Water quality detection device

Abstract

The invention relates to the technical field of water quality detection, and discloses a water quality detection device which is characterized in that an initial water sample to be detected is filtered through a filtering unit, so that the interference of impurities on a detection result is reduced; meanwhile, the water quality is detected by combining an electrode method and a spectrophotometric method, so that the advantage of high real-time performance of the electrode method is exerted, the detection precision is improved by utilizing the spectrophotometric method, and the error risk of a single detection method is effectively reduced; in the data processing unit, Kalman filtering and a multi-model correction algorithm are combined to carry out optimization processing on detection data, electrode drift errors are corrected more accurately, the long-term stability of data measured by an electrode method is effectively improved, the requirement of manual calibration is reduced, the measurement result always keeps high precision in long-term operation, and the measurement accuracy is improved. And the automation level of the equipment is improved. Therefore, according to the invention, an automatic correction mechanism is realized, frequent manual intervention is not needed, the operation and maintenance cost is reduced, and the stability and reliability of water quality monitoring are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality detection, and in particular to a water quality detection device. Background Art

[0002] The operational quality of sewage treatment plants is directly related to environmental safety and water resource protection, and the accurate measurement of water quality parameters is a key link in ensuring treatment effectiveness, optimizing process flows, and ensuring compliance with emission standards. During sewage treatment, real-time monitoring of core water quality indicators such as COD (chemical oxygen demand), ammonia nitrogen, total phosphorus, dissolved oxygen, and pH can reflect the efficiency of pollutant removal and guide adjustments to key processes such as chemical addition and aeration control. If water quality test data is inaccurate or discontinuous, it may lead to treatment process imbalances, which in the worst case may affect the sewage treatment effect, or even lead to excessive emissions, causing water pollution and even ecological safety issues. Therefore, how to achieve continuous and stable detection of water quality parameters while ensuring measurement accuracy has become an important direction for the development of water quality testing technology for sewage treatment plants.

[0003] Currently, some water quality testing systems attempt to combine the advantages of spectrophotometry and electrode methods, such as manually calibrating electrode data using spectrophotometry at fixed time intervals. However, such methods typically rely on simple linear corrections and cannot effectively address nonlinear drift caused by complex environmental factors, resulting in limited correction effectiveness. Furthermore, existing systems often require manual intervention in the calibration process, making it difficult to achieve intelligent and automated data correction. Therefore, current water quality testing technologies cannot simultaneously meet the requirements of high precision, real-time performance, and long-term stability. Summary of the Invention

[0004] In view of this, the present invention provides a water quality detection device to solve the problem that current water quality detection technology cannot simultaneously meet the requirements of high precision, real-time performance and long-term stability.

[0005] In a first aspect, the present invention provides a water quality detection device, comprising:

[0006] A filtering unit, a first water inlet pump, an electrode unit, a second water inlet pump, a spectrophotometer unit and a data processing unit; the filtering unit is used to filter the initial water sample to be tested to obtain a first water sample to be tested, and input the first water sample to be tested into the electrode unit through the first water inlet pump, and input the first water sample to be tested into the spectrophotometer unit through the first water inlet pump and the second water inlet pump; the electrode unit is used to perform water quality testing on the first water sample to be tested using the electrode method, and send the electrode signal and temperature data to the data processing unit according to the test results; the spectrophotometer unit is used to perform water quality testing on the first water sample to be tested using the spectrophotometer method, and send the absorbance signal to the data processing unit according to the test results; the data processing unit is used to perform water quality testing using Kalman filtering and a multi-model correction algorithm based on the electrode signal, temperature data and absorbance signal to obtain a water quality testing data set.

[0007] The water quality detection device provided by the present invention filters the initial water sample to be detected through a filtering unit, provides a clean water sample for subsequent detection, reduces the interference of impurities on the detection results, and improves the accuracy of detection. Furthermore, in the electrode unit, the electrode method is used to perform water quality detection on the first water sample to be detected, which can achieve high-frequency and continuous monitoring, and has a fast response speed and is suitable for dynamic changes in water quality during sewage treatment. At the same time, the temperature sensor measures the temperature data of the water sample and sends it to the data processing unit, which helps to perform temperature correction on the detection results later and improve the accuracy of detection. Furthermore, in the spectrophotometric unit, the spectrophotometric method is used to perform water quality detection on the first water sample to be detected, achieving high-precision detection, which not only gives play to the high real-time advantage of the electrode method, but also uses the spectrophotometric method to improve the detection accuracy, effectively reducing the error risk of a single detection method. Finally, in the data processing unit, the detection data of the electrode method and spectrophotometry method are optimized and processed through Kalman filtering and multi-model correction algorithms, which can more accurately correct electrode drift errors, effectively improve the long-term stability of the electrode method measurement data, reduce the need for manual calibration, and enable its measurement results to always maintain high accuracy during long-term operation, thereby improving the automation level of the equipment. Therefore, by implementing the present invention, an automatic correction mechanism is realized, eliminating the need for frequent manual intervention, reducing operation and maintenance costs, improving the stability and reliability of water quality monitoring, and providing a high-efficiency, low-cost, and intelligent water quality detection solution for sewage treatment plants.

[0008] In an optional embodiment, the filtration unit includes: a coarse filtration layer and a fine filtration layer;

[0009] The coarse filter layer is used to filter the initial water sample to be tested to obtain the second water sample to be tested; the fine filter layer is used to filter the first water sample to be tested to obtain the first water sample to be tested.

[0010] The water quality detection device provided by the present invention performs graded filtration on the initial water sample to be detected through a coarse filtration layer and a fine filtration layer, thereby avoiding the contamination of the electrode unit and the spectrophotometric unit sensor by impurities, preventing the adhesion of impurities from affecting the detection accuracy, extending the service life of the sensor, reducing the maintenance work and cost expenditure caused by impurities, and ensuring the stability of the detection process and the accuracy of the detection data.

[0011] In an optional embodiment, the filtration unit further includes: a backwashing module for flushing the coarse filter layer and the fine filter layer.

[0012] The water quality detection device provided by the present invention can automatically flush the coarse filter layer and the fine filter layer through the backwash module, thereby being able to promptly remove intercepted impurities and restore the filtering capacity of the filter layer, avoiding detection errors caused by reduced filtering effect, reducing the frequency and workload of manual cleaning, and improving the degree of automation of the filter unit.

[0013] In an optional embodiment, the electrode unit includes: a working electrode, a reference electrode and a temperature sensor; the working electrode and the reference electrode form a closed loop, which is used to perform water quality detection on the first water sample to be detected and generate an electrode signal; the temperature sensor is used to monitor the temperature of the first water sample to be detected and obtain temperature data.

[0014] The water quality testing device provided by the present invention forms a closed circuit using a working electrode and a reference electrode, enabling electrochemical testing of water samples and rapidly outputting an electrode signal reflecting the water quality. Simultaneously, a temperature sensor can simultaneously detect the water sample's temperature, providing data support for subsequent data processing.

[0015] In an optional embodiment, the electrode unit further includes: an ultrasonic cleaner for cleaning surface contaminants of the working electrode and the reference electrode.

[0016] The water quality detection device provided by the present invention can automatically and regularly clean the working electrode and the reference electrode through an ultrasonic cleaner. Compared with traditional cleaning methods, there is no need to disassemble the electrodes, the operation is simple and the cleaning effect is better. It can restore the electrode sensitivity in time, ensure the long-term stable operation of the electrodes, reduce measurement errors caused by electrode contamination, extend the service life of the electrodes, and reduce maintenance costs.

