A deep-sea aquaculture operation risk assessment method under a marine economy context

Abstract

The application discloses a kind of deep-sea aquaculture management risk assessment methods under the context of marine economy, belong to the technical field of marine economic risk assessment.The method is under the unified space grid to the environmental, facility and biomass perception data are spatio-temporal alignment;Environment risk field, facility damage field are generated in turn, and based on environment risk index and facility damage index dynamic adjustment fish population density baseline, calculate biomass loss rate and generate biomass loss field;Weighted aggregation obtains management risk comprehensive index, determines management risk grade.The application is through the integrated assessment of environment-facility-biology cascade coupling, complete reflection risk transmission chain, make up the deficiency that present method dimension is split, lack of biomass dynamic perception and management economic meaning, significantly improve the system and accuracy of assessment.

Description

Technical Field

[0001] This invention relates to the field of marine economic risk assessment technology, and in particular to a method for assessing the risks of deep-sea aquaculture operations in the context of marine economy. Background Technology

[0002] Deep-sea aquaculture is an important component of the marine economy, referring to intensive aquaculture activities conducted in open sea areas with depths exceeding 20 meters, utilizing facilities such as deep-sea cages, aquaculture vessels, or semi-submersible aquaculture platforms. As nearshore aquaculture space becomes saturated, the global marine aquaculture industry is rapidly expanding into deep-sea areas. However, deep-sea aquaculture faces far more complex environmental conditions than nearshore aquaculture, with frequent risk events such as extreme waves caused by typhoons, sudden changes in ocean currents, abnormal water temperatures, and red tide outbreaks, seriously threatening the safety of aquaculture operations.

[0003] Deep-sea aquaculture operational risks are characterized by multi-dimensional and multi-factor coupling. From the perspective of risk sources, aquaculture operational risks encompass at least three interactive levels: environmental stress (natural disaster factors such as typhoons, waves, ocean currents, and abnormal water quality), facility damage (engineering damage such as cage structure deformation, mooring system failure, and netting damage), and biological asset losses (losses of farmed organisms such as fish mortality, escape, and stunted growth). These three levels do not exist in isolation but are coupled through physical mechanisms: environmental stress drives facility damage, facility damage leads to organism escape, and water quality deterioration directly causes organism mortality. Disturbances at any level can be amplified into overall operational losses through this coupling chain. Therefore, accurate assessment of deep-sea aquaculture operational risks must focus on the coupling relationship between the environment, facilities, and organisms, and be conducted as an integrated assessment.

[0004] Current deep-sea aquaculture risk assessment methods suffer from the following shortcomings. First, the assessment dimensions are fragmented. Existing methods typically model a single dimension, such as facility safety, environmental disasters, or biological diseases, for example, assessing only the risk of cage structure damage or predicting only the probability of red tide occurrence. They lack cross-dimensional coupling mechanisms and cannot reflect the complete risk transmission chain from environmental events to facility damage, biological loss, and operational consequences. Second, there is a lack of effective means to perceive dynamic biomass data. Existing methods rely mainly on empirical formulas or periodic manual sampling to estimate fish biomass, failing to obtain real-time data on fish distribution density and survival numbers in large-area deep-sea cage environments. This results in lagging biomass data, insufficient spatial resolution, and a lack of empirical data to support the quantification of biological asset losses. Third, there is a lack of mechanisms to systematically integrate multiple risk factors within a unified spatiotemporal framework. Environmental data (such as waves and water temperature), facility status data (such as cage tension and node displacement), and biological status data (such as fish school echo intensity) differ in spatial coordinate system and time scale. Existing methods lack the technical means to align and integrate these heterogeneous data in a unified grid framework, making it difficult to establish a comprehensive risk profile with high spatiotemporal consistency.

[0005] In summary, existing technologies cannot achieve an integrated assessment of deep-sea aquaculture operation risks driven by the coupling of environmental stress, facility damage, and biomass loss. They are unable to provide quantitative risk classification results that reflect both the physical causal chain and have operational economic implications, and therefore cannot effectively support the risk management needs of deep-sea aquaculture operation decisions and from the perspective of marine economy. Summary of the Invention

[0006] To achieve the above objectives, this invention provides a method for risk assessment of deep-sea aquaculture operations in the context of marine economy, comprising the following steps:

[0007] Step 1: Acquire environmental perception data, facility status perception data, and biomass perception data of the target deep-sea aquaculture area. The environmental perception data includes significant wave height data and ocean current velocity data. The facility status perception data includes mooring tension data and nodal displacement data. The biomass perception data includes multi-frequency acoustic scattering data.

[0008] Step 2: Perform spatiotemporal alignment preprocessing on the data obtained in Step 1, establish a spatial grid framework, and map the preprocessed data to spatial grid cells to form a standardized grid dataset;

[0009] Step 3: Based on the standardized grid dataset, calculate the environmental risk index for each spatial grid cell using significant wave height data and ocean current velocity data to generate an environmental risk field;

[0010] Step 4: Based on the standardized grid dataset and the environmental risk field, calculate the facility damage index for each spatial grid cell using mooring tension data and nodal displacement data to generate the facility damage field;

[0011] Step 5: Based on the standardized grid dataset, environmental risk field, and facility damage field, obtain the fish area density of each spatial grid cell using multi-frequency acoustic scattering data, establish a fish density baseline value for each spatial grid cell, adjust the fish density baseline value downward based on the environmental risk index and facility damage index of the spatial grid cell, calculate the biomass loss rate based on the fish area density of the spatial grid cell and the adjusted fish density baseline value, and generate a biomass loss field;

[0012] Step 6: Calculate the comprehensive risk value of each spatial grid cell based on the environmental risk field, facility damage field, and biomass loss field. Weight and aggregate the comprehensive risk values ​​of all spatial grid cells to obtain the comprehensive operational risk index. Determine the operational risk level based on the comprehensive operational risk index and output the operational risk assessment results.

