Method and system for abnormal automatic identification and early warning of marine ecological protection red line

Abstract

The application provides a marine ecological protection red line anomaly automatic identification and early warning method and system, comprising the following steps: taking a marine ecological protection red line space unit as a basic analysis object; fusing multi-source marine monitoring data, combining a marine ecological protection red line type, and constructing a pressure and state index system; taking a historical benchmark period and a red line outside control area as a reference, establishing a protection effectiveness anomaly determination rule; and realizing automatic early warning output through anomaly type identification and anomaly degree classification. The technical route realizes a complete technical closed loop from "monitoring data" to "effectiveness anomaly determination" to "early warning information output".

Description

Technical Field

[0001] This invention relates to the field of marine ecological protection technology, and in particular to a method and system for automatic identification and early warning of anomalies in marine ecological protection red lines. Background Technology

[0002] Through the collection and analysis of domestic and international technical documents, the existing technology has the following problems: 1) Primarily based on management evaluation, lacking an anomaly identification mechanism. Existing technical documents related to ecological protection red lines and protected areas mainly focus on management evaluation and effectiveness evaluation, which are carried out periodically during the planning period. They emphasize the comprehensive analysis of historical data within a certain period, making it difficult to identify anomalies or management failures caused by external pressures or ecosystem degradation within the marine ecological protection red line in a timely manner. This fails to meet the needs for refined and dynamic management of marine ecological protection red lines in response to abnormal results.

[0003] 2) The comparison relationship between the inside and outside of the red line was not systematically introduced. Existing technical solutions all independently analyze monitoring data within ecological protection red lines or protected areas to determine the effectiveness of their management and protection. They do not systematically compare changes within ecological protection red lines with changes in control areas outside ecological protection red lines, resulting in a lack of spatial reference in the assessment results and an inability to accurately identify the characteristics of natural changes and fluctuations in marine ecosystems.

[0004] 3) Differentiated identification of various types of marine ecological protection red lines Existing technical solutions all treat ecological protection red lines and protected areas as a whole for evaluation, without differentiating between different types of areas. For different types of marine ecological protection red lines, such as coral reefs and seagrass beds, their core indicators and sensitivity to external pressures vary greatly. Therefore, it is necessary to differentiate and manage them in a targeted manner based on the type of marine ecological protection red line.

[0005] 4) An early warning mechanism for marine ecological protection red lines has not yet been established. Existing assessment results are mostly presented in the form of scores, indices or reports. However, marine ecological protection red lines are divided into eleven types, and the dominant ecological service functions and ecological characteristics of each type of red line are different. It is necessary to form an automated and hierarchical early warning output mechanism for the types of marine ecological protection red lines to directly support red line management decisions. Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide an automatic identification and early warning method and system for anomalies in marine ecological protection red lines. By comprehensively considering the trends of pressure change, state change, differences between inside and outside the red line, and deviations from historical benchmarks, a dual-dimensional coupled "time series and spatial comparison" scenario for judging protection effectiveness anomalies is constructed. This enables the differentiation, identification, and early warning output of different anomaly types, such as potential risks, degradation of protected objects, and failure of protection effectiveness. It avoids misjudgments caused by single-point-time or absolute numerical evaluations and improves the reliability and interpretability of anomaly identification.

[0007] In a first aspect, embodiments of the present invention provide a method for automatic identification and early warning of anomalies in marine ecological protection red lines, the method comprising: Acquire multi-source marine monitoring data, including marine ecological early warning monitoring data, dynamic supervision data of sea area use, and ship AIS activity data; Calculate pressure indicators, which include the intensity of marine development and use, nutrient concentration, and intensity of ship activity; Calculate state indicators, which include common indicators and characteristic indicators; Calculate the standardized values ​​of the status indicators within the red-line unit; Based on the pressure index and the state index, calculate the rate of change vector for adjacent time periods; wherein, the rate of change vector for adjacent time periods includes the rate of change vector for the pressure index and the rate of change vector for the state index. Construct a state index vector for the control area, and calculate the difference vector of state changes inside and outside the red line unit based on the state index vector for the control area and the state index change rate vector. A coupled anomaly identification model based on the relationship between pressure change trends and state response is constructed. The coupled anomaly identification model integrates the pressure index change rate vector, the state index change rate vector, the standardized value of the state index within the red line unit, and the state change difference vector inside and outside the red line unit for joint determination. The joint determination is based on both time series change characteristics and spatial comparison difference characteristics. Based on the determination results, the anomaly type identification results are output, which include pressure-leading anomalies, state response anomalies, inside-outside comparison anomalies, and baseline deviation anomalies.

