Artificial habitat cold water supply scheme engineering evaluation method and system

Abstract

The invention discloses an artificial habitat cold water supply scheme engineering evaluation method and system, and relates to the technical field of coral cultivation. According to the scheme, a Pareto optimal solution set is solved by adopting a multi-objective optimization algorithm, and the blindness and subjectivity of artificial experience selection are avoided by introducing environmental parameters to dynamically adjust the weights of time and space target values, so that a target cold water supply scheme can adapt to the requirements of different coral culture areas, and the coral culture efficiency is improved. And the final implementation effect of the coral reef artificial habitat cold water supply project and the coral protection efficiency are improved.

Description

Technical Field

[0001] This application relates to the field of coral aquaculture technology, and in particular to an engineering evaluation method and system for artificial habitat cold water supply schemes. Background Technology

[0002] Global warming has led to a severe bleaching crisis for coral reefs. To alleviate this crisis, artificial habitat creation technology has become a key approach, but existing technologies have significant limitations. Currently, the mainstream technologies fall into two categories: artificial upwelling technology uses powerful pumps to transport deep, cold seawater to the surface coral area. While this can alleviate thermal bleaching, long-distance pumping relies on high energy consumption, making it difficult to sustain long-term using only renewable energy sources. Nearshore water storage and cooling technology cools nearshore pools and discharges cold seawater, but this also faces high energy costs, and the cold seawater easily mixes and diffuses with surrounding water, resulting in significant cooling loss and a limited effective range.

[0003] Current cold water supply projects for artificial coral reef habitats generally suffer from significant cold water mixing and diffusion losses and low retention efficiency. The core challenge of cold water supply schemes for artificial coral reef habitats lies in the significant differences in the thermal anomalies, their frequency, duration, and coral population density, distribution, and heat tolerance across different sea areas. This can lead to a situation where the same cold water supply scheme effectively alleviates coral heat stress in sea area A, but almost none in sea area B (due to inconsistent environmental temperatures causing some effective cold water to reach higher temperatures, thus failing to alleviate the problem). Conversely, the same cold water supply scheme might have a good overall effect on alleviating coral heat stress in sea area C, but poor effect in sea area D. The uneven benefits of cold water supply projects in artificial coral reef habitats, characterized by "overcooling in some areas and ineffectiveness in others," severely hinder the widespread adoption of these projects. Therefore, in cold water supply projects for artificial coral reef habitats, the cold water supply scheme has a significant impact on alleviating thermal anomalies in the artificial habitat and is crucial to the project's success.

[0004] Meanwhile, different marine habitats have different priorities in different engineering projects. High-temperature abnormal marine areas require rapid and large-scale cooling (emphasizing the cold water mixing rate), while high coral density areas require long-term stable coverage (emphasizing the cold water retention time). However, existing technologies blindly and subjectively apply fixed artificial habitat cold water supply schemes based on human experience, leading to systemic risks in the projects. For example, using the block coral area scheme in dense staghorn coral areas (with poor heat resistance) resulted in a sharp drop in coral bleaching inhibition rate. Summary of the Invention

[0005] This specification provides an engineering evaluation method for artificial habitat cold water supply schemes to solve the problem of blindly and subjectively selecting artificial habitat cold water supply schemes based on human experience in the prior art.

[0006] Artificial coral farming is a necessary, urgent, and promising intervention to address the global coral reef crisis, protect biodiversity, maintain marine ecosystem functions and services, and safeguard human well-being. Artificial coral habitats are open protected areas created by deploying artificial reefs in shallow-water coral distribution areas within natural marine environments and equipping them with intelligent equipment for habitat creation.

[0007] In practical applications, coral reef artificial habitat creation technology often faces a contradiction: rapid cold water diffusion with a short duration or long duration with a small effective range. Increasing the water supply rate can expand the range, but it intensifies the convective mixing of cold water with the surrounding water, leading to rapid loss of cold energy. Conversely, decreasing the rate can extend the duration, but it limits the effective range. The root of this contradiction lies in the lack of a quantitative evaluation system that balances uniformity and sustainability. Existing evaluation systems cannot assess the effectiveness of cold water supply from both spatial coverage and temporal retention dimensions, nor can they dynamically adjust evaluation weights based on differences in marine environments (such as coral density and water temperature anomalies), resulting in a highly arbitrary selection of the optimal solution. Furthermore, the temperature diffusion of coral habitats is influenced by the coupling of multiple factors, including density current movement, heat exchange, and topography, making it difficult to separate the effects of a single factor. The conflict between the goals of a longer cold water duration and a wider cold water effective range necessitates a multi-criteria decision-making balance, further exacerbating the technical adaptation challenges.

