A multi-modal ecological environment monitoring element plug-in integrated system and method based on unified object modeling

Abstract

The application provides a multi-modal ecological environment monitoring element plug-in integrated system and method based on unified object modeling. First, an object model and inheritance system of monitoring equipment, data and algorithms are constructed; on this basis, a unified logical storage and metadata cataloging mechanism for heterogeneous databases such as relational, non-relational and file objects is designed and implemented; further, through a dynamic registration module, plug-and-play and full life cycle management of equipment, data and algorithm models are realized; relying on a unified service bus, object-level addition, deletion, modification, query, spatio-temporal correlation query and common API such as task scheduling and aggregation are provided externally. The hybrid storage engine cooperatively manages structured and unstructured data and maintains the topological relationship between objects. The scheme realizes dynamic integration and expansion of objects without interrupting operation, significantly improves the efficiency of multi-source heterogeneous data management and cross-modal analysis, and is suitable for various ecological environment monitoring scenes such as nature reserves, basin pollution supervision and disaster emergency response.

Description

Technical Field

[0001] This invention relates to the field of ecological environment monitoring and information management technology, specifically to a pluggable integrated system and method for multimodal ecological environment monitoring elements based on unified object-oriented modeling, which is particularly suitable for unified modeling, dynamic access, cross-modal data management and correlation analysis of multi-source heterogeneous environmental monitoring data. Background Technology

[0002] In the field of ecological and environmental monitoring, different business departments and regions usually need to build various information systems. Although these systems serve different application scenarios, they have a high degree of commonality in terms of data types, structural design, storage and management methods.

[0003] Currently, many database systems lack flexible scalability. When new data types or data structures exceed the original design, it is often necessary to restructure the database structure and upper-level business systems. This approach is particularly inefficient when dealing with multi-source, multi-modal data (such as satellite remote sensing imagery, video streams, ground monitoring data, sensor data, etc.). For example, in ecological and environmental monitoring, adding a satellite or a new monitoring device may require redesigning the database and related management systems, increasing the complexity of data access and system integration.

[0004] Furthermore, data preparation constitutes a significant portion of operations in areas such as ecological environment supervision and nature reserve management. Traditionally, data acquisition, processing, and integration often require manual intervention and repetitive tasks, failing to achieve "ready-to-use" functionality and hindering rapid deployment and iterative upgrades of business systems.

[0005] In existing technologies, some industry databases (such as ArcGIS Geodatabase and Oracle Spatial) simplify spatial data management by predefining data structures and interfaces specific to their respective domains. However, these solutions mostly focus on single-type data objects (such as vector and raster geospatial data) and lack the ability to manage non-data elements such as equipment and algorithm models in a unified manner. Furthermore, in terms of multimodal data management, these systems struggle to achieve integrated access and dynamic integration of equipment, data, and algorithm methods, and cannot perform plug-and-play module expansion as needed during operation.

[0006] Therefore, the existing information technology construction for ecological and environmental monitoring urgently needs a basic platform that can support unified modeling of multimodal objects and has dynamic expansion and hot-swappable integration capabilities, in order to reduce the cost of repeated construction, improve the integration efficiency of multi-source heterogeneous data and equipment, and enhance the maintainability and scalability of the system. Summary of the Invention

[0007] To overcome the problems in existing ecological and environmental monitoring information systems, such as frequent database reconstruction required for accessing multi-source heterogeneous data, poor system scalability, and complex cross-modal data management, this invention proposes a plug-and-play integrated system and method for multimodal ecological and environmental monitoring elements based on unified object-oriented modeling. This technical solution, through an object-oriented unified modeling approach combined with a dynamic registration mechanism, a unified service bus, and a hybrid storage engine, achieves plug-and-play access and management of monitoring equipment, data, and algorithm models, thereby improving the system's flexibility, scalability, and maintainability in equipment and data management.

[0008] In one embodiment of the present invention, a pluggable integrated system for multimodal ecological environment monitoring elements based on unified object-oriented modeling is provided, the system comprising: (1) Core object modeling layer, used to construct the inheritance hierarchy of monitoring equipment class, data class and algorithm model class, wherein: The monitoring equipment class defines the common attribute set (including device ID, geographic coordinates, acquisition frequency, and data format, etc.) and method set of satellites, cameras, and ground monitoring stations, and allows the addition of manufacturer-defined fields through extended annotations; Data classes organize the metadata structure and storage strategy of remote sensing images, video streams, and ground monitoring data through derivation relationships; The algorithm model class is bound to preprocessing methods, analysis service interfaces, and input / output data specifications. (2) Hybrid storage engine, based on object model driven logical cataloging mechanism, manages entity data and metadata in a unified manner and maps them to relational databases, non-relational databases, object storage systems and graph databases to support collaborative storage of structured and unstructured data and maintenance of topological relationships between objects; (3) Dynamic registration module, used to realize plug-and-play registration and full lifecycle management of devices, data and algorithm models, specifically including: Device instances are automatically generated by inheriting device class attributes and methods through declarative configuration, based on the device_profile message matching device template library. It describes heterogeneous data structures and spatiotemporal indexing rules through JSON Schema, and supports automatic mapping of metadata conforming to the ISO 19115 standard; Register the algorithm model and bind the input and output data classes using container images; (4) Unified service bus, used to provide service discovery and invocation routing based on the SPI mechanism, supports: Basic object-level operation interfaces (create, read, update, delete); A spatiotemporal correlation query interface for cross-modal data; Parallel task scheduling and aggregation interface based on partitioning and classification strategies.

