimization, and emergency response planning.

[0184] Reference is made to Figure 3Fig. 1 is a schematic diagram of a risk assessment model according to an example embodiment of the present application. The risk assessment model can obtain data of aquaculture biological disaster-bearing bodies and physical disaster-bearing bodies, and calculate individual first risk levels, overall first risk levels, second risk levels, and third risk levels according to marine environment characteristics.

[0185] Referring to Figure 4 Fig. 3 is a flowchart of a loss determination step according to an example embodiment of the present application. In this step, the total economic loss of a marine aquaculture area in a disaster process can be determined according to the determined first risk levels and third risk levels, and the original economic values of various disaster-bearing bodies.

[0186] Referring to Figure 5 Fig. 4 is a schematic diagram of another marine aquaculture area risk assessment method according to an example embodiment of the present application. This method can construct a multi-power element coupling numerical model, obtain dynamic disaster data through the model, and perform risk assessment on specific disaster-bearing body types through a multi-disaster factor risk assessment model to obtain physical disaster-bearing body and biological disaster-bearing body assessment results, and further obtain comprehensive disaster risk assessment results and produce visual products for users.

[0187] The marine aquaculture area risk assessment method provided by the present application realizes the joint simulation of water depth topography, atmospheric forcing field, typhoon moving path, tidal boundary, and seawater temperature and salinity flow and other multiple power elements by constructing a multi-dimensional power element coupling simulation model, which can obtain more realistic and fine marine environment characteristic information. Through bidirectional data exchange between the wind wave model and the hydrodynamic model, the key parameters such as sea wave radiation stress, tidal current velocity, and tidal water level can be updated synchronously, thereby significantly improving the simulation accuracy under extreme sea conditions. On this basis, the present application determines the risk levels of aquaculture biological and physical disaster-bearing entities respectively according to their differentiated characteristics, and further generates comprehensive risk assessment results for target areas, which can more accurately reflect the actual risk situation and improve the scientificity and reliability of disaster warning and management decision-making of marine aquaculture areas.

[0188] Referring to Figure 6 Fig. 5 is a schematic diagram of a marine aquaculture area risk assessment device according to an example embodiment of the present application. The device comprises:

[0189] The acquisition module 610 is configured to acquire power element data of a target area in multiple dimensions, wherein the multiple dimensions include multiple dimensions selected from the group consisting of water depth topography dimension, atmospheric forcing field dimension, typhoon pressure dimension, tidal boundary dimension, seawater temperature dimension, seawater salinity dimension, and seawater flow velocity dimension.

[0190] The simulation module 620 is configured to input the dynamic element data into a multi-dimensional dynamic element coupling simulation model to obtain marine feature information output by the multi-dimensional dynamic element coupling simulation model; the marine feature information comprises feature information of sea waves, tidal levels, flow velocities, temperatures and salinities; the multi-dimensional dynamic element coupling simulation model comprises a first sub-model and a second sub-model; the first sub-model is configured to perform numerical simulation of ocean and hydrodynamics based on the dynamic element data; the second sub-model is configured to perform numerical simulation of wind waves based on the dynamic element data; radiation stress data used by the first sub-model is determined and provided by the second sub-model; tidal flow velocities and tidal levels used by the second sub-model are determined and provided by the first sub-model;

[0191] The first determination module 630 is configured to determine a first risk level of a current marine environment to a cultured biological disaster-bearing entity in the target region based on the marine feature information, and determine a second risk level of the current marine environment to a physical disaster-bearing entity in the target region based on the marine feature information;

[0192] The second determination module 640 is configured to determine a third risk level of the physical disaster-bearing entity based on attribute information of the physical disaster-bearing entity itself and the second risk level.

[0193] The evaluation module 650 is configured to generate a risk evaluation result for the target region based on the first risk level and the third risk level.

[0194] Optionally, the simulation module 620 is further configured to:

[0195] construct a typhoon pressure model based on typhoon moving path data;

[0196] obtain a reanalysis pressure model provided by a database, and fuse the typhoon pressure model and the reanalysis pressure model to obtain a fused pressure model;

[0197] determine dynamic element data in the typhoon pressure dimension based on the fused pressure model.

