Intelligent management platform for wild animal and plant resource protection

Abstract

The invention discloses an intelligent management platform for wild animal and plant resource protection, and relates to the technical field of intelligent management, and the platform comprises a global static monitoring sub-network construction module which is used for laying a global static monitoring sub-network based on wild plant distribution data; the historical record data acquisition module is used for extracting an activity area, a migration channel and time point data; the key index screening module is used for screening key activity areas, migration channels and migration time points; the inspection path planning module is used for planning a primary dynamic inspection path; and a protection area monitoring module. The technical problems that in the prior art, a traditional wild animal and plant monitoring mode depends on fixed monitoring equipment or manual inspection, the protection range is not comprehensive, the monitoring efficiency is low, and the accuracy is low are solved, and the purposes that static monitoring and dynamic inspection are combined, and the monitoring efficiency is improved are achieved. And the technical effects of comprehensiveness, accuracy and high efficiency of wild animal and plant resource protection are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management technology, and in particular to an intelligent management platform for protecting wild animal and plant resources. Background Art

[0002] In the field of wildlife resource protection, with global climate change, ecological environmental destruction and increasing human activities, wildlife habitats are facing severe challenges, and the number of many wild species has dropped sharply or even become endangered. Therefore, it is particularly important to establish an efficient wildlife resource protection management system and conduct continuous and comprehensive monitoring and management.

[0003] Traditional field monitoring methods usually rely on manual inspections and fixed monitoring equipment. Although these methods have played a monitoring role to a certain extent, they have problems such as low inspection frequency, limited monitoring range, and untimely data analysis. They cannot meet the needs of real-time dynamic monitoring of wild animal and plant activities in large-scale and complex environments. Summary of the invention

[0004] The present application provides an intelligent management platform for the protection of wildlife resources, which is used to solve the technical problems in the prior art that the traditional wildlife monitoring method relies on fixed monitoring equipment or manual inspections, has an incomplete protection range, and has low monitoring efficiency and accuracy.

[0005] The present application provides an intelligent management platform for the protection of wild animal and plant resources, the platform comprising: a global static monitoring sub-network construction module, used to retrieve the wild plant distribution data of the target protected area, deploy global static monitoring devices based on the wild plant distribution data, and construct a global static monitoring sub-network; a historical record data acquisition module, used to retrieve K types of wild animals in the target protected area, retrieve the historical record data of the target protected area based on the K types of wild animals, and obtain K sets of historical main activity areas, K sets of migration channels and K sets of migration time points, where K is an integer greater than or equal to 1; a key indicator screening module, used to screen the The K sets of historical main activity areas, the K sets of migration channels and the K sets of migration time points are respectively screened centrally to determine K key activity areas, K key migration channels and K key migration time points; the inspection route planning module is used to plan the inspection routes of the first-level dynamic monitoring device based on the K key activity areas, K key migration channels and K key migration time points, and determine K first-level dynamic inspection routes; the protection area monitoring module is used to perform protection monitoring on the target protection area based on the global static monitoring subnetwork and the K first-level dynamic inspection routes, and transmit the monitoring data to the intelligent management platform for processing to obtain the protection area monitoring results.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The intelligent management platform for the protection of wild animal and plant resources provided by the present application relates to the field of intelligent management technology. By retrieving plant distribution data to set up a static monitoring subnetwork, the historical activity data of K wild animals are obtained, and key activity areas and migration channels are screened, dynamic inspection routes are planned, and static and dynamic monitoring are combined for data processing to achieve protected area monitoring. This solves the technical problems in the prior art that traditional wild animal and plant monitoring methods rely on fixed monitoring equipment or manual inspections, have an incomplete protection range, and have low monitoring efficiency and accuracy. This achieves the technical effect of improving the comprehensiveness, accuracy and efficiency of wild animal and plant resource protection by combining static monitoring with dynamic inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] 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.

[0008] Figure 1 A schematic diagram of the structure of an intelligent management platform for protecting wildlife resources provided in an embodiment of the present application; Figure 2 A schematic diagram of a process for determining K key activity areas, K key migration channels and K key migration time points in an intelligent management platform for protecting wildlife resources provided in an embodiment of the present application.

