A marine ecological restoration effect dynamic evaluation system and method based on digital twinning and multi-source perception

Abstract

The application discloses a kind of marine ecological restoration effect dynamic evaluation system and method based on digital twinning and multi-source perception, it is related to marine ecological management field, including: analysis module is used to set marine ecological restoration effect dynamic evaluation area, based on marine ecological restoration project area position, topography, hydrological information setting acquisition module deployment quantity and position;Acquisition module is used to collect marine ecological environment multidimensional parameter and repair engineering key design data;The application is scientifically delimited evaluation area and optimizes data acquisition layout, comprehensively captures multidimensional ecological environment parameters and repair engineering quantitative characteristic parameters under different time and space scales before and after marine ecological protection and restoration, relies on the coupling model of data construction Dynamic mapping regional topography, hydrology power, biological ecology (distribution, density, biodiversity etc.) change and the correlation of repair behavior, accurately simulate the interactive evolution process of ecology and repair engineering, realize the quantitative evaluation of ecological structure integrity and function recovery rate.

Description

Technical Field

[0001] This invention relates to the field of marine ecological management technology, specifically to a dynamic evaluation system and method for the effectiveness of marine ecological restoration based on digital twins and multi-source sensing. Background Technology

[0002] Marine ecological restoration focuses on habitat restoration and biodiversity recovery. It employs a combination of biological and engineering methods, such as artificial reef construction, mangrove / salt marsh planting, and stock enhancement, along with pollution interception and purification technologies, to improve the quality of the marine environment and help damaged ecosystems gradually restore their self-circulation capabilities.

[0003] The invention patent application with application number 202011237465.0 discloses a method for evaluating and diagnosing the quality of marine ecological environment. This application aims to solve the problem that "current discussions and research on the quality of marine ecological environment are mostly limited to qualitative discussions, quantitative research is somewhat lacking, and quantitative research on the diagnosis of the state of marine ecological environment quality is even more lacking".

[0004] However, current marine ecological restoration assessments still suffer from problems such as incomplete data acquisition, static and outdated assessment results, and difficulty in tracking changes in the restoration process in real time.

[0005] To this end, we propose a dynamic evaluation system and method for the effectiveness of marine ecological restoration based on digital twins and multi-source sensing. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a dynamic evaluation system and method for marine ecological restoration based on digital twin and multi-source sensing, which can effectively solve the problems of the existing technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions; This invention discloses a dynamic evaluation system for marine ecological restoration effects based on digital twins and multi-source sensing, comprising: The system comprises the following modules: Analysis Module: Defines the dynamic evaluation area for marine ecological restoration effects, and determines the number and location of data collection modules based on the location of the marine ecological restoration project area, topography, and hydrological information; Data Collection Module: Collects multi-dimensional parameters of the marine ecological environment and quantitative characteristic parameters of the restoration project; Construction Module: Constructs a digital twin model coupling marine ecology and restoration projects based on the collected data, mapping the dynamic correlation between species distribution, habitat morphology, and restoration behavior; Evaluation Module: Monitors twin model updates and quantitatively evaluates the degree of ecological structure and functional recovery based on the updated information; Early Warning Module: Receives the evaluation results from the evaluation module, determines whether the marine ecological restoration effect has deteriorated, and issues an early warning if it has; Feedback Module: Receives the results from the early warning module, simultaneously generates a marine ecological restoration effect report when the early warning module issues an early warning, and feeds it back to the preset receiving end; when the early warning module does not issue an early warning, it periodically feeds back the marine ecological restoration effect report to the preset receiving end according to a preset cycle. The marine ecological restoration effect report includes data on the operating results of each module of the system; The analysis module interacts with the acquisition module via a wireless network. The acquisition module interacts with the construction module and the evaluation module via a wireless network. The evaluation module interacts with the feedback module and the early warning module via a wireless network.

[0008] Furthermore, when determining the number and location of the acquisition modules, the analysis module manually divides the evaluation area into K contiguous and non-overlapping sub-evaluation areas. The number of acquisition modules deployed in each sub-area is calculated based on the deployment density of the sub-areas, and then summed to obtain the total number of acquisition modules deployed. ; In the formula: This represents the total number of data acquisition modules deployed. The total amount for the sub-evaluation area; The deployment density of the data acquisition modules in the k-th sub-evaluation region is expressed in units per square kilometer. Let k be the area of ​​the k-th sub-evaluation region; It is a rounding function; To deploy the density baseline coefficient; Let be the spatial variation coefficient of the ecological parameters of the k-th sub-evaluation region; The weight of the impact of the repair project on the k-th sub-evaluation region; The hydrodynamic exchange intensity of the k-th sub-evaluation region; The shape coefficient of the k-th sub-evaluation region; This is the ecological sensitivity correction coefficient for the k-th sub-evaluation region; Once the deployment density of the acquisition modules in each area is determined, it is rounded up and evenly distributed across the area.