[0198] Optionally, the first determination module 630 is configured to:

[0199] for any kind of cultured biological disaster-bearing entity in the target region, determine at least one target marine feature information from a plurality of marine feature information based on a type of the cultured biological disaster-bearing entity;

[0200] determine an individual first risk level of the cultured biological disaster-bearing entity based on each target marine feature information and a first weight corresponding to the target marine feature information under the cultured biological disaster-bearing entity;

[0201] Based on the second weight corresponding to each aquaculture disaster-bearing body and the individual first risk level, a total first risk level for the aquaculture disaster-bearing body as a whole is determined.

[0202] Optionally, the first determining module 630 is configured to:

[0203] For any kind of marine feature information, a corresponding danger level of the marine feature information is determined.

[0204] Based on each marine feature information and the corresponding third weight, a second risk level of the current marine environment to the physical disaster-bearing entity in the target region is determined.

[0205] Optionally, the first determining module 630 is further configured to:

[0206] The influence degree of the marine feature information on the physical disaster-bearing entity is analyzed by using the analytic hierarchy process to obtain a fourth weight.

[0207] The influence degree of the marine feature information on the physical disaster-bearing entity is analyzed by using the entropy method to obtain a fifth weight.

[0208] Based on the fourth weight and the fifth weight, the third weight corresponding to the marine feature information is determined.

[0209] Optionally, the second determining module 640 is configured to:

[0210] For any physical disaster-bearing entity in the target region, based on the aging degree and the wear degree of the physical disaster-bearing entity, a fourth risk level of the physical disaster-bearing entity is determined.

[0211] Based on the fourth risk level of each physical disaster-bearing entity and the second risk level, a third risk level of the physical disaster-bearing entity is determined.

[0212] Optionally, the evaluation module 650 is configured to:

[0213] Based on the first risk level, a damage rate of the aquaculture disaster-bearing entity in the target region is determined, and based on the third risk level, a damage probability of the physical disaster-bearing entity in the target region is determined.

[0214] Based on the damage rate and the damage probability, a predicted loss of the target region is determined.

[0215] The device further includes a visualization module 660 configured to:

[0216] generate a visual risk assessment map of the target area based on the first risk level, the third risk level and the risk assessment result; the visual risk assessment map includes risk levels corresponding to a plurality of positions in the target area and expected losses.

[0217] The seawater aquaculture area risk assessment device provided in the application realizes joint simulation of a plurality of dynamic elements such as water depth topography, atmospheric forcing field, typhoon moving path, tide boundary and seawater temperature and salinity flow, can obtain more real and fine marine environment characteristic information, and through bidirectional data exchange between the wind wave model and the hydrodynamic model, key parameters such as sea wave radiation stress, tidal current velocity and tidal water level can be updated synchronously, thereby significantly improving the simulation accuracy under extreme sea conditions. On this basis, the application determines the risk levels of aquaculture organisms and physical disaster-bearing entities respectively in combination with the differentiated characteristics of the aquaculture organisms and the physical disaster-bearing entities, and further generates a comprehensive risk assessment result for the target area, can more accurately reflect the actual risk situation, and improves the scientificity and reliability of disaster warning and management decision of the seawater aquaculture area.

[0218] The description of the processing flow of each module in the device and the interaction flow between the modules can refer to the related description in the above method embodiments, and will not be described in detail here.

[0219] The application also provides a computer device, as shown in the computer device structure schematic diagram shown in an example embodiment of the application. The computer device comprises: Figure 7 The computer device comprises:

[0220] The processor 71 and the memory 72; the memory 72 stores machine readable instructions executable by the processor 71, and the processor 71 is used for executing the machine readable instructions stored in the memory 72, and when the machine readable instructions are executed by the processor 71, the processor 71 executes the following steps:

[0221] Obtain dynamic element data of the target area in a plurality of dimensions; the plurality of dimensions include a plurality of dimensions in water depth topography dimension, atmospheric forcing field dimension, typhoon pressure dimension, tide boundary dimension, seawater temperature dimension, seawater salinity dimension and seawater flow velocity dimension;

[0222] inputting the dynamic element data into a multi-dimensional dynamic element coupling simulation model to obtain marine feature information output by the multi-dimensional dynamic element coupling simulation model; the marine feature information comprises feature information of sea waves, tidal levels, flow velocities, temperatures and salinities; the multi-dimensional dynamic element coupling simulation model comprises a first sub-model and a second sub-model; the first sub-model is used for numerical simulation of the ocean and water dynamics based on the dynamic element data; the second sub-model is used for numerical simulation of wind waves based on the dynamic element data; radiation stress data used by the first sub-model is determined and provided by the second sub-model; tidal flow velocities and tidal levels used by the second sub-model are determined and provided by the first sub-model;

