An ecological evaluation system for garden plant environment monitoring

Abstract

The application discloses an ecological evaluation system for garden plant environment monitoring, and belongs to the technical field of garden management. The system comprises the following modules: a data acquisition module, which acquires multi-dimensional ecological data in a garden ecological environment in real time; a data fusion module, which realizes deep correlation, semantic understanding and intelligent conversion of the multi-dimensional ecological data; a resilience evaluation module, which comprehensively evaluates the health condition and resilience level of an ecological system; an early warning and regulation module, which realizes real-time identification of an abnormal ecological environment, accurate prediction of potential ecological risks, and automatic generation of targeted regulation suggestions; a visualization module, which provides intuitive and intelligent user interaction experience and supports synchronous operation and data sharing across terminals; and an edge-cloud-end collaborative module, which constructs a distributed intelligent computing architecture and realizes efficient collaboration between edge nodes and cloud resources.

Description

Technical Field

[0001] This application relates to the field of landscape management technology, and more specifically, to an ecological assessment system for environmental monitoring of landscape plants. Background Technology

[0002] With the increasing severity of global ecological and environmental problems, gardens, as an important component of ecosystems, play a vital role in improving urban environmental quality, regulating climate, and protecting biodiversity. The health of garden ecosystems is directly related to the stability and sustainability of their functions. However, due to the combined effects of human activities and natural factors, the ecological balance in garden environments is facing increasing threats, such as soil degradation, climate change, and biodiversity loss. Therefore, how to efficiently and accurately monitor the growth environment and ecosystem status of garden plants has become an important research direction in related fields.

[0003] Currently, traditional methods of monitoring garden ecosystems mainly rely on manual data collection and periodic surveys, which have the following limitations: First, the data collection frequency is low, making it difficult to achieve real-time perception of environmental changes; second, the dimensions of the collected data are limited, failing to fully reflect the complexity of garden ecosystems; and third, the data analysis methods are relatively simplistic, lacking the ability to deeply correlate multi-source data and uncover ecological patterns. Furthermore, existing systems typically fail to effectively integrate real-time monitoring, ecological assessment, and decision support, making it difficult to provide targeted ecological risk prediction and control solutions. These problems result in a low level of intelligence in garden ecological environment monitoring and management, hindering the efficiency and effectiveness of garden ecosystem protection and restoration efforts.

[0004] In recent years, the development of the Internet of Things, artificial intelligence, and big data technologies has provided new technical means for intelligent monitoring of the garden ecological environment. For example, high-precision environmental sensors can achieve real-time acquisition of multi-dimensional data, the combination of edge computing and cloud computing can improve the efficiency of data processing and storage, and deep learning-based models can more accurately assess the state of the ecosystem. However, the individual application of these technologies still faces problems such as low data integration, imperfect ecological assessment systems, and insufficient real-time control capabilities.

[0005] In conclusion, how to construct a garden plant environmental monitoring system that can achieve real-time acquisition of multi-dimensional ecological data, deep fusion of multi-source data, comprehensive assessment of ecological health, and intelligent prediction and control of ecological risks has become an urgent technical problem to be solved. Summary of the Invention

[0006] To overcome a series of shortcomings in the existing technology, the purpose of this application is to provide an ecological assessment system for environmental monitoring of garden plants, comprising the following modules:

[0007] The data acquisition module collects multi-dimensional ecological data from the garden's ecological environment in real time.

[0008] The data fusion module enables deep correlation, semantic understanding, and intelligent transformation of multi-dimensional ecological data;

[0009] The resilience assessment module comprehensively evaluates the health status and resilience level of the ecosystem;

[0010] The early warning and control module can identify ecological and environmental anomalies in real time, accurately predict potential ecological risks, and automatically generate targeted control suggestions.

[0011] The visualization module provides an intuitive and intelligent user interaction experience, supporting cross-terminal synchronous operation and data sharing;

[0012] The edge-cloud collaboration module constructs a distributed intelligent computing architecture to achieve efficient collaboration between edge nodes and cloud resources.