[0009] Furthermore, the multi-dimensional parameters of the marine ecological environment collected by the acquisition module include core water quality parameters, biological community parameters, and bottom sediment environment parameters; The core water quality parameters include dissolved oxygen content, pH level, nitrogen and phosphorus nutrient concentration, and persistent organic pollutant concentration. The biological community parameters include the abundance of phytoplankton and zooplankton, the biomass of benthic organisms, and the population density and age structure of key indicator species of the ecosystem. Substrate environmental parameters include substrate particle size distribution, organic matter content, and accumulated heavy metal content; The quantitative characteristic parameters of the restoration project include the total area of ​​the restoration zone, the scale of restoration work in each zone, the duration of the restoration project, the density of the restoration spatial layout, the range of impact of restoration disturbances, and the temporal progress rate.

[0010] Furthermore, the digital twin model constructed by the building module is used to dynamically simulate the interactive evolution process between the marine ecological environment state and the effects of restoration engineering. The digital twin model performs twin mapping through real-time updates of multi-dimensional state vectors. ; In the formula: The ecological region-regional engineering coupled state vector of the digital twin model at time t in region t; This is the model update time step; The state self-evolution matrix; This refers to the total number of categories of ecological and environmental parameters and the total number of categories of restoration engineering parameters. Let be the dynamic coupling factor between the i-th type of ecological parameter and the j-th restoration project parameter at time t; Let be the standardized monitoring value of the i-th type of ecological parameter at time t; Let be the standardized value of the j-th engineering quantification parameter at time t; Let be the coupling vector between the i-th type of ecological parameter and the j-th restoration project parameter; Let be the model error correction vector at time t; This is the error correction weight matrix.

[0011] Furthermore, the dynamic coupling factor between the i-th type of ecological parameter and the j-th restoration project parameter at time t. The calculation formula is: ; In the formula: These are the coupling enhancement coefficient and coupling damping coefficient; Let be the association weight between the i-th type of ecological parameter and the j-th restoration project parameter. Similarly; This is the time decay coefficient; This refers to the initial moment when the repair project was launched. Let be the standardized monitoring value of the k-th type of ecological parameter at time t; The standardized operating value of the l-th repair project parameter at time t; This is the cumulative effect coefficient.

[0012] Furthermore, during the operation of the evaluation module, the quantitative evaluation of the degree of ecological structure restoration follows the following: ; In the formula: Let be the ecological structure integrity index at time t; These are the weighting coefficients for species abundance, community diversity, and habitat complexity. The number of biological species actually monitored in the region at time t is used to evaluate the number of biological species. This serves as a reference number of biological species in the ecologically healthy state of the region. This is a correction term for the time trend of species abundance; The biodiversity index of the biological community actually monitored at time t; It serves as a reference index for the biodiversity of the region under its ecologically healthy state. This is a correction term for the time trend of community diversity. The habitat structure complexity index is the actual monitored value at time t. This serves as a reference index for the complexity of habitat structure under the ecological health status of the region. This is a time trend correction term for habitat complexity.

[0013] Furthermore, during the operation phase of the evaluation module, when quantifying the degree of ecological function recovery, the recovery rate is used to characterize the degree of ecological function recovery, and it follows the principle of: ; In the formula; Let be the ecological function recovery rate at time t; The weight coefficient for the s-th type of core ecological function; Let t be the actual quantitative value of the s-th type of core ecological function at time t; is the stability correction coefficient for the s-th type of core ecological function; This serves as a reference quantitative value for the s-th type of core ecological function under ecologically healthy conditions. s represents the ecological function dimension identifier, with 1 corresponding to the material cycle function, 2 to the energy flow function, 3 to the environmental purification function, and 4 to the biological support function.

[0014] Furthermore, the early warning module determines whether the marine ecological restoration effect has deteriorated, and quantifies the comprehensive evaluation indicators. ; In the formula: , These are the weighting coefficients for the ecological structure integrity index and the ecological function recovery rate. Let be the ecological structure integrity index at time t; Let be the ecological function recovery rate at time t; when The model shows a continuous downward trend within a preset number of consecutive update cycles, and the magnitude of a single decrease exceeds a preset range, or When the value falls below the preset warning threshold, the warning module determines that the repair effect has deteriorated and generates warning information, which includes the decline rate and current value of each indicator.

[0015] On the other hand, a dynamic evaluation method for the effectiveness of marine ecological restoration based on digital twins and multi-source sensing includes: A dynamic evaluation area for marine ecological restoration effects is defined and divided into K contiguous and non-overlapping sub-regions. Based on geographical and ocean current information and deployment density calculation formulas, the total number of data collection nodes and their uniform deployment locations in each sub-region are determined. The system collects quantitative characteristic parameters of restoration projects, including water quality, biological communities, and seabed environmental parameters, such as the total area of ​​the restoration zone, the scale of restoration operations in each zone, the duration of restoration projects, the spatial layout density of restoration, the range of impact from restoration disturbances, and the temporal progression rate. Based on the collected data, a digital twin model coupling marine ecology and restoration projects is constructed. Through real-time updates of state vectors and quantification of coupling factors, the system dynamically maps the relationship between the two in their interactive evolution. Based on real-time updates from a digital twin model, and combined with the formulas for the ecological structure integrity index and the ecological function recovery rate, the integrity of the ecological structure and the degree of ecological function recovery are quantitatively evaluated respectively. The evaluation results of ecological structure and function are integrated through a comprehensive evaluation index formula. If the index continuously declines beyond a preset range or falls below the warning threshold for a preset period, the restoration effect is judged to have deteriorated and a warning message containing index data is generated. When the warning is triggered, a message on the marine ecological restoration effect is simultaneously fed back to the preset receiving end. If the warning is not triggered, a message containing system operation result data is periodically fed back according to the preset period.