[0008] Further, stress indicators are calculated, including: The intensity of sea area development and use is calculated by multiplying the sea area of ​​each sea area use type within the red line area during the assessment period by the intensity coefficient of each sea area use type. The nutrient concentrations were calculated using the regional annual average values ​​of inorganic nitrogen and reactive phosphate monitoring data from marine ecological early warning monitoring data. The intensity of vessel activity is calculated based on the sailing time of the vessel's AIS data during the assessment period within the red-line area; All of the pressure indicators are negative.

[0009] Furthermore, the common indicators include the proportion of area with excellent seawater quality and the marine biodiversity index, while the characteristic indicators are set according to the type of marine ecological protection red line; wherein, both the common indicators and the characteristic indicators are positive indicators.

[0010] Furthermore, based on the pressure index and the state index, the rate of change vector for adjacent time periods is calculated, including: Construct a pressure index vector based on the pressure index; Construct a state indicator vector based on the state indicators; Calculate the pressure index change rate vector for adjacent time periods based on the pressure index vector; The rate of change vector of the state index for the adjacent time periods is calculated based on the state index vector.

[0011] Furthermore, based on the judgment result, the anomaly type identification result is output, including: When the rate of change of the pressure index is greater than the threshold for a significant increase in pressure, and the rate of change of the state index is greater than or equal to the threshold for a stable state, it is determined that the red line unit has a pressure-leading anomaly. When the rate of change of the pressure index is less than the pressure stability threshold and the rate of change of the state index is less than the state significant decline threshold, it is determined that the red line unit has a state response anomaly.

[0012] Furthermore, based on the judgment result, the anomaly type identification result is output, including: When the difference vector of the state change inside and outside the red line unit is less than the difference judgment threshold inside and outside the red line, it is determined that there is an internal and external comparison anomaly in the red line unit; When the standardized value of the state index within the red line unit is less than the historical baseline deviation threshold, and the duration of the deviation exceeds a preset time threshold, it is determined that the red line unit has a baseline deviation anomaly.

[0013] Secondly, embodiments of the present invention provide an automatic identification and early warning system for anomalies in marine ecological protection red lines, the system comprising: The acquisition module is used to acquire multi-source marine monitoring data, including marine ecological early warning monitoring data, dynamic supervision data of sea area use, and ship AIS activity data. The pressure index calculation module is used to calculate pressure indices, including the intensity of marine development and use, nutrient concentration, and intensity of ship activity. A status indicator calculation module is used to calculate status indicators, which include common indicators and characteristic indicators. The standardized value calculation module is used to calculate the standardized values ​​of the status indicators within the redline unit; The rate of change vector calculation module is used to calculate the rate of change vector of adjacent time periods based on the pressure index and the state index; wherein, the rate of change vector of adjacent time periods includes the rate of change vector of the pressure index and the rate of change vector of the state index. The construction module is used to construct the state index vector of the control area, and calculate the state change difference vector inside and outside the red line unit based on the state index vector of the control area and the state index change rate vector. The joint judgment module is used to construct a coupled anomaly identification model based on the relationship between pressure change trends and state response. The coupled anomaly identification model performs joint judgment by comprehensively considering the pressure index change rate vector, the state index change rate vector, the standardized values ​​of the state index within the red line unit, and the state change difference vector inside and outside the red line unit. The joint judgment is based on both time series change characteristics and spatial comparison difference characteristics. Based on the judgment results, the module outputs anomaly type identification results, which include pressure-leading anomalies, state response anomalies, internal and external comparison anomalies, and baseline deviation anomalies.

[0014] Furthermore, the pressure index calculation module is specifically used for: The intensity of sea area development and use is calculated by multiplying the sea area of ​​each sea area use type within the red line area during the assessment period by the intensity coefficient of each sea area use type. The nutrient concentrations were calculated using the regional annual average values ​​of inorganic nitrogen and reactive phosphate monitoring data from marine ecological early warning monitoring data. The intensity of vessel activity is calculated based on the sailing time of the vessel's AIS data during the assessment period within the red-line area; All of the pressure indicators are negative.

[0015] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the method described above.

[0016] Fourthly, embodiments of the present invention provide a computer-readable medium having processor-executable non-volatile program code that causes the processor to perform the method described above.

