Intelligent water quality monitoring and early warning system and method of use

Abstract

The application discloses a water quality intelligent monitoring and early warning system and a use method, relates to the technical field of water quality monitoring, and comprises a main control unit, a data transmission unit, a power supply unit and an early warning output unit, further comprises a biological membrane in-situ electric signal coding monitoring module, a double signal mutual interpretation and analysis module, a micro-ecological self-purification ability coupling feedback evaluation module and a progressive early warning tracing module which are connected in a closed loop; in the application, the closed loop technology of biological membrane in-situ electric signal coding, double signal mutual interpretation and analysis, micro-ecological self-purification ability dynamic coupling and progressive early warning tracing is used to solve the problems that the existing system regards the biological membrane as an interference source, micro-ecological parameters cannot be acquired in real time, self-purification ability evaluation is static, early warning is lagging and monitoring dimension is single, realize real-time capture of micro-ecological information, accurate prediction of water quality in the future 96 hours, early warning in the future 72 hours, and full-process closed loop control of pollution tracing and customized intervention scheme pushing within 10 seconds.

Description

Technical Field

[0001] This invention relates to the field of water quality monitoring technology, specifically to an intelligent water quality monitoring and early warning system and its usage method. Background Technology

[0002] Intelligent water quality monitoring and early warning systems are the core of water environment management. Their core objective is to capture changes in water quality parameters in real time and predict pollution risks in advance. Most mainstream systems currently collect visible parameters such as COD, ammonia nitrogen, and turbidity through sensor probes, combine Kalman filtering and spectral feature decoupling algorithms to eliminate monitoring interference, assess self-purification capacity based on the Streeter-Phelps model, and finally trigger early warnings through parameter threshold matching.

[0003] However, existing systems have problems: the underlying monitoring logic is a one-way drive from dominant parameters to monitoring signals, generally treating biofilms formed on the sensor probe surface as sources of measurement error. While eliminating interference through anti-biofilm coatings and regular cleaning and calibration, they neglect the temporal drift of impedance signals caused by changes in bacterial colonization rate and metabolic activity during biofilm growth and evolution, which contains dynamic evolutionary information about the aquatic micro-ecosystem. Furthermore, existing signal processing algorithms focus on preserving dominant parameter signals, lacking mechanisms for extracting and analyzing the temporal characteristics of biofilm interference; even if signal anomalies are detected, they are often dismissed as errors.

[0004] Furthermore, existing monitoring of microbial community diversity and nitrogen and phosphorus cycle functional gene abundance relies on high-throughput sequencing in laboratories, making real-time on-site acquisition impossible. This prevents the system from linking microbial changes with dynamic self-purification potential, allowing assessments based only on historical data and failing to capture early warning signals of water quality deterioration. These shortcomings result in existing systems lagging behind actual water quality evolution trends in their early warning systems, with monitoring dimensions limited to explicit parameters, making it difficult to meet the demands of water environment management for precise and forward-looking early warnings. Therefore, this paper proposes an intelligent water quality monitoring and early warning system and its usage methods to overcome these problems. Summary of the Invention

[0005] The purpose of this invention is to provide a water quality intelligent monitoring and early warning system and its usage method to solve the problems mentioned in the background art.

[0006] To solve the above-mentioned technical problems, the present invention provides a water quality intelligent monitoring and early warning system, which includes a main control unit, a data transmission unit, a power supply unit and an early warning output unit, and also includes a biofilm in-situ electrical signal encoding monitoring module, a dual signal mutual translation and analysis module, a micro-ecology-self-purification capacity coupling feedback evaluation module and a progressive early warning and source tracing module connected in a closed loop in sequence. The in-situ electrical signal encoding and monitoring module for biomembranes transforms biomembranes into in vivo signal encoders, simultaneously acquiring extracellular electron transfer signals and impedance signals. The dual-signal translation and analysis module generates micro-ecological parameters in real time through dual-signal translation and decoding, and performs bidirectional calibration with dominant parameters; The micro-ecology-self-purification capacity coupled feedback assessment module constructs a dynamic coupling model based on micro-ecological parameters and explicit parameters, calculates the three-dimensional index of self-purification capacity, and predicts the water quality evolution trend. The progressive early warning and source tracing module constructs a three-level early warning system, simultaneously completing the source tracing of pollution and the delivery of intervention plans.