[0013] Preferably, the spatiotemporal alignment preprocessing in step 2 includes: performing temporal interpolation on significant wave height data, ocean current velocity data, mooring tension data, and nodal displacement data to unify the sampling time interval; performing water body layered echo integration processing on multi-frequency acoustic body scattering data; establishing a spatial grid framework with the center point of the target deep-sea aquaculture cage area as the origin, the east direction as the X-axis, and the north direction as the Y-axis, the spatial scale of the grid unit being determined according to the characteristic length of the aquaculture cage, wherein the characteristic length is the maximum diagonal length of the projection of a single cage onto the horizontal plane; and mapping the processed data to the corresponding spatial grid units according to geographic coordinates to obtain the standardized grid dataset.

[0014] Preferably, step 3 includes: for each spatial grid cell, based on the effective wave height data of the spatial grid cell, according to... Calculate the wave-induced disaster factor value, where Let (x, y) be the effective wave height of the spatial grid cell (x, y) at time t. The critical effective wave height to be withstood by the design of aquaculture cages; based on the ocean current velocity data of the aforementioned spatial grid cells, according to... Calculate the disaster-causing factor value of ocean currents, where Let (x, y) be the ocean current velocity in the spatial grid cell (x, y) at time t. The maximum flow velocity tolerable for the cultured organisms; the environmental risk index E(x,y,t) is determined as... and The maximum value in, i.e. The environmental risk field is generated by traversing all spatial grid cells.

[0015] Preferably, step 4 includes: converting mooring tension data into mooring safety factor values. Where T(x,y,t) is the mooring tension of the spatial grid cell (x,y) at time t. The minimum breaking tensile force of the mooring cable; converting nodal displacement data into deformation safety factor values. Where D(x,y,t) is the nodal displacement of the spatial grid element (x,y) at time t. The maximum allowable displacement of the cage node; according to Calculate the facility damage index, where The weighting coefficient for the mooring safety factor is determined by the importance level of the mooring system in the overall facility structure; the facility damage field is generated by traversing all spatial grid cells.

[0016] Preferably, step 5, which involves obtaining the fish area density of each spatial grid cell using multi-frequency acoustic scattering data, includes: performing frequency division processing on the multi-frequency acoustic scattering data to obtain the volume backscattering intensity at different frequencies; using a multi-frequency difference classification method to perform combined calculations on the volume backscattering intensities at different frequencies, separating the fish echo signal from the non-biological scattering body echo signal, and extracting the pure fish volume backscattering intensity. Based on the acoustic-density conversion coefficient k obtained from the target intensity calibration experiment of farmed fish fry, according to Calculate the fish area density N(x,y,t) of the spatial grid cell.

[0017] Preferably, step 5, which involves establishing a fish density baseline value for each spatial grid cell, includes: obtaining a time series of fish area density for the spatial grid cell over a continuous 72 hours prior to the current moment, calculating a moving average of the time series, and using the moving average as the fish density baseline value for the spatial grid cell at the current moment.

[0018] Preferably, step 5, which involves lowering the baseline value of the fish density based on the environmental risk index and facility damage index of the spatial grid unit, includes setting an environmental risk threshold. and facility damage threshold When the environmental risk index E(x,y,t) of the spatial grid cell is greater than At that time, the calculated environmental risk exceeded the range. And multiply the baseline value of the fish density by As the first downward adjustment value, among which The environmental risk reduction factor is preset; when the facility damage index S(x,y,t) of the spatial grid unit is greater than... At that time, the damage to the calculated facility exceeded the range. And multiply the baseline value of the fish density by As the second downward adjustment value, among which The preset facility damage reduction coefficient is used; the smaller of the first reduction value and the second reduction value is selected as the baseline value of the fish population density after reduction; if neither the environmental risk index nor the facility damage index exceeds its respective threshold, the original baseline value of the fish population density is maintained.

[0019] Preferably, the calculation of the comprehensive risk value for each spatial grid cell in step 6 uses the following formula: Where R(x,y,t) is the comprehensive risk value, and L(x,y,t) is the biomass loss rate of the spatial grid cell (x,y) at time t. For environmental risk weighting coefficients, For facility damage weighting coefficient, Let be the biomass loss weighting coefficient, and satisfy . The , and The value is set according to the risk preference type of the deep-sea aquaculture operators.

[0020] Preferably, the weighted aggregation of the comprehensive risk values ​​of all spatial grid cells in step 6 includes: using the expected economic value of the cultured organisms in each spatial grid cell as the weight, and according to... Calculate the comprehensive operational risk index G(t), where W(x,y) is the expected economic value of the cultured organism in the spatial grid cell (x,y). This indicates summing over all spatial grid cells.

[0021] Preferably, step 6, which involves determining the operational risk level based on the comprehensive operational risk index, includes: predefining five operational risk level ranges: when... When it is judged as low risk; when When it is judged as low risk; when It was determined to be of medium risk at that time; when When it is judged as a high risk; when It was determined to be high risk at that time.