[0016] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: This invention provides a dynamic evaluation system and method for marine ecological restoration based on digital twins and multi-source sensing. During execution, the system and method comprehensively capture multi-dimensional ecological environment parameters and quantitative characteristic parameters of restoration projects before and after marine ecological protection and restoration by delineating the evaluation area and optimizing the data collection layout. A coupled model constructed from the data dynamically maps the correlation between biological distribution, habitat morphology, and restoration behavior, accurately simulating the interactive evolution process between ecology and engineering. It also quantitatively assesses the integrity of ecological structure and the rate of functional recovery, promptly identifies trends of deteriorating restoration effects and issues early warnings, and simultaneously feeds back evaluation messages containing system operation results to a preset receiving end. This ensures the real-time nature and accuracy of the evaluation, provides a scientific basis for adjusting restoration plans, and effectively improves the targeting, efficiency, and effectiveness of marine ecological restoration. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a schematic diagram of the structure of a dynamic evaluation system for marine ecological restoration based on digital twins and multi-source sensing. Figure 2 This is a flowchart illustrating a dynamic evaluation method for the effectiveness of marine ecological restoration based on digital twins and multi-source sensing. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] The present invention will be further described below with reference to embodiments.

[0021] Example 1: This embodiment presents a dynamic evaluation system for marine ecological restoration effects based on digital twins and multi-source sensing, such as... Figure 1 As shown, it includes: The analysis module is used to set the dynamic evaluation area for the marine ecological restoration effect. Based on the location of the marine ecological restoration project area, topography, and hydrological information, the number and location of the data collection modules are set. When determining the number and location of the data acquisition modules, the analysis module manually divides the evaluation area into K contiguous and non-overlapping sub-evaluation areas. The number of data acquisition modules deployed in each sub-area is calculated based on the deployment density of the sub-areas, and then summed to obtain the total number of data acquisition modules deployed. ; In the formula: This represents the total number of data acquisition modules deployed. The total amount for the sub-evaluation area; The deployment density of the data acquisition modules in the k-th sub-evaluation region is expressed in units per square kilometer. Let k be the area of ​​the k-th sub-evaluation region; It is a rounding function; To deploy the density baseline coefficient; Let be the spatial variation coefficient of the ecological parameters of the k-th sub-evaluation region; The weight of the impact of the repair project on the k-th sub-evaluation region; The hydrodynamic exchange intensity of the k-th sub-evaluation region; The shape coefficient of the k-th sub-evaluation region; This is the ecological sensitivity correction coefficient for the k-th sub-evaluation region; The above formula combines the results of the evaluation area division, integrates key influencing factors such as spatial variation of ecological parameters, impact of restoration projects, and intensity of hydrodynamic exchange, introduces a baseline coefficient and an ecological sensitivity correction coefficient to reflect the differentiated characteristics of different sub-regions, and achieves accurate calculation of the number of data acquisition modules deployed by summing the product of area and density and rounding, making the deployment scheme more in line with the actual monitoring scenario. Once the deployment density of the data acquisition modules in each area is determined, it is rounded up and evenly distributed across the area. in, ∈ (0.5, 2.0), the higher the required evaluation accuracy of the evaluation area, the larger the value, and vice versa; ∈ (0, 1), characterized by the ratio of the standard deviation to the mean of the ecological parameters pre-collected in this sub-region; ∈ (0, 1), the impact weight of the restoration project in the k-th sub-evaluation region The value of is positively correlated with the work intensity and the depth of the repair work within the sub-region; the higher the work intensity or the greater the depth of the repair work, the higher the value of . The larger the value, the lower the workload or the shallower the depth of operation. The smaller the value; ∈ (0, 1), the faster the ocean current velocity, the shallower the water depth, and the shorter the water exchange period, the larger the value; the slower the ocean current velocity, the deeper the water depth, and the longer the water exchange period, the smaller the value. ∈ (0.1, 1), characterized by the ratio of the actual area of ​​the subregion to the area of ​​the circumscribed rectangle; ∈ (1.0, 3.0), the higher the ecological sensitivity level of the k-th sub-evaluation region, the better. The larger the value, the lower the value. The smaller the value; The data acquisition module is used to collect multi-dimensional parameters of the marine ecological environment and quantitative characteristic parameters of restoration projects; The data acquisition module collects multi-dimensional parameters of the marine ecological environment, including core water quality parameters, biological community parameters, and bottom sediment environment parameters. Core water quality parameters include dissolved oxygen content, pH level, nitrogen and phosphorus nutrient concentration, and persistent organic pollutant concentration. Biological community parameters include the abundance of phytoplankton and zooplankton, the biomass of benthic organisms, and the population density and age structure of key indicator species of the ecosystem. Substrate environmental parameters include substrate particle size distribution, organic matter content, and accumulated heavy metal content; The quantitative characteristic parameters of the restoration project include the total area of ​​the restoration zone, the scale of restoration work in each zone, the duration of the restoration project, the density of the restoration spatial layout, the range of impact of restoration disturbances, and the temporal progress rate. The sampling module's layout is matched with three major constraints: the scale of the sub-regional restoration space, the density of the restoration project's planar layout, and the regional ecological sensitivity level. The area of ​​the sub-evaluation region represents the scale of the restoration space, the regional shape coefficient represents the density of the restoration project's planar layout, and the ecological sensitivity correction coefficient represents the regional ecological sensitivity level. At the same time, the spatial variation coefficient of ecological parameters, the