[0009] Explanation of the reference numerals: global static monitoring sub-network construction module 11, historical record data acquisition module 12, key indicator screening module 13, inspection route planning module 14, protection area monitoring module 15. DETAILED DESCRIPTION

[0010] The present application provides an intelligent management platform for the protection of wildlife resources, which is used to solve the technical problems in the prior art that the traditional wildlife monitoring method relies on fixed monitoring equipment or manual inspections, has an incomplete protection range, and has low monitoring efficiency and accuracy.

[0011] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0012] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0013] Examples, such as Figure 1 As shown, the present application provides an intelligent management platform for the protection of wild animal and plant resources, the platform comprising: The global static monitoring sub-network construction module 11 is used to retrieve the wild plant distribution data of the target protected area, deploy global static monitoring devices based on the wild plant distribution data, and construct a global static monitoring sub-network.

[0014] Furthermore, the global static monitoring sub-network construction module 11 is also used to perform the following steps: P11: Extract plant height characteristics based on the wild plant distribution data to determine the wild plant height distribution characteristics; P12: Extract plant distribution density characteristics based on the wild plant distribution data to obtain wild plant density distribution characteristics; P13: Deploy global static monitoring devices based on the wild plant height distribution characteristics and the wild plant density distribution characteristics to construct a global static monitoring sub-network.

[0015] It should be understood that the global static monitoring sub-network construction module 11 of the present application has the main function of retrieving the wild plant distribution data of the target protected area, and based on these data, reasonably deploying the global static monitoring devices, and finally constructing the global static monitoring sub-network.

[0016] First of all, in the process of building a global static monitoring sub-network, plant height is an important factor that cannot be ignored. Based on the acquired wild plant distribution data, the height characteristics of the plants are extracted. The plant height characteristics refer to the average height of different types of plants in the protected area and their height variation range. The plant height characteristics can be automatically extracted from the wild plant distribution data through image processing technology and machine learning algorithms. By analyzing these height characteristics, the system can determine the coverage of plants in different areas and determine the viewing angle and coverage range when the monitoring equipment is installed. For example, in areas with high vegetation height, the monitoring equipment needs to be installed at a higher position to ensure that the entire monitoring area can be covered.

[0017] In addition to height, the distribution density of wild plants is also a key factor in determining the density and location of monitoring devices. Spatial analysis techniques and statistical models can be used to conduct in-depth analysis of wild plant distribution data and calculate the plant density distribution characteristics of each region. The plant distribution density characteristics describe the number or coverage of plants per unit area. These characteristics reveal the degree of aggregation and spatial distribution pattern of plant communities, and provide a scientific basis for optimizing the layout of monitoring devices. In high-density areas, more dense monitoring devices may be required to ensure full coverage; in low-density areas, the deployment density can be appropriately reduced to save costs.

[0018] Finally, the global static monitoring devices are deployed based on the plant height distribution characteristics and plant density distribution characteristics. Taking into account multiple factors such as terrain, climate, and light conditions, the optimal monitoring device deployment plan is planned through intelligent algorithms. The selection of monitoring devices is also customized according to specific needs, such as high-definition cameras, infrared sensors, vibration sensors, etc., to achieve all-weather, all-round monitoring of the target protection area. During the deployment process, interconnection and data sharing between monitoring devices should be achieved to ensure that the constructed global static monitoring subnetwork can form an organic whole. Through wireless communication technology, each monitoring device can transmit monitoring data to the smart management platform in real time to achieve centralized data processing and analysis.

[0019] The historical record data acquisition module 12 is used to retrieve K types of wild animals in the target protected area, and search the historical record data of the target protected area based on the K types of wild animals to obtain K sets of historical main activity areas, K sets of migration channels and K sets of migration time points, where K is an integer greater than or equal to 1.

[0020] Optionally, the function of the historical record data acquisition module 12 of the present application is to retrieve historical data related to K types of wild animals from the target protected area, and then conduct in-depth analysis of the activities and migration behaviors of these animals, and extract K sets of historical main activity areas, K sets of migration channels, and K sets of migration time points. Here, K represents the number of types of wild animals, which is an integer greater than or equal to 1.

[0021] First, retrieve historical records related to K types of wildlife in the target area. The types of wildlife refer to the different species observed in the protected area, and the activity habits, habitats, and migration behaviors of each species may be different. By selecting K representative or key species, the system can conduct more targeted monitoring and analysis of animal activities in the area.