[0022] The beneficial effects of this invention are:

[0023] 1. This invention constructs a three-tiered assessment framework consisting of an environmental risk field, a facility damage field, and a biomass loss field. This framework orderly couples risk information from three dimensions—environmental stress, facility structural damage, and fish biomass loss—within a unified spatial grid. It fully reveals the physical risk transmission chain of "environmental event → facility damage → biomass loss → operational consequences," fundamentally solving the problem that existing methods only assess a single dimension separately and cannot reflect cross-dimensional causal relationships. This enables the risk assessment results to cover all aspects from the causative factors to the final operational losses.

[0024] 2. This invention utilizes multi-frequency acoustic scattering data to separate pure fish school echo signals, obtains the spatial distribution of fish biomass, and dynamically adjusts the baseline value of fish density based on environmental risk index and facility damage index. It introduces environmental and facility risk information into the biomass loss assessment process in a causal-driven manner, making up for the shortcomings of traditional methods that rely on manual sampling or empirical formulas, resulting in lagging biomass data and insufficient spatial resolution. This gives the assessment results real-time and objective operational and economic meaning.

[0025] 3. This invention performs spatiotemporal alignment and weighted aggregation of multi-source heterogeneous sensing data under a unified grid framework. Using the expected economic value of farmed organisms as the weight, the comprehensive risk value of grid units is transformed into a comprehensive operational risk index, realizing a quantitative mapping from the physical risk field to the operational risk level. This provides deep-sea aquaculture operators with a comprehensive risk assessment tool that can be traced grid by grid and quantified hierarchically, significantly improving the systematicness and decision-making availability of aquaculture operational risk assessment under complex sea conditions. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a flowchart of the steps of the method of the present invention;

[0028] Figure 2 This is a flowchart illustrating the steps of establishing a baseline value for fish density for each spatial grid cell in the method of this invention. Detailed Implementation

[0029] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0030] Please see Figures 1-2 This invention provides a method for risk assessment of deep-sea aquaculture operations in the context of marine economy, which solves the technical problem that existing deep-sea aquaculture risk assessments are fragmented in terms of environmental data, facility status data and biomass data, and cannot reflect the complete risk chain of environmental stress transmitted through facility damage to biological asset loss.

[0031] The core concept of this invention lies in constructing a cascaded assessment architecture of "gridized data alignment → environmental risk field → facility damage field → biomass loss field → comprehensive operational risk index". This architecture, on a unified horizontal spatial grid framework, sequentially generates an environmental risk field reflecting the intensity of natural disaster-causing factors, a facility damage field reflecting the safety status of engineering facilities, and a biomass loss field reflecting the actual degree of fish biomass loss. There is an ordered causal relationship among the three risk fields: the environmental risk field is one of the inputs to the facility damage field, characterizing the intensity of environmental loads on the facility structure; the environmental risk field and the facility damage field together serve as inputs to the biomass loss field, determining the extent of downward adjustment of the fish density baseline value. Through this cascaded coupling mechanism, environmental risk information is ultimately transmitted to the biomass loss stage via facility damage, forming a complete physical causal transmission chain.

[0032] At the technical implementation level, the key aspects of this invention include the following: First, the spatiotemporal alignment of multi-source heterogeneous sensing data. In environmental sensing data, significant wave height and ocean current velocity are typically collected by buoys at fixed time intervals; in facility status sensing data, mooring tension and nodal displacement are recorded by sensors at a different sampling frequency; and in biomass sensing data, multi-frequency acoustic scattering data needs to cover the three-dimensional water space of the aquaculture area. This invention unifies the sampling time intervals of various data through temporal interpolation and establishes a horizontal spatial grid framework with the center point of the aquaculture cage area as the origin, east as the X-axis, and north as the Y-axis, mapping all data to the corresponding spatial grid cells according to their geographical coordinates. The spatial scale of the grid cells is determined by the characteristic length of the aquaculture cage, which refers to the maximum diagonal length of the projection of a single cage onto the horizontal plane. This value is chosen because dividing the grid at this scale can capture risk changes within the range of a single cage while avoiding computational redundancy caused by overly dense grids.

[0033] Second, the construction of the environmental risk field. For each spatial grid cell, the wave-induced disaster factor value is calculated using significant wave height data. Its physical meaning is the ratio of the current significant wave height to the critical significant wave height that the aquaculture cage is designed to withstand; the ocean current disaster factor value is calculated using ocean current velocity data. The environmental risk index (IRI) is physically defined as the ratio of the current ocean current velocity to the maximum velocity that the aquaculture organisms can tolerate. The maximum value between the wave-induced disaster factor and the ocean current-induced disaster factor is used as the environmental risk index for that grid cell. The physical basis of this maximum value synthesis rule is that the destructive effects of waves and ocean currents on the aquaculture system have a superposition and substitution effect; either wave or ocean current reaching a dangerous level constitutes an environmental risk event. Taking the maximum value conservatively reflects the effect of the most unfavorable environmental factor. Traversing all spatial grid cells generates the environmental risk field. The environmental risk index of each spatial grid cell in the environmental risk field constitutes a two-dimensional scalar field, where a larger value indicates a higher degree of environmental disaster factor stress at that location.

