Digital wetland ecological environment monitoring system

Abstract

The invention provides a digital wetland ecological environment monitoring system, which comprises an intelligent sensing terminal deployed in a wetland experiment sample area and used for collecting multi-source heterogeneous wetland monitoring data and transmitting the multi-source heterogeneous wetland monitoring data to information transmission equipment; the information transmission equipment is used for receiving, transmitting, summarizing, storing and analyzing the multi-source heterogeneous wetland monitoring data; and the wetland data center is used for acquiring the summarized multi-source heterogeneous wetland monitoring data through information transmission equipment, providing data analysis service on line by utilizing a distributed calculation and analysis technology and a data mining tool on the basis of the multi-source heterogeneous wetland monitoring data, and providing data service for the application service center and the intelligent decision center. According to the invention, multi-source heterogeneous data is integrated, wetland ecological monitoring related management applications and data services are integrated to a unified monitoring platform, and application of the system in ecological evaluation and simulation prediction is realized, so that informatization, digitization and intelligence of wetland ecological monitoring are further realized.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a digital wetland ecological environment monitoring system. Background Art

[0003] Traditional wetland ecological monitoring mainly uses satellite remote sensing to obtain large-scale, long-term, medium- and high-resolution wetland image data, to achieve wetland land use / cover classification, soil vegetation, water quality, disasters and other information extraction, which is helpful for analyzing wetland ecological change trends and influencing factors. In general, the current wetland ecological monitoring still has problems such as imperfect monitoring system and low level of informatization. The continuous monitoring and statistical means of wetland environment and biodiversity are still relatively simple, and some monitoring information is not open or shared, which cannot give full play to the role of monitoring data. There is an urgent need for a means and method that can monitor wetlands in real time, intelligently control, scientifically predict, and integrate and share wetlands, effectively solve the existing problems of wetland monitoring and management, empower protection and management with spatial information technology, and explore its ecological value through data sharing, so as to improve management efficiency and information, digitalization and intelligence levels. Summary of the invention

[0004] The purpose of the present invention is to provide a digital wetland ecological environment monitoring system, which integrates multi-source heterogeneous data obtained by macro satellite remote sensing and micro intelligent sensing Internet of Things, integrates wetland ecological monitoring related management applications and data services into a unified monitoring platform, and realizes its application in ecological evaluation and simulation prediction, so as to further realize the informatization, digitization and intelligence of wetland ecological monitoring.

[0005] To achieve the above-mentioned purpose, the present invention provides a digital wetland ecological environment monitoring system, the system comprising:

[0006] Intelligent sensing terminals are deployed in wetland experimental sample areas to collect multi-source heterogeneous wetland monitoring data and transmit the multi-source heterogeneous wetland monitoring data to information transmission equipment;

[0007] Information transmission equipment, used to send, receive, aggregate, store and analyze multi-source heterogeneous wetland monitoring data;

[0008] The wetland data center is used to obtain summarized multi-source heterogeneous wetland monitoring data through information transmission equipment. Based on the multi-source heterogeneous wetland monitoring data, it uses distributed computing, analysis technology and data mining tools to provide online data analysis services and provide data services for the application service center and intelligent decision-making center.

[0009] Furthermore, the information transmission device includes:

[0010] The remote terminal unit is connected to the intelligent sensing terminal and the gateway signal respectively, and is used to obtain multi-source heterogeneous wetland monitoring data from the intelligent sensing terminal, convert the abstract monitoring information into recognizable digital signals, and complete the reception, collection, storage and analysis of multi-source heterogeneous wetland monitoring data;

[0011] Gateway, used to realize data transmission between remote terminal units and wetland data centers.

[0012] Furthermore, the wetland data center includes:

[0013] The collection and aggregation layer is used to collect structured data and unstructured data, realize data aggregation, process the data, and send the processed data to the storage management layer for storage;

[0014] The storage management layer is used to establish a basic library and an entity library based on the received data, and also to establish a knowledge base, a GIS algorithm library, an indicator library, and a model library;

[0015] The analysis service layer is used to implement distributed computing and analysis through a distributed indexing engine and a distributed computing framework, as well as to provide BI tools.

