Marine ecological civilized development analysis method and system

Abstract

The invention relates to the technical field of marine data processing, in particular to a marine ecology civilization development analysis method which comprises the following steps: step 1, defining an analysis target area, collecting and analyzing multi-source data influencing marine ecology civilization development in the target area, and processing the multi-source data based on a space-time cooperative calculation model to obtain a spatial-temporal cooperative calculation model; obtaining a multi-source fusion data set; 2, performing standardization processing on the multi-source fusion data set, and constructing a PSR framework model based on the processed multi-source fusion data set to obtain a state index score SIS, a pressure index score PIS and a response index score RIS; according to the method, the complex relationship among the ecological environment dimension, the resource development dimension and the social participation dimension is converted into the dynamic quantitative relationship, so that the change trend of the marine ecological environment state along with time can be quantitatively described; the future development trend of the marine ecological environment under the dynamic change of development pressure, ecological state and response measures is predicted, and data support is provided for scientific research.

Description

Technical Field

[0001] The present invention relates to the technical field of marine data processing, and in particular to a method and system for analyzing the development of marine ecological civilization. Background Art

[0002] With the deterioration of the global ecological environment, especially the increasing fragility of the marine ecosystem, the construction of marine ecological civilization has become an important part of the global sustainable development goals. Marine ecological civilization aims to achieve the sustainable development and utilization of marine resources, the effective protection of the ecosystem and the coordinated development of human social and economic activities.

[0003] After searching, Chinese patent number CN202410917910.X discloses a method for simulating and predicting marine ecosystems based on data assimilation technology, including the following steps: S1: Initialize the marine ecological model, and establish a preliminary marine ecosystem model based on existing marine ecosystem data to simulate the interactions between marine biological, chemical and physical processes; S2: Collect multi-source marine environmental observation data, including satellite remote sensing data, ocean buoy records, and ship sampling to obtain real-time observation data on ocean temperature, salinity, and biomass. The above scheme can correct and optimize the initial state and parameters of the marine ecosystem model in real time by integrating multi-source marine environmental observation data and applying advanced data assimilation techniques, such as ensemble Kalman filtering, which not only enhances the model's reflection of the current state of the marine ecosystem, but also improves the accuracy and reliability of short-term and long-term predictions.

[0004] However, in the existing marine ecological civilization analysis schemes, ecological civilization assessment indicators are mostly concentrated on terrestrial ecology, and lack comprehensive indicators for marine characteristics, resulting in the failure to fully reflect the uniqueness of the ocean, including the openness, biodiversity and dynamic changes of marine ecosystems; in addition, in the analysis process, the complex interrelationships between ecological, economic and social dimensions are ignored, making it difficult to fully predict the results of marine ecological civilization development and its evolution laws, leading to deviations in policy making.

[0005] Therefore, a marine ecological civilization development analysis method and system are proposed to solve the above-mentioned problems. Summary of the invention

[0006] Technical issues solved In view of the above-mentioned shortcomings of the prior art, the present invention provides a method and system for analyzing the development of marine ecological civilization, which can effectively solve the problem that the prior art ignores the complex interrelationships among ecological, economic and social dimensions when analyzing the development of marine ecological civilization based on terrestrial ecological indicators.

[0007] Technical Solution To achieve the above objectives, the present invention is implemented through the following technical solutions: The present invention provides a method and system for analyzing the development of marine ecological civilization. The technical solution adopted by the present invention is as follows: comprising the following steps: Step 1: Define the target area for analysis, collect and analyze multi-source data that affect the development of marine ecological civilization in the target area, process the multi-source data based on the spatiotemporal collaborative computing model, and obtain a multi-source fusion data set; Step 2: Standardize the multi-source fusion data set, build the PSR framework model based on the processed multi-source fusion data set, and obtain the state index SIS, pressure index PIS and response index RIS; Step 3: Construct an ecological dynamic balance model based on the state index SIS, pressure index PIS and response index RIS to obtain the environmental status of the marine ecology The rate of change , Development Pressure , ecological status and response measures , used to divide the analysis target area into functional areas and evaluate the status of the marine ecological environment; Step 4: Develop stress , ecological status and response measures Construct a time series dataset with time as the dimension , extract the time series dataset Trend characteristics , Periodic characteristics and random perturbations ; Where Z is the item index, t is the time point, and g is the number of time points; Based on trend characteristics , Periodic characteristics and random perturbations The ecological dynamic balance model is updated, parameters in the updated ecological dynamic balance model are extracted, and the parameters are constructed as a fusion feature vector; Step 5: Input the fused feature vector into the pre-trained neural network model to predict the development rate of the marine ecological environment ; According to the development rate of marine ecological environment , judge the development of marine ecological civilization.

[0008] The multi-source data includes: Ecological and environmental dimension data: Water Quality Index WQI, Biodiversity Index BDI, Ocean Acidification Index OAI, Coral Reef Health Index CHI and Marine Litter Density Index MLDI; Resource development dimension data: development intensity index RDI, fishery resource development index FRI, coastline development index CLI and shipping density index SDI; Social participation dimension data: Ecological Protection Input Index EPI, Ecosystem Restoration Index ERI and Public Participation Index CPI.