[0007] Furthermore, the biofilm in-situ electrical signal encoding monitoring module includes a biofilm encoding acclimatization unit, a dual-signal synchronous acquisition unit, and an encoding environment steady-state unit. The biofilm encoding acclimatization unit uses a modified graphene-polyurethane composite carrier with a conductivity of 10-50 S / m and a pore size of 80-150 μm. After inoculating the in-situ bacterial community in the monitoring water body, it is acclimatized by three-stage gradient electrical stimulation of 0.1-0.5V. The dual-signal synchronous acquisition unit uses a three-electrode system to acquire extracellular electron transfer signals of 0.1-10Hz and an impedance signal to acquire impedance signals through a 100Hz-1MHz high-frequency impedance sensor. The sampling frequency is once every 30 seconds. The encoding environment steady-state unit controls the temperature at 20-25℃ and the dissolved oxygen at 2-5 mg / L, and applies electrical stimulation of 0.2V for 10 seconds every 2 hours.

[0008] Furthermore, the dual-signal translation and analysis module extracts the frequency, amplitude, and phase characteristics of the extracellular electron transfer signal and the colonization, metabolism, and shedding impedance characteristics of the impedance signal, and fuses them to generate a microecological coding feature vector. Based on the dual-signal coded microecological parameter decoding library constructed in the previous in-situ calibration experiment, it decodes and generates the Shannon index of microbial diversity, abundance of nitrogen and phosphorus cycling functional genes, and microbial activity parameters. The measurement deviation of dominant parameters is corrected by microecological parameters, and the dual-signal decoding accuracy is optimized by using dominant parameters in reverse.

[0009] Furthermore, the three-dimensional self-purification capacity index of the micro-ecology-self-purification capacity coupling feedback assessment module includes the pollutant degradation index, water body buffering index, and ecological restoration index, among which: Pollutant degradation index = 0.6 × abundance of nitrogen and phosphorus cycle functional genes + 0.4 × bacterial community activity. Water buffering index = 0.3 × Shannon index of microbial diversity + 0.7 × |7-pH value| Ecological restoration index = 0.5 × microbial colonization rate + 0.5 × metabolic activity; Based on dual-signal encoded feature vectors, three-dimensional self-purification capacity index, and time-series data of explicit parameters, the water quality evolution trend in the next 96 hours is predicted.

[0010] Furthermore, the progressive early warning and source tracing module has the following trigger conditions: Level 1 early warning is triggered when the frequency shift of extracellular electron transfer signal exceeds 10% or the rate of decline in microbial diversity exceeds 8% / h; Level 2 early warning is triggered when the three-dimensional index of self-purification capacity drops to the critical value corresponding to the water body type; and Level 3 early warning is triggered when the explicit parameter approaches or exceeds the preset threshold. All levels of early warning simultaneously analyze the root cause of pollution and push targeted intervention plans. The three levels of early warning are linked to the water environment management and control platform.

[0011] Furthermore, the biofilm in-situ electrical signal encoding monitoring unit is submerged at a depth of 0.8-1.2m in the monitored water body, while the main control unit and power supply unit are installed in a protective box on the shore. The data is uploaded to the cloud management platform via 5G or satellite network.

[0012] The intelligent water quality monitoring and early warning method includes the following steps: (1) System initialization and coding calibration: The in-situ bacterial community of the monitored water body was collected and inoculated onto a conductive biomimetic carrier. After 72 hours of three-stage gradient electrical stimulation acclimatization, dual signals and high-throughput sequencing data of water samples were collected simultaneously to construct a decoding library and set the warning threshold and critical value for the corresponding water body type. (2) Real-time monitoring and data analysis: Dual signals are collected and transmitted to the main control unit simultaneously. The dual signal mutual translation and analysis module generates micro-ecological parameters and calibrates them bidirectionally with the explicit parameters. The coupled feedback evaluation module calculates the three-dimensional index of self-purification capacity and predicts water quality trends. (3) Progressive early warning and intervention feedback: Within 10 seconds of triggering the corresponding early warning, the early warning information, pollution source and intervention plan are pushed out, the intervention effect is monitored in real time and the decoding library and coupling model are optimized.

[0013] Compared with the prior art, the beneficial effects of the present invention are: 1. Transform biofilms from traditional monitoring interference sources into live signal encoders, simultaneously collecting extracellular electron transfer signals and impedance signals to achieve real-time capture of microecological dynamic information and avoid waste of ecological information.

[0014] 2. Through the dual-signal mutual translation and analysis mechanism, without relying on laboratory high-throughput sequencing, microecological parameters such as bacterial community diversity and nitrogen and phosphorus cycle functional gene abundance can be decoded on-site in real time, and bidirectional calibration with dominant parameters can be performed to improve the accuracy of monitoring data.

