Water quality sensor efficient control system and method based on Internet of Things

Abstract

The invention discloses a detection device using a water quality detection sensor, and the detection device comprises a housing, the inner wall of the housing is provided with a mainboard, one side of one end of the mainboard is connected with a first support rod, one end of the first support rod is provided with an ultraviolet detector, the other side of one end of the mainboard is connected with a second support rod, and one end of the second support rod is connected with an ultraviolet light emitting diode. A probe front cover is clamped at one end of the shell, a temperature sensing metal cap is mounted in the middle of the probe front cover, conductivity probes are mounted on the two sides of the interior of the probe front cover, a receiving transparent cover is mounted at one end of the interior of the probe front cover, and a transmitting transparent cover is mounted on the other side of the interior of the probe front cover. According to the utility model, the transmitting transparent cover and the receiving transparent cover outside the ultraviolet light emitting diode and the ultraviolet detector are changed into glass materials, so that the anti-ultraviolet performance is better, the service life is prolonged, and the finger clamping structure is arranged to prevent the device from accidentally slipping when the device is held by a hand.

Description

Technical Field

[0001] This invention relates to the field of sensor control technology, specifically to an efficient control system and method for water quality sensors based on the Internet of Things. Background Technology

[0002] A sensor is a detection device that can sense the information being measured and transform that information into an electrical signal or other required form of information output according to a certain rule, in order to meet the requirements of information transmission, processing, storage, display, recording, and control. A water quality sensor control system is an automated system used to monitor and control water quality parameters. It plays an important role in water quality management, environmental protection, and industrial production. By combining the real-time, accurate, and scalable advantages of the Internet of Things (IoT), it provides a more efficient and intelligent solution for water quality management. Existing high-efficiency water quality sensor control systems basically meet user needs, but some shortcomings still exist: Firstly, existing water quality sensor control systems... While monitoring water quality, sensors operating underwater for extended periods may be affected by environmental factors, leading to equipment or parameter abnormalities and impacting continuous water quality monitoring. Furthermore, calibration and maintenance require manual intervention, increasing labor costs. Secondly, existing sensor control systems monitoring industrial wastewater discharge may be affected by external or human interference, impacting sensor detection performance and potentially causing environmental damage. Thirdly, existing water quality sensor control systems cannot analyze the source of water quality anomalies, making processing inconvenient and hindering water resource protection and sustainable development. Therefore, designing an efficient water quality sensor control system and method based on the Internet of Things (IoT) is essential. Summary of the Invention

[0003] The purpose of this invention is to provide an efficient control system and method for water quality sensors based on the Internet of Things, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a high-efficiency control system for water quality sensors based on the Internet of Things, comprising a central control module, a data collection module, an equipment feature module, a regional feature module, a data transmission module, an equipment positioning module, and a front-end control module. The central control module controls and connects to the data collection module, the equipment feature module, and the regional feature module respectively. The equipment feature module and the regional feature module both control and connect to the equipment positioning module. The equipment positioning module controls and connects to the front-end control module. The data collection module and the front-end control module both control and connect to the data transmission module. The data transmission module controls and connects to the central control module.

[0005] As a further technical solution of the present invention, the device positioning module controls the connection to the positioning transmission module, and the positioning transmission module controls the connection to the area positioning module.

[0006] As a further technical solution of the present invention, the positioning transmission module controls the connection to the data analysis module, the data analysis module controls the connection to the data transmission module, and both the data analysis module and the regional positioning module control the connection to the regional analysis module.

[0007] As a further technical solution of the present invention, the region analysis module controls the connection to the anomaly analysis module, and the anomaly analysis module controls the connection to the anomaly feedback module.

[0008] As a further technical solution of the present invention, the data collection module controls and connects to the data screening module, the data screening module controls and connects to the sandbox mirror module, and both the data screening module and the sandbox mirror module control and connect to the feature extraction module.