[0009] The spatiotemporal collaborative computing model processes multi-source data in the following ways: Extract the data features collected by time from multi-source data and record them as low-frequency time data sets , where E is the index of the data feature, n is the number of sampled low-frequency data features; the minimum sampling time frequency is defined as , define its maximum sampling time frequency as ; Based on the linear interpolation formula, all sampling time frequencies are non- The low-frequency time dataset is expanded to a high-frequency time dataset, denoted as , m is the number of high-frequency data features; the linear interpolation formula is: ; In the formula, and are the original sampling values ​​in the low-frequency time data set, corresponding to the time points and ; is the target data value after interpolation calculation, corresponding to the target time ; i and j represent the index of time point t, satisfying ; The high-frequency time data set is smoothed based on the sliding average method to obtain a time smoothed data set ; The processing formula is: ; In the formula, is the smoothed data point, indicating the time point The corresponding new value after smoothing; k is the size of the sliding window; Extract the data features collected at spatial resolution from multi-source data and record them as low-frequency spatial data sets ; Define its sampling resolution as , where h is the index of the sampling point; Based on the Kriging interpolation formula, the low-frequency spatial dataset is uniformly mapped to a resolution of The high-frequency spatial data set is obtained from the target grid points , where q and p are the horizontal and vertical indices of the target grid point; the Kriging interpolation formula is: ; In the formula, is the interpolation result on the target grid point; is the data value of the original sampling point in the low-frequency spatial data set; is the interpolation weight; u is the total number of sampling points in the low-frequency spatial dataset; Smoothing the dataset over time and high-frequency spatial datasets Fusion, get multi-source fusion data set ;in, is the water quality health index after treatment, is the biodiversity index after treatment, is the processed ocean acidification index, To process the Coral Reef Health Index, The density index of marine debris after treatment; To develop intensity index for post-treatment; To process the fishery resource exploitation index, To process the shoreline development index, is the shipping density index after treatment; is the ecological protection input index after treatment, is the ecosystem recovery index after treatment, is the public participation index after treatment.

[0010] The method of constructing the PSR framework model includes: Define the calculation formula for status indicator points: ; In the formula, , , , and are the weighting coefficients of the corresponding data features respectively; The calculation formula for defining the pressure index score is: ; In the formula, , , and are the weighting coefficients of the corresponding data features respectively; The calculation formula for defining the response index score is: ; In the formula, , and are the weighting coefficients of the corresponding data features.

[0011] The method of constructing the ecological dynamic balance model includes: The environmental status of marine ecological civilization is defined as , and its calculation formula is: ; In the formula, Indicates the environmental status of marine ecology The rate of change of To develop pressure; To develop stress weight parameters; For ecological status; is the weight parameter of ecological status; For response measures; is an exponential function; is the weight parameter of the response measure.

[0012] Wherein, the extracted trend characteristics , Periodic characteristics and random perturbations The methods include: For time series datasets Perform sliding window processing to extract trend characteristics The formula is: ; In the formula, r is the offset; For time series datasets Perform Fourier transform processing, the processing formula is: ; In the formula, is the frequency domain representation, including the amplitude and Phase ; N is the time series data set Length; is a complex exponential function; N is a time series data set The total length of ; find the frequency with the largest amplitude , that is, the dominant frequency ; Extract periodic characteristics The formula is: ; Extract random perturbations The formula is: .

[0013] Wherein, after updating the ecological dynamic balance model, the following is obtained: ; In the formula, is the trend characteristic of the ecological state, is the periodic characteristic of the ecological state. is the random disturbance of ecological state; To develop the trend characteristics of pressure, To exploit the cyclical nature of pressure, To develop random perturbations of pressure; In response to the trend characteristics of the measures, In response to the cyclical nature of the measures, for random perturbations of the response measures; Extract the above parameters to construct a fusion feature vector .

[0014] The training method of the neural network model includes: Define recursive neural network as the basic structure of neural network model, which includes input layer, hidden layer and output layer; Collect multi-source data of the marine ecological environment in the target area within a fixed period of time in the past, and construct a historical fusion feature vector after the corresponding processing is completed ; Fusion of history feature vector Expand according to the vector time step d to form sequence data as the training input of the neural network model, and label the sequence data according to the vector time step d; the label is the development rate of the marine ecological environment ; Development rate The annotation range is -100% to 100%; Define the loss function of the neural network model ; Where S is the length of the sequence data; is the loss weight function weighted according to the development rate; is the development rate of the dth vector time step; It is The labeled evolution rate of vector time steps ; It is The predicted value of the neural network model at vector time steps; The sequence data is passed to the input layer, and then passes through the hidden layer and the output layer in turn to output the predicted value; the value of the corresponding loss function is calculated, and the error gradient is back-propagated from the output layer to the input layer; according to the error gradient, the network parameters are updated using the optimization algorithm, and the sequence data is repeatedly trained until the neural network model converges or reaches the preset number of iterations, completing the training of the neural network model.

[0015] The methods for judging the development of marine ecological civilization include: The development rate of marine ecological civilization Set to five levels: The development rate is -100% to -30%, indicating that the marine ecological civilization is in an extremely unhealthy state; The development rate is -30% to 0%, indicating that the marine ecological civilization is at a relatively low level of ecological function operation; The development rate is 0% to 10%, indicating that the development of marine ecological civilization is at a critical state; The development rate is 10% to 50%, indicating that the marine ecological civilization is in good condition; The development rate is 50% to 100%, indicating that the development of marine ecological civilization is at an extremely healthy level; The predicted development rate of the marine ecological environment Compare with the five divided levels; determine the level it belongs to; output its development status according to the level it belongs to.

[0016] The method of dividing the analysis target area into functional areas includes: The target area for analysis is divided into grid cells of certain specifications. Each grid cell is represented by a number and coordinates as , where s is the grid number, a is the row number, and b is the column number; set the lower left corner coordinate of the analysis target area to , the center coordinates of each grid cell are ; horizontal axis The calculation formula is: , vertical coordinate The calculation formula is: ;in and The spacing of the grid cells in the row and column directions respectively; extract the rate of change of each grid cell , Development Pressure , ecological status and response measures , and the grid cells are represented as ;in represents the rate of change of the grid cell in row a and column b, represents the development pressure of the grid cell in row a and column b, represents the ecological status of the grid cell in row a and column b, represents the response measure of the grid cell in row a and column b; The rate of change based on each grid cell , Development Pressure , ecological status and response measures The grid units are divided into functional areas according to the scores. The functional areas include: Development Zone, the division conditions are: ; Ecologically sensitive areas are divided according to the following conditions: ; Restoration priority areas, divided according to the following criteria: ; Sustainable use area, divided into: ; The conflict area is divided according to the following conditions: .