[0017] In an optional embodiment, the spectrophotometric unit includes: a sample injection module, a reagent addition module, a mixing reaction module and an optical detection module;

[0018] The injection module is used to control the injection volume of the first water sample to be tested, and input the controlled quantitative water sample to be tested into the corresponding reaction pool; the reagent adding module is used to output the target reagent to the reaction pool when the quantitative water sample to be tested is input into the reaction pool; the mixing reaction module is used to mix the quantitative water sample to be tested and the target reagent in the reaction pool by bubble mixing, so that the quantitative water sample to be tested and the target reagent react to produce a mixed sample; the optical detection module is used to emit light of a target wavelength to the reaction pool after the reaction between the quantitative water sample to be tested and the target reagent is completed, and measure the absorbance of the mixed sample in the reaction pool to obtain an absorbance signal.

[0019] The water quality detection device provided by the present invention can control the injection of the water sample to be detected into the reaction pool through the injection module, and at the same time, inject the reagent sampling module in combination with the reagent sampling module. Furthermore, a bubble mixing method is adopted, and the quantitative water sample to be detected and the target reagent in the reaction pool are mixed by the mixing reaction module to ensure uniform mixing. At the same time, a stable mixed sample can be generated by fully mixing and completely reacting the water sample and the reagent. Finally, the optical detection module emits light of the target wavelength to the reaction pool, and the absorbance of the mixed sample can be measured and the corresponding absorbance signal can be obtained. Therefore, by implementing the present invention, precise control of spectrophotometry from sample processing to signal detection is achieved, and the accuracy, repeatability and stability of spectrophotometric detection are improved.

[0020] In an optional embodiment, the spectrophotometric unit further includes: a self-cleaning module and a waste liquid treatment module;

[0021] The self-cleaning module is used to input deionized water into the reaction tank so that the deionized water can rinse the reaction tank and generate cleaning waste liquid; the waste liquid treatment module is used to collect and treat the reaction liquid and cleaning waste liquid generated by the reaction tank.

[0022] The water quality testing device provided by the present invention uses a self-cleaning module to flush the reaction tank with deionized water, effectively removing residual reagents and reaction products, preventing residual substances from interfering with the next measurement and ensuring the accuracy and independence of each measurement. Furthermore, a waste liquid treatment module collects and processes reaction liquid and cleaning waste, preventing the discharge of harmful substances and complying with environmental protection requirements.

[0023] In an optional embodiment, the data processing unit includes: a signal acquisition module, a signal processing module, a calibration calculation module and an anomaly detection module;

[0024] A signal acquisition module is used to obtain electrode signals, temperature data and absorbance signals, and send the electrode signals, temperature data and absorbance signals to the signal processing module; the signal processing module is used to calculate a first measurement concentration value based on the electrode signal using the Nernst equation, and to calculate a second measurement concentration value based on the absorbance signal using the Lambert-Beer law, and to send the second measurement concentration value to the calibration calculation module and the anomaly detection module respectively; the signal processing module is also used to correct the first measurement concentration value using the temperature data to obtain a third measurement concentration value, and to send the third measurement concentration value to the calibration calculation module; the calibration calculation module is used to correct the third measurement concentration value based on the second measurement concentration value using the Kalman filter and the multi-model correction algorithm to obtain a fourth measurement concentration value, and to send the fourth measurement concentration to the anomaly detection module; the anomaly detection module is used to perform anomaly detection based on the second measurement concentration value, the third measurement concentration value and the fourth measurement concentration value and determine the water quality detection data set.

[0025] The water quality detection device provided by the present invention can calculate the first measurement concentration value corresponding to the electrode method and the second measurement concentration value corresponding to the spectrophotometric method respectively through the Nernst equation and the Lambert-Beer law. At the same time, the first measurement concentration value is corrected using temperature data, taking into account the influence of temperature on the measurement result, making the first measurement concentration value more accurate, and improving the accuracy and reliability of the measurement. Furthermore, based on the second measurement concentration value, the third measurement concentration value is corrected using Kalman filtering and a multi-model correction algorithm, effectively fusing the data of the two detection methods, reducing errors and uncertainties, reducing errors and uncertainties, and finally, the anomaly detection module can promptly detect anomalies in the measurement data through comprehensive analysis of multiple concentration values, thereby ensuring the quality and reliability of the final generated water quality detection data set.

[0026] In an optional implementation, the anomaly detection module is further configured to determine an anomaly handling solution according to an anomaly type, wherein different anomaly types correspond to different anomaly situations.

[0027] The water quality detection device provided by the present invention has an anomaly detection module that determines an anomaly handling plan based on the anomaly type, can accurately locate the root cause of the anomaly and take corresponding measures, changing the situation of blind handling or missed handling in traditional systems, improving the efficiency and accuracy of anomaly handling, reducing the impact of abnormal situations on water quality monitoring, and enhancing the stability and reliability of the device.

[0028] In an optional embodiment, the data processing unit further includes: a data storage module, configured to receive and store the water quality detection data set sent by the anomaly detection module.

[0029] The water quality detection device provided by the present invention receives and stores the water quality detection data set sent by the anomaly detection module through the data storage module, and can manage the data in an orderly manner, thereby facilitating users to trace water quality changes, analyze water quality change trends, evaluate the performance of detection equipment, etc., and provides data support for long-term decision-making such as optimizing the operation and adjusting the process of sewage treatment plants.

[0030] In an optional embodiment, the device is connected to a cloud server and a user terminal; the device further comprises: an intelligent monitoring unit;

[0031] The intelligent monitoring unit includes a data reading module, a display module, and a data upload module; the data reading module is used to obtain the water quality detection data set and send the water quality detection data set to the display module for display; the data upload module is used to receive the water quality detection data set sent by the data reading module and send the water quality detection data set to the cloud server and user terminal.

[0032] The water quality detection device provided by the present invention is connected to a cloud server and a user terminal via an intelligent monitoring unit. After obtaining a water quality detection data set, the data reading module can display it via a display module, making it convenient for on-site personnel to view it. At the same time, the data can be sent to a cloud server and a user terminal via a data upload module, enabling remote data sharing. Therefore, by implementing the present invention, managers can obtain real-time water quality data anytime and anywhere, keep abreast of water quality trends, and make scientific decisions. This breaks the limitations of time and space, improves management efficiency, and is particularly suitable for multi-site, wide-range water quality monitoring scenarios.

[0033] In an optional embodiment, the intelligent monitoring unit further includes: an alarm module and an interaction module;

[0034] The alarm module is used to, upon receiving the target abnormality sent by the abnormality detection module, issue an alarm based on the target abnormality and determine the target processing plan based on the target abnormality, and send the target processing plan to the user terminal; the interaction module is used to receive the first operation instruction sent by the user terminal or the second operation instruction directly input by the user, and respond to execute the first operation corresponding to the first operation instruction or the second operation corresponding to the second operation instruction.