[0034] Third, the construction of the facility damage field. The generation of the facility damage field incorporates information from the environmental risk field while utilizing facility condition sensing data. Mooring tension data is converted into mooring safety factor values. This means the ratio of the current mooring tension to the minimum breaking strength of the mooring cable; converting nodal displacement data into deformation safety factor values. The mooring safety factor is the ratio of the current node displacement to the maximum allowable displacement of the cage node. Both the mooring safety factor and deformation safety factor reflect the proximity of the facility's current load-bearing state to its ultimate load-bearing capacity; values ​​closer to 1 indicate that the facility is closer to failure. The facility damage index is obtained by weighted summation of the mooring safety factor and deformation safety factor values, with weighting coefficients... The value is determined by the importance level of the mooring system within the overall facility structure; a higher value is assigned when the mooring system is assessed as having greater structural importance. The existence of the facility damage field allows the impact of environmental loads on facilities to be quantified on a unified spatial grid, providing facility-level risk input for subsequent biomass loss assessment.

[0035] Fourth, the construction of the biomass loss field. This is the core creative aspect of the invention, incorporating information from both the environmental risk field and the facility damage field into the biomass loss assessment process. First, the multi-frequency acoustic backscattering data is processed by frequency division to obtain the volume backscattering intensity at different frequencies. Using a multi-frequency difference classification method, the volume backscattering intensities at different frequencies are combined and calculated to separate the fish school echo signal from the echo signals of abiotic scatterers such as zooplankton and suspended particles, extracting the pure fish school volume backscattering intensity. It should be noted that the physical principle underlying the multi-frequency difference classification method is as follows: the swim bladder, as the main acoustic scatterer of the fish body, exhibits a characteristic frequency response spectrum under different frequency sound wave illuminations. Zooplankton and suspended particles, lacking air cavities, exhibit significantly different frequency response characteristics compared to fish populations. By using the echo intensity difference between two or more frequency channels, a classification discriminant function can be constructed to separate the fish population signal from the mixed scatterers. Then, using the acoustic-density conversion coefficient (k) obtained from the target intensity calibration experiment of farmed fish species, the volumetric backscattering intensity of the pure fish population is linearly converted into the fish population area density (N(x,y,t)). The target intensity calibration experiment is conducted under controlled conditions. By placing a known number of farmed fish species in an acoustic measurement environment, their echo intensity at different frequencies is recorded, establishing a quantitative relationship between acoustic echo energy and fish population density. Based on the fish population area density time series data for the previous 72 consecutive hours, a fish population density baseline value is established for each spatial grid cell using a moving average method. This baseline value represents the fish population density level within that grid cell under normal conditions. Next, based on the environmental risk index and facility damage index of the spatial grid cell at the current moment, the baseline value of fish density is dynamically adjusted downwards. The physical meaning of this adjustment is that the outbreak of environmental risk events or the occurrence of facility structural damage will lead to a decrease in fish density due to stress response, escape, or death. Even if this is not fully reflected in the measured acoustic data, the expected value of normal density should be corrected based on the causal relationship. When the environmental risk index exceeds a preset threshold, the greater the excess, the higher the adjustment ratio; when the facility damage index exceeds a preset threshold, it is also adjusted downwards by the excess amount. Finally, the adjusted baseline value of fish density is compared with the measured fish area density to obtain the biomass loss rate. The biomass loss rate means that after excluding the expected impact of environmental and facility risk events on fish density, the remaining deviation in fish density represents the proportion of actual biomass loss caused by various factors. A biomass loss field is generated by traversing all spatial grid cells.

[0036] Fifth, the calculation of the comprehensive operational risk index and the determination of risk level. The comprehensive risk value (R(x,y,t)) is the weighted superposition result of the environmental risk field, facility damage field, and biomass loss field on the same spatial grid cell, with weighting coefficients... , and These indicators reflect the relative importance that aquaculture operators place on environmental risks, facility risks, and biomass losses, and are set according to the risk preferences of the operators. The comprehensive operational risk index (G(t)) is a weighted average of the comprehensive risk values ​​of all spatial grid units, with the weights (W(x,y)) representing the expected economic value of the cultured organisms in each grid unit. This weighted aggregation method, with economic value as the weight, transforms the physical-level risk assessment results into a comprehensive indicator with operational economic implications, directly linking risk level determination to the economic consequences of aquaculture operations, which aligns with the basic requirements for operational risk assessment in the context of the marine economy. The operational risk level is divided into five intervals, corresponding to low risk, relatively low risk, medium risk, relatively high risk, and high risk, respectively. The thresholds are set based on the dimensionless range of the risk index and the five-level classification standard commonly used in risk management practice.

[0037] The technical solution of the present invention will be further described below through an embodiment.

[0038] Example

[0039] This embodiment uses a typhoon passage process as the evaluation scenario for a deep-sea aquaculture project. The target aquaculture area is located near 25 degrees north latitude and consists of a group of 12 deep-water gravity cages. The cultured species is large yellow croaker, with an average individual weight of approximately 450 grams and a stocking density of approximately 8 fish per cubic meter. A set of marine environmental monitoring buoys is deployed around the cage group. Eight sets of tension sensors are installed on the mooring cables on the wave-facing side of the cages, and 12 sets of acceleration sensors are installed at key nodes of the cage frame. A multi-frequency split-beam echo sounder is deployed in the water, operating at frequencies of 38 kHz and 120 kHz.

