Water quality multi-parameter real-time monitoring system and method based on intelligent sensing

Abstract

The invention discloses a water quality multi-parameter real-time monitoring system and method based on intelligent sensing, and relates to the technical field of water quality analysis and testing. Comprising the following steps: detecting multi-dimensional water quality parameters by an intelligent sensing module, and modularly designing an adaptive standard interface; the data transmission module adopts a dual-mode communication architecture and an encryption algorithm for real-time transmission and verification; the data processing module generates standardized data and supports edge and cloud computing collaboration; the real-time monitoring platform supports multi-terminal access and integrates data visualization, trend analysis and report functions; multistage threshold values are built in the early warning module, and early warning information is synchronized through multiple channels; the automatic calibration module regularly calibrates the sensing unit, records data and generates a report; the power management module is powered by solar energy and lithium battery collaboratively. The system comprehensively monitors multiple parameters of water quality, guarantees stable transmission through dual-mode communication, reduces cost through remote operation and maintenance and data sharing, adapts to multi-scene requirements, and provides reliable support for water environment treatment and water safety guarantee.

Description

Technical Field

[0001] This invention relates to the field of water quality analysis and testing technology, and in particular to a real-time monitoring system and method for multiple parameters of water quality based on intelligent sensing. Background Technology

[0002] Water quality monitoring is a core component of water resource protection, water environment management, and water security. Its accuracy, real-time performance, and comprehensiveness directly impact the scientific validity of water resource management decisions. With the acceleration of industrialization and urbanization, water pollution types are becoming increasingly complex, involving not only conventional physicochemical parameters such as pH and dissolved oxygen, but also trace pollutants such as heavy metal ions and pathogens. Traditional water quality monitoring technologies are no longer sufficient to meet the multi-dimensional and demanding monitoring requirements.

[0003] Current water quality monitoring methods suffer from numerous technical shortcomings. Regarding parameter monitoring, traditional equipment often employs single-sensor technology, capable of detecting only a few parameters. Multi-parameter monitoring requires multiple devices, leading to high costs, cumbersome installation, and data synchronization issues. Electrochemical sensors are susceptible to interference from environmental factors such as temperature and turbidity; optical sensors exhibit decreased accuracy in complex water bodies; and biosensors face problems such as insufficient probe stability and high detection limits, making it difficult to accurately capture trace pollutants. In terms of data transmission, most monitoring devices use a single communication mode, prone to signal interruptions in outdoor or complex water environments. Furthermore, the lack of effective encryption mechanisms poses a risk of data leakage. In data processing, traditional systems rely heavily on cloud computing, lacking local processing capabilities, resulting in data delays. Additionally, outlier removal and multi-source data fusion are ineffective, impacting the reliability of monitoring results.

[0004] In terms of calibration and maintenance, existing equipment often requires regular on-site manual calibration, which is cumbersome and inefficient. Human error during calibration can easily affect sensor accuracy. Regarding anomaly handling, traditional systems can only provide simple alarms for exceeding limits, lacking the ability to trace pollution sources and diffusion paths, making it difficult to support rapid pollution control. Furthermore, the data formats of different monitoring devices are inconsistent, lacking standardized data sharing interfaces and hindering efficient integration with existing platforms of environmental protection and water resources departments, creating data silos. Simultaneously, in field monitoring scenarios, equipment relies on mains power or disposable batteries, resulting in limited battery life and restricting long-term continuous monitoring. These combined problems lead to deficiencies in monitoring efficiency, accuracy, convenience, and collaboration in traditional water quality monitoring systems, failing to meet the needs of modern water environment management. Summary of the Invention

[0005] The present invention proposes a real-time monitoring system and method for multiple parameters of water quality based on intelligent sensing, in order to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a real-time monitoring system and method for multiple water quality parameters based on intelligent sensing, comprising: The intelligent sensing module integrates electrochemical, optical, biological and physical sensing units to detect multi-dimensional water quality parameters. It adopts a modular design and connects to the system host through a standard interface. The data transmission module adopts a dual-mode communication architecture, integrates encryption algorithms, establishes a two-way data link, supports breakpoint resumption and data verification, and transmits data in real time. The data processing module processes sensor data through noise reduction, filtering, and normalization, removes outliers to generate standardized water quality data, and works in collaboration between edge computing and cloud computing. The real-time monitoring platform adopts a B / S architecture to support multi-terminal access and integrates data visualization, multi-parameter trend analysis, historical data query and report generation functions to centrally manage single sites and multiple sites. The early warning module classifies early warning levels based on the degree to which water quality parameters exceed standards, and provides early warning information through audible and visual alarms, SMS notifications, and platform push notifications. The early warning threshold can be customized and dynamically adjusted. The automatic calibration module is equipped with a standard solution storage unit and an automatic sampler. It periodically calibrates the sensing unit, records calibration coefficients and deviation data, and generates calibration reports. The power management module uses solar panels and lithium battery packs for power supply, and is equipped with a maximum power point tracking controller. The lithium battery packs support deep charge and discharge cycles.

[0007] Furthermore, it also includes an enhanced multi-parameter collaborative calibration module with a built-in standard solution library. The standard solution is automatically delivered through a micro peristaltic pump. During the calibration process, the sensor unit response signal and ambient temperature data are collected. The calibration results are corrected by combining temperature compensation algorithms. After calibration, the sensor probe is automatically cleaned, and the calibration data is uploaded to the monitoring platform in real time.

[0008] Furthermore, it also includes a water quality anomaly tracing module, which integrates an environmental parameter acquisition unit and a tracing algorithm model. The environmental parameter acquisition unit monitors water flow velocity, wind direction, light intensity, and the distribution data of surrounding pollution sources. The tracing algorithm model combines the changing trends of water quality parameters with environmental data to analyze the abnormal pollution diffusion path and source, generate a tracing report, and display it visually on the monitoring platform.