impact weight of restoration projects, and the intensity of hydrodynamic exchange are combined to collaboratively calculate the sampling deployment density of each sub-region, so as to achieve differentiated and precise deployment of sampling points according to the restoration space range, layout form, and ecological sensitivity differences. The module is used to build a digital twin model that couples marine ecology and restoration engineering based on the collected data, and to map the dynamic relationship between species distribution, habitat morphology and restoration behavior based on the built model; The digital twin model built by the module is used to dynamically simulate the interactive evolution process between the marine ecological environment state and the effects of restoration projects. The digital twin model performs twin mapping through real-time updates of multi-dimensional state vectors. ; The above formula is based on the time step and preserves the natural continuity of ecological and engineering states through the state self-evolution matrix. At the same time, it incorporates the standardized values ​​of multiple ecological parameters and restoration engineering parameters, dynamic coupling factors and coupling vectors, and combines error correction vectors and weight matrices to enable real-time mapping of the interactive evolution process of the two. In the formula: Let be the ecological region-regional engineering coupled state vector of the digital twin model at time t in region t, with dimension . It includes m ecological environment state components and n restoration engineering state components, each component corresponding to the real-time simulated value of ecological parameters and the dynamic response value of restoration engineering parameters. The model update time step is determined by the data sampling frequency of the acquisition module; The state self-evolution matrix has dimension 1. The diagonal elements are the self-preservation coefficients of each state component, ranging from 0.95 to 0.99, representing the natural continuity of the state over time. The off-diagonal elements are the intrinsic correlation coefficients between state components, ranging from -0.1 to 0.1, representing the degree of direct influence of the p-th state component on the q-th state component. p and q are matrix coordinates. Positive values ​​represent positive promoting effects, negative values ​​represent negative inhibiting effects, and the larger the absolute value, the higher the intensity of the cross-influence. This covers the interaction between different parameters within the ecological environment, the linkage effect between different parameters within the restoration project, and the indirect correlation between ecological environment parameters and restoration project parameters. This refers to the total number of categories of ecological and environmental parameters and the total number of categories of restoration engineering parameters. Let be the dynamic coupling factor between the i-th type of ecological parameter and the j-th restoration project parameter at time t; Let be the standardized monitoring value of the i-th type of ecological parameter at time t; Let j be the standardized value of the engineering quantitative parameter at time t, including the area of ​​the repair zone, the scale of the operation, the repair cycle, and the layout density. and The values ​​range from 0 to 1. Determined by the ratio of actual monitored values ​​to regional ecological health benchmark values. Determined by the ratio of actual operating values ​​to the optimal values ​​in the engineering design; Let be the coupling vector between the i-th type of ecological parameter and the j-th restoration project parameter; Let be the model error correction vector at time t, with dimension . The value is determined by an adaptive filtering algorithm based on the residual sequence between the model prediction and the actual monitoring value. The error correction weight matrix has dimensions of . The diagonal elements are the error correction weights for each state component, with values ​​ranging from 0.01 to 0.05, while the off-diagonal elements are 0, used to adjust the strength of the error correction. Among them, digital twin models are achieved through The formula achieves each The state update at the time step maps the dynamic coupling state of the marine ecological environment and restoration projects in real time, and through the coupling factor Quantify the interaction strength between the two through the state self-evolution matrix. Off-diagonal elements characterize the cross-effects of different state components; The dynamic coupling factor between the i-th type of ecological parameter and the j-th restoration project parameter at time t The calculation formula is: ; In the formula: These are the coupling enhancement coefficient and coupling damping coefficient; Let be the association weight between the i-th type of ecological parameter and the j-th restoration project parameter. Similarly; This is the time decay coefficient; This refers to the initial moment when the repair project was launched. Let be the standardized monitoring value of the k-th type of ecological parameter at time t; The standardized operating value of the l-th repair project parameter at time t; This is the cumulative effect coefficient; The above formula reflects the time dynamic characteristics of the coupling effect through the time decay coefficient and the cumulative effect coefficient. It combines the coupling enhancement coefficient and the damping coefficient to balance the interaction strength and background complexity, so as to achieve accurate quantification of the coupling strength under different time and different parameter combinations. in, For each ecological parameter of type i in (0,1), the more directly the i-th ecological parameter is affected by the j-th restoration project parameter, the more significant the mechanism of action, and the higher the regulation efficiency of the restoration project parameter on the ecological parameter, the larger the correlation weight value; conversely, the smaller the value. ∈ (1,2), its value increases with the significance of the interaction between ecological and engineering parameters, and its value decreases with the weaker the interaction. ∈ (0.1, 0.5), its value increases with the complexity of all eco-engineering parameters in relation to the overall interaction context, and decreases with the simplicity of the context; ∈ (0.01, 0.1), its value increases as the coupling effect decays over time, and decreases as the decay rate increases; ∈ (0.005, 0.05), its value is larger as the cumulative effect of the interaction of the repair engineering parameters is more significant, and smaller as the cumulative effect is weaker; The evaluation module is used to monitor twin model updates and quantitatively evaluate the degree of ecological structure and functional recovery based on twin model update information. During the evaluation module's operation phase, the quantitative evaluation of the degree of ecological structure restoration follows the following rules: ; In the formula: Let be the ecological structure integrity index at time t; These are the weighting coefficients for species abundance, community