Water quality monitoring method, device, system, electronic equipment and storage medium

Abstract

The application relates to the technical field of water environment supervision, and discloses a water quality monitoring method, device and system, electronic equipment and storage medium, wherein sensor-acquired water quality sensing data of pollutants at a monitoring point is used to determine a water quality monitoring level; when the water quality is abnormal, the monitoring point is determined as an alarm point; according to an alarm time, a preset water flow speed, a preset error value, a distance between an upstream outlet monitoring point of the alarm point and the alarm point, and a pollutant discharge peak time, the peak matching degree of the outlet is calculated; within a preset time window, pollutant correlation coefficients are determined according to water quality sensing data of the alarm point and the outlet; according to the pollutant correlation coefficients of the outlet, the peak matching degree, historical illegal records and a preset outlet weight, a pollutant discharge suspected degree is determined; and then a suspected outlet monitoring point is determined and sent to a user. Water quality sensing data is acquired by the sensor and analyzed in real time, suspected outlet monitoring points upstream of the alarm point are effectively determined, real-time water quality monitoring and timely pollution tracing are realized.

Description

Technical Field

[0001] This invention relates to the field of water environment monitoring technology, specifically to a water quality monitoring method, device, system, electronic equipment, and storage medium. Background Technology

[0002] The water environment supervision system must achieve the goals of clear understanding of the situation and full-process supervision. However, currently, by relying on regular manual monitoring or fixed water quality monitoring stations, the pollution has already ended when water quality exceeds the standard, making it difficult to collect evidence and hold people accountable.

[0003] In related technologies, monitoring is usually carried out by manually monitoring the discharge outlets periodically or by setting up fixed water quality monitoring stations. However, manual periodic monitoring is difficult to detect and collect evidence of instantaneous or intermittent illegal discharges due to its low frequency. Fixed water quality monitoring stations are costly and difficult to cover on a large scale, resulting in large blind spots in supervision. Both methods increase the difficulty of water environment supervision. Summary of the Invention

[0004] This application provides a water quality monitoring method, apparatus, system, electronic device, and storage medium to at least solve the problem of the inability to monitor water quality in real time in related technologies.

[0005] This application provides a water quality monitoring method, including: For any given monitoring point, water quality sensor data for at least one pollutant is acquired through sensors; the monitoring points include discharge outlet monitoring points and preset water flow monitoring points. For any pollutant at a monitoring point, the corresponding water quality monitoring level is determined based on the water quality sensor data corresponding to the pollutant. When the water quality monitoring level indicates abnormal water quality, the monitoring point is designated as the alarm point; Based on the alarm time, preset water flow velocity, preset error value, distance to at least one discharge monitoring point upstream of the alarm point, and peak emission time of pollutants corresponding to at least one discharge monitoring point, determine the peak matching degree of at least one discharge point corresponding to at least one discharge point. Within a preset time window, the pollutant correlation coefficient is determined based on at least one water quality sensor data of the pollutant at the alarm point and at least one water quality sensor data of the pollutant at at least one discharge outlet. For any discharge outlet, the pollutant emission suspicion level is determined based on the pollutant correlation coefficient, peak matching degree, historical violation records, and preset discharge outlet weights. Based on the suspected pollutant emission level, at least one suspected emission monitoring point is identified from at least one emission monitoring point, and the at least one suspected emission monitoring point is sent to the user so that the user can conduct on-site verification based on the suspected emission monitoring point.

[0006] This application also provides a water quality monitoring device, including: The acquisition module is used to acquire water quality sensor data of at least one pollutant for any given monitoring point through a sensor; wherein the monitoring points include discharge outlet monitoring points and preset water flow monitoring points; The level determination module is used to determine the corresponding water quality monitoring level for any pollutant at a monitoring point based on the corresponding water quality sensor data. The alarm point determination module is used to determine the monitoring point as the alarm point when the water quality monitoring level indicates abnormal water quality. The peak matching degree determination module is used to determine the peak matching degree of at least one discharge point corresponding to at least one discharge point based on the alarm time, preset water flow velocity, preset error value, distance of at least one discharge point monitoring point upstream of the alarm point, and peak discharge time of pollutants corresponding to the water quality sensor data of the alarm point. The correlation coefficient determination module is used to determine the correlation coefficient of pollutants within a preset time window, based on at least one water quality sensor data of pollutants at an alarm point and at least one water quality sensor data of pollutants at at least one discharge outlet. The suspicion determination module is used to determine the suspicion of pollutant emissions for any discharge outlet based on the pollutant correlation coefficient, peak matching degree, historical violation records and preset discharge outlet weights. The suspected discharge outlet identification module is used to identify at least one suspected discharge outlet monitoring point among at least one discharge outlet monitoring point based on the degree of suspicion of pollutant emission, and send at least one suspected discharge outlet monitoring point to the user so that the user can conduct on-site verification based on the suspected discharge outlet monitoring point.