[0015] 3. Based on the dynamic coupling model of micro-ecology and self-purification capacity, the three-dimensional index of self-purification capacity is calculated, and the actual evolution of water quality is matched in real time. The water quality changes and abrupt change points in the next 96 hours are accurately predicted, thus solving the problem of lag in static assessment.

[0016] 4. Construct a three-level progressive early warning system to capture early signs of microecological changes 72 hours in advance, simultaneously analyze the root causes of pollution, and push customized intervention plans within 10 seconds. The three-level early warning linkage control platform enables closed-loop control of the entire process.

[0017] 5. Biofilms have both monitoring and ecological restoration auxiliary functions, and the shed functional bacterial communities can enhance the self-purification capacity of water bodies; intervention plans dynamically adapt to changes in water quality and can achieve cross-seasonal trend prediction through decoding library updates; they intuitively present the causal link between micro-ecology, self-purification capacity, and visible parameters, helping to achieve precise management. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the intelligent water quality monitoring and early warning system and its usage method of the present invention. Detailed Implementation

[0019] 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.

[0020] Please see Figure 1 The present invention provides a technical solution: See Figure 1 The following is an example of a water quality intelligent monitoring and early warning system and its usage: I. System: 1. System Overall Architecture: The purpose of this invention is to address the shortcomings of existing systems by using a closed-loop technical solution centered on the in-situ electrical signal encoding mechanism of biofilms. This solution constructs a closed-loop system comprising a biofilm live coding monitoring module, a dual-signal mutual translation and analysis module, a microecological self-purification capacity coupling feedback evaluation module, and a progressive early warning and tracing module. The core of this invention is to transform the biofilm into a live signal encoder. Through the mutual translation of extracellular electron transfer signals and impedance signals, real-time decoding of microecological parameters is achieved on-site. Then, through a coupled feedback model linking self-purification capacity and dominant parameters, the entire process of generating a solution for early warning, trend prediction, and root cause tracing is ultimately realized. All modules work synergistically, simultaneously solving all existing problems.

[0021] 2. Core Modules: 2.1 Biofilm in-situ electrical signal encoding and monitoring module: The core of this module is the biofilm in vivo encoding mechanism. By modifying the biofilm growth environment and signal acquisition method, the biofilm becomes a living signal encoder of microecological information, simultaneously acquiring its encoded extracellular electron transfer signals and impedance signals. Each component is connected in series with the signal preprocessing unit via wires, specifically: 2.1.1 Biofilm Encoding Acclimation Unit: In-situ bacterial communities in the monitored water body were selected, separated by centrifugation, and then inoculated onto a conductive biomimetic carrier. The conductive biomimetic carrier was a modified graphene-polyurethane composite carrier with a conductivity of 10-50 S / m and a pore size of 80-150 μm. The carrier was fixed 2 cm below the electrode assembly. Specific extracellular electron transfer behavior of the bacterial communities was induced through gradient electrical stimulation. The gradient electrical stimulation used a DC voltage of 0.1-0.5V, divided into three stages, each lasting 24 hours. The voltage was 0.1-0.2V in the first stage, 0.2-0.35V in the second stage, and 0.35-0.5V in the third stage. Different bacterial communities, different metabolic activities, and different colonization densities corresponded to extracellular electron transfer signals of different frequencies and amplitudes, realizing a natural mapping from microecological dynamics to extracellular electron transfer signal encoding, making the biofilm a living signal encoder.

[0022] 2.1.2 Dual-Signal Synchronous Acquisition Unit: This unit integrates an extracellular electron transfer signal acquisition electrode assembly and a high-frequency impedance sensor, spaced 5 cm apart, to simultaneously acquire dual signals. The extracellular electron transfer signal acquisition electrode assembly employs a three-electrode system: a working electrode made of glassy carbon, a reference electrode made of Ag / AgCl, and a counter electrode made of platinum-iridium alloy. All electrodes have a diameter of 5 mm and a spacing of 3 mm, with an acquisition frequency of 0.1-10 Hz, accurately capturing the time-series data of extracellular electron transfer signals generated by biomembrane metabolism. The high-frequency impedance sensor, with a frequency range of 100 Hz-1 MHz, simultaneously acquires biomembrane impedance signals, capturing impedance drift caused by bacterial colonization and shedding. Both signals are sampled once every 30 seconds and transmitted to the signal preprocessing unit via shielded cables. The shielded cables are wrapped with a copper mesh shielding layer to prevent signal interference.