[0017] A marine ecological civilization development analysis system, comprising: Data collection module: used to collect multi-source data on the development of marine ecological civilization; Data processing module: used to process multi-source data to obtain a multi-source fusion data set; also used to process the multi-source fusion data set to obtain a state index SIS, a pressure index PIS and a response index RIS; Model building module: used to build an ecological dynamic balance model and obtain the rate of change , Development Pressure , ecological status and response measures ; Data update module: used to reduce development pressure , ecological status and response measures Build a time series dataset to extract trend characteristics , Periodic characteristics and random perturbations , and through the trend characteristics , Periodic characteristics and random perturbations Update the ecological dynamic balance model; Development assessment module: used to extract parameters from the updated ecological dynamic balance model and construct them into fused feature vectors, which are then input into the pre-trained neural network model to predict the development rate of the marine ecological environment. ; According to the development rate of marine ecological environment , determine the balance point of the development of marine ecological civilization.

[0018] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. In the present invention, multi-source data is processed based on the spatiotemporal collaborative computing model, which can solve the problems of different data sources, inconsistent temporal resolution and spatial scale, realize the unified processing of multi-source data, and use time interpolation, space interpolation and other technologies to standardize heterogeneous data (such as water quality health index, marine garbage density index, etc.) into a unified time step and spatial grid, generate a unified multi-source fusion data set, and provide a high-quality data foundation for subsequent analysis.

[0019] 2. In the present invention, the complex relationship between the ecological environment dimension, resource development dimension and social participation dimension is transformed into a dynamic quantitative relationship through the ecological dynamic balance model, which can quantitatively describe the environmental status of the marine ecology. Changes over time, predicting the marine ecological environment under development pressure , ecological status and response measures The dynamic equilibrium model can not only predict the future development trend of the marine ecological environment under the dynamic changes of the marine environment, but also assess the impact of the marine ecological environment on the development pressure. , ecological status and response measures Which value ranges are , find the critical point of dynamic balance or collapse of marine ecological environment development, optimize policies and resource allocation, support the balance between marine ecological environment protection and economic development, and achieve sustainable management of marine ecological environment development.

[0020] 3. In the present invention, the development rate of the marine ecological environment is By quantifying the data and comparing them with the corresponding classification levels, we can make a quantitative assessment of the development status of marine ecological civilization and form a unified evaluation standard. The five levels from extremely unhealthy to extremely healthy cover the complete state of marine ecosystems from severe degradation to a highly coordinated state. This scientific assessment method can provide targeted guidance for the development of ecological civilization in different marine regions, clarify the priority of marine ecological protection, and optimize policy formulation and resource development allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a simplified flowchart of the marine ecological civilization development analysis method in Example 1 of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0023] Embodiment 1 Reference Figure 1 , this case proposes a marine ecological civilization development analysis method, including the following steps: Step 1: Define the target area for analysis, collect and analyze multi-source data that affect the development of marine ecological civilization in the target area, process the multi-source data based on the spatiotemporal collaborative computing model, and obtain a multi-source fusion data set; Step 2: Standardize the multi-source fusion data set, build the PSR framework model based on the processed multi-source fusion data set, and obtain the state index SIS, pressure index PIS and response index RIS; Step 3: Construct an ecological dynamic balance model based on the state index SIS, pressure index PIS and response index RIS to obtain the environmental status of the marine ecology The rate of change , Development Pressure , ecological status and response measures , used to divide the analysis target area into functional areas and evaluate the status of the marine ecological environment; Step 4: Develop stress , ecological status and response measures Construct a time series dataset with time as the dimension and extract the trend characteristics of the time series dataset , Periodic characteristics and random perturbations , where Z is the item index, based on trend characteristics , Periodic characteristics and random perturbations The ecological dynamic balance model is updated, parameters in the updated ecological dynamic balance model are extracted, and the parameters are constructed as a fusion feature vector; Step 5: Input the fused feature vector into the pre-trained neural network model to predict the development rate of the marine ecological environment ; According to the development rate of marine ecological environment , judge the development of marine ecological civilization.

[0024] Specifically, in this case, the multi-source data includes: Data on ecological environment dimensions: Water Quality Health Index (WQI), obtained through experimental analysis of measurement data such as chemical oxygen demand, nutrient concentration, and heavy metals, reflects the overall health of marine water quality and is an important basis for measuring whether the ecosystem is polluted; Biodiversity Index (BDI), obtained through survey data, reflects the stability and health of the marine ecosystem and is used to assess marine species richness, biological population distribution and changes; Ocean Acidification Index (OAI), based on long-term monitoring of ocean pH values, quantifies ocean acidification trends and is used to assess the impact of carbon dioxide absorption on marine ecology; Coral Reef Health Index (CHI), coral reefs are an important habitat for marine biodiversity and reflect the health of coral reef ecosystems and are used to assess the impact of climate change (such as ocean acidification and temperature changes) on corals; Marine Litter Density Index (MLDI), used to assess the severity of marine litter pollution, especially the threat of plastic litter to marine ecology; Resource development dimension data: Development intensity index RDI, quantifies the development pressure of human activities on marine resources and identifies over-exploited areas; fishery resource development index FRI, assesses whether fishery activities exceed sustainable levels and warns of overfishing risks; coastline development index CLI, used to assess the degree of damage to the marine ecology caused by coastline development; shipping density index SDI, used to assess the pressure of shipping activities on the marine ecology (such as noise pollution, oil spills, etc.); Data on social participation: The Ecological Protection Input Index (EPI) is used to assess the degree of attention paid to marine ecological protection in a region and quantify the intensity of investment in protecting resources; the Ecosystem Restoration Index (ERI) quantifies the effect of ecological restoration and evaluates the effectiveness of governance measures; the Public Participation Index (CPI) reflects the public’s participation in marine ecological protection.

