Intelligent ecological system service partition management and control system

Abstract

The invention discloses an intelligent ecological system service partition management and control system, and relates to the technical field of ecological system management and control, and the system comprises a data collection module which is responsible for collecting and sorting ecological data from various sources, and providing basic original data for system partition management and control; the data processing module is responsible for processing the collected original data, and the ecological partition module is used for dividing ecological management and control partitions according to service characteristics of an ecological system and defining a space management and control boundary; the ecological evaluation module is used for carrying out quantitative evaluation on the ecological system condition of the whole region based on the characteristic condition of the partition; the strategy generation module generates differentiated management and control measures and treatment strategies of the ecological function areas based on the ecological zoning result and the ecological evaluation conclusion; and the data storage module is responsible for efficiently storing massive heterogeneous data and ensuring the traceability of the data of the whole system. According to the invention, accurate partitioning of the ecological system can be realized, and a personalized management and control strategy is generated according to the ecological condition in the partition.

Description

Technical Field

[0001] The present invention relates to the technical field of ecosystem management and control, and in particular to an intelligent ecosystem service zoning management and control system. Background Art

[0002] Ecosystem services refer to the various benefits that ecosystems provide in ecological and environmental processes. With the rapid advancement of urbanization, industrialization, and urbanization, human activities are increasingly interfering with natural ecosystems, challenging the function and stability of regional ecosystems and further jeopardizing the natural-social-economic system. The importance of ecosystem management has become even more prominent. Due to regional geographical differences and uneven socioeconomic development, there are also mismatches between and within regional ecosystem services, making it difficult to meet regional ecosystem service needs. Therefore, identifying regional ecosystem services has important theoretical and practical implications for promoting the coordinated development of regional human-land relations and clarifying the priorities for regional spatial zoning and regulation.

[0003] At present, the research on ecological zoning management can be summarized as follows: First, ecological space control based on different spatial scales, such as ecological zoning management based on the watershed scale, exploring the spatiotemporal variation characteristics of ecological security within the watershed, and discussing ecological security restoration work from the perspective of the watershed; there is also ecological zoning management within administrative regions of different levels, such as provinces, counties, urban agglomerations and urban circles, such as the Yangtze River Delta and the Pearl River Delta; second, ecological zoning research based on different control objects, such as wetlands, farmlands, water sources, ecological protection areas, etc. Existing research focuses on mountainous and hilly areas, arid and semi-arid areas, and important grain-producing areas, and then divides urban core areas, farmland protection areas, ecological protection areas, etc.; third, in terms of research methods, early ecological space zoning research relied on ecosystem service research methods, based on direct market methods, conditional value methods, environmental model selection methods, etc. to conduct an overall assessment of the internal ecosystem of the region, and then spatially divide different ecosystem services.

[0004] However, existing systems for zoning ecosystems do not accurately zoning ecosystems based on their service characteristics. Furthermore, the allocation of ecological characteristics fails to account for the dynamics of environmental conditions. This lacks a rational allocation of effective environmental characteristics, leading to inaccurate evaluations of ecological zoning and, consequently, the inability to formulate appropriate management and control strategies based on the characteristics of these zoning areas. These shortcomings, to a certain extent, limit the effectiveness of ecological and environmental zoning management and control, making it difficult to fully meet the growing demand for environmental protection and management. Summary of the Invention

[0005] The present invention provides an intelligent ecosystem service zoning management and control system to solve the technical problem in the prior art that the ecosystem cannot be accurately zoned, resulting in the inability to generate personalized management and control strategies based on the ecological conditions within the zone.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] The present invention provides an intelligent ecosystem service zoning management and control system, comprising:

[0008] Data collection module: responsible for collecting and organizing ecological data from various sources, providing basic raw data for system partition management;

[0009] Data processing module: responsible for processing the collected raw data and generating standardized data for subsequent analysis;

[0010] Ecological zoning module: divide ecological control zones according to the service characteristics of the ecosystem and clarify the spatial control boundaries;

[0011] Ecological evaluation module: quantitatively evaluates the status of the ecosystem in the entire region based on the characteristics of the sub-regions;

[0012] Strategy generation module: Based on the ecological zoning results and ecological assessment conclusions, it generates differentiated management and control measures and governance strategies for ecological functional zones;

[0013] Data storage module: responsible for efficiently storing massive heterogeneous data and ensuring the traceability of data in the entire system.