[0008] Coral reef artificial habitat creation technology is a technique that uses artificially introduced cold seawater to create artificial upwelling or stored water for cooling and discharge, thereby creating a suitable temperature, water flow, and nutrient environment in the coral growth area.

[0009] To solve the above-mentioned technical problems, the embodiments in this specification are implemented as follows: Firstly, the embodiments of this specification provide an engineering evaluation method for an artificial habitat cold water supply scheme, including: Multiple cold water supply schemes for the coral farming area to be evaluated are obtained; each cold water supply scheme includes a set of engineering parameters, which at least include water supply method, water supply flow rate, water inlet height and water supply pipe diameter. All the aforementioned cold water supply schemes are input into a random forest model to obtain the target value of each cold water supply scheme output by the random forest model; the target value includes a time target value and a spatial target value; the target value is used to quantify the cold water supply effect of the coral farming area to be evaluated from both spatial and temporal dimensions. A multi-objective optimization algorithm is used to solve for the Pareto optimal solution set, which is used to characterize multiple candidate cold water supply schemes that weigh spatial objective values ​​against temporal objective values. The weights of the time target value and the spatial target value are adjusted according to the environmental parameters of the coral farming area to be evaluated, and the target cold water supply scheme is determined from multiple candidate cold water supply schemes.

[0010] Secondly, the embodiments of this specification provide an engineering evaluation system for an artificial habitat cold water supply scheme, comprising: The acquisition module is used to acquire multiple cold water supply schemes for the coral farming area to be evaluated; each cold water supply scheme includes a set of engineering parameters, which at least include water supply method, water supply flow rate, water inlet height and water supply pipe diameter. The first determining module is used to input all the cold water supply schemes into a random forest model to obtain the target value of each cold water supply scheme output by the random forest model; the target value includes a time target value and a spatial target value; the target value is used to quantify the cold water supply effect of the coral farming area to be evaluated from the spatial and temporal dimensions. The solution module is used to solve for the Pareto optimal solution set using a multi-objective optimization algorithm. The Pareto optimal solution set is used to characterize multiple candidate cold water supply schemes that weigh spatial objective values ​​against time objective values. The second determining module is used to adjust the weights of the time target value and the spatial target value according to the environmental parameters of the coral farming area to be evaluated, and to determine the target cold water supply scheme from multiple candidate cold water supply schemes.

[0011] One embodiment of this specification achieves the following beneficial effects: by using a multi-objective optimization algorithm to solve the Pareto optimal solution set, and by introducing environmental parameters to dynamically adjust the weights of time and space objective values, the blindness and subjectivity of human experience selection are avoided, enabling the target cold water supply scheme to adapt to the needs of different coral farming areas, thereby improving the final implementation effect of the coral reef artificial habitat cold water supply project and the protection efficiency of corals. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart illustrating an engineering evaluation method for an artificial habitat cold water supply scheme provided in the embodiments of this specification; Figure 2 A schematic diagram illustrating the water supply method provided in the embodiments of this specification; Figure 3 This is a schematic diagram of the structure of an engineering evaluation system for an artificial habitat cold water supply scheme provided in the embodiments of this specification.

[0014] Explanation of reference numerals in the attached figures: 1. Artificial reefs; 2. Water inlet. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of one or more embodiments of this specification clearer, the technical solutions of one or more embodiments of this specification will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of them. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of one or more embodiments of this specification.

[0016] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0017] The engineering evaluation method for an artificial habitat cold water supply scheme provided in the embodiments of the specification will be described in detail with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart illustrating an engineering evaluation method for an artificial habitat cold water supply scheme provided in an embodiment of this specification. From a programming perspective, the entity executing the process can be a program mounted on an application server or an application client. From a hardware perspective, the entity executing the process can be a terminal device; this embodiment does not impose any particular limitation on this.

[0019] like Figure 1 As shown, the process may include the following steps: S110: Obtain multiple cold water supply schemes for the coral farming area to be evaluated; each cold water supply scheme includes a set of engineering parameters, which at least include the water supply method, water supply flow rate, water inlet height, and water supply pipe diameter.