[0025] The spatiotemporal collaborative computing model processes multi-source data in the following ways: Extract the data features collected by time from the multi-source data (including water quality health index WQI, ocean acidification index OAI, development intensity index RDI, fishery resource development index FRI, fishery resource development index FRI and public participation index CPI) and record them as low-frequency time datasets , where E is the index of the data feature, n is the number of sampled low-frequency data features; the minimum sampling time frequency is defined as , define its maximum sampling time frequency as ,Right now ; Based on the linear interpolation formula, all sampling time frequencies are non- The low-frequency time dataset is expanded to a high-frequency time dataset, denoted as , m is the number of high-frequency data features; the linear interpolation formula is: ; In the formula, and are the original sampling values ​​in the low-frequency time data set, corresponding to the time points and , these values ​​are known; The target data value after interpolation calculation, corresponding to the target time point , which is used to fill the gaps in the low-frequency time data set at the high-frequency time points; i and j represent the index of the time point t, satisfying ; Then, the high-frequency time data set is smoothed based on the sliding average method to eliminate noise and fluctuations and extract a smoother data trend, thus obtaining a time-smoothed data set, which is recorded as ; The processing formula is: ; In the formula, is the smoothed data point, indicating the time point The corresponding new value after smoothing takes into account the mean of the surrounding data points, which can reduce the noise interference in the original high-frequency data; k is the size of the sliding window, which is the total number of data points in the window range used for sliding averaging (should be an odd number).

[0026] Extract data features collected at spatial resolution from multi-source data (including biodiversity index BDI, coral reef health index CHI, marine debris density index MLDI, coastline development index CLI, shipping density index SDI, and ecological protection input index ERI) and record them as low-frequency spatial datasets ; Define its sampling resolution as , where h is the index of the sampling point. Based on the Kriging interpolation formula, all low-frequency spatial datasets are uniformly mapped to a resolution of The high-frequency spatial data set is obtained from the target grid points , where q and p are the horizontal and vertical indices of the target grid point; the Kriging interpolation formula is: ; In the formula, is the interpolation result on the target grid point, indicating the target grid point The estimated value on ; is the data value of the original sampling point in the low-frequency spatial data set; is the interpolation weight, which represents the original sampling point h to the target grid point The contribution of is calculated through the covariance matrix and semivariogram function; u is the total number of sampling points in the low-frequency spatial dataset.

[0027] In summary, processing multi-source data based on the spatiotemporal collaborative computing model can solve the problems of different data sources, inconsistent temporal resolution and spatial scale, realize unified processing of multi-source data, and use time interpolation, spatial interpolation and other technologies to standardize heterogeneous data (such as water quality health index, marine debris density index, etc.) into a unified time step and spatial grid, generate a unified multi-source fusion data set, and provide a high-quality data foundation for subsequent analysis.

[0028] Smoothing the dataset over time and high-frequency spatial datasets Fusion, get a multi-source fusion data set, denoted as ;in, , , , , , , , , , , and These are all data features processed by the spatiotemporal collaborative computing model; The data in the multi-source fusion dataset X are cleaned and normalized to remove outliers and fill missing values, and all data are standardized to the interval [0, 1]. A PSR framework model is constructed based on the multi-source fusion dataset X to analyze the changes in the marine ecological environment caused by the pressure of resource development on the ecosystem, as well as the protection and restoration capabilities of social and policy responses to the marine ecological environment. The construction method is as follows: Define the calculation formula for status indicator points: ; In the formula, , , , and They are weighted coefficients of the corresponding data features, indicating the importance of each data feature in the state evaluation, which are usually determined based on statistical analysis; is the water quality health index after treatment, is the biodiversity index after treatment, is the processed ocean acidification index, To process the Coral Reef Health Index, It is the density index of marine debris after treatment; by integrating the ecological environment dimension data in the marine ecological environment, the state index SIS is formed to assess the health level of the marine ecological environment; The calculation formula for defining the pressure index score is: ; In the formula, , , and They are weighted coefficients of the corresponding data features, indicating the importance of each data feature in stress assessment, which are usually determined based on statistical analysis; To develop intensity index for post-treatment; To process the fishery resource exploitation index, To process the shoreline development index, The processed shipping density index is formed by integrating the resource development dimension data in the marine ecological environment to form a pressure index PIS, which is used to identify potential problem areas and high-risk activities; The calculation formula for defining the response index score is: ; In the formula, , and They are weighting coefficients corresponding to the data characteristics, indicating the importance of each data adjustment in the response evaluation, usually determined based on statistical analysis; is the ecological protection input index after treatment, is the ecosystem recovery index after treatment, It is the processed public participation index; by integrating the data of social participation dimensions in the marine ecological environment, a response index RIS is formed to evaluate the intensity and effectiveness of marine ecological environment restoration and protection.