[0017] This invention provides a method and system for automatic identification and early warning of anomalies in marine ecological protection red lines, comprising: acquiring multi-source marine monitoring data, including marine ecological early warning monitoring data, dynamic monitoring data of sea area use, and ship AIS activity data; calculating pressure indicators, including sea area development and use intensity, nutrient concentration, and ship activity intensity; calculating state indicators, including common indicators and characteristic indicators; calculating standardized values ​​of state indicators within the red line unit; calculating the rate of change vector of adjacent time periods based on the pressure indicators and state indicators; wherein the rate of change vector of adjacent time periods includes the rate of change vector of pressure indicators and the rate of change vector of state indicators; constructing a state indicator vector of a control area, and calculating the difference vector of state changes inside and outside the red line unit based on the state indicator vector of the control area and the rate of change vector of state indicators; and constructing a coupling based on the relationship between pressure change trend and state response. An anomaly identification model is coupled with a comprehensive anomaly identification model that uses the pressure index change rate vector, the state index change rate vector, the standardized values ​​of state indices within the red line unit, and the state change difference vector inside and outside the red line unit for joint judgment. The joint judgment is based on both time series change characteristics and spatial comparison difference characteristics. Based on the judgment results, the model outputs anomaly type identification results, including pressure-leading anomalies, state response anomalies, internal and external comparison anomalies, and baseline deviation anomalies. By comprehensively considering the pressure change trend, state change trend, internal and external comparison differences within the red line, and the degree of historical baseline deviation, a dual-dimensional coupled protection effectiveness anomaly judgment scenario of "time series and spatial comparison" is constructed. This enables the differentiation and identification of different anomaly types such as potential risks, degradation of protected objects, and failure of protection effectiveness, avoiding misjudgments caused by single time point or absolute value evaluation, and improving the reliability and interpretability of anomaly identification.

[0018] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 The flowchart of the automatic identification and early warning method for anomalies in marine ecological protection red lines provided in Embodiment 1 of the present invention is shown below. Figure 2 The flowchart of the automatic identification and early warning method for anomalies in marine ecological protection red lines provided in Embodiment 1 of the present invention is as follows: Figure 3 This is a schematic diagram of the automatic identification and early warning system for abnormal marine ecological protection red lines provided in Embodiment 2 of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] The ecological protection red line system is a new type of regional ecological management system proposed by China based on the continuous practice of various regional ecological management systems. As a major policy decision in the field of marine ecological civilization construction, my country prioritizes the designation of areas with extremely important marine ecological functions and extremely vulnerable marine ecosystems within the marine ecological protection red line for strict protection. In 2023, approximately 150,000 square kilometers of marine ecological protection red lines were delineated, covering various types of ecosystems including mangroves, seagrass beds, coral reefs, coastal salt marshes, important estuaries, and important islands. Real-time and dynamic assessment and early warning of the ecological protection status of the marine ecological protection red lines after their delineation are crucial. This is not only the foundation for improving the long-term management and control of the marine ecological protection red lines, but also an important requirement for carrying out targeted management, restoration, and risk prevention measures based on abnormal situations, thereby enhancing the diversity, stability, and sustainability of the ecosystems within the marine ecological protection red line areas.

[0024] Currently, regarding terrestrial ecological protection red lines, the Ministry of Environmental Protection has issued the "Technical Specifications for Supervision and Evaluation of the Protection Effectiveness of Ecological Protection Red Lines (Trial Implementation)," which establishes an indicator system for evaluating the protection effectiveness of ecological protection red lines from four dimensions: area, function, nature, and management. The evaluation assesses the protection effectiveness of ecological protection red lines through scores on five sub-indices: protected area index, land use nature index, ecological function index, management capacity index, and characteristic indicator index. For the evaluation of the protection effectiveness of marine protected spaces, the World Commission on Protected Areas (WCPA) has proposed a widely adopted framework that emphasizes the planning, inputs, processes, outputs, and outcomes of protected areas. The International Union for Conservation of Nature (IUCN)'s "Guidelines for Performance Evaluation of Marine Protected Areas" further refines this approach by incorporating biophysical, socio-economic, and governance indicators. Domestic researchers have conducted case studies on the protection effectiveness of marine protected areas from the perspectives of habitat, environmental quality, and biological resources. All the aforementioned work related to ecological protection red lines or marine protected areas aims at comprehensive evaluation and grading; currently, there is no dedicated technical process for the anomaly identification and early warning of marine ecological protection red lines.

[0025] This application takes the spatial units of marine ecological protection red lines as the basic analytical object; it integrates multi-source marine monitoring data and combines them with the types of marine ecological protection red lines to construct a pressure and status indicator system; it establishes rules for judging abnormal protection effectiveness using historical baseline periods and control areas outside the red lines as references; and it achieves automated early warning output through anomaly type identification and anomaly degree classification. This technical approach realizes a complete technical closed loop from "monitoring data" to "judgment of abnormal effectiveness" and then to "early warning information output".

[0026] To facilitate understanding of this embodiment, the embodiments of the present invention will be described in detail below.

[0027] Example 1: Figure 1 The flowchart illustrates the automatic identification and early warning method for anomalies in marine ecological protection red lines provided in Embodiment 1 of the present invention.

[0028] Reference Figure 1 The method includes the following steps: Step S101: Obtain multi-source marine monitoring data, which includes marine ecological early warning monitoring data, dynamic supervision data of sea area use, and ship AIS activity data; Specifically, multi-source marine monitoring data includes marine ecological early warning monitoring data reflecting the state of the ecological environment, dynamic regulatory data on sea area use reflecting the intensity of human activities, and ship AIS activity data reflecting the intensity of ship operations. Preprocessing of multi-source marine monitoring data includes time scale unification, spatial location matching, and indicator direction consistency processing to ensure that monitoring data from different sources are processed within the same analytical framework.