[0013] Preferably, the data acquisition module includes the following components:

[0014] The environmental sensing unit uses high-precision sensors to collect real-time parameters such as temperature, humidity, light, soil moisture, and air quality in the garden's ecological environment.

[0015] The data acquisition and control unit is responsible for coordinating the operation of each sensor and monitoring the integrity and accuracy of the data in real time.

[0016] The adaptive sampling unit dynamically adjusts the sampling frequency according to environmental changes and real-time requirements;

[0017] The data compression unit compresses the large amount of data collected, reducing the pressure on data storage and transmission;

[0018] The data caching unit temporarily stores the collected data to prevent data loss and performs batch processing when the system load is high.

[0019] The basic data preprocessing unit performs noise reduction and filtering on the collected data.

[0020] Preferably, the data fusion module includes the following components:

[0021] The feature extraction and analysis unit is responsible for extracting feature vectors from the preprocessed multidimensional ecological data and performing multi-level data analysis.

[0022] Semantic association unit: Constructs semantic mapping relationships between heterogeneous data to realize association analysis and interoperability of multi-source data;

[0023] Knowledge graph construction unit: builds and updates knowledge graphs to achieve deep integration and intelligent transformation of data and knowledge;

[0024] The data feature analysis unit performs feature analysis and pattern recognition on the data based on knowledge graphs and semantic associations;

[0025] The data quality assessment unit performs quality assessment and verification on the merged data.

[0026] Preferably, the toughness assessment module includes the following components:

[0027] Ecosystem stability assessment unit assesses whether the structure and function of an ecosystem are stable, analyzes the interrelationships and stability thresholds of its elements, and identifies potential vulnerable links.

[0028] The resilience assessment unit analyzes the performance of an ecosystem when subjected to external disturbances, and assesses its response capacity and recovery speed.

[0029] The self-repair capacity assessment unit evaluates the ability of an ecosystem to recover to a normal state after being damaged, and analyzes the efficiency and effectiveness of its self-repair process;

[0030] Ecological diversity assessment unit measures the resilience of ecosystems by assessing species diversity, genetic diversity, and ecological function diversity;

[0031] The long-term ecological stress assessment unit assesses the cumulative impact of long-term environmental stress factors on ecosystems and outputs the assessment results.

[0032] The resilience trend analysis unit analyzes the changing trends of ecosystem resilience based on historical data and existing assessment results.

[0033] Preferably, the early warning and control module includes the following components:

[0034] The real-time anomaly detection unit quickly identifies abnormal conditions in the ecological environment based on real-time analysis of multi-dimensional data.

[0035] The predictive analysis unit predicts environmental change trends and performs pattern mining.

[0036] The environmental correlation analysis unit conducts correlation analysis on multiple factors in the ecological environment, reveals the mutual influence between factors, and accurately identifies potential relationships that may cause risks.

[0037] The regulation strategy generation unit automatically generates targeted regulation suggestions or intervention measures;

[0038] The decision support unit provides comprehensive decision-making suggestions and optimized control solutions.

[0039] Preferably, the visualization module includes the following components:

[0040] The chart rendering unit generates various visual charts based on real-time data to intuitively display environmental changes, trends, and key indicators;

[0041] The 3D visualization unit uses 3D modeling technology to present the spatial layout and changes of the ecological environment;

[0042] The interactive control unit provides intelligent interactive functions, allowing users to customize the content and format of data display by clicking or dragging.

[0043] The data filtering and screening unit allows users to select and filter the displayed data dimensions and time ranges according to different needs;

[0044] The data display unit receives synchronized data from the edge-cloud collaboration module to ensure consistency in display across multiple terminals;

[0045] The analysis results display unit presents relevant analysis results and decision-making suggestions based on the user's operating habits.

[0046] Preferably, the edge-cloud collaboration module includes the following components:

[0047] Edge computing node units are deployed at the network edge and are responsible for real-time processing and analysis of local data.

[0048] The cloud computing resource unit provides powerful computing capabilities and storage resources, and is responsible for processing and analyzing large amounts of data;

[0049] A unified data transmission unit enables data transmission and synchronization between various modules of the system, as well as between edge nodes and the cloud.

