Ecological pattern-based ecological management evaluation method and ecological integrated monitoring system

Abstract

The invention relates to the field of ecological protection data processing, in particular to an ecological pattern-based ecological management evaluation method and an ecological integrated monitoring system. According to the method provided by the invention, an ecological pattern evaluation matrix based on six dimensions is determined according to an ecological pattern data set, and a dynamic weight distribution model is obtained based on an ecological management target; and multiplying the weight matrix obtained in the dynamic weight distribution model by the ecological pattern evaluation matrix to obtain evaluation score conditions based on different ecological management targets, so that the completion conditions of the ecological management targets can be judged. The method focuses on the ecological pattern level, comprehensively integrates multi-dimensional ecological information, and systematically reflects the overall characteristics and the operation state of an ecological system; a unified weight adjustment model is applied to flexibly adapt to different management targets such as ecological protection, ecological planning and ecological restoration, and a complicated evaluation system does not need to be independently constructed for each target.

Description

Technical Field

[0001] The present invention relates to the field of ecological protection data processing, and in particular to an ecological management assessment method based on ecological pattern and an ecological integrated monitoring system. Background Art

[0002] In recent years, the importance of ecological management has become increasingly prominent. With the rapid development of human society, the ecological environment faces numerous severe challenges, such as deforestation, wetland degradation, and biodiversity loss. Ecosystems are the foundation of life on Earth. They not only provide essential resources such as food, water, and air, but also maintain crucial ecological services such as climate regulation, soil and water conservation, and soil fertility maintenance.

[0003] There are some urgent problems to be solved in the evaluation of ecological management. First, there is a lack of unified and effective quantitative indicators. At present, the evaluation of ecological management based on different objectives involves many indicators, but these indicators are often difficult to compare and measure uniformly in different regions and different ecosystems. Furthermore, manual statistics have many disadvantages. When manually collecting and processing ecological data, subjective biases and recording errors are prone to occur. In addition, manual statistics are inefficient and cannot reflect the rapid changes in the ecosystem in a timely manner, resulting in delayed evaluation results and difficulty in meeting the timeliness requirements of ecological management. These problems seriously restrict the accuracy and effectiveness of ecological management work. Therefore, there is a need for a method that can effectively evaluate the effectiveness of ecological management work under different ecological management objectives. Summary of the Invention

[0004] The purpose of the present invention is to provide an ecological management assessment method and an ecological integrated monitoring system based on ecological pattern, wherein the method proposed in the present invention can effectively evaluate the effectiveness of ecological management work under different ecological management objectives.

[0005] According to a first aspect of the present invention, an ecological management assessment method based on ecological pattern is proposed, comprising: S1. Train the dynamic weight allocation model based on the ecological management objectives and management operation datasets; S2. Determine the first weight matrix according to the dynamic weight allocation model ; S3. Determine the ecological pattern evaluation matrix based on the ecological pattern dataset of the target area ; S4. Determine the ecological pattern assessment score SC = W1 × D1 based on the first weight matrix W1 and the ecological pattern assessment matrix D1; S5. Determine the level of completion of ecological management objectives based on the ecological pattern assessment score SC.

[0006] According to some embodiments, in the method of the first aspect of the present invention, the ecological management goal includes any one of ecological planning T1, ecological protection T2, and ecological restoration T3; The dynamic weight allocation model includes a reinforcement learning model, and the reinforcement learning model includes a state space and an action space; The state space records ecosystem parameters, including the ecological connectivity ratio, ecosystem composition ratio, ecosystem type area change rate, ecosystem spatial pattern characteristics, ecosystem comprehensive dynamics, ecosystem classification dynamics, as well as ecological planning T1, ecological protection T2, and ecological restoration T3. It is used to enable the intelligent agent to perceive the current environmental status of the ecosystem and provide a basis for the intelligent agent's decision-making; The action space includes the first weight matrix W1, as well as the value range and adjustment step of each data in the first weight matrix W1, which are used to limit the action mode of the intelligent agent so that the intelligent agent knows which feasible actions can be selected.

[0007] According to some embodiments, in the method of the first aspect of the present invention, step S1. training based on the management operation dataset to obtain a dynamic weight allocation model includes: Set different reward functions for different ecological management goals ; Perform preprocessing operations on the management operation data set to obtain training samples; The agent of the reinforcement learning model selects a first weight matrix W1 from the action space according to the current state as the action to be performed; Evaluate the impact of the selected first weight matrix W1 on the ecological management goal according to the reward function, and feed back the reward signal and new state information to the agent; Based on the reward signal and new state information, the agent uses the reinforcement learning algorithm to update and learn its weight distribution plan to determine the final reinforcement learning model.

[0008] According to some embodiments, in the method of the first aspect of the present invention, the intelligent agent of the reinforcement learning model is the core component for performing decision-making and learning, and is used to perceive the current state of the environment, select appropriate actions from its action space to execute, and then adjust its behavioral strategy according to the reward signal fed back by the environment.

[0009] According to some embodiments, in the method of the first aspect of the present invention, the state space further includes a connectivity index COH, a first aggregation index AI, a total number of patches NP, an average patch area, a boundary density ED, and a second aggregation index C; the action space further includes a second weight matrix and a third weight matrix; and the method of the first aspect of the present invention further includes: Determine the second weight matrix W2= according to the dynamic weight allocation model And the third weight matrix W3= .

[0010] According to some embodiments, in the method of the first aspect of the present invention, step S3. determining the ecological pattern evaluation matrix D1 based on the ecological pattern dataset of the target area includes: The connectivity index COH, the first aggregation index AI, the total number of patches NP, and the average patch area were calculated based on the ecological pattern data set. , boundary density ED, second aggregation index C; The ecological connectivity ratio is calculated based on the connectivity index COH and the first aggregation index AI, which follows the following formula: ; According to the total number of plaques NP, average plaque area , boundary density ED and the second aggregation index C are used to calculate the spatial pattern characteristics of the ecosystem according to the following formula: .

[0011] According to some embodiments, in the method of the first aspect of the present invention, step S5. determining the level of completion of the ecological management goal based on the ecological pattern assessment score SC includes: When the ecological pattern assessment score SC is less than or equal to the first scoring value, the completion level is poor; When the ecological pattern assessment score SC is greater than the first scoring value and less than or equal to the second scoring value, the completion level is average; When the ecological pattern assessment score SC is greater than the second scoring value and less than or equal to the third scoring value, the completion level is good; When the ecological pattern assessment score SC is greater than the third scoring value, the completion level is excellent.