[0009] As a further technical solution of the present invention, the feature extraction module controls and connects to the behavior analysis module and the feature recognition module respectively, and both the behavior analysis module and the feature recognition module control and connect to the anomaly feedback module, which in turn controls and connects to the central control module.

[0010] As a further technical solution of the present invention, the data collection module includes a data processing module, a data noise reduction module, a data deduplication module and a data compression module. The data processing module controls and connects to the data noise reduction module and the data deduplication module respectively. The data noise reduction module and the data deduplication module both control and connect to the data compression module. The data compression module controls and connects to the data transmission module.

[0011] As a further technical solution of the present invention, the device feature module controls and connects to the device monitoring module and the parameter monitoring module respectively, the parameter monitoring module controls and connects to the anomaly extraction module, the anomaly extraction module controls and connects to the autonomous calibration module, the autonomous calibration module controls and connects to the calibration confirmation module, and the calibration confirmation module controls and connects to the central control module.

[0012] As a further technical solution of the present invention, the equipment monitoring module, the anomaly extraction module and the calibration confirmation module are all controlled and connected to the anomaly alarm module, the anomaly alarm module is controlled and connected to the equipment feedback module, and the equipment feedback module is controlled and connected to the central control module.

[0013] An efficient control method for water quality sensors based on the Internet of Things includes the following steps: Step 1, parameter adjustment; Step 2, data collection and processing; Step 3, data analysis and judgment; Step 4, data screening and identification; Step 5, anomaly feedback processing.

[0014] In step one above, the parameters of the water quality sensor are adjusted to a suitable range according to actual needs.

[0015] In step two above, the data collection module collects the equipment operation data and the physical data detected by the sensors, and the data processing module controls the data noise reduction module and the data deduplication module to perform noise reduction and deduplication processing on the data to ensure the accuracy and consistency of the data. Then, the data is compressed by the data compression module and transmitted through the data transmission module.

[0016] In step three above, the processed data is analyzed by the data analysis module to determine whether there are any anomalies in the data;

[0017] In step four above, the abnormal behavior of the data chain is investigated by the data screening module, and then the behavior and characteristics of the malware are analyzed and detected by the feature recognition module and the behavior analysis module.

[0018] In step five above, the anomaly feedback module feeds back abnormal data and the behavioral characteristics of malicious software to the central control module, enabling administrators to detect and handle them in a timely manner.

[0019] Compared with existing technologies, the beneficial effects of this invention are as follows: This efficient water quality sensor control system and method based on the Internet of Things (IoT) monitors the equipment's operating status and parameters in real time through a device feature module that controls the device monitoring module and parameter monitoring module. When the equipment's operating status is abnormal, the device monitoring module transmits the abnormal information to the abnormal alarm module. When parameters are abnormal, the abnormal information is extracted by the abnormal extraction module and transmitted to the self-calibration module. The self-calibration module completes the self-calibration parameter settings without manual intervention, reducing labor costs. Then, the calibration confirmation module confirms whether the calibration is complete. If the self-calibration is incomplete or does not meet the requirements, the calibration confirmation module and the abnormal extraction module transmit the abnormal parameters to the device feedback module through the abnormal alarm module. The device feedback module then feeds back the abnormalities in equipment operation and parameters to the central control module, enabling managers to view and respond promptly. The system employs a data screening module to control the sandbox mirror module for testing the device's operating software. A feature extraction module extracts feature fields, and the feature recognition and behavior analysis modules are used to perform feature recognition and behavior analysis to detect the presence of malicious software. Any anomalies are promptly reported via the anomaly feedback module to prevent sensors from being modified or affected by unknown software, thus avoiding environmental damage caused by wastewater discharge. Furthermore, the system accurately locates the sensors within water resource areas using the device positioning and regional positioning modules. The data analysis and regional analysis modules then analyze and compare water quality changes in different water resource areas. The anomaly analysis module analyzes and determines the causes of water quality changes and performs reverse location analysis. Finally, the anomaly feedback module provides feedback, enabling managers to accurately identify the causes and sources of abnormal water quality changes, effectively safeguarding water resource protection and sustainable development. Attached Figure Description