[0009] Furthermore, the monitoring equipment class has the following characteristics: (1) Video stream data is pushed through the RTMP interface; (2) Access to satellite data is achieved through FTP retrieval service; (3) It can be extended to bind other communication protocols to adapt to different manufacturers' devices. This extended interface is automatically bound through annotation.

[0010] Furthermore, the dynamic registration module implements the following functions: (1) Device hot-swap mechanism: when the device comes online, the instance is generated and mounted by triggering the MQTT registration message; (2) Zero-configuration data access mechanism, which adapts non-standard data to the target object model through the XSLT converter; (3) Access quality assessment mechanism: Based on data latency, packet integrity rate and sampling accuracy, an access score is generated and communication parameters are dynamically optimized. When the score is lower than the threshold, communication parameter optimization or data retransmission is triggered.

[0011] In one embodiment of the present invention, a plug-and-play integration method for multimodal ecological environment monitoring elements based on unified object-oriented modeling is provided, the method comprising the following steps: (1) Construct a domain object model library, and define the inheritance tree of monitoring equipment classes, the versioning schema of data classes, and the dependency graph of algorithm models; (2) Perform dynamic loading, including: parsing the device description file, instantiating the device object, registering the service endpoint; extracting spatiotemporal metadata, building the spatial index, and triggering the correlation analysis pipeline; (3) Provide a unified service interface, including but not limited to multimodal aggregation query based on GraphQL, real-time video analysis based on gRPC-streaming, and object-level CRUD, cross-modal spatiotemporal association and parallel task scheduling and aggregation interface; (4) Implement performance optimization strategies, including: metadata caching, SQL query pushdown, and edge node load balancing; (5) Implement exception handling mechanisms, including: device compatibility check, data integrity verification, and object model version rollback.

[0012] Furthermore, the performance optimization strategy further includes: (1) Use Redis to cache frequently accessed device status information; (2) Compile the spatial range filtering conditions into an SQL WHERE clause and push it down to the database for execution; (3) Schedule the nearest edge computing node to perform analysis tasks based on the device’s geographical location.

[0013] Furthermore, the dynamic loading process also includes an exception handling mechanism, which includes: (1) Compare the MD5 hash values ​​of the device driver library to perform compatibility verification; (2) Verify block-level data consistency using checksums; (3) The object model is maintained based on the snapshot mechanism and a rollback is triggered when an exception occurs. The rollback mechanism ensures that the system does not stop running.

[0014] Alternatively, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements all the steps of the above method.

[0015] Alternatively, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program to implement all the steps of the above method.

[0016] Based on the above technical solutions, this invention proposes a plug-and-play integrated system for multimodal ecological environment monitoring elements based on unified object-oriented modeling. This system comprises a core object modeling layer, a dynamic registration module, a unified service bus, and a hybrid storage engine, enabling unified modeling and integrated management of monitoring equipment, data, and algorithm models. During the access, storage, retrieval, and retrieval of multi-source heterogeneous data, this invention supports plug-and-play access of devices, data, and algorithms without requiring reconstruction of the underlying database structure, thereby significantly shortening the system construction and upgrade cycle.

[0017] This invention effectively solves the problems in existing technologies, such as the need for repetitive database design for multi-source heterogeneous data access, poor system scalability, and the inability to uniformly manage cross-modal data. Through object-oriented modeling, the system can define classes and attributes based on the characteristics of different devices and data types, and achieve rapid adaptation through derivation and extension. Combined with a dynamic registration mechanism, the system can complete the registration and unloading of devices and data while running, reducing downtime and improving operational continuity and stability.

[0018] Furthermore, the unified service bus not only supports spatiotemporal relational queries of cross-modal data, but also provides end-to-end management of object lifecycles, ensuring the standardization and consistency of data and device management. The hybrid storage engine combines the advantages of relational databases, object storage, and graph databases to achieve efficient collaborative management of structured and unstructured data, and supports the visualization and analysis of complex relationships between objects.

[0019] Furthermore, the performance optimization strategies and anomaly handling mechanisms proposed in this invention, including metadata caching, query pushdown, load balancing, device compatibility checks, data integrity verification, and version rollback, ensure that the system can maintain high response speed and stability under high concurrency and multi-task scenarios, effectively reducing the risks caused by data corruption or device incompatibility.

[0020] The technical solution of this invention is applicable to various ecological and environmental monitoring scenarios, such as multi-source data acquisition in nature reserves, watershed pollution monitoring and source tracing, urban air and water quality monitoring, and disaster emergency response. The system can be rapidly deployed and achieves on-demand expansion and rapid response through object-oriented configuration and dynamic integration, thereby improving the overall level of the ecological and environmental monitoring system in terms of data access efficiency, cross-modal analysis capabilities, and system scalability.

[0021] In summary, this invention achieves plug-and-play access and unified management of multimodal ecological environment monitoring elements through the collaborative application of unified object-oriented modeling, dynamic registration, a unified service bus, and a hybrid storage engine. This solution not only reduces the workload of database reconstruction and system modification but also significantly improves the unified management and analysis capabilities of cross-modal data. Furthermore, the system's built-in performance optimization strategies and anomaly handling mechanisms ensure stable operation in high-concurrency, large-scale data environments, providing solid technical support for the standardized construction, modular evolution, and sustainable upgrading of ecological environment information platforms.

