Comprehensive water quality evaluation method and device based on multi-source fusion sensing network and medium

Abstract

The invention relates to the field of water quality monitoring, and discloses a water quality evaluation comprehensive method and device based on a multi-source fusion sensing network and a medium, and the device comprises a floating detection node, a LoRa gateway and a cloud platform; the floating detection node communicates with the LoRa gateway through a wireless network; the LoRa gateway communicates with the cloud platform through a wireless network; the floating type detection node comprises a plurality of water quality detection sensors and is used for detecting water quality parameters and transmitting the water quality parameters to the LoRa gateway; the LoRa gateway transmits the water quality parameters to a cloud platform, and the cloud platform performs comprehensive analysis to obtain a water quality evaluation result; according to the invention, the automation degree and the data processing efficiency of water quality monitoring are greatly improved, the efficient data processing capability ensures that a user can rapidly obtain water quality information and make a decision, and the response speed of environment management is improved.

Description

Technical Field

[0001] The present invention relates to the field of water quality monitoring, and in particular to a comprehensive method, equipment and medium for water quality evaluation based on a multi-source fusion sensing network. Background Art

[0002] At present, water quality monitoring is an important part of environmental protection. Especially in the case of increasingly serious water pollution, it is particularly important to grasp the changes in water quality in real time and effectively. Traditional water quality monitoring methods usually rely on manual sampling and laboratory analysis, which have shortcomings such as time lag, limited sampling range, and high operating costs. In recent years, with the rapid development of sensor technology, wireless communication technology, and Internet of Things technology, using sensor networks to achieve real-time monitoring of water quality has become an important research direction.

[0003] However, existing sensor network solutions face many challenges in practical applications. The water environment is complex and changeable, and a single sensor is difficult to fully and accurately reflect the water quality. Traditional sensor systems usually rely on fixed nodes, have limited coverage, and cannot adapt to the dynamic changes of a wide range of waters. At the same time, how to effectively transmit the data collected by sensors and perform cloud-based analysis and processing is still one of the current technical difficulties. Summary of the invention

[0004] The purpose of the present invention is to provide a comprehensive method, equipment and medium for water quality evaluation based on a multi-source fusion perception network to solve the shortcomings of existing water quality monitoring systems in wide-area coverage and real-time performance, especially the technical problems of high deployment cost, limited coverage and poor real-time performance of traditional fixed monitoring stations.

[0005] The present invention provides a comprehensive water quality evaluation device based on a multi-source fusion sensing network, comprising:

[0006] Floating detection nodes, LoRa gateways and cloud platforms;

[0007] The floating detection node communicates with the LoRa gateway through a wireless network;

[0008] The LoRa gateway communicates with the cloud platform via a wireless network;

[0009] The floating detection node includes multiple water quality detection sensors, which are used to detect water quality parameters and transmit them to the LoRa gateway;

[0010] The LoRa gateway transmits water quality parameters to the cloud platform, which conducts comprehensive analysis to obtain water quality evaluation results.

[0011] A comprehensive method for water quality evaluation based on a multi-source fusion perception network is applied to a comprehensive device for water quality evaluation based on a multi-source fusion perception network. The method comprises the following steps:

[0012] S1. Place the floating detection node in the water body to be detected;

[0013] S2. Use the Beidou positioning module to obtain the node geographical location information, use the turbidity detector and the suspended matter concentration detector to obtain the water turbidity and suspended matter concentration, and convert the above information into a format that can be received by the first MCU controller through the communication protocol conversion module;

[0014] S3, communicating with the second LoRa communication module in the LoRa gateway through the first LoRa communication module, and the second LoRa communication module communicating with the cloud platform through the 4G communication module; or directly communicating with the cloud platform through the NB-IoT module;

[0015] S4. The cloud platform obtains the water quality parameters collected by the floating detection node, and uses the principal component analysis (PCA) method to obtain the comprehensive water quality evaluation index, and the isolation forest method to adjust the dynamic alarm threshold.