[0020] Figure 1 This is a partial flowchart of the present invention;

[0021] Figure 2 This is a schematic diagram of the overall process of the present invention;

[0022] Figure 3 This is a flowchart illustrating the data collection module in this invention;

[0023] Figure 4 This is a flowchart illustrating the device feature module in this invention;

[0024] Figure 5 This is a flowchart of the method of the present invention;

[0025] In the diagram: 1. Central Control Module; 2. Data Collection Module; 3. Equipment Feature Module; 4. Area Feature Module; 5. Data Transmission Module; 6. Equipment Positioning Module; 7. Front-end Control Module; 8. Positioning Transmission Module; 9. Area Positioning Module; 10. Data Analysis Module; 11. Area Analysis Module; 12. Anomaly Analysis Module; 13. Anomaly Feedback Module; 14. Data Screening Module; 15. Sandbox Mirroring Module; 16. Feature Extraction Module; 17. Behavior Analysis Module; 18. Feature Recognition Module; 19. Data Processing Module; 20. Data Denoising Module; 21. Data Deduplication Module; 22. Data Compression Module; 23. Equipment Monitoring Module; 24. Parameter Monitoring Module; 25. Anomaly Alarm Module; 26. Anomaly Extraction Module; 27. Autonomous Calibration Module; 28. Calibration Confirmation Module; 29. ​​Equipment Feedback Module. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Please see the appendix Figure 1 -Appendix Figure 4This invention provides an embodiment of an efficient water quality sensor control system based on the Internet of Things (IoT), comprising a central control module 1, a data collection module 2, an equipment feature module 3, a regional feature module 4, a data transmission module 5, an equipment positioning module 6, and a front-end control module 7. The central control module 1 controls and connects to the data collection module 2, the equipment feature module 3, and the regional feature module 4. The equipment feature module 3 and the regional feature module 4 are both controlled and connected to the equipment positioning module 6. The equipment positioning module 6 controls and connects to the front-end control module 7. The data collection module 2 and the front-end control module 7 are both controlled and connected to the data transmission module 5. 5. Control module connects to central control module 1. Equipment positioning module 6 controls and connects to positioning transmission module 8. Positioning transmission module 8 controls and connects to area positioning module 9. Positioning transmission module 8 and data transmission module 5 both control and connect to data analysis module 10. Data analysis module 10 and area positioning module 9 both control and connect to area analysis module 11. Area analysis module 11 controls and connects to anomaly analysis module 12. Anomaly analysis module 12 controls and connects to anomaly feedback module 13. Data collection module 2 controls and connects to data investigation module 14. Data investigation module 14 controls and connects to sandbox mirror module 15. Data investigation module 14 and sandbox mirror module 15... The data collection module 2 includes a data processing module 19, a data denoising module 20, a data deduplication module 21, and a data compression module 22. The data processing module 19 controls and connects to the data denoising module 20 and the data deduplication module 21. The data denoising module 20 and the data deduplication module 21 both control and connect to the data compression module 22. Block 22 controls and connects to the data transmission module 5. The device feature module 3 controls and connects to the device monitoring module 23 and the parameter monitoring module 24 respectively. The parameter monitoring module 24 controls and connects to the anomaly extraction module 26. The anomaly extraction module 26 controls and connects to the autonomous calibration module 27. The autonomous calibration module 27 controls and connects to the calibration confirmation module 28. The calibration confirmation module 28 controls and connects to the central control module 1. The device monitoring module 23, the anomaly extraction module 26, and the calibration confirmation module 28 all control and connect to the anomaly alarm module 25. The anomaly alarm module 25 controls and connects to the device feedback module 29. The device feedback module 29 controls and connects to the central control module 1.