[0009] Furthermore, it also includes a biosensing enhancement module, which optimizes the biological probe immobilization process by using covalent bonding, enhances the binding specificity of the probe to the target pathogen by modifying it with nanomaterials, has a built-in pathogen database, and determines the type and concentration of pathogens by the intensity of fluorescence signals.

[0010] Furthermore, it also includes a remote control module, which adopts a hierarchical permission management mechanism. Operation permissions are divided into three levels: administrator, maintenance personnel, and ordinary users. Administrators can remotely adjust the sampling frequency, calibration cycle, and early warning threshold of the sensor unit through the monitoring platform. Maintenance personnel can remotely start the calibration process and probe cleaning procedure. Ordinary users can view the data. Remote commands are transmitted to the system host in encrypted form, and the execution results are fed back to the platform in real time.

[0011] Furthermore, it also includes an anti-interference processing module, which uses an electromagnetic shielding shell, collects ambient temperature in real time through a temperature sensor and dynamically adjusts the operating parameters of the sensing unit, is equipped with an ultrasonic cleaning device to regularly remove impurities adhering to the probe surface, and combines digital filtering algorithms to eliminate interference signals.

[0012] Furthermore, it also includes a data sharing interface module, which adopts a standardized data transmission protocol to connect with environmental protection department monitoring platforms, water conservancy management systems, and scientific research data analysis platforms. It customizes data fields and transmission frequencies according to the needs of the connected platforms, de-identifies shared data, and records data transmission logs.

[0013] Furthermore, it also includes the following steps: In the parameter sensing and acquisition process, the intelligent sensing module collects water quality parameters at a preset sampling frequency, the electrochemical sensing unit converts electrical signals through electrode reactions, the optical sensing unit converts signals through light absorption intensity, and the biosensing unit feeds back the detection results through fluorescence signals. The data encryption transmission process involves analog-to-digital conversion and SM4 encryption of sensor signals. The communication mode is automatically selected based on the strength of the communication signal. After encryption, the water quality data is transmitted to the data processing module in real time. During the transmission process, CRC32 data verification is performed. The data preprocessing and fusion step involves using the Kalman filter algorithm to reduce noise in the sensor data, removing outliers, unifying parameter standards through a normalization algorithm, and integrating the data from each sensor unit using a multi-source data fusion model to generate a water quality monitoring dataset. Real-time analysis and calibration steps: The real-time monitoring platform analyzes the pre-processed dataset from multiple dimensions, compares it with preset thresholds to determine the water quality compliance status, and the automatic calibration module periodically starts the calibration process, extracts standard solutions to calibrate the sensing unit, and updates the calibration coefficients to correct detection deviations. The abnormal early warning process involves the early warning module determining the early warning level based on the degree to which water quality parameters exceed the standard, simultaneously triggering early warning notifications through multiple channels, and pushing details of abnormal parameters and monitoring point location information. In the data storage and sharing process, the monitoring platform stores standardized water quality data, calibration records, and early warning information in a cloud database, and the data sharing interface module connects with external platforms according to a preset protocol.

[0014] Furthermore, it also includes a dynamic error compensation step, using the formula... Calculate the error compensation value, where This refers to the concentration compensation value for water quality parameters. This is the temperature influence coefficient. To actually monitor the temperature, For standard calibration temperature, Humidity influence coefficient This represents the actual ambient humidity. For standard humidity calibration, The sensing drift coefficient is... This is the time interval since the last calibration.

[0015] Furthermore, it also includes multi-dimensional data tracing steps, collecting data on water flow velocity, wind direction, light intensity, and the distribution of surrounding pollution sources when water quality anomalies occur, combining the time change curves of abnormal parameters with spatial diffusion trends, using cluster analysis algorithms to divide the pollution impact range, using path analysis models to trace the pollution source, and generating a tracing report.

[0016] Compared with existing technologies, the beneficial effects of this invention are: The present invention provides a real-time water quality multi-parameter monitoring system and method based on intelligent sensing, which achieves comprehensive innovation to address the pain points of traditional technologies, with significant core advantages, and provides an efficient, accurate and intelligent solution for water quality monitoring.

[0017] In terms of monitoring coverage and accuracy, the intelligent sensing module integrates multiple types of sensing units, enabling simultaneous detection of physical, chemical, and biological parameters across multiple dimensions without the need for multiple devices, significantly improving monitoring efficiency. By optimizing the biological probe immobilization process and modifying it with nanomaterials, the stability and specificity of biological sensing are enhanced. Optical sensing employs ultraviolet-visible spectrophotometry, and the electrochemical sensing utilizes a modular design. Combined with the electromagnetic shielding, temperature compensation, and probe cleaning functions of the anti-interference processing module, the impact of environmental interference on detection results is effectively reduced, improving the stability and accuracy of monitoring data.

[0018] Data transmission and processing capabilities have been significantly optimized. The dual-mode communication architecture adapts to complex aquatic environments, ensuring real-time data transmission. Encryption algorithms and data verification mechanisms guarantee data transmission security and integrity. The data processing module supports collaborative edge and cloud computing, enabling rapid local processing of critical data. Multi-source data fusion algorithms effectively eliminate outliers, generating standardized data and improving processing efficiency and accuracy. The automatic calibration module achieves automatic standard solution delivery, calibration, and probe cleaning without manual intervention, reducing operational errors and ensuring long-term stable operation of the sensing unit.