[0022] Next, for each type of wild animal, we use data analysis techniques, such as big data analysis and machine learning algorithms, to conduct an in-depth analysis of the behavioral patterns of each wild animal, and further analyze the main activity areas of each animal, including the key areas where wild animals live and forage on a daily basis. By analyzing the activity data of animals over a period of time, we obtain a set of K historical main activity areas, thereby determining the specific locations where animals frequently move in the protected area.

[0023] Subsequently, for wild animals with migratory habits, special attention is paid to their migration paths. By tracking historical migration records, a set of migration channels is extracted. The migration channel refers to the path that animals move along in different seasons or when the environment changes, which is usually closely related to natural terrain, food sources and climatic conditions. By analyzing these migration channels, the system can predict the possible future migration routes of animals and adjust the layout of the monitoring network according to these routes.

[0024] Finally, a set of migration time points for each wild animal is extracted, that is, when the animal starts migrating and the duration of the migration. These time points are often closely related to seasonal changes, food supply, and breeding cycles. By mastering these key time points, the system can strengthen monitoring during the peak period of animal migration and ensure real-time tracking of animal behavior.

[0025] After the analysis and processing of the above steps, the historical main activity area set, migration channel set and migration time point set corresponding to K types of wild animals are integrated and output to the smart management platform in a structured form. These results not only provide an important basis for subsequent inspection route planning, protected area monitoring and other work, but also provide valuable ecological data support for scientific researchers and decision makers.

[0026] The key indicator screening module 13 is used to centrally screen the K historical main activity area sets, the K migration channel sets and the K migration time point sets respectively, to determine K key activity areas, K key migration channels and K key migration time points.

[0027] Further, such as Figure 2 As shown, the key indicator screening module 13 is also used to perform the following steps: P31: Perform regional intersection identification on the K sets of main historical activity areas respectively to determine K historical main activity intersection areas; P32: Based on the overlap of the K sets of main historical activity areas, perform edge transition expansion on the K historical main activity intersection areas to determine K key activity areas; P33: Perform regional edge concentrated screening on the sets of K migration channels to obtain the K key migration channels; P34: Perform time point concentrated density screening on the sets of K migration time points to determine the K key migration time points.

[0028] In a possible embodiment of the present application, the main function of the key indicator screening module 13 of the present application is to centrally screen K historical main activity area sets, K migration channel sets and K migration time point sets to determine the key activity areas, migration channels and migration time points.

[0029] First, the regional intersection identification is performed on the K sets of historical main activity areas, that is, the overlapping parts of multiple historical activity areas of wild animals are identified. The regional intersection refers to the overlapping or intersecting parts of multiple animal activity areas in space. These intersection areas often represent key areas where multiple animals frequently move. The regional intersection identification algorithm in spatial analysis technology is used to compare the geographical locations and boundaries of areas in different sets to find out the overlapping parts between them, that is, the K historical main activity intersection areas. These intersection areas may be the areas with the richest biodiversity in the ecosystem, and are also the areas that should be focused on in conservation work.

[0030] Next, considering that the activity range of wild animals is not fixed and may be affected by multiple factors such as season, climate, and food supply, the identified intersection areas are expanded by edge transition based on the overlap of the K historical main activity area sets. Exemplarily, these intersection areas are expanded using edge transition expansion technology. This technology simulates the movement paths and range expansion trends of animals in the natural environment based on the activity habits and ecological needs of wild animals, thereby determining K more extensive and reasonable key activity areas. These areas not only cover the original intersection areas, but also take into account the possible activity boundaries and potential habitats of wild animals.

[0031] Furthermore, migration corridors are key paths that wild animals pass through during migration, and are crucial to protecting the smooth passage of migratory animals. Therefore, each channel in the migration corridor set is carefully analyzed using the path optimization and edge detection algorithms in spatial analysis. By identifying the edge features and width changes of the channels, migration paths with moderate width, good connectivity and high utilization rate are screened out, and K key migration corridors are obtained. These channels are the main movement paths of animals during migration and can serve as key areas for inspection and monitoring.

[0032] Finally, the K migration time point sets are screened for time point concentration density. Migration time points are key time nodes in the migration process of wild animals and are of great significance for the formulation of protection strategies and emergency response plans. Time series analysis and density clustering algorithms are used to process the migration time point set. By calculating the density and frequency distribution characteristics of migration activities at each time point, the K time points with the most concentrated and critical migration activities are identified. These time points will serve as time nodes that require special attention and preparation in conservation work to ensure that migratory animals can smoothly cross the protected area and reach their destination safely.