diversity, and habitat complexity. The number of biological species actually monitored in the region at time t is used to evaluate the number of biological species. This serves as a reference number of biological species in the ecologically healthy state of the region. This is a correction term for the time trend of species abundance; The biodiversity index of the biological community actually monitored at time t; It serves as a reference index for the biodiversity of the region under its ecologically healthy state. This is a correction term for the time trend of community diversity. The habitat structure complexity index is the actual monitored value at time t. This serves as a reference index for the complexity of habitat structure under the ecological health status of the region. This is a time trend correction term for habitat complexity; The above formula selects three core dimensions: species quantity, community diversity, and habitat complexity. It reflects the restoration basis by the ratio of actual monitoring values ​​to health status reference values, introduces a time trend correction term to capture the dynamic change trend of indicators, and sets customizable weight coefficients. It not only comprehensively covers the key evaluation elements of ecological structure, but also allows the importance of each dimension to be adjusted according to restoration goals and regional characteristics, providing personalized and multi-dimensional quantitative assessment conditions for the degree of ecological structure restoration. in, , , The values ​​range from 0.8 to 1.2, and are determined by the slope of the change in the number of species within a preset time period before and after time t. The slope of the community diversity index change within a preset time period before and after time t is determined. The slope of the habitat complexity index change within a preset time period before and after time t is determined. ; , , All parameters are preset. The sum is 1, and all values ​​are positive, with each value defined by the user on the system side. The initial value is set to 1 / 3; , , All data are collected by the acquisition module, either directly or indirectly. During the evaluation module's operational phase, when quantifying the degree of ecological function recovery, the recovery rate is used to characterize the degree of ecological function recovery, and it follows the principle of: ; In the formula; Let be the ecological function recovery rate at time t; The weight coefficient for the s-th type of core ecological function; Let t be the actual quantitative value of the s-th type of core ecological function at time t; is the stability correction coefficient for the s-th type of core ecological function; This serves as a reference quantitative value for the s-th type of core ecological function under ecologically healthy conditions. The above formula compares the actual quantitative value with the health status reference value, and combines the stability correction term to reflect the dynamic stability of the function. This ensures the comprehensive integration of different ecological function recovery statuses and adapts to the differentiated priority requirements of different restoration projects for functional recovery, making the ecological function recovery evaluation more targeted and comprehensive. s represents the ecological function dimension identifier, with 1 corresponding to the material cycle function, 2 to the energy flow function, 3 to the environmental purification function, and 4 to the biological support function. in, Its value is defined by the system user and follows the condition that all weight coefficients are positive and are initially set to 0.25; The following results were obtained through comprehensive calculation of key ecological parameters in the corresponding dimensions: the material cycling function corresponds to the nutrient conversion efficiency, the energy flow function corresponds to the biomass transfer efficiency, the environmental purification function corresponds to the pollutant degradation rate, and the biological support function corresponds to the species reproduction success rate. ∈ (0.9, 1.1), characterized by the ratio of the quantized value of the s-th type of function at time t to the quantized value of the previous monitoring cycle; This is the default value; The early warning module is used to receive the evaluation results from the evaluation module, determine whether the marine ecological restoration effect has deteriorated based on the evaluation results, and issue an early warning if it has deteriorated. The early warning module determines whether the marine ecological restoration effect has deteriorated, and quantifies the comprehensive evaluation indicators. ; In the formula: , These are the weighting coefficients for the ecological structure integrity index and the ecological function recovery rate. Let be the ecological structure integrity index at time t; Let be the ecological function recovery rate at time t; when The model shows a continuous downward trend within a preset number of consecutive update cycles, and the magnitude of a single decrease exceeds a preset range, or When the value falls below the preset warning threshold, the warning module determines that the repair effect has deteriorated and generates warning information, which includes the decline rate and current value of each indicator. in, , All are positive numbers, and their sum is 1. They also follow the condition that: when the restoration project's core objective is to restore ecological structural attributes such as biological community structure and habitat integrity, or when the degree of damage to the regional ecological structure is significant... The larger the value, the more likely the restoration effort is to address issues other than ecological structural damage, or when the regional ecological structure itself has been relatively minor. The smaller the value, the better; when the core objective of the restoration project is to restore the ecosystem service functions of the ocean, such as supply, regulation, and support, or when the degradation of regional ecological functions has a significant impact on the surrounding environment and human activities. The larger the value, the less severe the impact of ecological function degradation, or the less the restoration priority is on functional recovery. The smaller the value; The feedback module is used to receive the operation results of the early warning module. When the early warning module issues an early warning, it synchronously generates a marine ecological restoration effect report and feeds it back to the preset receiving end. When the early warning module does not issue an early warning, it periodically feeds back the marine ecological restoration effect report to the preset receiving end based on a preset cycle. The marine ecological restoration effect report includes data on the operating results of each module of the system; The analysis module interacts with the acquisition module via a wireless network. The acquisition module interacts with the construction module and the evaluation module via a wireless network. The evaluation module interacts with the feedback module and the early warning module via a wireless network.