[0022] The core innovation of this invention lies in proposing a unified object-oriented modeling methodology, which can semantically abstract and inherit monitoring equipment, data, and algorithm models; and through a dynamic registration mechanism and a unified API interface system, it realizes the rapid integration and flexible scheduling of multimodal monitoring elements, supports the unified management of heterogeneous data and the efficient invocation of algorithm services, and meets the comprehensive needs of the field of ecological and environmental monitoring for multi-source data fusion, dynamic expansion and parallel processing. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings will be briefly described below.

[0024] Figure 1 The schematic diagram of the overall structure of the pluggable integrated system for multimodal ecological environment monitoring elements based on unified object-oriented modeling provided in the embodiments of the present invention shows the composition and interrelationship of the core object modeling layer, dynamic registration module, unified service bus and hybrid storage engine.

[0025] Figure 2 This is a schematic diagram of the core object modeling layer and its class inheritance structure in an embodiment of the present invention. It shows the hierarchical structure and derivation relationship of the monitoring equipment class, data class, and algorithm model class, as well as the binding methods of key attributes and methods of various objects.

[0026] Figure 3 This is a flowchart illustrating the collaborative workflow between the dynamic registration module and the unified service bus in this embodiment of the invention. It shows the main processing steps and interaction processes for device access, data registration, algorithm model mounting, and cross-modal data query.

[0027] Figure 4 The flowchart of the plug-and-play integration method for multimodal ecological environment monitoring elements provided in the embodiments of the present invention illustrates the complete method steps from domain object model construction, dynamic loading, unified interface calling to performance optimization and anomaly handling. Detailed Implementation

[0028] Example 1: System Overall Structure like Figure 1 As shown, the pluggable integrated system for multimodal ecological environment monitoring elements based on unified object-oriented modeling provided in this embodiment adopts a layered modular architecture, including a core object modeling layer, a dynamic registration module, a unified service bus, and a hybrid storage engine.

[0029] The core object modeling layer is the foundation of this system. Its function is to perform unified semantic abstraction and modeling of various element objects involved in the field of ecological and environmental monitoring, covering three aspects: monitoring equipment classes, data classes, and algorithm model classes. The monitoring equipment class defines the common attributes and general methods of different types of monitoring equipment, such as satellites, cameras, and ground monitoring stations. Specific model or function subclasses can be derived from this to adapt to different monitoring needs. The data class describes the metadata structure and storage strategies of different types of monitoring data, including remote sensing image data, video stream data, and ground monitoring data. It is extended through inheritance to quickly adapt to new data types. The algorithm model class binds data processing preprocessing methods, analysis service interfaces, and input / output data specifications, realizing integrated management of data and algorithms.

[0030] The dynamic registration module serves as the system's access and integration hub, enabling plug-and-play registration of devices, data, and algorithm models through declarative configuration. When a device is connected, the system automatically parses its description information and matches it to the corresponding template library, generating a device instance and adding it to the unified management system. When data is connected, the system automatically maps and indexes data classes based on standardized metadata descriptions; for data that does not conform to standards, format conversion and structure mapping are performed using a data conversion adapter. When an algorithm model is connected, the system can deploy the model in a containerized manner and automatically bind it to input and output data classes, allowing it to be directly invoked within the system.

[0031] The unified service bus serves as the system's operational hub, providing cross-module data access and service invocation capabilities. It supports service discovery and invocation routing based on standard interface protocols and possesses spatiotemporal correlation query capabilities for cross-modal data, enabling users to retrieve and analyze different types of data in a unified manner. Simultaneously, the unified service bus is responsible for the full lifecycle management of objects, including creation, updating, deletion, and decommissioning operations, ensuring the consistency and maintainability of the system during long-term operation.

[0032] The hybrid storage engine provides multimodal data storage support for the system, consisting of a relational database, an object storage system, and a graph database. The relational database stores structured metadata, such as device attribute information and data indexes; the object storage system manages unstructured observation data, such as image, video, and audio files; and the graph database maintains the topological relationships and semantic associations between objects, facilitating complex relational queries and analysis. Depending on actual business needs, the system can also incorporate a time-series database for storing and analyzing high-frequency monitoring data, thereby further improving the efficiency of time-series data processing.

[0033] The system's overall architecture balances scalability and ease of use. At the application level, users do not need to concern themselves with the specific implementation of the underlying database, nor do they need to redesign data structures for new devices or data types; they can simply complete access and management through configuration. This design significantly lowers the technical barrier to system expansion, improves data access efficiency and system adaptability, making it suitable for widespread application in various ecological and environmental monitoring scenarios such as nature reserves, biodiversity surveys, pollution source monitoring, and disaster emergency response.

[0034] Example 2: Core Object Modeling Layer Structure like Figure 2 As shown, the core object modeling layer in this embodiment adopts a unified object-oriented modeling method, abstracting the main components of the ecological environment monitoring field into three major categories: monitoring equipment classes, data classes, and algorithm model classes. A hierarchical relationship between objects is established through an inheritance system, and attribute fields and methods are bound to objects to achieve integrated management of data, equipment, and algorithms.

[0035] 2.1 Object Class Design and Inheritance Structure 1. Monitoring Equipment The monitoring equipment class first abstracts a set of common attributes applicable to various types of monitoring equipment, including equipment ID, geographical location, acquisition frequency, data format, manufacturer information, and operating status.