[0012] According to a second aspect of the present invention, an ecological management assessment device based on ecological pattern is proposed, comprising: The evaluation matrix generation module is used to determine the ecological pattern evaluation matrix D1 based on the ecological pattern dataset of the target area: ; in, Represents ecological connectivity, Indicates the proportion of ecosystem composition, represents the rate of change in area of ecosystem type, Represents the spatial pattern characteristics of the ecosystem, represents the comprehensive dynamics of the ecosystem, Indicates the dynamics of ecosystem classification; The weight model training module is used to train based on the management operation data set to obtain a dynamic weight distribution model; A weight matrix generation module is used to determine the first weight matrix according to the dynamic weight distribution model ; A score calculation module is used to determine an ecological pattern assessment score SC = W1 × D1 based on the first weight matrix W1 and the ecological pattern evaluation matrix D1; The grade evaluation module is used to determine the completion level of ecological management objectives based on the ecological pattern assessment score SC.

[0013] According to a third aspect of the present invention, an ecological integrated monitoring system is proposed, comprising: Data collection unit, including satellite remote sensing monitoring module, drone aerial photography monitoring module and Internet of Things collection module, used to collect ecological pattern data sets and management operation data sets; A data analysis unit comprising the ecological management assessment device based on ecological pattern according to the second aspect of the present invention, configured to execute the ecological management assessment method based on ecological pattern according to the first aspect of the present invention; The data visualization unit includes a visual data display platform for displaying the analysis results of the data analysis unit.

[0014] The solution proposed by the present invention has the following beneficial effects: 1. The proposed method utilizes a unified weight adjustment model that flexibly adapts to diverse management objectives, such as ecological protection, ecological planning, and ecological restoration, eliminating the need to construct complex assessment systems for each objective. By dynamically adjusting weights, it accurately highlights key ecological factors for each management objective, making the assessment process more targeted and effective. This robust adaptability improves assessment efficiency, conserves resources, and ensures the reliability and consistency of assessment results across diverse ecological management scenarios, providing strong support for ecological management decision-making.

[0015] 2. The proposed method focuses on the ecological landscape, comprehensively integrating multidimensional ecological information to systematically reflect the overall characteristics and operational status of ecosystems. From this perspective, it allows for a comprehensive analysis of the interrelationships and spatial distribution patterns of ecosystem elements, avoiding the one-sidedness of single-metric assessments. This provides a more macroscopic and comprehensive perspective for ecological management, aiding in the development of more systematic and scientific ecological management strategies, and ultimately achieving comprehensive ecosystem protection and sustainable development.

[0016] 3. The proposed method can rapidly calculate an assessment score based on real-time ecological pattern data. Through dynamic weight allocation, it reflects the effectiveness of ecological management efforts in real time, allowing for the timely identification of problems and deficiencies in the management process. Compared to traditional assessment methods, this method offers significant timeliness advantages, providing a scientific basis for timely adjustments to ecological management strategies and effectively improving the accuracy and flexibility of management efforts. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without exceeding the scope of protection claimed by the present invention.

[0018] Figure 1 1 is a flow chart of an ecological management assessment method 1000 based on ecological pattern according to the present invention; Figure 2 Schematic diagram of the process of an ecological management assessment method 2000 based on ecological pattern of the present invention; Figure 3 for Figure 1 A flowchart of the detailed steps of step S1 in an ecological management assessment method 1000 based on ecological pattern; Figure 4 for Figure 1 A flowchart of the detailed steps of step S3 in an ecological management assessment method 1000 based on ecological pattern; Figure 5 for Figure 1 A flowchart of the detailed steps of step S5 in the ecological management assessment method 1000 based on ecological pattern; Figure 6 Schematic diagram of the structure of an ecological management assessment device 3000 based on ecological pattern of the present invention; Figure 7 Schematic diagram of the structure of an ecological integrated monitoring system 4000 of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0020] Figure 1 FIG. 1 is a flow chart of an ecological management assessment method 1000 based on ecological pattern of the present invention. Figure 1 As shown, method 1000 includes steps S1 to S5.

[0021] In step S1, an ecological management assessment device based on ecological pattern (such as a processor, which is described below using a processor as an example) is trained based on ecological management goals and management operation data sets to obtain a dynamic weight allocation model.

[0022] In some specific embodiments, in step S1, the dynamic weight allocation model is a reinforcement learning model, and the reinforcement learning model includes a state space and an action space.

[0023] In some specific embodiments, step S1 defines the state space of the reinforcement learning model. Optionally, the state space includes the ecological connectivity ratio, ecosystem composition ratio, ecosystem type area change rate, ecosystem spatial pattern characteristics, ecosystem comprehensive dynamics, ecosystem classification dynamics, as well as ecological planning T1, ecological protection T2, and ecological restoration T3. Optionally, the state space also includes other environmental variables related to ecological management, such as regional climate conditions and human activity intensity, to more comprehensively reflect the actual state of the ecosystem and external influencing factors.

[0024] In some specific embodiments, in step S1, the action space of the reinforcement learning model is defined, and the action space represents the weight allocation strategy for the six dimensional data in the ecological pattern evaluation matrix. Optionally, the action space includes a first weight matrix W1, as well as the value range and adjustment step size of each data in the first weight matrix W1. For example, the value range of the weight data can be set within the interval [0,1], and the adjustment step size for iteratively updating each weight data is 0.01. By reasonably setting the value range and adjustment step size of the weight data, the flexibility and accuracy of the weight allocation are ensured.

[0025] In some specific embodiments, in step S1, the processor sets different reward functions for different ecological management goals. .

[0026] In some specific embodiments, in step S1, preprocessing operations are performed on the data in the management operation dataset, including data cleaning and normalization, to obtain training samples. Optionally, the management operation dataset includes historical ecological data under different ecological management objectives and corresponding management operation records, specifically including changes in the six dimensions of the ecological pattern assessment matrix at different time points, as well as the response results under different management operations (such as the implementation of ecological restoration projects and adjustments to ecological protection policies).