[0023] 2.1.3. The steady-state coding environment unit consists of a temperature control module, a dissolved oxygen regulation module, and a nutrient replenishment module. These three modules are connected to the main control unit via wires. Through the synergistic regulation of temperature, humidity, dissolved oxygen, and nutrients, the stable coding environment of the biofilm is maintained. The temperature is controlled at 20-25℃ with a temperature control accuracy of ±0.5℃; dissolved oxygen is controlled at 2-5 mg / L with an adjustment accuracy of ±0.1 mg / L; and the nutrient concentration is consistent with the monitored water body, achieved through timed and quantitative replenishment. A periodic wake-up mechanism is also implemented, applying a 0.2V electrical stimulus for 10 seconds every 2 hours to ensure the biofilm remains in an active coding state and to prevent signal loss due to bacterial dormancy.

[0024] It should be noted here that the biological membrane is trained to have specific signal encoding capabilities through gradient electrical stimulation.

[0025] 2.2 Dual-signal translation and parsing module: The core of this module is the extracellular electron transfer impedance dual-signal mutual decoding mechanism. Through the mutual translation and verification of the two signals, it achieves real-time on-site decoding of microbial ecological parameters without relying on high-throughput sequencing. The module is connected to the signal preprocessing unit of the biofilm in-situ electrical signal encoding and monitoring module via a data line, specifically: 2.2.1 Dual-Signal Feature Fusion Extraction: An encoding feature decoding algorithm is employed, embedded in the processor of the main control unit. This algorithm extracts three types of encoding features from the extracellular electron transfer signal and three types of evolutionary features from the impedance signal. Feature fusion is then used to generate a microbial ecosystem encoding feature vector. The frequency features of the extracellular electron transfer signal correspond to the bacterial community type, the amplitude features correspond to the bacterial community activity, and the phase features correspond to the abundance of functional genes. The colonization impedance, metabolic impedance, and shedding impedance features of the impedance signal help verify the accuracy of the extracellular electron transfer encoding, avoiding misinterpretation by a single signal.

[0026] 2.2.2 Microbial Ecosystem Parameter Decoding and Generation: A dual-signal encoded microbial ecosystem parameter decoding library was constructed and stored in the main control unit's storage module, built through prior in-situ calibration experiments. Biofilm dual signals under different water qualities were collected along with corresponding high-throughput sequencing data. A unique correspondence was established between microbial ecosystem parameters and dual-signal encoded feature vectors. Microbial ecosystem parameters include bacterial diversity, Shannon index, nitrogen and phosphorus cycling, functional gene abundance, and bacterial community activity. Real-time generation of microbial ecosystem parameters was achieved through encoding-matching decoding, with high decoding accuracy and no offline detection steps required.

[0027] 2.2.3 Dual-signal cross-calibration and dominant parameter fusion: A microecological dominant parameter cross-calibration mechanism is introduced. The microecological parameters generated by decoding are used to correct the monitoring error of dominant parameters. Dominant parameters include COD, ammonia nitrogen, and turbidity. When the activity of nitrogen-cycling bacteria decreases, the measurement deviation of the ammonia nitrogen sensor is corrected. At the same time, the dominant parameters are used to optimize the dual-signal decoding accuracy. When the COD concentration changes abruptly, the weight of the corresponding bacterial community code in the decoding library is adjusted to achieve bidirectional calibration of microecological parameters and dominant parameters, solving the defect of independent parameter processing without correlation in the existing system.

[0028] It should be noted here that the construction of a dual-signal mutual decoding mechanism directly correlates the encoding features of extracellular electron transfer signals with microbial ecological parameters, achieving a technological breakthrough in real-time acquisition of microbial ecological parameters without the need for machine learning mapping.

[0029] 2.3 Microbial Ecosystem-Self-Purification Capacity Coupled Feedback Evaluation Module: The core of this module is a coupled feedback assessment mechanism. It constructs a dynamic coupled feedback model for the explicit parameters of micro-ecological self-purification capacity, enabling real-time assessment of self-purification capacity and accurate prediction of water quality evolution trends. This module is connected to the dual-signal translation and analysis module via a data bus, specifically: 2.3.1 Dynamic Coupling Model of Self-Purification Capacity: A self-purification capacity assessment model driven by the micro-ecology core is constructed. Real-time decoded micro-ecological parameters are used as the core input, combined with explicit parameters and water physicochemical parameters, including temperature and pH. A three-dimensional self-purification capacity index is calculated, including a pollutant degradation index, a water buffering index, and an ecological restoration index. The pollutant degradation index is calculated based on the abundance of nitrogen and phosphorus cycle functional genes and microbial activity. The calculation formula is as follows: Pollutant degradation index = 0.6 × abundance of nitrogen and phosphorus cycle functional genes + 0.4 × bacterial community activity; The water buffering index is calculated based on the coupling of microbial diversity and pH value. The calculation formula is as follows: Water buffering index = 0.3 × Shannon index of microbial diversity + 0.7 × (7 - absolute pH value). The ecological restoration index is calculated based on the colonization rate and metabolic activity of microbial communities. The calculation formula is as follows: Ecological restoration index = 0.5 × microbial colonization rate + 0.5 × metabolic activity.