[0014] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0015] The present invention can collect diverse environmental data with the help of a data collection module, and conduct in-depth analysis through a data processing module to provide high-quality data support.

[0016] The present invention utilizes an ecological zoning module and adopts a self-organizing mapping algorithm to assign neuron weights to different data to achieve weight division of data importance, thereby accurately dividing ecological regions.

[0017] Through the ecological evaluation module, this paper selects cultivated land, construction land, and unused land data as threat factors, and uses the InVEST model to calculate the comprehensive habitat quality value of each patch in the core area, and then derives the ecological environment quality score of the sub-region, providing solid data support for subsequent management and control strategies.

[0018] The present invention constructs an ecological resistance evaluation index system through a strategy generation module, simulates the minimum cost path, and formulates a path management and control strategy for biological migration, providing a reference plan for subsequent management and control of the ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] 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 ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is a system diagram of the intelligent ecosystem service zoning management and control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0022] This embodiment provides an intelligent ecosystem service zoning management and control system.

[0023] 1. Data Collection Module

[0024] The data collection module is responsible for collecting and organizing ecological data from various sources, providing basic raw data for system partition management and control, including:

[0025] The collected data include basic topographic data, environmental and ecological data, and social and cultural data. Basic topographic data include topographic and geomorphological data, river hydrological data, and vegetation and soil data. Environmental and ecological data include climate and meteorological data and species sample data. Social and cultural data include administrative area data, road traffic data, and land use data.

[0026] The valid range of basic terrain data should be less than 6 months, the valid range of environmental and ecological data should be less than 1 month, and the valid range of social and cultural data should be less than 12 months.

[0027] It should be noted that basic terrain data is obtained through the National Basic Geographic Information Database, environmental and ecological data is obtained through ground monitoring stations, and social and cultural data is obtained through the National Land and Space Planning Database of the Ministry of Natural Resources.

[0028] 2. Data Processing Module

[0029] Data processing includes data preprocessing, data standardization, and data discretization. Data preprocessing is used to clean and correct the collected data. Data standardization is used to remove the dimensional differences and order of magnitude differences of multi-source indicators. Data discretization is used to convert continuous values ​​into finite management levels.

[0030] Data preprocessing uses the random forest algorithm to calculate the contribution of each indicator in the original data set, eliminates redundant indicators whose contribution is lower than the set threshold, and uses the average to fill the missing values;

[0031] It should be noted that the threshold range is T1. If the data type is environmental factor, the value of T1 is 0.05. If the data type is social factor, the value of T1 is 0.03. The environmental factor type refers to basic terrain data and environmental ecological data, and the social data refers to social and humanistic data.

[0032] Data standardization integrates geospatial data with regional boundary data to generate an ecological background layer with a unified coordinate system, and synchronizes real-time collected data with historical monitoring time series through spatiotemporal alignment technology;

[0033] It should be noted that geospatial data refers to data collected from satellite images, ground observation stations, and drone data, and regional boundary data refers to administrative divisions and control unit boundaries. The integration of geospatial data and regional boundary data is achieved by rasterizing the boundary data vectors and converting them into 1000×1000 grids and establishing a coordinate system.

[0034] Indicators with statutory thresholds are divided into levels using the hard cutting method, and relative indicators without clear thresholds are divided into levels using the statistical quantile method. After the data are divided into levels, the discretization results are converted into vector boundaries, and topological correction and speckle processing are performed. The negative list and development intensity threshold in the management and control rule database are associated with each discrete unit.

[0035] 3. Ecological Zoning Module

[0036] The ecological zoning module uses the self-organizing map algorithm to realize the division of ecological regions. Different ecological spaces are partitioned according to the spatial similarity of indicators, and the self-organizing map algorithm is used to identify and divide ecological space partitions.