[0027] In some specific embodiments, in step S1: the processor performs preprocessing operations on the management operation data set to obtain training samples; the intelligent agent of the reinforcement learning model selects a first weight matrix W1 from the action space as an action to be executed according to the current state; the processor evaluates the impact of the selected first weight matrix W1 on the ecological management goal according to the reward function, and feeds back the reward signal and the new state information to the intelligent agent; the intelligent agent uses the reinforcement learning algorithm to update and learn its weight distribution plan according to the reward signal and the new state information to determine the final reinforcement learning model.

[0028] In some specific embodiments, in step S1, the dynamic weight allocation model is a linear regression model. In some specific embodiments, it is assumed that there is a linear relationship between the completion of ecological management objectives and the six dimensional data in the ecological pattern evaluation matrix, that is, the goal completion score can be expressed as a linear combination of the six dimensional data. Using a linear regression algorithm, the model is trained using historical data from the management operation dataset, the six dimensional data as independent variables, and the actual completion of the ecological management objectives as the dependent variable. The regression coefficient corresponding to each dimensional data is obtained. This regression coefficient can then be used as a mapping matrix between ecological management objectives and weight allocation.

[0029] In some embodiments, in step S1, the dynamic weight allocation model is an artificial neural network model. In some embodiments, training is used to learn the complex mapping relationship between the six dimensions of data and the achievement of ecological management objectives. During training, the neural network's connection weights are continuously adjusted, ultimately converging to weights that effectively predict objective achievement. These weights reflect the importance of the six dimensions of data in the ecological pattern assessment matrix.

[0030] In step S2, a first weight matrix is determined according to the dynamic weight distribution model. In some specific embodiments, in step S2, the processor can obtain the specific value of the first weight matrix W1 corresponding to the ecological management target based on the trained reinforcement learning model. For example, in some specific embodiments, the ecological management target is the ecological planning T1, and the first weight matrix ; The ecological management goal is ecological protection T2, the first weight matrix ; The ecological management goal is ecological restoration T3, the first weight matrix .

[0031] In step S3, the processor determines the ecological pattern evaluation matrix based on the ecological pattern data set of the target area. .

[0032] in, represents the ecological connectivity ratio, Indicates the proportion of ecosystem composition, represents the rate of change in area of ecosystem type, Represents the spatial pattern characteristics of the ecosystem, represents the comprehensive dynamics of the ecosystem, Represents the dynamics of ecosystem classification.

[0033] In some specific embodiments, the processor calculates the connectivity index COH and the first aggregation index AI according to the ecological pattern data set, and further calculates the ecological connectivity ratio according to the connectivity index COH and the first aggregation index AI. .

[0034] In some specific embodiments, the processor sets the second weight matrix W2= In some specific embodiments, the processor obtains a second weight matrix W2= according to the dynamic weight allocation model.

[0035] In some specific embodiments, the processor calculates the connectivity index COH and the first aggregation index AI to obtain an ecological connectivity ratio according to the following formula: .

[0036] In some specific embodiments, the connectivity index COH mainly reflects the connectivity between different ecological units in the ecosystem, such as the degree of physical connectivity between habitat patches, while the first aggregation index AI focuses on reflecting the degree of spatial aggregation of ecological elements. By combining the two to calculate the ecological connectivity ratio, the connectivity status of the ecosystem can be assessed more comprehensively and accurately. For example, an ecosystem may have a high degree of connectivity, indicating that there are more connecting channels between habitat patches, but if the aggregation of these patches is low and the distribution is more dispersed, then the overall connectivity of the ecosystem may not be ideal. The ecological connectivity ratio can comprehensively reflect this complex situation.

[0037] In some specific embodiments, the processor calculates the ecosystem composition ratio based on the ecological pattern dataset according to the following formula: ; in, represents the area of ecosystem type i, and TS represents the total area of the target area for assessment.

[0038] In some specific embodiments, the processor calculates the ecosystem area change rate based on the ecological pattern dataset according to the following formula: ; in, represents the area of ecosystem type i at the beginning of the period, represents the area of ecosystem type i at the end of the period.

[0039] In some specific embodiments, the processor calculates the total number of patches NP, the average patch area, and the , boundary density ED and second aggregation index C, and then further calculations are made to obtain the spatial pattern characteristics of the ecosystem.

[0040] In some embodiments, the processor sets the third weight matrix by a preset method. In some specific embodiments, the processor obtains a third weight matrix according to the dynamic weight allocation model. .

[0041] In some embodiments, the processor calculates the number of plaques NP, the average plaque area, , boundary density ED and the second aggregation index C are used to calculate the spatial pattern characteristics of the ecosystem according to the following formula: .

[0042] In some specific embodiments, the processor calculates the comprehensive dynamics of the ecosystem based on the ecological pattern dataset according to the following formula: ; Among them, ECOi represents the area of ecosystem type i at the beginning of the period, It represents the absolute value of the area of ecosystem type i converted to non-ecosystem type i.

[0043] In some specific embodiments, the processor calculates the ecosystem classification dynamics based on the ecological pattern dataset according to the following formula: ; Where i represents the ecosystem type at the beginning of the period, j represents the ecosystem type at the end of the period, represents the area of the ecosystem type, Aij represents the proportion of the i-th ecosystem type at the beginning of the period transformed into the j-th ecosystem type at the end of the period, and Bij represents the proportion of the j-th ecosystem type at the end of the period transformed from the i-th ecosystem type at the beginning of the period.

[0044] In step S4, the ecological pattern evaluation score SC = W1 × D1 is determined based on the first weight matrix W1 and the ecological pattern evaluation matrix D1. In some specific embodiments, the ecological pattern evaluation score SC = .

[0045] In step S5, the completion level of the ecological management goal is determined based on the ecological pattern assessment score (SC). In some specific embodiments, if the ecological pattern assessment score (SC) is less than or equal to a first scoring value, the completion level is poor; if the ecological pattern assessment score (SC) is greater than the first scoring value and less than or equal to a second scoring value, the completion level is fair; if the ecological pattern assessment score (SC) is greater than the second scoring value and less than or equal to a third scoring value, the completion level is good; and if the ecological pattern assessment score (SC) is greater than the third scoring value, the completion level is excellent.

[0046] Optionally, the first scoring value, the second scoring value, and the third scoring value are 0.5, 0.6, and 0.8, respectively.