[0030] The model introduces a feedback adjustment factor to receive real-time changes in dominant parameters and microecological parameters, dynamically adjusts the evaluation weights, and achieves real-time updates of self-purification capacity at a frequency of once every 3 minutes.

[0031] 2.3.2 Water Quality Evolution Trend Prediction Mechanism: A coupled feedback prediction model is constructed and embedded in the processor of the main control unit. Based on the time series data of the three-dimensional exponential explicit parameters of self-purification capacity using dual-signal encoded feature vectors, the model predicts the water quality evolution trend in the next 96 hours through an encoded trend extrapolation algorithm. This includes the trend of microecological changes, the trend of self-purification capacity decay or improvement, and the trend of explicit parameter changes. At the same time, it identifies trend abrupt change points, such as a sudden drop in self-purification capacity caused by microecological imbalance, thus solving the deficiency of existing systems in being unable to predict future evolution trends.

[0032] It should be noted here that the introduction of a feedback mechanism into the self-purification capacity assessment enables real-time linkage between microecological parameters and self-purification capacity, rather than static calculation, thus achieving real-time matching between the self-purification capacity assessment and the actual evolution trend of water quality.

[0033] 2.4 Progressive Early Warning and Source Tracing Module: The core of this module is a progressive early warning and source tracing mechanism, constructing a three-level early warning system based on precursor signals, trend signals, and threshold signals. It also achieves real-time linkage between early warning and root cause tracing intervention plans. This module is connected to the micro-ecology-self-purification capacity coupling feedback assessment module via data lines, specifically: 2.4.1 Level 1 Early Warning for Microbial Ecosystem Precursors: When a sudden change occurs in the dual-signal encoding feature vector, i.e., the extracellular electron transfer signal frequency shift exceeds 10% or the rate of decline in microbial diversity exceeds 8% / h, an early warning is triggered. At this time, the dominant parameters and self-purification capacity are still within the normal range. The system analyzes the root cause of the mutation in real time, including the absence of specific functional microbial communities and the invasion of exogenous pollutants, and pushes early warnings for microbial ecosystem anomalies and preliminary intervention suggestions, such as targeted introduction of functional microbial communities, and prediction of water quality deterioration risk 72 hours in advance.

[0034] 2.4.2 Level II Early Warning Self-purification Capacity Trend Warning: When the three-dimensional index of self-purification capacity drops to the critical value, which is calibrated according to the water body type, and the critical value for drinking water sources is 0.4, a trend warning is triggered. The system predicts that the visible parameters will approach the threshold within the next 24-48 hours, analyzes the core reasons for the decline in self-purification capacity, including microecological imbalance and insufficient dissolved oxygen, and pushes the self-purification capacity decline warning and customized intervention plan, such as a combination of artificial oxygenation and functional bacteria introduction.

[0035] 2.4.3 Level 3 Early Warning Explicit Parameter Threshold Warning: When an explicit parameter approaches or exceeds a preset threshold, a threshold warning is triggered. The system combines the warning information from the first two levels to accurately locate the root cause of pollution, including COD exceeding the standard due to microecological imbalance and ammonia nitrogen exceeding the standard due to external pollution. It also links with the local water environment management platform to push out a water quality exceeding warning root cause tracing report and emergency response plan, while tracking the intervention effect.

[0036] It should be noted here that by advancing the early warning node to the micro-ecological coding mutation stage, real-time linkage of the early warning and source tracing scheme is achieved, rather than just triggering the early warning, thus solving the shortcomings of the existing system in that the early warning is delayed and lacks source tracing.

[0037] 3. System Hardware Configuration and Deployment: Core hardware configuration: ① Main control unit: adopts an industrial-grade embedded motherboard, model RK3588, with high-speed signal processing and data storage capabilities, storage capacity of not less than 128GB, and processor frequency of not less than 2.0GHz; ② Biomembrane coding monitoring unit: includes conductive biomimetic carrier extracellular electron transfer signal acquisition electrode group, high-frequency impedance sensor coding environment steady-state regulation module, and each component is connected in series by wires; ③ Data transmission unit: Adopts 5G+satellite dual-mode transmission module to ensure real-time data upload in complex water environments, with a transmission rate of no less than 10Mbps; ④ Power supply unit: It consists of a solar panel and a lithium battery emergency power generation module. The solar panel power is not less than 100W, the lithium battery capacity is not less than 100Ah, and the battery life is more than 15 days. ⑤ Warning output unit: It consists of a mobile APP management platform for the audible and visual alarm. The alarm volume of the audible and visual alarm is not less than 80dB, and it can push warnings to multiple terminals.

