Risk assessment method and device for mariculture area

Abstract

According to the mariculture area risk assessment method and device provided by the invention, by constructing the multi-dimensional dynamic element coupling simulation model, joint simulation of various dynamic elements such as water depth topography, an atmosphere forced field, a typhoon moving path, a tide boundary and seawater temperature and salt flow is realized, and more real and fine marine environment characteristic information can be obtained. Bidirectional data exchange is carried out between the storm model and the hydrodynamic model, so that key parameters such as sea wave radiation stress, tidal current speed and tidal water level are synchronously updated, and the simulation precision under the extreme sea condition is remarkably improved. On the basis, the risk levels of the cultured organisms and the physical disaster-bearing entities are respectively determined by combining the differentiation characteristics of the cultured organisms and the physical disaster-bearing entities, and a comprehensive risk assessment result oriented to the target area is further generated, so that the actual risk condition can be reflected more accurately, and the scientificity and reliability of disaster early warning and management decision making 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: In a first aspect, the present application provides a mariculture area risk assessment method, comprising: 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; 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; determining a first risk level of the current marine environment to a breeding 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; determining 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; generate a risk assessment result for the target area based on the first risk level and the third risk level.

[0006] Optionally, the dynamic element data under the atmospheric forcing field dimension is determined by the following steps: construct a typhoon pressure model based on typhoon moving path data; 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; determine the dynamic element data under the typhoon pressure dimension based on the fused pressure model.

[0007] Optionally, based on the marine feature information, a first risk level of a current marine environment to a target area in a target area is determined, including: for any one of the target area in the target area, based on the type of the target area in the target area, at least one target marine feature information is determined from a plurality of marine feature information; based on each target marine feature information and the first weight corresponding to the target marine feature information under the target area in the target area, the individual first risk level of the target area in the target area is determined; based on the second weight and the individual first risk level corresponding to each target area in the target area, the overall first risk level of the target area in the target area is determined.

[0008] Optionally, based on the marine feature information, a second risk level of a current marine environment to a target area in a target area is determined, including: for any one of the marine feature information, determine the corresponding risk level of the marine feature information; based on each marine feature information and its corresponding third weight, the second risk level of the current marine environment to the physical disaster bearing entity in the target area is determined.

[0009] Optionally, the third weight corresponding to the marine feature information is determined by the following steps: using the analytic hierarchy process, the influence degree of the marine feature information on the physical disaster bearing entity is analyzed to obtain a fourth weight; using the entropy method, the influence degree of the marine feature information on the physical disaster bearing entity is analyzed to obtain a fifth weight; based on the fourth weight and the fifth weight, the third weight corresponding to the marine feature information is determined.

[0010] 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, including: For any one of the physical disaster-bearing entities in the target area, 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; Based on the fourth risk level of each of the physical disaster-bearing entities and the second risk level, a third risk level of the physical disaster-bearing entity is determined.

[0011] Optionally, the risk assessment result for the target area is generated based on the first risk level and the third risk level, including: Based on the first risk level, a damage rate of the aquaculture biological 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; Based on the damage rate and the damage probability, a predicted 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 result, a visual risk assessment map of the target area is generated; the visual risk assessment map includes the risk degree and the predicted loss corresponding to a plurality of positions in the target area.

[0012] In a second aspect, the present application also provides a marine aquaculture area risk assessment device, including: 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. A simulation module 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 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 configured to perform numerical simulation of the ocean and water dynamics 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 velocity and tidal level used by the second sub-model are determined and provided by the first sub-model; A first determination module is configured to determine a first risk level of an aquaculture biological disaster-bearing entity in the target area based on the marine feature information, and determine a second risk level of a physical disaster-bearing entity in the target area based on the marine feature information; a second determining module, 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; an evaluation module, configured to generate a risk evaluation result for the target region based on the first risk level and the third risk level.

[0013] Optionally, the simulation module is further configured to: construct a typhoon pressure model based on typhoon moving path data; 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; determine dynamic element data under the typhoon pressure dimension based on the fused pressure model.

[0014] Optionally, the first determining module is configured to: for any kind of aquaculture biological disaster-bearing body 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 aquaculture biological disaster-bearing body; determine an individual first risk level of the aquaculture biological disaster-bearing body based on each target marine feature information and a first weight corresponding to the target marine feature information under the aquaculture biological disaster-bearing body; determine a total first risk level for the aquaculture biological carrier as a whole based on a second weight corresponding to each aquaculture biological disaster-bearing body and the individual first risk level.

[0015] Optionally, the first determining module is configured to: for any kind of marine feature information, determine a danger level corresponding to the marine feature information; determine a second risk level of the physical disaster-bearing entity in the target region based on each marine feature information and a third weight corresponding thereto.