[0029] Based on the state index SIS, pressure index PIS and response index RIS, an ecological dynamic balance model is constructed to comprehensively analyze the interactive relationship between ecology, economy and society, and dynamically evaluate the ecological, economic and social benefits of different policy measures; the construction methods include: The environmental status of marine ecological civilization is defined as , and its calculation formula is: ; In the formula, Indicates the environmental status of marine ecology The rate of change of , indicating the environmental status of the marine ecosystem In growth or recovery (ecological improvement); if , indicating the environmental status of the marine ecosystem in decline or deterioration (ecological degradation); if , indicating the environmental status of the marine ecosystem Maintain balance (dynamic balance or stable state); based on the adjustment status index SIS, pressure index PIS and response index RIS, achieve , thereby deriving the restoration and development of the marine ecological environment; Indicates the negative impact of resource development on the marine ecological environment. Decrease (negative direction) indicates that the increase in development pressure has deteriorated the marine ecological environment, thus affecting the environmental status of the marine ecology. The rate of descent; is the development pressure, which means the pressure index score PIS at time point t. It is a positive number with a value range of , the larger represents a higher development intensity; when When it is smaller, that is, when the development pressure is lower, , indicating that a small increase in development may have a huge negative impact on the fragile marine ecological environment; After the increase, the negative effects gradually stabilized. As the development pressure increased significantly, the ecological carrying capacity of some parts was destroyed and could not be restored, leading to the collapse of the ecosystem; To develop the pressure weight parameters, Indicates the environmental status of marine ecosystems affected by development pressure Deterioration sensitivity coefficient; if If the development activity is high, it means that the development activity has a great impact on the marine ecological environment (for example, the coral reef ecology is extremely sensitive to environmental changes); If it is low, it means that the marine ecological environment is more adaptable to development pressure (e.g. wetlands with higher pollution tolerance); Indicates the positive impact on the marine ecological environment. Increase (positive direction), indicating that a healthy marine ecological environment can alleviate development pressure and help restore the environmental status of the marine ecology ; is the ecological state, indicating the state index SIS at time point t, , in theory, The higher the value, the stronger the self-resilience of the marine ecological environment; is the weight parameter of the ecological state, Indicates ecological status Environmental status of marine ecosystems The recovery of If the value is high, it means that the marine ecological environment has a strong self-recovery ability (such as the strong recovery ability of tropical rainforests and mangroves); Low, indicating that the recovery capacity of the marine ecological environment is weak Indicates the positive impact of the social participation dimension. Increase (positive direction), indicating that social measures have a positive supporting effect on the marine ecological environment; is the response measure, i.e., the response index score RIS at time point t; is an exponential function, emphasizing the response measure The marginal effect decreases. When the response index RIS is small (such as when the protection measures are just launched), the environmental status of the marine ecology The improvement effect is significant; when the response index RIS is large (such as protection measures have been fully implemented), the environmental status of the marine ecology Improvements tend to stabilize, and marginal improvements decrease; is the weight parameter of the response measure, The value of indicates the response measure The effect of When higher, it indicates the response measures Environmental status of marine ecosystems The recovery effect is obvious; when When lower, indicate the response measures Environmental status of marine ecosystems The improvement effect is limited; ,when If it is larger, explain the response measures The effect is very significant on a small scale. When it is smaller, it indicates the response measures Greater investment is needed to produce significant results.

[0030] In summary, the ecological dynamic balance model transforms the complex relationship between the ecological environment dimension, resource development dimension and social participation dimension into a dynamic quantitative relationship, which can quantitatively describe the environmental status of the marine ecology. Changes over time, predicting the marine ecological environment under development pressure , ecological status and response measures The dynamic equilibrium model can not only predict the future development trend of the marine ecological environment under the dynamic changes of the marine environment, but also assess the impact of the marine ecological environment on the development pressure. , ecological status and response measures Which value ranges are , find the critical point of dynamic balance or collapse of marine ecological environment development, optimize policies and resource allocation, support the balance between marine ecological environment protection and economic development, and achieve sustainable management of marine ecological environment development.

[0031] The target area for analysis is divided into grid cells of certain specifications. Each grid cell is represented by a number and coordinates as , where s is the grid number, a is the row number, and b is the column number; set the lower left corner coordinate of the analysis target area to , the center coordinates of each grid cell are ; horizontal axis The calculation formula is: , vertical coordinate The calculation formula is: ;in and The spacing of the grid cells in the row and column directions respectively; extract the rate of change of each grid cell , Development Pressure , ecological status and response measures , and the grid cells are represented as ;in represents the rate of change of the grid cell in row a and column b, represents the development pressure of the grid cell in row a and column b, represents the ecological status of the grid cell in row a and column b, represents the response measure of the grid cell in row a and column b; The rate of change based on each grid cell , Development Pressure , ecological status and response measures The grid units are divided into functional areas according to the scores. The functional areas include: Development Zone, the division conditions are: , indicating that the development intensity of this grid unit is high, and the potential impact of development behavior on the marine ecological environment needs to be further monitored; Ecologically sensitive areas are divided according to the following conditions: , indicating that the marine ecological status of this grid unit is poor, the response measures are insufficient, and the marine ecological environment is deteriorating. Priority protection is needed to prevent further deterioration of the marine ecological environment; Restoration priority areas, divided according to the following criteria: , indicating that the ecological status of the grid unit is poor, but the response measures have been strengthened and the marine ecological environment is gradually recovering. Such areas need to concentrate resources to accelerate ecological restoration; Sustainable use area, divided into: , indicating that the development pressure of this grid unit is low, the ecological status and response measures are good, and the marine ecological environment tends to be stable or improved, which is suitable for reasonable development on the basis of ensuring the balance of marine ecology; The conflict area is divided according to the following conditions: , indicating that high development pressure in this grid cell is encroaching on ecologically sensitive areas, and the marine ecological environment is deteriorating, requiring focused policy intervention; In summary, by dividing the target area into regular grids, we can more accurately evaluate the functional status of different marine ecosystems, and calculate the environmental status of marine ecology through the ecological dynamic balance model. The rate of change , dividing grid units into development zones, ecologically sensitive zones, restoration priority zones, sustainable use zones, and conflict zones can detect ecological risks in advance, provide a basis for policy making, help concentrate resources for ecological protection and restoration, and avoid further ecological deterioration.