[0016] The application presentation layer is used to build a GIS platform based on the microservice architecture, and to publish related application services online based on wetland characteristics and user needs.

[0017] Furthermore, the data sources of the collection and aggregation layer include land surveying and mapping data, ecological environment big data platform data services, intelligent sensing terminals and data sharing and exchange services from other sources.

[0018] Furthermore, the collection and aggregation layer processes the data, specifically: preprocessing the collected data, and after preprocessing, cleaning, processing, and quality inspection of the data on the GIS platform based on the spatial data engine and GIS middleware technology to achieve standard storage of monitoring data.

[0019] Furthermore, the application presentation layer builds a GIS platform based on the microservice architecture. Specifically, based on the microservice architecture, different functions and services are split into microservices. At the same time, the existing wetland monitoring-related application systems are integrated on the GIS platform, and aggregated into a microservice service library through microservice routing for invocation by the application service center.

[0020] Furthermore, the smart decision-making center is used to conduct a comprehensive evaluation of wetland ecological health, including the following operations:

[0021] Select the study area, combine multi-source heterogeneous wetland monitoring data, and construct a comprehensive evaluation index system for wetland ecosystem health;

[0022] Based on expert decision-making method and analytic hierarchy process, determine the weight of each indicator in the comprehensive evaluation index system;

[0023] The comprehensive index of wetland health evaluation is calculated by weighted summation, and the calculation results are graded to obtain the final evaluation result.

[0024] Furthermore, the intelligent decision-making center is used to predict the land use / cover process in the study area, specifically including the following operations:

[0025] According to the area of ​​the study area and its land use type, the Markov model is used to obtain the land use transfer area matrix, transfer probability matrix and transfer suitability image set;

[0026] Construct a CA filter, create a weight factor with significant spatial significance according to the distance between the neighbor and the cell, and make it act on the cell to determine the state change of the cell;

[0027] Determine the number of iterations of the CA filter, which is used as the time interval for model simulation prediction;

[0028] The Kappa coefficient is used to test the simulation accuracy of the model and analyze the model operation results.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. Intelligent sensing technology is currently mostly used in small-scale scenarios such as agricultural Internet of Things and fire fighting, and is rarely deployed in large areas such as wetlands. The digital wetland ecological environment monitoring system provided by the present invention expands the technical means of obtaining monitoring data through the intelligent sensing Internet of Things, realizes the innovation of research ideas in the acquisition of wetland-related data resources, and comprehensively improves the intelligent level of monitoring information collection, processing, and transmission;

[0031] 2. The present invention integrates multi-source heterogeneous wetland monitoring data into a unified wetland data center for real-time analysis and display, further upgrading the technical means for ecological monitoring information integration and analysis, and achieving innovation in research ideas in resource integration;

[0032] 3. The current application of wetland monitoring data is mostly limited to the testing and verification of a single method, and there are few means of multi-model integration. However, there are differences in the ecological status of a large range of wetlands. The present invention improves the scientific nature of management decisions by constructing a comprehensive analysis and evaluation system with multiple influencing factors and multiple evaluation dimensions, based on multivariate heterogeneous wetland monitoring data and using simulation prediction models. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 It is a schematic diagram of the overall structure of a digital wetland ecological environment monitoring system provided by an embodiment of the present invention.

[0035] Figure 2 It is a schematic diagram of the wetland ecological health assessment process provided by an embodiment of the present invention.

[0036] Figure 3 It is a schematic diagram of the CA-Markov model prediction process provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The enumerated embodiments are only used to explain the present invention and are not used to limit the scope of the present invention.

[0038] Reference Figure 1 , this embodiment provides a digital wetland ecological environment monitoring system, the system includes an intelligent sensing terminal, information transmission equipment and a wetland data center.

[0039] Among them, the intelligent sensing terminal is deployed in the wetland experimental sample area to collect multi-source heterogeneous wetland monitoring data and transmit the collected multi-source heterogeneous wetland monitoring data to the information transmission equipment.