[0036] This category can be further divided into subcategories such as satellite, camera, and ground monitoring station. Satellites are further divided into optical satellites and radar satellites, with additional attributes such as orbital altitude, resolution, band type, and revisit period added to the subclasses. The camera class can be derived into visible light cameras and infrared cameras, with parameters such as field of view, frame rate, and video protocol added to the subclasses; Ground monitoring stations can be derived into meteorological monitoring stations and water quality monitoring stations, with specific monitoring indicator types and measurement ranges defined in the subclasses.

[0037] Through inheritance, the system can quickly expand to new device types without modifying the core class.

[0038] 2. Data Class Data classes are used for unified description and management of multimodal data.

[0039] Remote sensing image data classes define metadata such as spatial extent, resolution, number of bands, and coordinate system, and specify storage strategies; The video stream data class defines metadata such as resolution, frame rate, and encoding format, and binds real-time playback and archive retrieval methods; The ground monitoring data class defines the collection time interval, indicator type, unit system, etc., and includes batch entry and real-time update methods.

[0040] Through subclass inheritance and versioned schema mechanisms, the system can adapt to data from different sources and in different formats, and ensure compatibility during business evolution.

[0041] 3. Algorithm Model Class The algorithm model class contains attributes such as model identifier, version information, function category, input data type, and output data type, and is bound to the calling interface, preprocessing and postprocessing methods.

[0042] For example, a target recognition model binds video stream data as input and outputs vectorized target data; The remote sensing inversion model binds image data classes as input and outputs pollution index raster data.

[0043] Algorithm model classes can be deployed in a containerized manner and automatically bound to data classes, enabling the model to be invoked within the system.

[0044] 2.2 Methodological Three-Model To ensure the universality and scalability of object-oriented modeling, this invention proposes a unified object and data methodology framework: 1. Object abstract model (ID + attribute + method + relationship) Objects are identified by a unique ID; Attribute fields describe the characteristics of an object; Methods define the behavior of an object; Relationships describe the dependencies or interactions between objects.

[0045] For example, a water quality monitoring station object can define attributes (latitude and longitude, sampling frequency, index type), methods (data collection, reporting results), and relationships (spatial inclusion relationship with the objects in its watershed).

[0046] 2. Organizational Management Model (ID + Time + Space + Cataloging) The time field is used to describe the time of collection or generation; Spatial fields are used to describe the spatial location or range of data, and can be points, lines, polygons, or raster grids; Cataloging fields are used to categorize objects and manage their versions.

[0047] For example, remote sensing image objects are located by capture time and coverage area and categorized under the "Optical Satellite-Image Data-v1.2" directory.

[0048] 3. Data Model (ID + Data Entity + Metadata) The data entity is the original data content (images, videos, numerical tables); Metadata includes format, resolution, sampling rate, and verification information; IDs ensure the unique location of data.

[0049] For example, the data entity of a PM2.5 monitoring record is a specific numerical value, while the metadata includes the collection time, instrument model, and quality control identifier.

[0050] 2.3 Integration with System Modules Relationship with hybrid storage engines: In the three-method model, "data entities" and "metadata" are mapped to object storage systems and relational databases respectively, and relational fields are entered into graph databases to achieve unified logical cataloging.

[0051] Relationship with the dynamic registration module: During registration, the template library is matched based on the object abstract model, a spatiotemporal index is established based on the organization and management model, and the layered mounting of data and metadata is completed based on the data model.

[0052] Relationship with the unified service bus: The object abstraction model ensures the operation unit of the unified API (CRUD, spatiotemporal correlation, parallel statistics), while the organization and management model and data model ensure the standardization and parallel decomposition of query conditions.

[0053] In this embodiment, the full lifecycle management of objects relies on object-level CRUD operations. When the system performs operations such as object creation, updating, and deletion, it automatically triggers corresponding event notification and response mechanisms, for example: When a new device object is added, the driver loading and initial configuration events are triggered. When updating data objects, index rebuilding and data consistency verification are triggered; Dependency checks and server endpoint recycling are triggered when an algorithm model object is deleted; During the read operation, access logs and security audit events are triggered.

[0054] Through the above mechanism, the system can maintain consistency with event-driven processes across different lifecycle stages of objects (creation, operation, maintenance, and deregistration), ensuring the integrity and traceability of object states. This modeling and lifecycle management approach not only improves the system's stability and scalability but also provides a solid foundation for subsequent dynamic registration and unified service bus scheduling.

[0055] Example 3: Dynamic Registration and Unified Service Bus Collaboration Process Based on the object abstraction model, organization management model, and data model proposed in Embodiment 2, the dynamic registration module of this embodiment can automatically match object templates, construct spatiotemporal indexes, and complete the mounting of data and metadata according to the registration information.

[0056] like Figure 3 As shown, the dynamic registration module in this embodiment works in conjunction with the unified service bus to enable plug-and-play access and management of devices, data and algorithm models in the multimodal ecological environment monitoring system, avoiding system downtime for maintenance or underlying reconstruction due to the addition of new objects.

[0057] Regarding device access, when a new monitoring device connects to the system, the dynamic registration module receives a registration message containing metadata information such as device type, manufacturer information, communication protocol, and data acquisition parameters. After parsing, it matches the message with the device template library to determine the corresponding class definition and initial configuration scheme, generates a device instance, and mounts it to the unified service bus. Furthermore, after device mounting is complete, the system automatically executes an access quality assessment task, collecting and analyzing key indicators such as real-time data latency, data packet integrity rate, and sampling accuracy to generate an access quality score. When the score falls below a preset threshold, the system can automatically trigger adaptive sampling rate adjustment, communication parameter optimization, or data retransmission mechanisms to ensure that the data meets availability and stability requirements immediately after registration.