[0020] In the embodiments of this specification, each cold water supply scheme includes a set of key engineering parameters. These parameters may include the water supply method, water flow rate, water inlet height, and water supply pipe diameter. The water supply method includes upward water supply, downward water supply, and horizontal radial water supply. These engineering parameters cover the cold water supply effect from both spatial and temporal dimensions, overcoming the shortcomings of existing technologies that rely on single technical indicators.

[0021] Figure 2 This is a schematic diagram of the water supply method provided in the embodiments of this specification.

[0022] like Figure 2As shown, upward, downward, and horizontal radial water supply are commonly used high-flow-rate water supply methods. Upward and downward water supply methods use vertical cylindrical pipes with openings pointing upwards or downwards to supply cold water; these are commonly found in upflow systems, industrial wastewater discharge systems, and downflow systems. Horizontal radial water supply uses a circumferentially open jet device to spray a negatively buoyant stream of cold water radially; this type of device is often used in ocean thermal energy conversion plants (OTEC).

[0023] S120: Input all the cold water supply schemes into the random forest model to obtain the target value of each cold water supply scheme output by the random forest model; the target value includes a time target value and a spatial target value; the target value is used to quantify the cold water supply effect of the coral farming area to be evaluated from the spatial and time dimensions.

[0024] In the embodiments of this specification, for each input cold water supply scheme, the random forest model can output corresponding time target value and spatial target value. The time target value can reflect the effective duration of cold water in the coral cultivation area, and the spatial target value can measure the range and uniformity of cold water coverage, thus quantifying the effect of cold water supply from both spatial and temporal dimensions.

[0025] S130: A multi-objective optimization algorithm is used to solve for the Pareto optimal solution set, which is used to characterize multiple candidate cold water supply schemes that weigh spatial objective values ​​against temporal objective values.

[0026] In the embodiments described in this specification, a multi-objective optimization algorithm (NSGA-II) is employed to weigh the time and space objective values ​​and obtain a Pareto optimal solution set. The Pareto optimal solution set contains a series of candidate cold water supply schemes that perform well in both time and space, providing decision-makers with a variety of options.

[0027] The steps for multi-objective optimization using the NSGA-II algorithm are as follows: Initialize the population: Each individual in the population represents a cold water supply scheme (i.e., a set of engineering parameters: water supply method, water supply flow rate, water inlet height, water supply pipe diameter), and the population size is set to 100.

[0028] Fitness assessment: For each individual, the objective function value is calculated based on the engineering parameter values ​​predicted by the random forest.

[0029] Non-dominated ranking: stratifying individuals in a population according to their dominance (Pareto hierarchy).

[0030] Crowding degree calculation: In the same non-dominated layer, calculate the crowding degree of each individual (the solution density around the individual).

[0031] Selection, crossover, and mutation: Binary tournament selection (considering non-dominant rank and crowding) is used, followed by simulated binary crossover (SBX) and polynomial mutation to generate offspring populations.

[0032] Merge parent and offspring populations: Perform non-dominated sorting and crowding comparison to select the next generation population.

[0033] Iteration: Repeat the above process until the maximum number of iterations is reached (e.g., 10,000 generations).

[0034] Output Pareto optimal frontier: The final set of non-dominated solutions is the Pareto optimal solution, which weighs multiple objectives.

[0035] S140: Adjust the weights of the time target value and the spatial target value according to the environmental parameters of the coral farming area to be evaluated, and determine the target cold water supply scheme from multiple candidate cold water supply schemes.

[0036] In the embodiments of this specification, environmental parameters may include the frequency of water temperature anomalies, the duration of temperature anomalies, the temperature anomaly values, and coral population density. The weights of time-based and spatial target values ​​are adjusted based on these environmental parameters to select the most suitable target cold water supply scheme for the coral farming area to be evaluated from the Pareto optimal solution set. For example, in areas with high heat anomalies and low coral density, a scheme with a higher spatial target value may be preferred to quickly reduce the temperature over a large area; while in areas with high coral density but low heat anomalies, a scheme with a higher time target value may be preferred to ensure that cold water can cover and protect the corals for an extended period.

[0037] By combining a random forest model and a multi-objective optimization algorithm, a comprehensive evaluation and optimization of the cold water supply scheme for artificial coral reef habitats was achieved.

[0038] It should be understood that the order of some steps in the methods described in one or more embodiments of this specification may be interchanged according to actual needs, or some steps may be omitted or deleted.