[0029] Step S102: Calculate the pressure indicators, which include the intensity of marine development and use, nutrient concentration, and intensity of ship activity. Step S103: Calculate the status indicators, which include common indicators and characteristic indicators; Step S104: Calculate the standardized values ​​of the state indicators within the redline unit; Step S105: Calculate the rate of change vector for adjacent time periods based on the pressure index and the state index; wherein, the rate of change vector for adjacent time periods includes the rate of change vector for the pressure index and the rate of change vector for the state index. Step S106: Construct the state index vector of the control area, and calculate the state change difference vector inside and outside the red line unit based on the state index vector of the control area and the state index change rate vector; wherein, the spatial location of the control area is adjacent to the red line unit or within the preset buffer distance, and the ecological type of the control area is consistent with or has similar characteristics to the red line unit. Step S107: Construct a coupled anomaly identification model based on the relationship between pressure change trend and state response. The coupled anomaly identification model integrates the pressure index change rate vector, the state index change rate vector, the standardized value of the state index within the red line unit, and the state change difference vector inside and outside the red line unit for joint judgment. The joint judgment is based on time series change characteristics and spatial comparison difference characteristics. The anomaly type identification result is output according to the judgment result. The anomaly type identification result includes pressure-leading anomaly, state response anomaly, internal and external comparison anomaly, and baseline deviation anomaly.

[0030] Further, stress indicators are calculated, including: Step S201: Calculate the intensity of marine development and use by multiplying the marine area of ​​each marine use type within the red line area during the assessment period by the intensity coefficient of each marine use type. Step S202: Calculate nutrient concentrations using the regional annual average values ​​of inorganic nitrogen and reactive phosphate monitoring data from marine ecological early warning monitoring data; Step S203: Calculate the intensity of ship activity based on the sailing time of the ship's AIS data during the assessment period within the red-line area; All of the pressure indicators are negative.

[0031] Specifically, the pressure indicators include three categories: intensity of marine area development and use, nutrient concentration, and intensity of ship activity. The intensity of marine area development and use is calculated by multiplying the sea area of ​​each type of sea use within the red line area during the assessment period by the intensity coefficient of each sea area use type. The intensity coefficient of sea area use type is determined with reference to existing methods. The nutrient concentration is calculated by using the regional annual average value of inorganic nitrogen and reactive phosphate monitoring data from marine ecological early warning monitoring data. The intensity of ship activity is calculated based on the sailing time of ships within the red line area during the assessment period according to AIS data.

[0032] Furthermore, the common indicators include the proportion of area with excellent seawater quality and the marine biodiversity index, while the characteristic indicators are set according to the type of marine ecological protection red line; among them, both the common and characteristic indicators are positive indicators.

[0033] Specifically, the status indicators include common indicators and characteristic indicators. The common indicators include the proportion of area with excellent seawater quality and the marine biodiversity index. The characteristic indicators are set according to the types of marine ecological protection red lines. There are eleven types of marine ecological protection red lines, namely mangroves, coral reefs, seagrass beds, coastal salt marshes, important tidal flats and shallow waters, important estuaries, important spawning grounds of fishery resources, extremely important coastal protection areas, extremely vulnerable coastal erosion areas, concentrated distribution areas of rare and endangered species, and specially protected islands.

[0034] Based on the characteristics of each type of red line, its characteristic status indicators are determined. The characteristic indicator for mangrove type red line areas is mangrove area; for seagrass bed type red line areas, it is seagrass bed area; for coral reef type red line areas, it is coral reef area; for coastal salt marsh type red line areas, it is coastal salt marsh area; for important tidal flats and shallow sea areas red line areas, it is the density of macrobenthic organisms; for important estuary red line areas and important fishery resource spawning ground red line areas, it is the density of fish eggs and larvae; for extremely important coastal protection areas and extremely vulnerable coastal erosion areas red line areas, it is the maximum erosion distance; for areas with concentrated distribution of rare and endangered species red line areas, it is the population density of rare and endangered species; and for specially protected island red line areas, it is the island's vegetation coverage. All common and characteristic indicators are positive indicators.

[0035] Furthermore, based on the pressure and state indicators, the rate of change vector between adjacent time periods is calculated, including the following steps: Step S301: Construct a pressure index vector based on the pressure index; Step S302: Construct a state index vector based on the state index; Step S303: Calculate the pressure index change rate vector for adjacent time periods based on the pressure index vector; Step S304: Calculate the state index change rate vector for adjacent time periods based on the state index vector.