[0033] Through the above steps, the most guiding key indicators for wildlife resource protection are extracted from massive historical data, including K key activity areas, K key migration corridors and K key migration time points. The determination of these key indicators will provide strong data support and scientific basis for subsequent inspection route planning, protection area monitoring and protection strategy formulation.

[0034] Furthermore, step P32 of the embodiment of the present application also includes: P32-1: Perform pre-expansion area identification on the K historical main activity intersection areas according to the preset transition step size, and determine K pre-expansion areas; P32-2: Calculate K pre-expansion factors of the K pre-expansion areas according to the overlap of the K historical main activity area sets, wherein the pre-expansion factor is the ratio of the area of ​​the overlapping area in each pre-expansion area to the area of ​​the pre-expansion area; P32-3: Perform edge transition expansion license certification based on the K pre-expansion factors. If the certification is passed, perform edge transition identification based on the K pre-expansion areas to determine K stage expansion areas; P32-4: Merge the K stage expansion areas with the K historical main activity intersection areas to obtain K stage historical main activity intersection expansion areas; P32-5: Continue to perform edge transition expansion according to the preset transition step size and the K stage historical main activity intersection expansion areas to obtain the K key activity areas.

[0035] Specifically, in order to more accurately determine the K key activity areas, a more detailed edge transition expansion process is introduced. First, the pre-expansion area identification is performed on the K historical main activity intersection areas according to the preset transition step length, and K pre-expansion areas are determined. The preset transition step length can be a fixed distance or proportion derived from ecological and animal behavior research for simulating the expansion of wildlife activity range. The transition step length refers to the area increment during expansion, which is usually preset according to the natural fluctuation range of wildlife activities. The pre-expansion area identification is performed on the K historical main activity intersection areas according to the preset transition step length, and the buffer analysis algorithm in the spatial analysis technology is used to expand the specified step length outward around the boundary of each intersection area to form K pre-expansion areas. These areas represent potential areas where wildlife may expand their activity range in the future.

[0036] Next, in order to evaluate the rationality and importance of the pre-expansion area, the pre-expansion factor of each pre-expansion area is calculated based on the overlap of the K historical main activity areas. The pre-expansion factor is a ratio that represents the proportion of the area of ​​the overlapping area in the pre-expansion area to the area of ​​the entire pre-expansion area. The higher this ratio is, the higher the overlap is in the activity range of multiple wildlife types in the area, and therefore it is more likely to be a key area for conservation work.

[0037] After obtaining the pre-expansion factors, edge transition expansion license certification is carried out based on these factors. The certification process verifies the rationality of the expansion of each pre-expansion area to ensure that the expansion does not deviate from the actual animal activity patterns and avoids excessive expansion leading to waste or destruction of protected resources. If the certification is passed, edge transition identification is performed based on the K pre-expansion areas to further determine the K stage expansion areas. The movement paths and range expansion trends of wild animals in the natural environment are simulated using edge detection algorithms and animal behavior models in spatial analysis. K stage expansion areas are determined by identifying the edge features of the pre-expansion areas and taking into account the migration habits and ecological needs of animals. This stage of expansion is to optimize the pre-expansion areas based on the certification results to make them more in line with the actual animal activity range.

[0038] Subsequently, the K-stage expansion areas are merged with the K historical main activity intersection areas to obtain the K-stage historical main activity intersection expansion areas. This operation aims to integrate the newly expanded areas with the original activity intersection areas to ensure that the expanded areas retain the core information of the historical activity areas and cover the possible activity expansion range.

[0039] Finally, according to the preset transition step length and the intersection expansion area of ​​the main historical activities in the K stages, the edge transition expansion is continued, and K key activity areas are finally determined. These areas not only cover the current main activity range of wild animals, but also fully consider their future activity trends and expansion potential, and will serve as the key areas for subsequent monitoring and inspections of the system. Through this progressive expansion and optimization process, the rationality of the monitoring area and the comprehensiveness of the coverage are ensured.