[0223] based on the marine feature information, determining a first risk level of the current marine environment to a cultured biological disaster-bearing entity in the target area; and based on the marine feature information, determining a second risk level of the current marine environment to a physical disaster-bearing entity in the target area;

[0224] based on attribute information of the physical disaster-bearing entity itself and the second risk level, determining a third risk level of the physical disaster-bearing entity;

[0225] based on the first risk level and the third risk level, generating a risk assessment result for the target area.

[0226] Optionally, the processor 71 further performs:

[0227] based on typhoon moving path data, constructing a typhoon pressure model;

[0228] obtaining a reanalysis pressure model provided by a database, and fusing the typhoon pressure model and the reanalysis pressure model to obtain a fused pressure model;

[0229] based on the fused pressure model, determining dynamic element data in the typhoon pressure dimension.

[0230] Optionally, based on the marine feature information, determining a first risk level of the current marine environment to a cultured biological disaster-bearing entity in the target area comprises:

[0231] for any kind of cultured biological disaster-bearing entity in the target area, based on the type of the cultured biological disaster-bearing entity, determining at least one target marine feature information from a plurality of marine feature information;

[0232] based on each target marine feature information and a first weight corresponding to the target marine feature information for the cultured biological disaster-bearing entity, determining an individual first risk level of the cultured biological disaster-bearing entity;

[0233] determine a total first risk level for the aquaculture disaster-bearing body as a whole based on the second weight corresponding to each type of aquaculture disaster-bearing body and the individual first risk level.

[0234] Optionally, based on the marine feature information, a second risk level of the current marine environment to the physical disaster-bearing entity in the target area is determined, including:

[0235] For any type of marine feature information, a corresponding danger level of the marine feature information is determined.

[0236] Based on each type of marine feature information and its corresponding third weight, a second risk level of the current marine environment to the physical disaster-bearing entity in the target area is determined.

[0237] Optionally, the processor 71 further performs:

[0238] The influence of the marine feature information on the physical disaster-bearing entity is analyzed using the analytic hierarchy process to obtain a fourth weight.

[0239] The influence of the marine feature information on the physical disaster-bearing entity is analyzed using the entropy method to obtain a fifth weight.

[0240] Based on the fourth weight and the fifth weight, a third weight corresponding to the marine feature information is determined.

[0241] Optionally, the determination of the third risk level of the physical disaster-bearing entity based on the attribute information of the physical disaster-bearing entity itself and the second risk level includes:

[0242] For any physical disaster-bearing entity in the target area, a fourth risk level of the physical disaster-bearing entity is determined based on the aging degree and the wear degree of the physical disaster-bearing entity.

[0243] Based on the fourth risk level of each type of physical disaster-bearing entity and the second risk level, a third risk level of the physical disaster-bearing entity is determined.

[0244] Optionally, the generation of the risk assessment result for the target area based on the first risk level and the third risk level includes:

[0245] Based on the first risk level, a damage rate of the aquaculture disaster-bearing entity in the target area is determined, and based on the third risk level, a damage probability of the physical disaster-bearing entity in the target area is determined.

[0246] Based on the damage rate and the damage probability, a predicted loss of the target area is determined.

[0247] The processor 71 further performs:

[0248] generate a visual risk assessment map of the target area based on the first risk level, the third risk level, and the risk assessment result; the visual risk assessment map includes risk levels and expected losses corresponding to a plurality of positions in the target area.

[0249] The memory 72 includes an internal memory 721 and an external memory 722. The internal memory 721 is also referred to as an internal storage, and is used to temporarily store operation data in the processor 71 and exchange data with the external memory 722 such as a hard disk. The processor 71 exchanges data with the external memory 722 through the internal memory 721.

[0250] The specific execution process of the instructions can refer to the steps of the mariculture area risk assessment method described in the embodiments of the present application, which will not be described here.

[0251] For the device embodiment, since it basically corresponds to the method embodiment, the related parts can refer to the part of the method embodiment. The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present application. Those skilled in the art can understand and implement without creative labor.