[0058] Regarding data access, the dynamic registration module supports both standardized and non-standardized access paths. For metadata descriptions conforming to international or industry standards, the system directly maps them to the corresponding data classes and establishes spatiotemporal and semantic indexes. For non-standard data, the system can call a data adapter or XSLT converter to complete the structure and format conversion and register it in the target object model. Preferably, in high-concurrency data registration scenarios, the system can enable a multi-dimensional index building optimization algorithm to dynamically adjust the construction order of the spatiotemporal index and full-text search index based on the current system load, data scale, and query hotspots, thereby reducing access latency.

[0059] Regarding algorithm model access, the dynamic registration module allows models to be deployed in a containerized image manner. During registration, the input and output data classes and calling interfaces are specified. The unified service bus will generate a unique service endpoint for it and automatically route the call request according to the data type at runtime, realizing dynamic calling of the model and return of results.

[0060] To provide consistent service capabilities to upper-layer applications, the unified service bus exposes a set of common APIs after object mounting, whose functions include: 1. Object-level basic operation interface (CRUD): Supports the creation, reading, updating and deletion of device, data and model objects, ensuring consistency in object lifecycle management; 2. Cross-modal spatiotemporal correlation interface: Supports retrieving spatiotemporal joint results from multiple data sources in a single query, for example: Spatial overlay analysis of remote sensing images and concurrent ground monitoring data; Perform time alignment and joint querying of video stream slices and sensor time-series data.

[0061] 3. Parallel Task Scheduling and Aggregation Interface: This interface enables efficient processing and aggregation of large-scale environmental monitoring data in high-concurrency and large-scale multimodal data scenarios through a "task splitting—parallel scheduling—result merging" mechanism. The system supports task splitting by spatial unit (e.g., county, city, province), object type, or attribute, and supports various common aggregation calculations (mean, extreme values, variance, interpolation, etc.). This interface can be extended to a MapReduce-style task interface to achieve distributed subtask execution and result merging, ensuring high efficiency and stability even in large-scale computing scenarios.

[0062] To further enhance the flexibility and practicality of parallel scheduling, the system supports various spatial task partitioning strategies. Users can select a suitable spatial partitioning method through interface parameters, including but not limited to: Regular grid division: Spatial slicing is performed according to fixed grids (such as 1km × 1km, 10km × 10km), which is suitable for monitoring data with uniform spatial distribution; Administrative division: Tasks are divided based on standard provincial, municipal, and county administrative boundaries, applicable to environmental statistics, policy evaluation, and other business operations; Custom region division: Supports uploading custom boundary layers (such as watersheds, ecological blocks, etc.), and the system can automatically build spatial indexes and apply them to task scheduling.

[0063] During task scheduling and result merging, the system follows the process as follows: The system scheduler breaks down the overall query or statistics task into several subtasks based on the selected partitioning strategy; Each subtask is bound to a spatial region and is distributed to the corresponding compute node or container instance for parallel execution; During execution, the scheduler monitors node load in real time, dynamically optimizes resource allocation, and improves computing efficiency; After the subtask is completed, its result is returned through the service bus, and the aggregation engine automatically performs operations such as merging, sorting, completing or weighting the result. Users can configure the aggregated output format according to their business needs, such as a stitched raster map, a partitioned statistical table, or a combined analysis result.

[0064] For example, when a user needs to calculate the annual average PM2.5 value for each county-level administrative region in a city since 2020, the system will automatically divide the task by county and district and perform parallel calculations on multiple nodes, finally aggregating the data to form a complete statistical report and returning it. As another example, when performing the annual maximum value synthesis operation on multi-temporal remote sensing images, the system can divide the data into blocks based on a regular grid, distribute them to multiple nodes for parallel processing, and then automatically merge the results, effectively improving processing efficiency.

[0065] Through the aforementioned unified API layer, this invention not only enables standardized access to objects, but also provides efficient and scalable technical support for cross-modal analysis and large-scale statistics.

[0066] Example 4: Method Flow This method is based on the three-model architecture (object abstraction model, organization management model, and data model) described in Example 2 and the collaborative mechanism of the unified service bus and dynamic registration module in Example 3. It realizes the whole process of object modeling, dynamic access, cross-modal query and parallel statistics, and is adapted to multi-source heterogeneous devices and multi-modal data environments.

[0067] like Figure 4 As shown, this embodiment provides a pluggable integration method for multimodal ecological environment monitoring elements based on unified object-oriented modeling, which mainly includes the following five steps: Step 1: Build a domain object model library Before system deployment, a unified object model library needs to be built based on the target application scenario and data types. The library should include: Inheritance tree for monitoring equipment classes: Define common equipment classes and their derived subclasses (such as satellite equipment, infrared cameras, ground sensors, etc.) to ensure extensibility for new equipment; Data versioning schema: It adopts schema specifications to define various data structures (such as images, videos, monitoring indicators, etc.) and supports version evolution management; Algorithm model dependency graph: Defines the algorithm's input / output data types, calling interfaces, and pre- and post-processing logic to support automatic mounting and scheduling.

[0068] This step provides a unified template and verification basis for subsequent device registration, data loading, and algorithm mounting.