[0007] This application also provides a water quality monitoring system, including: at least one quantum dot spectral sensor, a water quality monitoring and analysis module, and a user interaction module; A quantum dot spectrometer is used to acquire water quality sensing data of at least one pollutant at a monitoring point and send the water quality sensing data to a water quality monitoring and analysis module; wherein, the quantum dot spectrometer is deployed at the monitoring point; The water quality monitoring and analysis module receives water quality sensing data from the quantum dot spectral sensor. For any pollutant at a monitoring point, it determines the corresponding water quality monitoring level based on the water quality sensing data. If the water quality monitoring level indicates abnormal water quality, the monitoring point is designated as an alarm point. Based on the alarm time, preset water flow velocity, preset error value, distance to at least one upstream discharge monitoring point, and peak discharge time of the pollutant, the module determines the peak matching degree of the discharge point. Within a preset time window, it determines the pollutant correlation coefficient based on at least one water quality sensing data of the pollutant at the alarm point and at least one water quality sensing data of the pollutant at at least one discharge point. Based on the pollutant correlation coefficient, peak matching degree, historical violation records, and discharge point weight, the module determines the pollutant discharge suspicion level. Based on the pollutant discharge suspicion level, at least one suspected discharge monitoring point is identified among at least one discharge monitoring point, and this suspected discharge monitoring point is sent to the user interaction module. The user interaction module is used to push alarm information to users, provide at least one suspected discharge outlet monitoring point and corresponding navigation information, and provide a port for filling in handling information.

[0008] This application also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described water quality monitoring methods.

[0009] This application also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of any of the above-described water quality monitoring methods.

[0010] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the above-described water quality monitoring methods.

[0011] This application utilizes sensor data to acquire pollutant water quality at monitoring points, determining the water quality monitoring level. When the water quality is deemed abnormal, the monitoring point is designated as an alarm point. Based on the alarm time, preset water flow velocity, preset error value, distance to the upstream discharge monitoring point, and peak pollutant discharge time, the peak matching degree of the discharge point is calculated. Within a preset time window, the pollutant correlation coefficient is determined based on the water quality sensor data between the alarm point and the discharge point. The pollutant suspicion level is then determined based on the discharge point's correlation coefficient, peak matching degree, historical violation records, and preset discharge point weight. Suspected discharge monitoring points are then identified and sent to the user. By acquiring and analyzing water quality sensor data in real time, the suspected discharge monitoring point upstream of the alarm point is effectively identified, enabling real-time water quality monitoring and timely pollution source tracing. Attached Figure Description

[0012] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A schematic flowchart of the water quality monitoring method provided in the embodiments of this application; Figure 2 A schematic diagram of the structure of the integrated sensing device provided in the embodiments of this application; Figure 3 A schematic diagram of the mooring-type installation method of the integrated sensing device provided in the embodiments of this application; Figure 4 A schematic diagram of the cylindrical mounting method of the integrated sensing device provided in the embodiments of this application; Figure 5 A schematic flowchart of the water quality monitoring method provided in the embodiments of this application; Figure 6 A schematic flowchart illustrating the water quality traceability method provided in this application embodiment; Figure 7 This is a schematic diagram of the structure of the water quality monitoring device provided in the embodiments of this application; Figure 8 This is a schematic diagram of the structure of the water quality monitoring system provided in the embodiments of this application; Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0015] It should be noted that, in the description of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. The terms "first," "second," etc., in this application are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0016] The current water environment monitoring system suffers from large blind spots. The existing model, primarily based on manual monitoring and fixed automatic water quality stations, is costly and time-consuming to construct, failing to achieve large-scale coverage of hundreds or even thousands of scattered and concealed discharge outlets into rivers within the jurisdiction. Many outlets exist in a regulatory vacuum, making it difficult to detect illegal discharges in a timely manner. Regulatory response is poor; traditional manual monitoring is infrequent, typically once a month, with long data feedback cycles, usually several days, completely unable to capture instantaneous and intermittent pollution events. By the time water quality exceedances are detected, the pollution has already ended, leading to difficulties in evidence collection and accountability, resulting in a passive situation of repeated exceedances and subsequent compensation payments. Pollution source tracing is inaccurate, lacking minute-level, in-situ water quality data support, making it difficult to quickly and accurately trace upstream pollution sources after water quality exceeds standards at river sections. Source tracing work is time-consuming, labor-intensive, and ineffective.

[0017] Regulatory personnel conduct on-site sampling at a few key discharge outlets monthly or quarterly. Samples are sent to qualified laboratories for chemical analysis, and test reports are issued. However, the data is severely delayed; it typically takes 3-5 days from sampling to report issuance. The data reflects past water quality conditions and is completely unsuitable for real-time decision-making. It fails to capture instantaneous or intermittent discharges, as most illegal discharges occur at night or during rainy days and are short-lived. The low frequency of manual sampling makes it extremely difficult to detect such events, resulting in numerous illegal discharges remaining in regulatory blind spots. Faced with hundreds or even thousands of discharge outlets throughout the district, limited regulatory manpower cannot achieve high-frequency, wide-coverage sampling, leaving the vast majority of outlets in a regulatory vacuum year-round.