[0037] The partitioning step involves assigning small random values ​​to the connection weights from the input layer neurons to the output layer neurons, setting the initial neighborhood and learning rate of the output neurons, and providing new input sample vectors. The Euclidean distance between the input sample and the output neuron is calculated, and the winning output unit is the neuron with the smallest Euclidean distance, as shown in the following formula:

[0038]

[0039] Where, d j represents the output value of the neuron; X represents the sample vector; N represents the total number of samples; W j represents the weight vector; x i (t) represents the i-th input neuron at time t; w ij(t) represents the random initial value of the connection weight between the i-th input neuron and the i-th output neuron at time t;

[0040] After the winning neuron outputs, the winning neighborhood at time t is defined, and the weights of the winning neuron and the neurons in its neighborhood are updated as follows:

[0041] △w ij =η(t)·[x i (t)-u ij (t)],j∈N k (t)

[0042] In the formula, Δw ij represents the updated neuron weight; η(t) represents the learning rate parameter; N k (t) represents the range of the winning neighborhood.

[0043] It should be noted that when implementing the self-organizing map algorithm, each indicator is normalized to ensure consistency and comparability.

[0044] 4. Ecological Evaluation Module

[0045] The ecological assessment module selects cultivated land, construction land, and unused land data as threat factors, and sets the habitat suitability of each land type for the threat factor. The habitat suitability includes relative weight, maximum impact distance, and spatial decay type. The default relative weight of cultivated land data is 0.5, the maximum impact distance is 3, and the spatial decay type is linear. The default relative weight of construction land data is 1, the maximum impact distance is 8, and the spatial decay type is exponential. The default relative weight of unused land data is 0.6, the maximum impact distance is 5, and the spatial decay type is linear.

[0046] It should be noted that the threat factor parameters here are dynamically generated by training the machine learning model with historical ecological disaster data, and actually need to be dynamically adjusted based on the regional ecological background.

[0047] The ecological evaluation module uses the InVEST model to calculate the comprehensive habitat quality value of each patch in the core area based on the service value and utilization threat index of the ecosystem in the zone, as shown in the following formula:

[0048]

[0049] Where Q xj represents the comprehensive habitat quality value; H j represents the habitat suitability of land use type; Z represents the normalization constant; represents the stress level of grid x in land use type j; k represents the half-saturation parameter.

[0050] It should be noted that the higher the comprehensive habitat quality value, the more suitable it is for constructing the core area of ​​the ecological network.

[0051] 5. Strategy Generation Module

[0052] The strategy generation module constructs an ecological resistance evaluation index system, uses the OWA analysis method to evaluate the weight of each resistance factor, and forms an ecological resistance surface by weighted superposition, as shown in the following formula:

[0053]

[0054] Where D xj represents the ecological resistance value of the sample parameter x under the jth ecological resistance factor; R represents the total number of ecological resistance factors; Y c represents the total number of categories considered in calculating ecological resistance; w r represents the weight of the rth ecological resistance factor; r y represents the resistance value of the yth category under the rth ecological resistance factor; i rxy represents the specific impact factor of sample parameter x under the yth category and the rth ecological resistance factor; β x represents the adjustment factor associated with the xth object; S jr represents the correlation coefficient between the jth criterion and the rth ecological resistance factor;

[0055] The strategy generation module uses the minimum cumulative resistance model based on the ecological resistance surface to calculate the minimum cumulative resistance distance between the source and the target, simulate the minimum cost path, and generate a path control strategy for biological migration, as shown in the following formula:

[0056]

[0057] Where, MCR represents the minimum cumulative resistance value; f min A function representing the positive correlation between minimum resistance and ecological process; D pq represents the migration distance of species from source unit q to landscape unit p; R p It represents the resistance coefficient of landscape unit p to biological migration, which is determined by the resistance surface.

[0058] The minimum cost path is obtained based on the connectivity analysis of landscape units, as shown in the following formula:

[0059]

[0060] Where IIC represents the degree of connectivity between patches in the landscape; n represents the total number of patches in the landscape; and nl represents the area of ​​the i-th and j-th patches respectively; ij represents the distance between the i-th patch and the j-th patch; Indicates the total area of ​​the landscape.

[0061] 6. Data Storage Module

[0062] The data storage module stores data by establishing a three-level storage structure consisting of the original collection layer, the preprocessing layer, and the analysis application layer. Each data record is associated with spatiotemporal coordinates, collection device information, and accuracy specifications. A temporal database is used to record the effective time interval of key data. The hash value of data modifications and the operator identity are stored in the blockchain.

[0063] The original acquisition layer records the original sensor readings, remote sensing images and associated spatiotemporal metadata through the object storage service; the preprocessing layer stores the topologically corrected control unit boundaries in the spatial database and associates them with the original layer data identifiers; the analysis application layer stores the discretization partitioning results and binding control rules.