[0039] In the embodiments described in this specification, a multi-objective optimization algorithm is used to solve the Pareto optimal solution set. By introducing environmental parameters to dynamically adjust the weights of time and space objective values, the blindness and subjectivity of human experience selection are avoided, enabling the target cold water supply scheme to adapt to the needs of different coral farming areas, thereby improving the final implementation effect of the coral reef artificial habitat cold water supply project and the protection efficiency of corals.

[0040] based on Figure 1 In addition to the method described in the embodiments of this specification, some specific implementation schemes of the method are also provided, which will be described below.

[0041] Optionally, in the embodiments of this specification, before inputting all the described cold water supply schemes into the random forest model, the method includes: Numerical simulation of the first cold water supply scheme was performed based on computational fluid dynamics to obtain the first evaluation parameter; the evaluation parameter is used to characterize the cold water supply effect of the coral farming area to be evaluated after the implementation of the cold water supply scheme; the first cold water supply scheme is one of the multiple schemes in the cold water supply scheme. The random forest model is trained by constructing training samples based on the first cold water supply scheme and the first evaluation parameters to obtain the optimized random forest model.

[0042] In practice, 273 parameter combination schemes, namely the first cold water supply scheme, are generated by using water supply method (upward water supply, downward water supply and horizontal radial water supply), flow velocity (0.1-0.3m / s, step size 0.02m / s), pipe diameter (50-90mm, step size 5mm) and height (0-500mm, step size 50mm) as variables.

[0043] Computational fluid dynamics software was used to simulate the diffusion and retention process of cold water in the coral cultivation area. The analysis focused on fluid dynamics, cold volume scale, spatial trajectory and global diffusion pattern. After performing simulation for each scheme, the first evaluation parameter was extracted.

[0044] Evaluation parameters may include abnormal heat accumulation value, colder water volume, effective cold water volume, effective cold water absorbable heat value, effective cold water diffusion distance, and effective cold water duration. Among these, abnormal heat accumulation value quantifies the accumulation of abnormal temperatures in the water within the artificial habitat, directly reflecting the mitigation effect of cold water on coral heat stress. Colder water volume is the total volume of cold water with a temperature lower than the ambient water temperature, aiding in the analysis of cold distribution. Effective cold water volume is the volume of cold water that can both alleviate heat stress and prevent overcooling, relating to cooling efficiency and ecological safety. Effective cold water absorbable heat value is the amount of heat that the effective cold water volume can absorb, assessing cooling capacity. Effective cold water diffusion distance is the farthest distance cold water diffuses from the inlet, characterizing spatial coverage. Effective cold water duration is the length of time cold water maintains an effective mitigation state, reflecting the temporal dimension of effect.

[0045] The formula for calculating the Abnormal Heat Accumulation Value (AHA) is: ;in, for Abnormal heat per unit volume of water at any given time; Specific heat capacity of water; The density of water changes with temperature. The volume of a unit body of water; The abnormal temperature value per unit of water body is the difference between the temperature and the warning temperature threshold for coral bleaching. and These represent the start and end times of the study period. When the water temperature in the coral habitat area exceeds the coral temperature threshold T, it is considered an abnormal temperature. As abnormal heat accumulates, the AHA (Arctic Harmonic Effect) gradually increases. The artificial coral reef habitat cooling water supply system can slow down the growth of AHA.

[0046] colder water volume The calculation formula is: A cylindrical coordinate system is used, where, For unit radius, In unit angle, In units of height, To find the volume of colder water as a function, colder water is defined as water with a temperature 6% lower than the ambient temperature. It is used to distinguish between cold water in a broad sense and ambient water.

[0047] Effective cold water volume The calculation formula is: ,in, To find the effective cold water volume as a function, the effective cold water is the cold water that is lower than the coral bleaching threshold.

[0048] Effective cold water heat absorption value The calculation formula is: ,in, This is the difference between the ambient water temperature and the effective cold water temperature. The effective volume of cold water is expressed per unit.

[0049] Effective cold water diffusion distance The calculation formula is: ,in, Let be the effective cold water diffusion distance at time i.

[0050] Effective cold water duration The calculation formula is: ,in, The time when cold water is retained in the habitat area.