[0016] A storage medium stores instructions and data for realizing a comprehensive water quality evaluation device based on a multi-source fusion perception network.

[0017] The beneficial effects provided by the present invention are:

[0018] 1. The Beidou positioning module built into the floating detection node can accurately obtain the geographical location information of the detection node and associate it with the water quality data, thus realizing the spatial positioning of the water quality information. This real-time positioning function enables users to understand the water quality status of a specific location, which is helpful for accurate environmental monitoring and data analysis.

[0019] 2. The present invention designs two data transmission modes: LoRa and NB-IoT. In areas covered by the LoRa network, the floating detection node can transmit data through the LoRa gateway carried by the drone; in areas without LoRa coverage, the data is directly uploaded to the cloud through NB-IoT. This flexible transmission mode ensures the stable transmission and real-time performance of water quality data and adapts to various network conditions.

[0020] 3. The floating detection node is lightweight and can be flexibly deployed in different waters such as rivers, lakes, and ponds, solving the problem of limited coverage of traditional fixed monitoring stations. Combined with the LoRa gateway carried by the drone, the data collection area is expanded through flight, realizing dynamic monitoring of wide-area waters, greatly improving the system's coverage capability and deployment efficiency.

[0021] 4. Compared with traditional manual sampling and laboratory analysis methods, the present invention greatly improves the automation level and data processing efficiency of water quality monitoring. The cloud platform uses principal component analysis (PCA) to integrate multiple indicators and generate comprehensive water quality evaluation results in real time. At the same time, the alarm threshold is dynamically adjusted through the isolation forest algorithm to improve the accuracy and timeliness of the alarm. This efficient data processing capability ensures that users can quickly obtain water quality information and make decisions, improving the response speed of environmental management. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the system structure of the present invention;

[0023] Figure 2 Schematic diagram of the communication structure of the floating detection node, the UAV mobile LoRa gateway and the cloud platform in an embodiment of the present invention;

[0024] Figure 3 Schematic diagram of the communication mode of each internal functional module of the floating detection node in an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of the internal structure of the UAV mobile LoRa gateway in an embodiment of the present invention;

[0026] Reference numerals:

[0027] 1- floating detection node; 11- solar power module; 12- first LoRa communication module; 13- NB-IoT communication module; 14- first MCU controller; 15- communication protocol conversion module; 16- Beidou positioning module; 17- turbidity detector; 18- suspended matter concentration detector; 2- UAV mobile LoRa gateway; 21- second LoRa communication module; 22- second MCU controller; 23- 4G communication module; 3- cloud platform; 31- PC terminal; 32- mobile terminal. DETAILED DESCRIPTION

[0028] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0029] Before formally describing the present invention, the scheme of the present invention is first generally described for easy understanding.

[0030] Please refer to Figure 1 , Figure 1 It is a schematic diagram of the system structure of the present invention.

[0031] The present invention provides a comprehensive water quality evaluation device based on a multi-source fusion sensing network, comprising:

[0032] Floating detection nodes, LoRa gateways and cloud platforms;

[0033] The floating detection node communicates with the LoRa gateway through a wireless network;

[0034] The LoRa gateway communicates with the cloud platform via a wireless network;

[0035] The floating detection node includes multiple water quality detection sensors, which are used to detect water quality parameters and transmit them to the LoRa gateway;

[0036] The LoRa gateway transmits water quality parameters to the cloud platform, which conducts comprehensive analysis to obtain water quality evaluation results.

[0037] It should be noted that the floating detection node includes: a Beidou positioning module, a turbidity detector, a suspended matter concentration detector, a communication protocol conversion module, a first MCU controller, a first LoRa communication module, a NB-IoT communication module and a solar power module.

[0038] The Beidou positioning module, turbidity detector, suspended matter concentration detector, communication protocol conversion module, first LoRa communication module, and NB-IoT communication module are all electrically connected to the first MCU controller; the solar power module provides power for the floating detection node.