[0016] Optionally, the first determining module is further configured to: analyze the influence degree of the marine feature information on the physical disaster-bearing entity by using an analytic hierarchy process to obtain a fourth weight; analyze the influence degree of the marine feature information on the physical disaster-bearing entity by using an entropy method to obtain a fifth weight; determine the third weight corresponding to the marine feature information based on the fourth weight and the fifth weight.

[0017] Optionally, the second determining module is configured to: For any one of the physical disaster-bearing entities in the target area, a fourth risk level of the physical disaster-bearing entity is determined based on an aging degree and a wear degree of the physical disaster-bearing entity. A third risk level of each of the physical disaster-bearing entities is determined based on the fourth risk level of each of the physical disaster-bearing entities and the second risk level.

[0018] Optionally, the evaluation module is configured to: a damage rate of the aquaculture biological disaster-bearing entity in the target area is determined based on the first risk level, and a damage probability of the physical disaster-bearing entity in the target area is determined based on the third risk level; a predicted loss of the target area is determined based on the damage rate and the damage probability. The apparatus further includes a visualization module configured to: a visualization risk assessment map of the target area is generated based on the first risk level, the third risk level and the risk assessment result, wherein the visualization risk assessment map includes risk degrees and predicted losses of a plurality of positions in the target area.

[0019] In a third aspect, an embodiment of the present application further provides a computer device, which includes 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 executed by the processor perform the steps of the first aspect or any possible implementation manner of the first aspect.

[0020] 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 performs the steps of the first aspect or any possible implementation manner of the first aspect when the computer program is run.

[0021] The risk assessment method and apparatus for a seawater aquaculture area provided in the embodiments of the present application can realize 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 by constructing a multi-dimensional dynamic element 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, so that the simulation accuracy under extreme sea conditions is significantly improved. On this basis, the embodiments of the present application determine risk levels of aquaculture biological disaster-bearing entities and physical disaster-bearing entities respectively in combination with the differentiated characteristics of the aquaculture biological disaster-bearing entities and the physical disaster-bearing entities, and further generate comprehensive risk assessment results 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 aquaculture area. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a risk assessment method for a marine aquaculture area, as shown in an exemplary embodiment of this application; Figure 2 This is a schematic diagram of a multi-dimensional dynamic element coupling simulation model illustrated in an exemplary embodiment of this application; Figure 3 This is a schematic diagram illustrating a risk assessment model according to an exemplary embodiment of this application; Figure 4 This is a flowchart illustrating the loss determination steps in an exemplary embodiment of this application; Figure 5 This is a flowchart illustrating another risk assessment method for marine aquaculture areas, as shown in an exemplary embodiment of this application; Figure 6 This is a schematic diagram of a risk assessment device for a marine aquaculture area, as illustrated in an exemplary embodiment of this application; Figure 7 This is a schematic diagram of a computer device illustrated in an exemplary embodiment of this application. Detailed Implementation

[0023] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0024] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0025] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0026] It is found through research that at present, quantitative risk assessment for a disaster process is mainly based on post-disaster risk assessment investigation or a single-angle linear dynamic disaster assessment model.

[0027] Therefore, the application provides a seawater breeding area risk assessment method and device, which realizes joint simulation of various dynamic elements 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 element coupling simulation model, and can obtain more real 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 application determines the risk levels of breeding organisms and physical disaster-bearing entities respectively according to their differentiated characteristics, and further generates comprehensive risk assessment results 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.

[0028] The above-mentioned defects of the prior art are the results of the inventors after careful research and practice, and therefore, the discovery process of the above-mentioned problems and the solutions proposed by the application to solve the above-mentioned problems should be the contributions of the inventors to the application.

[0029] 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.

[0030] 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 includes S101-S105, wherein: S101, acquire 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, tidal boundary, seawater temperature, seawater salinity, and seawater flow velocity.

[0031] In this step, the dynamic element data required for building the multi-dimensional dynamic element coupling simulation model can be acquired. The target area can refer to an area including the mariculture area, and the size of the area can be set according to actual requirements, and can include the mariculture area, the surrounding sea area and land area.

[0032] 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.

[0033] The water depth topography dimension is used to describe the topographic features of the target sea area, including water depth distribution, seabed slope, seabed shape and other information, which determines the spatial structure of the hydrodynamic 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 is an important external input 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 the extreme wind wave, storm surge and strong current field changes caused by the typhoon. 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 the 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 mariculture 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 of tidal current, sea current distribution and mixing process. The seawater flow velocity dimension is used to describe the flow velocity and flow direction information of the sea area, including tidal current, residual current, density current and other different cause flows, which is a key parameter for evaluating seawater exchange capacity, material transport characteristics and facility structure stability.