[0047] According to Figure 1 In the implementation method shown, the solution proposed in the present invention uses a unified weight adjustment model to flexibly adapt to different management goals such as ecological protection, ecological planning, and ecological restoration, without the need to construct a complex evaluation system for each goal separately; focusing on the ecological pattern level, comprehensively integrating multi-dimensional ecological information, and systematically reflecting the overall characteristics and operating status of the ecosystem; based on the real-time acquired ecological pattern data, the evaluation score is quickly calculated, and the effectiveness of ecological management work is reflected in real time through dynamic weight allocation, so that problems and deficiencies in the management process can be discovered in a timely manner.

[0048] Figure 2 FIG2 is a flow chart of an ecological management assessment method 2000 based on ecological pattern of the present invention. Figure 2 As shown, method 2000 includes steps S201 to S206, wherein step S202 and steps S205 to S206 are the same as step S2 and steps S4 to S5 in method 1000, and are not described in detail here.

[0049] In step S201, training is performed based on the ecological management objectives and the management operation data set to obtain a dynamic weight allocation model.

[0050] In some specific embodiments, in step S201, the dynamic weight allocation model is a reinforcement learning model, and the state space of the reinforcement learning model is further defined to include the connectivity index COH, the first aggregation index AI, the total number of plaques NP, the average plaque area , boundary density ED, second aggregation index C, the action space also includes the second weight matrix and the third weight matrix .

[0051] In some specific embodiments, in step S201, a reward function is designed under different ecological management objectives, wherein the reward function includes a target reward function , Ecological Connectivity Ratio Reward Function and ecosystem spatial pattern characteristic reward function .

[0052] Optionally, the target reward function Different combinations can be configured according to specific ecological management goals, see Figure 4 Optionally, the ecological connectivity ratio reward function = , ecosystem spatial pattern characteristic reward function = .in, 、 、 、 、 、 They are connectivity index COH, first aggregation index AI, total number of plaques NP, average plaque area , boundary density ED, and the change value of the second aggregation index C.

[0053] In some specific embodiments, in step S201, the agent in the reinforcement learning model selects a combination of a first weight matrix W1, a second weight matrix W2, and a third weight matrix W3 from the action space based on its current state to execute as an action. In some specific embodiments, the reinforcement learning model includes a decision-making entity, i.e., the agent, which learns by interacting with the environment. In some specific embodiments, the agent in the reinforcement learning model is placed in a simulated ecological management environment. Based on its current state (including six-dimensional data, ecological management goals, etc.), the agent selects a weight allocation strategy (i.e., a combination of the first weight matrix W1, the second weight matrix W2, and the third weight matrix W3) from the action space to execute as an action.

[0054] In some embodiments, in step S201, the impact of the selected weight allocation strategy on the ecological management objectives is evaluated based on a reward function, and a reward signal and new state information are fed back to the agent. In some embodiments, the reward signal is the change in the six dimensions of the ecological landscape evaluation matrix after weight adjustment, and the state information is the corresponding response of the ecosystem. In step S201, the agent uses a reinforcement learning algorithm to update and learn its weight allocation strategy based on the reward signal and new state information to determine the final reinforcement learning model. In some embodiments, the reinforcement learning algorithm includes any one of a Q-learning algorithm, a deep deterministic policy gradient algorithm, a SARSA algorithm, and a deep Q-network.

[0055] In step S203, a second weight matrix is determined according to the dynamic weight allocation model. and the third weight matrix

[0056] In some specific embodiments, the processor can obtain the second weight matrix W2 and the third weight matrix W3 corresponding to the ecological management goal based on the already trained reinforcement learning model. The specific value of .

[0057] In step S204, the ecological pattern evaluation matrix is determined based on the ecological pattern data set of the target area. .

[0058] in, represents the ecological connectivity ratio, Indicates the proportion of ecosystem composition, represents the rate of change in area of ecosystem type, Represents the spatial pattern characteristics of the ecosystem, represents the comprehensive dynamics of the ecosystem, Represents the dynamics of ecosystem classification.

[0059] In some specific embodiments, in step S204, the ecological connectivity ratio is calculated based on the ecological pattern dataset according to the following formula: .

[0060] When evaluating regional ecological conditions, the Ecological Connectivity Ratio can help identify connectivity bottlenecks within individual ecosystems. For example, some regions may have relatively high connectivity, but low concentrations restrict ecological flows (such as species migration and gene exchange), becoming a key constraint on ecosystem connectivity. Conversely, some regions, despite high concentrations, lack connectivity, also impacting ecosystem connectivity. By analyzing the Ecological Connectivity Ratio, we can pinpoint these critical areas and provide clear priorities and directions for ecological restoration and conservation efforts.

[0061] In some specific embodiments, in step S204, the spatial pattern characteristics of the ecosystem are calculated based on the ecological pattern dataset according to the following formula: .

[0062] When evaluating the ecological situation of a region: the total number of patches NP reflects the number of different ecological units in the ecosystem. By analyzing the total number of patches NP, we can understand the complexity and diversity of the ecosystem; the average patch area It reflects the size of each patch in the ecosystem. A larger average patch area usually means that each patch can support more biological populations and ecological processes. The boundary density ED reflects the ratio of the boundary length between different patches in the ecosystem to the total area. A higher boundary density usually means that there are more boundaries between patches in the ecosystem, which may increase the edge effect.

[0063] Figure 3 for Figure 1 A flowchart of the detailed steps of step S1 in the ecological management assessment method 1000 based on ecological pattern. Figure 3 As shown, step S1 includes steps S11 to S16.

[0064] In some specific embodiments, in step S1, the dynamic weight allocation model includes a reinforcement learning model, which includes a state space and an action space. The state space is used to enable the intelligent agent to perceive the current environmental conditions of the ecosystem and provide a basis for the intelligent agent's decision-making. The action space is used to limit the intelligent agent's action mode, allowing the intelligent agent to know which possible actions to choose.

[0065] Optionally, the state space includes the ecological connectivity ratio, ecosystem composition ratio, ecosystem type area change rate, ecosystem spatial pattern characteristics, ecosystem comprehensive dynamics, ecosystem classification dynamics, as well as ecological planning T1, ecological protection T2, and ecological restoration T3. Optionally, the action space includes the first weight matrix W1, as well as the value range and adjustment step size of each data in the first weight matrix W1. For example, the value range of the weight data can be set in the interval [0, 1], and the adjustment step size for iterative updates of each weight data is 0.01. By properly setting the value range and adjustment step size of the weight data, flexibility and accuracy of weight allocation can be ensured.