[0035] The water quality testing device provided by the present invention features an alarm module that not only issues an alarm upon receiving an abnormality but also determines a targeted treatment plan based on the abnormality and sends it to the user terminal, providing detailed problem-solving guidance and helping users quickly resolve the abnormality. Furthermore, the interactive module supports users entering operational instructions directly or through the user terminal, enabling flexible control of the device, improving user convenience and the system's intelligence, enabling users to customize monitoring plans based on their actual needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 is a structural block diagram of a water quality detection device according to an embodiment of the present invention;

[0038] Figure 2 is a structural block diagram of a filtering unit according to an embodiment of the present invention;

[0039] Figure 3 is a structural block diagram of an electrode unit according to an embodiment of the present invention;

[0040] Figure 4 is a structural block diagram of a spectrophotometric unit according to an embodiment of the present invention;

[0041] Figure 5 is a structural block diagram of a data processing unit according to an embodiment of the present invention;

[0042] Figure 6 4 is a structural block diagram of an intelligent monitoring unit according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0044] Wastewater treatment plants widely use spectrophotometry and electrode methods for water quality monitoring. However, both methods have limitations and struggle to meet the demand for continuous, stable, and high-precision monitoring of water quality parameters. Spectrophotometry calculates pollutant concentrations by measuring light absorption at specific wavelengths, offering high measurement accuracy. However, this method has a long testing cycle, typically requiring several hours to obtain a single measurement result, making it difficult to meet the needs of real-time monitoring. Electrode methods, which directly measure water parameters using ion-selective electrodes or other sensors, enable high-frequency, continuous monitoring with a fast response, making them suitable for dynamic changes in water quality during wastewater treatment. However, electrode methods have significant drawbacks. Their measurement accuracy is easily affected by external environmental factors, leading to data drift. Over time, electrodes may become passivated or contaminated, affecting measurement stability. Therefore, while electrode methods offer real-time monitoring capabilities, long-term accuracy is difficult to guarantee and requires frequent manual calibration, which increases maintenance costs and compromises monitoring reliability.

[0045] Currently, some water quality detection systems have attempted to combine the advantages of spectrophotometry and electrode methods, such as using spectrophotometry to manually calibrate electrode data at fixed time intervals. However, such methods usually rely on simple linear corrections and cannot effectively deal with nonlinear drift caused by complex environmental factors, resulting in limited correction effects. At the same time, existing systems often require manual intervention in the calibration process, making it difficult to achieve intelligent and automated data correction. Therefore, current water quality monitoring technology still faces problems such as data discontinuity, measurement drift, and high maintenance costs, and cannot simultaneously meet the requirements of high precision, real-time performance, and long-term stability.

[0046] Embodiments of the present invention provide a water quality testing device that uses spectrophotometric measurement results as a high-precision benchmark to regularly calibrate data obtained using an electrode method, thereby ensuring the long-term accuracy of electrode measurement data. Furthermore, by optimizing the detection data from both the electrode and spectrophotometric methods using a Kalman filter and a multi-model correction algorithm, electrode drift errors can be more accurately corrected, effectively improving the long-term stability of electrode measurement data and reducing the need for manual calibration, ensuring that measurement results remain highly accurate over the long term.

[0047] In this embodiment, a water quality detection device is provided. Figure 1 As shown, the water quality detection device 1 is connected to the cloud server 2 and the user terminal 3 respectively. The water quality detection device 1 includes a filtering unit 11, a first water inlet pump 12, an electrode unit 13, a second water inlet pump 14, a spectrophotometer unit 15, a data processing unit 16 and an intelligent monitoring unit 17.

[0048] Optionally, the filtering unit 11 is used to filter the initial water sample to be tested to obtain a first water sample to be tested, and input the first water sample to be tested into the electrode unit 13 through the first water inlet pump 12, and input the first water sample to be tested into the spectrophotometer unit 15 through the first water inlet pump 12 and the second water inlet pump 14.

[0049] Among them, such as Figure 2 As shown, the filter unit 11 includes a coarse filter layer 111 , a fine filter layer 112 and a backwash module 113 .

[0050] The coarse filter layer 111 is made of filter materials with relatively large pore sizes, such as quartz sand, nylon filter mesh, etc. Its main function is to intercept larger suspended solids and particulate impurities in the water sample, such as mud, leaf fragments, and larger algae clumps.

[0051] Fine filter layer 112 uses materials with smaller pore sizes and higher filtration precision, such as activated carbon and microporous membranes. It can further filter out impurities such as smaller suspended solids, colloids, microorganisms, and some organic matter in the water sample. For example, activated carbon can absorb pigments, odors, and some organic pollutants in the water, while microporous membranes can intercept tiny particles and microorganisms.

[0052] Specifically, after the initial water sample to be tested enters the filter unit 11, it first flows into the coarse filter layer 111. Through the preliminary filtration of the coarse filter layer 111, large particles of impurities, such as mud and sand, in the initial water sample to be tested can be removed, so that the initial water sample to be tested is preliminarily purified, thereby preventing the clogging of the fine filter layer 112.

[0053] Furthermore, the second water sample to be tested after being filtered by the coarse filter layer 111 continues to flow into the fine filter layer 112. Through the deep filtration of the fine filter layer 112, small particle impurities, alternations, microorganisms and other tiny impurities in the second water sample to be tested that cannot be intercepted by the coarse filter layer 111 are removed, and then the first water sample to be tested with higher purity can be obtained, thereby improving the cleanliness of the water sample.

[0054] Furthermore, during the long-term operation of the coarse filter layer 111 and the fine filter layer 112, the intercepted impurities will gradually accumulate on the surface and in the pores of the filter material, resulting in increased filtration resistance and decreased filtration efficiency. In this case, the coarse filter layer 111 and the fine filter layer 112 can be flushed using the backwash module 113.

[0055] For example, after backwash module 113 is activated, it can flush the coarse filter layer 111 with reverse high-pressure water flow to remove large particles. Simultaneously, it can flush the fine filter layer 112 with short pulses to prevent particle deposition. Furthermore, during the flushing process, the water flow direction is opposite to that during normal filtration. The impact of the water flow flushes impurities from the surface and pores of the filter material, restoring the filter material's filtration performance and ensuring that the coarse filter layer 111 and the fine filter layer 112 return to normal filtration conditions.

[0056] Furthermore, the backwashing process usually lasts for a certain period of time to ensure that impurities are fully rinsed away. After the backwashing is completed, the filter unit 11 can continue to work normally, thereby maintaining a stable filtering effect, ensuring the continuity and reliability of the water quality testing process, and reducing the frequency and workload of manual maintenance.

[0057] Finally, the filtered first water sample to be tested can be input into the electrode unit 13 through the first water inlet pump 12 , and can be input into the spectrophotometer unit 15 through the first water inlet pump 12 and the second water inlet pump 14 .

[0058] Optionally, the electrode unit 13 is used to perform water quality detection on the first water sample to be detected using an electrode method, and send electrode signals and temperature data to the data processing unit 16 according to the detection results.

[0059] Among them, such as Figure 3 As shown, the electrode unit 13 includes: a working electrode 131 , a reference electrode 132 , a temperature sensor 133 and an ultrasonic cleaner 134 .

[0060] Specifically, the working electrode 131 is typically made of a material that is electrochemically active toward a specific target substance. Therefore, when the first water sample to be tested, after being pre-treated by the filtration unit 11, flows into the electrode unit 13, the target substance (e.g., ionic contaminants) in the first water sample to be tested can undergo an oxidation-reduction reaction on the surface of the working electrode 131.

[0061] Furthermore, during the redox reaction, electrons are transferred. These electrons flow between the working electrode and the reference electrode, thereby forming an electric current. Since the working electrode 131 and the reference electrode 132 are connected by wires and immersed in the water sample together, forming a complete electrochemical circuit, electrons can flow smoothly from the area where the oxidation reaction occurs to the area where the reduction reaction occurs.