[0040] Furthermore, step P32-3 of the embodiment of the present application also includes: P32-31: Determine whether the K pre-expansion factors are greater than or equal to the preset expansion factor threshold. If so, the authentication is passed; P32-32: If not, the authentication fails, and the K historical main activity intersection areas are used as the K key activity areas.

[0041] Optionally, in order to more strictly control the rationality of edge transition expansion, a certification process based on a pre-expansion factor is added to determine whether the expansion operation can continue by judging the pre-expansion factor.

[0042] First, the calculated K pre-expansion factors are checked one by one and compared with the preset expansion factor threshold. The preset expansion factor threshold is a reference value set based on ecological research, animal behavior data and the specific conditions of the protected area, which is used to assess whether the pre-expansion area has sufficient ecological importance and conservation value. If the value of a pre-expansion factor is greater than or equal to this threshold, it means that the pre-expansion area has a high overlap ratio in the activity ranges of multiple wildlife types and has a high conservation priority, so it is certified. This means that the pre-expansion area may become an important area for future wildlife activities and is worthy of further edge jump expansion.

[0043] If the value of a pre-expansion factor is less than the preset expansion factor threshold, it means that the pre-expansion area has a low overlap ratio in the activity ranges of multiple wildlife types and may not be a key area for future wildlife activities. Therefore, the certification fails. In this case, in order to avoid unnecessary waste of resources and potential risks of ecological damage, the original K historical main activity intersection areas are directly used as K key activity areas. This not only retains the current main activity area information of wildlife, but also avoids the problems that may be caused by over-expansion.

[0044] Through this expansion certification mechanism, the scientificity and rationality of each expansion can be ensured, unnecessary expansion operations can be avoided, and the data of historical activity areas can be fully utilized to define key activity areas for wildlife.

[0045] Furthermore, step P32-3 of the embodiment of the present application also includes: P32-33: Extract edge points with the most overlaps from the edges of the K pre-expansion areas to obtain K expansion edge starting points; P32-34: Construct K iterative edge point screening areas with the K expansion edge starting points as the center and the preset point expansion step as the radius, and determine the K first iterative edge points with the most overlaps in the K iterative edge point screening areas according to the overlap of the K historical main activity area sets; P32-35: Continue to iterate in the K pre-expansion areas based on the K first iterative edge points, and stop iterating when the iteratively updated K Nth iterative edge points coincide with the K expansion edge starting points to obtain K stage expansion areas.

[0046] In a possible embodiment of the present application, in order to more accurately determine the expansion trend of the activity range of wild animals, an expansion area identification method based on edge point iteration is introduced.

[0047] First, the edge points with the most overlaps were extracted from the edges of the K pre-expansion areas. These edge points are the most critical locations on the boundaries of the intersection of multiple historical major activity areas, because they represent the intersection of the activity ranges of different types of wild animals and are also the starting points for possible expansion of activity ranges in the future. By calculating the number of overlaps of each edge point (i.e., how many historical major activity areas it is included in), K expansion edge starting points were identified.

[0048] Next, K iterative edge point screening areas are constructed with the K expansion edge starting points as the center and the preset point expansion step as the radius. These areas are circular or elliptical areas formed by extending a certain distance around the expansion edge starting point, which are used to screen new edge points in subsequent iterations. At the same time, based on the overlap of the K historical main activity area sets, the K first iteration edge points with the most overlaps in each iterative edge point screening area are determined. These first iteration edge points represent new locations that may be reached along the trend of wildlife activity starting from the expansion edge starting point.

[0049] Then, the iteration is continued in the K pre-expansion areas based on the K first iteration edge points. Each iteration will take the current iteration edge point as the starting point, build a new iteration edge point screening area, and determine the new iteration edge point. This process will be repeated until the K iteratively updated Nth iteration edge points coincide with the initial K expansion edge starting points. K stage expansion areas will be determined based on the final iteration edge points. These areas represent the staged expansion results that the wildlife activity range may achieve in the future, and will serve as the basis for further expansion or optimization, providing a reference for the final key activity areas.

[0050] Furthermore, step P34 of the embodiment of the present application also includes: P34-1: Perform fluctuation variance identification on the K migration time point sets to obtain the fluctuation variance of the K migration time points; P34-2: When the fluctuation variance of the K migration time points is greater than or equal to the preset fluctuation variance, perform data cleaning on the K migration time point sets, and perform mean calculation on the cleaned K cleaned migration time point sets to obtain the K key migration time points; P34-3: When the fluctuation variance of the K migration time points is less than the preset fluctuation variance, perform mean calculation on the K migration time point sets to obtain the K key migration time points.