[0252] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program. When the computer program is run by a processor, the steps of the mariculture area risk assessment method described in the method embodiments are executed. The storage medium can be a volatile or non-volatile computer readable storage medium.

[0253] The embodiments of the present application also provide a computer program product, which includes computer programs / instructions. When the computer programs / instructions are executed by a processor, the mariculture area risk assessment method provided by the embodiments of the present application is implemented.

[0254] The computer program product can be specifically implemented by hardware, software or a combination thereof. In one optional embodiment, the computer program product is specifically embodied as a computer storage medium. In another optional embodiment, the computer program product is specifically embodied as a software product, such as a software development kit (SDK) and the like.

[0255] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the foregoing method embodiment, and will not be repeated here. In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some communication interfaces, devices or units, and can be electrical, mechanical or other forms.

[0256] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. can be located in one place or can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0257] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0258] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solutions of the present application or the essential part or part of the technical solutions that make contributions to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and various program code storage media.

[0259] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present application, and are not intended to limit the technical solutions of the present application. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that any modification or easy-to-think change, or equivalent replacement of part of the technical features of the foregoing embodiments can still be made within the technical range disclosed by the present application. The essence of the corresponding technical solution does not deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0260] The above-described embodiments are merely preferred embodiments of the present application and are not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method of risk assessment of a marine farming area, c h a r a c t e r i s e d in that The method includes: Acquire dynamic element data of the target area in multiple dimensions; the multiple dimensions include multiple dimensions such as water depth and topography, atmospheric forcing field, typhoon pressure, tidal boundary, seawater temperature, seawater salinity, and seawater current velocity. The dynamic element data is input into a multi-dimensional dynamic element coupled simulation model to obtain ocean characteristic information output by the multi-dimensional dynamic element coupled simulation model. The ocean characteristic information includes characteristic information of waves, tide level, current velocity, temperature, and salinity. The multi-dimensional dynamic element coupled simulation model includes a first sub-model and a second sub-model. The first sub-model is used for numerical simulation of ocean and hydrodynamics based on the dynamic element data. The second sub-model is used for numerical simulation of wind and waves based on the dynamic element data. The radiation stress data used by the first sub-model is determined and provided by the second sub-model. The tidal current velocity and tidal level used by the second sub-model are determined and provided by the first sub-model. Based on the marine characteristic information, a first risk level is determined for the current marine environment to aquaculture organisms in the target area that are subject to disaster; and a second risk level is determined for the current marine environment to physical entities in the target area that are subject to disaster. The first risk level is used to characterize the danger posed by the marine characteristic information to aquaculture organisms that are subject to disaster; the second risk level is used to characterize the danger posed by the marine characteristic information to physical entities that are subject to disaster. Based on the attribute information of the physical disaster-bearing entity itself and the second risk level, the third risk level of the physical disaster-bearing entity is determined; Based on the first risk level and the third risk level, a risk assessment result for the target area is generated; Determining the first risk level of the current marine environment for aquaculture organisms in the target area includes: For any type of aquaculture organism in the target area, based on the type of aquaculture organism, at least one target marine feature is determined from multiple marine feature information. Based on each type of target marine feature information and the first weight corresponding to that type of target marine feature information under the disaster-bearing organism, the individual first risk level of the disaster-bearing organism is determined; Based on the second weight corresponding to each type of aquaculture organism and the first risk level of the individual organism, the overall first risk level for the aquaculture organism as a whole is determined. When the aquatic organism that bears the disaster is an aquatic animal, the target marine characteristic information includes the temperature change range and the salinity change range; when the aquatic organism that bears the disaster is an aquatic plant, the target marine characteristics include the maximum wind speed, the maximum tide height exceeding the extreme high water level value, the maximum water level rise, the maximum wave height, and the maximum current velocity. The first weight is used to represent the different degrees of impact of different marine feature information on the risk of the cultured organism.

2. The method of claim 1, wherein, The following steps are used to determine the dynamic element data under the atmospheric forcing field dimension: A typhoon pressure model is constructed based on typhoon movement path data; Obtain the reanalysis pressure model provided by the database, and fuse the typhoon pressure model with the reanalysis pressure model to obtain a fused pressure model; Based on the fused pressure model, the dynamic element data under the typhoon pressure dimension are determined.