[0036] Specifically, for each red line unit k In time t Construct a stress indicator vector:

[0037] For the first i The pressure indicator is in the red line unit. k ,time t The value to be taken below; m This represents the number of stress indicators; stress indicators are used to characterize the intensity of human activities' disturbance to the ecosystem within the redline unit.

[0038] For each red line unit k In time t Construct a state indicator vector:

[0039] For the first i Each status indicator is within the red line unit. k ,time t The value to be taken below; n This represents the number of status indicators.

[0040] Common state indicators: applicable to all types of marine ecological protection red line units, used to characterize basic ecological state; Characteristic status indicators: These are set separately for different types of marine ecological protection red line units and are used to characterize the status characteristics of specific ecological functions or ecological objects.

[0041] Selecting a period of historical stability as the benchmark period T 0, for each indicator X i Calculate its baseline mean U i Standard deviation from the benchmark .

[0042] Standardize the indicators for the current monitoring period:

[0043] in, As an indicator i In the red line unit k ,time t The original value below; These are the standardized indicator values; and Let be the mean and standard deviation of indicator i over the baseline period. Standardized indicators are used to measure the degree of deviation of the current state from its historical steady state.

[0044] Calculate the rate of change of the pressure index vector over adjacent time periods: = -

[0045] in, This represents the vector of the rate of change of the pressure index.

[0046] Calculate the rate of change of the state index vector over adjacent time periods: = -

[0047] in: This represents the vector of rate of change of state indicators.

[0048] For each redline unit k, spatially adjacent areas outside the redline with similar ecological characteristics are selected as control areas, and a state index vector for the control areas is constructed. Calculate the difference in state changes inside and outside the red line: = -

[0049] in, This represents the vector of differences in state changes inside and outside the red line unit.

[0050] Furthermore, refer to Figure 2 The determination of whether there is an anomaly in the red-line unit is based on the pressure index change rate vector, the state index change rate vector, the standardized values ​​of the state index within the red-line unit, and the state change difference vector inside and outside the red-line unit. This includes the following steps: Step S401: When the rate of change vector of the pressure index is greater than the threshold for a significant increase in pressure, and the rate of change vector of the state index is greater than or equal to the threshold for a stable state, it is determined that there is a pressure-leading anomaly in the red line unit. Step S402: When the rate of change of the pressure index is less than the pressure stability threshold and the rate of change of the state index is less than the state significant decline threshold, it is determined that the red line unit has a state response anomaly.

[0051] Specifically, pressure-leading anomalies: When the pressure index change rate vector satisfies > The vector of rate of change of state index satisfies ≥ ; in, The threshold for a significant increase in pressure. If the threshold for stable ecological conditions is reached, an anomaly is determined to exist within the red-lined unit. This anomaly is used to identify potential risk scenarios where human activity intensity has significantly increased before any obvious changes in the ecological state have occurred. The state indicators can be used to assess overall stability based on common state indicators, and combined with characteristic state indicators to assist in the analysis of potential risks to core protected objects.

[0052] State-response anomalies: When the pressure index change rate vector satisfies The vector of rate of change of state index satisfies < ; in, The pressure stability threshold, If the threshold for a significant decline in status is reached, then an anomaly is determined to exist within the red-line unit. In this case, judgment is primarily based on characteristic status indicators to identify the risk of premature degradation of core protected objects within the red-line area, even when the overall ecological state has not changed significantly. Even if common status indicators remain stable, a significant decline in characteristic status indicators can still be considered an anomaly.

[0053] Furthermore, based on the pressure index change rate vector, the state index change rate vector, the standardized values ​​of the state indices within the red-line unit, and the state change difference vector inside and outside the red-line unit, it is determined whether there is an anomaly in the red-line unit, including the following steps: Step S501: When the difference vector of the state change inside and outside the red line unit is less than the difference judgment threshold inside and outside the red line, it is determined that there is an internal and external control type anomaly in the red line unit. Step S502: When the standardized value of the status index within the red line unit is less than the historical benchmark deviation threshold, and the duration of the deviation exceeds the preset time threshold, it is determined that the red line unit has a benchmark deviation anomaly.

[0054] Specifically, in vitro-internal control type abnormalities: When the trend of state change within the red-lined unit is significantly worse than that of the control area outside the red line... < ; in, If the threshold for judging the difference between inside and outside the red line is set, then the red line unit is judged to have an anomaly. This anomaly is mainly judged based on common state indicators, which are used to evaluate the actual protection effectiveness of the red line area under the changing background environment. Most characteristic state indicators are not comparable and have significant differences inside and outside the red line, and are not used as discrimination conditions. Characteristic state indicators can be used to help explain the cause of the anomaly.