[0050] The collaborative scheduling unit intelligently schedules the allocation of tasks between edge nodes and the cloud based on the complexity and resource requirements of the tasks.

[0051] The intelligent load balancing unit monitors the load status of edge nodes and the cloud in real time and dynamically adjusts resource allocation.

[0052] The security and privacy protection unit ensures the security and privacy of data during edge-cloud collaboration.

[0053] Compared with the prior art, this application has the following beneficial effects:

[0054] This application comprehensively assesses the health and resilience of the garden's ecological environment through real-time data acquisition, deep data fusion, and intelligent transformation. Combined with predictive analysis and early warning control, it provides an intelligent and intuitive user experience. Furthermore, the edge-cloud collaboration module implements a highly efficient distributed intelligent computing architecture, ensuring system security and privacy while improving data processing efficiency and accuracy. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the structure of an ecological assessment system for environmental monitoring of garden plants disclosed in an embodiment of this application.

[0056] Figure 2 This is a flowchart of the long-term ecological stress assessment unit in the embodiments of this application. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some embodiments of this invention, but not all embodiments.

[0058] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0060] like Figure 1 As shown, an ecological assessment system for environmental monitoring of garden plants includes the following modules:

[0061] The data acquisition module collects multi-dimensional ecological data from the garden's ecological environment in real time, establishing a comprehensive and accurate environmental data acquisition network.

[0062] The data fusion module, through semantic association and knowledge graph, enables deep association, semantic understanding and intelligent transformation of multi-dimensional ecological data;

[0063] The resilience assessment module comprehensively evaluates the health status and resilience level of the ecosystem, including ecosystem stability, resistance to disturbance, and self-repair capacity.

[0064] The early warning and control module can identify ecological and environmental anomalies in real time, accurately predict potential ecological risks, and automatically generate targeted control suggestions.

[0065] The visualization module provides an intuitive and intelligent user interaction experience through multi-dimensional visualization charts and real-time data display, and supports cross-terminal synchronous operation and data sharing;

[0066] The edge-cloud collaboration module constructs a distributed intelligent computing architecture to achieve efficient collaboration between edge nodes and cloud resources.

[0067] In this embodiment, the ecological assessment system establishes a comprehensive monitoring network for the garden ecological environment through real-time, accurate data collection and semantic association, effectively improving the timeliness and accuracy of data. The combination of data fusion and knowledge graphs enables deep correlation and intelligent transformation of ecological data, enhancing the scientific rigor and intelligence of the ecological assessment. The resilience assessment module provides comprehensive quantitative assessment indicators for the health status and adaptive capacity of the ecosystem, supporting scientific decision-making. The early warning and control module, through real-time anomaly identification and prediction, improves the initiative and accuracy of ecological risk management, effectively preventing environmental problems. The visualization module, through intuitive interaction and multi-terminal synchronization, greatly improves user experience and decision-making efficiency. The edge-cloud collaborative architecture further optimizes the utilization of computing resources, enhancing the system's scalability and operational efficiency. Overall, by integrating the functions of multiple modules, this system provides efficient, intelligent, and comprehensive technical support for the scientific management and protection of the garden ecological environment.

[0068] Furthermore, the data acquisition module includes the following components:

[0069] The environmental sensing unit uses high-precision sensors to collect real-time parameters such as temperature, humidity, light, soil moisture, and air quality in the garden's ecological environment.

[0070] The data acquisition and control unit is responsible for coordinating the work of each sensor, ensuring the orderly progress of the acquisition task, and monitoring the integrity and accuracy of the data in real time.

[0071] The adaptive sampling unit dynamically adjusts the sampling frequency according to environmental changes and real-time requirements to ensure that the most effective data is obtained under different conditions.

[0072] The data compression unit compresses the large amount of data collected, reducing the pressure on data storage and transmission and improving system operating efficiency.

[0073] The data caching unit temporarily stores the collected data to prevent data loss and performs batch processing when the system load is high.

[0074] The basic data preprocessing unit performs noise reduction and filtering on the collected data, providing basic data support for subsequent feature extraction.