[0034] For example, dynamic element data for the water depth topography dimension can be obtained from publicly available databases, and then interpolated onto a high-resolution model grid. For the dynamic element data for the atmospheric forcing field dimension, wind field forecasts for a future period can be obtained, and data such as wind speed, air pressure, and long-wave and short-wave radiation can be processed into gridded data suitable for numerical models. For the dynamic element data for the typhoon pressure dimension, typhoon tracks can be obtained from data released by meteorological data centers, and typhoon pressure models can be established using these tracks. For the dynamic element data for the tidal boundary dimension, multiple major tidal constituents (such as M2, S2, K1, O1, P1, N2, Q1, K2) can be referenced, and the harmonic parameters of the tidal boundary can be determined by a global high-precision tidal model, serving as an open boundary for forcing. Seawater temperature, salinity, and current velocity dimensions can be obtained from global high-resolution ocean reanalysis datasets, driving the model on the open boundary.

[0035] In some embodiments, a typhoon pressure model can be constructed based on typhoon movement path data; then, a reanalysis pressure model provided by a database is obtained, and the typhoon pressure model is fused with the reanalysis pressure model to obtain a fused pressure model; finally, based on the fused pressure model, the dynamic element data under the typhoon pressure dimension is determined.

[0036] 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: ; ; ; .

[0037] 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: ; ; .

[0038] wherein, is a synthetic pressure field (i.e., a fused pressure field), is an ERA5 reanalysis pressure field, is an impact factor representing 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 between the calculation point and the center of the typhoon increases, and n can be 9.

[0039] 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 to perform numerical simulation of the ocean and water dynamics based on the dynamic element data; the second sub-model is used 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 water levels used by the second sub-model are determined and provided by the first sub-model.

[0040] 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 water levels to the second sub-model, and the second sub-model can transmit radiation stress data to the first sub-model, which can meet the demand of large-scale high-resolution multi-scale, has high accuracy, high efficiency, and is more stable.

[0041] The first sub-model is the core computational module for ocean and hydrodynamic processes, used to numerically simulate tides, currents, and three-dimensional hydrodynamic fields in a regional sea area based on externally input dynamic element data. For example, the first model can use unstructured networks (such as triangular meshes) to construct the computational domain, effectively adapting to complex coastlines, islands, reefs, shoals, and other terrain features, thus improving simulation accuracy. The first sub-model can solve shallow water equations or three-dimensional hydrodynamic equations, outputting oceanic elements such as tidal levels, tidal current velocities, and temperature-salinity structure. Furthermore, it supports high-resolution local densification, suitable for multi-level nested simulations ranging from large to small scales. Simultaneously, the first sub-model can receive radiation stress data from the second sub-model and update the hydrodynamic equations, enabling the hydrodynamic field to reflect the influence of wind and waves on ocean currents and tidal levels.

[0042] The second sub-model is used to simulate wind-generated wave processes in the target sea area. It can dynamically reconstruct wave elements based on atmospheric forcing field data, typhoon pressure model data, and tidal current information. For example, the second sub-model can use spectral or grid methods to simulate wave evolution, characterizing wave generation, propagation, refraction, and breaking. Based on input wind field data, pressure field data, and tidal current velocity and water level information provided by the first sub-model, it can update the wave energy spectrum in real time, generating wave characteristic information including significant wave height, average wave direction, average period, and wave energy distribution. Simultaneously, 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 and waves on seawater motion (such as wind-induced wave rise and current enhancement).

[0043] In this way, through a two-way coupling mechanism, the integrated simulation process of ocean dynamics and wind and wave dynamics can be realized. Various physical fields are coupled in a nonlinear way through mutual influence and feedback, so that the final ocean characteristic information obtained is more consistent with the real sea conditions.

[0044] See Figure 2 The diagram shown is a schematic representation of a multi-dimensional dynamic element coupling simulation model according to an exemplary embodiment of this application. This 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 dynamic element data of each dimension into the corresponding sub-model. The first and second sub-models are coupled and can exchange data. The storage module can store the calculation results output by the first and second sub-models, and can classify and store them using time tags. 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).

[0045] S103, determining a first risk level of the current marine environment to a 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.

[0046] In this step, the risk levels of the biological disaster-bearing entity and the physical disaster-bearing entity can be determined. The biological disaster-bearing entity can refer to animals or plants, such as fish, shellfish, crabs, algae, etc., which are cultivated in the marine aquaculture area. The physical disaster-bearing entity can refer to equipment, buildings, tools, etc. in the marine aquaculture area.

[0047] In this embodiment, the analytic hierarchy process (AHP) and the entropy method can be used to weight the risk indicators (i.e., marine feature information), and a comprehensive weight can be constructed based thereon to improve the objectivity and reliability of the evaluation model.