[0066] Optionally, in step S1, the state space also includes other environmental variables related to ecological management, such as regional climate conditions and intensity of human activities, so as to more comprehensively reflect the actual state of the ecosystem and external influencing factors.

[0067] In some specific embodiments, the action space represents a weight allocation strategy for the six dimensions of the ecological landscape evaluation matrix. Optionally, the action space includes a first weight matrix W1, as well as the value ranges and adjustment steps for each dimension in the first weight matrix W1. For example, the weight value range can be set within the interval [0, 1], and the weight adjustment step size for each dimension is 0.01. This ensures flexibility and accuracy in weight allocation.

[0068] In some specific embodiments, in step S11, the processor designs different reward functions for different ecological management goals. .

[0069] In some specific embodiments, the ecological management goal is ecological planning T1. The reward function of ecological planning T1 should focus on the reasonable layout and long-term sustainable development of the ecosystem. The formula of the reward function includes: .in, 、 、 The changes in the proportion of ecosystem composition are , the change value of the ecosystem type area change rate , the change value of the spatial pattern characteristics of the ecosystem The weight coefficient of .

[0070] In some specific embodiments, the ecological management goal is ecological protection T2. The reward function for ecological protection T2 should focus on maintaining the stability and connectivity of the ecosystem. The formula of the reward function includes: .in, 、 、 are the changes in the ecological connectivity ratio , Change value of ecosystem composition ratio , the change value of the ecosystem type area change rate The weight coefficient of .

[0071] In some specific embodiments, the ecological management goal is ecological restoration T3. The reward function of ecological restoration T3 should focus on the recovery speed and quality of the ecosystem. The formula of the reward function includes: .in, 、 、 are the change values of the ecosystem type area change rate , comprehensive dynamic change value of ecosystem , the change value of ecosystem classification dynamics The weight coefficient of .

[0072] In some specific embodiments, in order to adapt the reinforcement learning model to different ecological management goals, a comprehensive reward function is designed in step S11, and the weight of the reward function is dynamically adjusted according to the current management goal, for example: ,in, 、 、 is a weight coefficient that is dynamically adjusted according to the current ecological management objectives, and When the ecological management goal is ecological planning T1, add The value of and When the ecological management goal is ecological protection T2, increase The value of and When the ecological management goal is ecological restoration T3, increase The value of and value.

[0073] In step S12, the processor preprocesses the management operation dataset to obtain training samples. Optionally, the management operation dataset includes historical ecological data under different ecological management objectives and corresponding management operation records. Specifically, it includes the changes in the six dimensions of the ecological pattern assessment matrix at different points in time, as well as the responses to different management operations (such as the implementation of ecological restoration projects and adjustments to ecological protection policies). In step S13, the processor performs preprocessing to ensure the quality and consistency of the data, so that it can serve as valid training samples for input into the reinforcement learning model.

[0074] In step S13, the agent in the reinforcement learning model selects a first weight matrix W1 from the action space based on its current state as an action to be executed. In some embodiments, the reinforcement learning model includes a decision-making entity, i.e., the agent, which learns by interacting with the environment. In some embodiments, the agent in the reinforcement learning model is placed in a simulated ecological management environment. Based on its current state (including six-dimensional data, ecological management objectives, etc.), the agent selects a weight allocation strategy (i.e., the first weight matrix W1) from the action space as an action to be executed.

[0075] In step S14, the impact of the selected first weight matrix W1 on the ecological management objective is evaluated based on the reward function, and a reward signal and new state information are fed back to the agent. In some embodiments, the reward signal is the change in the six dimensions of the ecological landscape evaluation matrix after the weight adjustment, and the state information is the corresponding response of the ecosystem.

[0076] In step S15, the agent updates and learns its weight distribution plan using a reinforcement learning algorithm based on the reward signal and the new state information to determine the final reinforcement learning model. In some embodiments, the reinforcement learning algorithm includes any one of a Q-learning algorithm, a deep deterministic policy gradient algorithm, a SARSA algorithm, and a deep Q-network.

[0077] In some specific embodiments, in step S15, through continuous interaction with the environment and trial and error, the intelligent agent can gradually learn which weight allocation strategies can obtain higher cumulative rewards under different ecological management goals and ecosystem states, thereby optimizing its weight allocation decisions and ultimately obtaining a reinforcement learning model.

[0078] According to the above implementation scheme, the ecological management assessment method based on ecological pattern proposed in the present invention can achieve cross-target transfer learning and dynamic adaptation to environmental changes. Because the reinforcement learning model accumulates experience and knowledge of weight allocation under different ecological management goals during the training process, when switching from one ecological management goal to another, the model can use some of the previously learned general knowledge and patterns to adapt to the new target scenario more quickly and find a weight allocation strategy that adapts to the new goal in fewer training iterations. The reinforcement learning weight adjustment model has good dynamic adaptability and can adjust the weight allocation strategy in real time according to changes in the state of the ecosystem and interference from external environmental factors.

[0079] Figure 4 for Figure 1 A flow chart of the detailed steps of step S3 in the ecological management assessment method 1000 based on ecological pattern. Figure 4 As shown, step S3 includes steps S31 to S33.

[0080] In step S31, the connectivity index COH, the first aggregation index AI, the total number of patches NP, and the average patch area are calculated based on the ecological pattern data set. , boundary density ED, and second aggregation index C.

[0081] Optionally, the connectivity index COH is calculated according to the following formula: ; in, represents the plaque perimeter, represents the plaque area, and A represents the total number of plaques in the target area.

[0082] Optionally, the first aggregation index AI is calculated according to the following formula: ; in, represents the number of adjacent patches of type i, and max represents the maximum number of adjacent patches of type i. It represents the proportion of landscapes containing type i patches within the target area.

[0083] In some embodiments, the processor calculates the average plaque area The calculation follows the following formula: ; Among them, Aij represents the area of the jth patch of the i-th ecosystem, and Ni represents the total number of patches of the i-th ecosystem.

[0084] In some specific embodiments, the processor calculates the boundary density ED according to the following formula: ; Where Lij represents the length of the boundary between the i-th ecosystem patch and the adjacent j-th ecosystem patch, and Ai represents the total area of the i-th ecosystem.

[0085] In some specific embodiments, the processor calculates the second concentration index C according to the following formula:

[0086] Where Pij represents the probability that patch types i and j are adjacent, n represents the total number of patches of each type of ecosystem, and Cmax represents the maximum value of the Pij parameter.