[0062] Furthermore, as the electrons continue to transfer and the current is generated, a potential difference is generated between the working electrode 131 and the reference electrode 132 and a corresponding electrode signal (potential signal) is formed.

[0063] The magnitude of the electrode signal is closely related to the concentration of the target substance in the water sample. Generally speaking, the higher the concentration of the target substance, the more substances participate in the redox reaction, the more electrons are transferred, and the larger the electrode signal generated.

[0064] Furthermore, temperature changes can alter chemical reaction rates, affecting the activity and movement of particles involved in redox reactions. Generally speaking, increasing temperature accelerates reaction rates, enhances particle activity, and may increase the potential signal; decreasing temperature slows reaction rates and may decrease the potential signal. Temperature also affects the physicochemical properties of electrode materials, altering the internal resistance of the electrode and the double-layer structure on the electrode surface, indirectly affecting the stability and accuracy of the potential signal.

[0065] Therefore, in order to eliminate the influence of temperature on the measurement result of the electric potential signal, the temperature sensor 133 can measure the temperature of the first water sample to be detected in real time, providing support for subsequent data processing.

[0066] Furthermore, the obtained electrode signal and temperature data are transmitted to the data processing unit 16 .

[0067] Furthermore, during the detection process, the ultrasonic cleaner 134 can be activated at regular intervals. When in operation, the ultrasonic cleaner 134 emits high-frequency vibrations, generating powerful ultrasonic energy. Furthermore, the generated ultrasonic energy can effectively remove contaminants such as colloids, sediments, and microbial membranes attached to the electrode surfaces, thereby restoring the working electrode 131 and the reference electrode 132 to their optimal working state. This ensures that the working electrode 131 and the reference electrode 132 can accurately react with the target substance during subsequent detection, maintaining high measurement accuracy and stability.

[0068] Compared with traditional cleaning methods, ultrasonic cleaners can automatically clean the working electrode and reference electrode regularly without disassembling the electrodes. It is easy to operate and has better cleaning effects. It can restore the electrode sensitivity in time, ensuring the long-term stable operation of the electrode, reducing measurement errors caused by electrode contamination, extending the service life of the electrode, and reducing maintenance costs.

[0069] Optionally, the spectrophotometric unit 15 is configured to perform a water quality test on the first water sample to be tested using spectrophotometry, and send an absorbance signal to the data processing unit 16 according to the test result.

[0070] Among them, such as Figure 4 As shown, the spectrophotometric unit 15 includes: a sample injection module 151 , a reagent addition module 152 , a mixing reaction module 153 , an optical detection module 154 , a self-cleaning module 155 and a waste liquid treatment module 156 .

[0071] Specifically, after the first water sample to be tested enters the spectrophotometric unit 15 through the first water inlet pump 12 and the second water inlet pump 14, the injection pump (such as a peristaltic pump or a syringe pump) in the injection module 151 controls the injection volume of the first water sample to be tested into the reaction cell, and then injects the controlled quantitative water sample to be tested into the reaction cell. By controlling the injection volume of the water sample to be tested to be consistent each time it is injected into the reaction cell, it is possible to ensure that water quality monitoring is performed under the same reaction conditions, thereby making the monitoring results comparable.

[0072] Furthermore, after a quantitative amount of the test water sample is injected into the reaction cell, the reagent injection module 152 can inject the target reagent required for a specific color development reaction with the target pollutant in the test water sample according to a pre-set chemical reaction ratio and experimental standards. The pre-set chemical reaction ratio and experimental standards ensure that the test water sample in the reaction cell fully reacts with the target reagent, producing a stable and detectable color change.

[0073] Furthermore, after the target reagent is injected into the reaction pool, the mixing reaction module can use a bubble mixing method to fully mix the quantitative water sample to be tested and the target reagent in the reaction pool. At the same time, a constant temperature and fixed reaction time can be set during the mixing process.

[0074] Among them, by setting a constant temperature, it helps to maintain the chemical reaction in a stable environment, avoiding the impact of temperature fluctuations on the reaction rate and product generation; by setting a fixed reaction time, it can be ensured that the reaction can be fully completed and the color development can be stable.

[0075] Furthermore, after thorough mixing and reaction of the quantitative water sample to be tested and the target reagent, a mixed sample with specific chemical composition and optical properties is produced in the reaction cell. Furthermore, this mixed sample may contain components originally present in the water sample, a color-forming substance generated by reaction with the target test substance, as well as incompletely reacted reagents and byproducts generated during the reaction.

[0076] Furthermore, after the reaction is complete, the optical detection module 154 can use a light source to emit light of a specific wavelength into the reaction cell. When the light illuminates the mixed sample generated in the reaction cell, the chromogenic substance in the mixed sample absorbs some of the light. At this time, the optical detection module 154 can use an integrated sensor such as a photodetector to measure the intensity of the light transmitted through the mixed sample and convert it into a corresponding electrical signal, namely an absorbance signal.

[0077] Furthermore, the obtained absorbance signal may be sent to the data processing unit 16 .

[0078] Furthermore, after the measurement is completed, the self-cleaning module 155 can be used to rinse the reaction cell.

[0079] For example, the self-cleaning module 155 may inject deionized water into the reaction tank and use the deionized water to rinse the reaction tank to remove residual reagents and reaction products in the reaction tank.

[0080] Furthermore, when the reaction tank is rinsed with deionized water, corresponding cleaning waste liquid is generated. Furthermore, the cleaning waste liquid may contain residual reaction liquid components, deionized water itself, and various ions and organic matter dissolved therein.

[0081] Furthermore, the reaction liquid and cleaning waste liquid generated by the reaction tank can be collected and processed by the waste liquid processing module 156. The reaction liquid is the residual solution after the reaction between the water sample to be tested and the target reagent in the reaction tank is completed, and may contain unreacted oxidant, reaction products, other substances in the water sample that did not participate in the reaction, and various auxiliary reagent components added during the reaction process.

[0082] For example, the waste liquid treatment module 156 can discharge the collected reaction liquid and cleaning waste liquid into a corresponding waste liquid collection device, which can then regularly treat the waste liquid to meet environmental protection requirements.

[0083] Optionally, the data processing unit 16 is used to perform water quality detection based on the electrode signal, temperature data and absorbance signal using Kalman filtering and a multi-model correction algorithm to obtain a water quality detection data set.

[0084] Among them, such as Figure 5 As shown, the data processing unit 16 includes: a signal acquisition module 161 , a signal processing module 162 , a calibration calculation module 163 , an anomaly detection module 164 and a data storage module 165 .

[0085] Specifically, when the electrode unit 13 and the spectrophotometer unit 15 are performing water quality detection, the signal acquisition module 161 can collect the electrode signal and temperature data generated by the spectrophotometer unit 15 , as well as the absorbance signal generated by the spectrophotometer unit 15 in real time.

[0086] Among them, time synchronization is performed during the collection process to ensure that the collection time is aligned.

[0087] In some optional implementations, the signal acquisition module 161 may further perform preliminary processing on the received signal, which may include operations such as amplification and filtering.

[0088] Furthermore, the signal processing module 162 is used to calculate the first measurement concentration value based on the electrode signal using the Nernst equation, and to calculate the second measurement concentration value based on the absorbance signal using the Lambert-Beer law, and to send the second measurement concentration value to the calibration calculation module 163 and the abnormality detection module 164 respectively.