[0069] Step 2: Execute the dynamic loading process During system operation, the dynamic registration module enables plug-and-play loading of devices, data, and algorithm models. Specifically, this includes: Device registration and instantiation: The system automatically parses the device description file (device_profile), matches it with the model library, generates a device instance, and completes the service endpoint registration at the same time; Data access and index building: The system automatically extracts the temporal and spatial metadata of the data and builds spatial indexes, temporal indexes and semantic tags to facilitate subsequent joint retrieval; Algorithm model mounting: The system can load containerized model images, bind input / output data classes and runtime parameters, and complete the mounting of the unified service bus.

[0070] This step enables hot-swappable loading of devices and data without interrupting operation, significantly improving the system's scalability and maintainability.

[0071] Step 3: Provide a unified service interface To improve the integration efficiency of upper-layer business systems, the system provides a set of standardized API interfaces to support unified access and manipulation of multimodal data objects. These mainly include: 1. Object-level operation interface (CRUD): Supports creation, reading, updating, and deletion operations on device objects, data objects, and algorithm model objects, enabling full lifecycle management; 2. Cross-modal spatiotemporal correlation interface: Enables spatial overlay and temporal alignment of remote sensing imagery, video streams, and ground sensor data, for example: Retrieve satellite remote sensing data and ground monitoring point observations for a specific time period; Synchronously analyze target events and corresponding sensor data in the video stream; 3. Parallel Task Scheduling and Aggregation Interface: Used in large-scale, multimodal data scenarios, this interface achieves efficient distributed processing and rapid result aggregation through a mechanism of "task partitioning—parallel scheduling—result merging." It supports various spatial and attribute partitioning strategies, including: Tasks are divided according to administrative regions, rule grids, or user-defined areas; Perform aggregation operations based on object type or attribute characteristics; It provides common statistical methods such as mean, extreme values, variance, and time series interpolation; Supports MapReduce-style sharded parallel computing and automatic result merging.

[0072] For example, the system can receive a request for "Monthly average PM2.5 values ​​in Beijing districts from January to December 2021", automatically schedule parallel tasks, execute them in segments, and finally aggregate them to generate a complete statistical report.

[0073] Step 4: Implement performance optimization strategies To ensure system stability and response efficiency under high concurrency and large data volume environments, the system integrates the following multi-dimensional performance optimization mechanisms: Metadata caching mechanism: Frequently accessed device status and metadata are cached in Redis to reduce database pressure; Query pushdown optimization: Translate spatial / temporal filtering conditions into SQL query conditions, complete the filtering at the data source, and reduce network transmission overhead; Edge computing load balancing: Based on the device's geographical location and network latency, computing tasks are scheduled to be executed on the nearest node, shortening response time; Dynamic index building scheduling: The index building order is dynamically adjusted based on data volume, node load, and query hotspots to avoid system congestion.

[0074] This optimization system enables the system to maintain a response time within seconds even when dealing with millions of objects and terabytes of data.

[0075] Step 5: Implement exception handling and version rollback mechanisms The system has a comprehensive mechanism to ensure operational stability, including: Device compatibility verification: By comparing the MD5 value of the device driver library with the registration information, we ensure that the new device is compatible with the system protocol; Data integrity verification: Block verification is performed based on the checksum algorithm to prevent data tampering or loss during transmission or storage; Object model snapshot and rollback mechanism: The system automatically generates a version snapshot for each model structure update. When an abnormal operation or incompatible change is detected, it can be rolled back to a stable version with one click to ensure that the system runs without interruption.

[0076] For example, if the model is incompatible with the old data after a new type of video capture device is added, the system can immediately trigger a version rollback to restore the historical model state, thus avoiding service interruption.

[0077] This methodology, through unified modeling, dynamic registration, a unified service interface, and optimized execution strategies, achieves fully automated management of multimodal ecological environment monitoring elements from access and fusion to analysis. It is widely applicable to scenarios such as nature reserves, urban environmental monitoring, watershed pollution control, and emergency disaster response, possessing excellent deployment flexibility and application potential.

[0078] Example 5: Joint Monitoring of Urban Air and Water Quality In a pilot project of a municipal ecological and environmental bureau, the plug-and-play integrated system for multimodal ecological and environmental monitoring elements based on unified object-oriented modeling, as described in this invention, was deployed in the municipal data center to achieve joint monitoring and management of air and water quality. The system, through a core object modeling layer and a dynamic registration module, uniformly connects to the following multi-source heterogeneous monitoring devices and data sources: (1) 12 air quality monitoring stations are used to collect air pollution indicators such as PM2.5, PM10, SO2, and NO2; (2) Eight sets of online surface water quality monitoring stations are used to monitor water quality parameters such as dissolved oxygen, ammonia nitrogen, and chemical oxygen demand (COD); (3) Three low-orbit remote sensing satellites (optical resolution 1.5 m, revisit period 2 days) are used for large-scale environmental image acquisition; (4) Six-channel urban traffic cameras (1080p, real-time video stream) are used for monitoring traffic flow and road environment conditions.

[0079] During system operation, a unified service bus enables spatiotemporal correlation queries of cross-modal data, and a hybrid storage engine is used to complete the collaborative management of structured and unstructured data. Compared with the original independent management system, the embodiments of the present invention show significant advantages in terms of new device access time, data registration latency, cross-modal aggregation query response time, index building latency, and data integrity verification success rate. The test comparison results are shown in Table 1.