[0039] As an embodiment, the floating detection node has a dual data transmission mode, including: performing data communication with a LoRa gateway disposed on the drone through the first LoRa communication module, and the LoRa gateway uploading the received data to a cloud platform;

[0040] Or you can choose to upload the collected water quality data directly to the cloud platform through the NB-IoT communication module, so as to flexibly switch the data transmission mode according to different network coverage and transmission requirements. The Beidou positioning module is used to obtain the geographical location information of the floating detection node, and upload the location information together with the water quality data; the turbidity detector and suspended matter concentration detector are used to monitor the turbidity and suspended matter concentration of the water sample respectively, and generate corresponding detection data; the communication protocol conversion module is used to convert the detection data into a protocol format compatible with the first MCU controller to ensure efficient data transmission between modules; the solar power module is used to provide continuous power support for the floating detection node to ensure the stable operation of each module under various environmental conditions.

[0041] It should be noted that the LoRa gateway is arranged on the drone, including a 4G communication module, a second MCU controller, and a second LoRa communication module; the 4G communication module, the second LoRa communication module and the second MCU controller are electrically connected;

[0042] The second LoRa communication module communicates with the first LoRa communication module to obtain the collected water quality parameters; the second MCU controller integrates the water quality parameters and transmits them to the cloud platform through the 4G communication module.

[0043] As an embodiment, the LoRa gateway is set on the drone, and exchanges data with multiple floating detection nodes through the second LoRa communication module to receive and aggregate water quality data collected by multiple floating detection nodes. Subsequently, the LoRa gateway uploads the received water quality data to the cloud platform through the 4G communication module to achieve long-distance data transmission and real-time monitoring.

[0044] It should be noted that the cloud platform performs data analysis and processing, and uses the principal component analysis (PCA) algorithm to integrate multiple indicators to form a comprehensive water quality evaluation index. The cloud platform uses the isolation forest algorithm to analyze historical data and real-time changes, and dynamically adjusts the water quality alarm threshold.

[0045] As an embodiment, the cloud platform is used to receive and process data from the LoRa gateway and the NB-IoT communication module, generate comprehensive water quality evaluation results through data analysis and processing, and provide the mobile phone and PC device access and monitoring functions so that users can view and manage water quality conditions in real time. In order to improve the intelligence level of the system, the cloud platform uses principal component analysis (PCA) to fuse multiple indicators to form a comprehensive water quality evaluation index. In addition, the isolation forest algorithm is used to analyze historical data and real-time changes, dynamically adjust the alarm threshold, improve the accuracy and timeliness of the alarm, and ensure the reliability of water quality monitoring.

[0046] A comprehensive method for water quality evaluation based on a multi-source fusion perception network is applied to a comprehensive device for water quality evaluation based on a multi-source fusion perception network. The method comprises the following steps:

[0047] S1. Place the floating detection node in the water body to be detected;

[0048] S2. Use the Beidou positioning module to obtain the node geographical location information, use the turbidity detector and the suspended matter concentration detector to obtain the water turbidity and suspended matter concentration, and convert the above information into a format that can be received by the first MCU controller through the communication protocol conversion module;

[0049] S3, communicating with the second LoRa communication module in the LoRa gateway through the first LoRa communication module, and the second LoRa communication module communicating with the cloud platform through the 4G communication module; or directly communicating with the cloud platform through the NB-IoT module;

[0050] S4. The cloud platform obtains the water quality parameters collected by the floating detection node, and uses the principal component analysis (PCA) method to obtain the comprehensive water quality evaluation index, and the isolation forest method to adjust the dynamic alarm threshold.

[0051] For a better explanation of the present invention, please refer to Figure 1-Figure 4 .