[0038] In-situ deployment method: The biofilm coding monitoring unit is submerged in the monitored water body at a depth of 0.8-1.2m to ensure full contact between the carrier and the water body; the main control unit and power supply unit are installed in a protective box on the shore. The protective box is waterproof and dustproof, with a protection level of IP65; real-time data is uploaded to the cloud management platform via 5G or satellite network to realize remote monitoring data query and early warning management. The deployment process does not require complicated construction and is suitable for various water environments such as rivers, lakes and reservoirs.

[0039] II. Method: 1. System initialization and encoding calibration: Hardware Deployment and Debugging: Install each module according to the deployment requirements. Connect the biofilm coding monitoring unit and the main control unit using shielded cables. Connect the main control unit to the data transmission unit, power supply unit, and early warning output unit using wires. Debug the accuracy of the dual-signal acquisition unit: the extracellular electron transfer signal acquisition accuracy is ±0.01μA, and the impedance signal accuracy is ±1Ω. Test the data transmission stability and power supply reliability. Conduct continuous testing for 24 hours, ensuring a data transmission success rate of no less than 99% and uninterrupted power supply, ensuring that all modules work together effectively.

[0040] In-situ acclimatization and coding calibration of biofilms: Water samples were collected from the monitored water body, and in-situ bacterial communities were obtained through centrifugation and screening. These communities were then inoculated onto a conductive biomimetic carrier. The coding environment steady-state unit was activated, and gradient electrical stimulation acclimatization was implemented in three stages over a total of 72 hours. During this period, dual signals and water samples were simultaneously collected every 30 seconds. The water samples were then sent to the laboratory for high-throughput sequencing to obtain microecological parameters. A decoding library of dual-signal coding feature vectors and microecological parameters was constructed and stored in the storage module of the main control unit. Coding calibration was completed to ensure that the decoding accuracy reached over 90%.

[0041] Early warning threshold and critical value setting: Based on the functional positioning of the monitored water bodies, including drinking water sources and landscape water bodies, and in conjunction with local water quality standards, set the micro-ecological coding mutation threshold, the three-dimensional index critical value of self-purification capacity, and the explicit parameter threshold, and input them into the early warning module of the main control unit to complete the system initialization.

[0042] 2. Real-time monitoring and data analysis: Dual-signal synchronous acquisition: When the system is turned on, the coding environment steady-state unit maintains the active coding state of the biomembrane, and the dual-signal acquisition unit acquires extracellular electron transfer signals and impedance signals in real time, which are transmitted to the main control unit through shielded cables with a transmission delay of no more than 1 second.

[0043] Dual-signal translation and micro-ecological parameter decoding: The main control unit extracts dual-signal features and fuses them to generate coded feature vectors through the dual-signal translation and parsing module. Based on the decoding library, it decodes and generates real-time micro-ecological parameters. Then, through the micro-ecological explicit parameter mutual calibration mechanism, it optimizes the accuracy of micro-ecological parameters and explicit parameters, with the parameter optimization error not exceeding 5%.

[0044] Coupled feedback assessment and trend prediction: The coupled feedback assessment module takes real-time micro-ecological parameters as input, calculates the three-dimensional index of self-purification capacity through a dynamic coupling model of self-purification capacity, and then predicts the water quality evolution trend in the next 96 hours through a coupled feedback prediction model, identifies the trend change point, and the prediction error does not exceed 10%.

[0045] 3. Progressive early warning and intervention feedback: Level 3 Early Warning Triggering and Source Tracing: The early warning and source tracing module monitors the explicit parameters of the three-dimensional index of self-cleaning capacity in the encoded feature vector in real time. After triggering the corresponding early warning, it analyzes the root cause of pollution in real time and pushes the early warning information and intervention plan to each terminal within 10 seconds, including the mobile APP management platform of the audible and visual alarm.

[0046] Intervention effect feedback and model optimization: After users implement the intervention plan, the system monitors the changes in the self-purification capacity index and dominant parameters of the dual-signal microecological parameters in real time, collecting data every 30 seconds to evaluate the intervention effect. The feedback data is entered into the decoding library and coupled with the evaluation model to optimize the encoding and decoding accuracy and trend prediction accuracy, enabling the system to self-iterate. After iteration, the decoding accuracy improves by no less than 3%, and the prediction accuracy improves by no less than 5%.