[0019] The ease of operation and maintenance, as well as application, has been significantly improved. The remote control module adopts hierarchical access control, supporting remote parameter adjustment and initiation of calibration and cleaning processes, reducing operation and maintenance costs. The early warning module synchronizes early warning information through multiple channels to ensure timely handling of abnormal situations; the anomaly tracing module combines environmental data and water quality change trends to quickly trace the source and spread path of pollution, providing scientific support for pollution control. The data sharing interface module adopts standardized protocols to achieve efficient integration with multi-department platforms, breaking down data silos and facilitating cross-departmental collaborative management. The power management module is adapted for outdoor scenarios without mains power, extending continuous working time and broadening the system's application scope.

[0020] Overall, through multi-module collaborative innovation, the system achieves real-time, accurate, and comprehensive monitoring of multiple water quality parameters, balancing monitoring precision, ease of operation and maintenance, and data synergy. It is adaptable to various application scenarios, including drinking water sources, aquaculture areas, and wild water bodies. It not only improves the intelligence level and management efficiency of water quality monitoring but also provides reliable data support for water resource protection and water environment governance, which is of great significance for ensuring water security. Attached Figure Description

[0021] Figure 1 This is a schematic block diagram of a real-time water quality multi-parameter monitoring system based on intelligent sensing proposed in this invention. Figure 2 This is a schematic block diagram of a real-time monitoring method for multiple water quality parameters based on intelligent sensing proposed in this invention. Figure 3 A diagram showing the comparison of monitoring parameter coverage and accuracy in different scenarios; Figure 4 A diagram showing the comparison of response times for monitoring different parameter types; Figure 5 This is a diagram illustrating the comparison of anti-interference capabilities under complex environments. Figure 6 A diagram illustrating the multi-dimensional performance comparison of water quality monitoring systems; Figure 7 This diagram illustrates the comparison of annual operation and maintenance costs across different scenarios. Detailed Implementation

[0022] 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.

[0023] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0024] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0025] Reference Figures 1 to 7 A real-time monitoring system and method for multiple water quality parameters based on intelligent sensing, comprising: The intelligent sensing module integrates an electrochemical sensing unit, an optical sensing unit, a biological sensing unit, and a physical sensing unit. The electrochemical sensing unit detects pH value, dissolved oxygen, and heavy metal ion concentration. The optical sensing unit uses ultraviolet-visible spectrophotometry to detect COD, ammonia nitrogen, and total phosphorus. The biological sensing unit detects pathogenic bacteria through specific biological probes. The physical sensing unit monitors water temperature, turbidity, and water depth. Each sensing unit adopts a modular design and connects to the system host through a standard interface. The data transmission module adopts a 5G and LoRa dual-mode communication architecture, integrates TCP / IP protocol stack and SM4 encryption algorithm, establishes a two-way data link between the sensing module and the back-end platform, supports breakpoint resume and data verification, achieves real-time data transmission in complex water environment monitoring scenarios, and controls transmission latency to the millisecond level. The data processing module has a built-in multi-source data fusion algorithm to perform noise reduction, filtering, and normalization on sensor data. It also removes outliers by combining the correlation between water quality parameters, generates standardized water quality data, supports collaborative work between edge computing and cloud computing, and ensures real-time processing of key data locally. The real-time monitoring platform is deployed on a cloud server and adopts a B / S architecture to support multi-terminal access. It integrates data visualization, multi-parameter trend analysis, historical data query and report generation functions, and supports centralized management and control of single and multi-site monitoring. Managers can view the water quality parameters and status of each monitoring point in real time. The early warning module has a built-in multi-level early warning threshold system. It divides the early warning level according to the degree of exceedance of water quality parameters and synchronizes early warning information through multiple channels such as audible and visual alarms, SMS notifications, and platform push. The early warning threshold supports custom configuration and dynamic adjustment. The automatic calibration module is equipped with a standard solution storage unit and an automatic sampler. It periodically extracts standard solutions of different concentration gradients to calibrate the sensing unit, records the calibration coefficients and deviation data, generates a calibration report, and ensures the accuracy of the sensing detection. The power management module uses solar panels and lithium battery packs for power supply, and is equipped with a maximum power point tracking controller to improve solar energy utilization. The lithium battery pack supports deep charge and discharge cycles, and the low power consumption design extends the continuous working time of the system, making it suitable for outdoor scenarios without mains power.

[0026] This invention also includes an enhanced multi-parameter collaborative calibration module, which has a built-in standard solution library for core parameters such as pH, dissolved oxygen, and COD, containing standard reagents with five concentration gradients. The standard solutions are automatically delivered via a micro peristaltic pump. During the calibration process, the response signals of the sensing unit and ambient temperature data are collected simultaneously, and the calibration results are corrected by combining a temperature compensation algorithm. After calibration, the sensing probe is automatically cleaned to reduce cross-contamination, and the calibration data is uploaded to the monitoring platform in real time, supporting calibration effect traceability and anomaly analysis.

[0027] This invention also includes a water quality anomaly tracing module, which integrates an environmental parameter acquisition unit and a tracing algorithm model. The environmental parameter acquisition unit monitors water flow velocity, wind direction, light intensity, and the distribution data of surrounding pollution sources. The tracing algorithm model combines the changing trends of water quality parameters with environmental data to analyze the diffusion path and possible sources of abnormal pollution, generate a tracing report, and visualize it on the monitoring platform, providing data support for pollution control and supporting multi-monitoring data linkage analysis to improve the accuracy of tracing.

[0028] This invention also includes a biosensing enhancement module, which optimizes the immobilization process of the biological probe by using covalent bonding to improve stability, enhances the binding specificity of the probe to the target pathogen by modifying it with nanomaterials, and has a built-in pathogen database containing the biological characteristic parameters of common aquatic pathogens. During the detection process, the type and concentration of pathogens are determined by the intensity of the fluorescence signal. The detection limit is lower than that of traditional biological detection methods, making it suitable for water quality safety monitoring in drinking water sources and aquaculture areas.