[0075] In this embodiment, the data acquisition module significantly improves the comprehensiveness, accuracy, and efficiency of garden ecological environment data acquisition through the collaborative operation of multiple components. The environmental sensing unit ensures real-time acquisition of multi-dimensional ecological data, providing a comprehensive environmental information foundation for the system. The data acquisition control unit, through coordination and monitoring, ensures the efficiency and accuracy of the data acquisition process, avoiding data loss due to sensor conflicts or malfunctions. The adaptive sampling unit dynamically adjusts the sampling frequency according to the environment, improving the flexibility and targeting of data acquisition and helping to capture key information under limited resources. The data compression unit effectively reduces storage and transmission pressure, improving operational efficiency, while the data caching unit enhances robustness and reliability through buffering and batch processing. The basic data preprocessing unit provides high-quality input for subsequent data analysis, ensuring the stability and accuracy of subsequent processing steps. Overall, this module, through multi-dimensional collaboration, lays a solid foundation for the efficient operation of the garden ecological monitoring system.

[0076] Specifically, the environmental sensing unit has the following parameter detection ranges: temperature sensor range: -40℃ to 85℃, accuracy: ±0.2℃; humidity sensor range: 0-100%RH, accuracy: ±2%RH; light sensor range: 0-150000Lux, accuracy: ±2%; soil moisture sensor range: 0-100%, accuracy: ±3%; air quality sensor can detect PM2.5 concentration range: 0-500μg / m³, accuracy: ±10%.

[0077] Specifically, the sampling frequency adjustment range of the adaptive sampling unit is as follows: 5-15 minutes in normal mode; 1-3 minutes in early warning mode; and 30-60 minutes in energy-saving mode. The data caching unit adopts a circular storage strategy, which can save high-frequency sampling data from the last 7 days and regular sampling data from the last 30 days, with a single node storage capacity of 16GB.

[0078] Furthermore, the data fusion module includes the following components:

[0079] The feature extraction and analysis unit is responsible for extracting feature vectors from the preprocessed multidimensional ecological data and performing multi-level data analysis.

[0080] Semantic association unit: Constructs semantic mapping relationships between heterogeneous data to realize association analysis and interoperability of multi-source data;

[0081] The knowledge graph construction unit, based on the knowledge base and expert rules of the ecological environment, constructs and updates the knowledge graph to achieve deep integration and intelligent transformation of data and knowledge;

[0082] The data feature analysis unit performs feature analysis and pattern recognition on the data based on knowledge graphs and semantic associations;

[0083] The data quality assessment unit performs quality assessment and verification on the merged data to ensure its reliability.

[0084] In this embodiment, the data fusion module significantly enhances the analytical depth and intelligence level of ecological monitoring data through the collaborative action of meticulously designed multiple components. The feature extraction and analysis unit extracts key features from multi-dimensional ecological data and conducts multi-level analysis, providing data-driven foundational support for subsequent processing. The semantic association unit constructs semantic relationships between heterogeneous data, achieving deep data association and interoperability, effectively integrating multi-source information. The knowledge graph construction unit combines ecological knowledge bases with expert rules to build a dynamically updated knowledge graph, deeply integrating data and knowledge and providing strong support for intelligent analysis. The data feature analysis unit utilizes knowledge graphs and semantic association technologies to perform pattern recognition and feature analysis on the data, further enhancing its application value. The data quality assessment unit ensures the reliability of the fused data, guaranteeing the credibility of the system's output results. Overall, this module strengthens the scientific nature and decision support capabilities of ecological monitoring through multi-dimensional data fusion and intelligent analysis.