[0018] Fixed monitoring stations are constructed at key river sections or near large discharge outlets, equipped with large-scale online monitoring instruments based on wet chemical analysis for automated sampling and analysis. However, the construction cost is extremely high, with the cost of a single station, including the station building, instruments, and land acquisition, making large-scale deployment for a vast number of discharge outlets impossible. Operation and maintenance are complex and generate secondary pollution; the instruments rely on highly toxic chemical reagents, requiring regular replacement by professionals, and produce large amounts of hazardous waste, introducing new environmental risks and operational burdens. Furthermore, a stable municipal power supply, leveled land, and dedicated water intake and distribution systems are required, making this solution unsuitable for over 90% of discharge outlets.

[0019] The method employs an ultraviolet-visible (UV-Vis) spectrometer, directly immersed in water, to calculate pollutant concentrations by analyzing the water's absorption spectrum, achieving in-situ, reagent-free real-time monitoring. However, size and cost are bottlenecks. Its core spectroscopic elements, such as gratings and rotating filters, are precision optical-mechanical structures, difficult to miniaturize, resulting in large equipment size and still high cost, limiting the economic viability of high-density grid deployment. In real-world environments like river outfalls with high turbidity, high color, and complex composition, suspended particles, bubbles, and dissolved organic matter in the water strongly interfere with the spectrum, leading to a sharp decline in measurement accuracy and long-term stability, resulting in insufficient data reliability. Traditional spectrometers require stable light sources and precise mechanical control, resulting in high overall power consumption, making it difficult to achieve long-term independent operation for months or more solely powered by batteries.

[0020] To address the aforementioned technical problems, this application provides a water quality monitoring method, apparatus, system, electronic device, and storage medium. The method includes: acquiring water quality sensing data for at least one pollutant at any monitoring point using a sensor; wherein the monitoring point includes a discharge outlet monitoring point and a preset water flow monitoring point; determining the corresponding water quality monitoring level for any pollutant at the monitoring point based on the corresponding water quality sensing data; determining the monitoring point as an alarm point if the water quality monitoring level indicates abnormal water quality; and determining the alarm point based on the alarm time, preset water flow velocity, preset error value, and the distance to at least one discharge outlet monitoring point upstream of the alarm point, and the pollutant's... The peak emission time of pollutants is determined, and the peak matching degree of at least one discharge outlet corresponding to at least one discharge outlet monitoring point is determined. Within a preset time window, the pollutant correlation coefficient is determined based on at least one water quality sensor data of the pollutant at the alarm point and at least one water quality sensor data of the pollutant at at least one discharge outlet. For any discharge outlet, the pollutant emission suspicion degree is determined based on the pollutant emission correlation coefficient, peak matching degree, historical violation records, and preset discharge outlet weight. Based on the pollutant emission suspicion degree, at least one suspected discharge outlet monitoring point is determined among at least one discharge outlet monitoring point, and at least one suspected discharge outlet monitoring point is sent to the user so that the user can conduct on-site verification based on the suspected discharge outlet monitoring point.

[0021] The method provided by the above scheme determines the water quality monitoring level by acquiring pollutant water quality sensor data at monitoring points using sensors. When the water quality is abnormal, the monitoring point is designated as an alarm point. Based on the alarm time, preset water flow velocity, preset error value, distance to the upstream discharge monitoring point, and peak pollutant discharge time, the peak matching degree of the discharge point is calculated. Within a preset time window, the pollutant correlation coefficient is determined based on the water quality sensor data of the alarm point and the discharge point. Based on the pollutant correlation coefficient, peak matching degree, historical violation records, and preset discharge point weight, the pollutant discharge suspicion degree is determined. Then, the suspected discharge monitoring point is identified and sent to the user. By acquiring water quality sensor data through sensors and performing real-time analysis, the suspected discharge monitoring point upstream of the alarm point is effectively identified, realizing real-time water quality monitoring and timely pollution source tracing.

[0022] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] The specific application environment architecture or specific hardware architecture on which the water quality monitoring method depends is described here.

[0024] This application provides a water quality monitoring method for real-time water quality monitoring and timely source tracing when water quality anomalies occur. The execution subject of this application embodiment is an electronic device, such as a server, desktop computer, laptop computer, tablet computer, and other electronic devices that can be used for water quality monitoring.

[0025] like Figure 1 The diagram shown is a flowchart illustrating a water quality monitoring method provided in an embodiment of this application. The method includes: Step 201: For any monitoring point, acquire water quality sensing data of at least one pollutant through a sensor.

[0026] The monitoring points include outlet monitoring points and pre-set water flow monitoring points; Specifically, regulatory agencies can dynamically adjust or add / remove monitoring points based on changes in river water quality or management priorities. They can focus on monitoring pre-set monitoring points at sewage outlets and rivers, which will serve as sewage outlet monitoring points and pre-set water flow monitoring points, respectively. Quantum dot spectral sensors will be deployed at the monitoring points to acquire various water quality parameters, including COD, ammonia nitrogen, turbidity, dissolved oxygen, pH, temperature, and the concentration of various pollutants. The water quality sensor data and corresponding time stamps will be stored.