[0080] Table 1 Comparison of System Deployment Results

[0081] As shown in Table 1, the embodiments of the present invention can significantly shorten the device access and data registration time, reduce the latency of cross-modal data query and index construction, and improve the success rate of data integrity verification. These performance improvements directly reflect the technical effectiveness of the present invention in the integration and operation efficiency of multi-source heterogeneous environmental monitoring systems, and help ecological and environmental regulatory departments achieve more efficient and reliable joint monitoring and decision support.

[0082] Example 6: Multi-source ecological monitoring in nature reserves In a multi-source ecological monitoring project in a nature reserve, the plug-and-play integrated system for multimodal ecological environment monitoring elements based on unified object-oriented modeling, as described in this invention, was deployed at the reserve's management center to achieve comprehensive monitoring and analysis of wildlife, water quality, and vegetation changes. The system, through a core object modeling layer and a dynamic registration module, uniformly connects to the following monitoring devices and data sources: (1) Ten infrared cameras for monitoring wildlife activity at night; (2) Four sets of water quality monitoring buoys were used to monitor dissolved oxygen, ammonia nitrogen, pH value and temperature in the lake water. (3) Two drones were used for aerial photography of vegetation coverage, terrain changes, and other image data; (4) One satellite data interface is used to acquire multispectral remote sensing data from Landsat-8 / 9 satellites.

[0083] During three consecutive months of actual operation, the system of this invention demonstrated the following technical effects: 1. Through a dynamic registration mechanism, 100% online access to all devices and data sources is achieved without downtime maintenance; 2. By combining performance optimization strategies (including Redis caching, SQL query pushdown, and edge node scheduling), the average latency for real-time analysis of multimodal data was reduced to 500 milliseconds; 3. Through the built-in exception handling mechanism, data stream transmission was automatically restored within 15 seconds in all four communication interruption events; 4. During the data fusion analysis, the system identified three abnormal declines in vegetation coverage, which were highly correlated with the decline in water quality indicators during the same period. Based on this, the management took remedial measures two weeks in advance, effectively curbing the potential trend of ecological degradation.

[0084] This embodiment demonstrates that the present invention can not only operate stably in protected area scenarios, but also improve the early warning and response capabilities for ecological and environmental problems through multi-source data fusion and rapid anomaly detection, thereby providing reliable technical support for the scientific management of protected areas.

[0085] Example 7: Disaster Emergency Response Scenario In a sudden flood emergency response, the plug-and-play integrated system for multimodal ecological environment monitoring elements based on unified object-oriented modeling, as proposed in this invention, was temporarily deployed at the emergency command center to support rapid post-disaster assessment and rescue decision-making. The system, through a dynamic registration module and a unified service bus, quickly completed the access and processing of the following multi-source heterogeneous data: (1) Satellite optical images and radar images (including comparison of pre-disaster and post-disaster data) are used to obtain information on the extent of flooding and changes in terrain. (2) Low-altitude aerial video stream from UAVs, used for real-time observation and detailed analysis of local areas; (3) Water level sensor data (sampling period 10 seconds) is used for real-time water level change tracking at key monitoring points.

[0086] Within 5 minutes of receiving the first batch of disaster data, the system automatically completed the following processing steps: 1. Automatic data association and georeferencing enable accurate overlay of data from different sources in a unified spatial coordinate system; 2. Cross-modal visualization displays synchronize satellite imagery, drone videos, and sensor data, facilitating comprehensive judgment by emergency command personnel; 3. Automatic labeling and early warning of high-risk areas, including information prompts for areas with rapidly rising flood levels and areas with traffic disruptions.

[0087] Compared to traditional manual data aggregation methods (which take approximately one hour), this invention reduces data processing and analysis time by 91.6%, significantly improving emergency response speed. This result verifies that the invention possesses comprehensive capabilities for rapid deployment, rapid access, multimodal analysis, and real-time early warning in sudden disaster emergency scenarios, providing efficient technical support for scientific decision-making and emergency dispatch at disaster sites.

[0088] Practical application effect This method demonstrates high adaptability and stability in ecological and environmental monitoring scenarios. For example, in watershed pollution incidents, the system can quickly access multiple water quality monitoring devices and high-resolution satellite imagery, eliminate abnormal monitoring point data through multi-source consistency verification, and then utilize dynamic registration and cross-modal correlation analysis functions, combined with a progressive data recovery mechanism, to ensure uninterrupted real-time analysis, thereby providing reliable real-time data support for emergency decision-making.

[0089] Applicable Scenarios and Implementation Effects The plug-and-play integrated system and method for multimodal ecological environment monitoring elements proposed in this invention, based on unified object-oriented modeling, has high versatility and scalability, and can be widely applied to scenarios with prominent needs for multi-source heterogeneous equipment management, multimodal data fusion analysis, and rapid deployment.

[0090] I. Typical Applicable Scenarios 1. Ecological monitoring in nature reserves In long-term monitoring of nature reserves, various types of equipment are required, including infrared cameras, drone aerial photography equipment, and ground sensors. This invention can quickly complete device registration and access, uniformly manage multimodal data such as animal activity images, voiceprint data, and meteorological parameters, and perform correlation analysis with historical remote sensing images to form a record of species distribution and activity changes.