[0051] Two independent random forest regressors are employed to predict spatial and temporal objectives respectively, enhancing the model's specificity. Using 273 cold water supply schemes and corresponding evaluation parameters generated by CFD simulations as training samples, the random forest model is trained to learn the nonlinear mapping relationship between engineering parameters and cold water supply effects. The final output is an optimized model capable of predicting the temporal and spatial objective values ​​for any scheme.

[0052] Ten-fold cross-validation was used to calculate the root mean square error (RMSE) and coefficient of determination (R²) between the predicted and CFD simulation values. R² > 0.85 was required; otherwise, model parameters were adjusted or the number of simulation samples was increased.

[0053] In practice, a trained random forest model is used to predict combinations of engineering parameters that have not been simulated. For example, within the range of engineering parameters (flow velocity: 0.1~0.3m / s, pipe diameter: 50~90mm, height: 0~500mm), a large number of parameter combinations (e.g., 1000 sets) are generated at certain step sizes (e.g., flow velocity step size 0.02m / s, pipe diameter step size 5mm, height step size 50mm). Then, the random forest model is used to predict the target values ​​corresponding to these 1000 sets of parameters.

[0054] Further, optionally, the training of the random forest model based on the first cold water supply scheme and the first evaluation parameters as described in the embodiments of this specification specifically includes: The first evaluation parameters are weighted and fused to obtain the first spatial target value and the first temporal target value; The random forest model is trained by constructing training samples based on the first engineering parameters, the first spatial target value, and the first temporal target value.

[0055] In the embodiments of this specification, the first engineering parameter refers to the engineering parameters included in the first cold water supply scheme. The first evaluation parameter is weighted and fused to obtain the first spatial target value and the first temporal target value. The weights are determined by the Analytic Hierarchy Process (AHP), the entropy weight method, or the expert experience method.

[0056] Optionally, before using a multi-objective optimization algorithm to solve for the Pareto optimal solution set as described in the embodiments of this specification, the method includes: The subjective weights of the evaluation parameters corresponding to the cold water supply scheme are determined by the analytic hierarchy process (AHP), and the objective weights of the evaluation parameters corresponding to the cold water supply scheme are determined by the entropy weight method. The subjective weights and objective weights are combined to obtain the comprehensive weights of the evaluation parameters; The evaluation parameters corresponding to each cold water supply scheme are weighted using the comprehensive weight to obtain the comprehensive evaluation score of each cold water supply scheme.

[0057] In the embodiments described in this specification, the evaluation parameters are normalized according to different normalization formulas to obtain a normalized decision matrix. The normalization formulas are shown in Table 1.

[0058] Table 1

[0059] In the table, record Positive indicator The standardized value, Let R be the decision matrix R. i The first water supply scheme j Performance metrics for attribute parameters For the firstj The maximum value of each attribute parameter. For the first j The minimum value of each attribute parameter.

[0060] The judgment matrix is ​​constructed using the analytic hierarchy process (AHP) and the pairwise comparison method. A This represents the experts' subjective preferences for the standard and alternatives. The subjective weight for each standard is calculated. :

[0061] ,in, A To determine the matrix, For feature vectors, For matrix A The largest eigenvalue, This represents the importance scale value of the elements in the matrix.

[0062]

[0063] Where R is the decision matrix composed of different cold water supply schemes, showing the performance of m feasible alternative cold water supply schemes relative to n evaluation attribute parameters (standards); The entropy value for each standard; This represents the overall entropy information of the sample, and the standardized value of the indicator. Here, the entropy weights of each indicator are given, , It follows the properties of entropy.

[0064] By effectively combining the subjectivity of the analytic hierarchy process (AHP) with the objectivity of the entropy weight method, the normalized decision variables are weighted and summed to obtain a comprehensive weight. The calculation formula is: , ,in, W For comprehensive weighting In matrix form.

[0065] For the i-th cold water supply scheme, calculate the comprehensive evaluation score. ,in, To evaluate parameter values, multi-dimensional evaluation parameters are transformed into comprehensive evaluation scores to provide input for subsequent multi-objective optimization algorithms, such as serving as objective functions or constraints.

[0066] Optionally, the adjustment of the weights of the time target value and the spatial target value based on the environmental parameters of the coral farming area to be evaluated, as described in the embodiments of this specification, specifically includes: The environmental parameters are input into the environmentally sensitive weight allocation system to obtain the time target weight value and spatial target weight value output by the environmentally sensitive weight allocation system; the environmentally sensitive weight allocation system is used to characterize the mapping relationship between environmental parameters and weight values.

