Intelligent environment monitoring and GIS data fusion system

Abstract

The invention relates to the technical field of environment monitoring, and discloses an intelligent environment monitoring and GIS (Geographic Information System) data fusion system, which comprises the following steps: a data fusion algorithm module, performing deep fusion on preprocessed environment data and GIS information through space-time correlation analysis, a machine learning or deep learning algorithm and a weighted average method or a maximum value method to obtain a GIS data fusion module; according to the intelligent environment monitoring and GIS data fusion system, space-time correlation analysis, a machine learning or deep learning algorithm and a weighted average method or a maximum value method mathematical method are adopted, and the preprocessed environment data and GIS information are deeply fused. The environmental data set with spatial distribution characteristics is generated, a scientific basis is provided for environmental condition evaluation, trend prediction and anomaly detection, the environmental condition and the association between the environmental condition and the geographic position can be more comprehensively understood through data fusion, and the efficiency and accuracy of environmental management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, in particular to an intelligent environmental monitoring and GIS data fusion system. Background Art

[0002] As global environmental problems become increasingly serious, real-time monitoring and data analysis of environmental parameters have become the key to environmental protection and sustainable development. Traditional environmental monitoring methods rely on manual sampling and laboratory analysis, which is not only time-consuming and labor-intensive, but also difficult to achieve real-time data updates and comprehensive coverage. In order to solve the limitations of traditional environmental monitoring methods, intelligent sensor technology came into being. Intelligent sensors have the characteristics of high precision, high sensitivity and strong real-time performance, and can realize continuous monitoring of environmental parameters and remote data transmission. However, relying solely on intelligent sensors for environmental monitoring also has certain limitations, such as insufficient data processing capabilities and non-intuitive information display. It can be seen that we urgently need an intelligent environmental monitoring and GIS data fusion system. Summary of the invention

[0003] In view of the above technical deficiencies, the purpose of the present invention is to provide an intelligent environmental monitoring and GIS data fusion system to solve the problems raised in the above background technology. In order to solve the limitations of traditional environmental monitoring methods, intelligent sensor technology has come into being. Intelligent sensors have the characteristics of high precision, high sensitivity and strong real-time performance, and can realize continuous monitoring of environmental parameters and remote data transmission. However, relying solely on intelligent sensors for environmental monitoring also has certain limitations, such as insufficient data processing capabilities and non-intuitive information display.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent environment monitoring and GIS data fusion system, comprising the following steps: Smart sensor module: used to arrange multiple smart sensors in a preset area to collect data on the atmospheric environment, water environment, soil environment, acoustic environment, radiation environment and biological environment in real time; Data preprocessing module: receives data from the intelligent sensor module, cleans, filters and organizes the collected data parameters, and removes abnormal values ​​and noise data; GIS integration module: It stores and updates geographic spatial information and spatial location information associated with environmental data through the geographic information database, providing the system with spatial data processing and analysis capabilities; Data fusion algorithm module: through spatiotemporal correlation analysis, machine learning or deep learning algorithm and weighted average method or maximum method, the pre-processed environmental data is deeply integrated with GIS information to generate an environmental data set with spatial distribution characteristics; Real-time analysis module: Based on the fused data set, it uses machine learning or deep learning models to perform environmental status assessment, trend prediction, and anomaly detection; Visualization display module: The analysis results are displayed intuitively in the form of maps, charts and reports, which facilitates users to customize views and interactive queries; Permission management module: assign different data access and operation permissions according to user roles to ensure data security; System interface module: provides API interface to support seamless connection with other environmental management systems or third-party platforms; System maintenance and upgrade module: responsible for the daily maintenance, performance monitoring and software version updates of the system to ensure stable operation of the system.

[0005] Preferably, in the smart sensor module step, the smart sensor module supports remote configuration and self-organizing network, and can dynamically adjust the monitoring range and frequency according to environmental changes.

[0006] Preferably, in the smart sensor module step, the smart sensors include air quality sensors, meteorological parameter sensors, water quality sensors, water body parameter sensors, soil pollutant sensors, soil temperature sensors, humidity sensors, pH sensors, noise sensors, radiation sensors, and biological monitoring sensors.

[0007] Preferably, in the data preprocessing module step, a data cleaning algorithm is used to remove errors, outliers and noise in the data to improve the quality of the data and facilitate subsequent data analysis.

