Water quality online monitoring system and method based on spectral electrode method and big data analysis

Abstract

The invention relates to the technical field of environmental monitoring, and particularly discloses a water quality online monitoring system and method based on a spectral electrode method and big data analysis, and the system comprises a water sampling unit which is used for extracting a water sample from a to-be-detected water body and conveying the water sample to a detection water tank; the monitoring instrument unit comprises a physical sensor group and a big data analysis module, the physical sensor group adopts a sensor based on the principle of a spectrum method or an electrode method and is used for directly measuring multiple basic physical and chemical parameters of a water sample, and the big data analysis module is in communication connection with the physical sensor group and is used for analyzing the basic physical and chemical parameters of the water sample. A trained AI big data analysis model is built in. According to the water quality on-line monitoring system and method based on the spectral electrode method and the big data analysis, the purposes that no chemical reagent is needed in the core monitoring process, and high cost and secondary pollution risks caused by reagent purchase, storage and waste liquid treatment are thoroughly eliminated are achieved.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, specifically to an online water quality monitoring system and method based on spectral electrode method and big data analysis. Background Technology

[0002] Traditional online water quality monitoring methods, especially for key indicators such as chemical oxygen demand (COD), ammonia nitrogen, total nitrogen, and total phosphorus, mostly employ chemical reagent methods. While these methods offer high accuracy, they have significant drawbacks: 1) High construction and maintenance costs: The need for regular replenishment and replacement of expensive chemical reagents, along with wastewater treatment, results in high total lifecycle costs; 2) Low monitoring frequency: Chemical reactions require time, typically resulting in monitoring cycles of several hours, making high-frequency, real-time monitoring difficult; 3) Risk of secondary pollution: The use of chemical reagents and wastewater discharge may cause secondary pollution to the environment; 4) Complex systems and frequent maintenance: Involving complex flow paths, reaction units, and reagent storage units, these systems have a high failure rate and require substantial maintenance workload.

[0003] In recent years, physical detection technologies, such as spectroscopic and electrode methods, have attracted attention due to their advantages of being reagent-free, having fast response times, and producing no secondary pollution. However, when relying solely on physical sensors to directly measure certain complex water quality indicators (such as total nitrogen and total phosphorus), problems such as weak anti-interference capabilities, insufficient measurement accuracy, and poor correlation with national standard methods often exist, limiting their application in the field of serious environmental supervision.

[0004] Therefore, there is an urgent need for a new online water quality monitoring solution that can combine the advantages of physical detection speed and economy with the accuracy of chemical methods. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides the following technical solution: an online water quality monitoring system and method based on spectral electrode method and big data analysis, comprising: The water sampling unit is used to extract water samples from the water body to be tested and transport them to the testing tank; The monitoring instrument unit includes a physical sensor group and a big data analysis module. The physical sensor group uses sensors based on the principles of spectroscopy or electrode method to directly measure multiple basic physical and chemical parameters of the water sample. The big data analysis module is communicatively connected to the physical sensor group and has a built-in trained AI big data analysis model. The model uses at least two basic parameters measured by the physical sensor group as input features and performs calculations and analyses through a preset algorithm model to indirectly deduce the total nitrogen concentration and total phosphorus concentration in the water sample. The system control unit is electrically connected to the water sampling unit and the monitoring instrument unit, and is used for the automated operation, data acquisition and processing, and remote communication of the control system. The power supply unit is used to provide power to all units in the system; Auxiliary units include cabinets, surge protection modules, and insulation and heat dissipation devices.

[0006] Preferably, the physical sensor group includes sensors for measuring at least two of water temperature, conductivity, pH, dissolved oxygen, turbidity, ammonia nitrogen, and permanganate index (COD).

[0007] Preferably, the AI ​​big data analysis model is trained using a dataset containing historical physical sensor data and corresponding national standard method true values ​​for total nitrogen and total phosphorus. The AI ​​big data analysis model is constructed in the following way: Collect historical datasets containing various basic parameters measured by devices based on the same principle as the physical sensor group, as well as accurate total nitrogen and total phosphorus values ​​of corresponding water samples measured by national standard chemical methods; The historical dataset was trained using machine learning algorithms to establish a predictive model for total nitrogen and total phosphorus concentrations, based on multidimensional basic parameters.

[0008] Preferably, the system control unit includes: The intelligent controller is equipped with a touchscreen human-machine interface; The process control module is configured to execute a periodic monitoring process, which includes at least the following steps: drainage, rinsing, water intake, sedimentation, and data acquisition and storage. The data management module is used to store raw measurement data and calculated total nitrogen / total phosphorus data; The communication module supports wireless networks and is used to remotely upload monitoring data.