[0067] In the embodiments described in this specification, the Pareto front provides multiple optimal solutions. The specific solution selected depends on the characteristics of the marine environment (via environmentally sensitive weights). The difference in the weighting of the two objectives reflects a comprehensive consideration of the value of individual corals and the population under the condition of limited system cooling capacity. Essentially, it is a difference in water supply strategy: is it to ensure that a small area of ​​corals is always at a suitable temperature, while other areas face a greater risk of bleaching, i.e., sacrificing some coral protection rate in exchange for 100% survival in the core breeding area; or is it to increase the cooling range and reduce the cooling amplitude, so that most areas of the habitat have only a slight risk of bleaching, thereby improving the overall survival of corals?

[0068] An environmentally sensitive weighting system was constructed, with cooling strategies adapting to environmental changes. Decision preferences were driven by quantifying environmental conditions, comprehensively considering environmental parameters such as the frequency, duration, and value of temperature anomalies, as well as coral population density, and converting them into standardized scores of 0-1. When the environment falls between the values ​​of the environmental parameters in the table, the corresponding score was obtained using the difference method. The calculation of the target weight scores is shown in Table 2.

[0069] Table 2

[0070] The system can alleviate the conflict between limited space and time in coral bleaching. The basic strategy is to ensure that corals do not face the risk of bleaching while appropriately sacrificing time to maximize space. The abnormal temperature difference is calculated using the following formula:

[0071] in, The temperature of the artificial habitat is the actual seawater temperature. The temperature threshold for early warning of coral bleaching in artificial marine habitats.

[0072] Calculate the corresponding The main scores were calculated based on the reference table. The weighted main scores set an upper limit for the spatial target value (uniformity) score, which serves as a critical threshold for effectively mitigating the risk of coral bleaching. If the spatial target value score exceeds this critical threshold, it may lead to a decline in the overall system performance, thus failing to effectively mitigate the negative effects of heat stress. The abnormal heat accumulation reference table is shown in Table 3.

[0073] Table 3

[0074] DHW is an indicator for quantifying heat stress in corals, combining the intensity and duration of thermal anomalies, but it has low accuracy and is difficult to apply to short-term timescale assessments.

[0075] The study aims to maximize the protection of coral areas. However, for coral communities with high overall heat tolerance, i.e., a high proportion of heat-sensitive and moderately heat-resistant corals, the system should prioritize the survival of sensitive groups to minimize the cooling range and create an environment with lower temperatures and longer periods of cold water, thereby leveraging their high resilience to amplify the protection benefits.

[0076] After calculating the main weighted scores, due to the varying spatial distribution of coral reefs in marine environments at risk of bleaching, further auxiliary score calculations are needed to ensure that cooling strategies also consider the survival of heat-sensitive corals in core breeding areas. By maintaining the uniformity score unchanged and appropriately increasing the time target value (persistence) score, the selection of water supply methods for coral artificial habitat engineering can be effectively guided for different marine conditions, enhancing the system's versatility. See Table 4.

[0077] Table 4

[0078] Finally, based on Table 4, the comprehensive score of the artificial habitat system's objective weights is calculated, and the comprehensive weight of the system is calculated using a linear weighting function, the formula of which is:

[0079] in, The weights for the time target value, The weights are the spatial objective values, with CS being the primary score and US being the secondary score. The overall score of the artificial habitat system's effectiveness is calculated using the weighted average formula: ,in, For the time target value, This represents the target value in space.

[0080] The environmentally sensitive weight allocation system can adjust the weights based on "abnormal water temperature and coral density", and is applicable to different sea areas with different coral bleaching risks, such as Sanya, the Great Barrier Reef, and Swindon Island.

[0081] In practice, key engineering parameters are obtained through computational fluid dynamics (CFD) simulation, and a comprehensive evaluation is performed using the principal hierarchical analysis method (AHP) and the entropy weight method (EWM). Data is then expanded using a random forest model, and a Pareto optimal set is obtained through multi-objective optimization. By constructing an environmentally adaptive decision-making system, the optimal cold water supply scheme (water supply method, water supply velocity, water inlet height, and water supply pipe diameter) adapted to the specific marine environment is finally output. This provides scientific basis and technical support for the protection of coral reef ecosystems and serves as guidance for engineering promotion.