[0008] Preferably, in the GIS integration module step, the geographic information database supports the integration of multi-source geographic information data, including but not limited to satellite remote sensing data, drone aerial photography data and ground survey data.

[0009] Preferably, in the real-time analysis module step, an early warning threshold is set according to environmental standards and regulatory requirements, and an early warning is triggered when the environmental parameters exceed the threshold.

[0010] Preferably, in the visualization module step, mobile devices and web access are supported, and flexible data visualization options are provided, such as heat maps, scatter plots, and trend lines.

[0011] Preferably, in the permission management module step, a multi-level permission control strategy is adopted to ensure secure access to sensitive data.

[0012] Preferably, in the system interface module step, RESTful API is supported to facilitate efficient integration with other systems.

[0013] Compared with the prior art, the beneficial effects achieved by the present invention are: First, the present invention identifies the spatiotemporal patterns in environmental data through spatiotemporal correlation analysis, such as the diffusion path of pollutants, the seasonal law of water quality changes, etc., and then maps the environmental data to the geographic space in combination with the spatial analysis capability of the GIS integrated module, thereby intuitively displaying the spatial distribution characteristics of the environmental conditions, and then identifies the complex relationships in the environmental data through machine learning algorithms, such as the correlation between pollutant concentrations and meteorological conditions. The deep learning algorithm can process image and video data, thereby extracting richer environmental information. By training these algorithms, the system can automatically fuse environmental data and GIS information to generate a more accurate environmental data set, and then use the weighted average method to assign weights according to the accuracy and reliability of different sensors to obtain more accurate environmental data. The maximum value rule is used to identify extreme values ​​or abnormal situations in environmental data, such as the highest concentration value of pollutants. These methods can be combined with spatiotemporal correlation analysis and machine learning algorithms to further improve the accuracy and practicality of data fusion, thereby fusing information from different data sources to generate an environmental data set with spatial distribution characteristics, providing a scientific basis for environmental status assessment, trend prediction and anomaly detection. Through data fusion, the environmental status and its relationship with the geographical location can be more comprehensively understood, and the efficiency and accuracy of environmental management can be improved.

[0014] Second, the present invention uses machine learning or deep learning models to conduct in-depth mining and analysis on the fused data set, extract key features from a large amount of environmental data, and quantitatively evaluate the environmental conditions based on these features. For example, for air quality monitoring, the model can analyze the concentration, source and diffusion trend of pollutants to derive the air quality index. For water quality monitoring, the model can evaluate the chemical composition, biological community and ecological health of the water body to determine whether the water quality meets the standards. At the same time, historical data and real-time data are used for trend prediction to identify the laws and trends of environmental changes, such as seasonal changes and cyclical fluctuations. Then, by setting thresholds and monitoring rules according to environmental standards and regulatory requirements, the module can promptly detect abnormal conditions in environmental data. When environmental parameters exceed the set thresholds, the real-time analysis module will automatically trigger the early warning mechanism, and promptly remind relevant personnel to pay attention to environmental changes through text messages, emails, phone calls and other methods, so as to achieve a rapid response to environmental changes.

[0015] Third, the present invention intuitively displays the analysis results to users in the form of maps, charts and reports, and supports user-defined views and interactive query functions. Users can choose different display methods and data dimensions for viewing and analysis as needed. At the same time, the visualization display module also supports mobile devices and Web access, and provides flexible data visualization options, such as heat maps, scatter plots and trend lines, etc., which makes it convenient for users to obtain environmental information anytime and anywhere. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flowchart of the overall steps of the present invention; Figure 2 It is a flowchart of the steps of the intelligent sensor module of the present invention; Figure 3 It is a flowchart of the steps of the data preprocessing module of the present invention; Figure 4 It is a flowchart of the steps of the GIS integration module of the present invention; Figure 5 It is a flowchart of the steps of the data fusion algorithm module of the present invention; Figure 6 It is a flowchart of the steps of the real-time analysis module of the present invention. DETAILED DESCRIPTION

[0017] The specific implementation modes of the present invention will be further described in detail below with reference to the accompanying drawings. Specific implementation method 1 The following is a specific implementation method of the intelligent environmental monitoring and GIS data fusion system.