[0064] Furthermore, it should be noted that the present invention may be provided as a method, apparatus, or computer program product. Thus, embodiments of the present invention may take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media containing computer-usable program code.

[0065] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the process in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0066] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0067] It should also be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. The terms "comprises," "includes," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further restrictions, an element defined by the phrase "comprises a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0068] Finally, it should be noted that the above is a preferred embodiment of the present invention. It should be noted that although the preferred embodiment of the present invention has been described, it is clear that those skilled in the art, once they understand the basic inventive concept of the present invention, can make various improvements and modifications without departing from the principles of the present invention. Such improvements and modifications should also be considered as within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the embodiments of the present invention.

Claims

1. An intelligent ecosystem service zoning management and control system, characterized by: include: Data collection module: responsible for collecting and organizing ecological data from various sources, providing basic raw data for system partition management; Data processing module: responsible for processing the collected raw data and generating standardized data for subsequent analysis; Ecological zoning module: divide ecological control zones according to the service characteristics of the ecosystem and clarify the spatial control boundaries; Ecological evaluation module: quantitatively evaluates the status of the ecosystem in the entire region based on the characteristics of the sub-regions; Strategy generation module: Based on the ecological zoning results and ecological assessment conclusions, it generates differentiated management and control measures and governance strategies for ecological functional zones; Data storage module: responsible for efficiently storing massive heterogeneous data and ensuring the traceability of data in the entire system.

2. The intelligent ecosystem service zoning management and control system according to claim 1, characterized in that: The data collection module is responsible for collecting and organizing ecological data from various sources to provide basic raw data for system partition management and control, including: The collected data includes basic topographic data, environmental and ecological data, and social and cultural data. The basic topographic data includes topographic and geomorphological data, river hydrological data, and vegetation and soil data. The environmental and ecological data includes climate and meteorological data and species sample data. The social and cultural data includes administrative area data, road traffic data, and land use data. The valid data range of the basic terrain data should be less than 6 months, the valid data range of the environmental ecological data should be less than 1 month, and the valid data range of the social and cultural data should be less than 12 months.

3. The intelligent ecosystem service zoning management and control system according to claim 1, characterized in that: The data processing module is responsible for processing the collected raw data and generating standardized data for subsequent analysis, wherein: The data processing includes data preprocessing, data standardization, and data discretization. The data preprocessing is used to clean and correct the collected data. The data standardization is used to remove the dimensional differences and order of magnitude differences of multi-source indicators. The data discretization is used to convert continuous values ​​into finite management levels. The data preprocessing uses the random forest algorithm to calculate the contribution of each indicator in the original data set, eliminates redundant indicators whose contribution is lower than the set threshold, and uses the average to fill the missing values; The data standardization integrates geospatial data with regional boundary data to generate an ecological background layer with a unified coordinate system, and synchronizes real-time collected data with historical monitoring time series through spatiotemporal alignment technology; The indicators of the data with legal thresholds are divided into levels using the hard cutting method, and the relative indicators of the data without clear thresholds are divided into levels using the statistical quantile method. After the data are divided into levels, the discretization results are converted into vector boundaries, and topological correction and speckle processing are performed to associate the negative list and development intensity threshold in the management and control rule database with each discrete unit.

4. The intelligent ecosystem service zoning management and control system according to claim 1, characterized in that: The ecological zoning module divides ecological control zones according to the service characteristics of the ecosystem and clarifies the spatial control boundaries, including: The ecological zoning module uses the self-organizing map algorithm to realize the division of ecological regions. Different ecological spaces are partitioned according to the spatial similarity of indicators, and the self-organizing map algorithm is used to identify and divide ecological space partitions. The partitioning step includes assigning a small random value to the connection weight from the input layer neuron to the output layer neuron, setting the initial neighborhood and learning rate of the output neuron, and providing a new input sample vector, calculating the Euclidean distance between the input sample and the output neuron. The winning output unit is the neuron with the smallest Euclidean distance, as shown in the following formula: Where, d j represents the output value of the neuron; X represents the sample vector; N represents the total number of samples; W j represents the weight vector; x i (t) represents the i-th input neuron at time t; w ij (t) represents the random initial value of the connection weight between the i-th input neuron and the i-th output neuron at time t; After the winning neuron is output, the winning neighborhood at time t is defined, and the weights of the winning neuron and the neurons in its neighborhood are updated, as shown in the following formula: Δw ij =η(t)·[x i (t)-w ij (t)],j∈N k (t) In the formula, Δw ij represents the updated neuron weight; η(t) represents the learning rate parameter; N k (t) represents the range of the winning neighborhood.