[0051] It should be understood that in order to extract representative key migration time points from the set of K migration time points, a mechanism for identifying and processing the fluctuation variance of the migration time points is introduced.

[0052] First, the fluctuation variance of the K migration time point set is identified. The fluctuation variance of the migration time point is an important indicator to measure the degree of dispersion of each time point in the migration time point set. The fluctuation variance of the K migration time points is obtained by calculating the average value (i.e., variance) of the square of the difference between each migration time point and the set mean. If the fluctuation variance is large, it means that there is a large uncertainty in the migration time points of animals in different years or conditions; if the fluctuation variance is small, it means that the migration time is relatively stable and the difference is not large. By calculating the fluctuation variance of the K migration time points, the volatility of these time points is quantitatively analyzed.

[0053] When the fluctuation variance of K migration time points is greater than or equal to the preset fluctuation variance, it means that the dispersion of the migration time points is high and there may be outliers or noise data. Therefore, the migration time point set is cleaned to remove outliers or unreasonable data points. Specific methods of data cleaning may include threshold filtering, cluster analysis, outlier detection, etc. After cleaning, the remaining K cleaned migration time point sets are mean-calculated to obtain K key migration time points. These key migration time points represent the central trend of migration activities and are an important reference for subsequent analysis and protection work.

[0054] If the fluctuation variance of the K migration time points is less than the preset fluctuation variance, it means that the dispersion of the migration time points is low and the data is relatively stable. The original set of K migration time points can be directly averaged to obtain K key migration time points. Since the data itself is highly stable, the key migration time points can be directly obtained without data cleaning.

[0055] With the technical support of fluctuation variance analysis and data processing, the system can flexibly respond to different fluctuations in migration time. Regardless of whether there are large fluctuations in migration time points, the system can extract accurate key migration time points through cleaning and mean calculation.

[0056] The inspection route planning module 14 is used to plan the inspection route of the first-level dynamic monitoring device based on the K key activity areas, K key migration channels and K key migration time points, and determine K first-level dynamic inspection routes.

[0057] Furthermore, the inspection path planning module 14 is also used to perform the following steps: P41: Constructing a patrol path identifier; P42: Using the patrol path identifier to identify the K key activity areas, K key migration channels and K key migration time points, and determining K first-level dynamic patrol paths.

[0058] Optionally, the main function of the patrol route planning module 14 of the present application is to generate patrol routes for the first-level dynamic monitoring device based on K key activity areas, K key migration channels and K key migration time points to ensure that the monitoring equipment can efficiently cover the main activity and migration areas of wild animals.

[0059] Before planning the inspection route, you first need to build an inspection route identifier. This identifier is a comprehensive system that integrates spatial analysis, time series analysis, and path optimization algorithms. It can use the spatial data management capabilities of the geographic information system (GIS) and combine time series analysis technology to deeply explore the spatiotemporal characteristics of wildlife activities. At the same time, by integrating path optimization algorithms (such as genetic algorithms, ant colony algorithms, etc.), optimal or suboptimal inspection routes can be quickly generated in complex environments. It is built based on spatial data processing technology, combined with geographic information systems (GIS) and path optimization algorithms, so that the spatial distribution of different regions can be efficiently analyzed, and the optimal inspection route can be planned according to the laws of wildlife activities.

[0060] Next, the inspection path identifier is used to identify K key activity areas, K key migration channels and K key migration time points. First, the identifier analyzes the geographical characteristics and activity frequency of wild animals in each area based on the spatial distribution of these key areas. For key activity areas, the identifier will focus on identifying the core areas of these areas to ensure that the monitoring equipment can cover the main activity areas of animals; for key migration channels, the identifier will determine the best inspection route based on the length, width and flow of animal migration to cover the channel route of migrating animals; for key migration time points, the inspection schedule is optimized based on the characteristics of the time period to ensure effective dynamic monitoring during the peak migration period.

[0061] Through the analysis of the identifier, these key factors are comprehensively considered to generate K first-level dynamic inspection paths. Each path is optimized to cover the activity areas and migration paths of wild animals to the greatest extent, while ensuring the operating efficiency and resource utilization of the inspection equipment. These paths will become the basis of the inspection tasks, ensuring that monitoring at key times and key areas is effectively implemented.