[0029] This invention also includes a remote control module, which adopts a hierarchical permission management mechanism, dividing operation permissions into three levels: administrator, maintenance personnel, and ordinary users. Different levels correspond to different operation ranges. Administrators can remotely adjust the sampling frequency, calibration cycle, and early warning threshold of the sensing unit through the monitoring platform. Maintenance personnel can remotely start the calibration process and probe cleaning procedure. Ordinary users only have data viewing permissions. Remote commands are transmitted to the system host in encrypted form, and the execution results are fed back to the platform in real time.

[0030] This invention also includes an anti-interference processing module, which addresses interference factors such as electromagnetic interference, temperature fluctuations, and impurity adhesion in the aquatic environment by using an electromagnetic shielding shell to reduce electromagnetic interference, using a temperature sensor to collect ambient temperature in real time and dynamically adjust the operating parameters of the sensing unit, using an ultrasonic cleaning device to periodically remove impurities from the probe surface, and using a digital filtering algorithm to eliminate interference signals, thereby maintaining the stability and accuracy of sensing data in complex aquatic environments.

[0031] This invention also includes a data sharing interface module, which adopts a standardized data transmission protocol to support data docking with environmental protection department monitoring platforms, water conservancy management systems, and scientific research data analysis platforms. The data sharing format supports multiple types such as JSON and CSV. Data fields and transmission frequency can be customized according to the needs of the docking platform. The shared data is de-identified to protect sensitive information, and it also has a data transmission log recording function for easy traceability.

[0032] This invention also includes the following steps: In the parameter sensing and acquisition process, each unit of the intelligent sensing module synchronously collects water quality parameters such as pH value, dissolved oxygen, heavy metal ions, COD, ammonia nitrogen, total phosphorus, pathogenic bacteria, water temperature, turbidity, and water depth at a preset sampling frequency. The electrochemical sensing unit converts electrical signals through electrode reactions, the optical sensing unit converts signals through light absorption intensity, and the biosensing unit feeds back the detection results through fluorescence signals. In the data encryption transmission step, the data transmission module performs analog-to-digital conversion and SM4 encryption on the collected sensor signals, automatically selects 5G or LoRa communication mode according to the communication signal strength, and transmits the encrypted water quality data to the data processing module in real time. During the transmission process, CRC32 data verification is performed to ensure data integrity. In the data preprocessing and fusion step, the data processing module uses the Kalman filter algorithm to denoise the sensor data, remove outliers that exceed the reasonable range, and uses the normalization algorithm to unify the parameters of different dimensions. Combined with the multi-source data fusion model, the data from each sensor unit are integrated to generate a comprehensive water quality monitoring dataset. The real-time analysis and calibration process involves the real-time monitoring platform performing multi-dimensional analysis on the pre-processed dataset, comparing it with preset thresholds to determine whether the water quality meets the standards, and the automatic calibration module periodically initiating the calibration process, extracting standard solutions to calibrate the sensing unit, and updating the calibration coefficients to correct detection deviations. The abnormal early warning process involves the early warning module determining the early warning level based on the degree to which water quality parameters exceed the standard, simultaneously triggering early warning notifications through multiple channels, and pushing details of abnormal parameters and monitoring point location information to facilitate timely handling by relevant personnel. In the data storage and sharing process, the monitoring platform stores standardized water quality data, calibration records, and early warning information in a cloud database. The data sharing interface module connects with external platforms according to a preset protocol to achieve cross-platform sharing and application of water quality monitoring data.

[0033] This invention also includes a dynamic error compensation step, which is achieved through a formula. Calculate the error compensation value, where This refers to the concentration compensation value for water quality parameters. This is the temperature influence coefficient. To actually monitor the temperature, For standard calibration temperature, Humidity influence coefficient This represents the actual ambient humidity. For standard humidity calibration, The sensing drift coefficient is... The monitoring results are corrected by incorporating temperature, humidity, and sensor drift factors to adjust the time interval since the last calibration, thereby improving the monitoring accuracy under different environmental conditions.

[0034] This invention also includes a multi-dimensional data tracing step, which collects data on water flow velocity, wind direction, light intensity, and distribution of surrounding pollution sources when water quality anomalies occur. Combining the time variation curves and spatial diffusion trends of the abnormal parameters, the pollution impact range is divided through cluster analysis algorithms, and the pollution source is traced using a path analysis model. A tracing report containing the pollution source, diffusion path, and impact range is generated, providing a scientific basis for the formulation of pollution control plans. At the same time, the tracing data is linked and stored with the monitoring data for easy subsequent analysis.

[0035] The following two examples further illustrate specific embodiments of the present invention: Example 1: Application of Real-time Monitoring of Multiple Parameters of Drinking Water Sources This embodiment is applied to surface drinking water sources. This scenario requires long-term stable monitoring of key parameters such as pH, dissolved oxygen, heavy metal ions, COD, ammonia nitrogen, total phosphorus, and pathogens. The environment is subject to diurnal temperature fluctuations and electromagnetic interference, requiring the system to have high precision, anti-interference capabilities, and remote operation and maintenance capabilities. The specific implementation process is as follows: I. Execution of Core Processes and Key Steps System Deployment and Module Integration: The intelligent sensing module is fixed inside the monitoring buoy at the water source. The electrochemical sensing unit uses a glass electrode to detect pH value, a Clark electrode to detect dissolved oxygen, and an ion-selective electrode to detect heavy metal ions such as lead and cadmium. The optical sensing unit incorporates a UV-Vis spectrophotometer to detect COD, ammonia nitrogen, and total phosphorus through characteristic absorption peaks. The biosensing unit uses covalently bonded specific biological probes modified with gold nanoparticles to enhance the signal and includes a built-in database of biological characteristics of common pathogens such as E. coli and Salmonella. The physical sensing unit monitors water temperature, turbidity, and water depth. Each sensing unit connects to the system host via a standard interface, and the host casing is made of electromagnetic shielding material.