[0009] A water quality online monitoring method based on a spectral electrode method and big data analysis system includes the following steps: S1: System initialization, setting the data acquisition cycle and process parameters; S2: Execute automated monitoring process: Control the water sampling unit to complete drainage, rinsing, water intake and sedimentation operations; S3: After the sedimentation step is completed, start the monitoring instrument unit: synchronously collect multiple basic parameters of the water sample through the physical sensor group; S4: Big data analysis steps: Input the multiple basic parameters collected in S3 into the preset AI big data analysis model in real time. The model performs calculations and analysis, and outputs the total nitrogen concentration and total phosphorus concentration of the current water sample in real time. S5: Data integration and upload: Package the basic parameter data directly measured in S3 and the total nitrogen and total phosphorus data calculated in S4, store and upload them to the remote platform; S6: Return to the waiting state until the next acquisition cycle begins, then jump to S2.

[0010] Preferably, the automated monitoring process in step S2 specifically includes: drainage, rinsing, water intake, and sedimentation operations.

[0011] Preferably, the basic parameters collected in step S3 include at least two of the following: water temperature, conductivity, pH, dissolved oxygen, turbidity, ammonia nitrogen, and COD.

[0012] It has the following beneficial effects: Reagent-free and low-cost: The core monitoring process does not require the use of any chemical reagents, completely eliminating the high costs and secondary pollution risks associated with reagent procurement, storage, and waste disposal.

[0013] High frequency and real-time performance: Based on the rapid response characteristics of physical sensors and combined with efficient AI analysis algorithms, it can achieve a data acquisition frequency of minutes, providing high time-series resolution data support for real-time monitoring, early warning and trend analysis of the water environment.

[0014] High precision and reliability: Through AI big data analysis models, the measurement information of multiple physical parameters is deeply integrated and intelligently compensated, which effectively overcomes the problem of insufficient accuracy when measuring total nitrogen and total phosphorus with a single physical sensor, making the measurement results closer to the accuracy of the national standard chemical method.

[0015] Intelligent and highly integrated: The system is highly integrated and automated, with functions such as intelligent process control, remote monitoring, data breakpoint resume, and anomaly alarm, and is easy to operate and maintain.

[0016] Methodological innovation: It creatively proposes a technical approach that combines simultaneous measurement of multiple parameters using spectroscopy / electrode methods with AI big data analysis to indirectly obtain complex indicators such as total nitrogen and total phosphorus, providing a brand-new technical solution for water quality monitoring. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the outdoor miniature automatic online water quality monitoring station system of the present invention. Detailed Implementation

[0018] 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.

[0019] For the first embodiment, please refer to... Figure 1 This invention provides a technical solution: an online water quality monitoring system and method based on spectral electrode method and big data analysis. like Figure 1 As shown, the system in this embodiment includes a water sampling unit, a monitoring instrument unit, a system control unit, a power supply unit, and an auxiliary unit, all integrated into an outdoor cabinet. Water sampling unit: A self-priming pump is used to draw water samples from the water body to the detection water tank inside the cabinet. The water tank is equipped with a liquid level switch. Monitoring instrument unit: Physical sensor group: Integrated and installed in the water tank, including: thermistor temperature sensor, electrode conductivity sensor, fluorescence dissolved oxygen sensor, glass electrode pH sensor, transmission turbidity sensor, ion-selective electrode ammonia nitrogen sensor, and ultraviolet-visible spectroscopy COD sensor. Big data analysis module: It is integrated into the system control unit in the form of an embedded industrial control computer. Its built-in AI model has been pre-trained. The model takes seven parameters collected in real time, namely water temperature, conductivity, pH, dissolved oxygen, turbidity, ammonia nitrogen and COD, as inputs and outputs the predicted concentration values ​​of total nitrogen and total phosphorus through a three-layer neural network model. System control unit: The core is an Android industrial computer with a touch screen; Process control: The industrial computer controls the water pump and solenoid valve by controlling the relay. A typical operating cycle (30 minutes) is as follows: waiting → drainage → rinsing → water intake → sedimentation → data acquisition and AI calculation → data storage and upload → entering the next cycle and waiting. Communication: Built-in 4G module, supports HJ212 environmental communication protocol, can upload data packets containing 9 parameter values ​​to the monitoring platform; Work process: After the equipment is installed, powered on, and configured, the system will run automatically. Once the data collection time is reached, the control unit automatically completes water collection, rinsing, and sedimentation according to a preset procedure. After sedimentation, the physical sensor array simultaneously measures and transmits seven basic parameters back to the user. The big data analysis module immediately inputs the seven parameter values ​​into the loaded neural network model and outputs the predicted concentrations of total nitrogen and total phosphorus within milliseconds. The control unit packages the 7 directly measured parameters and the 9 calculated parameters (TN and TP) into a single package, stores it locally, and uploads it to the cloud platform. Once completed, the system enters a waiting state, which repeats continuously.