[0055] Abnormal deviation from benchmark: When the standardized value of the state index within the red line unit meets < Furthermore, the above conditions persist for more than the window time W. Here, W is the historical baseline deviation threshold, and W is the time continuity threshold. When a common or characteristic state indicator within a red-line unit deviates significantly from the historical baseline period, and this deviation continues to exceed a preset time threshold, the red-line unit is determined to have a historical baseline deviation anomaly. This anomaly is used to identify long-term degradation trends in the ecological state of the red-line area and reflects the risk of persistent failure of protection effectiveness.

[0056] The main advantages of this application include: 1) To achieve phased and predictable identification of anomalies in protection effectiveness oriented towards marine ecological protection red lines; Based on the coupling relationship between human activity pressure and ecosystem status, this application constructs a multi-scenario protection effectiveness anomaly identification mechanism, forming a complete technical closed loop from "monitoring data input - trend analysis - coupling judgment - anomaly type identification - early warning output". It can not only identify situations where the ecological status has obviously degraded, but also discover potential risks in advance when pressure changes precede ecological response. It realizes phased anomaly identification from risk-leading identification to protection effectiveness failure judgment, improving the foresight and scientific nature of red line protection effectiveness supervision.

[0057] 2) It can accurately distinguish between human interference and natural background fluctuations, improving the reliability of anomaly detection; This application comprehensively analyzes the trends of pressure change, state change, and the comparison between inside and outside the red line to make a multidimensional judgment on the causes of anomalies. It can effectively distinguish between ecological changes caused by natural fluctuations and regional background changes and abnormal changes caused by increased human activity interference, avoid misjudging background fluctuations as red line protection failure, and improve the objectivity and reliability of the results of anomaly identification of protection effectiveness.

[0058] 3) It highlights the risk identification of core protected objects, and the results are intuitive and easy to manage and apply; This application strengthens the ability to identify changes in core protected objects by conducting structured analysis of the ecological state, based on an overall ecological state assessment. It can promptly detect the degradation risks of key ecological elements even when the overall ecological environment has not changed significantly.

[0059] 4) The anomaly detection logic is clear, the results are highly interpretable, and it is easy to manage and apply.

[0060] This application constructs multiple scenarios for determining abnormal protection effectiveness, distinguishing and identifying different types of anomalies such as pressure-led risks, abnormal ecological state responses, and long-term failure of protection effectiveness. The anomaly determination logic is clear, and the determination results have clear management implications, providing direct technical support for the dynamic monitoring, risk warning, and management decision-making of marine ecological protection red lines.

[0061] The following example illustrates the situation using non-abnormal background ecological changes based on internal and external comparisons: This embodiment illustrates the process by which this application determines non-abnormal situations under conditions of regional background change. During continuous monitoring of a marine ecological protection redline unit, a downward trend was observed in the state indicators within the redline area, but the pressure indicators did not change significantly during the same period, and the state indicators in the adjacent control area outside the redline showed a similar downward trend. According to the pressure-state coupled anomaly determination model of this invention, the above changes do not meet the anomaly determination conditions caused by enhanced human activity interference, and are more likely to reflect ecological changes caused by regional natural fluctuations or climate background changes, thereby avoiding misjudging background fluctuations as anomalies in the effectiveness of redline protection.

[0062] The following example illustrates the identification of protection effectiveness anomalies based on pressure-state coupling: This embodiment illustrates the identification process for abnormal conservation effectiveness under conditions of significantly increased human activity pressure. In monitoring a marine ecological protection redline unit, pressure indicators showed a significant increase, while status indicators within the redline area remained stable or showed a downward trend. Simultaneously, no similar changes were observed in the control area outside the redline. According to the anomaly detection model of this application, these changes meet the criteria for pressure-led or redline-inside / outside-control anomalies, and can be identified as an anomaly in conservation effectiveness caused by increased human activity interference, providing a basis for subsequent management and intervention.

[0063] Example 2: Figure 3 This is a schematic diagram of the automatic identification and early warning system for abnormal marine ecological protection red lines provided in Embodiment 2 of the present invention.

[0064] Reference Figure 3 The system includes: The acquisition module is used to acquire multi-source marine monitoring data, including marine ecological early warning monitoring data, dynamic supervision data of sea area use, and ship AIS activity data. The pressure index calculation module is used to calculate pressure indices, including the intensity of marine development and use, nutrient concentration, and intensity of ship activity. The status indicator calculation module is used to calculate status indicators, which include common indicators and characteristic indicators. The standardized value calculation module is used to calculate the standardized values ​​of the status indicators within the redline unit; The rate of change vector calculation module is used to calculate the rate of change vector of adjacent time periods based on pressure indicators and state indicators; wherein, the rate of change vector of adjacent time periods includes the rate of change vector of pressure indicators and the rate of change vector of state indicators. The module is used to construct the state index vector of the control area and calculate the difference vector of state changes inside and outside the red line unit based on the state index vector of the control area and the state index change rate vector. The comprehensive judgment module is used to construct a coupled anomaly identification model based on the relationship between pressure change trends and state response. The coupled anomaly identification model performs joint judgment based on the pressure index change rate vector, the state index change rate vector, the standardized values ​​of state indices within the red line unit, and the state change difference vector inside and outside the red line unit. The joint judgment is based on both time series change characteristics and spatial comparison difference characteristics. Based on the judgment results, the module outputs anomaly type identification results, which include pressure-leading anomalies, state response anomalies, internal and external comparison anomalies, and baseline deviation anomalies.