[0048] The analytic hierarchy process (AHP) is a subjective weighting method, which constructs a judgment matrix and introduces expert experience for pairwise comparison, and then calculates the relative importance of each index. This method is suitable for situations where there is a strong hierarchical structure relationship between indexes, and it is more subjective to reflect the importance difference of different risk factors.

[0049] 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, the more information, and the higher the weight. Therefore, the entropy method can effectively overcome the deviation caused by subjective judgment, and make the evaluation result more objective.

[0050] 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.

[0051] The first risk level can be used to represent the risk of the biological disaster-bearing entity. The first risk level can represent the threat of the disaster-causing factor (i.e., marine feature information) to the biological disaster-bearing entity, and can be graded according to the biological characteristics of the biological disaster-bearing entity.

[0052] In some embodiments, the risk level can be calculated by selecting suitable marine feature information for a specific disaster-bearing entity of the cultivated organism. Specifically, for any disaster-bearing entity of the cultivated organism 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 disaster-bearing entity of the cultivated organism. Then, an individual first risk level of the disaster-bearing entity of the cultivated organism can be determined based on each target marine feature information and a first weight corresponding to the target marine feature information under the disaster-bearing entity of the cultivated organism. Finally, an overall first risk level for the disaster-bearing entity of the cultivated organism as a whole can be determined based on the second weight corresponding to each disaster-bearing entity of the cultivated organism and the individual first risk level.

[0053] For example, for large yellow croaker, a target marine feature information can include temperature variation amplitude and salinity variation amplitude. For Sargassum, the target marine feature can include maximum wind speed, maximum tide height exceeding extreme high water level value, maximum water level rise, maximum wave height, and maximum flow rate. In this embodiment, a corresponding relationship between the type of the organism and the marine feature information can be established according to the sensitivity difference of different cultivated organisms to environmental factors.

[0054] The first weight is used to represent the different influence degrees of different marine feature information on the risk of the cultivated 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 disaster-bearing entity of the cultivated organism under the current environment can be quantitatively evaluated.

[0055] For example, the individual first risk level can be as follows: ; wherein S is the individual first risk level, which can be expressed by 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.

[0056] The second weight is used to represent the proportion, importance, or economic value of the cultivated organism in the entire cultivation area. For example, when a certain organism has a high proportion in the cultivation area, a large economic value, or a high ecological vulnerability, a high second weight can be assigned to it. The individual first risk level of each disaster-bearing entity of the cultivated organism and the corresponding second weight are weighted and summed up to generate an overall first risk level for the disaster-bearing entity of the cultivated organism as a whole.

[0057] Exemplarily, the correspondence between different target marine feature information of Pseudosciaena crocea and threat indexes (I to V, from high to low) can be as follows: Temperature change (℃): <±3℃—V; ±3℃~±6℃—Ⅳ; ±6℃~±9℃—Ⅲ; ±9℃~±12℃—Ⅱ; ±12℃~±15℃—I; Salinity decrease (%): [0,3)—V; [3,3.5)—Ⅳ; [3.5,4)—Ⅲ; [4,5)—Ⅱ; [5,+∞)—I.

[0058] The correspondence between different target marine feature information of Pseudosciaena crocea and the first weight can be as follows: Temperature—0.5; salinity 0.5.

[0059] The correspondence between different target marine feature information of Sargassum fusiforme and threat indexes (I to V) can be as follows: Maximum wind speed (m / s): <10—V; 10~20—Ⅳ; 20~30—Ⅲ; 30~40—Ⅱ; >40—I; Maximum tide height exceeding extreme high water level value (cm): <30—V; 30~60—Ⅳ; 60~90—Ⅲ; 90~120—Ⅱ; >120—I; Maximum water level rise (cm): <30—V; 30~50—Ⅳ; 50~100—Ⅲ; 100~150—Ⅱ; 150~200—I; Maximum wave height (m): <2—V; 2~4—Ⅳ; 4~6—Ⅲ; 6~9—Ⅱ; 9~12—I; Maximum flow rate (m / s): <0.3—V; 0.3~0.6—Ⅳ; 0.6~0.9—Ⅲ; 0.9~1.2—Ⅱ; >1.2—I.

[0060] The correspondence between different target marine feature information of Sargassum fusiforme and the first weight can be as follows: Maximum wind speed—0.278; maximum tide height exceeding extreme high water level value—0.122; maximum water level rise—0.133; maximum wave height—0.278; maximum flow rate—0.269.

[0061] Among them, Sargassum fusiforme is more sensitive to wind speed, seawater flow rate and wave height in the breeding area, so the weight proportion of maximum wind speed, maximum flow rate and maximum wave height in the evaluation of Sargassum fusiforme breeding can be appropriately increased.