[0085] Furthermore, semantic mapping relationships are constructed between heterogeneous data to achieve correlation analysis and interoperability of multi-source data, including the following steps:

[0086] For each heterogeneous data source, identify the key entities and their corresponding attributes;

[0087] For entities from different data sources and The semantic similarity between them is calculated using similarity measurement methods: ,in, It is a physical entity and Semantic similarity between them; and Representing entities respectively and Word vectors;

[0088] Based on the calculated semantic similarity, a semantic mapping matrix M is constructed, where each element... Representative Entity and Semantic similarity between them;

[0089] Based on the semantic mapping matrix M, a weighted summation method is used to integrate data from different data sources, expressed by the formula: ,in, It is the feature representation after data fusion, representing the entities in data source D1 based on semantic mapping and fusion. and entities in data source D2 The result after fusing the features; These are weighting coefficients used to control the impact of different semantic mappings; It generates entities in data source D1. A function of characteristics; It generates entities in data source D2. A function of characteristics;

[0090] Perform correlation analysis on the integrated data to identify potential relationships and patterns between the data.

[0091] Intelligent decision support solutions are generated based on the analysis results, enabling interoperability between data sources and optimizing ecological environment monitoring and regulation.

[0092] Furthermore, the resilience assessment module includes the following components:

[0093] Ecosystem stability assessment unit assesses whether the structure and function of an ecosystem are stable, analyzes the interrelationships and stability thresholds of its elements, and identifies potential vulnerable links.

[0094] The resilience assessment unit analyzes the performance of an ecosystem when subjected to external disturbances, and assesses its response capacity and recovery speed.

[0095] The self-repair capacity assessment unit evaluates the ability of an ecosystem to recover to a normal state after being damaged, and analyzes the efficiency and effectiveness of its self-repair process;

[0096] Ecological diversity assessment unit measures the resilience of ecosystems by assessing species diversity, genetic diversity, and ecological function diversity;

[0097] The long-term ecological stress assessment unit assesses the cumulative impact of long-term environmental stress factors on ecosystems and outputs the assessment results.

[0098] The resilience trend analysis unit analyzes the changing trends of ecosystem resilience based on historical data and existing assessment results.

[0099] In this embodiment, the resilience assessment module effectively enhances the scientific understanding and monitoring capabilities of ecosystem health and adaptability through comprehensive assessment components. The ecosystem stability assessment unit reveals the robustness of ecosystem structure and function, helping to identify potential vulnerabilities and providing a basis for early intervention. The disturbance resistance assessment unit assesses the system's response and recovery capabilities to external disturbances, providing important references for ecological risk prevention and control strategies. The self-repair capability assessment unit focuses on the system's self-repair efficiency and effectiveness, supporting the optimized design of ecological restoration measures. The biodiversity assessment unit provides important evidence for measuring ecological resilience by measuring diversity indicators, strengthening the comprehensive assessment of ecological functions. The long-term ecological stress assessment unit reveals the potential threats of cumulative environmental stress to ecosystems, aiding in long-term planning and management. The resilience trend analysis unit provides in-depth insights into the dynamic changes in ecological resilience through historical data analysis, helping to predict future development trends. Overall, this module provides a systematic tool for comprehensive and dynamic ecosystem health assessment and optimization, promoting the refined and intelligent development of ecological protection and management.

[0100] like Figure 2 As shown, assessing the cumulative impacts of long-term environmental stressors on ecosystems and outputting the assessment results includes the following steps:

[0101] Identify and define long-term environmental stressors affecting ecosystems and represent them as time-series vectors. ,in Indicates the first Environmental stress factors over time The value at time;

[0102] For each environmental stress factor Establish a separate response model This describes the ecosystem's response to each stressor, where... Indicates the first Environmental stress factors Response to the ecosystem; Indicating environmental stress factors and time Response function to the ecosystem;

[0103] Response to each environmental stress factor Perform time integration to calculate the cumulative effect of this factor. , represented as: ,in, The time interval indicates the time range for the evaluation; Indicates based on the first Response to environmental stress factors The weighting function; This represents the time increment, which is a tiny time step used in the integration process;

[0104] Introducing repair capabilities To correct for the cumulative effect of each stress factor, the formula is as follows: ,in, After considering repair capabilities, the first The modified cumulative impact of individual environmental stress factors; For repair capabilities to the first An adjustment function for the response to environmental stress factors;

[0105] The cumulative impact is standardized to output the final evaluation result: ,in, Represents the standardized first The ultimate cumulative impact of individual environmental stressors; Indicates the first The maximum possible impact value of each environmental stress factor;

[0106] Assign a weight to each stress factor and calculate the total cumulative effect, expressed as: ,in, For the first The weight of each environmental stress factor; Indicates the number of environmental stressors; This represents the comprehensive final assessment result of all environmental stress factors.