[0028] Please see the appendix Figure 5 The present invention provides an embodiment of an efficient control method for water quality sensors based on the Internet of Things, comprising the following steps: Step 1, parameter adjustment; Step 2, data collection and processing; Step 3, data analysis and judgment; Step 4, data screening and identification; Step 5, anomaly feedback processing.

[0029] In step one above, the parameters of the water quality sensor are adjusted to a suitable range according to actual needs.

[0030] In step two above, the data collection module 2 collects the equipment operation data and the physical data detected by the sensors, and the data processing module 19 controls the data noise reduction module 20 and the data deduplication module 21 to perform noise reduction and deduplication processing on the data to ensure the accuracy and consistency of the data. Then, the data is compressed by the data compression module 22 and transmitted through the data transmission module 5.

[0031] In step three above, the data analysis module 10 analyzes the processed data and determines whether there are any anomalies in the data.

[0032] In step four above, the abnormal behavior of the data chain is investigated by the data screening module 14, and then the behavior and characteristics of the malware are analyzed and detected by the feature recognition module 18 and the behavior analysis module 17.

[0033] In step five above, the abnormal feedback module 13 feeds back abnormal data and the behavioral characteristics of malicious software to the central control module 1, enabling managers to detect and handle them in a timely manner.

[0034] Working Principle: During use, the equipment monitoring module 23 and parameter monitoring module 24 are controlled by the equipment feature module 3 to monitor the equipment's operating status and parameters in real time. When the equipment's operating status is abnormal, the equipment monitoring module 23 transmits the abnormal information to the abnormal alarm module 25. When the parameters are abnormal, the abnormal information is extracted by the abnormal extraction module 26 and transmitted to the self-calibration module 27. The self-calibration module 27 completes the self-calibration parameter settings without manual intervention, reducing labor costs. Then, the calibration confirmation module 28 confirms whether the calibration is complete. If the self-calibration is incomplete or does not meet the requirements, the calibration confirmation module 28 and the abnormal extraction module 26 transmit the abnormal parameters to the equipment feedback module 29 through the abnormal alarm module 25. The equipment feedback module 29 feeds back the abnormalities in equipment operation and parameters to the central control module 1, enabling managers to view and handle them in a timely manner. The data investigation module 14... The sandbox mirror module 15 tests the device's operating software, the feature extraction module 16 extracts feature fields, and the feature recognition module 18 and behavior analysis module 17 are controlled to perform feature recognition and behavior analysis to detect the presence of malicious software. In case of anomalies, the anomaly feedback module 13 reports the anomaly in a timely manner to prevent the sensor from being modified or affected by unknown software, thereby avoiding damage to the external environment caused by wastewater discharge. The device positioning module 6 and the area positioning module 9 accurately locate the sensor's position in the water resource area. Then, the data analysis module 10 and the area analysis module 11 analyze and compare the water quality changes in each water resource area. The anomaly analysis module 12 analyzes and judges the cause of the water quality change and performs reverse location. Then, the anomaly feedback module 13 provides feedback, enabling managers to accurately confirm the cause and source of the water quality anomaly, which effectively guarantees the protection and sustainable development of water resources.

[0035] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A high-efficiency control system for water quality sensors based on the Internet of Things, comprising a central control module (1), a data collection module (2), an equipment feature module (3), a regional feature module (4), a data transmission module (5), an equipment positioning module (6), and a front-end control module (7), characterized in that: The central control module (1) controls and connects to the data collection module (2), the equipment feature module (3) and the area feature module (4) respectively. The equipment feature module (3) and the area feature module (4) both control and connect to the equipment positioning module (6). The equipment positioning module (6) controls and connects to the front-end control module (7). The data collection module (2) and the front-end control module (7) both control and connect to the data transmission module (5). The data transmission module (5) controls and connects to the central control module (1).