[0062] The second risk level can be used to represent the dangerousness of the disaster-causing factor to the physical disaster-bearing entity. In some embodiments, for any kind of marine feature information, a dangerousness level corresponding to the marine feature information can be determined; then based on each kind 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 region is determined. In determining the second risk level, calculation can be performed for each spatial grid point in the unstructured grid, and then normalized to obtain the total second risk level.

[0063] The corresponding relationship between the marine feature information and the dangerousness level (I to V, from high to low) can be as follows: Maximum wind speed (m / s): <10—V; 10~20—Ⅳ; 20~30—Ⅲ; 30~40—Ⅱ; >40—I; Maximum tide height exceeding extreme high water level value (cm): <30—V; 30~60—Ⅳ; 60~90—Ⅲ; 90~120—Ⅱ; >120—I; Maximum water level rise (cm): <30—V; 30~50—Ⅳ; 50~100—Ⅲ; 100~150—Ⅱ; 150~200—I; Maximum wave height (m): <2—V; 2~4—Ⅳ; 4~6—Ⅲ; 6~9—Ⅱ; 9~12—I; 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.

[0064] Since the analytic hierarchy process is greatly affected by subjective experience, and the entropy method completely relies on sample data and cannot be combined with actual conditions, there is a certain limitation in single weighting, and therefore, the application can combine the analytic hierarchy process and the entropy method by using a subjective and objective balanced weighting method, average weighting of the two calculation results, construct a comprehensive weight model, take into account expert experience and objective data facts, and realize the organic combination of subjective and objective weighting.

[0065] In some embodiments, the third weight can be determined in the following manner: 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; The influence of the marine feature information on the physical disaster-bearing entity is analyzed using the entropy method to obtain a fifth weight; Based on the fourth weight and the fifth weight, the third weight corresponding to the marine feature information is determined.

[0066] For example, the third weight can be as follows: ; wherein, is a fusion weight (i.e., a third weight), is an analytic hierarchy process weight (i.e., a fourth weight), is an entropy value method weight (i.e., a fifth weight), may be 0.5.

[0067] In some embodiments, the correspondence between the marine feature information and the third weight can be as follows: Maximum wind speed (m / s): 0.188; Maximum tide height exceeding extreme high water level value (cm): 0.139; Maximum water level rise (cm): 0.152; Maximum wave height (m): 0.265; Maximum flow velocity (m / s): 0.256.

[0068] S104, determining 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.

[0069] In this step, the vulnerability index of the physical disaster-bearing entity itself can be determined based on the attribute information of the physical disaster-bearing entity itself, and the third risk level of the physical disaster-bearing entity can be determined based on the vulnerability index and the second risk level.

[0070] In some embodiments, for any kind of physical disaster-bearing entity in the target area, the fourth risk level of the physical disaster-bearing entity can be determined based on the aging degree and the wear degree of the physical disaster-bearing entity, and the third risk level of the physical disaster-bearing entity can be determined based on the fourth risk level of each kind of physical disaster-bearing entity and the second risk level.

[0071] For example, taking a raft support as an example, the aging degree and the wear degree can correspond to a plurality of evaluation indexes, which are V level to I level from light to heavy. The evaluation indexes of the aging degree can be as follows: Grade I: very serious, indicating serious corrosion and cracks, affecting function; Grade II: serious, indicating that the material is aging in a large area, with obvious cracks and corrosion; Grade III: moderate, indicating that the discoloration is obvious, with moderate cracks, and regular inspection is required; Grade IV: slight, indicating that there is slight discoloration and cracking, and there is no significant impact on strength and durability; Grade V: perfect, indicating no signs of aging and excellent corrosion resistance.

[0072] The evaluation indexes of the wear degree can be as follows: Grade I: very serious, indicating severe wear, reduced load capacity, and potential safety hazard, and the object should be immediately stopped using; Grade II: serious, indicating obvious wear or deformation, affecting the use; Grade III: moderate, indicating obvious wear, and possible local weak points; Grade IV: slight, indicating slight wear, and not affecting the use and function; Grade V: perfect, indicating no wear, and fully functional.

[0073] 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.

[0074] Exemplarily, the fourth risk grade can be represented by the following formula: ; wherein, 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 related marine characteristic information, and can be represented by , and the fourth risk grade can be decomposed according to different physical hazard-bearing entities, and can be represented by , so that ; wherein, 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.

[0075] In S105, a risk assessment result for the target area is generated based on the first risk grade and the third risk grade.

[0076] 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.

[0077] For example, for a physical hazard-recipient, when the fourth risk level R is less than 0.5, it can be determined that the marine environment in which the physical hazard-recipient is located is safe; when R is greater than 0.7, it can be determined that the physical hazard-recipient 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 the linear interpolation method.