[0062] The protection area monitoring module 15 is used to perform protection monitoring on the target protection area based on the global static monitoring subnetwork and the K first-level dynamic inspection paths, and transmit the monitoring data to the smart management platform for processing to obtain the protection area monitoring results.

[0063] Specifically, the main function of the protection area monitoring module 15 of the present application is to conduct comprehensive monitoring of the target protection area based on the global static monitoring subnetwork and K first-level dynamic inspection paths, and transmit the monitoring data to the smart management platform for processing, so as to obtain the monitoring results of the protection area.

[0064] First, the module integrates data from the global static monitoring sub-network. The global static monitoring sub-network refers to monitoring equipment (such as cameras, sensors, etc.) that are fixedly deployed in the protected area. These devices can continuously monitor the activities of wild animals and plants in real time, especially in areas with dense plant distribution. Static monitoring devices cover the regular animal activity areas. Through long-term monitoring, the system can collect stable activity data in the protected area to ensure that the activity trajectory of wild animals and changes in the ecological environment can be captured.

[0065] At the same time, supplementary monitoring is carried out based on the monitoring data of K first-level dynamic inspection paths. The first-level dynamic inspection path is an inspection route for dynamic monitoring of the protected area through drones or other mobile monitoring equipment. These paths are optimized to focus on key activity areas and migration channels of wild animals and conduct efficient monitoring during key time periods. Dynamic inspections can flexibly respond to animal migration or other emergencies, and supplement and optimize static monitoring.

[0066] Next, the monitoring data obtained from the static monitoring subnetwork and dynamic inspection paths are integrated and transmitted to the smart management platform. The smart management platform is the core of the entire protected area monitoring system and is responsible for processing and analyzing the transmitted data. The platform uses big data analysis, pattern recognition and other technologies to conduct in-depth analysis of the activity patterns, migration patterns and environmental changes of wild animals, thereby generating accurate protected area monitoring results. The monitoring results include the distribution of wild animals and plants, changes in migration routes, dynamic changes in habitat environments, and early warnings of possible threats (such as illegal activities, habitat destruction, etc.). These results will help managers respond quickly and adjust monitoring plans and protection measures according to actual conditions.

[0067] In summary, the embodiments of the present application have at least the following technical effects: This application retrieves the wild plant distribution data of the target protected area, deploys global static monitoring devices, builds a static monitoring sub-network, and retrieves the historical record data of K wild animals to obtain their activity areas, migration channels and time points. It also screens and determines the key activity areas, migration channels and time points. Based on this, it plans dynamic inspection routes, combines the static monitoring network to conduct comprehensive monitoring of the protected area, transmits the monitoring data to the smart management platform for processing, and generates monitoring results for the protected area.

[0068] The technical effect of improving the comprehensiveness, accuracy and efficiency of wildlife and plant resource protection has been achieved by combining static monitoring with dynamic inspections.

[0069] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0070] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0071] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An intelligent management platform for the protection of wild animal and plant resources, characterized by: The platform includes: A global static monitoring sub-network construction module is used to retrieve the wild plant distribution data of the target protected area, deploy global static monitoring devices based on the wild plant distribution data, and construct a global static monitoring sub-network; A historical record data acquisition module is used to retrieve K types of wild animals in the target protected area, and based on the K types of wild animals, retrieve the historical record data of the target protected area to obtain K sets of historical main activity areas, K sets of migration channels and K sets of migration time points, where K is an integer greater than or equal to 1; A key indicator screening module, used to centrally screen the K historical main activity area sets, the K migration channel sets and the K migration time point sets, respectively, to determine K key activity areas, K key migration channels and K key migration time points; A patrol route planning module, used to plan the patrol routes of the first-level dynamic monitoring device based on the K key activity areas, the K key migration channels and the K key migration time points, and determine K first-level dynamic patrol routes; The protection area monitoring module is used to perform protection monitoring on the target protection area based on the global static monitoring subnetwork and the K first-level dynamic inspection paths, and transmit the monitoring data to the intelligent management platform for processing to obtain the protection area monitoring results.