[0091] 2. Watershed water quality and air pollution supervision In cross-regional river basin or urban atmospheric monitoring, it is necessary to integrate water quality monitoring station data, air quality monitoring station data, and satellite remote sensing data simultaneously. This invention can unify the modeling and access of multiple types of data, enabling real-time monitoring, historical data backtracking, and spatial correlation analysis. For example, when pollution levels exceed standards at a monitoring point, the system can automatically retrieve relevant upstream monitoring data and corresponding satellite imagery to assist in tracing the pollution source.

[0092] 3. Disaster Emergency Response and Rapid Deployment In emergencies such as floods, forest fires, and chemical spills, this invention can complete the access to sensors, drone video streams, and emergency satellite imagery within minutes, and call upon analytical models to process the data in real time. For example, in a forest fire scenario, the system can access infrared video streams from multiple drones to analyze the fire's spread trend in real time, providing a basis for emergency dispatch decisions.

[0093] 4. Comprehensive Urban Environmental Management In comprehensive urban environmental governance, this invention can unify the management of multiple data sources, such as air quality, noise, water quality, and green coverage, enabling cross-departmental data sharing and collaboration. For example, by correlating traffic flow monitoring data with air quality data, it can provide a scientific basis for formulating traffic restriction or optimization plans.

[0094] II. Implementation Results Through testing and actual deployment verification, this invention has the following significant advantages: High access efficiency: The access time for new satellites, sensors, and monitoring equipment can be reduced from several weeks to less than 10 minutes.

[0095] Strong cross-modal data correlation capability: It can quickly establish spatiotemporal correlations between different types of data such as video, images, and sensor records.

[0096] Stable and flexible operation: Supports hot-swappable expansion, allowing object loading and unloading to be completed without interrupting operation.

[0097] Significant performance optimization: In high-concurrency environments, the response time for cross-modal aggregation queries can be consistently kept below 500 milliseconds.

[0098] It has great adaptability and promotion potential: it can be extended to multiple fields such as agriculture, mine safety, and smart ports, reducing redundant R&D investment.

[0099] In summary, this invention not only has significant application value in the field of ecological environment monitoring, but also provides a general technical platform for other industries that integrate and dynamically combine multi-source data, and has broad prospects for promotion and significant economic and social benefits.

[0100] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, various modifications and improvements can be made to the technical solutions of the present invention without departing from the spirit and essence of the invention, and all such equivalent substitutions or variations should be covered within the protection scope of the present invention.

[0101] The scope of protection of this invention is defined by the claims. The specification and drawings are only used to explain the principles and technical solutions of this invention and do not constitute a limitation on the scope of protection of this invention. Unless expressly defined in the claims, the steps and order of implementing this invention can be adjusted without affecting its technical effect, and the module division of the system can also be merged, split, or functionally adjusted according to actual needs.

[0102] Any combination of the technical features disclosed in this invention, and any alternative solution that can achieve the same or similar technical effects, is considered an equivalent solution of this invention and should be given equal protection. This invention can also be combined with other technologies to form new technical solutions; as long as such combinations do not exceed the scope of ordinary knowledge of those skilled in the art, they are all within the protection scope of this invention.

Claims

1. A plug-and-play integrated system for multimodal ecological environment monitoring elements based on unified object-oriented modeling, characterized in that, The system includes: (1) Core object modeling layer, used to construct the inheritance hierarchy of monitoring equipment class, data class and algorithm model class, wherein: The monitoring equipment class defines the common attribute set and method set for satellites, cameras, and ground monitoring stations, and allows the addition of manufacturer-defined fields through extended annotations; wherein the common attribute set includes device ID, geographic coordinates, acquisition frequency, and data format; Data classes organize the metadata structure and storage strategy of remote sensing images, video streams, and ground monitoring data through derivation relationships; The algorithm model class is bound to preprocessing methods, analysis service interfaces, and input / output data specifications. (2) Hybrid storage engine, based on object model driven logical cataloging mechanism, manages entity data and metadata in a unified manner and maps them to relational databases, non-relational databases, object storage systems and graph databases to support collaborative storage of structured and unstructured data and maintenance of topological relationships between objects; (3) Dynamic registration module, used to realize plug-and-play registration and full lifecycle management of devices, data and algorithm models, specifically including: Device instances are automatically generated by inheriting device class attributes and methods through declarative configuration, based on the device_profile message matching device template library. It describes heterogeneous data structures and spatiotemporal indexing rules through JSON Schema, and supports automatic mapping of standard-compliant metadata; Register the algorithm model and bind the input and output data classes using container images; (4) Unified Service Bus, used to provide service discovery and invocation routing based on the SPI mechanism, supports: Object-level basic operation interfaces; the basic operations include creation, reading, updating, and deletion; A spatiotemporal correlation query interface for cross-modal data; Parallel task scheduling and aggregation interface based on partitioning and classification strategies.

2. The system according to claim 1, characterized in that, The monitoring equipment category further includes: (1) Video stream data is pushed through the RTMP interface; (2) Access to satellite data is achieved through FTP retrieval service; (3) It can be extended to bind other communication protocols to adapt to different manufacturers' equipment.

3. The system according to claim 1, characterized in that, The dynamic registration module also includes: (1) Device hot-swap mechanism: when the device comes online, the instance is generated and mounted by triggering the MQTT registration message; (2) Zero-configuration data access mechanism, which adapts non-standard data to the target object model through the XSLT converter; (3) Access quality assessment mechanism: Based on data latency, packet integrity rate and sampling accuracy, an access score is generated and communication parameters are dynamically optimized.

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