[0078] For a cultured biological hazard-recipient, the risk assessment can be performed according to the first risk level to calculate the damage rate.

[0079] In some embodiments, the risk assessment result can include the loss rate of each hazard-recipient and the expected economic loss.

[0080] In some embodiments, the method can further generate a visual risk assessment map of the target area for reference by the user.

[0081] For example, based on the first risk level, the third risk level, and the risk assessment result, a visual risk assessment map of the target area can be generated. The risk assessment map is used to graphically display the risk level and the expected loss at different locations in the target area, so that the management personnel can intuitively understand the risk distribution and the potential damage scale. For example, the target area can be divided into a plurality of grid units or spatial location points, and a corresponding risk index can be generated for each location 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.

[0082] In addition, the visual risk assessment map can also show the expected loss corresponding to each spatial location, including the loss amount of cultured biological hazard-recipient, the damage degree of physical structure, and the comprehensive economic loss, etc. 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.

[0083] In some embodiments, the above-mentioned visual risk assessment map can be stored according to time.

[0084] The visual risk assessment map in this embodiment can realize the spatialization expression of risk information, making the risk assessment result more understandable and decision-making, improving 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.

[0085] Referring to Figure 3 is a schematic diagram of a risk assessment model according to an example embodiment of the present application. The risk assessment model can obtain the relevant data of the cultured biological hazard-recipient and the physical hazard-recipient, and calculate the individual first risk level, the overall first risk level, the second risk level, and the third risk level according to the marine environment characteristics.

[0086] Referring to Figure 4 Fig. 6 is a flow chart of a step of determining loss according to an example embodiment of the present application. In this step, the total economic loss of the marine aquaculture area in a disaster process can be determined according to the determined first risk level and third risk level and the original economic value of various hazard-affected bodies.

[0087] Referring to Figure 5 Fig. 7 is a schematic diagram of another method of risk assessment of a marine aquaculture area according to an example embodiment of the present application. This method can construct a multi-power element coupling numerical model, calculate the dynamic disaster-causing data through the model, and perform risk assessment on a specific hazard-affected body type through a multi-disaster factor risk assessment model to obtain the assessment results of the physical hazard-affected body and the biological hazard-affected body, and further obtain the comprehensive disaster risk assessment results and produce visual products for users.

[0088] The method of risk assessment of a marine aquaculture area provided by the present application realizes the joint simulation of various dynamic elements 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 element coupling simulation model, and can obtain more real and fine marine environment characteristic information. 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 present application determines the risk levels of the aquaculture organisms and the physical hazard-affected entities respectively according to their differentiated characteristics, and further generates comprehensive risk assessment results for the target area, which can more accurately reflect the actual risk situation and improve the scientificity and reliability of the disaster warning and management decision of the marine aquaculture area.

[0089] Referring to Figure 6 Fig. 8 is a schematic diagram of a device for risk assessment of a marine aquaculture area according to an example embodiment of the present application. The device comprises: The acquisition module 610 is configured to acquire dynamic 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. 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; The first determination module 630 is configured to determine a first risk level of a 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; 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. 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.

[0090] Optionally, the simulation module 620 is further configured to: construct a typhoon pressure model based on typhoon moving path data; 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; determine dynamic element data in the typhoon pressure dimension based on the fused pressure model.

[0091] Optionally, the first determination module 630 is configured to: 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 a type of the breeding biological disaster-bearing entity; 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; determine a total first risk level for the breeding biological carrier as a whole based on a second weight corresponding to each breeding biological disaster-bearing entity and the individual first risk level.

[0092] Optionally, the first determination module 630 is configured to: For any kind of marine feature information, determine the danger level corresponding to the marine feature information; Based on each kind of marine feature information and its corresponding third weight, determine the second risk level of the current marine environment to the physical disaster-bearing entity in the target area.

[0093] Optionally, the first determination module 630 is further configured to: Use the analytic hierarchy process to analyze the influence degree of the marine feature information on the physical disaster-bearing entity, and obtain a fourth weight; Use the entropy method to analyze the influence degree of the marine feature information on the physical disaster-bearing entity, and obtain a fifth weight; Based on the fourth weight and the fifth weight, determine the third weight corresponding to the marine feature information.

[0094] Optionally, the second determination module 640 is configured to: For any kind of physical disaster-bearing entity in the target area, based on the aging degree and the wear degree of the physical disaster-bearing entity, determine the fourth risk level of the physical disaster-bearing entity; Based on the fourth risk level of each kind of physical disaster-bearing entity and the second risk level, determine the third risk level of the physical disaster-bearing entity.