[0087] In step S32, the ecological connectivity ratio is calculated based on the connectivity index COH and the first aggregation index AI, which follows the following formula: .

[0088] In some specific embodiments, the processor sets the second weight matrix W2= In some specific embodiments, the processor obtains a second weight matrix W2= according to the dynamic weight allocation model. .

[0089] In step S33, according to the total number of plaques NP, the average plaque area , boundary density ED and the second aggregation index C are used to calculate the spatial pattern characteristics of the ecosystem according to the following formula: .

[0090] In some specific embodiments, in step S13, the processor sets the third weight matrix In some specific embodiments, the processor obtains a third weight matrix according to the dynamic weight allocation model. .

[0091] According to Figure 4 In the embodiment shown, the technical solution proposed by the present invention can determine the weighted sum of detailed features during the calculation of the ecological connectivity ratio and ecosystem spatial pattern characteristics through a preset or dynamic weight allocation model. The embodiment shown in Figure 3 enables more flexible calculation of the ecological connectivity ratio and ecosystem spatial pattern characteristics, adapting to different ecological management objectives or other different ecological management requirements.

[0092] Figure 5 for Figure 1 A flowchart of the detailed steps of step S5 in the ecological management assessment method 1000 based on ecological pattern. Figure 5 As shown, step S5 includes steps S51 to S54.

[0093] In step S51, if the ecological pattern assessment score (SC) is less than or equal to the first scoring value, the completion level is poor. In step S52, if the ecological pattern assessment score (SC) is greater than the first scoring value and less than or equal to the second scoring value, the completion level is fair. In step S53, if the ecological pattern assessment score (SC) is greater than the second scoring value and less than or equal to the third scoring value, the completion level is good. In step S54, if the ecological pattern assessment score (SC) is greater than the third scoring value, the completion level is excellent.

[0094] In steps S51-S54, the completion level reflects the degree of achievement of the ecological management objectives and can serve as a basis for subsequent improvements to ecological management operations. Optionally, the first, second, and third rating values are 0.5, 0.6, and 0.8, respectively. Alternatively, the first, second, and third rating values can be flexibly set based on ecological management objectives or other assessment-related requirements.

[0095] Figure 6 FIG. 3 is a schematic diagram of the structure of an ecological management assessment device 3000 based on ecological pattern of the present invention. Figure 6 As shown, the device 3000 includes a weight model training module 31, a weight matrix generation module 32, an evaluation matrix generation module 33, a score calculation module 34 and a grade judgment module 35.

[0096] In some specific embodiments, the weight model training module 31 performs training based on the ecological management objectives and the management operation data set to obtain a dynamic weight allocation model. In some specific embodiments, the dynamic weight allocation model is a reinforcement learning model, which includes a state space and an action space.

[0097] In some specific embodiments, the weight model training module 31 defines the state space of the reinforcement learning model. Optionally, the state space includes the ecological connectivity ratio, the proportion of ecosystem components, the rate of change of ecosystem type area, the spatial pattern characteristics of the ecosystem, the comprehensive dynamics of the ecosystem, the dynamics of the ecosystem classification, as well as ecological planning T1, ecological protection T2, and ecological restoration T3. Optionally, the state space also includes other environmental variables related to ecological management, such as regional climate conditions and the intensity of human activities, to more comprehensively reflect the actual state of the ecosystem and external influencing factors.

[0098] In some specific embodiments, the weight model training module 31 defines the action space of the reinforcement learning model, and the action space represents the weight allocation strategy for the six dimensional data in the ecological pattern evaluation matrix. Optionally, the action space includes a first weight matrix W1, and the value range and adjustment step of each data in the first weight matrix W1. For example, the value range of the weight data can be set in the interval [0,1], and the adjustment step for iteratively updating each weight data is 0.01. By reasonably setting the value range and adjustment step of the weight data, the flexibility and accuracy of the weight allocation can be ensured.

[0099] In some specific embodiments, the weight model training module 31 sets different reward functions for different ecological management goals. Specifically, the reward function The corresponding ecological management objectives are ecological planning T1, ecological protection T2, and ecological restoration T3.

[0100] In some specific embodiments, the weight model training module 31 preprocesses the data in the management operation dataset to obtain training samples. Preprocessing operations specifically include data cleaning and normalization. Optionally, the management operation dataset includes historical ecological data under different ecological management objectives and corresponding management operation records, specifically including changes in the six dimensions of the ecological pattern assessment matrix at different time points, as well as responses to different management operations (such as the implementation of ecological restoration projects and adjustments to ecological protection policies).

[0101] In some specific embodiments, the weight model training module 31 performs preprocessing operations on the management operation data set to obtain training samples; the intelligent agent of the reinforcement learning model selects a first weight matrix W1 from the action space as an action to be executed according to the current state; the weight model training module 31 evaluates the impact of the selected first weight matrix W1 on the ecological management goal according to the reward function, and feeds back the reward signal and the new state information to the intelligent agent; the intelligent agent uses the reinforcement learning algorithm to update and learn its weight distribution plan based on the reward signal and the new state information to determine the final reinforcement learning model.

[0102] In some specific embodiments, the dynamic weight allocation model is a linear regression model. In some specific embodiments, it is assumed that there is a linear relationship between the completion of ecological management objectives and the six dimensions of data in the ecological pattern evaluation matrix. That is, the goal completion score can be expressed as a linear combination of the six dimensions. Using a linear regression algorithm, using historical data from the management operation dataset, the six dimensions of data are used as independent variables, and the actual completion of ecological management objectives is used as the dependent variable to train the model. The regression coefficient corresponding to each dimension of data is obtained. This regression coefficient can be used as a mapping matrix between ecological management objectives and weight allocation.

[0103] In some specific embodiments, the dynamic weight allocation model is an artificial neural network model. In some specific embodiments, training learns the complex mapping relationship between the six dimensions of data and the achievement of ecological management objectives. During training, the neural network's connection weights are continuously adjusted, ultimately converging to weights that effectively predict objective achievement. These weights reflect the importance of the six dimensions of data in the ecological pattern assessment matrix.