[0052] The embodiment of the present invention provides a comprehensive water quality evaluation system based on a multi-source fusion perception network, including a floating detection node 1, a LoRa gateway 2 and a cloud platform 3. The floating detection node 1 has a built-in Beidou positioning module 16, a turbidity detector 17, a suspended matter concentration detector 18, a communication protocol conversion module 15, a first MCU controller 14, a first LoRa communication module 12, a NB-I oT communication module 13 and a solar power module 11. The LoRa gateway 2 is set on the drone, and internally includes a 4G communication module 23, a second MCU controller 22 and a second LoRa communication module 21. The cloud platform 3 is used to connect a variety of terminal devices to receive, process and display water quality data.

[0053] The floating detection node has a dual data transmission mode, including: data communication with the LoRa gateway 2 set on the drone through the first LoRa communication module 12, and the LoRa gateway 2 uploads the received data to the cloud platform 3; or chooses to upload the collected water quality data directly to the cloud platform 3 through the NB-I oT communication module 13, so as to flexibly switch the data transmission mode according to different network coverage and transmission requirements. The Beidou positioning module 16 is used to obtain the geographical location information of the floating detection node, and upload the location information together with the water quality data; the turbidity detector 17 and the suspended matter concentration detector 18 are used to monitor the turbidity and suspended matter concentration of the water sample respectively, and generate corresponding detection data; the communication protocol conversion module 15 is used to convert the detection data into a protocol format compatible with the first MCU controller 14 to ensure efficient data transmission between modules; the solar power module 11 is used to provide continuous power support for the floating detection node 1 to ensure that each module operates stably under various environmental conditions.

[0054] The LoRa gateway 2 is set on the drone, and exchanges data with multiple floating detection nodes 1 through the second LoRa communication module 21, and receives and aggregates water quality data collected by multiple floating detection nodes 1. Subsequently, the LoRa gateway 2 uploads the received water quality data to the cloud platform 3 through the 4G communication module 23, realizing long-distance data transmission and real-time monitoring.

[0055] It should be further explained that the dual data transmission mode of the floating detection node 1 can be automatically switched according to the network coverage of the area. In the area covered by the LoRa gateway 2, the node communicates with the LoRa gateway 2 through the first LoRa communication module 12, and the LoRa gateway 2 uploads to the cloud platform 3 through the 4G communication module 23; in the area without LoRa coverage, the node automatically switches to the NB-I oT communication mode and transmits the data directly to the cloud platform 3.

[0056] As a result, this design can flexibly cope with complex water environments, ensuring stable data transmission and maintaining real-time performance, whether in nearshore or remote areas. In this way, the limitations of a single communication mode can be effectively avoided, improving the reliability of the overall system.

[0057] The LoRa gateway 2 is set on the drone, and exchanges data with multiple floating detection nodes 1 through the second LoRa communication module 21, and receives and aggregates water quality data collected by multiple floating detection nodes 1. Subsequently, the LoRa gateway 2 uploads the received data to the cloud platform 3 through the 4G communication module 23, realizing long-distance data transmission and real-time monitoring.

[0058] It should be further explained that the mobility of drones enables LoRa Gateway 2 to cover a wider area of ​​water, effectively solving the problem of limited coverage of traditional fixed sites, and is particularly suitable for dynamic monitoring of large areas of water. In addition, the flight path and mission of the drone are remotely controlled by the cloud platform, which can be flexibly adjusted according to specific monitoring needs, thereby improving the monitoring efficiency of the system.

[0059] The cloud platform is used to receive and process data from the LoRa gateway 2 and the NB-I oT communication module 13, generate comprehensive water quality evaluation results through data analysis and processing, and provide mobile phone 32 and PC 31 device access and monitoring functions so that users can view and manage water quality conditions in real time.

[0060] It should be further explained that the platform not only supports multi-terminal data access, but also has data storage, analysis and alarm functions. When abnormal water quality parameters are detected, alarm information can be sent to users immediately so that users can take countermeasures quickly. Furthermore, through the long-term data accumulation of the cloud platform, it can help users understand the changing trend of water quality and provide scientific support for decision-making.