[0095] Optionally, the evaluation module 650 is configured to: Based on the first risk level, determine the damage rate of the aquaculture biological disaster-bearing entity in the target area; and based on the third risk level, determine the damage probability of the physical disaster-bearing entity in the target area; Based on the damage rate and the damage probability, determine the expected loss of the target area; The device further comprises a visualization module 660, configured to: Based on the first risk level, the third risk level and the risk evaluation result, generate a visual risk evaluation map of the target area; the visual risk evaluation map comprises the risk degree and the expected loss corresponding to a plurality of positions in the target area.

[0096] The risk assessment device for marine aquaculture areas provided in this application, through the construction of a multi-dimensional dynamic element coupled simulation model, achieves joint simulation of various dynamic elements such as water depth and topography, atmospheric forcing field, typhoon movement path, tidal boundary, and seawater temperature, salinity, and current, enabling the acquisition of more realistic and detailed marine environmental characteristic information. By enabling bidirectional data exchange between the wind and wave model and the hydrodynamic model, key parameters such as wave radiation stress, tidal velocity, and tidal level are updated synchronously, significantly improving the simulation accuracy under extreme sea conditions. Based on this, this application, considering the differentiated characteristics of aquaculture organisms and physically affected entities, determines their risk levels separately and further generates comprehensive risk assessment results for the target area, more accurately reflecting the actual risk situation and improving the scientific rigor and reliability of disaster early warning and management decisions for marine aquaculture areas.

[0097] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0098] This application also provides a computer device, such as... Figure 7 The diagram shown is a schematic representation of a computer device structure according to an exemplary embodiment of this application. The computer device includes: A processor 71 and a memory 72; the memory 72 stores machine-readable instructions executable by the processor 71, and the processor 71 executes the machine-readable instructions stored in the memory 72. When the machine-readable instructions are executed by the processor 71, the processor 71 performs the following steps: 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; and a second risk level is determined for the current marine environment to physical entities in the target area. 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; generate a risk assessment result for the target area based on the first risk level and the third risk level.

[0099] Optionally, the processor 71 further performs: construct a typhoon pressure model based on typhoon moving path data; 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; determine dynamic element data in the typhoon pressure dimension based on the fused pressure model.

[0100] 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: 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; 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; 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.

[0101] 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: for any kind of marine feature information, a corresponding risk level of the marine feature information is determined; based on each marine feature information and a third weight corresponding thereto, a second risk level of a current marine environment to a physical disaster-bearing entity in the target area is determined.

[0102] Optionally, the processor 71 further performs: using the analytic hierarchy process, the influence degree of the marine feature information on the physical disaster-bearing entity is analyzed to obtain a fourth weight; using the entropy method, the influence degree of the marine feature information on the physical disaster-bearing entity is analyzed to obtain a fifth weight; based on the fourth weight and the fifth weight, a third weight corresponding to the marine feature information is determined.

[0103] 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, comprising: For any kind of 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. The third risk level of the physical disaster-bearing entity is determined based on the fourth risk level of each kind of physical disaster-bearing entity and the second risk level.

[0104] Optionally, the risk assessment result for the target area is generated based on the first risk level and the third risk level, comprising: A damage rate of the breeding biological disaster-bearing entity in the target area is determined based on the first risk level, and a damage probability of the physical disaster-bearing entity in the target area is determined based on the third risk level. The expected loss of the target area is determined based on the damage rate and the damage probability. The processor 71 further performs: A visual risk assessment map of the target area is generated based on the first risk level, the third risk level and the risk assessment result; the visual risk assessment map comprises the risk degree and the expected loss corresponding to a plurality of positions in the target area.

[0105] The memory 72 comprises an internal memory 721 and an external memory 722; the internal memory 721 is also referred to as an internal storage, used for temporarily storing operation data in the processor 71 and exchanging 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.

[0106] The specific execution process of the above instructions can refer to the steps of the method for risk assessment of a mariculture area described in the embodiments of the present application, which will not be described here again.

[0107] 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 embodiment described above is only illustrative, wherein the units described as separate components can be or can not be physically separated, and the components displayed as units can be or can not be physical units, that is, they can be located in one place or distributed on multiple network units. According to actual needs, part or all of the modules can be selected to achieve the purpose of the present application. Those skilled in the art can understand and implement without creative labor.

[0108] The embodiment of the present application further provides a computer readable storage medium, and the computer readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the method for evaluating the risk of the mariculture zone are executed. The storage medium can be a volatile or non-volatile computer readable storage medium.

[0109] The embodiment of the present application further provides a computer program product, which includes computer programs / instructions, and when the computer programs / instructions are executed by a processor, the method for evaluating the risk of the mariculture zone is implemented.

[0110] The computer program product can be implemented by hardware, software or a combination thereof. In an optional embodiment, the computer program product is embodied as a computer storage medium. In another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK) or the like.