[0022] In this embodiment, the analysis module sets up a dynamic evaluation area for marine ecological restoration effects. Based on the location of the marine ecological restoration project area, topography, and hydrological information, it sets the number and location of the data acquisition modules. The acquisition modules then collect multi-dimensional parameters of the marine ecological environment and quantitative characteristic parameters of the restoration project. The construction module further constructs a digital twin model coupling the marine ecology and the restoration project based on the collected data. Based on the constructed model, it maps the dynamic correlation between the distribution of biological species, habitat morphology, and restoration behavior. The evaluation module then monitors the updates of the twin model and quantitatively evaluates the degree of ecological structure and functional recovery based on the updated information. The evaluation results are received by the early warning module, which determines whether the marine ecological restoration effect has deteriorated. If it has deteriorated, an early warning is issued. Finally, the feedback module receives the results of the early warning module. When the early warning module issues an early warning, it simultaneously generates a marine ecological restoration effect report and sends it back to the preset receiving end. When the early warning module does not issue an early warning, it periodically sends the marine ecological restoration effect report back to the preset receiving end based on a preset cycle.

[0023] In the above embodiments, when the system is applied to the dynamic evaluation of marine ecological restoration effects, it can capture dynamic data related to marine ecology and restoration projects in real time, accurately depict the interactive evolution process between the two, scientifically quantify the restoration status of ecological structure and function, promptly detect signs of deterioration in restoration effects and provide feedback on key information, effectively improving the pertinence, efficiency and stability of marine ecological restoration.

[0024] Example 2: A dynamic evaluation method for the effectiveness of marine ecological restoration based on digital twins and multi-source sensing includes: A dynamic evaluation area for marine ecological restoration effects was defined and divided into K contiguous and non-overlapping sub-regions. Based on geographical and ocean current information and deployment density calculation formulas, the total number of data collection nodes and the uniform deployment location of each sub-region were determined. The quantitative characteristic parameters of the restoration project, including water quality, biological community, and bottom sediment environmental parameters in the marine ecological environment, are collected. These parameters include the total area of ​​the restoration zone, the scale of the restoration operation in each zone, the duration of the restoration project, the density of the spatial layout of the restoration, the range of the impact of the restoration disturbance, and the temporal progress rate. Based on the collected data, a digital twin model coupling marine ecology and restoration engineering is constructed. Through real-time updates of state vectors and quantification of coupling factors, the relationship between the two interactive evolution is dynamically mapped. Based on real-time updated information from the digital twin model, and combined with the formulas for the ecological structure integrity index and the ecological function recovery rate, the integrity of the ecological structure and the degree of ecological function recovery are quantitatively evaluated respectively. By integrating the evaluation results of ecological structure and function through a comprehensive evaluation index formula, if the index continues to decline beyond the preset range or falls below the warning threshold for a preset period, the restoration effect is judged to be deteriorating and a warning information containing index data is generated. When an early warning is triggered, a message about the marine ecological restoration effect is simultaneously sent to a preset receiver. If no early warning is triggered, a message containing system operation results data is sent periodically according to a preset cycle.