[0104] In some specific embodiments, the weight matrix generation module 32 determines the first weight matrix according to the dynamic weight distribution model. In some specific embodiments, the weight matrix generation module 32 can obtain the specific value of the first weight matrix W1 corresponding to the ecological management target based on the trained reinforcement learning model. For example, in some specific embodiments, the ecological management target is the ecological planning T1, and the first weight matrix ; The ecological management goal is ecological protection T2, the first weight matrix ; The ecological management goal is ecological restoration T3, the first weight matrix .

[0105] In some specific embodiments, the evaluation matrix generation module 33 determines the ecological pattern evaluation matrix according to the ecological pattern data set of the target area. .

[0106] in, represents the ecological connectivity ratio, Indicates the proportion of ecosystem composition, represents the rate of change in area of ecosystem type, Represents the spatial pattern characteristics of the ecosystem, represents the comprehensive dynamics of the ecosystem, Represents the dynamics of ecosystem classification.

[0107] In some specific embodiments, the evaluation matrix generation module 33 calculates the connectivity index COH and the first aggregation index AI according to the ecological pattern data set, and further calculates the ecological connectivity ratio according to the connectivity index COH and the first aggregation index AI. .

[0108] In some specific embodiments, the evaluation matrix generation module 33 sets the second weight matrix W2= .

[0109] In some specific embodiments, the evaluation matrix generation module 33 obtains the second weight matrix W2= .

[0110] In some specific embodiments, the evaluation matrix generation module 33 calculates the connectivity index COH and the first aggregation index AI to obtain an ecological connectivity ratio according to the following formula: .

[0111] In some specific embodiments, the connectivity index COH mainly reflects the connectivity between different ecological units in the ecosystem, such as the degree of physical connectivity between habitat patches, while the first aggregation index AI focuses on reflecting the degree of spatial aggregation of ecological elements. By combining the two to calculate the ecological connectivity ratio, the connectivity status of the ecosystem can be assessed more comprehensively and accurately. For example, an ecosystem may have a high degree of connectivity, indicating that there are more connecting channels between habitat patches, but if the aggregation of these patches is low and the distribution is more dispersed, then the overall connectivity of the ecosystem may not be ideal. The ecological connectivity ratio can comprehensively reflect this complex situation.

[0112] In some specific embodiments, the evaluation matrix generation module 33 calculates the ecosystem composition ratio based on the ecological pattern data set according to the following formula: ; in, represents the area of ecosystem type i, and TS represents the total area of the target area for assessment.

[0113] In some specific embodiments, the evaluation matrix generation module 33 calculates the ecosystem area change rate based on the ecological pattern dataset according to the following formula: ;in, represents the area of ecosystem type i at the beginning of the period, represents the area of ecosystem type i at the end of the period.

[0114] In some specific embodiments, the evaluation matrix generation module 33 calculates the total number of patches NP, the average patch area, and the number of patches NP based on the ecological pattern data set. , boundary density ED and second aggregation index C, and then further calculations are made to obtain the spatial pattern characteristics of the ecosystem.

[0115] In some specific embodiments, the weight matrix generation module 32 sets the third weight matrix by a preset method. In some specific embodiments, the weight matrix generation module 32 obtains the third weight matrix according to the dynamic weight distribution model. .

[0116] In some specific embodiments, the evaluation matrix generation module 33 is based on the total number of plaques NP, the average plaque area , boundary density ED and the second aggregation index C are used to calculate the spatial pattern characteristics of the ecosystem according to the following formula: .

[0117] In some specific embodiments, the evaluation matrix generation module 33 calculates the comprehensive dynamic degree of the ecosystem based on the ecological pattern data set according to the following formula: ; Among them, ECOi represents the area of ecosystem type i at the beginning of the period, It represents the absolute value of the area of ecosystem type i converted to non-ecosystem type i.

[0118] In some specific embodiments, the evaluation matrix generation module 33 calculates the ecosystem classification dynamics based on the ecological pattern dataset according to the following formula: ; where i represents the ecosystem type at the beginning of the period, j represents the ecosystem type at the end of the period, represents the area of the ecosystem type, Aij represents the proportion of the i-th ecosystem type at the beginning of the period transformed into the j-th ecosystem type at the end of the period, and Bij represents the proportion of the j-th ecosystem type at the end of the period transformed from the i-th ecosystem type at the beginning of the period.

[0119] In some specific embodiments, the score calculation module 34 determines the ecological pattern evaluation score SC=W1×D1 based on the first weight matrix W1 and the ecological pattern evaluation matrix D1. In some specific embodiments, the ecological pattern evaluation score SC= .

[0120] In some specific embodiments, the grade evaluation module 35 determines the completion grade of the ecological management target based on the ecological pattern assessment score SC. In some specific embodiments, if the ecological pattern assessment score SC is less than or equal to a first score value, the completion grade is poor; if the ecological pattern assessment score SC is greater than the first score value and less than or equal to a second score value, the completion grade is fair; if the ecological pattern assessment score SC is greater than the second score value and less than or equal to a third score value, the completion grade is good; and if the ecological pattern assessment score SC is greater than the third score value, the completion grade is excellent.

[0121] Optionally, the first scoring value, the second scoring value, and the third scoring value are 0.5, 0.6, and 0.8, respectively.

[0122] Figure 7 FIG. 4 is a schematic diagram of the structure of an ecological integrated monitoring system 4000 of the present invention. Figure 7 As shown, the system 4000 includes a data acquisition unit 41 , a data analysis unit 42 and a data visualization unit 43 .

[0123] In some specific embodiments, data acquisition unit 41 includes a satellite remote sensing monitoring module, an unmanned aerial vehicle (UAV) aerial photography monitoring module, and an Internet of Things (IoT) acquisition module, configured to collect an ecological pattern dataset and a management operation dataset. The satellite remote sensing monitoring module, the UAV aerial photography monitoring module, and the IoT acquisition module are collectively configured to monitor ecological conditions in the target area, such as soil change trends, water flow changes, and forestland changes, and to generate an ecological pattern dataset and a management operation dataset from the collected data.

[0124] Among them, the satellite remote sensing monitoring module conducts meteorological monitoring through satellite imaging and remote sensing data, the drone aerial monitoring monitors organisms, soil, patches and various biodiversity components, and the Internet of Things acquisition module includes a variety of networked sensor devices. The Internet of Things acquisition module can directly monitor data on substances such as the atmosphere, soil, and water quality in the target area.

[0125] In some specific embodiments, the data analysis unit 42 includes: Figure 6 The ecological management assessment device based on ecological pattern is used to perform the following Figure 1-5 Any of the methods described.