[0089] The first measured concentration value is a result of measuring the concentration of the target substance in the first water sample to be detected based on the electrode method; the second measured concentration value is a result of measuring the concentration of the target substance in the first water sample to be detected based on the spectrophotometry method.

[0090] The Nernst equation is an equation used to quantitatively describe the relationship between the electrode potential and the concentration and temperature of substances involved in the electrode reaction, as shown in the following equation (1):

[0091]

[0092] Where: E represents the electrode potential; E 0 represents the standard electrode potential; R represents the gas constant; T represents the absolute temperature; n represents the number of electrons transferred in the reaction; F represents the Faraday constant; and α represents the activity ratio of the oxidized state to the reduced state.

[0093] Lambert-Beer law is a law that describes the relationship between the degree of light absorption by a substance and the concentration of the absorbing substance and the thickness of the liquid layer, as shown in the following equation (2):

[0094] A=εbc (2)

[0095] Where: A represents absorbance; ε represents molar absorptivity; b represents optical path length; and c represents solution concentration.

[0096] Specifically, in water quality monitoring, the potential signal generated by the electrode is related to the concentration of the target substance in the water. According to the description of the electrode unit 13, the electrode signal is used to reflect the potential difference generated between the working electrode 131 and the reference electrode 132.

[0097] Therefore, when the concentration of the water sample to be detected is detected in the electrode unit 13, the potential difference between the working electrode 131 and the reference electrode 132 will change with the concentration in the water sample to be detected. At this time, the measured potential difference can be converted into the corresponding concentration, i.e., the first concentration value, according to the Nernst equation.

[0098] Furthermore, according to the description of spectrophotometric unit 15, the absorbance signal can reflect the intensity of light transmitted through the mixed sample, that is, the absorbance of the mixed sample at the target wavelength. Therefore, based on the known optical path length (fixed by the instrument) and the molar absorptivity (a characteristic constant of a specific substance at a specific wavelength), the concentration of the target substance in the mixed sample can be calculated using the Lambert-Beer law, i.e., the second measured concentration value.

[0099] Finally, the calculated second measured concentration value may be sent to the calibration calculation module 163 and the abnormality detection module 164 respectively.

[0100] Furthermore, the signal processing module 162 is further configured to correct the first measured concentration value using the temperature data to obtain a third measured concentration value, and send the third measured concentration value to the calibration calculation module 163 .

[0101] Since temperature has a significant impact on concentration measurement using the electrode method, it is necessary to use a temperature sensor to measure the water sample temperature for correction.

[0102] Specifically, temperature changes can alter chemical reaction rates and electrode material properties, thereby affecting the electrode potential and measured concentration. Furthermore, according to the Nernst equation, temperature T is a variable in the equation.

[0103] Therefore, in the actual correction process, a correction model for temperature and concentration measured by electrode method can be established.

[0104] Furthermore, the electrode potential of standard solutions of known concentrations at different temperatures is first experimentally measured to obtain data on the relationship between temperature and electrode potential. A calibration curve or correction formula is then fitted based on this data. When actually measuring water samples, the electrode concentration calculated based on the Nernst equation is corrected using the correction formula based on the measured temperature data, eliminating errors caused by temperature and improving measurement accuracy.

[0105] Finally, the corrected third measured concentration value may be sent to the calibration calculation module 163 .

[0106] Furthermore, the calibration calculation module 163 is used to correct the third measured concentration value based on the second measured concentration value using Kalman filtering and a multi-model correction algorithm to obtain a fourth measured concentration value, and send the fourth measured concentration to the abnormality detection module 164 .

[0107] Specifically, the calibration calculation module 163 can use Kalman filtering and multi-model correction algorithm to perform real-time correction on the third measured concentration value, which is specifically divided into three steps: prediction, observation correction and error covariance update to ensure the accuracy and stability of the electrode method measurement data.

[0108] (1) Prediction step.

[0109] At time k, the electrode method measurement value at the previous moment, that is, the third measured concentration value X k-1 And the system state transition model predicts the electrode concentration value at the current moment As shown in the following equation (3):

[0110]

[0111] Where: A represents the state transfer matrix, which can be obtained by electrode aging experiments and recursive least squares fitting; B represents the coupling coefficient matrix, which can be determined by offline experiments and used to determine the compensation weight of the spectrophotometric reference pair electrode drift; U k Represents the absorbance measured spectrophotometrically, providing baseline data at the sampling time point.

[0112] At the same time, the prediction error covariance is updated as shown in the following relationship (4):

[0113]

[0114] Where: It represents the prediction error covariance, which indicates the uncertainty of the current state. The larger its value is, the lower the reliability of the prediction result is. k-1 represents the prediction error covariance at the previous moment (k-1); Q represents the process noise covariance, which can be set according to the electrode stability test results. It is used to characterize the system noise of the electrode method measurement value, that is, to reflect the influence of inevitable interference factors on the measurement results during the measurement process.

[0115] (2) Observation and correction steps.

[0116] When there is no spectrophotometric data, only the prediction step is performed. Furthermore, since there is no spectrophotometric method to provide more accurate benchmark data, the prediction result of the previous moment can only be used as the measurement value of the current moment, and the prediction error covariance remains unchanged, that is,

[0117] When the spectrophotometric measurement value, i.e. the second measured concentration value Y k When the predetermined time interval is available, the Kalman gain can be calculated using the following relationship (5):

[0118]

[0119] Where: K k represents the Kalman gain, which is used to determine the weights of the predicted value and the observed value in the correction calculation; C represents the concentration conversion matrix, which is determined by the calibration experiment to determine the range ratio relationship between the electrode method and the spectrophotometric method, that is, C reflects the conversion relationship between the two measurement methods when measuring the concentration of the same substance; R represents the observation noise covariance, which is used to describe the uncertainty of the spectrophotometric measurement value and can be determined by calculating the sample variance through the spectrophotometric repeatability test.

[0120] Furthermore, after calculating the Kalman gain, the following relationship (6) can be used to update the electrode method measurement value so that it is closer to the spectrophotometric observation value:

[0121]

[0122] Where: X k represents the corrected electrode concentration value, i.e., the fourth measured concentration value; C1 represents the observation matrix.

[0123] (3) Error covariance update.

[0124] Specifically, the following relationship (7) can be used to update the error covariance matrix to reduce the uncertainty of electrode measurement:

[0125]

[0126] Where: I represents the unit matrix.

[0127] By adjusting the prediction error covariance, the uncertainty of the electrode method measurement is reduced and the reliability of the measurement is improved. Furthermore, with each measurement and calibration process, the error covariance is continuously updated, making the measurement results more stable and accurate.

[0128] Finally, through the above processing, the fourth measured concentration is sent to the abnormality detection module 164.

[0129] Furthermore, the anomaly detection module 164 is configured to perform anomaly detection based on the second measured concentration value, the third measured concentration value, and the fourth measured concentration value and determine a water quality detection data set.

[0130] The water quality detection data set may include data such as real-time concentration, data quality, historical trends, and device status. The real-time concentration may include the second measured concentration value and the fourth measured concentration value; data quality may be determined through anomaly detection; device status may be determined based on anomaly detection results; and historical trends may be determined based on information such as the real-time concentration and measurement time of each measurement stored in the data storage module 165.

[0131] Specifically, the normal measurement range can be preset to X min -X max Further, the electrode method measurement data, that is, the third measurement concentration value A k Compare with this range, if A k <X min or A k >X max , it means that the measurement data exceeds the length range. At this time, the abnormal situation is recorded and compensation or cleaning measures are triggered.