[0082] Figure 3 This is a schematic diagram of the structure of an engineering evaluation system for an artificial habitat cold water supply scheme provided in the embodiments of this specification.

[0083] Corresponding to the method embodiment, this embodiment also provides an engineering evaluation system for artificial habitat cold water supply schemes, which may include: The acquisition module 302 is used to acquire multiple cold water supply schemes for the coral farming area to be evaluated; each cold water supply scheme includes a set of engineering parameters, which at least include water supply method, water supply flow rate, water inlet height and water supply pipe diameter. The first determining module 304 is used to input all the cold water supply schemes into a random forest model to obtain the target value of each cold water supply scheme output by the random forest model; the target value includes a time target value and a spatial target value; the target value is used to quantify the cold water supply effect of the coral farming area to be evaluated from the spatial and time dimensions. The solver module 306 is used to solve for the Pareto optimal solution set using a multi-objective optimization algorithm. The Pareto optimal solution set is used to characterize multiple candidate cold water supply schemes that weigh spatial objective values ​​against time objective values. The second determining module 308 is used to adjust the weights of the time target value and the spatial target value according to the environmental parameters of the coral farming area to be evaluated, and to determine the target cold water supply scheme from multiple candidate cold water supply schemes.

[0084] Optionally, in the embodiments of this specification, before inputting all the described cold water supply schemes into the random forest model, the system includes: Numerical simulation of the first cold water supply scheme was performed based on computational fluid dynamics to obtain the first evaluation parameter; the evaluation parameter is used to characterize the cold water supply effect of the coral farming area to be evaluated after the implementation of the cold water supply scheme; the first cold water supply scheme is one of the multiple schemes in the cold water supply scheme. The random forest model is trained by constructing training samples based on the first cold water supply scheme and the first evaluation parameters to obtain the optimized random forest model.

[0085] Optionally, before employing a multi-objective optimization algorithm to solve for the Pareto optimal solution set as described in the embodiments of this specification, the system includes: The subjective weights of the evaluation parameters corresponding to the cold water supply scheme are determined by the analytic hierarchy process (AHP), and the objective weights of the evaluation parameters corresponding to the cold water supply scheme are determined by the entropy weight method. The subjective weights and objective weights are combined to obtain the comprehensive weights of the evaluation parameters; The evaluation parameters corresponding to each cold water supply scheme are weighted using the comprehensive weight to obtain the comprehensive evaluation score of each cold water supply scheme.

[0086] Optionally, the adjustment of the weights of the time target value and the spatial target value based on the environmental parameters of the coral farming area to be evaluated, as described in the embodiments of this specification, specifically includes: The environmental parameters are input into the environmentally sensitive weight allocation system to obtain the time target weight value and spatial target weight value output by the environmentally sensitive weight allocation system; the environmentally sensitive weight allocation system is used to characterize the mapping relationship between environmental parameters and weight values.

[0087] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0088] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. The various embodiments in this specification are described in a progressive manner; similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments.

[0089] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0090] The above description is merely an embodiment of this specification and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. An engineering evaluation method for an artificial habitat cold water supply scheme, characterized in that, include: Multiple cold water supply schemes for the coral farming area to be evaluated are obtained; each cold water supply scheme includes a set of engineering parameters, which at least include water supply method, water supply flow rate, water inlet height and water supply pipe diameter. All the aforementioned cold water supply schemes are input into a random forest model to obtain the target value of each cold water supply scheme output by the random forest model; the target value includes a time target value and a spatial target value; the target value is used to quantify the cold water supply effect of the coral farming area to be evaluated from both spatial and temporal dimensions. A multi-objective optimization algorithm is used to solve for the Pareto optimal solution set, which is used to characterize multiple candidate cold water supply schemes that weigh spatial objective values ​​against temporal objective values. The weights of the time target value and the spatial target value are adjusted according to the environmental parameters of the coral farming area to be evaluated, and the target cold water supply scheme is determined from multiple candidate cold water supply schemes.

2. The method according to claim 1, characterized in that, Before inputting all the aforementioned cold water supply schemes into the random forest model, the method includes: Numerical simulation of the first cold water supply scheme was performed based on computational fluid dynamics to obtain the first evaluation parameter; the evaluation parameter is used to characterize the cold water supply effect of the coral farming area to be evaluated after the implementation of the cold water supply scheme; the first cold water supply scheme is one of the multiple schemes in the cold water supply scheme. The random forest model is trained by constructing training samples based on the first cold water supply scheme and the first evaluation parameters to obtain the optimized random forest model.