3. The method of claim 1, wherein, Based on the aforementioned marine characteristic information, a second risk level is determined for the current marine environment to physically vulnerable entities in the target area, including: For any type of marine feature information, determine the corresponding hazard level of the marine feature information; Based on each marine feature and its corresponding third weight, a second risk level is determined for the current marine environment to the physically vulnerable entities in the target area.

4. The method of claim 3, wherein, The third weight corresponding to the marine feature information is determined through the following steps: The analytic hierarchy process (AHP) was used to analyze the impact of the marine feature information on physical disaster-bearing entities, resulting in a fourth weight. The fifth weight is obtained by analyzing the impact of the marine feature information on the physical disaster-bearing entities using the entropy method. Based on the fourth and fifth weights, the third weight corresponding to the marine feature information is determined.

5. The method according to claim 1, characterized in that, The step of determining the third risk level of the physical disaster-bearing entity based on its own attribute information and the second risk level includes: For any physical disaster-bearing entity in the target area, the fourth risk level of the physical disaster-bearing entity is determined based on its aging and wear levels. Based on the fourth risk level of each of the physical disaster-bearing entities and the second risk level, the third risk level of the physical disaster-bearing entity is determined.

6. The method according to claim 1, characterized in that, The step of generating a risk assessment result for the target area based on the first risk level and the third risk level includes: Based on the first risk level, determine the damage rate of aquatic organisms within the target area; and based on the third risk level, determine the damage probability of physical entities within the target area. Based on the damage rate and the damage probability, the expected loss of the target area is determined; The method further includes: Based on the first risk level, the third risk level, and the risk assessment results, a visual risk assessment map of the target area is generated; the visual risk assessment map includes the risk level and expected loss corresponding to multiple locations in the target area.

7. A risk assessment device for marine aquaculture areas, characterized in that, include: The acquisition module is used to acquire dynamic element data of the target area in multiple dimensions; the multiple dimensions include multiple dimensions such as water depth and topography, atmospheric forcing field, typhoon pressure, tidal boundary, seawater temperature, seawater salinity, and seawater flow velocity. The simulation module is used to input the dynamic element data into a multi-dimensional dynamic element coupled simulation model to obtain the ocean feature information output by the multi-dimensional dynamic element coupled simulation model; the ocean feature information includes feature information of waves, tide level, current velocity, temperature and salinity; The multi-dimensional dynamic element coupled simulation model includes a first sub-model and a second sub-model; the first sub-model is used for numerical simulation of ocean and hydrodynamics based on the dynamic element data; the second sub-model is used for numerical simulation of wind and waves based on the dynamic element data; the radiation stress data used by the first sub-model is determined and provided by the second sub-model; the tidal current velocity and tidal level used by the second sub-model are determined and provided by the first sub-model. The first determining module is used to determine the first risk level of the current marine environment to the aquatic organisms in the target area based on the marine feature information. Furthermore, based on the aforementioned marine characteristic information, a second risk level is determined for the current marine environment to the physically vulnerable entities in the target area; The first risk level is used to characterize the danger posed by marine characteristic information to aquaculture organisms bearing disaster; the second risk level is used to characterize the danger posed by marine characteristic information to physically bearing disaster. The second determining module is used to determine the third risk level of the physical disaster-bearing entity based on its own attribute information and the second risk level. An assessment module is used to generate a risk assessment result for the target area based on the first risk level and the third risk level; The first determining module is used for: For any type of aquaculture organism in the target area, based on the type of aquaculture organism, at least one target marine feature is determined from multiple marine feature information. Based on each type of target marine feature information and the first weight corresponding to that type of target marine feature information under the disaster-bearing organism, the individual first risk level of the disaster-bearing organism is determined; Based on the second weight corresponding to each type of aquaculture organism and the first risk level of the individual organism, the overall first risk level for the aquaculture organism as a whole is determined. When the aquatic organism that bears the disaster is an aquatic animal, the target marine characteristic information includes the temperature change range and the salinity change range; when the aquatic organism that bears the disaster is an aquatic plant, the target marine characteristics include the maximum wind speed, the maximum tide height exceeding the extreme high water level value, the maximum water level rise, the maximum wave height, and the maximum current velocity. The first weight is used to represent the different degrees of impact of different marine feature information on the risk of the cultured organism.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 6.

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