[0132] Furthermore, the following relationship (8) can be used to calculate the sudden change or drift of the measurement data:

[0133] D k =|X k -X k-1 |(8)

[0134] Where: X k-1 represents the fourth measured concentration value at the previous moment (k-1); D k The concentration difference is used to reflect the sudden change or drift of the measurement data.

[0135] Furthermore, D k With the preset threshold D threshold Compare, if D k >D threshold , it means that the data changes too much, and the change may be caused by external interference, sensor contamination or failure.

[0136] Furthermore, the Kalman filter can be used to calculate the residual R between the electrode method measurement value and the predicted value k , as shown in the following relation (9):

[0137] R k =|A k -A k-1 |(9)

[0138] Where: A k-1 Indicates the third measured concentration value at the previous moment (k-1).

[0139] Furthermore, R k and the preset threshold R threshold Compare, if R k >R threshold , it means that the data deviation is too large, which may be caused by electrode drift or interference signal.

[0140] Furthermore, the deviation Δ between the spectrophotometric method and the electrode method data can be calculated using the following relation (10): k :

[0141] Δ k =|Y k -A k | (10)

[0142] Furthermore, Δ k and the preset threshold Δ threshold Compare, if Δ k >Δ threshold , it means that there is a significant deviation between the two measurement methods, which may be caused by electrode contamination, abnormal spectrophotometric reagents or sensor failure.

[0143] Furthermore, the detected abnormality type can be determined through the above processing.

[0144] Furthermore, by integrating the data changes of the electrode method and spectrophotometry method, it can be inferred whether the system operation status is normal.

[0145] For example, when the above-mentioned data range anomalies, trend anomalies, residual anomalies, or data deviation anomalies of the two measurement methods occur, it may indicate that a system operation failure has occurred.

[0146] Furthermore, frequent residual anomalies in electrode method measurement data may mean that there is a fault in the electrode sensor; and abnormal deviations between spectrophotometry and electrode method data may be related to the operating conditions of the reagent addition module, mixing reaction module, etc.

[0147] Furthermore, the anomaly detection module 164 may also determine an anomaly handling solution based on the anomaly type, wherein different anomaly types correspond to different anomaly situations.

[0148] Specifically, you can take adaptive processing measures based on the type of exception:

[0149] (1) If there is a short-term abnormality, the data will be adaptively adjusted to improve measurement reliability.

[0150] (2) If abnormalities persist for a long time, trigger ultrasonic cleaning to remove contamination.

[0151] (3) If the measured value deviates seriously, restart the electrode sensor and perform a self-test.

[0152] (4) If the abnormality continues to occur, a remote alarm will be triggered and maintenance personnel will be notified.

[0153] Furthermore, through the above analysis and processing process, the corresponding real-time concentration, data quality, historical trend and equipment status data can be determined and the corresponding water quality detection data set can be formed.

[0154] Finally, the obtained water quality detection data set can be sent to the corresponding data storage module 165 for storage.

[0155] Optional, such as Figure 6 As shown, the intelligent monitoring unit 17 includes: a data reading module 171 , a display module 172 , a data uploading module 173 , an alarm module 174 and an interaction module 175 .

[0156] Specifically, the data reading module 171 can read the latest water quality detection data set from the data storage module 165 and send the read water quality detection data set to the display module 172. Further, the display module 172 can display the received water quality detection data set on the screen for users or administrators to view.

[0157] Furthermore, the data reading module 171 can also remotely transmit the latest water quality detection data set read to the corresponding cloud server 2 and user terminal 3 through the data uploading module 173, thereby realizing remote data sharing.

[0158] Furthermore, upon receiving the target abnormality sent by the abnormality detection module, the alarm module 174 issues an alarm according to the target abnormality, determines a target processing solution according to the target abnormality, and sends the target processing solution to the user terminal.

[0159] According to the description of the abnormality detection module 164 , when the abnormality continues to occur, a remote alarm is triggered and maintenance personnel are notified, that is, the target abnormality indicates that the abnormality detection module 164 detects the abnormality and the abnormality continues to occur.

[0160] Specifically, when the abnormality detection module 604 detects an abnormality in the target, it triggers a remote alarm, and the alarm module 174 is activated. The alarm module 174 can then obtain an abnormality code from the abnormality detection module 604, such as electrode drift, measurement data exceeding the standard, etc., and finally issue an alarm prompt through various forms such as screen display, buzzer, text message, and app push.

[0161] Furthermore, the alarm module 174 can also provide a suggested treatment plan based on the abnormality code, that is, determine a target treatment plan, such as "Please check the electrode status" or "Recommend replacing the reagent". Finally, the alarm module 174 can send the determined target treatment plan to the user terminal 3 so that the user can view and handle the abnormality.

[0162] Furthermore, the interaction module 175 is configured to receive a first operation instruction sent by the user terminal 3 or a second operation instruction directly input by the user, and respond by executing a first operation corresponding to the first operation instruction or a second operation corresponding to the second operation instruction.

[0163] The interaction module 175 supports local and remote operations, that is, the interaction module 175 not only supports local operations by users, but also supports remote operations by users.

[0164] Specifically, when the user performs relevant operations on user terminal 3 (such as clicking a specific function button, entering a specific instruction code, etc.), user terminal 3 can encapsulate the corresponding first operation instruction according to a pre-set communication protocol (such as HTTP, MQTT, etc.) and send it to the interaction module 175.

[0165] Furthermore, when the interaction module 175 receives the instruction data packet, it parses it according to the communication protocol, identifies the specific content and operation intention of the first operation instruction, and further starts the operation according to the identified specific content and operation intention, that is, responds to execute the first operation corresponding to the first operation instruction.

[0166] Furthermore, when the user directly inputs a second operation instruction in the interaction module 175 , the second operation corresponding to the second operation instruction is directly responded and executed.

[0167] Furthermore, the interaction module 175 can also support maintenance management, such as starting sensor cleaning or triggering device self-test.

[0168] The water quality detection device provided in this embodiment filters the initial water sample to be detected through the filtering unit, provides a clean water sample for subsequent detection, reduces the interference of impurities on the detection results, and improves the accuracy of the detection. Furthermore, the electrode method is used in the electrode unit to perform water quality detection on the first water sample to be detected, which can achieve high-frequency and continuous monitoring, and has a fast response speed and is suitable for the dynamic changes of water quality in the sewage treatment process. At the same time, the temperature sensor measures the temperature data of the water sample and sends it to the data processing unit, which helps to perform temperature correction on the test results later and improve the accuracy of the detection. Furthermore, the spectrophotometry method is used in the spectrophotometry unit to perform water quality detection on the first water sample to be detected, achieving high-precision detection, which not only gives play to the high real-time advantage of the electrode method, but also uses the spectrophotometry method to improve the detection accuracy, effectively reducing the error risk of a single detection method. Finally, in the data processing unit, the detection data of the electrode method and spectrophotometry method are optimized and processed through Kalman filtering and multi-model correction algorithms, which can more accurately correct electrode drift errors, effectively improve the long-term stability of the electrode method measurement data, reduce the need for manual calibration, and enable its measurement results to always maintain high accuracy during long-term operation, thereby improving the automation level of the equipment. Therefore, by implementing the present invention, an automatic correction mechanism is realized, eliminating the need for frequent manual intervention, reducing operation and maintenance costs, improving the stability and reliability of water quality monitoring, and providing a high-efficiency, low-cost, and intelligent water quality detection solution for sewage treatment plants.