[0036] Power and Data Transmission Configuration: The power management module is equipped with a monocrystalline silicon solar panel and a lithium iron phosphate battery pack. The maximum power point tracking controller tracks the peak solar power in real time. The lithium battery pack supports more than 2000 deep charge-discharge cycles, and the standby current is less than 10mA in low-power mode, ensuring continuous operation in outdoor scenarios without mains power. The data transmission module monitors the communication signal strength in real time. When the 5G signal strength is higher than -90dB, 5G transmission is used; when it is lower than -90dB, it automatically switches to LoRa communication. It integrates a TCP / IP protocol stack and SM4 encryption algorithm to perform 128-bit encryption processing on sensor data, controlling the transmission latency to within 50 milliseconds, and supporting breakpoint resumption and CRC32 data verification.

[0037] Parameter Acquisition and Anti-interference Processing: The intelligent sensing module acquires data at a preset frequency, with conventional parameters sampled once per minute and pathogens sampled once per hour. The anti-interference processing module reduces electromagnetic interference from surrounding electrical equipment through an electromagnetic shielding shell. The temperature sensor collects ambient temperature data every 10 seconds, dynamically adjusting the operating voltage and response time of the sensing unit. The ultrasonic cleaning device automatically starts at 3:00 AM daily to remove biofilm and impurities from the probe surface, with each cleaning session lasting 30 seconds. A Kalman filter algorithm is used to eliminate pulse interference signals from the sensing data.

[0038] Data processing and dynamic error compensation: The data processing module first performs noise reduction and filtering on the sensor data. Then, using a normalization algorithm, it converts parameters of different dimensions, such as pH (0-14) and dissolved oxygen (0-20 mg / L), to a standard range of 0-100. Outliers are then eliminated based on the correlation between water quality parameters. A formula is used... Perform dynamic error compensation and set the temperature influence coefficient. =0.002, actual monitored temperature =25℃, standard calibration temperature =20℃, humidity influence coefficient =0.001, actual ambient humidity =60%, standard calibrated humidity =50%, Sensing drift coefficient =0.0005, the time interval since the last calibration. =24 hours. Calculated =0.032, this compensation value is used to correct the measurement results of concentration parameters such as COD and ammonia nitrogen.

[0039] Automatic Calibration and Remote Control: The automatic calibration module is equipped with standard solution storage units for five concentration gradients, at concentrations of 0.5 times, 1 times, 1.5 times, 2 times, and 2.5 times the standard limit, respectively. Standard solutions are delivered via a micro-peristaltic pump at a flow rate of 1 mL / min. The system automatically initiates the calibration process every 7 days, first cleaning the sensor probe, then sequentially injecting standard solutions of different concentrations, recording the sensor unit's response signal, calculating the calibration coefficient and updating it to the system, and generating a calibration report which is uploaded to the monitoring platform. Administrators, with administrator privileges on the remote control module, can adjust the sensor unit's sampling frequency and remotely initiate the probe cleaning program on the monitoring platform. Commands are transmitted encrypted, and the system host provides feedback on the execution results within 10 seconds.

[0040] Monitoring Platform and Early Warning & Source Tracing: The real-time monitoring platform adopts a B / S architecture, allowing administrators to access it via computers and mobile devices to view real-time data and 7-day trend curves of water quality parameters at the water source. It supports historical data export and custom report generation. The early warning module has built-in three-level warning thresholds: exceeding the standard by less than 10% triggers a Level 1 warning, which is pushed to the platform; exceeding the standard by 10%-30% triggers a Level 2 warning, which also includes SMS notifications; and exceeding the standard by more than 30% triggers a Level 3 warning, simultaneously triggering on-site audible and visual alarms. The water quality anomaly source tracing module collects water flow velocity and wind direction data, combines this with information on the distribution of surrounding pollution sources, uses cluster analysis algorithms to divide the pollution impact range, employs path analysis models to trace the pollution source, generates a source tracing report, and displays it visually on the platform.

[0041] Data Sharing and Storage: The data sharing interface module uses the MQTT standardized transmission protocol to push anonymized water quality data to the environmental protection department's monitoring platform hourly. The data format is JSON, including fields such as monitoring time, monitoring point location, and concentration values ​​of various parameters. The monitoring platform stores standardized water quality data, calibration records, and early warning information in a cloud database for a period of 3 years, supporting multi-user access and querying according to permissions.

[0042] II. Data Representation and Interpretation Table 1: Comparison of Water Quality Monitoring Performance of Drinking Water Sources

[0043] Table 1 clearly shows the core advantages of the present invention in the monitoring scenario of drinking water sources. The traditional monitoring system has limited monitoring parameters, only covering less than 6 conventional indicators. The data transmission is vulnerable to environmental interference, with an accuracy rate of only about 85%. Manual on-site calibration is required monthly, and the operation and maintenance costs are high. Through the integration of multiple types of sensing units, the present invention realizes the synchronous monitoring of more than 10 parameters. The dual-mode communication and anti-interference design ensure stable data transmission. The dynamic error compensation and automatic calibration mechanism improve the data accuracy rate to more than 98%. Quick warning is issued within 30 seconds, and the 7-day automatic calibration greatly reduces the operation and maintenance costs, fully meeting the requirements of high-precision and long-term stable monitoring of drinking water sources.