[0040] Exemplarily, the intelligent sensing terminal includes satellite remote sensing, low-altitude aerial photography, ground-based video and intelligent sensing equipment such as meteorology, air quality, hydrology and water quality, soil and birds.

[0041] Information transmission equipment is used to send, receive, aggregate, store and analyze multi-source heterogeneous wetland monitoring data.

[0042] In this embodiment, the information transmission equipment includes a remote terminal unit (RTU) and a gateway.

[0043] Among them, the remote terminal unit is connected to the intelligent sensing terminal and the gateway signal respectively, and is used to obtain multi-source heterogeneous wetland monitoring data from the intelligent sensing terminal, convert the abstract monitoring information into recognizable digital signals, complete the reception, collection, storage and analysis of multi-source heterogeneous wetland monitoring data, and realize the non-electrical measurement of physical quantities, chemical quantities and biomass of the monitored objects.

[0044] The gateway is used to realize data transmission between the remote terminal unit and the wetland data center.

[0045] The system provided in this embodiment is based on wireless networks, 4G / 5G networks, and multiple air-space-ground sensors such as satellite remote sensing, low-altitude aerial photography by drones, ground-based video, meteorology, air quality, hydrology and water quality, soil, and birds to form an Internet of Things. The final data is transmitted to the wetland data center through a network protocol.

[0046] The wetland data center includes a collection and aggregation layer, a storage management layer, an analysis service layer and an application presentation layer.

[0047] Among them, the collection and aggregation layer is used to collect structured data and unstructured data, realize data aggregation, process the data, and send the processed data to the storage management layer for storage.

[0048] In this embodiment, the data sources of the collection and convergence layer include land surveying and mapping data, ecological environment big data platform data services, intelligent sensing terminals and data sharing and exchange services from other sources. Among them, the data from land surveying and mapping data, ecological environment big data platform data services and other sources of data sharing and exchange services are structured data, and land surveying and mapping data can be managed through a relational database management system (RDBMS). The ecological environment big data platform data services and other sources of data sharing and exchange services can be obtained through the API interface. Unstructured data mainly comes from data collected by intelligent sensing terminals.

[0049] As an optional implementation, the data collection and aggregation layer processes the data, specifically: pre-processing the collected data, and after pre-processing, cleaning, processing and quality inspection of the data on the GIS platform based on the spatial data engine and GIS middleware technology, so as to realize the standard storage and standardized management of monitoring data. In particular, for remote sensing images from various sources, matching image interpretation and classification methods can be used to process them according to their image spatial resolution, spectral resolution and other characteristics.

[0050] The storage management layer is used to establish a basic library and an entity library based on the received data, and also to establish a knowledge base, a GIS algorithm library, an indicator library and a model library.

[0051] Specifically, the storage management layer first stores the received basic data such as hydrology and water quality, air quality, soil vegetation, acoustic environment, and meteorology into the basic database, and then further constructs entity libraries such as basic geographic information data, wetland resource thematic data, wetland ecological monitoring data, wetland management business data, and wetland knowledge support data according to the entities corresponding to the basic data. At the same time, it integrates GIS algorithm library, indicator library, and model library to provide data support for the analysis service layer and application presentation layer.

[0052] The analysis service layer is used to implement distributed computing and analysis through a distributed index engine and a distributed computing framework, as well as to provide BI tools. BI (Business Intelligence) tools are a type of software application that converts large amounts of raw data into meaningful information to help users make better decisions. BI tools enable users to easily explore and understand data through functions such as data collection, cleaning, analysis, and visualization, thereby discovering trends, patterns, and potential problems. In this embodiment, BI tools are mainly used to implement data query statistics, spatial analysis, data mining, and evaluation and assessment.

[0053] The application presentation layer is used to build a GIS platform based on the microservice architecture, and to publish relevant application services online based on wetland characteristics and user needs.