[0019] See also Figure 1-6 , the intelligent environmental monitoring and GIS data fusion system includes the following steps: 1. Smart sensor module: used to arrange multiple smart sensors in a preset area to collect data on the atmospheric environment, water environment, soil environment, acoustic environment, radiation environment and biological environment in real time; In the smart sensor module step, the smart sensor module supports remote configuration and self-organizing network, and can dynamically adjust the monitoring range and frequency according to environmental changes; In the smart sensor module step, the smart sensors include air quality sensors, meteorological parameter sensors, water quality sensors, water body parameter sensors, soil pollutant sensors, soil temperature sensors, humidity sensors, pH sensors, noise sensors, radiation sensors, and biological monitoring sensors.

[0020] Air quality sensors are installed in the preset area to collect data on the concentration of pollutants in the atmospheric environment in real time. Meteorological parameter sensors include temperature sensors, humidity sensors, wind speed sensors, and wind direction sensors. Meteorological parameters such as atmospheric temperature, humidity, wind speed, and wind direction in the atmospheric environment are monitored in real time to provide data support for assessing the degree of atmospheric pollution. Water quality sensors are used to monitor pollutants in water bodies. Water parameter sensors include water temperature sensors, pH sensors, dissolved oxygen sensors, and turbidity sensors. They are used to collect and measure water temperature, pH value, dissolved oxygen content, and turbidity parameters to provide data support for assessing water pollution conditions. Soil pollutant sensors are used to monitor pollutants in the soil. Soil temperature sensors, humidity sensors, and pH (pH value) sensors are used to monitor pollutants in the soil. Sensors monitor the physical and chemical properties of soil and provide data support for assessing the degree of soil pollution. Noise sensors are used to monitor the noise level in the environment, including traffic noise, industrial noise and construction noise, to provide data support for assessing noise pollution and protecting people's hearing health. Radiation sensors are used to monitor ionizing radiation and non-ionizing radiation in the environment, such as X-rays, gamma rays, ultraviolet rays, and visible light, and measure the intensity and dose rate of radiation in real time to provide a scientific basis for protecting public health and safety. Biological monitoring sensors include ecological monitoring stations and remote sensing technology to monitor biological species and biological communities to assess the health of the ecosystem, and then transmit these real-time detection data to the data processing center through wireless networks, thereby achieving the effect of real-time monitoring and collection of environmental data.

[0021] 2. Data preprocessing module: Receives data from the intelligent sensor module, cleans, filters and organizes the collected data parameters, and removes abnormal values ​​and noise data; In the data preprocessing module step, a data cleaning algorithm is used to remove errors, outliers and noise in the data to improve the quality of the data and facilitate subsequent data analysis.

[0022] The collected environmental data is cleaned comprehensively. Since the data collection process may be affected by various factors, such as equipment failure, signal interference, etc., which may lead to abnormal values, duplicate values ​​or missing values ​​in the data, data cleaning algorithms and technologies are used to automatically identify and correct these abnormal data to ensure the accuracy and completeness of the data. In addition to data cleaning, denoising is also performed. During the data collection process, due to the accuracy limitations of sensor equipment or the interference of environmental noise, some noise data may be generated. These data will interfere with subsequent analysis and evaluation. Therefore, by using filtering algorithms and noise reduction technologies, the collected data is denoised to eliminate the impact of noise on the evaluation results, thereby improving the quality of the data and facilitating subsequent data analysis.

[0023] 3. GIS integration module: It stores and updates geographic spatial information and spatial location information associated with environmental data through the geographic information database, providing the system with spatial data processing and analysis capabilities.

[0024] In the GIS integration module step, the geographic information database supports the integration of multi-source geographic information data, including but not limited to satellite remote sensing data, drone aerial photography data and ground survey data.

[0025] Through the geographic information database, it supports the integration of multi-source geographic information data, and stores satellite remote sensing data, drone aerial photography data and ground survey data in the geographic information database, providing the system with powerful spatial data processing and analysis capabilities. Through GIS technology, environmental data can be visualized on the map to help users intuitively understand the spatial distribution characteristics of environmental conditions. Therefore, geographic information databases can be used to store and update geographic spatial information, including topography, administrative divisions, transportation networks, and spatial location information associated with environmental data, and then associate the data collected by smart sensors with the corresponding spatial location information. This association enables the system to intuitively display the distribution of environmental data in geographic space, making it easier for users to analyze and understand environmental conditions, thereby achieving accurate matching of environmental data with geographic location.