[0044] Embodiment 2: Application of multi-parameter real-time monitoring of water quality in aquaculture areas This embodiment is applied to large-scale aquaculture ponds. This scenario requires key monitoring of parameters such as dissolved oxygen, ammonia nitrogen, nitrite, pH value, water temperature, turbidity, etc. It requires the system to provide real-time feedback on water quality changes, support multi-pond linkage monitoring and quick warning, and adapt to the characteristics of high humidity and dust in the aquaculture environment. The specific implementation process is as follows: I. Execution of core processes and key steps System deployment and module integration: The intelligent sensing module is fixed on the monitoring bracket on the shore of the aquaculture pond, and some probes extend to a depth of 50 cm underwater. The electrochemical sensing unit focuses on optimizing the detection accuracy of dissolved oxygen, ammonia nitrogen, and nitrite, and uses a membrane electrode to reduce the interference of water impurities; the optical sensing unit simplifies the detection process of COD and total phosphorus and adapts to the high-turbidity environment of aquaculture water; the biological sensing unit focuses on the detection of aquatic pathogenic bacteria such as vibrio, and the biological probe is treated with an anti-pollution coating; the physical sensing unit encrypts the acquisition frequency of water temperature and turbidity. The system host adopts a waterproof and dustproof design, and the protection level meets the requirements for outdoor use.

[0045] Power supply and data transmission configuration: The power management module adopts a dual-power supply mode of solar panels and mains power. Mains power is used preferentially, and when the mains power is interrupted, it automatically switches to the lithium battery pack for power supply. The capacity of the lithium battery pack meets the requirement of continuous operation for more than 72 hours. The data transmission module is mainly based on 5G communication and uses LoRa communication as a backup. Considering the characteristics of multi-pond distribution in the aquaculture area, a star-shaped networking method is adopted, and each monitoring point establishes an independent communication link with the background platform, and the transmission delay is controlled within 30 milliseconds to ensure the synchronous upload of multi-pond data.

[0046] Parameter Acquisition and Anti-Interference Processing: The intelligent sensing module adjusts the sampling frequency according to the breeding cycle. The sampling frequency during the fry cultivation period is once every 30 seconds, and during the adult fish breeding period, it is once per minute. The anti-interference processing module optimizes the electromagnetic shielding structure for electromagnetic interference generated by equipment such as water pumps and aerators in the breeding area, using a double-layer shielding material; the temperature sensor continuously collects the water temperature and dynamically adjusts the electrode response time of the electrochemistry sensing unit; the ultrasonic cleaning device is started once in the morning and once in the evening every day to remove algae and silt attached to the probe surface, and combined with the median filtering algorithm to eliminate abnormal data caused by water flow disturbance.

[0047] Data Processing and Dynamic Error Compensation: The data processing module classifies the sensing data collected from multiple ponds, establishes data files according to the breeding pond numbers, and compares the water quality parameters of different breeding ponds in the same area through a multi-source data fusion model to identify abnormal fluctuations. Use the formula for dynamic error compensation, set the temperature influence coefficient = 0.003, the actual monitored temperature = 28 °C, the standard calibration temperature = 20 °C, the humidity influence coefficient = 0.0015, the actual environmental humidity = 75%, the standard calibration humidity = 50%, the sensing drift coefficient = 0.0008, the time interval since the last calibration = 48 hours. Calculate = 0.0999, and the corrected parameters such as dissolved oxygen and ammonia nitrogen are more in line with the actual situation of the breeding water body.

[0048] Automatic Calibration and Remote Control: The standard solution library of the automatic calibration module adapts to the range of breeding water quality parameters, including dissolved oxygen, ammonia nitrogen, and nitrite standard reagents with low, medium, and high concentrations, and realizes precise delivery through a micro peristaltic pump. The system automatically starts the calibration process every 5 days, and at the same time supports the management staff to remotely trigger the calibration through the operation and maintenance authority. During the calibration process, the environmental temperature and humidity data are synchronously recorded. After the calibration is completed, the probe is automatically cleaned, and the calibration data is associated and stored with the water quality data of the breeding pond. The remote control module supports batch adjustment of the warning thresholds of multiple breeding pond monitoring points. The operation and maintenance personnel can remotely start the linkage control of the aerator, and when the dissolved oxygen is lower than 5 mg / L, the start signal of the aerator is automatically triggered.

[0049] Monitoring Platform and Early Warning Tracing: The real-time monitoring platform supports centralized management and control of more than 20 aquaculture ponds. Managers can switch to view the water quality parameters of each pond, judge the water quality change rules through multi-parameter trend analysis, and predict possible over-standard situations. The thresholds of the early warning module are custom-configured according to the aquaculture species. The lower limit of dissolved oxygen warning for grass carp aquaculture ponds is set at 5mg / L, and the upper limit of ammonia nitrogen warning is set at 0.5mg / L. When over-standard, synchronous early warnings are sent through platform push, SMS notification and on-site sound and light alarms. The water quality anomaly tracing module collects the water change records, feeding amounts, and surrounding pollution source distribution data of aquaculture ponds, and combines with the water quality parameter change curves to trace the causes of anomalies and provide data support for aquaculture adjustment.

[0050] Data Sharing and Storage: The data sharing interface module is docked with the aquaculture management platform, and pushes the water quality summary data at a frequency of once a day, including information such as the daily average value, maximum value, minimum value of each parameter, and the data format supports JSON and CSV types. The monitoring platform stores all data in the local server and cloud database for dual backup, with a storage period of 2 years, and supports querying historical data according to conditions such as aquaculture cycle and parameter type.