3. The method according to claim 2, characterized in that, The step of training the random forest model by constructing training samples based on the first cold water supply scheme and the first evaluation parameters specifically includes: The first evaluation parameters are weighted and fused to obtain the first spatial target value and the first temporal target value; The random forest model is trained by constructing training samples based on the first engineering parameters, the first spatial target value, and the first temporal target value.

4. The method according to claim 2, characterized in that, Before employing a multi-objective optimization algorithm to solve for the Pareto optimal solution set, the method includes: The subjective weights of the evaluation parameters corresponding to the cold water supply scheme are determined by the analytic hierarchy process (AHP), and the objective weights of the evaluation parameters corresponding to the cold water supply scheme are determined by the entropy weight method. The subjective weights and objective weights are combined to obtain the comprehensive weights of the evaluation parameters; The evaluation parameters corresponding to each cold water supply scheme are weighted using the comprehensive weight to obtain the comprehensive evaluation score of each cold water supply scheme.

5. The method according to claim 1, characterized in that, The step of adjusting the weights of the time target value and the spatial target value based on the environmental parameters of the coral farming area to be evaluated specifically includes: The environmental parameters are input into the environmentally sensitive weight allocation system to obtain the time target weight value and spatial target weight value output by the environmentally sensitive weight allocation system; the environmentally sensitive weight allocation system is used to characterize the mapping relationship between environmental parameters and weight values.

6. The method according to claim 2, characterized in that, The evaluation parameters include abnormal heat accumulation value, colder water volume, effective cold water volume, effective cold water absorbable heat value, effective cold water diffusion distance, and effective cold water duration.

7. An engineering evaluation system for an artificial habitat cold water supply scheme, characterized in that, include: The acquisition module is used to acquire multiple cold water supply schemes for the coral farming area to be evaluated; each cold water supply scheme includes a set of engineering parameters, which at least include water supply method, water supply flow rate, water inlet height and water supply pipe diameter. The first determining module is used to input all the cold water supply schemes into a random forest model to obtain the target value of each cold water supply scheme output by the random forest model; the target value includes a time target value and a spatial target value; the target value is used to quantify the cold water supply effect of the coral farming area to be evaluated from the spatial and temporal dimensions. The solution module is used to solve for the Pareto optimal solution set using a multi-objective optimization algorithm. The Pareto optimal solution set is used to characterize multiple candidate cold water supply schemes that weigh spatial objective values ​​against time objective values. The second determining module is used to adjust the weights of the time target value and the spatial target value according to the environmental parameters of the coral farming area to be evaluated, and to determine the target cold water supply scheme from multiple candidate cold water supply schemes.

8. The system according to claim 7, characterized in that, Before inputting all the aforementioned cold water supply schemes into the random forest model, the system includes: Numerical simulation of the first cold water supply scheme was performed based on computational fluid dynamics to obtain the first evaluation parameter; the evaluation parameter is used to characterize the cold water supply effect of the coral farming area to be evaluated after the implementation of the cold water supply scheme; the first cold water supply scheme is one of the multiple schemes in the cold water supply scheme. The random forest model is trained by constructing training samples based on the first cold water supply scheme and the first evaluation parameters to obtain the optimized random forest model.

9. The system according to claim 8, characterized in that, Before employing a multi-objective optimization algorithm to solve for the Pareto optimal solution set, the system includes: The subjective weights of the evaluation parameters corresponding to the cold water supply scheme are determined by the analytic hierarchy process (AHP), and the objective weights of the evaluation parameters corresponding to the cold water supply scheme are determined by the entropy weight method. The subjective weights and objective weights are combined to obtain the comprehensive weights of the evaluation parameters; The evaluation parameters corresponding to each cold water supply scheme are weighted using the comprehensive weight to obtain the comprehensive evaluation score of each cold water supply scheme.

10. The system according to claim 7, characterized in that, The step of adjusting the weights of the time target value and the spatial target value based on the environmental parameters of the coral farming area to be evaluated specifically includes: The environmental parameters are input into the environmentally sensitive weight allocation system to obtain the time target weight value and spatial target weight value output by the environmentally sensitive weight allocation system; the environmentally sensitive weight allocation system is used to characterize the mapping relationship between environmental parameters and weight values.

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