[0126] In some embodiments, data visualization unit 43 includes a visual data display platform for displaying the analysis results of the data analysis unit. In some embodiments, the visual data display platform includes an interactive page and a processor that can intuitively display the management status of the ecosystem based on the evaluation results of data unit 42 and generate corresponding tables, charts, reports, etc.

[0127] The embodiments of the present invention are described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only intended to help understand the method of the present invention and its core concept. At the same time, changes or modifications made by those skilled in the art based on the concept of the present invention, the specific implementation methods of the present invention, and the scope of application are all within the scope of protection of the present invention. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. An ecological management assessment method based on ecological pattern, characterized in that: include: S1. Train the dynamic weight allocation model based on the ecological management objectives and management operation datasets; S2. Determine the first weight matrix according to the dynamic weight allocation model ; S3. Determine the ecological pattern evaluation matrix based on the ecological pattern dataset of the target area ; in, represents the ecological connectivity ratio, Indicates the proportion of ecosystem composition, represents the rate of change in area of ecosystem type, Represents the spatial pattern characteristics of the ecosystem, represents the comprehensive dynamics of the ecosystem, Indicates the dynamics of ecosystem classification; S4. Determine an ecological pattern assessment score SC = W1 × D1 based on the first weight matrix W1 and the ecological pattern evaluation matrix D1; S5. Determine the completion level of the ecological management target based on the ecological pattern assessment score SC.

2. The ecological management assessment method based on ecological pattern according to claim 1, characterized in that: The ecological management goal is any one of ecological planning T1, ecological protection T2, and ecological restoration T3; The dynamic weight distribution model includes a reinforcement learning model, and the reinforcement learning model includes a state space and an action space; The state space records ecosystem parameters, including the ecological connectivity ratio, ecosystem composition ratio, ecosystem type area change rate, ecosystem spatial pattern characteristics, ecosystem comprehensive dynamics, ecosystem classification dynamics, as well as ecological planning T1, ecological protection T2, and ecological restoration T3; and is used to enable the intelligent agent to perceive the current environmental status of the ecosystem and provide a basis for the intelligent agent's decision-making; The action space includes the first weight matrix W1, and the value range and adjustment step of each data in the first weight matrix W1, which are used to limit the action mode of the intelligent agent so that the intelligent agent knows which possible actions can be selected.

3. The ecological management assessment method based on ecological pattern according to claim 2, characterized in that: The S1. training based on the management operation data set to obtain a dynamic weight allocation model includes: Set different reward functions for different ecological management goals ; Performing a preprocessing operation on the management operation data set to obtain a training sample; The agent of the reinforcement learning model selects a first weight matrix W1 from the action space as an action to be performed according to the current state; Evaluate the impact of the selected first weight matrix W1 on the ecological management goal according to the reward function, and feed back the reward signal and new state information to the agent; The agent updates and learns its weight distribution plan using a reinforcement learning algorithm based on the reward signal and new state information to determine the final reinforcement learning model.

4. The ecological management assessment method based on ecological pattern according to claim 3, characterized in that: The intelligent agent of the reinforcement learning model is the core component for decision-making and learning. It is used to perceive the current state of the environment, select appropriate actions from its action space to execute, and then adjust its behavior strategy based on the reward signal fed back by the environment.

5. The ecological management assessment method based on ecological pattern according to claim 2, characterized in that: The state space also includes connectivity index COH, first aggregation index AI, total number of plaques NP, average plaque area , boundary density ED, a second aggregation index C, the action space also includes a second weight matrix and the third weight matrix The ecological management assessment method based on ecological pattern also includes: Determine a second weight matrix according to the dynamic weight allocation model and the third weight matrix .

6. The ecological management assessment method based on ecological pattern according to claim 5, characterized in that: The step S3. determining the ecological pattern evaluation matrix D1 based on the ecological pattern dataset of the target area includes: The connectivity index COH, the first aggregation index AI, the total number of patches NP, and the average patch area were calculated based on the ecological pattern data set. , boundary density ED, second aggregation index C; The ecological connectivity ratio is calculated based on the connectivity index COH and the first aggregation index AI, and follows the following formula: ; According to the total number of plaques NP, the average plaque area , the boundary density ED and the second aggregation index C are used to calculate the spatial pattern characteristics of the ecosystem, following the following formula: 。 7. The ecological management assessment method based on ecological pattern according to any one of claims 1 to 6, characterized in that: S5. Determining the level of completion of the ecological management objectives based on the ecological pattern assessment score SC includes: When the ecological pattern assessment score SC is less than or equal to the first scoring value, the completion level is poor; When the ecological pattern assessment score SC is greater than the first scoring value and less than or equal to the second scoring value, the completion level is average; When the ecological pattern assessment score SC is greater than the second scoring value and less than or equal to the third scoring value, the completion level is good; When the ecological pattern assessment score SC is greater than the third scoring value, the completion level is excellent.

8. An ecological management assessment device based on ecological pattern, characterized in that: include: Evaluation matrix generation module, used to determine the ecological pattern evaluation matrix based on the ecological pattern dataset of the target area ; in, Represents ecological connectivity, Indicates the proportion of ecosystem composition, represents the rate of change in area of ecosystem type, Represents the spatial pattern characteristics of the ecosystem, represents the comprehensive dynamics of the ecosystem, Indicates the dynamics of ecosystem classification; The weight model training module is used to train based on ecological management objectives and management operation data sets to obtain a dynamic weight allocation model; A weight matrix generation module is used to determine a first weight matrix according to the dynamic weight allocation model ; A score calculation module, configured to determine an ecological pattern assessment score SC = W1 × D1 based on the first weight matrix W1 and the ecological pattern evaluation matrix D1; The grade evaluation module is used to determine the completion grade of the ecological management target according to the ecological pattern assessment score SC.

9. An ecological integrated monitoring system, characterized in that: include: Data collection unit, including satellite remote sensing monitoring module, drone aerial photography monitoring module and Internet of Things collection module, used to collect ecological pattern data sets and management operation data sets; a data analysis unit comprising the ecological management assessment device based on ecological pattern according to claim 8, configured to execute the ecological management assessment method based on ecological pattern according to any one of claims 1 to 7; The data visualization unit includes a visualization data display platform for displaying the analysis results of the data analysis unit.

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