[0040] Step 1: Obtain raw data for the target deep-sea aquaculture area

[0041] Environmental sensing data is collected in real time by buoys, including effective wave height data. Ocean current velocity data were recorded by wave sensors at 0.5-hour intervals. Acoustic Doppler current profilers recorded data at 0.5-hour intervals, surface water temperature data were recorded at 0.5-hour intervals by a CTD (Conductivity, Temperature, Depth) instrument, and chlorophyll concentration data retrieved from satellite remote sensing were acquired in the form of daily spatial raster data.

[0042] In the facility status sensing data, mooring tension data (T) is continuously recorded by a tension sensor at a sampling frequency of 10 Hz. The raw data is downsampled to a 1-minute average before storage. Nodal acceleration data is recorded by an accelerometer at a sampling frequency of 50 Hz. After double integration and detrending processing, it is converted into nodal displacement data (D), which is also downsampled to a 1-minute average. In this embodiment, the integration calculation uses the trapezoidal rule numerical integration, and the detrending processing uses a high-pass filter with a cutoff frequency of 0.03 Hz to eliminate the cumulative error caused by sensor drift.

[0043] In the biomass sensing data, multi-frequency acoustic scattering data was collected by a multi-frequency split-beam scientific echo sounder scanning around the cage group according to a pre-set cruise path. The beam angle of the 38 kHz channel was 7 degrees, the beam angle of the 120 kHz channel was 7 degrees, the pulse width was 0.256 milliseconds, the pulse repetition frequency was 5 times per second, and the cruising speed was controlled within 2 knots to ensure spatial sampling density.

[0044] Step 2: Preprocess the raw data and establish a unified spatial grid framework.

[0045] Step 2.1: Perform linear time-series interpolation on the significant wave height data and ocean current velocity data, and unify the sampling time interval of each environmental sensing data to 1 hour; perform bilinear spatial interpolation on the chlorophyll concentration data retrieved from satellite remote sensing, and unify the spatial resolution to 100 meters by 100 meters to match the target grid scale; perform water body layered echo integration processing on the multi-frequency acoustic body scattering data, with the layering interval set to 1 meter, and the integration range from 3 meters below the transducer surface to 5 meters below the bottom of the cage.

[0046] Step 2.2: Perform outlier detection and removal on the mooring tension data and nodal displacement data. Outlier detection adopts the three-standard-deviation criterion. Based on the mean and standard deviation of the data within the sliding window, numerical points exceeding the mean plus or minus three standard deviations are marked as outliers and removed. Missing positions are filled by linear interpolation between two adjacent normal values.

[0047] Using the center point of the target aquaculture cage area as the origin, a horizontal spatial grid framework is established with east as the X-axis and north as the Y-axis. In this embodiment, the 12 cages are arranged in a matrix of 3 rows and 4 columns, with a center-to-center distance of 150 meters between adjacent cages. Each cage is circular with a diameter of 120 meters, so the maximum diagonal length of the projection of a single cage onto the horizontal plane is the diameter of 120 meters. The characteristic length is set to 120 meters, and based on this, the spatial scale of the grid unit is determined to be 120 meters by 120 meters, covering the entire aquaculture area and forming a total of 80 spatial grid units in 10 columns horizontally and 8 rows vertically.

[0048] Step 2.3: Map the preprocessed environmental sensing data, facility status sensing data, and biomass sensing data at each time section to the corresponding spatial grid cells according to their actual geographic coordinates. For sensor data crossing grid boundaries, the nearest neighbor principle is used to assign it to the grid cell whose center point is closest to the sensor's latitude and longitude coordinates. For acoustic water body stratified echo integral data, map each stratified integral value to the corresponding spatial grid cell according to its horizontal projection position. This forms a standardized grid dataset.

[0049] Step 3: Construct an environmental risk field based on a standardized grid dataset

[0050] Step 3.1: For each spatial grid cell, calculate the wave-induced disaster factor value based on the effective wave height data of that spatial grid cell. In this embodiment, the critical effective wave height that the aquaculture cage design can withstand is... The value is 6 meters. This represents the effective wave height at a certain moment during the typhoon's influence in grid cell 7. The measured value is 4.8 meters, therefore the wave-induced disaster factor value is:

[0051]

[0052] Step 3.2: For each spatial grid cell, calculate the ocean current hazard factor value based on the ocean current velocity data of that grid cell. The maximum tolerable current velocity for the cultured species, large yellow croaker. The value was taken as 1.5 meters per second based on data from aquaculture biology research. This refers to the ocean current velocity at the same moment in grid cell number 12. The measured value is 1.2 meters per second, therefore the ocean current disaster factor value is:

[0053]

[0054] Step 3.3: For each spatial grid cell, the environmental risk index is determined according to the maximum value synthesis rule using the wave-induced disaster factor value and the ocean current-induced disaster factor value. For grid cell number 7, if... , ,but:

[0055]

[0056] Step 3.4: Traverse all 80 spatial grid cells to generate an environmental risk field containing the environmental risk index of each spatial grid cell. In this embodiment, during the typhoon's passage, the environmental risk index of the wave-facing grid cells reaches a maximum of 0.92, while the environmental risk index of the wave-avoiding grid cells ranges from 0.45 to 0.60.

[0057] Step 4: Construct a facility damage field based on a standardized grid dataset and an environmental risk field.