[0026] 4. Data fusion algorithm module: The pre-processed environmental data is deeply integrated with GIS information through spatiotemporal correlation analysis, machine learning or deep learning algorithm and weighted average method or maximum method to generate an environmental data set with spatial distribution characteristics; Through spatiotemporal correlation analysis, the spatiotemporal patterns in environmental data are identified, such as the diffusion path of pollutants and the seasonal laws of water quality changes. Then, combined with the spatial analysis capabilities of the GIS integrated module, the environmental data are mapped to the geographic space, so as to intuitively display the spatial distribution characteristics of the environmental conditions. Then, the machine learning algorithm is used to identify the complex relationships in the environmental data, such as the correlation between pollutant concentrations and meteorological conditions. The deep learning algorithm can process image and video data to extract richer environmental information. By training these algorithms, the system can automatically fuse environmental data and GIS information to generate a more accurate environmental data set. Then, the weighted average method is used to assign weights according to the accuracy and reliability of different sensors to obtain more accurate environmental data. The maximum value rule is used to identify extreme values ​​or abnormal situations in environmental data, such as the highest concentration value of pollutants. These methods can be combined with spatiotemporal correlation analysis and machine learning algorithms to further improve the accuracy and practicality of data fusion, so as to fuse information from different data sources and generate environmental data sets with spatial distribution characteristics, providing a scientific basis for environmental status assessment, trend prediction and anomaly detection. Through data fusion, the environmental status and its relationship with geographical location can be more comprehensively understood, and the efficiency and accuracy of environmental management can be improved.

[0027] Specifically, the preprocessed environmental data are first aligned with the GIS information to ensure their consistency in time and space. Then, machine learning or deep learning algorithms are used to extract key features from the environmental data, such as the concentration distribution of pollutants and the changing trends of meteorological parameters. Then, by combining the spatial analysis capabilities of GIS, the extracted features are associated with the geographic location to reveal the spatial distribution characteristics of the environmental conditions. Then, by using mathematical methods such as weighted averaging or maximum method, the information from different data sources is fused to generate an environmental data set with spatial distribution characteristics.

[0028] 5. Real-time analysis module: Based on the fused data set, use machine learning or deep learning models to perform environmental status assessment, trend prediction, and anomaly detection; In the real-time analysis module, set warning thresholds according to environmental standards and regulatory requirements. When environmental parameters exceed the thresholds, an alarm is triggered. By using machine learning or deep learning models to deeply mine and analyze the fused data set, key features can be extracted from a large amount of environmental data, and the environmental conditions can be quantitatively evaluated based on these features. For example, for air quality monitoring, the model can analyze the concentration, source and diffusion trend of pollutants to derive the air quality index. For water quality monitoring, the model can evaluate the chemical composition, biological community and ecological health of the water body to determine whether the water quality meets the standard. At the same time, historical data and real-time data can be used for trend prediction to identify the laws and trends of environmental changes, such as seasonal changes and cyclical fluctuations. Then, by setting thresholds and monitoring rules according to environmental standards and regulatory requirements, the module can promptly detect anomalies in environmental data. When environmental parameters exceed the set thresholds, the real-time analysis module will automatically trigger the early warning mechanism, and promptly remind relevant personnel to pay attention to environmental changes through text messages, emails, phone calls, etc., so as to achieve a rapid response to environmental changes.

[0029] 6. Visual display module: The analysis results are intuitively displayed in the form of maps, charts and reports, which facilitates users to customize views and interactive queries; The visualization module supports mobile devices and web access, and provides flexible data visualization options such as heat maps, scatter plots, and trend lines. By intuitively displaying the analysis results to users in the form of maps, charts and reports, and supporting user-defined views and interactive query functions, users can choose different display methods and data dimensions for viewing and analysis as needed. At the same time, the visualization display module also supports mobile devices and Web access, providing flexible data visualization options such as heat maps, scatter plots and trend lines, etc., so that users can obtain environmental information anytime and anywhere.

[0030] 7. Permission management module: assign different data access and operation permissions according to user roles to ensure data security; In the permission management module step, a multi-level permission control strategy is adopted to ensure secure access to sensitive data A multi-level permission control strategy is adopted to strictly divide and manage permissions for users at different levels. Through the permission management module, users' access and operation permissions to sensitive data are restricted to prevent data leakage and abuse and ensure data security.