[0027] Correspondingly, by setting up quantum dot spectral sensors at pre-set monitoring points at each sewage outlet and river, it is possible to more accurately identify situations where pollutants exceed standards. At the same time, compared with setting up water quality monitoring stations, the sensor equipment does not require land acquisition, mains power, or other infrastructure construction. A single person can complete the installation of one point within one hour. The convenient and quick deployment method, as well as the lower deployment cost, expands the coverage of water quality monitoring and improves the flexibility of monitoring equipment deployment.

[0028] Step 202: For any pollutant at the monitoring point, determine the corresponding water quality monitoring level based on the water quality sensor data corresponding to the pollutant.

[0029] Step 203: If the water quality monitoring level indicates abnormal water quality, determine the monitoring point as the alarm point.

[0030] Correspondingly, since it's possible for the pollutant concentration at a single discharge outlet to be below the static threshold, but the total discharge from multiple outlets could exceed the alarm threshold, real-time monitoring of pollutant concentration in the water flow at monitoring points prevents water quality anomalies caused by such situations. Compared to manual periodic monitoring, the monitoring frequency has increased from monthly to minute-level, and data latency has been reduced from several days to seconds. The system can issue early warnings within minutes of a discharge incident, nipping pollution in the bud at the source or during transmission, preventing pollutants from spreading downstream.

[0031] Step 204: Based on the alarm time, preset water flow velocity, preset error value, distance of at least one discharge monitoring point upstream of the alarm point, and peak discharge time of pollutants corresponding to at least one discharge monitoring point, determine the peak matching degree of at least one discharge point corresponding to at least one discharge monitoring point.

[0032] Specifically, data is linked with the GIS pipeline network source tracing map. When a water quality alarm is triggered at a preset water flow monitoring point or outlet monitoring point, the current time is determined to be... Immediately locate several key drainage outlet nodes within a preset kilometer range upstream of the alarm point, and denot them as the set. The system retrieves water quality sensor data from these upstream outlets over the past few hours. For any given upstream outlet, it determines the peak matching degree based on the distance between the outlet and the alarm point, the peak discharge time of pollutants, the alarm time corresponding to the water quality sensor data of the alarm point, the preset water flow velocity, and the preset error value. The peak matching degree of several key outlet nodes is then calculated sequentially.

[0033] Step 205: Within a preset time window, determine the pollutant correlation coefficient based on at least one water quality sensor data of the pollutant at the alarm point and at least one water quality sensor data of the pollutant at at least one discharge outlet.

[0034] Specifically, the preset time window is usually greater than or equal to the theoretical transport time of the pollutant. Water quality sensor data of the pollutant within the preset time window are retrieved from the alarm point to generate the corresponding pollutant concentration sequence. For any outlet Retrieve water quality sensor data of the pollutant from the discharge outlet within a preset time window, and generate the corresponding pollutant concentration sequence. The data retrieval times for alarm points and outlets are shifted based on the theoretical transmission time. For example, the data retrieval time for alarm points is... The data retrieval time of the outlet is .

[0035] Step 206: For any discharge outlet, determine the suspected pollutant emission level based on the pollutant correlation coefficient, peak matching degree, historical violation records, and preset discharge outlet weight.

[0036] Specifically, the pollutant emission suspicion level characterizes the degree of correlation between the emission outlet and the pollutant exceedance event at the alarm point.

[0037] Step 207: Based on the suspected pollutant emission level, identify at least one suspected emission monitoring point among at least one emission monitoring point, and send the at least one suspected emission monitoring point to the user so that the user can conduct on-site verification based on the suspected emission monitoring point.

[0038] Specifically, based on the suspected pollutant emission level, the corresponding discharge outlets are sorted in descending order, and a list of suspected discharge outlet monitoring points is output to the user. The pollution transmission path is highlighted on the GIS map, narrowing the investigation scope from several kilometers of river to a range of hundreds of meters upstream of the specific discharge outlet, so that users can conduct on-site verification in a timely manner based on the list.

[0039] Correspondingly, compared to regular manual monitoring, which often results in delays in obtaining evidence when illegal discharges occur, this system, by combining alarm points and water quality sensor data from each discharge point with spatiotemporal correlation analysis and digital pipeline route maps, enables rapid, semi-automatic identification of suspected upstream discharge points from downstream water quality anomalies. This reduces tracing efficiency from days to minutes. Furthermore, the continuous and reliable monitoring data provided by the system can serve as legally valid electronic evidence. Combined with precise tracing algorithms, it can clearly pinpoint the discharge time, responsible discharge point, and even suspected upstream pollution sources, thereby enhancing regulatory deterrence.