[0065] Furthermore, the pressure index calculation module is specifically used for: The intensity of marine development and use is calculated by multiplying the sea area of ​​each marine use type within the red line area during the assessment period by the intensity coefficient of each marine use type. Nutrient concentrations were calculated using the regional annual average values ​​of inorganic nitrogen and reactive phosphate monitoring data from marine ecological early warning monitoring data. The intensity of ship activity is calculated based on the sailing time of ships within the red-line area during the assessment period using AIS data. All of the pressure indicators are negative.

[0066] This application breaks through the existing technical approach that mainly relies on single-state evaluation or ex-post effectiveness assessment. Based on the coupling relationship between changes in human activity pressure and ecosystem state response, it constructs a method for identifying anomalies in the protection effectiveness of marine ecological protection red lines. By jointly analyzing the changing trends and interrelationships of pressure indicators and state indicators, it can effectively distinguish between abnormal changes caused by natural fluctuations and human interference. It achieves hierarchical judgment from identifying pressure-leading anomalies, identifying state response anomalies, to identifying protection effectiveness failures, providing technical support for dynamic monitoring and risk warning of protection effectiveness in red line areas.

[0067] 1) Anomaly identification mechanism based on "pressure-state sequence relationship"; By introducing changes in human activity stress as leading information, and analyzing the temporal relationship between stress indicators and state indicators, two types of identification scenarios are constructed: stress-leading anomalies and state-response anomalies. This approach enables the early identification of potential risks to conservation effectiveness before significant changes occur in the ecological state, unlike existing technologies that rely solely on state indicator thresholds.

[0068] 2) Differentiated invocation mechanism for common / characteristic status indicators in different abnormal scenarios; This application does not use ecological status as a single overall indicator for judgment. Instead, based on the differences in marine ecological protection red line types, it structurally breaks down the status indicators into common status indicators and characteristic status indicators. Different judgment strategies are adopted in different anomaly identification scenarios, so that anomaly identification can simultaneously reflect the overall ecological stability of the red line area and the safety of the core protected objects, and achieve refined identification of risks to different protection targets.

[0069] 3) A multi-scenario method for determining protection effectiveness anomalies based on trend changes, spatial comparison, and deviation from historical benchmarks was constructed.

[0070] This application constructs a dual-dimensional coupled protection effectiveness anomaly judgment scenario by comprehensively considering the trends of pressure change, state change, differences between inside and outside the red line, and deviation from historical benchmarks. This enables the differentiation and identification of different anomaly types such as potential risks, degradation of protected objects, and failure of protection effectiveness, avoiding misjudgments caused by single time point or absolute numerical evaluation, and improving the reliability and interpretability of anomaly identification.

[0071] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the automatic identification and early warning method for abnormal marine ecological protection red lines provided in the above embodiments.

[0072] This invention also provides a computer-readable medium having processor-executable non-volatile program code, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the above-described method for automatic identification and early warning of anomalies in marine ecological protection red lines.

[0073] The computer program product provided in this embodiment of the invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0074] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0075] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0076] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0077] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0078] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do 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, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for automatic identification and early warning of anomalies in marine ecological protection red lines, characterized in that, The method includes: Acquire multi-source marine monitoring data, including marine ecological early warning monitoring data, dynamic supervision data of sea area use, and ship AIS activity data; Calculate pressure indicators, which include the intensity of marine development and use, nutrient concentration, and intensity of ship activity; Calculate state indicators, which include common indicators and characteristic indicators; Calculate the standardized values ​​of the status indicators within the red-line unit; Based on the pressure index and the state index, calculate the rate of change vector for adjacent time periods; wherein, the rate of change vector for adjacent time periods includes the rate of change vector for the pressure index and the rate of change vector for the state index. Construct a state index vector for the control area, and calculate the difference vector of state changes inside and outside the red line unit based on the state index vector for the control area and the state index change rate vector. A coupled anomaly identification model based on the relationship between pressure change trends and state response is constructed. The coupled anomaly identification model integrates the pressure index change rate vector, the state index change rate vector, the standardized value of the state index within the red line unit, and the state change difference vector inside and outside the red line unit for joint determination. The joint determination is based on both time series change characteristics and spatial comparison difference characteristics. Based on the determination results, the anomaly type identification results are output, which include pressure-leading anomalies, state response anomalies, inside-outside comparison anomalies, and baseline deviation anomalies.