[0061] It should be further explained that during the data analysis process, the cloud platform uses the principal component analysis (PCA) statistical method to perform comprehensive processing by combining monitoring data from different sensors. This method first standardizes each water quality indicator to eliminate the impact of different dimensions on the results. PCA then calculates the covariance matrix of these indicators, extracts the main components, and identifies the key factors that explain the data variation. This process not only considers the importance of each indicator, but also identifies the correlation between the data to form a comprehensive water quality evaluation index, allowing users to understand the water quality status more intuitively.

[0062] It should be further explained that in terms of dynamic threshold adjustment, the cloud platform uses the Isolation Forest algorithm for anomaly detection. This algorithm can effectively identify anomalies in water quality monitoring data by constructing multiple random trees. The system trains historical water quality data to establish a distribution model for normal data. Then when new data enters the system, the Isolation Forest algorithm calculates the anomaly score for each data point and dynamically adjusts the alarm threshold based on these scores. The advantage of this method lies in its unsupervised learning characteristics, which can flexibly adapt to complex water quality environments, reduce the risk of false alarms and missed alarms, and thus improve the accuracy and timeliness of water quality monitoring.

[0063] Specifically, in the embodiment of the invention, the first MCU controller 14 and the second MCU controller 22 are single chip microcomputers STM32L151 C8T6.

[0064] Therefore, the single-chip microcomputer STM32L151 C8T6 is used as a control chip, which is small in size, and this controller has the characteristics of low power consumption and high efficiency, and is suitable for continuous and stable data processing tasks.

[0065] Specifically, in the embodiment of the present invention, the Beidou positioning module 16 is Air 700E.

[0066] Specifically, in the embodiment of the present invention, the turbidity detector 17 and the suspended matter concentration detector 18 are BSK-TUR-100.

[0067] Specifically, in the embodiment of the present invention, the first LoRa communication module 12 and the second LoRa communication module 21 are sx1268, which can provide long-distance, low-power wireless communication;

[0068] Specifically, in the embodiment of the present invention, the NB-I oT communication module 13 is BC260Y-CN, which is suitable for wide coverage and low bandwidth data transmission.

[0069] Specifically, in the embodiment of the present invention, the cloud platform 3 can be deployed in mainstream cloud service platforms such as Baidu, Alibaba Cloud, and Tencent Cloud.

[0070] Finally, a storage medium is provided, wherein the storage medium stores instructions and data for realizing a comprehensive water quality evaluation device based on a multi-source fusion perception network.

[0071] The beneficial effects of the present invention are:

[0072] 1. The Beidou positioning module built into the floating detection node can accurately obtain the geographical location information of the detection node and associate it with the water quality data, thus realizing the spatial positioning of the water quality information. This real-time positioning function enables users to understand the water quality status of a specific location, which is helpful for accurate environmental monitoring and data analysis.

[0073] 2. The present invention designs two data transmission modes: LoRa and NB-IoT. In areas covered by the LoRa network, the floating detection node can transmit data through the LoRa gateway carried by the drone; in areas without LoRa coverage, the data is directly uploaded to the cloud through NB-IoT. This flexible transmission mode ensures the stable transmission and real-time performance of water quality data and adapts to various network conditions.

[0074] 3. The floating detection node is lightweight and can be flexibly deployed in different waters such as rivers, lakes, and ponds, solving the problem of limited coverage of traditional fixed monitoring stations. Combined with the LoRa gateway carried by the drone, the data collection area is expanded through flight, realizing dynamic monitoring of wide-area waters, greatly improving the system's coverage capability and deployment efficiency.

[0075] 4. Compared with traditional manual sampling and laboratory analysis methods, the present invention greatly improves the automation level and data processing efficiency of water quality monitoring. The cloud platform uses principal component analysis (PCA) to integrate multiple indicators and generate comprehensive water quality evaluation results in real time. At the same time, the alarm threshold is dynamically adjusted through the isolation forest algorithm to improve the accuracy and timeliness of the alarm. This efficient data processing capability ensures that users can quickly obtain water quality information and make decisions, improving the response speed of environmental management.