[0040] Based on the above embodiments, as an implementable approach, in one embodiment, for any pollutant at a monitoring point, the corresponding water quality monitoring level is determined according to the water quality sensor data corresponding to the pollutant, including: Step 2021: For any pollutant at the monitoring point, determine the first statistical data and the second statistical data based on the historical sensor data corresponding to the pollutant in the current time interval; wherein, the first statistical data represents the average concentration of the pollutant in the current time interval, and the second statistical data represents the degree of concentration fluctuation of the pollutant in the current time interval. Step 2022: Determine the concentration fluctuation range based on the first and second statistical data. Step 2023: Determine the corresponding water quality monitoring level based on the current sensor data and concentration fluctuation range of the pollutant; wherein, the water quality sensor data corresponding to the pollutant includes historical sensor data and current sensor data.

[0041] Specifically, the platform collects no less than 168 hours, or 7 days, of historical sensor data for each monitoring point, divides a 24-hour day into 96 time periods, with each time period consisting of 15 minutes, and calculates the average value of each water quality sensor data within each time period. ) and standard deviation ( This establishes a unique dynamic baseline range for the monitoring site, namely the concentration fluctuation range. Each pollutant corresponds to a concentration fluctuation range. The first statistical data can be the mean of water quality sensor data at any time, and the second statistical data can be the standard deviation of water quality sensor data at any time period.

[0042] The concentration fluctuation range is determined based on the following formula:

[0043] in, This represents the lower limit of the concentration fluctuation range. This represents the upper limit of the concentration fluctuation range. This represents the first statistical data. This indicates the second statistical data.

[0044] For any pollutant, if the concentration represented by its water quality sensor data exceeds the concentration fluctuation range of the corresponding time period for a continuous preset time period, the water quality monitoring level is determined to be Level 1, which means it is judged as a suspected abnormal sewage discharge event, indicating the existence of abnormal water quality.

[0045] Accordingly, The range covers approximately 95% of normal fluctuation data, and the preset duration can effectively filter false alarms caused by instantaneous noise from the sensor. Together, these two factors ensure a balance between the accuracy and sensitivity of the alarm. Even if the real-time data does not exceed the national standard static threshold, the system will still trigger an alarm as long as it deviates from its normal fluctuation pattern, thereby effectively capturing hidden behaviors such as dilution emissions and intermittent illegal discharges. The system automatically updates the dynamic fluctuation range daily to achieve real-time updates of the model.

[0046] Specifically, in one embodiment, when the slope, second derivative, or other characteristics of the monitored data sequence undergo abrupt changes, an early warning of water quality deterioration trends is issued. If the pollutant concentration characterized by the deterioration trend exceeds the concentration fluctuation range of the corresponding time period, the water quality monitoring level is determined to be Level II, which is used to prompt users to pay attention to the pollutant concentration in the future period and determine whether there is a real abnormal sewage discharge event.

[0047] Based on the above embodiments, as an implementable approach, in one embodiment, the peak matching degree of at least one discharge point corresponding to at least one discharge point is determined according to the alarm time, preset water flow velocity, preset error value, distance of at least one discharge point monitoring point upstream of the alarm point, and peak discharge time of pollutants, based on the water quality sensor data corresponding to the alarm point, including: Step 2041: Determine the theoretical transmission time based on the preset water flow velocity and the distance to at least one discharge monitoring point upstream of the alarm point; Step 2042: Based on the alarm time, preset error value, and theoretical transmission time corresponding to the water quality sensor data of the alarm point, and the peak emission time of pollutants at at least one discharge monitoring point upstream of the alarm point, determine the peak matching degree of at least one discharge point corresponding to at least one discharge monitoring point.

[0048] Specifically, in one embodiment, the theoretical transmission time can be determined based on the following formula:

[0049] in, Indicates the theoretical transmission time. This indicates the distance between the monitoring point and the alarm point at the discharge outlet. The preset water flow velocity can be measured in real time by the terminal's built-in flow velocity sensor, deduced from the historical transmission time between the outlet and the downstream section, or obtained from empirical flow velocity values ​​in hydrological yearbooks.

[0050] Specifically, in one embodiment, the peak matching degree can be determined based on the following formula:

[0051] in, Indicates peak matching degree. This indicates the alarm time corresponding to the water quality sensor data at the alarm point. This indicates the peak emission time of pollutants from at least one monitoring point at an outlet upstream of the alarm point. Indicates the theoretical transmission time. This represents the preset error value, which characterizes the allowable error time for pollutant propagation, for example, 5 minutes.

[0052] Specifically, if the outlet Peak occurrence time of pollutants satisfy Then determine the outlet Highly correlated with pollutant exceedance events at alarm points, several key upstream discharge outlets were calculated sequentially. The peak matching degree.

[0053] Specifically, in one embodiment, the pollutant correlation coefficient can be determined based on the following formula:

[0054] in, Represents the correlation coefficient of pollutants. Indicates the alarm point is at the 1st Minute-level water quality sensor data on pollutants, Indicates the outlet is at the Minute-level water quality sensor data on pollutants, This indicates the average concentration of pollutants at the alarm point within the time window. This indicates the average concentration of pollutants at the discharge outlet within a time window. Indicates the duration of the preset time window.