[0031] 8. System interface module: provides API interface to support seamless connection with other environmental management systems or third-party platforms; In the system interface module step, RESTful API is supported to facilitate efficient integration with other systems By providing an API interface, it supports seamless connection with other environmental management systems or third-party platforms. By supporting the RESTful API standard, it facilitates efficient integration and data exchange with other systems, thereby realizing information sharing and collaborative work between different systems, and improving the overall efficiency and level of environmental management.

[0032] 9. System maintenance and upgrade module: responsible for the daily maintenance, performance monitoring and software version updates of the system to ensure stable operation of the system.

[0033] Regularly inspect and test the system to promptly identify and fix potential problems and vulnerabilities, so that it can operate stably and meet changing business needs. Through the work of system maintenance and upgrade modules, the system's service life can be extended and the system's stability and reliability can be improved.

[0034] Although specific embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit thereof, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Intelligent environmental monitoring and GIS data fusion system, characterized by: The following steps are involved: Smart sensor module: used to arrange multiple smart sensors in a preset area to collect data on the atmospheric environment, water environment, soil environment, acoustic environment, radiation environment and biological environment in real time; Data preprocessing module: receives data from the intelligent sensor module, cleans, filters and organizes the collected data parameters, and removes abnormal values ​​and noise data; GIS integration module: It stores and updates geographic spatial information and spatial location information associated with environmental data through the geographic information database, providing the system with spatial data processing and analysis capabilities; Data fusion algorithm module: through spatiotemporal correlation analysis, machine learning or deep learning algorithm and weighted average method or maximum method, the pre-processed environmental data is deeply integrated with GIS information to generate an environmental data set with spatial distribution characteristics; Real-time analysis module: Based on the fused data set, it uses machine learning or deep learning models to perform environmental status assessment, trend prediction, and anomaly detection; Visualization display module: The analysis results are displayed intuitively in the form of maps, charts and reports, which facilitates users to customize views and interactive queries; Permission management module: assign different data access and operation permissions according to user roles to ensure data security; System interface module: provides API interface to support seamless connection with other environmental management systems or third-party platforms; System maintenance and upgrade module: responsible for the daily maintenance, performance monitoring and software version updates of the system to ensure stable operation of the system.

2. The intelligent environment monitoring and GIS data fusion system according to claim 1 is characterized by: In the smart sensor module step, the smart sensor module supports remote configuration and self-organizing network, and can dynamically adjust the monitoring range and frequency according to environmental changes.

3. The intelligent environment monitoring and GIS data fusion system according to claim 1 is characterized in that: In the smart sensor module step, the smart sensors include air quality sensors, meteorological parameter sensors, water quality sensors, water body parameter sensors, soil pollutant sensors, soil temperature sensors, humidity sensors, pH sensors, noise sensors, radiation sensors, and biological monitoring sensors.

4. The intelligent environment monitoring and GIS data fusion system according to claim 1 is characterized in that: In the data preprocessing module step, a data cleaning algorithm is used to remove errors, outliers and noise in the data to improve the quality of the data and facilitate subsequent data analysis.

5. The intelligent environment monitoring and GIS data fusion system according to claim 1 is characterized in that: In the GIS integration module step, the geographic information database supports the integration of multi-source geographic information data, including but not limited to satellite remote sensing data, drone aerial photography data and ground survey data.

6. The intelligent environment monitoring and GIS data fusion system according to claim 1 is characterized by: In the real-time analysis module step, warning thresholds are set according to environmental standards and regulatory requirements. When environmental parameters exceed the thresholds, warnings are triggered.

7. The intelligent environment monitoring and GIS data fusion system according to claim 1 is characterized by: The visualization module supports access from mobile devices and the web, and provides flexible data visualization options such as heat maps, scatter plots, and trend lines.

8. The intelligent environment monitoring and GIS data fusion system according to claim 1 is characterized by: In the permission management module step, a multi-level permission control strategy is adopted to ensure secure access to sensitive data.

9. The intelligent environment monitoring and GIS data fusion system according to claim 1, characterized in that: In the system interface module step, RESTful API is supported to facilitate efficient integration with other systems.

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