[0054] In this embodiment, the application presentation layer includes an application service center and a smart decision-making center. With the support of the wetland data center and microservice architecture, the application presentation layer splits different functions and services into microservices. At the same time, it integrates existing wetland monitoring-related application systems on the GIS platform, aggregates them into a microservice service library through microservice routing, and supplies them to the application service center for calling. Exemplarily, the microservices include but are not limited to data services, map services, algorithm services, spatial analysis services, real-time rendering services, etc.

[0055] In response to the business requirements of wetland ecological monitoring, the application service center can also include multiple business applications such as comprehensive display of wetland monitoring results, resource management, and monitoring equipment management. In addition, the reserved interface can facilitate the expansion of application services required for the improvement of wetland protection and management requirements, thereby ensuring that the system can keep pace with the times and continuously meet the changing needs of wetland management.

[0056] Data is the primary production material of information systems. The data involved in the ecological environment monitoring system includes historical data, business data of meteorological departments, GIS data, remote sensing data and statistical data, as well as real-time monitoring data. The data sources are diverse and the data structure is not uniform. The system provided in this embodiment uses geographic entities as carriers to associate related business data. At the same time, it realizes data management functions such as data cleaning, quality inspection and format conversion to support the efficient integration and update of monitoring data, and finally builds a wetland data center with basic geographic information data, wetland resource thematic data, wetland management business data, knowledge support data and wetland ecological monitoring data as the main body.

[0057] As a possible implementation method, refer to Figure 2 The smart decision-making center is used to conduct a comprehensive evaluation of wetland ecological health. By selecting the research area and combining multi-source data such as monitoring data, remote sensing images and statistical data, a comprehensive evaluation index system for wetland ecosystem health is constructed. GIS spatial analysis technology is used to conduct a comprehensive, classified and zoned evaluation of wetland ecological health, explore its main control factors and spatial distribution patterns, and compare the health status of different types of wetlands. The specific operations include the following:

[0058] S101. Select a research area and combine multi-source heterogeneous wetland monitoring data to construct a comprehensive evaluation index system for wetland ecosystem health.

[0059] S102. Based on the expert decision-making method and the hierarchical analysis method, determine the weight of each indicator in the comprehensive evaluation index system.

[0060] S103, calculating the comprehensive index of wetland health evaluation according to the weighted summation method, classifying the calculation results, and obtaining the final evaluation result.

[0061] Exemplarily, the calculation formula for the comprehensive index of wetland health evaluation is:

[0062]

[0063] In the above formula, I is the comprehensive index of wetland health evaluation, ranging from 0 to 1, n is the number of evaluation indicators, and W i is the weight of the i-th evaluation index, and E i is the standardized value of the ith indicator.

[0064] As another possible implementation, the smart decision-making center is also used to predict the land use / cover process in the study area. The dynamic change process of land use / cover can be regarded as a Markov process, and the pixel can be regarded as a cell in the CA model. The land use / cover type of the pixel is the cell state, the adjacent pixels are the cell neighbors, and the pixel size is the scale of the cell. Applying the CA-Markov model to the study of land use / cover change can ultimately determine the land use / cover type of the central cell based on the land use / cover type of the central cell and its neighbors at the previous moment, using conversion rules.

[0065] In this embodiment, reference Figure 3 , the intelligent decision-making center predicts the land use / cover process in the study area, including the following operations:

[0066] S201. According to the area of ​​the study region and its land use type, the Markov model is used to obtain the land use transfer area matrix, transfer probability matrix and transfer suitability image set.

[0067] S202, constructing a CA filter, creating a weight factor with significant spatial significance according to the distance between the neighbor and the cell, and making it act on the cell, thereby determining the state change of the cell.

[0068] S203: Determine the number of iterations of the CA filter and use it as the time interval for model simulation prediction.

[0069] S204. Use the Kappa coefficient to test the simulation accuracy of the model and analyze the model operation results.

[0070] This embodiment creates an intelligent decision-making center for wetland ecological monitoring with the goal of "smart wetlands" through the aggregation of algorithm libraries, indicator libraries, model libraries, and knowledge bases. It supports decision-making services such as query and retrieval of monitoring indicators, statistical analysis of monitoring indicators, comprehensive evaluation of monitoring indicators, and trend forecasting and analysis. It enables intelligent ecological monitoring and auxiliary decision-making analysis through the empowerment of "big data + AI" technology.