[0055] Specifically, the closer the pollutant correlation coefficient is to 1, the more likely it is that the emission waveform of the discharge outlet is completely reproduced at the alarm point after a time of τ, which indicates that the discharge outlet is highly correlated with the pollutant exceedance event at this alarm point.

[0056] Specifically, in one embodiment, the suspicion level of pollutant emissions can be determined based on the following formula:

[0057] in, Indicates the sequence number is The suspected pollutant emission level of the discharge outlet Represents the correlation coefficient of pollutants. Indicates peak matching degree. Indicates historical illegal records, Indicates the sequence number is The weight of the outlet. , , and This indicates the preset weighting coefficient. Historical violation records are determined based on the ratio of actual violations to preset violation counts.

[0058] Specifically, in one embodiment, the sensor employs a novel spectral sensing chip based on quantum dot materials. Quantum dots of different sizes can accurately respond to light of different wavelengths. The chip integrates a miniature LED light source, a quantum dot detector array, and a microfluidic sample cell, enabling simultaneous in-situ spectral analysis of multiple key water quality parameters, including COD, ammonia nitrogen, turbidity, dissolved oxygen, pH, temperature, and various pollutants. Furthermore, the entire optical engine is integrated onto a centimeter-scale chip, with an average operating power consumption of less than 1 watt and a standby power consumption in the microwatt range. This allows the device to be powered by a small battery and operate for extended periods.

[0059] Correspondingly, compared to quantum dots used as tracers, the technical principle of releasing encoded quantum dot particles at the sewage outlet as tracers and diffusing with the water flow, and retrieving the pollution source by tracking their trajectory, is a passive and reactive dynamic tracer. Moreover, quantum dots pose a secondary environmental risk after entering the water body. In this application, quantum dots of different sizes are fixed on a chip. When illuminated by a micro LED light source, the target pollutant changes its emission spectrum through interaction with the quantum dots, such as fluorescence quenching or energy transfer. The system infers the pollutant concentration by analyzing the spectral changes. This is a static, in-situ quantitative analysis method without secondary pollution.

[0060] For example, such as Figure 2The diagram shows the structure of the integrated sensing device provided in this application embodiment. It integrates a quantum dot spectral sensor, a low-power MCU main control unit, a 4G / 5G / NB-IoT wireless communication module, a high-energy lithium battery pack, and a GPS or BeiDou positioning module all within a high-protection cylindrical or square housing. The integrated sensing device is cylindrical with a diameter ≤120mm, a height ≤370mm, an overall volume not exceeding 500cm³, an overall weight ≤5kg, an average operating power consumption ≤1W, and a standby power consumption ≤10μW. The built-in lithium battery pack has a capacity ≥80Ah, allowing for continuous operation for ≥480 hours (20 days) in pure battery power mode. An optional solar charging module enables long-term maintenance-free operation. The wireless communication module supports 4G / 5G / NB-IoT standards, data transmission follows the HJ / T 212-2017 standard protocol, supports breakpoint resume function, and achieves a data transmission success rate ≥98% and a transmission delay ≤3 seconds in an environment with a signal strength ≥-85dBm. Optionally, for scenarios with stable mains power or ample sunlight, a hybrid power supply module combining solar panels and batteries can be added, or the system can be directly connected to mains power to support higher-frequency data acquisition, such as 30 seconds / time, or additional functions like video surveillance. In remote areas with poor 4G / 5G signals, low-power wide-area network technologies such as LoRaWAN and NB-IoT can be used. By setting up gateways at nearby high points, data from multiple terminals can be aggregated and transmitted back in a unified manner. For core organic pollutants, such as COD, and nutrients, such as ammonia nitrogen and total phosphorus, quantum dot spectrometers are used to ensure performance, while the five conventional parameters, such as temperature, pH, conductivity, dissolved oxygen, and turbidity, can be combined using more mature and lower-cost electrochemical or optical probes to further optimize the overall system's cost-effectiveness.

[0061] For example, such as Figure 3 The diagram shown illustrates the structure of the integrated sensing device with an anchored installation method according to an embodiment of this application. This anchored installation method is suitable for areas with stable riverbeds, such as rivers with gentle bank slopes, remote locations, areas with limited human activity requiring protection, or areas without a fixed, rigid surface nearby. The terminal is connected to a counterweight, such as a concrete pier or anchor, at the bottom of the riverbed via a waterproof cable. The weight of the counterweight is determined based on the river flow rate, allowing the terminal to float at a fixed depth below the water surface. This method is resistant to water flow impact, simple to install, and uses weights to horizontally fix the monitoring device, causing the buoy to float on the water surface.