[0047] 4. System maintenance and updates: The biofilm coding monitoring unit is maintained every 10 days, including replacing the aging conductive biomimetic carrier, re-inoculating the in-situ bacterial flora, and undergoing a short-term acclimatization period of 24 hours to ensure stable biofilm coding capabilities. The dual-signal acquisition unit is calibrated monthly using a standard signal source, with a calibration error not exceeding 2%. The decoding library and coupling evaluation model are updated quarterly, and parameters are optimized based on the microecological changes characteristic of different seasons to ensure long-term stable operation and monitoring accuracy. Parameter optimization improves monitoring accuracy by at least 5%.

[0048] Summarize: 1. Solving the problems of biofilm interference and lack of microecological information: Through in-situ coding and domestication of biofilms, biofilms are transformed from interference sources into live signal encoders, simultaneously collecting dual signals and decoding microecological parameters, without relying on high-throughput sequencing, to achieve real-time on-site acquisition of microecological information.

[0049] 2. Solve the problems of one-way signal processing and information waste: Through a dual-signal mutual translation and analysis mechanism, extract the micro-ecological information encoded by the biofilm, rather than removing interference signals, realize the value transformation of interference signals into ecological information and then into monitoring data, and build a two-way correlation between micro-ecology and dominant parameters.

[0050] 3. Solve the problem of static self-purification capacity assessment: By using a coupled feedback assessment model with real-time micro-ecological parameters as the core driver, the self-purification capacity can be updated in real time and the water quality evolution trend can be predicted. This avoids static assessment based on historical data and makes the assessment results highly consistent with the actual evolution trend.

[0051] 4. Solving the problems of delayed early warning and single monitoring dimensions: Through a progressive early warning and source tracing mechanism, the early warning node is brought forward to the micro-ecological coding mutation stage, realizing 72-hour forward-looking early warning. At the same time, it covers three monitoring dimensions of the micro-ecological self-purification capacity explicit parameters, solving the defects of delayed early warning and single monitoring dimensions.

[0052] 5. Real-time visualization of the causal chain of micro-ecology-water quality evolution: Through dual-signal encoding analysis and coupled feedback evaluation, the system displays in real time the causal chain of micro-ecological encoding mutations leading to the decline of self-purification capacity and thus changes in explicit parameters. It intuitively presents the source and transmission process of water quality deterioration, providing a precise basis for source tracing and process intervention for water environment management.

[0053] 6. The triple value of biofilm encoding monitoring and restoration: Biofilms not only serve as living signal encoders of microecological information, but their domesticated functional microbial communities also shed into the water during periodic renewal, helping to enhance the water's self-purification capacity and realizing the dual value of monitoring carrier and ecological restoration aid; at the same time, the restoration effect is fed back in real time through encoded signals, forming a closed loop of monitoring and restoration feedback.

[0054] 7. Dynamic adaptability of intervention programs: Based on real-time microecological coding characteristics and self-purification capacity, the system generates customized intervention programs, including precise control of the types and dosages of specific functional bacteria and the intensity of oxygenation, rather than general programs. The programs can be adjusted in real time according to the intervention effect to adapt to different water quality change scenarios.

[0055] 8. Cross-seasonal water quality evolution prediction effect: Through the quarterly update of the decoding library and the self-iteration of the coupled model, the system captures the changing patterns of micro-ecological coding characteristics in different seasons, accurately predicts the cross-seasonal water quality evolution trend, such as the decline in self-purification capacity caused by the decrease in bacterial activity in winter, and formulates response strategies in advance.

Claims

1. A water quality intelligent monitoring and early warning system, comprising a main control unit, a data transmission unit, a power supply unit, and an early warning output unit, characterized in that, It also includes a biofilm in-situ electrical signal encoding and monitoring module, a dual signal mutual translation and analysis module, a micro-ecology-self-purification capacity coupling feedback assessment module, and a progressive early warning and source tracing module, which are connected in a closed loop in sequence. The in-situ electrical signal encoding and monitoring module for biomembranes transforms biomembranes into in vivo signal encoders, simultaneously acquiring extracellular electron transfer signals and impedance signals. The dual-signal translation and analysis module generates micro-ecological parameters in real time through dual-signal translation and decoding, and performs bidirectional calibration with dominant parameters; The micro-ecology-self-purification capacity coupled feedback assessment module constructs a dynamic coupling model based on micro-ecological parameters and explicit parameters, calculates the three-dimensional index of self-purification capacity, and predicts the water quality evolution trend. The progressive early warning and source tracing module constructs a three-level early warning system, simultaneously completing the source tracing of pollution and the delivery of intervention plans.