[0032] Extracting development pressure from ecological dynamic equilibrium models , ecological status and response measures , and construct it into a time series dataset with time as the dimension, respectively recorded as , where Z is the project index (i.e. development pressure , ecological status and response measures ); The time series dataset It is divided into three parts, namely , extract the time series dataset Trend characteristics in , Periodic characteristics and random perturbations ; Among them, trend characteristics It is a time series dataset The part that reflects long-term changes removes short-term fluctuations and cyclical components, which is usually smooth and indicates the overall upward or downward trend of the data; the extraction formula is: ; In the formula, r is the offset, which is used to define the position of the data point in the sliding window relative to the current time point t; Periodic characteristics It is a time series dataset The regularly repeated part reflects the accuracy change pattern of the data at any time. Based on the Fourier transform of the time series data set The processing formula is: ; In the formula, is a frequency domain representation, which is a time series data set The Fourier transform result represents the complex value at frequency f, which contains two core information: amplitude (describing the strength of the contribution of frequency f) and phase (Describes the degree of deviation of frequency f); N is the time series data set Length; is a complex exponential function used to convert the time series data set Project onto different frequencies; find the frequency with the largest amplitude , i.e., dominant frequency, represents a time series data set The most significant periodic feature in , calculate the corresponding period ; Extract periodic characteristics The formula is: ; Random Perturbations It is a time series dataset The unpredictable part of , which appears as random noise or outliers, is calculated as: ; The trend characteristics , Periodic characteristics and random perturbations Return to the ecological dynamic balance model and update it to obtain: ;in, is the trend characteristic of the ecological state, is the periodic characteristic of the ecological state. is the random disturbance of ecological state; To develop the trend characteristics of pressure, To exploit the cyclical nature of pressure, To develop random perturbations of pressure; In response to the trend characteristics of the measures, Due to the cyclical nature of the response measures, is the random perturbation of the response measure.

[0033] In summary, through the time series data set The decomposition of the ocean will affect the development pressure of the marine ecological environment , ecological status and response measures Split and get its trend characteristics , Periodic characteristics and random perturbations , can more comprehensively analyze the dynamic changes of the marine ecological environment; specifically: trend characteristics Can reflect long-term changes and periodic characteristics Can capture seasonal fluctuations, random disturbances Can reflect unpredictable emergencies; thus, the trend characteristics , Periodic characteristics and random perturbations Substituting it into the ecological dynamic balance model can more accurately predict the environmental status of marine ecology Changes over time.

[0034] Extract the updated parameters of the ecological dynamic balance model and construct them into a fusion feature vector, recorded as ; Input feature vector The pre-trained neural network model is used to predict the environmental status of the marine ecosystem. The rate of change ; According to the environmental status of marine ecology The rate of change , determine the balance point of the development of the marine ecological environment; The training methods of neural network models include: Define recursive neural network as the basic structure of neural network model, which includes input layer, hidden layer and output layer; The input layer is used to receive the fused feature vector As input, where d represents the vector time step; the hidden layer consists of c recurrent neurons; The calculation formula for each recurrent neuron is: ;in, is the current vector time step The hidden state vector of is the activation function, such as tanh or ReLU; is the weight matrix from the input layer to the hidden layer; is the recurrent weight matrix of the hidden layer; is the bias vector of the hidden layer; The output layer calculates the predicted value based on the output of the hidden layer ;in, is the activation function of the output layer; for example, a linear function or softmax; is the weight matrix from the hidden layer to the output layer; is the bias vector of the output layer, the predicted value The predicted environmental status of the marine ecosystem The rate of change ; Collect multi-source data of the marine ecological environment in the target area within a fixed period of time in the past, and construct a historical fusion feature vector after the corresponding processing is completed ; Fusion of history feature vector Expand according to the vector time step d to form sequence data as the training input of the neural network model, and label the sequence data according to the vector time step d; the label is the development rate of the marine ecological environment (i.e. the environmental status of the marine ecosystem The rate of change ); Development rate The marking range is from -100% to 100%; it depends on the actual situation.

[0035] Initialize the network parameters of the neural network model; the network parameters include the weight matrix from the input layer to the hidden layer , the recurrent weight matrix of the hidden layer , the bias vector of the hidden layer , the weight matrix from hidden layer to output layer and the bias vector of the output layer ; Define the loss function of the neural network model ; Where S is the length of the sequence data; is the loss weight function weighted according to the development rate; is the development rate of the dth vector time step; It is The labeled evolution rate of vector time steps ; It is The predicted value of the neural network model at vector time steps; The sequence data is passed to the input layer, and then passes through the hidden layer and the output layer in turn to output the predicted value; the corresponding loss function value is calculated, and the error gradient is back-propagated from the output layer to the input layer; according to the error gradient, the network parameters are updated using an optimization algorithm (such as gradient descent, Adam, etc.) to reduce the value of the loss function; the sequence data is repeatedly trained until the neural network model converges (that is, the value of the loss function no longer changes) or reaches the preset number of iterations to complete the training of the neural network model.

[0036] In summary, the neural network model can capture the key variables of marine ecological environment development (such as development pressure , ecological status and response measures ) can more comprehensively reflect the dynamic changes of the marine ecological environment, predict the changing trends of the marine ecosystem in the future, and provide a scientific basis for future policy making.

[0037] Development rate Set in the range of -100% to 100%; for this range, the development rate The threshold interval is used to distinguish whether the marine ecological civilization is in a healthy development trend; the development rate of marine ecological civilization is calculated based on the development scenario requirements of marine ecological civilization, historical data statistical analysis and expert experience. Set to five levels: The development rate is -100% to -30%, indicating that the marine ecological civilization is in an extremely unhealthy state and may lead to ecological collapse; The development rate is -30% to 0%, indicating that the marine ecological civilization is at a relatively low level of ecological function operation, but has not yet reached the point of complete collapse; The development rate is between 0% and 10%, indicating that the development of marine ecological civilization is at a critical state, indicating that the health of the marine ecosystem has not yet reached a complete state, but is still within the stable operating range; The development rate is between 10% and 50%, indicating that the marine ecological civilization is in good condition, the development pressure is under control, the marine ecosystem can maintain stable function, and the protection and development are relatively balanced; The development rate is 50% to 100%, indicating that the development of marine ecological civilization is at an extremely healthy level, and the marine ecosystem has strong self-recovery and stress resistance capabilities; The predicted development rate of the marine ecological environment Compare with the five divided levels; determine the level it belongs to; output the analysis results according to the level it belongs to.