2. The method for automatic identification and early warning of anomalies in marine ecological protection red lines according to claim 1, characterized in that, Calculate stress indicators, including: The intensity of sea area development and use is calculated by multiplying the sea area of ​​each sea area use type within the red line area during the assessment period by the intensity coefficient of each sea area use type. The nutrient concentrations were calculated using the regional annual average values ​​of inorganic nitrogen and reactive phosphate monitoring data from marine ecological early warning monitoring data. The intensity of vessel activity is calculated based on the sailing time of the vessel's AIS data during the assessment period within the red-line area; All of the pressure indicators are negative.

3. The method for automatic identification and early warning of anomalies in marine ecological protection red lines according to claim 1, characterized in that, The common indicators include the proportion of area with excellent seawater quality and the marine biodiversity index, while the characteristic indicators are set according to the type of marine ecological protection red line; wherein, both the common indicators and the characteristic indicators are positive indicators.

4. The method for automatic identification and early warning of anomalies in marine ecological protection red lines according to claim 1, characterized in that, Based on the pressure index and the state index, calculate the rate of change vector for adjacent time periods, including: Construct a pressure index vector based on the pressure index; Construct a state indicator vector based on the state indicators; Calculate the pressure index change rate vector for adjacent time periods based on the pressure index vector; The rate of change vector of the state index for the adjacent time periods is calculated based on the state index vector.

5. The method for automatic identification and early warning of anomalies in marine ecological protection red lines according to claim 1, characterized in that, Based on the judgment result, the anomaly type identification result is output, including: When the rate of change of the pressure index is greater than the threshold for a significant increase in pressure, and the rate of change of the state index is greater than or equal to the threshold for a stable state, it is determined that the red line unit has the pressure-leading anomaly. When the rate of change of the pressure index is less than the pressure stability threshold and the rate of change of the state index is less than the state significant decline threshold, it is determined that the red line unit has the state response anomaly.

6. The method for automatic identification and early warning of anomalies in marine ecological protection red lines according to claim 1, characterized in that, Based on the judgment result, the anomaly type identification result is output, including: When the difference vector of the state change inside and outside the red line unit is less than the difference judgment threshold inside and outside the red line, it is determined that there is an internal and external comparison anomaly in the red line unit; When the standardized value of the state index within the red line unit is less than the historical baseline deviation threshold, and the duration of the deviation exceeds a preset time threshold, it is determined that the red line unit has the baseline deviation anomaly.

7. An automatic identification and early warning system for anomalies in marine ecological protection red lines, characterized in that, The system includes: The acquisition module is used to acquire multi-source marine monitoring data, including marine ecological early warning monitoring data, dynamic supervision data of sea area use, and ship AIS activity data. The pressure index calculation module is used to calculate pressure indices, including the intensity of marine development and use, nutrient concentration, and intensity of ship activity. A status indicator calculation module is used to calculate status indicators, which include common indicators and characteristic indicators. The standardized value calculation module is used to calculate the standardized values ​​of the status indicators within the redline unit; The rate of change vector calculation module is used to calculate the rate of change vector of adjacent time periods based on the pressure index and the state index; wherein, the rate of change vector of adjacent time periods includes the rate of change vector of the pressure index and the rate of change vector of the state index. The construction module is used to construct the state index vector of the control area, and calculate the state change difference vector inside and outside the red line unit based on the state index vector of the control area and the state index change rate vector. The joint judgment module is used to construct a coupled anomaly identification model based on the relationship between pressure change trends and state response. The coupled anomaly identification model performs joint judgment by comprehensively considering the pressure index change rate vector, the state index change rate vector, the standardized values ​​of the state index within the red line unit, and the state change difference vector inside and outside the red line unit. The joint judgment is based on both time series change characteristics and spatial comparison difference characteristics. Based on the judgment results, the module outputs anomaly type identification results, which include pressure-leading anomalies, state response anomalies, internal and external comparison anomalies, and baseline deviation anomalies.

8. The automatic identification and early warning system for anomalies in marine ecological protection red lines according to claim 7, characterized in that, The pressure index calculation module is specifically used for: The intensity of sea area development and use is calculated by multiplying the sea area of ​​each sea area use type within the red line area during the assessment period by the intensity coefficient of each sea area use type. The nutrient concentrations were calculated using the regional annual average values ​​of inorganic nitrogen and reactive phosphate monitoring data from marine ecological early warning monitoring data. The intensity of vessel activity is calculated based on the sailing time of the vessel's AIS data during the assessment period within the red-line area; All of the pressure indicators are negative.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the method described in any one of claims 1 to 6.

10. A computer-readable medium having processor-executable non-volatile program code, characterized in that, The program code causes the processor to execute the method described in any one of claims 1 to 6.

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