[0107] Furthermore, the early warning and control module includes the following components:

[0108] The real-time anomaly detection unit quickly identifies abnormal conditions in the ecological environment based on real-time analysis of multi-dimensional data.

[0109] The predictive analysis unit integrates historical data, trend analysis, and environmental change patterns to predict environmental change trends and perform pattern mining.

[0110] The environmental correlation analysis unit conducts correlation analysis on multiple factors in the ecological environment, reveals the mutual influence between factors, and accurately identifies potential relationships that may cause risks.

[0111] The regulation strategy generation unit automatically generates targeted regulation suggestions or intervention measures based on the results of anomaly detection and risk prediction.

[0112] The decision support unit integrates various analytical results, taking into account external environmental factors, policy requirements, and resource conditions, to provide comprehensive decision-making suggestions and optimized control solutions.

[0113] In this embodiment, the early warning and control module significantly improves the response speed and decision-making accuracy of ecological environment management through the collaborative work of multiple components. The real-time anomaly detection unit can quickly identify abnormal conditions in the ecological environment, providing the first line of defense for timely response to potential risks. The predictive analysis unit, by integrating historical data and trend analysis, predicts environmental change trends in advance and uncovers potential patterns, thus providing data support for the formulation of preventative measures. The environmental correlation analysis unit reveals the interrelationships between multiple factors in the ecological environment, helping to accurately identify key factors that may trigger risks. The control strategy generation unit automatically generates targeted control measures based on real-time detection and predictive analysis results, ensuring the targeted and efficient nature of risk response. The decision support unit, by integrating analysis results and external environmental factors, provides comprehensive and optimized decision-making solutions, further enhancing the scientific nature and operability of control measures. Overall, this module improves the management efficiency and response capabilities of the ecological environment through accurate anomaly identification and risk prediction, intelligent control strategies, and decision support, contributing to the sustainable development of the ecosystem.

[0114] Specifically, the predictive analysis unit uses a time series prediction algorithm to predict environmental change trends over 24 hours, 72 hours, and 7 days, with a prediction accuracy of no less than 75%; the environmental correlation analysis unit can simultaneously process the correlation relationships between no less than 20 environmental factors, with a confidence level of no less than 90%.

[0115] Furthermore, the visualization module includes the following components:

[0116] The chart rendering unit generates various visual charts based on real-time data to intuitively display environmental changes, trends, and key indicators;

[0117] The 3D visualization unit uses 3D modeling technology to present the spatial layout and changes of the ecological environment, allowing users to intuitively view the dynamic changes of the overall and local environment.

[0118] The interactive control unit provides intelligent interactive functions, allowing users to customize the content and format of data display by clicking or dragging, enabling flexible view adjustments;

[0119] The data filtering and screening unit allows users to select and filter the data dimensions and time ranges displayed according to different needs, ensuring that the displayed information matches the user's focus.

[0120] The data display unit receives synchronized data from the edge-cloud collaboration module to ensure consistency in display across multiple terminals;

[0121] The analysis results display unit presents relevant analysis results and decision-making suggestions based on the user's operating habits.

[0122] In this embodiment, the visualization module significantly enhances the understandability of ecological and environmental data and the user's interactive experience through the synergistic effect of multiple components. The chart rendering unit transforms real-time data into intuitive charts, making environmental changes, trends, and key indicators readily apparent and enhancing the effectiveness of information delivery. The 3D visualization unit utilizes 3D modeling technology to display the spatial layout and changes of the ecological environment, helping users dynamically observe environmental changes from both overall and local perspectives, improving the spatial awareness of the analysis. The interactive control unit provides users with flexible customizable view functions, allowing them to freely adjust the displayed content and format according to their needs, enhancing operational flexibility. The data filtering and screening unit enables users to accurately select the displayed dimensions and time range according to specific needs, ensuring that the displayed data aligns with the user's key concerns. The data display unit ensures synchronous display across multiple terminals, guaranteeing data consistency between different devices. The analysis results display unit presents analysis results and decision-making suggestions according to user habits, making the decision-making process more efficient and personalized. Overall, this module enhances users' understanding of ecological and environmental data, improves operational convenience, and enhances the accuracy of decision-making through intuitive and flexible display methods.