[0038] In summary, by taking the development rate of marine ecological environment as By quantifying the data and comparing them with the corresponding classification levels, we can make a quantitative assessment of the development status of marine ecological civilization and form a unified evaluation standard. The five levels from extremely unhealthy to extremely healthy cover the complete state of marine ecosystems from severe degradation to a highly coordinated state. This scientific assessment method can provide targeted guidance for the development of ecological civilization in different marine regions, clarify the priority of marine ecological protection, and optimize policy formulation and resource development allocation.

[0039] Embodiment 2 Based on the first embodiment, this case proposes a marine ecological civilization development analysis system, including: Data collection module: used to collect multi-source data on the development of marine ecological civilization; Data processing module: used to process multi-source data to obtain a multi-source fusion data set; also used to process the multi-source fusion data set to obtain a state index SIS, a pressure index PIS and a response index RIS; Model building module: used to build an ecological dynamic balance model and obtain the rate of change , Development Pressure , ecological status and response measures ; Data update module: used to reduce development pressure , ecological status and response measures Build a time series dataset to extract trend characteristics , Periodic characteristics and random perturbations , and through the trend characteristics , Periodic characteristics and random perturbations Update the ecological dynamic balance model; Development assessment module: used to extract parameters from the updated ecological dynamic balance model and construct them into fused feature vectors, which are then input into the pre-trained neural network model to predict the development rate of the marine ecological environment. ; According to the development rate of marine ecological environment , determine the balance point of the development of marine ecological civilization.

[0040] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing the development of marine ecological civilization, characterized in that: The following steps are involved: Step 1: Define the target area for analysis, collect and analyze multi-source data that affect the development of marine ecological civilization in the target area, process the multi-source data based on the spatiotemporal collaborative computing model, and obtain a multi-source fusion data set; Step 2: Standardize the multi-source fusion data set, build the PSR framework model based on the processed multi-source fusion data set, and obtain the state index SIS, pressure index PIS and response index RIS; Step 3: Construct an ecological dynamic balance model based on the state index SIS, pressure index PIS and response index RIS to obtain the environmental status of the marine ecology The rate of change , Development Pressure , ecological status and response measures , used to divide the analysis target area into functional areas and evaluate the status of the marine ecological environment; Step 4: Develop stress , ecological status and response measures Construct a time series dataset with time as the dimension , extract the time series dataset Trend characteristics , Periodic characteristics and random perturbations ; Where Z is the item index, t is the time point, and g is the number of time points; Based on trend characteristics , Periodic characteristics and random perturbations The ecological dynamic balance model is updated, parameters in the updated ecological dynamic balance model are extracted, and the parameters are constructed as a fusion feature vector; Step 5: Input the fused feature vector into the pre-trained neural network model to predict the development rate of the marine ecological environment ; According to the development rate of marine ecological environment , judge the development of marine ecological civilization.

2. A method for analyzing the development of marine ecological civilization as claimed in claim 1, characterized in that: The multi-source data includes: Ecological and environmental dimension data: Water Quality Index WQI, Biodiversity Index BDI, Ocean Acidification Index OAI, Coral Reef Health Index CHI and Marine Litter Density Index MLDI; Resource development dimension data: development intensity index RDI, fishery resource development index FRI, coastline development index CLI and shipping density index SDI; Social participation dimension data: Ecological Protection Input Index EPI, Ecosystem Restoration Index ERI and Public Participation Index CPI.

3. A method for analyzing the development of marine ecological civilization as claimed in claim 2, characterized in that: The spatiotemporal collaborative computing model processes multi-source data in the following ways: Extract the data features collected by time from multi-source data and record them as low-frequency time data sets , where E is the index of the data feature, n is the number of sampled low-frequency data features; the minimum sampling time frequency is defined as , define its maximum sampling time frequency as ; Based on the linear interpolation formula, all sampling time frequencies are non- The low-frequency time dataset is expanded to a high-frequency time dataset, denoted as , m is the number of high-frequency data features; the linear interpolation formula is: ; In the formula, and are the original sampling values ​​in the low-frequency time data set, corresponding to the time points and ; The target data value after interpolation calculation, corresponding to the target time point ; i and j represent the index of time point t, satisfying ; The high-frequency time data set is smoothed based on the sliding average method to obtain a time smoothed data set ; The processing formula is: ; In the formula, is the smoothed data point, indicating the time point The corresponding new value after smoothing; k is the size of the sliding window; Extract the data features collected at spatial resolution from multi-source data and record them as low-frequency spatial data sets ; Define its sampling resolution as , where h is the index of the sampling point; Based on the Kriging interpolation formula, the low-frequency spatial dataset is uniformly mapped to a resolution of The high-frequency spatial data set is obtained from the target grid points , where q and p are the horizontal and vertical indices of the target grid point; the Kriging interpolation formula is: ; In the formula, is the interpolation result on the target grid point; is the data value of the original sampling point in the low-frequency spatial data set; is the interpolation weight; u is the total number of sampling points in the low-frequency spatial dataset; Smoothing the dataset in time and high-frequency spatial datasets Fusion, get multi-source fusion data set ;in, is the water quality health index after treatment, is the biodiversity index after treatment, is the processed ocean acidification index, To process the Coral Reef Health Index, The density index of marine debris after treatment; To develop intensity index for post-treatment; To process the fishery resource exploitation index, To process the shoreline development index, is the shipping density index after treatment; is the ecological protection input index after treatment, is the ecosystem recovery index after treatment, is the public participation index after treatment.

4. A method for analyzing the development of marine ecological civilization as claimed in claim 3, characterized in that: The method of constructing the PSR framework model includes: Define the calculation formula for status indicator points: ; In the formula, , , , and are the weighting coefficients of the corresponding data features respectively; The calculation formula for defining the pressure index score is: ; In the formula, , , and are the weighting coefficients of the corresponding data features respectively; The calculation formula for defining the response index score is: ; In the formula, , and are the weighting coefficients of the corresponding data features.