[0058] Step 4.1: Convert the mooring tension data into a mooring safety factor value. In this embodiment, the mooring cable used is a 44 mm diameter ultra-high molecular weight polyethylene cable with a minimum breaking strength... The value is 1960 kN. On the wave-facing mooring cable of the cage corresponding to grid cell 7, the tension sensor records a mooring tension (T(7,7,t)) of 1274 kN. Therefore, the mooring safety factor value is:

[0059]

[0060] Step 4.2: Convert the node displacement data into deformation safety factor values. The maximum allowable displacement of the cage nodes. Based on the cage structure design scheme, the value is taken as 0.8 meters. At the cage frame node corresponding to the 7th grid cell, the displacement data (D(7,7,t)) is 0.44 meters. Therefore, the deformation safety factor value is:

[0061]

[0062] Step 4.3: Calculate the facility damage index based on the mooring safety factor value and the deformation safety factor value. In this embodiment, the mooring system is rated as highly important in the overall facility structure. After assessment by facility safety experts, the mooring safety factor weight coefficient is... The value is set at 0.65. Therefore, the facility damage index is:

[0063]

[0064] Step 4.4: Traverse all 80 spatial grid cells to generate a facility damage field containing the facility damage index for each spatial grid cell. In this embodiment, the spatial distribution of the facility damage index during the typhoon shows a pattern of high on the wave-facing side and low on the wave-avoiding side, which is highly consistent with the orientation of the cage group facing the direction of the typhoon.

[0065] Step 5: Construct a biomass loss field based on a standardized grid dataset, environmental risk field, and facility damage field.

[0066] Step 5.1: Perform frequency division processing on the multi-frequency acoustic volume backscattering data to obtain the volume backscattering intensity data for the 38 kHz and 120 kHz frequency channels, denoted as . and The volume backscattering intensity difference between two frequency channels is calculated using a multi-frequency difference classification method. The difference in target intensity between the swim bladder of large yellow croaker at 38 kHz and 120 kHz was determined in the laboratory, and the threshold range of the difference was statistically determined to be 2 dB to 8 dB. When the signal falls within this range, it is determined that the scattering signal originates from the fish school and is extracted as the volume backscattering intensity of the pure fish school. When the difference is less than 2 dB, it is determined to be a zooplankton scatterer; when the difference is greater than 8 dB, it is determined to be suspended particulate matter or other non-biological scatterers.

[0067] Step 5.2: Convert the volumetric backscattering intensity of a pure fish swarm into the fish area density. The acoustic-density conversion coefficient (k) was obtained through a large yellow croaker target intensity calibration experiment. The calibration experiment was conducted in an experimental net cage with a diameter of 5 meters and a depth of 6 meters. A known number of large yellow croaker individuals were placed in the cage, and the volumetric backscattering intensity was measured using echo sounders at 38 kHz and 120 kHz. A regression relationship between the number density and the volumetric backscattering intensity was established, and the fitted value of (k) was 0.87 fish per square meter. In grid cell number 7, the fish area density was obtained after converting the volumetric backscattering intensity of the pure fish swarm:

[0068]

[0069] Step 5.3: Based on the fish population area density time series data for the consecutive 72 hours prior to the current moment, establish a baseline value for fish population density for each spatial grid cell. Taking grid cell 7 as an example, fish population area density data for 72 hourly intervals were obtained within the 72 hours before the typhoon's arrival. A moving average was calculated for this time series, with a moving window width of 12 hours. The baseline value for fish population density after the moving average for grid cell 7 is 8.5 fish per square meter, reflecting the typical level of fish population density in this grid cell under normal meteorological conditions over 72 hours.

[0070] Step 5.4: Based on the environmental risk index and facility damage index of the spatial grid cell, the baseline value of fish density is lowered. In this embodiment, a preset environmental risk threshold is used. The facility damage threshold is 0.60. The environmental risk downgrade coefficient is 0.50. The value is 0.45, which is the facility damage downgrade factor. The value is 0.55. Taking grid cell number 7 as an example, its environmental risk index (E(7,7,t)=0.80) is greater than... Environmental risks exceed the limit The first downward adjustment value is:

[0071]

[0072] The facility damage index (S(7,7,t)=0.615) is greater than... The damage to the facilities exceeded the limit. The second downward adjustment value is:

[0073]

[0074] The smaller of the first downward adjustment value of 7.735 fish per square meter and the second downward adjustment value of 7.962 fish per square meter, 7.735 fish per square meter, was selected as the baseline value for the adjusted fish density. .

[0075] Step 5.5: Compare the adjusted baseline fish density value with the measured fish area density of the current spatial grid cell to calculate the biomass loss rate. Measured fish area density of grid cell 7 at the current time. If the number of biomass strips is 6.2 per square meter, then the biomass loss rate is:

[0076]

[0077] The actual meaning of a biomass loss rate of 19.8% is that, after excluding the expected pressure drop effect of environmental and facility risk events on fish density, there is still an abnormal decrease in fish density in approximately 19.8% of the grid cell. This decrease is attributed to the combined effects of fish escape, disease, or other unperceived factors.

[0078] Step 5.6: Traverse all 80 spatial grid cells to generate a biomass loss field containing the biomass loss rate of each spatial grid cell. In this embodiment, during the typhoon's passage, the biomass loss rate of the first row of cages on the wave-facing side is between 18% and 35%, the biomass loss rate of the middle cages is between 10% and 18%, and the biomass loss rate of the cages on the wave-avoiding side is between 5% and 12%.

[0079] Step 6: Calculate the comprehensive business risk index and determine the business risk level.