[0062] For example, such as Figure 4The diagram shown is a structural schematic of the cylindrical installation method of the integrated sensing device provided in this application embodiment. The cylindrical installation method is suitable for vertical riverbanks or areas with anti-theft requirements, such as vertical riverbanks, areas with fast river flow and large impacts during flood season, areas with many large floating objects on the river surface, areas requiring impact resistance, areas with theft risks, or areas with a lot of garbage in the main river channel or irregularly low water levels that significantly interfere with monitoring. The cylindrical installation mainly uses 300mm diameter PVC pipes, laid along the riverbank. Several meters of PVC pipe are fixed with expansion bolts using hose clamps. The device is placed inside the cylinder, with the upper end secured to the pipe opening with ropes, and the lower end ensuring the device is submerged to a certain depth. Simultaneously, the underwater portion of the pipe is designed with a water flow through-hole to ensure water flow. The upper part is covered and locked to effectively prevent vandalism and theft.

[0063] Alternatively, for shallow open channels, a plug-in installation can be used, where the terminal is directly fixed to a rod inserted into the bottom sediment.

[0064] Accordingly, the integrated sensing device achieves an IP68 protection rating, can be completely submerged in water, is corrosion-resistant, resistant to water flow impact, and adaptable to the harsh environment of the discharge outlet.

[0065] Specifically, a list of suspected discharge outlets is provided to users via mobile devices. Users can monitor in real time through their mobile devices, viewing the latest data and sensor status of all deployed monitoring points in map or list format, including online, offline, and low battery status. The system can receive various alarm information pushed from the cloud in real time, including alarm type, exceeding indicators, suspected pollution source location, and suggested action routes. Users can navigate to the abnormal discharge outlet with one click, allowing regulatory personnel to take photos and videos on-site, upload enforcement documents, and fill in the handling situation online. For example, if it is found that a company is illegally discharging, the site has been sealed off, and the incident has been closed. The handling results are returned to the cloud platform to optimize the AI ​​early warning model. False alarms automatically expand the concentration fluctuation range, while missed alarms automatically shrink the range, enabling online incremental learning of the alarm model.

[0066] For example, such as Figure 5The diagram illustrates the flow chart of the water quality monitoring method provided in this application embodiment. Monitoring points are set up at river outfalls, etc. Water quality sensing data is acquired through a quantum dot spectral sensor in an integrated sensing device. The water quality sensing data is analyzed in real time. Based on the historical sensing data corresponding to the pollutant in the current time interval, a first statistical data point and a second statistical data point are determined, thereby determining the concentration fluctuation range. When the current sensing data corresponding to the pollutant exceeds the concentration fluctuation range, the water quality is determined to be abnormal. Optionally, an early warning judgment is also made based on future trends, and an alarm is triggered. After an alarm is triggered at the monitoring point, the peak matching degree and pollutant correlation coefficient are determined based on the water quality sensing data, alarm time, preset water flow velocity, etc., and spatiotemporal correlation analysis is performed. Based on the pollutant correlation coefficient, peak matching degree, historical violation records, and preset outfall weights of the outfall, the pollutant discharge suspicion degree is determined, a suspected outfall list is generated, and the suspected outfall list is sent to the user. The user can perform real-time monitoring, receive alarm information, and navigate to suspected outfalls through a mobile device. For example, such as Figure 6 The diagram shows a flowchart of the water quality tracing method provided in this application embodiment. After determining the alarm time, the theoretical transmission time of each upstream discharge outlet is calculated, and a preset time window is determined. Water quality sensor data within the preset time window is retrieved. Based on the water quality sensor data of pollutants at the alarm point and the water quality sensor data of pollutants at the discharge outlet, the pollutant correlation coefficient is determined. Based on the pollutant correlation coefficient, peak matching degree, historical violation records, and preset discharge outlet weight, the pollutant discharge suspicion degree is determined. The pollutants are sorted in descending order according to the discharge suspicion degree to obtain a list of suspected discharge outlets.

[0067] The water quality monitoring method provided in this application includes: acquiring water quality sensing data of at least one pollutant at any monitoring point using a sensor; wherein the monitoring point includes a discharge outlet monitoring point and a preset water flow monitoring point; determining the corresponding water quality monitoring level for any pollutant at the monitoring point based on the corresponding water quality sensing data; determining the monitoring point as an alarm point if the water quality monitoring level indicates abnormal water quality; and determining at least one alarm point based on the alarm time, preset water flow velocity, preset error value, distance to at least one discharge outlet monitoring point upstream of the alarm point, and the peak discharge time of the pollutant, according to the water quality sensing data of the alarm point. The peak matching degree of at least one discharge outlet corresponding to a discharge outlet monitoring point; within a preset time window, the pollutant correlation coefficient is determined based on at least one water quality sensor data of pollutants at the alarm point and at least one water quality sensor data of pollutants at at least one discharge outlet; for any discharge outlet, the pollutant discharge suspicion degree is determined based on the pollutant discharge suspicion degree, peak matching degree, historical violation records and preset discharge outlet weight; based on the pollutant discharge suspicion degree, at least one suspected discharge outlet monitoring point is determined among at least one discharge outlet monitoring point, and at least one suspected discharge outlet monitoring point is sent to the user so that the user can conduct on-site verification based on the suspected discharge outlet monitoring point.