[0123] Furthermore, the edge-cloud collaboration module includes the following components:

[0124] Edge computing node units are deployed at the network edge and are responsible for real-time processing and analysis of local data, reducing cloud pressure and improving response speed and data processing efficiency;

[0125] The cloud computing resource unit provides powerful computing capabilities and storage resources, and is responsible for processing and analyzing large amounts of data;

[0126] A unified data transmission unit enables data transmission and synchronization between various modules of the system, as well as between edge nodes and the cloud.

[0127] The collaborative scheduling unit intelligently schedules the allocation of tasks between edge nodes and the cloud based on task complexity and resource requirements, thereby optimizing the efficiency of computing resource utilization.

[0128] The intelligent load balancing unit monitors the load status of edge nodes and the cloud in real time, dynamically adjusts resource allocation, and ensures efficient system operation and optimal resource utilization.

[0129] The security and privacy protection unit ensures the security and privacy of data during edge-cloud collaboration by employing encryption, authentication, and other technologies to prevent data leakage and unauthorized access.

[0130] In this embodiment, the edge-cloud collaboration module significantly improves the system's data processing efficiency, response speed, and resource utilization by optimizing the combination of edge computing and cloud computing. Edge computing node units perform real-time data processing and analysis at the network edge, reducing the burden on the cloud and improving rapid response capabilities in emergencies. The cloud computing resource unit provides powerful computing and storage capabilities, ensuring unrestricted processing and analysis of large-scale data. The unified data transmission unit ensures data flow and synchronization between system modules and between the edge and the cloud, guaranteeing information consistency and real-time performance. The collaborative scheduling unit intelligently allocates computing tasks based on task complexity and resource requirements, optimizing the configuration of computing resources. The intelligent load balancing unit dynamically adjusts resource allocation, ensuring efficient system operation and maximized resource utilization. The security and privacy protection unit ensures data security during edge-cloud collaboration through encryption, authentication, and other technologies, preventing data leakage and unauthorized access. Overall, this module optimizes computing resource configuration, improves overall system performance, and strengthens data security through an efficient edge-cloud collaboration mechanism.

[0131] Specifically, the edge computing node unit has a processing capacity of no less than 0.5 TFLOPS and a local storage capacity of no less than 16 GB; the cloud computing resource unit provides a computing capacity of no less than 100 TFLOPS and a storage capacity of no less than 100 TB; the unified data transmission unit has a data transmission rate of no less than 1 Gbps and supports simultaneous access to no less than 1,000 edge nodes.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An ecological assessment system for environmental monitoring of garden plants, characterized in that, It includes the following modules: a data acquisition module, which collects multi-dimensional ecological data in the garden ecological environment in real time; The data fusion module enables deep correlation, semantic understanding, and intelligent transformation of multi-dimensional ecological data; the resilience assessment module comprehensively assesses the health status and resilience level of the ecosystem. The early warning and control module can identify ecological and environmental anomalies in real time, accurately predict potential ecological risks, and automatically generate targeted control suggestions. The visualization module provides an intuitive and intelligent user interaction experience, supporting cross-terminal synchronous operation and data sharing; the edge-cloud collaboration module builds a distributed intelligent computing architecture to achieve efficient collaboration between edge nodes and cloud resources.

2. The ecological assessment system for environmental monitoring of garden plants according to claim 1, characterized in that, The data acquisition module includes the following components: an environmental sensing unit, which uses high-precision sensors to collect real-time parameters of temperature, humidity, light, soil moisture, and air quality in the garden ecological environment; The data acquisition and control unit is responsible for coordinating the operation of each sensor and monitoring the integrity and accuracy of the data in real time. The adaptive sampling unit dynamically adjusts the sampling frequency according to environmental changes and real-time requirements; the data compression unit compresses the large amount of collected data to reduce the pressure of data storage and transmission. The data caching unit temporarily stores the collected data to prevent data loss and performs batch processing when the system load is high; the basic data preprocessing unit performs noise reduction and filtering on the collected data.