[0080] Step 6.1: Based on the environmental risk field, facility damage field, and biomass loss field, calculate the comprehensive risk value for each spatial grid cell. In this embodiment, the risk preference type of the aquaculture operator is assessed as "balanced," and the corresponding weighting coefficient is set to [value missing]. , , Taking grid cell number 7 as an example:

[0081]

[0082] Step 6.2: Weight the comprehensive risk values ​​of all spatial grid units using the expected economic value of the cultured organisms in each grid unit as the weight. The expected economic value of the cultured organisms (W(x,y)) is calculated by combining the stocking density, the current market price of the cultured species, and the area of ​​the grid unit. In this embodiment, the market price of large yellow croaker is 65 yuan per kilogram, the average individual weight is 450 grams, the stocking density is 8 croakers per square meter, and the area of ​​each grid unit is 14,400 square meters. Without considering the value differences between different grid units, the area ratio of the grid units is used as a simplified weight, and the areas of all 80 grid units are equal. At this point, the comprehensive risk index degenerates into the arithmetic mean of the comprehensive risk values ​​of all grid units. Calculations show:

[0083]

[0084] Step 6.3: Determine the operational risk level based on the comprehensive operational risk index. (G(t)=0.612), falling within the range The current operational risk level is determined to be relatively high.

[0085] Step 6.4: Output the operational risk assessment results, including the comprehensive operational risk index of 0.612, the operational risk level of relatively high risk, and the spatial distribution map of the comprehensive risk value of each spatial grid unit.

[0086] To further illustrate the technical effectiveness of this embodiment, using the same typhoon scenario data as input, conventional single-dimensional assessment methods were employed as comparative examples. Comparative example one used only the environmental risk index for single-dimensional assessment, with the average environmental risk index of 80 grid units as the assessment conclusion; comparative example two used a two-dimensional weighted assessment of environmental risk and facility damage, but did not include the biomass loss field; comparative example three used the cascade assessment method proposed in this embodiment. The assessment results of the three methods are compared in the table below:

[0087] Evaluation methods Comprehensive Business Risk Index Determine the risk level Does it include biomass loss information? Comparative Example 1 (Environmental Dimension Only) 0.68 Higher risk no Comparative Example 2 (Environment + Facilities Dimension) 0.59 Medium risk no This embodiment (environment + facility + biomass cascade) 0.61 Higher risk yes

[0088] As can be seen from the table above, Comparative Example 1, which relies solely on the environmental dimension, overestimates the overall risk level because it does not consider the real physical scenario where the facility structure may have a certain safety redundancy, thus partially offsetting the effects of environmental stress. Comparative Example 2, which only performs a two-dimensional weighting of the environment and facilities, underestimates the overall risk level because it ignores the important operational consequence that environmental and facility risk events have already begun to lead to actual losses in fish biomass. In contrast, the cascade assessment method in this embodiment, by incorporating the causal transmission relationship between the environment, facilities, and organisms, can more comprehensively and accurately reflect the risk level of the entire chain from environmental disaster factors to final operational losses. Furthermore, the output results include the spatial distribution information of biomass loss rate in each grid unit, which can provide a direct basis for aquaculture managers to formulate grid-based precise emergency measures.

[0089] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0090] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for risk assessment of deep-sea aquaculture operations within the context of marine economy, characterized in that, Includes the following steps: Step 1: Acquire environmental perception data, facility status perception data, and biomass perception data of the target deep-sea aquaculture area. The environmental perception data includes significant wave height data and ocean current velocity data. The facility status perception data includes mooring tension data and nodal displacement data. The biomass perception data includes multi-frequency acoustic scattering data. Step 2: Perform spatiotemporal alignment preprocessing on the data obtained in Step 1, establish a spatial grid framework, and map the preprocessed data to spatial grid cells to form a standardized grid dataset; Step 3: Based on the standardized grid dataset, calculate the environmental risk index for each spatial grid cell using significant wave height data and ocean current velocity data to generate an environmental risk field; Step 4: Based on the standardized grid dataset and the environmental risk field, calculate the facility damage index for each spatial grid cell using mooring tension data and nodal displacement data to generate the facility damage field; Step 5: Based on the standardized grid dataset, environmental risk field, and facility damage field, obtain the fish area density of each spatial grid cell using multi-frequency acoustic scattering data, establish a fish density baseline value for each spatial grid cell, adjust the fish density baseline value downward based on the environmental risk index and facility damage index of the spatial grid cell, calculate the biomass loss rate based on the fish area density of the spatial grid cell and the adjusted fish density baseline value, and generate a biomass loss field; Step 6: Calculate the comprehensive risk value of each spatial grid cell based on the environmental risk field, facility damage field, and biomass loss field. Weight and aggregate the comprehensive risk values ​​of all spatial grid cells to obtain the comprehensive operational risk index. Determine the operational risk level based on the comprehensive operational risk index and output the operational risk assessment results.

2. The method for risk assessment of deep-sea aquaculture operations in the context of marine economy, as described in claim 1, is characterized in that... Step 2, the spatiotemporal alignment preprocessing, includes: performing temporal interpolation on significant wave height data, ocean current velocity data, mooring tension data, and nodal displacement data to unify the sampling time interval; performing water-layered echo integration on multi-frequency acoustic scattering data; establishing a spatial grid framework with the center point of the target deep-sea aquaculture cage area as the origin, the east direction as the X-axis, and the north direction as the Y-axis, the spatial scale of the grid unit being determined according to the characteristic length of the aquaculture cage, wherein the characteristic length is the maximum diagonal length of the projection of a single cage onto the horizontal plane; and mapping the processed data to the corresponding spatial grid units according to geographic coordinates to obtain the standardized grid dataset.