[0169] In one example, a water quality monitoring device based on the fusion of spectrophotometry and electrode method is provided, including a filtration unit, a water inlet pump 10, an electrode unit, a water inlet pump 20, a spectrophotometry unit, a data processing unit and an intelligent monitoring unit. The device uses the measurement results of the spectrophotometry method as a high-precision benchmark to regularly calibrate the data measured by the electrode method, thereby ensuring the long-term accuracy of the electrode measurement data. Unlike traditional simple linear correction, the device uses Kalman filtering and multi-model correction, which can more accurately correct the electrode drift error, so that its measurement results always maintain high accuracy in long-term operation. In addition, the device implements an automatic correction mechanism, which does not require frequent manual intervention, reduces operation and maintenance costs, improves the stability and reliability of water quality monitoring, and provides a high-efficiency, low-cost, and intelligent water quality monitoring solution for sewage treatment plants.

[0170] During water quality monitoring, incoming water first passes through a filtration unit to remove suspended impurities. It then enters the electrode unit and then the spectrophotometer via inlet pump 10. Inlet pump 20 then pumps the water sample into the spectrophotometer unit, where the electrode and spectrophotometer units test the water quality using electrode and spectrophotometry, respectively. The tested water samples are then discharged as the outlet water, while the wastewater generated by the spectrophotometer unit is discharged separately. The signals obtained from the electrode and spectrophotometer units are input into the data processing unit for correction and processing. The signals are then transmitted to the intelligent monitoring unit for display and remotely transmitted to the sewage treatment plant monitoring system.

[0171] Furthermore, the water inlet pump 10 works continuously, and the electrode unit continuously measures the filtered inlet water to detect dynamic changes in water quality; while the water inlet pump 20 is turned on at a fixed time, and the spectrophotometric unit regularly measures the filtered inlet water.

[0172] Specifically, the filtration unit consists of a coarse filter layer, a fine filter layer, and a backwash module. The coarse filter layer removes large particles of impurities, such as silt, from the wastewater, preventing clogging of the fine filter layer. The fine filter layer further removes small particles of impurities, colloids, and microorganisms, improving the cleanliness of the water sample. The backwash module uses reverse high-pressure water flow to flush the coarse filter layer to remove large particles of blockage, and uses short pulses to flush the fine filter layer to prevent particle deposition, ensuring that the coarse and fine filter layers return to normal filtration.

[0173] Furthermore, sewage often contains a large amount of suspended matter, which can adversely affect water quality monitoring. During operation, the sewage first passes through the coarse and fine filtration layers, fully removing suspended matter. Simultaneously, the backwash module is periodically activated to automatically clean the filter element, ensuring stable filtration. The filtered water sample then enters the electrode unit and spectrophotometer unit via the inlet pump 10 for testing, ensuring it is not affected by suspended matter in the sewage.

[0174] Furthermore, the electrode unit includes a working electrode, a reference electrode, a temperature sensor, and an ultrasonic cleaner. After the water sample passes through the filtration unit and flows into the electrode pool, the working electrode reacts with the target substance and outputs an electric potential signal. The reference electrode provides a stable electric potential reference, forming a closed loop and generating a signal related to the concentration. The temperature sensor measures the water sample temperature for automatic compensation. The final signal is transmitted to the data processing unit. The ultrasonic cleaner is activated periodically to remove colloids, sediments, and microbial films attached to the electrode surface, restoring the electrode sensitivity.

[0175] Furthermore, the spectrophotometric unit includes an injection module, a reagent loading module, a mixing reaction module, an optical detection module, a self-cleaning module and a waste liquid treatment module. After the water inlet pump 20 is turned on regularly, the spectrophotometric unit starts to detect the water sample using spectrophotometry. The water sample is first injected into the reaction pool by controlling the injection volume of the injection module; the required reagent is then accurately added by the reagent loading module; the mixing reaction module mixes the water sample and the reagent by bubble mixing to ensure uniform mixing, and at the same time sets a constant temperature and a fixed time to ensure that the reaction is fully completed and the color development is stable; after the reaction is completed, the optical detection module uses a light source and a photodetector to measure the absorbance of the mixed sample at a specific wavelength; after the measurement is completed, the signal is transmitted to the data processing unit, and the self-cleaning module uses deionized water to rinse the reaction pool and pipeline to prevent reagent residues from affecting the next measurement; the waste liquid treatment module collects the reaction liquid and the cleaning waste liquid and discharges them into the waste liquid collection device, and processes them regularly.

[0176] Furthermore, the data processing unit includes a signal acquisition module, a signal processing module, a calibration calculation module, an anomaly detection module, and a data storage module. The signal acquisition module collects electrode signals and temperature from the electrode unit and absorbance signals from the spectrophotometer unit, and performs time synchronization to ensure that the acquisition times are aligned. The signal is then transmitted to the signal processing module, which calculates the measured concentrations by the electrode method and spectrophotometer method respectively based on the Nernst equation and Lambert-Beer's law, and corrects the electrode method concentration using temperature. The concentration is then transmitted to the calibration calculation module, and the acquired data is tested by the anomaly detection module. The data is finally stored in the data storage module.

[0177] Among them, the calibration calculation module uses Kalman filtering and multi-model correction algorithm to perform real-time correction on the measurement data, which specifically includes three steps: prediction, observation correction and error covariance update. Please refer to the above description of the calibration calculation module 163, which will not be repeated here.

[0178] The anomaly detection module monitors the stability of electrode measurement data, the consistency of spectrophotometry data, and the system operation status in real time. The specific steps are described in the above description of the anomaly detection module 164 and will not be repeated here.

[0179] Furthermore, the intelligent monitoring unit includes a data reading module, a display module, a data upload module, an alarm module, and an interaction module. The data reading module reads the latest water quality monitoring data from the data processing unit, including real-time concentration, data quality, historical trends, and equipment status; the display module then displays this data on the screen; and the data upload module remotely transmits the above data to the cloud server and user terminal.

[0180] Furthermore, when the abnormality detection module detects an abnormal situation, the alarm module is activated. First, it obtains the abnormality code from the abnormality detection module, such as electrode drift, measurement data exceeding the standard, etc., and then issues an alarm prompt through various forms such as screen display, buzzer, text message, APP push, etc., and provides recommended processing solutions based on the abnormality code, such as "Please check the electrode status", "Recommend replacing the reagent", etc.; the interactive module supports local and remote operations, supports parameter settings, such as adjusting the measurement frequency, setting the alarm threshold, etc., and supports maintenance management, such as starting sensor cleaning or triggering equipment self-test, etc.

[0181] The water quality monitoring device provided in this example, which integrates spectrophotometry and electrode methods, has the following effects:

[0182] (1) This example uses a measurement method that combines spectrophotometry and electrode method to achieve both high precision and high real-time performance. Spectrophotometry, as a high-precision benchmark, can provide accurate ammonia nitrogen concentration measurements, while the electrode method can achieve continuous monitoring and ensure real-time data. Through the complementary integration of the two, this example can provide continuous data monitoring capabilities while maintaining measurement accuracy, making it suitable for scenarios such as sewage treatment plants that require real-time monitoring of water quality changes.