[0051] II. Data Representation and Interpretation Table 2: Comparison of Water Quality Monitoring Performance in Aquaculture Areas

[0052] The data in Table 2 highlights the adaptation advantages of the present invention in aquaculture areas. The traditional monitoring system can only manage and control within 5 ponds, with a slow response speed, a data accuracy rate of only about 80%, requiring manual on-site calibration, a single early warning method, and it is difficult to adapt to the needs of large-scale aquaculture. The present invention supports centralized management and control of more than 20 ponds, quickly responds to water quality changes within 30 seconds, the data accuracy rate is increased to over 97%, automatic calibration and remote calibration greatly reduce the operation and maintenance difficulty, multi-channel early warnings ensure timely handling of anomalies, combined with custom thresholds and linkage control according to aquaculture species, adapting to the needs of different aquaculture stages, providing accurate data support for scientific aquaculture, and effectively reducing aquaculture losses caused by water quality problems.

[0053] Refer to Figure 3This chart visually illustrates the core advantages of this invention across multiple scenarios, stemming from the multi-unit integration and anti-interference design of the intelligent sensing module. Traditional systems, limited by single-sensor technology, can only cover 4-6 conventional parameters, and their accuracy drops to as low as 75% in complex scenarios such as industrial wastewater outlets, failing to meet comprehensive monitoring needs. This invention, through the modular integration of electrochemical, optical, biological, and physical sensing units, achieves simultaneous detection of 10-12 multi-dimensional parameters. Simultaneously, leveraging electromagnetic shielding, temperature compensation, and dynamic error compensation algorithms, it maintains an accuracy rate above 95% in various scenarios. This combination of multi-parameter coverage and high accuracy makes water quality monitoring more comprehensive and precise, adaptable to different monitoring needs such as drinking water sources and industrial wastewater outlets.

[0054] Reference Figure 4 The chart clearly demonstrates the rapid response advantage of this invention, which is primarily due to the optimization of data transmission and processing mechanisms. Traditional systems rely on single communication and centralized cloud processing, resulting in long response times, especially for pathogen detection which requires 120 seconds, making it impossible to promptly report sudden changes in water quality. This invention employs dual-mode 5G and LoRa communication, controlling transmission latency to the millisecond level, while simultaneously supporting collaborative edge computing and cloud computing for rapid local processing of critical data. For pathogen detection, through bioprobe nano-modification and signal enhancement technology, the response time is shortened to 15 seconds, and for conventional parameters, it only requires 2-5 seconds, significantly improving the ability to rapidly handle water quality anomalies and buying time for pollution control.

[0055] Reference Figure 5 This chart highlights the superior anti-interference performance of this invention, which is key to the comprehensive design of the anti-interference processing module. Traditional systems lack targeted anti-interference measures, resulting in error rates exceeding 15% in scenarios with high turbidity and temperature fluctuations, severely impacting data reliability. This invention reduces electromagnetic interference through an electromagnetic shielding shell, dynamically adjusts sensing parameters using a temperature sensor, removes probe impurities using an ultrasonic cleaning device, and eliminates interference signals using a digital filtering algorithm. The error rate is only 4% in high turbidity scenarios and only 3% in temperature fluctuation scenarios. Error rates are controlled within 5% in various complex environments, ensuring the stability and accuracy of monitoring data and making it suitable for complex monitoring environments such as fieldwork and industrial areas.

[0056] Reference Figure 6This radar chart comprehensively demonstrates the integrated performance advantages of this invention, the core of which stems from the collaborative innovation of its various modules. Traditional systems have significant shortcomings, scoring only 50 points in response speed, 45 points in ease of operation and maintenance, and 40 points in data sharing, making it difficult to meet the needs of modern monitoring. This invention improves parameter coverage through multi-sensor unit integration, optimizes response speed through dual-mode communication and edge computing, ensures accuracy through anti-interference design and error compensation, enhances ease of operation and maintenance through automatic calibration and remote control, and achieves efficient data sharing through standardized interfaces. All dimensions score around 90 points, achieving integrated monitoring that is "comprehensive, fast, accurate, convenient, and collaborative," significantly surpassing the single-item optimization model of traditional systems.

[0057] Reference Figure 7 The chart clearly demonstrates the operational cost advantages of this invention, primarily due to the design of its automatic calibration and remote control modules. Traditional systems rely on manual on-site calibration, probe cleaning, and parameter adjustments, resulting in annual maintenance costs of 62,000 yuan for drinking water sources alone and as high as 80,000 yuan for industrial wastewater outlets, placing a heavy burden on long-term maintenance. This invention achieves automatic calibration every 7 days through its automatic calibration module, periodically removes probe impurities using an ultrasonic cleaning device, and supports parameter adjustment and process initiation through its remote control module, eliminating the need for frequent on-site operations. Annual maintenance costs for all scenarios are controlled within 13,000 yuan, only 1 / 5 to 1 / 6 of traditional systems, significantly reducing monitoring costs while minimizing human error and ensuring long-term stable system operation.

[0058] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A real-time monitoring system for multiple water quality parameters based on intelligent sensing, characterized in that, Includes the following modules: The intelligent sensing module integrates electrochemical, optical, biological and physical sensing units to detect multi-dimensional water quality parameters. It adopts a modular design and connects to the system host through a standard interface. The data transmission module adopts a dual-mode communication architecture, integrates encryption algorithms, establishes a two-way data link, supports breakpoint resumption and data verification, and transmits data in real time. The data processing module processes sensor data through noise reduction, filtering, and normalization, removes outliers to generate standardized water quality data, and works in collaboration between edge computing and cloud computing. The real-time monitoring platform adopts a B / S architecture to support multi-terminal access and integrates data visualization, multi-parameter trend analysis, historical data query and report generation functions to centrally manage single sites and multiple sites. The early warning module classifies early warning levels based on the degree to which water quality parameters exceed standards, and provides early warning information through audible and visual alarms, SMS notifications, and platform push notifications. The early warning threshold can be customized and dynamically adjusted. The automatic calibration module is equipped with a standard solution storage unit and an automatic sampler. It periodically calibrates the sensing unit, records calibration coefficients and deviation data, and generates calibration reports. The power management module uses solar panels and lithium battery packs for power supply, and is equipped with a maximum power point tracking controller. The lithium battery packs support deep charge and discharge cycles.