[0025] In summary, the systems and methods described above, by scientifically delineating evaluation areas and optimizing data collection layout, comprehensively capture multi-dimensional ecological and environmental parameters and quantitative characteristic parameters of restoration projects before and after marine ecological protection and restoration. Based on the coupled model constructed from the data, they dynamically map the correlation between changes in regional topography, hydrodynamics, and biological ecology (distribution, density, biodiversity, etc.) and restoration behavior. This accurately simulates the interactive evolution process between ecology and engineering, enabling quantitative assessment of ecological structural integrity and functional recovery rate. It can promptly determine the deterioration trend of restoration effects and issue early warnings, simultaneously feeding back evaluation messages containing system operation results to a preset receiving end. This ensures both the real-time nature and accuracy of the evaluation, provides a scientific basis for adjusting restoration plans, effectively improves the targeting and efficiency of marine ecological restoration, and facilitates the rapid and stable recovery of ecosystems. It balances evaluation accuracy and practicality, providing reliable technical support for the management and control of marine ecological restoration effects.

[0026] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic evaluation system for marine ecological restoration effects based on digital twins and multi-source sensing, characterized in that, include: The analysis module is used to set the dynamic evaluation area for the marine ecological restoration effect. Based on the location of the marine ecological restoration project area, topography, and hydrological information, the number and location of the data collection modules are set. The data acquisition module is used to collect multi-dimensional parameters of the marine ecological environment and quantitative characteristic parameters of restoration projects; The module is used to build a digital twin model that couples marine ecology and restoration engineering based on the collected data, and to map the dynamic relationship between species distribution, habitat morphology and restoration behavior based on the built model; The evaluation module is used to monitor twin model updates and quantitatively evaluate the degree of ecological structure and functional recovery based on twin model update information. The early warning module is used to receive the evaluation results from the evaluation module, determine whether the marine ecological restoration effect has deteriorated based on the evaluation results, and issue an early warning if it has deteriorated. The feedback module is used to receive the operation results of the early warning module. When the early warning module issues an early warning, it synchronously generates a marine ecological restoration effect report and feeds it back to the preset receiving end. When the early warning module does not issue an early warning, it periodically feeds back the marine ecological restoration effect report to the preset receiving end based on a preset cycle. The marine ecological restoration effect report includes data on the operating results of each module of the system.

2. The dynamic evaluation system for marine ecological restoration based on digital twin and multi-source sensing according to claim 1, characterized in that, When determining the number and location of the acquisition modules, the analysis module manually divides the evaluation area into K contiguous and non-overlapping sub-evaluation areas. The number of acquisition modules deployed in each sub-area is calculated based on the deployment density of the sub-areas, and then summed to obtain the total number of acquisition modules deployed. ； In the formula: This represents the total number of data acquisition modules deployed. The total amount for the sub-evaluation area; The deployment density of the data acquisition modules in the k-th sub-evaluation region is expressed in units per square kilometer. Let k be the area of ​​the k-th sub-evaluation region; It is a rounding function; To deploy the density baseline coefficient; Let be the spatial variation coefficient of the ecological parameters of the k-th sub-evaluation region; The weight of the impact of the repair project on the k-th sub-evaluation region; The hydrodynamic exchange intensity of the k-th sub-evaluation region; The shape coefficient of the k-th sub-evaluation region; This is the ecological sensitivity correction coefficient for the k-th sub-evaluation region; Once the deployment density of the acquisition modules in each area is determined, it is rounded up and evenly distributed across the area.

3. The dynamic evaluation system for marine ecological restoration based on digital twins and multi-source sensing as described in claim 1, characterized in that, The data acquisition module collects multi-dimensional parameters of the marine ecological environment, including core water quality parameters, biological community parameters, and bottom sediment environment parameters. The core water quality parameters include dissolved oxygen content, pH level, nitrogen and phosphorus nutrient concentration, and persistent organic pollutant concentration. The biological community parameters include the abundance of phytoplankton and zooplankton, the biomass of benthic organisms, and the population density and age structure of key indicator species of the ecosystem. Substrate environmental parameters include substrate particle size distribution, organic matter content, and accumulated heavy metal content; The quantitative characteristic parameters of the restoration project include the total area of ​​the restoration zone, the scale of restoration work in each zone, the duration of the restoration project, the density of the restoration spatial layout, the range of impact of restoration disturbances, and the temporal progress rate.

4. The dynamic evaluation system for marine ecological restoration based on digital twin and multi-source sensing as described in claim 1, characterized in that, The digital twin model constructed by the building module is used to dynamically simulate the interactive evolution process between the marine ecological environment state and the effects of restoration projects. The digital twin model performs twin mapping through real-time updates of multi-dimensional state vectors. ； In the formula: The ecological region-regional engineering coupled state vector of the digital twin model at time t in region t; This is the model update time step; The state self-evolution matrix; This refers to the total number of categories of ecological and environmental parameters and the total number of categories of restoration engineering parameters. Let be the dynamic coupling factor between the i-th type of ecological parameter and the j-th restoration project parameter at time t; Let be the standardized monitoring value of the i-th type of ecological parameter at time t; Let be the standardized value of the j-th engineering quantification parameter at time t; Let be the coupling vector between the i-th type of ecological parameter and the j-th restoration project parameter; Let be the model error correction vector at time t; This is the error correction weight matrix.