[0076] 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 comprehensive water quality assessment device based on a multi-source fusion perception network, characterized by: include: Floating detection nodes, LoRa gateways and cloud platforms; The floating detection node communicates with the LoRa gateway through a wireless network; The LoRa gateway communicates with the cloud platform via a wireless network; The floating detection node includes multiple water quality detection sensors, which are used to detect water quality parameters and transmit them to the LoRa gateway; The LoRa gateway transmits water quality parameters to the cloud platform, which conducts comprehensive analysis to obtain water quality evaluation results.

2. The water quality evaluation comprehensive device based on multi-source fusion perception network as claimed in claim 1, characterized in that: The floating detection node includes: a Beidou positioning module, a turbidity detector, a suspended matter concentration detector, a communication protocol conversion module, a first MCU controller, a first LoRa communication module, a NB-IoT communication module and a solar power module.

3. The comprehensive water quality assessment device based on a multi-source fusion sensing network as claimed in claim 2, characterized in that: The NB-IoT communication module communicates directly with the cloud platform.

4. The water quality evaluation comprehensive device based on multi-source fusion perception network as claimed in claim 2, characterized in that: The Beidou positioning module, turbidity detector, suspended matter concentration detector, communication protocol conversion module, first LoRa communication module, and NB-IoT communication module are all electrically connected to the first MCU controller; the solar power module provides power for the floating detection node.

5. The water quality evaluation comprehensive device based on multi-source fusion sensing network as claimed in claim 4, characterized in that: The LoRa gateway is arranged on the drone, and includes a 4G communication module, a second MCU controller, and a second LoRa communication module; the 4G communication module, the second LoRa communication module and the second MCU controller are electrically connected; The second LoRa communication module communicates with the first LoRa communication module to obtain the collected water quality parameters; the second MCU controller integrates the water quality parameters and transmits them to the cloud platform through the 4G communication module.

6. The water quality evaluation comprehensive device based on multi-source fusion sensing network as claimed in claim 5, characterized in that: The cloud platform is used to receive and process data from the LoRa gateway and the NB-IoT communication module, generate comprehensive water quality evaluation results through data analysis and processing, and provide terminal devices with access and monitoring functions so that users can view and manage water quality conditions in real time.

7. The comprehensive water quality assessment device based on a multi-source fusion sensing network as claimed in claim 6, characterized in that: The cloud platform performs data analysis and processing, and uses the principal component analysis (PCA) algorithm to fuse multiple indicators to form a comprehensive water quality evaluation index.

8. The water quality evaluation comprehensive device based on multi-source fusion perception network as claimed in claim 6, characterized in that: The cloud platform analyzes historical data and real-time changes through the isolation forest algorithm and dynamically adjusts the water quality alarm threshold.

9. A comprehensive method for water quality evaluation based on a multi-source fusion perception network, applied to a comprehensive device for water quality evaluation based on a multi-source fusion perception network as claimed in any one of claims 1 to 8, characterized in that: The method comprises the following steps: S1. Place the floating detection node in the water body to be detected; S2. Use the Beidou positioning module to obtain the node geographical location information, use the turbidity detector and the suspended matter concentration detector to obtain the water turbidity and suspended matter concentration, and convert the above information into a format that can be received by the first MCU controller through the communication protocol conversion module; S3, communicating with the second LoRa communication module in the LoRa gateway through the first LoRa communication module, and the second LoRa communication module communicating with the cloud platform through the 4G communication module; or directly communicating with the cloud platform through the NB-IoT module; S4. The cloud platform obtains the water quality parameters collected by the floating detection node, and uses the principal component analysis (PCA) method to obtain the comprehensive water quality evaluation index, and the isolation forest method to adjust the dynamic alarm threshold.

10. A storage medium, characterized in that: The storage medium stores instructions and data for implementing the comprehensive method for water quality evaluation based on a multi-source fusion perception network.

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