2. The real-time water quality multi-parameter monitoring system based on intelligent sensing according to claim 1, characterized in that, It also includes an enhanced multi-parameter collaborative calibration module with a built-in standard solution library. The standard solution is automatically delivered through a micro peristaltic pump. During the calibration process, the sensor unit response signal and ambient temperature data are collected. The calibration results are corrected by combining temperature compensation algorithms. After calibration, the sensor probe is automatically cleaned, and the calibration data is uploaded to the monitoring platform in real time.

3. The real-time water quality multi-parameter monitoring system based on intelligent sensing according to claim 1, characterized in that, It also includes a water quality anomaly tracing module, which integrates an environmental parameter acquisition unit and a tracing algorithm model. The environmental parameter acquisition unit monitors water flow velocity, wind direction, light intensity, and the distribution data of surrounding pollution sources. The tracing algorithm model combines the changing trends of water quality parameters with environmental data to analyze the abnormal pollution diffusion path and source, generate a tracing report, and display it visually on the monitoring platform.

4. The real-time water quality multi-parameter monitoring system based on intelligent sensing according to claim 1, characterized in that, It also includes a biosensing enhancement module, which optimizes the biological probe immobilization process by using covalent bonding, enhances the binding specificity of the probe to the target pathogen by modifying it with nanomaterials, has a built-in pathogen database, and determines the type and concentration of pathogens by the intensity of fluorescence signals.

5. The real-time water quality multi-parameter monitoring system based on intelligent sensing according to claim 1, characterized in that, It also includes a remote control module, which adopts a hierarchical permission management mechanism. Operation permissions are divided into three levels: administrator, maintenance personnel, and ordinary users. Administrators can remotely adjust the sampling frequency, calibration cycle, and early warning threshold of the sensor unit through the monitoring platform. Maintenance personnel can remotely start the calibration process and probe cleaning procedure. Ordinary users can view the data. Remote commands are transmitted to the system host in encrypted form, and the execution results are fed back to the platform in real time.

6. The real-time water quality multi-parameter monitoring system based on intelligent sensing according to claim 1, characterized in that, It also includes an anti-interference processing module, which uses an electromagnetic shielding shell, collects ambient temperature in real time through a temperature sensor and dynamically adjusts the operating parameters of the sensing unit, is equipped with an ultrasonic cleaning device to regularly remove impurities adhering to the probe surface, and combines digital filtering algorithms to eliminate interference signals.

7. The real-time water quality multi-parameter monitoring system based on intelligent sensing according to claim 1, characterized in that, It also includes a data sharing interface module, which adopts a standardized data transmission protocol to connect with environmental protection department monitoring platforms, water conservancy management systems, and scientific research data analysis platforms. It customizes data fields and transmission frequencies according to the needs of the connected platforms, de-identifies shared data, and records data transmission logs.

8. A method for applying the real-time monitoring system for multiple water quality parameters based on intelligent sensing as described in any one of claims 1-7, characterized in that, Includes the following steps: In the parameter sensing and acquisition process, the intelligent sensing module collects water quality parameters at a preset sampling frequency, the electrochemical sensing unit converts electrical signals through electrode reactions, the optical sensing unit converts signals through light absorption intensity, and the biosensing unit feeds back the detection results through fluorescence signals. The data encryption transmission process involves analog-to-digital conversion and SM4 encryption of sensor signals. The communication mode is automatically selected based on the strength of the communication signal. After encryption, the water quality data is transmitted to the data processing module in real time. During the transmission process, CRC32 data verification is performed. The data preprocessing and fusion step involves using the Kalman filter algorithm to reduce noise in the sensor data, removing outliers, unifying parameter standards through a normalization algorithm, and integrating the data from each sensor unit using a multi-source data fusion model to generate a water quality monitoring dataset. Real-time analysis and calibration steps: The real-time monitoring platform analyzes the pre-processed dataset from multiple dimensions, compares it with preset thresholds to determine the water quality compliance status, and the automatic calibration module periodically starts the calibration process, extracts standard solutions to calibrate the sensing unit, and updates the calibration coefficients to correct detection deviations. The abnormal early warning process involves the early warning module determining the early warning level based on the degree to which water quality parameters exceed the standard, simultaneously triggering early warning notifications through multiple channels, and pushing details of abnormal parameters and monitoring point location information. In the data storage and sharing process, the monitoring platform stores standardized water quality data, calibration records, and early warning information in a cloud database, and the data sharing interface module connects with external platforms according to a preset protocol.

9. The method for real-time monitoring of multiple water quality parameters based on intelligent sensing according to claim 8, characterized in that, It also includes a dynamic error compensation step, using the formula Calculate the error compensation value, where This refers to the concentration compensation value for water quality parameters. This is the temperature influence coefficient. To actually monitor the temperature, For standard calibration temperature, Humidity influence coefficient This represents the actual ambient humidity. For standard humidity calibration, The sensing drift coefficient is... This is the time interval since the last calibration.

10. The method for real-time monitoring of multiple water quality parameters based on intelligent sensing according to claim 8, characterized in that, It also includes multi-dimensional data tracing steps, collecting data on water flow velocity, wind direction, light intensity and distribution of surrounding pollution sources when water quality anomalies occur, combining the time change curves of abnormal parameters with spatial diffusion trends, using cluster analysis algorithms to divide the pollution impact range, using path analysis models to trace the pollution source, and generating a tracing report.

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