3. The method for risk assessment of deep-sea aquaculture operations in the context of marine economy, as described in claim 2, is characterized in that... Step 3 includes: for each spatial grid cell, based on the effective wave height data of the spatial grid cell, according to... Calculate the wave-induced disaster factor value, where Let (x, y) be the effective wave height of the spatial grid cell (x, y) at time t. The critical effective wave height to be withstood by the design of aquaculture cages; based on the ocean current velocity data of the aforementioned spatial grid cells, according to... Calculate the disaster-causing factor value of ocean currents, where Let (x, y) be the ocean current velocity in the spatial grid cell (x, y) at time t. The maximum flow velocity tolerable for the cultured organisms; the environmental risk index E(x,y,t) is determined as... and The maximum value in, i.e. The environmental risk field is generated by traversing all spatial grid cells.

4. The method for risk assessment of deep-sea aquaculture operations in the context of marine economy, as described in claim 3, is characterized in that... Step 4 includes: converting mooring tension data into mooring safety factor values. Where T(x,y,t) is the mooring tension of the spatial grid cell (x,y) at time t. The minimum breaking tensile force of the mooring cable; converting nodal displacement data into deformation safety factor values. Where D(x,y,t) is the nodal displacement of the spatial grid element (x,y) at time t. The maximum allowable displacement of the cage node; according to Calculate the facility damage index, where The weighting coefficient for the mooring safety factor is determined by the importance level of the mooring system in the overall facility structure; the facility damage field is generated by traversing all spatial grid cells.

5. The method for risk assessment of deep-sea aquaculture operations in the context of marine economy, as described in claim 4, is characterized in that... Step 5, which involves obtaining the fish area density of each spatial grid cell using multi-frequency acoustic scattering data, includes: performing frequency division processing on the multi-frequency acoustic scattering data to obtain the volume backscattering intensity at different frequencies; using a multi-frequency difference classification method to perform combined calculations on the volume backscattering intensities at different frequencies, separating the fish echo signal from the non-biological scattering body echo signal, and extracting the pure fish volume backscattering intensity. Based on the acoustic-density conversion coefficient k obtained from the target intensity calibration experiment of farmed fish fry, according to Calculate the fish area density N(x,y,t) of the spatial grid cell.

6. The method for risk assessment of deep-sea aquaculture operations in the context of marine economy, as described in claim 5, is characterized in that... Step 5, which establishes a fish density baseline value for each spatial grid cell, includes: obtaining the fish area density time series of the spatial grid cell over a continuous 72 hours prior to the current moment, calculating the moving average of the time series, and using the moving average as the fish density baseline value of the spatial grid cell at the current moment.

7. The method for risk assessment of deep-sea aquaculture operations in the context of marine economy, as described in claim 6, is characterized in that... Step 5, which involves lowering the baseline value of the fish density based on the environmental risk index and facility damage index of the spatial grid cell, includes: setting an environmental risk threshold. and facility damage threshold When the environmental risk index E(x,y,t) of the spatial grid cell is greater than At that time, the calculated environmental risk exceeded the range. And multiply the baseline value of the fish density by As the first downward adjustment value, among which The environmental risk reduction factor is preset; when the facility damage index S(x,y,t) of the spatial grid unit is greater than... At that time, the damage to the calculated facility exceeded the range. And multiply the baseline value of the fish density by As the second downward adjustment value, among which The preset facility damage reduction coefficient is used; the smaller of the first reduction value and the second reduction value is selected as the baseline value of the fish population density after reduction; if neither the environmental risk index nor the facility damage index exceeds its respective threshold, the original baseline value of the fish population density is maintained.

8. The method for risk assessment of deep-sea aquaculture operations in the context of marine economy, as described in claim 7, is characterized in that... Step 6, which calculates the comprehensive risk value for each spatial grid cell, uses the following formula: Where S(x,y,t) is the facility damage index of the spatial grid cell, R(x,y,t) is the comprehensive risk value, and L(x,y,t) is the biomass loss rate of the spatial grid cell (x,y) at time t. For environmental risk weighting coefficients, For facility damage weighting coefficient, Let be the biomass loss weighting coefficient, and satisfy . The , and The value is set according to the risk preference type of the deep-sea aquaculture operators.

9. The method for risk assessment of deep-sea aquaculture operations in the context of marine economy, as described in claim 8, is characterized in that... Step 6, which involves weighted aggregation of the comprehensive risk values ​​of all spatial grid cells, includes: using the expected economic value of the cultured organisms in each spatial grid cell as the weight, and then... Calculate the comprehensive operational risk index G(t), where W(x,y) is the expected economic value of the cultured organism in the spatial grid cell (x,y). This indicates summing over all spatial grid cells.

10. The method for risk assessment of deep-sea aquaculture operations in the context of marine economy, as described in claim 9, is characterized in that... Step 6, which describes determining the operational risk level based on the comprehensive operational risk index, includes: predefining five operational risk level ranges: when... When it is judged as low risk; when When it is judged as a low risk; when It was determined to be of medium risk at that time; when When it is judged as a high risk; when It was determined to be high risk at that time.

Images