5. A method for analyzing the development of marine ecological civilization as claimed in claim 4, characterized in that: The method of constructing the ecological dynamic balance model includes: The environmental status of marine ecological civilization is defined as , and its calculation formula is: ； In the formula, Indicates the environmental status of marine ecology The rate of change of To develop pressure; To develop stress weight parameters; For ecological status; is the weight parameter of ecological status; For response measures; is an exponential function; is the weight parameter of the response measure.

6. A method for analyzing the development of marine ecological civilization as claimed in claim 5, characterized in that: The extracted trend characteristics , Periodic characteristics and random perturbations The methods include: For time series datasets Perform sliding window processing to extract trend characteristics The formula is: ; In the formula, r is the offset; For time series datasets Perform Fourier transform processing, the processing formula is: ; In the formula, is the frequency domain representation, including the amplitude and Phase ; N is the time series data set Length; is a complex exponential function; N is a time series data set The total length of ; find the frequency with the largest amplitude , that is, the dominant frequency ; Extract periodic characteristics The formula is: ; Extract random perturbations The formula is: .

7. A method for analyzing the development of marine ecological civilization as claimed in claim 6, characterized in that: After updating the ecological dynamic balance model, we get: ; In the formula, is the trend characteristic of the ecological state, is the periodic characteristic of the ecological state. is the random disturbance of ecological state; To develop the trend characteristics of pressure, To exploit the cyclical nature of pressure, To develop random perturbations of pressure; In response to the trend characteristics of the measures, Due to the cyclical nature of the response measures, for random perturbations of the response measures; Extract the above parameters to construct a fusion feature vector .

8. A method for analyzing the development of marine ecological civilization as claimed in claim 7, characterized in that: The training method of the neural network model includes: Define recursive neural network as the basic structure of neural network model, which includes input layer, hidden layer and output layer; Collect multi-source data of the marine ecological environment in the target area within a fixed period of time in the past, and construct a historical fusion feature vector after the corresponding processing is completed ; Fusion of history feature vector Expand according to the vector time step d to form sequence data as the training input of the neural network model, and label the sequence data according to the vector time step d; the label is the development rate of the marine ecological environment ; Development rate The annotation range is -100% to 100%; Define the loss function of the neural network model ; Where S is the length of the sequence data; is the loss weight function weighted according to the development rate; is the development rate of the dth vector time step; It is The annotated evolution rate of vector time steps; It is The predicted value of the neural network model at vector time steps; The sequence data is passed to the input layer, and then passes through the hidden layer and the output layer in sequence to output the predicted value; the corresponding loss function value is calculated, and the error gradient is back-propagated from the output layer to the input layer; according to the error gradient, the network parameters are updated using the optimization algorithm, and the sequence data is repeatedly trained until the neural network model converges or reaches the preset number of iterations, completing the training of the neural network model; The methods for judging the development of marine ecological civilization include: The development rate of marine ecological civilization Set to five levels: The development rate is -100% to -30%, indicating that the marine ecological civilization is in an extremely unhealthy state; The development rate is -30% to 0%, indicating that the marine ecological civilization is at a relatively low level of ecological function operation; The development rate is 0% to 10%, indicating that the development of marine ecological civilization is at a critical state; The development rate is 10% to 50%, indicating that the marine ecological civilization is in good condition; The development rate is 50% to 100%, indicating that the development of marine ecological civilization is at an extremely healthy level; The predicted development rate of the marine ecological environment Compare with the five divided levels; determine the level it belongs to; output its development status according to the level it belongs to.

9. A method for analyzing the development of marine ecological civilization as claimed in claim 6, characterized in that: The method of dividing the analysis target area into functional areas includes: The target area for analysis is divided into grid cells of certain specifications. Each grid cell is represented by a number and coordinates as , where s is the grid number, a is the row number, and b is the column number; set the lower left corner coordinate of the analysis target area to , the center coordinates of each grid cell are ; horizontal axis The calculation formula is: , vertical coordinate The calculation formula is: ;in and The spacing of the grid cells in the row and column directions respectively; extract the rate of change of each grid cell , Development Pressure , ecological status and response measures , and the grid cells are represented as ;in represents the rate of change of the grid cell in row a and column b, represents the development pressure of the grid cell in row a and column b, represents the ecological status of the grid cell in row a and column b, represents the response measure of the grid cell in row a and column b; The rate of change based on each grid cell , Development Pressure , ecological status and response measures The grid units are divided into functional areas according to the scores. The functional areas include: Development Zone, the division conditions are: ； Ecologically sensitive areas are divided according to the following conditions: ； Restoration priority areas, divided according to the following criteria: ； Sustainable use area, divided into: ； The conflict area is divided according to the following conditions: 。 10. A marine ecological civilization development analysis system, which is used to implement a marine ecological civilization development analysis method according to any one of claims 1 to 9, characterized in that: include: Data collection module: used to collect multi-source data on the development of marine ecological civilization; Data processing module: used to process multi-source data to obtain multi-source fusion data sets; It is also used to process multi-source fusion data sets to obtain state index points SIS, pressure index points PIS and response index points RIS; Model building module: used to build an ecological dynamic balance model and obtain the rate of change , Development Pressure , ecological status and response measures ; Data update module: used to reduce development pressure , ecological status and response measures Build a time series dataset to extract trend characteristics , Periodic characteristics and random perturbations , and through the trend characteristics , Periodic characteristics and random perturbations Update the ecological dynamic balance model; Development assessment module: used to extract parameters from the updated ecological dynamic balance model and construct them into fused feature vectors, which are then input into the pre-trained neural network model to predict the development rate of the marine ecological environment. ; According to the development rate of marine ecological environment , determine the balance point of the development of marine ecological civilization.

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