5. The intelligent ecosystem service zoning management and control system according to claim 1, characterized in that: The ecological assessment module: quantitatively assesses the status of the ecosystem of the entire region based on the characteristic status of the sub-regions, including: The ecological assessment module selects cultivated land, construction land, and unused land data as threat factors, and sets the habitat suitability of each land type for the threat factor. The habitat suitability includes relative weight, maximum impact distance, and spatial decay type. The default relative weight of the cultivated land data is 0.5, the maximum impact distance is 3, and the spatial decay type is linear. The default relative weight of the construction land data is 1, the maximum impact distance is 8, and the spatial decay type is exponential. The default relative weight of the unused land data is 0.6, the maximum impact distance is 5, and the spatial decay type is linear. The ecological evaluation module uses the InVEST model to calculate the comprehensive habitat quality value of each patch in the core area based on the service value and utilization threat index of the ecosystem in the zone, as shown in the following formula: Where Q xj represents the comprehensive habitat quality value; H j represents the habitat suitability of land use type; Z represents the normalization constant; represents the stress level of grid x in land use type j; k represents the half-saturation parameter.

6. The intelligent ecosystem service zoning management and control system according to claim 1, characterized in that: The strategy generation module generates differentiated management and control measures and governance strategies for ecological functional zones based on the ecological zoning results and ecological assessment conclusions, including: The strategy generation module constructs an ecological resistance evaluation index system, uses the OWA analysis method to evaluate the weight of each resistance factor, and forms an ecological resistance surface by weighted superposition, which is specifically as follows: Where D xj represents the ecological resistance value of the sample parameter x under the jth ecological resistance factor; R represents the total number of ecological resistance factors; Y c represents the total number of categories considered in calculating ecological resistance; w r represents the weight of the rth ecological resistance factor; r y represents the resistance value of the yth category under the rth ecological resistance factor; i rxy represents the specific impact factor of sample parameter x under the yth category and the rth ecological resistance factor; β x represents the adjustment factor associated with the xth object; S jr represents the correlation coefficient between the jth criterion and the rth ecological resistance factor; The strategy generation module uses the minimum cumulative resistance model based on the ecological resistance surface to calculate the minimum cumulative resistance distance between the source and the target, simulate the minimum cost path, and generate a path control strategy for biological migration, as shown in the following formula: Where, MCR represents the minimum cumulative resistance value; f min A function representing the positive correlation between minimum resistance and ecological process; D pq represents the migration distance of species from source unit q to landscape unit p; R p It represents the resistance coefficient of landscape unit p to biological migration, which is determined by the resistance surface.

7. The strategy generation module according to claim 6 uses a minimum cumulative resistance model based on the ecological resistance surface to calculate the minimum cumulative resistance distance between the source and the target, simulate the minimum cost path, and generate a biological migration path management strategy, characterized by: The minimum cost path is obtained based on the connectivity analysis of the landscape units, as shown in the following formula: Where IIC represents the degree of connectivity between patches in the landscape; n represents the total number of patches in the landscape; and nl represents the area of ​​the i-th and j-th patches respectively; ij represents the distance between the i-th patch and the j-th patch; Indicates the total area of ​​the landscape.

8. The intelligent ecosystem service zoning management and control system according to claim 1, characterized in that: The data storage module is responsible for efficiently storing massive heterogeneous data and ensuring the traceability of data in the entire system, including: The data storage module stores data by establishing a three-level storage structure consisting of an original acquisition layer, a preprocessing layer, and an analysis application layer. Each data record is associated with spatiotemporal coordinates, acquisition device information, and accuracy specifications. A temporal database is used to record the effective time interval of key data. The hash value of data modifications and the operator identity are stored in the blockchain. The original acquisition layer records the original sensor readings, remote sensing images and associated spatiotemporal metadata through the object storage service; the preprocessing layer stores the topologically corrected control unit boundaries in the spatial database and associates them with the original layer data identifiers; the analysis application layer stores the discretization partitioning results and binding control rules.

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