[0020] Through the above system and method, reagent-free, high-frequency, and low-cost online monitoring of nine water quality parameters, especially total nitrogen and total phosphorus, has been achieved. Comparative experiments show that the TN and TP data output by the system of this invention have good consistency with the laboratory national standard method measurement results (correlation coefficient R²>0.85), which fully meets the accuracy requirements of routine surface water monitoring.

[0021] Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art and related fields based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described and explained in the present invention, unless otherwise specified or limited, shall be implemented according to conventional means in the art.

Claims

1. A water quality online monitoring system based on spectral electrode method and big data analysis, characterized in that, include: The water sampling unit is used to extract water samples from the water body to be tested and transport them to the testing tank; The monitoring instrument unit includes a physical sensor group and a big data analysis module. The physical sensor group uses sensors based on the principles of spectroscopy or electrode method to directly measure multiple basic physical and chemical parameters of the water sample. The big data analysis module is communicatively connected to the physical sensor group and has a built-in trained AI big data analysis model. The model uses at least two basic parameters measured by the physical sensor group as input features and performs calculations and analyses through a preset algorithm model to indirectly deduce the total nitrogen concentration and total phosphorus concentration in the water sample. The system control unit is electrically connected to the water sampling unit and the monitoring instrument unit, and is used for the automated operation, data acquisition and processing, and remote communication of the control system. The power supply unit is used to provide power to all units in the system; Auxiliary units include cabinets, surge protection modules, and insulation and heat dissipation devices.

2. The online water quality monitoring system based on spectral electrode method and big data analysis according to claim 1, characterized in that: The physical sensor set includes sensors for measuring at least two of the following: water temperature, conductivity, pH, dissolved oxygen, turbidity, ammonia nitrogen, and permanganate index.

3. The online water quality monitoring system based on spectral electrode method and big data analysis according to claim 1, characterized in that: The AI ​​big data analysis model was trained using a dataset that included historical physical sensor data and the corresponding national standard values ​​for total nitrogen and total phosphorus.

4. The online water quality monitoring system based on spectral electrode method and big data analysis according to claim 3, characterized in that: The AI ​​big data analysis model is constructed in the following way: Collect historical datasets containing various basic parameters measured by devices based on the same principle as the physical sensor group, as well as accurate total nitrogen and total phosphorus values ​​of corresponding water samples measured by national standard chemical methods; The historical dataset was trained using machine learning algorithms to establish a predictive model for total nitrogen and total phosphorus concentrations, based on multidimensional basic parameters.

5. The online water quality monitoring system based on spectral electrode method and big data analysis according to claim 1, characterized in that: The system control unit includes a process control module, which is configured to execute a periodic monitoring process, including the steps of drainage, rinsing, water intake, sedimentation, and data acquisition.

6. The online water quality monitoring system based on spectral electrode method and big data analysis according to claim 5, characterized in that: The system control unit also includes: The intelligent controller is equipped with a touchscreen human-machine interface; The data management module is used to store raw measurement data and calculated total nitrogen / total phosphorus data; The communication module supports wireless networks and is used to remotely upload monitoring data.

7. A method for online water quality monitoring based on the system described in any one of claims 1-4, characterized in that, Includes the following steps: S1: System initialization; S2: Execute the automated monitoring process to complete the replacement and preparation of water samples; S3: Simultaneously collect multiple basic parameters of water samples through a group of physical sensors; S4: Input the multiple basic parameters collected in S3 into the preset AI big data analysis model to calculate and output the total nitrogen concentration and total phosphorus concentration of the current water sample; S5: Integrate and upload basic parameter data and calculated total nitrogen and total phosphorus data; S6: Return to the waiting state until the next acquisition cycle begins, then jump to S2.

8. The online water quality monitoring method according to claim 7, characterized in that: The automated monitoring process in step S2 specifically includes: drainage, rinsing, water intake, and sedimentation operations.

9. The online water quality monitoring method according to claim 7, characterized in that: The basic parameters collected in step S3 include at least two of the following: water temperature, conductivity, pH, dissolved oxygen, turbidity, ammonia nitrogen, and COD.

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