3. The ecological assessment system for environmental monitoring of garden plants according to claim 1, characterized in that, The data fusion module includes the following components: a feature extraction and analysis unit, which is responsible for extracting feature vectors from preprocessed multidimensional ecological data and performing multi-level data analysis; The semantic association unit constructs semantic mapping relationships between heterogeneous data to achieve association analysis and interoperability of multi-source data; the knowledge graph construction unit constructs and updates knowledge graphs to achieve deep integration and intelligent transformation of data and knowledge. The data feature analysis unit performs feature analysis and pattern recognition on the data based on knowledge graphs and semantic associations; the data quality assessment unit performs quality assessment and verification on the fused data.

4. An ecological assessment system for environmental monitoring of garden plants according to claim 1, characterized in that, The resilience assessment module includes the following components: an ecosystem stability assessment unit, which assesses the stability of the ecosystem's structure and function, analyzes the interrelationships and stability thresholds of various elements, and identifies potential vulnerabilities; a disturbance resistance assessment unit, which analyzes the ecosystem's performance when subjected to external disturbances, assessing its response capacity and recovery speed; a self-repair capacity assessment unit, which assesses the ecosystem's ability to recover to a normal state after being damaged, analyzing the efficiency and effectiveness of its self-repair process; an ecodiversity assessment unit, which measures the ecosystem's resilience by assessing species diversity, genetic diversity, and ecological function diversity; and a long-term ecological stress assessment unit, which assesses the cumulative impact of long-term environmental stress factors on the ecosystem and outputs the assessment results. The resilience trend analysis unit analyzes the changing trends of ecosystem resilience based on historical data and existing assessment results.

5. An ecological assessment system for environmental monitoring of garden plants according to claim 1, characterized in that, The early warning and control module includes the following components: a real-time anomaly detection unit, which quickly identifies abnormal conditions in the ecological environment based on real-time analysis of multi-dimensional data; and a predictive analysis unit, which predicts environmental change trends and performs pattern mining. The environmental correlation analysis unit conducts correlation analysis on multiple factors in the ecological environment, reveals the mutual influence between factors, and accurately identifies potential relationships that may cause risks. The regulation strategy generation unit automatically generates targeted regulation suggestions or intervention measures; the decision support unit provides comprehensive decision suggestions and optimized regulation plans.

6. An ecological assessment system for environmental monitoring of garden plants according to claim 1, characterized in that, The visualization module includes the following components: a chart rendering unit that generates various visualization charts based on real-time data to intuitively display environmental changes, trends, and key indicators; The 3D visualization unit uses 3D modeling technology to present the spatial layout and changes of the ecological environment; the interactive control unit provides intelligent interactive functions, allowing users to customize the content and form of data display by clicking or dragging. The data filtering and screening unit allows users to select and filter the displayed data dimensions and time ranges according to different needs; The data display unit receives synchronized data from the edge-cloud collaboration module to ensure consistency across multiple terminals; the analysis results display unit presents relevant analysis results and decision-making suggestions based on user operating habits.

7. An ecological assessment system for environmental monitoring of garden plants according to claim 1, characterized in that, The edge-cloud collaboration module includes the following components: an edge computing node unit, deployed at the network edge, responsible for real-time processing and analysis of local data; a cloud computing resource unit, providing powerful computing capabilities and storage resources, responsible for processing and analyzing large amounts of data; a unified data transmission unit, enabling data transmission and synchronization between system modules and between edge nodes and the cloud; a collaborative scheduling unit, intelligently scheduling task allocation between edge nodes and the cloud based on task complexity and resource requirements; an intelligent load balancing unit, monitoring the load status of edge nodes and the cloud in real time and dynamically adjusting resource allocation; and a security and privacy protection unit, ensuring data security and privacy during edge-cloud collaboration.

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