2. The intelligent water quality monitoring and early warning system as described in claim 1, characterized in that: The biofilm in-situ electrical signal encoding monitoring module includes a biofilm encoding acclimatization unit, a dual-signal synchronous acquisition unit, and an encoding environment steady-state unit. The biofilm encoding acclimatization unit uses a modified graphene-polyurethane composite carrier with a conductivity of 10-50 S / m and a pore size of 80-150 μm. After inoculating the in-situ bacterial community in the monitoring water body, it is acclimatized by three-stage gradient electrical stimulation of 0.1-0.5V. The dual-signal synchronous acquisition unit uses a three-electrode system to acquire extracellular electron transfer signals of 0.1-10Hz and an impedance signal to acquire impedance signals through a 100Hz-1MHz high-frequency impedance sensor. The sampling frequency is once every 30 seconds. The encoding environment steady-state unit controls the temperature at 20-25℃ and the dissolved oxygen at 2-5 mg / L, and applies electrical stimulation of 0.2V for 10 seconds every 2 hours.

3. The intelligent water quality monitoring and early warning system as described in claim 1, characterized in that: The dual-signal translation and analysis module extracts the frequency, amplitude, and phase characteristics of extracellular electron transfer signals and the colonization, metabolism, and shedding impedance characteristics of impedance signals, and fuses them to generate a microbial ecosystem coding feature vector. Based on the dual-signal coding microbial ecosystem parameter decoding library constructed from previous in-situ calibration experiments, it decodes and generates the Shannon index of microbial diversity, abundance of nitrogen and phosphorus cycling functional genes, and microbial activity parameters. The measurement bias of dominant parameters is corrected by microbial ecosystem parameters, and the dual-signal decoding accuracy is optimized by using dominant parameters in reverse.

4. The intelligent water quality monitoring and early warning system as described in claim 1, characterized in that: The micro-ecology-self-purification capacity coupled feedback assessment module includes three-dimensional indices for self-purification capacity, such as pollutant degradation index, water buffering index, and ecological restoration index. Pollutant degradation index = 0.6 × abundance of nitrogen and phosphorus cycle functional genes + 0.4 × bacterial community activity. Water buffering index = 0.3 × Shannon index of microbial diversity + 0.7 × |7-pH value| Ecological restoration index = 0.5 × microbial colonization rate + 0.5 × metabolic activity; Based on dual-signal encoded feature vectors, three-dimensional self-purification capacity index, and time-series data of explicit parameters, the water quality evolution trend in the next 96 hours is predicted.

5. The intelligent water quality monitoring and early warning system as described in claim 1, characterized in that: The first-level warning trigger condition of the progressive early warning and source tracing module is that the frequency deviation of the extracellular electron transfer signal exceeds 10% or the rate of decline of microbial diversity exceeds 8% / h; the second-level warning trigger condition is that the three-dimensional index of self-purification capacity drops to the critical value corresponding to the water body type; and the third-level warning trigger condition is that the explicit parameter approaches or exceeds the preset threshold. All levels of early warning systems simultaneously analyze the root causes of pollution and push out targeted intervention plans, linking the three-level early warning system to the water environment management and control platform.

6. The intelligent water quality monitoring and early warning system as described in claim 1, characterized in that: The biofilm in-situ electrical signal encoding monitoring unit is submerged at a depth of 0.8-1.2m in the monitored water body. The main control unit and power supply unit are installed in a protective box on the shore and the data is uploaded to the cloud management platform via 5G or satellite network.

7. A water quality intelligent monitoring and early warning method, applied to the system described in any one of claims 1-7, characterized in that: Includes the following steps: (1) System initialization and coding calibration: The in-situ bacterial community of the monitored water body was collected and inoculated onto a conductive biomimetic carrier. After 72 hours of three-stage gradient electrical stimulation acclimatization, dual signals and high-throughput sequencing data of water samples were collected simultaneously to construct a decoding library and set the warning threshold and critical value for the corresponding water body type. (2) Real-time monitoring and data analysis: Dual signals are collected and transmitted to the main control unit simultaneously. The dual signal mutual translation and analysis module generates micro-ecological parameters and calibrates them bidirectionally with the explicit parameters. The coupled feedback evaluation module calculates the three-dimensional index of self-purification capacity and predicts water quality trends. (3) Progressive early warning and intervention feedback: Within 10 seconds of triggering the corresponding early warning, the early warning information, pollution source and intervention plan are pushed out, the intervention effect is monitored in real time and the decoding library and coupling model are optimized.

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