[0183] (2) This example proposes a dynamic calibration mechanism that uses spectrophotometric measurement results to regularly correct electrode method data. Through Kalman filtering and a multi-model correction algorithm, this example can automatically calculate the deviation of electrode measurement data and adaptively adjust it to keep it consistent with the spectrophotometric measurement value, thereby solving the problem of long-term drift of the electrode method. This method effectively improves the long-term stability of electrode measurement data, reduces the need for manual calibration, and improves the automation level of the equipment.

[0184] (3) This example integrates an intelligent anomaly detection module that can monitor the data change trends of the electrode method and spectrophotometry method in real time, and identify abnormal drift, sudden data changes, and equipment failures. When abnormal deviations occur in the electrode data, or when the spectrophotometry measurement results deviate from the normal range, the system can automatically trigger an alarm and adjust the measurement parameters to ensure the reliability of water quality monitoring. At the same time, the equipment has self-diagnosis capabilities and can promptly remind operation and maintenance personnel to perform maintenance, reducing monitoring errors caused by equipment failures.

[0185] (4) This example supports remote monitoring and intelligent management, enabling remote data upload, alarm push, and remote control through the communication unit. Managers can view water quality data in real time through the cloud platform and receive alarm notifications when abnormal conditions occur. In addition, the system supports remote adjustment of equipment parameters and even remote activation of automatic calibration functions, significantly reducing on-site maintenance requirements and improving the intelligence level of equipment operation. It is suitable for the management needs of large-scale water quality monitoring networks.

[0186] (5) This example also optimizes equipment maintenance. An ultrasonic cleaning device is used to automatically clean the electrodes, reducing the impact of contamination on measurement results. At the same time, the reagent dosing module accurately controls reagent consumption, reducing operating costs. Combined with remote monitoring and intelligent calibration mechanisms, this example can operate stably and long-term in an unattended environment, significantly reducing operation and maintenance costs and improving the overall reliability of water quality monitoring.

[0187] In summary, this example solves problems such as accuracy drift, high maintenance costs, and insufficient remote management capabilities in traditional water quality monitoring systems through dynamic calibration, intelligent anomaly detection, remote monitoring, and highly integrated innovative design. It provides the water quality monitoring industry with a high-precision, high-real-time, low-maintenance, and intelligent monitoring solution with broad application prospects and market value.

[0188] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A water quality detection device, characterized in that: The device comprises: a filtering unit, a first water inlet pump, an electrode unit, a second water inlet pump, a spectrophotometric unit and a data processing unit; The filtering unit is used to filter the initial water sample to be tested to obtain a first water sample to be tested, and input the first water sample to be tested into the electrode unit through the first water inlet pump, and input the first water sample to be tested into the spectrophotometer unit through the first water inlet pump and the second water inlet pump; The electrode unit is used to perform water quality testing on the first water sample to be tested using an electrode method, and send electrode signals and temperature data to the data processing unit according to the test results; The spectrophotometric unit is used to perform a water quality test on the first water sample to be tested using spectrophotometry, and send an absorbance signal to the data processing unit according to the test result; The data processing unit is used to perform water quality detection based on the electrode signal, the temperature data and the absorbance signal using Kalman filtering and a multi-model correction algorithm to obtain a water quality detection data set.

2. The device according to claim 1, characterized in that The filtering unit comprises a coarse filtering layer and a fine filtering layer; The coarse filter layer is used to filter the initial water sample to be tested to obtain a second water sample to be tested; The fine filtration layer is used to filter the first water sample to be detected to obtain the first water sample to be detected.

3. The device according to claim 2, characterized in that The filtering unit further includes: A backwash module is used for flushing the coarse filter layer and the fine filter layer.

4. The device according to claim 1, characterized in that The electrode unit includes: a working electrode, a reference electrode and a temperature sensor; The working electrode and the reference electrode form a closed loop for performing water quality detection on the first water sample to be detected and generating the electrode signal; The temperature sensor is used to monitor the temperature of the first water sample to be detected and obtain temperature data.

5. The device according to claim 4, characterized in that The electrode unit further includes: An ultrasonic cleaner is used to clean surface contaminants of the working electrode and the reference electrode.

6. The device according to claim 1, characterized in that The spectrophotometric unit includes: a sample injection module, a reagent addition module, a mixing reaction module and an optical detection module; The sampling module is used to control the sampling volume of the first water sample to be tested, and input the controlled quantitative water sample to be tested into the corresponding reaction pool; The reagent loading module is used to output the target reagent to the reaction pool when the quantitative water sample to be tested is input into the reaction pool; The mixing reaction module is used to mix the quantitative water sample to be detected and the target reagent in the reaction pool by using a bubble mixing method, so that the quantitative water sample to be detected and the target reagent react to produce a mixed sample; The optical detection module is used to emit light of a target wavelength to the reaction pool after the reaction between the quantitative water sample to be detected and the target reagent is completed, and to measure the absorbance of the mixed sample in the reaction pool and obtain the absorbance signal.

7. The device according to claim 6, characterized in that The spectrophotometric unit further includes: a self-cleaning module and a waste liquid treatment module; The self-cleaning module is used to input deionized water into the reaction tank, so that the deionized water rinses the reaction tank and generates cleaning waste liquid; The waste liquid treatment module is used to collect and treat the reaction liquid generated by the reaction tank and the cleaning waste liquid.

8. The device according to claim 1, characterized in that The data processing unit includes: a signal acquisition module, a signal processing module, a calibration calculation module and an anomaly detection module; The signal acquisition module is used to obtain the electrode signal, the temperature data and the absorbance signal, and send the electrode signal, the temperature data and the absorbance signal to the signal processing module; The signal processing module is configured to calculate a first measured concentration value using the Nernst equation according to the electrode signal, calculate a second measured concentration value using the Lambert-Beer law according to the absorbance signal, and send the second measured concentration value to the calibration calculation module and the abnormality detection module respectively; The signal processing module is further configured to correct the first measured concentration value using the temperature data to obtain a third measured concentration value, and send the third measured concentration value to the calibration calculation module; the calibration calculation module is configured to correct the third measured concentration value based on the second measured concentration value using the Kalman filter and the multi-model correction algorithm to obtain a fourth measured concentration value, and send the fourth measured concentration to the abnormality detection module; The anomaly detection module is used to perform anomaly detection based on the second measured concentration value, the third measured concentration value, and the fourth measured concentration value and determine the water quality detection data set.

9. The device according to claim 8, characterized in that The anomaly detection module is further configured to determine an anomaly handling solution according to an anomaly type, wherein different anomaly types correspond to different anomaly situations.

10. The device according to claim 8, characterized in that The data processing unit further includes: The data storage module is used to receive and store the water quality detection data set sent by the abnormality detection module.

11. The device according to claim 10, characterized in that The device is connected to a cloud server and a user terminal; the device further comprises: an intelligent monitoring unit; The intelligent monitoring unit includes a data reading module, a display module, and a data uploading module; The data reading module is used to obtain the water quality detection data set and send the water quality detection data set to the display module for display; The data uploading module is configured to receive the water quality detection data set sent by the data reading module, and send the water quality detection data set to the cloud server and the user terminal.

12. The device according to claim 11, characterized in that The intelligent monitoring unit further includes: an alarm module and an interaction module; The alarm module is configured to, upon receiving the target abnormality sent by the abnormality detection module, generate an alarm according to the target abnormality, determine a target processing solution according to the target abnormality, and send the target processing solution to the user terminal; The interaction module is configured to receive a first operation instruction sent by the user terminal or a second operation instruction directly input by the user, and to respond by executing a first operation corresponding to the first operation instruction or a second operation corresponding to the second operation instruction.

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