2. The efficient control system for water quality sensors based on the Internet of Things according to claim 1, characterized in that: The device positioning module (6) controls the connection to the positioning transmission module (8), and the positioning transmission module (8) controls the connection to the area positioning module (9).

3. The efficient control system for water quality sensors based on the Internet of Things according to claim 2, characterized in that: The positioning transmission module (8) controls the connection to the data analysis module (10), the data analysis module (10) controls the connection to the data transmission module (5), and both the data analysis module (10) and the regional positioning module (9) control the connection to the regional analysis module (11).

4. The efficient control system for water quality sensors based on the Internet of Things according to claim 3, characterized in that: The region analysis module (11) controls the connection to the anomaly analysis module (12), and the anomaly analysis module (12) controls the connection to the anomaly feedback module (13).

5. The efficient control system for water quality sensors based on the Internet of Things according to claim 1, characterized in that: The data collection module (2) controls the connection to the data screening module (14), the data screening module (14) controls the connection to the sandbox mirror module (15), and both the data screening module (14) and the sandbox mirror module (15) control the connection to the feature extraction module (16).

6. The efficient control system for water quality sensors based on the Internet of Things according to claim 5, characterized in that: The feature extraction module (16) controls the behavior analysis module (17) and the feature recognition module (18) respectively. Both the behavior analysis module (17) and the feature recognition module (18) control the abnormal feedback module (13), and the abnormal feedback module (13) controls the central control module (1).

7. A high-efficiency control system for water quality sensors based on the Internet of Things according to claim 5, characterized in that: The data collection module (2) includes a data processing module (19), a data noise reduction module (20), a data deduplication module (21), and a data compression module (22). The data processing module (19) controls the data noise reduction module (20) and the data deduplication module (21) respectively. The data noise reduction module (20) and the data deduplication module (21) both control the data compression module (22). The data compression module (22) controls the data transmission module (5).

8. The efficient control system for water quality sensors based on the Internet of Things according to claim 1, characterized in that: The device feature module (3) controls and connects to the device monitoring module (23) and the parameter monitoring module (24), respectively. The parameter monitoring module (24) controls and connects to the anomaly extraction module (26). The anomaly extraction module (26) controls and connects to the autonomous calibration module (27). The autonomous calibration module (27) controls and connects to the calibration confirmation module (28). The calibration confirmation module (28) controls and connects to the central control module (1).

9. A high-efficiency control system for water quality sensors based on the Internet of Things according to claim 8, characterized in that: The equipment monitoring module (23), the anomaly extraction module (26), and the calibration confirmation module (28) are all connected to the anomaly alarm module (25). The anomaly alarm module (25) is connected to the equipment feedback module (29). The equipment feedback module (29) is connected to the central control module (1).

10. A highly efficient control method for water quality sensors based on the Internet of Things, comprising the following steps: Step 1, parameter adjustment; Step 2, data collection and processing; Step 3, data analysis and judgment; Step 4, data screening and identification; Step 5, anomaly feedback handling; Its features are: In step one above, the parameters of the water quality sensor are adjusted to a suitable range according to actual needs. In step two above, the data collection module (2) collects the equipment operation data and the physical data detected by the sensor, and the data processing module (19) controls the data noise reduction module (20) and the data deduplication module (21) to perform noise reduction and deduplication processing on the data to ensure the accuracy and consistency of the data. Then, the data is compressed by the data compression module (22) and transmitted through the data transmission module (5). In step three above, the processed data is analyzed by the data analysis module (10) to determine whether there are any abnormalities in the data; In step four above, the abnormal behavior of the data chain is investigated by the data investigation module (14), and then the behavior and characteristics of the malware are analyzed and detected by the feature recognition module (18) and the behavior analysis module (17). In step five above, the abnormal data and the behavioral characteristics of malicious software are fed back to the central control module (1) through the anomaly feedback module (13), so that the managers can discover and deal with them in a timely manner.

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