[0111] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and the device described above can refer to the corresponding process in the foregoing method embodiments, and will not be described here. In the several embodiments provided by the present application, it should be understood that the disclosed system, device and method can be implemented by 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.

[0112] 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, that is, they can be located in one place, or can be distributed on a plurality of network units. According to actual needs, some or all of the units can be selected to achieve the purpose of the embodiment.

[0113] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0114] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a nonvolatile computer readable storage medium executable by a processor. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number 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 methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0115] Finally, it should be noted that: the above-described embodiments are merely specific embodiments of the present application, which are used to illustrate the technical solutions of the present application, but not to limit them, 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, those skilled in the art should understand: any person skilled in the art within the technical range disclosed by the present application, they can still modify or easily think of changes to the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications, changes or replacements do not make the corresponding technical solutions 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.

[0116] The above-described is only the preferred embodiment of the present application, and does not 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 comprises: acquiring dynamic element data of a target area in multiple dimensions, wherein the multiple dimensions include multiple dimensions selected from a water depth topography dimension, an atmospheric forcing field dimension, a typhoon pressure dimension, a tidal boundary dimension, a seawater temperature dimension, a seawater salinity dimension, and a seawater flow velocity dimension; 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, wherein 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 water levels used by the second sub-model are determined and provided by the first sub-model; determining a first risk level of a 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; determining 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; generating a risk assessment result for the target area based on the first risk level and the third risk level.

2. The method of claim 1, wherein, The dynamic element data in the atmospheric forcing field dimension is determined by the following steps: constructing a typhoon pressure model based on typhoon moving path data; acquiring 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; determining the dynamic element data in the typhoon pressure dimension based on the fused pressure model.

3. The method of claim 1, wherein, The first risk level of the current marine environment to the cultured biological disaster-bearing entity in the target area is determined based on the marine feature information, which comprises: for any kind of cultured biological disaster-bearing entity in the target area, determining at least one target marine feature information from a plurality of marine feature information based on the type of the cultured biological disaster-bearing entity; determining 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; determining an overall first risk level for the cultured biological disaster-bearing entity as a whole based on a second weight corresponding to each cultured biological disaster-bearing entity and the individual first risk level.

4. The method of claim 1, wherein, The second risk level of the current marine environment to the physical disaster-bearing entity in the target area is determined based on the marine feature information, which comprises: determining a danger level corresponding to each marine feature information; determining the second risk level of the current marine environment to the physical disaster-bearing entity in the target area based on each marine feature information and a third weight corresponding to the marine feature information.

5. The method of claim 4, wherein, The third weight corresponding to the marine feature information is determined by the following steps: 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; 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; The third weight corresponding to the marine feature information is determined based on the fourth weight and the fifth weight.

6. The method of claim 1, wherein, 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, including: For any kind of 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; The third risk level of the physical disaster-bearing entity is determined based on the fourth risk level of each kind of physical disaster-bearing entity and the second risk level.

7. The method of claim 1, wherein, The risk assessment result for the target area is generated based on the first risk level and the third risk level, including: Based on the first risk level, a damage rate of a cultured organism disaster-bearing entity in the target area is determined; and based on the third risk level, a damage probability of a physical disaster-bearing entity in the target area is determined; Based on the damage rate and the damage probability, a predicted 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 result, a visual risk assessment map of the target area is generated; the visual risk assessment map includes the risk degree and the predicted loss corresponding to multiple positions in the target area.

8. A marine farming area risk assessment device, characterized by, It includes: An acquisition module is configured to acquire dynamic element data of a target area in multiple dimensions, wherein the multiple dimensions include multiple dimensions selected from the group consisting of a water depth and topography dimension, an atmospheric forcing field dimension, a typhoon pressure dimension, a tidal boundary dimension, a seawater temperature dimension, a seawater salinity dimension, and a seawater flow rate dimension. A simulation module 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, wherein the marine feature information includes feature information of sea waves, tidal levels, flow rates, temperatures, and salinities. The multi-dimensional dynamic element coupling simulation model includes a first sub-model and a second sub-model, wherein the first sub-model is configured to perform numerical simulation of ocean and water dynamics 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, and tidal flow velocity and tidal water level used by the second sub-model are determined and provided by the first sub-model. A first determination module is configured to determine a first risk level of a cultured organism disaster-bearing entity in the target area based on the marine feature information, and determine a second risk level of a physical disaster-bearing entity in the target area based on the marine feature information. ​ A 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; An evaluation module is configured to generate a risk evaluation result for the target region based on the first risk level and the third risk level.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps of the method of any one of claims 1 to 7.

10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the steps of the method of any one of claims 1 to 7.

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