[0071] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A digital wetland ecological environment monitoring system, characterized in that: The system comprises: Intelligent sensing terminals are deployed in wetland experimental sample areas to collect multi-source heterogeneous wetland monitoring data and transmit the multi-source heterogeneous wetland monitoring data to information transmission equipment; Information transmission equipment, used to send, receive, aggregate, store and analyze multi-source heterogeneous wetland monitoring data; The wetland data center is used to obtain summarized multi-source heterogeneous wetland monitoring data through information transmission equipment. Based on the multi-source heterogeneous wetland monitoring data, it uses distributed computing, analysis technology and data mining tools to provide online data analysis services and provide data services for the application service center and intelligent decision-making center.

2. A digital wetland ecological environment monitoring system according to claim 1, characterized in that: The information transmission device comprises: The remote terminal unit is connected to the intelligent sensing terminal and the gateway signal respectively, and is used to obtain multi-source heterogeneous wetland monitoring data from the intelligent sensing terminal, convert the abstract monitoring information into recognizable digital signals, and complete the reception, collection, storage and analysis of multi-source heterogeneous wetland monitoring data; Gateway, used to realize data transmission between remote terminal units and wetland data centers.

3. A digital wetland ecological environment monitoring system according to claim 1, characterized in that: The wetland data center includes: The collection and aggregation layer is used to collect structured data and unstructured data, realize data aggregation, process the data, and send the processed data to the storage management layer for storage; The storage management layer is used to establish a basic library and an entity library based on the received data, and also to establish a knowledge base, a GIS algorithm library, an indicator library, and a model library; The analysis service layer is used to implement distributed computing and analysis through a distributed indexing engine and a distributed computing framework, as well as to provide BI tools. The application presentation layer is used to build a GIS platform based on the microservice architecture, and to publish related application services online based on wetland characteristics and user needs.

4. A digital wetland ecological environment monitoring system according to claim 3, characterized in that: The data sources of the collection and aggregation layer include land surveying and mapping data, ecological environment big data platform data services, intelligent sensing terminals and data sharing and exchange services from other sources.

5. A digital wetland ecological environment monitoring system according to claim 3, characterized in that: The collection and aggregation layer processes the data, specifically: preprocessing the collected data. After preprocessing, based on the spatial data engine and GIS middleware technology on the GIS platform, the data is cleaned, processed, and quality-checked to achieve standard storage of monitoring data.

6. A digital wetland ecological environment monitoring system according to claim 3, characterized in that: The application presentation layer builds a GIS platform based on the microservice architecture. Specifically, based on the microservice architecture, different functions and services are split into microservices. At the same time, the existing wetland monitoring-related application systems are integrated on the GIS platform and aggregated into a microservice service library through microservice routing for invocation by the application service center.

7. A digital wetland ecological environment monitoring system according to claim 1, characterized in that: The smart decision-making center is used to conduct a comprehensive evaluation of wetland ecological health, including the following operations: Select the study area, combine multi-source heterogeneous wetland monitoring data, and construct a comprehensive evaluation index system for wetland ecosystem health; Based on the expert decision-making method and the hierarchical analysis method, the weight of each indicator in the comprehensive evaluation index system is determined; The comprehensive index of wetland health evaluation is calculated by weighted summation, and the calculation results are graded to obtain the final evaluation result.

8. A digital wetland ecological environment monitoring system according to claim 1, characterized in that: The intelligent decision-making center is used to predict the land use / cover process in the study area, including the following operations: According to the area of ​​the study area and its land use type, the Markov model is used to obtain the land use transfer area matrix, transfer probability matrix and transfer suitability image set; Construct a CA filter, create a weight factor with significant spatial significance according to the distance between the neighbor and the cell, and make it act on the cell to determine the state change of the cell; Determine the number of iterations of the CA filter, which is used as the time interval for model simulation prediction; The Kappa coefficient is used to test the simulation accuracy of the model and analyze the model operation results.

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