5. The dynamic evaluation system for marine ecological restoration based on digital twin and multi-source sensing according to claim 4, characterized in that, The dynamic coupling factor between the i-th type of ecological parameter and the j-th restoration project parameter at time t. The calculation formula is: ； In the formula: These are the coupling enhancement coefficient and coupling damping coefficient; Let be the association weight between the i-th type of ecological parameter and the j-th restoration project parameter. Similarly; This is the time decay coefficient; This refers to the initial moment when the repair project was launched. Let be the standardized monitoring value of the k-th type of ecological parameter at time t; The standardized operating value of the l-th repair project parameter at time t; This is the cumulative effect coefficient.

6. The dynamic evaluation system for marine ecological restoration based on digital twin and multi-source sensing according to claim 1, characterized in that, During the operation of the evaluation module, the quantitative evaluation of the degree of ecological structure restoration follows the following: ； In the formula: Let be the ecological structure integrity index at time t; These are the weighting coefficients for species abundance, community diversity, and habitat complexity. The number of biological species actually monitored in the region at time t is used to evaluate the number of biological species. This serves as a reference number of biological species in the ecologically healthy state of the region. This is a correction term for the time trend of species abundance; The biodiversity index of the biological community actually monitored at time t; It serves as a reference index for the biodiversity of the region under its ecologically healthy state. This is a correction term for the time trend of community diversity. The habitat structure complexity index is the actual monitored value at time t. This serves as a reference index for the complexity of habitat structure under the ecological health status of the region. This is a time trend correction term for habitat complexity.

7. The dynamic evaluation system for marine ecological restoration based on digital twin and multi-source sensing according to claim 6, characterized in that, During the operation of the evaluation module, when quantifying the degree of ecological function recovery, the recovery rate is used to characterize the degree of ecological function recovery, and it follows the following: ； In the formula; Let be the ecological function recovery rate at time t; The weight coefficient for the s-th type of core ecological function; Let t be the actual quantitative value of the s-th type of core ecological function at time t; is the stability correction coefficient for the s-th type of core ecological function; This serves as a reference quantitative value for the s-th type of core ecological function under ecologically healthy conditions. s represents the ecological function dimension identifier, with 1 corresponding to the material cycle function, 2 to the energy flow function, 3 to the environmental purification function, and 4 to the biological support function.

8. The dynamic evaluation system for marine ecological restoration based on digital twin and multi-source sensing according to claim 1, characterized in that, The early warning module determines whether the marine ecological restoration effect has deteriorated, and quantifies comprehensive evaluation indicators. ; In the formula: , These are the weighting coefficients for the ecological structure integrity index and the ecological function recovery rate. Let be the ecological structure integrity index at time t; Let be the ecological function recovery rate at time t; when The model shows a continuous downward trend within a preset number of consecutive update cycles, and the magnitude of a single decrease exceeds a preset range, or When the value falls below the preset warning threshold, the warning module determines that the repair effect has deteriorated and generates warning information, which includes the decline rate and current value of each indicator.

9. A dynamic evaluation system for marine ecological restoration based on digital twins and multi-source sensing as described in claim 1, characterized in that, The analysis module interacts with the acquisition module via a wireless network. The acquisition module interacts with the construction module and the evaluation module via a wireless network. The evaluation module interacts with the feedback module and the early warning module via a wireless network.

10. A method for dynamic evaluation of marine ecological restoration effects based on digital twins and multi-source sensing, wherein the method is an implementation method of the dynamic evaluation system for marine ecological restoration effects based on digital twins and multi-source sensing as described in any one of claims 1-9, characterized in that, include: A dynamic evaluation area for marine ecological restoration effects was defined and divided into K contiguous and non-overlapping sub-regions. Based on geographical and ocean current information and deployment density calculation formulas, the total number of data collection nodes and the uniform deployment location of each sub-region were determined. The quantitative characteristic parameters of the restoration project, including water quality, biological community, and bottom sediment environmental parameters in the marine ecological environment, are collected. These parameters include the total area of ​​the restoration zone, the scale of the restoration operation in each zone, the duration of the restoration project, the density of the spatial layout of the restoration, the range of the impact of the restoration disturbance, and the temporal progress rate. Based on the collected data, a digital twin model coupling marine ecology and restoration engineering is constructed. Through real-time updates of state vectors and quantification of coupling factors, the relationship between the two interactive evolution is dynamically mapped. Based on real-time updated information from the digital twin model, and combined with the formulas for the ecological structure integrity index and the ecological function recovery rate, the integrity of the ecological structure and the degree of ecological function recovery are quantitatively evaluated respectively. By integrating the evaluation results of ecological structure and function through a comprehensive evaluation index formula, if the index continues to decline beyond the preset range or falls below the warning threshold for a preset period, the restoration effect is judged to be deteriorating and a warning information containing index data is generated. When an early warning is triggered, a message about the marine ecological restoration effect is simultaneously sent to a preset receiver. If no early warning is triggered, a message containing system operation results data is sent periodically according to a preset cycle.

Images