2. The intelligent management platform for wildlife and plant resource protection according to claim 1, characterized in that: The K historical main activity area sets, the K migration channel sets and the K migration time point sets are respectively screened to determine K key activity areas, K key migration channels and K key migration time points, including: Performing area intersection identification on the K sets of historical main activity areas respectively to determine K historical main activity intersection areas; Based on the overlap of the K sets of historical main activity areas, edge transition expansion is performed on the intersection areas of the K historical main activities to determine K key activity areas; Performing regional edge concentrated screening on the K migration channel sets to obtain the K key migration channels; The K migration time point sets are screened based on time point concentration density to determine the K key migration time points.

3. The intelligent management platform for wildlife resource protection according to claim 2, characterized in that: Based on the overlap of the K historical main activity area sets, edge transition expansion is performed on the intersection area of ​​the K historical main activities to determine K key activity areas, including: Performing pre-expansion area identification on the K historical main activity intersection areas according to a preset transition step length to determine K pre-expansion areas; Calculating K pre-expansion factors of the K pre-expansion areas according to the overlap of the K historical main activity area sets, wherein the pre-expansion factor is the ratio of the area of ​​the overlapping area in each pre-expansion area to the area of ​​the previous pre-expansion area; Performing edge transition expansion license authentication based on the K pre-expansion factors, and if the authentication is passed, performing edge transition identification based on the K pre-expansion areas to determine K stage expansion areas; Merging the K stage expansion areas with the K historical main activity intersection areas to obtain the K stage historical main activity intersection expansion areas; The edge transition expansion is continued according to the preset transition step length and the intersection expansion area of ​​the K stage historical main activities to obtain the K key activity areas.

4. The intelligent management platform for wildlife and plant resource protection according to claim 3, characterized in that: Performing edge transition expansion license authentication based on the K pre-expansion factors, and if the authentication is passed, performing edge transition identification based on the K pre-expansion areas, and determining K stage expansion areas, including: Determine whether the K pre-expansion factors are greater than or equal to a preset expansion factor threshold, if so, the authentication is passed; If not, the authentication fails, and the K historical main activity intersection areas are used as the K key activity areas.

5. The intelligent management platform for wildlife and plant resource protection according to claim 4, characterized in that: Performing edge transition expansion license authentication based on the K pre-expansion factors, and if the authentication is passed, performing edge transition identification based on the K pre-expansion areas, and determining K stage expansion areas, including: Extracting edge points with the largest number of overlaps from the edges of the K pre-expanded regions to obtain K expansion edge starting points; Taking the K expansion edge starting points as the center of the circle and the preset point expansion step as the radius, construct K iterative edge point screening areas, and determine the K first iterative edge points with the largest number of overlaps in the K iterative edge point screening areas according to the overlap of the K historical main activity area sets; Iteration is continued in the K pre-expansion regions based on the K first iteration edge points, and when the iteratively updated K Nth iteration edge points coincide with the K expansion edge starting points, iteration is stopped to obtain K stage expansion regions.

6. The intelligent management platform for wildlife resource protection according to claim 1, characterized in that: The K migration time point sets are screened based on the concentration density of the time points to determine the K key migration time points, including: Performing fluctuation variance identification on the K migration time point sets to obtain fluctuation variances of the K migration time points; When the fluctuation variance of the K migration time points is greater than or equal to the preset fluctuation variance, data cleaning is performed on the K migration time point sets, and mean calculation is performed on the cleaned K cleaned migration time point sets to obtain the K key migration time points; When the fluctuation variance of the K migration time points is less than the preset fluctuation variance, the mean of the K migration time point sets is calculated to obtain the K key migration time points.

7. The intelligent management platform for wildlife resource protection according to claim 1, characterized in that: Retrieve the wild plant distribution data of the target protected area, deploy global static monitoring devices based on the wild plant distribution data, and build a global static monitoring subnetwork, including: Extracting plant height characteristics based on the wild plant distribution data to determine wild plant height distribution characteristics; Extracting plant distribution density characteristics from the wild plant distribution data to obtain wild plant density distribution characteristics; Based on the wild plant height distribution characteristics and the wild plant density distribution characteristics, global static monitoring devices are deployed to construct a global static monitoring subnetwork.

8. The intelligent management platform for wildlife and plant resource protection according to claim 1, characterized in that: include: Build an inspection path identifier; The inspection path identifier is used to identify the K key activity areas, the K key migration channels and the K key migration time points, and determine K first-level dynamic inspection paths.

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