[0068] The method provided by the above scheme determines the water quality monitoring level by acquiring pollutant water quality sensor data at monitoring points using sensors. When the water quality is abnormal, the monitoring point is designated as an alarm point. Based on the alarm time, preset water flow velocity, preset error value, distance to the upstream discharge monitoring point, and peak pollutant discharge time, the peak matching degree of the discharge point is calculated. Within a preset time window, the pollutant correlation coefficient is determined based on the water quality sensor data of the alarm point and the discharge point. Based on the pollutant correlation coefficient, peak matching degree, historical violation records, and preset discharge point weight, the pollutant discharge suspicion degree is determined. Then, the suspected discharge monitoring point is identified and sent to the user. By acquiring water quality sensor data through sensors and performing real-time analysis, the suspected discharge monitoring point upstream of the alarm point is effectively identified, realizing real-time water quality monitoring and timely pollution source tracing.

[0069] Furthermore, by installing quantum dot spectral sensors at pre-set monitoring points at each sewage outlet and river, situations where pollutants exceed standards can be identified more accurately. Compared to setting up water quality monitoring stations, the sensor equipment requires no land acquisition or power supply infrastructure, and a single person can complete the installation at one point within one hour. This convenient and quick deployment method, along with lower deployment costs, expands the coverage of water quality monitoring and improves the flexibility of monitoring equipment deployment. Since it is possible for the pollutant concentration at a single sewage outlet to be below the static threshold, but the total discharge from multiple outlets exceeds the alarm threshold, real-time monitoring of pollutant concentration in the water flow at monitoring points prevents water quality anomalies caused by such situations. Compared to manual periodic monitoring, the monitoring frequency has increased from monthly to minute-level, and data latency has been reduced from days to seconds. The system can issue early warnings within minutes of a sewage discharge event, nipping pollution in the bud at the source or during transmission, preventing pollutants from spreading downstream. Compared to regular manual monitoring, which often results in delays in obtaining evidence when illegal discharges occur, this new system leverages alarm points and water quality sensor data from each discharge point. Through spatiotemporal correlation analysis and integration with digital pipeline network maps, it enables rapid, semi-automated identification of suspected upstream discharge points from downstream water quality anomalies. This reduces tracing efficiency from days to minutes. Furthermore, the continuous and reliable monitoring data provided by the system serves as legally valid electronic evidence. Combined with precise tracing algorithms, it can clearly pinpoint the discharge time, responsible discharge point, and even suspected upstream pollution sources, thereby enhancing regulatory deterrence. The range covers approximately 95% of normal fluctuation data, and the preset duration effectively filters false alarms caused by instantaneous sensor noise. Both factors ensure a balance between alarm accuracy and sensitivity. Even if real-time data does not exceed the national standard static threshold, the system will still trigger an alarm if it deviates from its normal fluctuation pattern, effectively capturing hidden behaviors such as dilution discharge and intermittent illegal discharge. The system automatically updates the dynamic fluctuation range daily, enabling real-time model updates. Compared to quantum dots used as tracers, whose technical principle involves releasing encoded quantum dot particles at the discharge outlet as tracers that diffuse with the water flow and track their trajectory to invert the pollution source, this is a passive, reactive dynamic tracer. Furthermore, quantum dots pose a secondary environmental risk after entering the water body. This application fixes quantum dots of different sizes onto a chip. When illuminated by a miniature LED light source, the target pollutant changes its emission spectrum through interaction with the quantum dots, such as fluorescence quenching or energy transfer. The system infers the pollutant concentration by analyzing the spectral changes, providing a static, in-situ quantitative analysis method without secondary pollution. This integrated sensing device achieves an overall IP68 protection rating, allowing it to be completely submerged in water. It is corrosion-resistant, water flow-resistant, and adaptable to harsh environments at discharge outlets.

[0070] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.

[0071] The embodiments of this application also provide a water quality monitoring device for performing the water quality monitoring method provided in the above embodiments.

[0072] like Figure 7 The diagram shown is a structural schematic of the water quality monitoring device provided in this embodiment of the application. The water quality monitoring device 70 includes: an acquisition module 701, a level determination module 702, an alarm point determination module 703, a peak value matching degree determination module 704, a correlation coefficient determination module 705, a suspicion degree determination module 706, and a suspected discharge outlet determination module 707.

[0073] The system includes the following modules: an acquisition module for acquiring water quality sensor data of at least one pollutant at any given monitoring point; the monitoring points include outlet monitoring points and preset water flow monitoring points; a level determination module for determining the corresponding water quality monitoring level for any pollutant at a monitoring point based on the corresponding water quality sensor data; an alarm point determination module for determining an alarm point when the water quality monitoring level indicates abnormal water quality; and a peak matching degree determination module for determining at least one outlet monitoring point based on the alarm time, preset water flow velocity, preset error value, distance to at least one outlet monitoring point upstream of the alarm point, and the peak discharge time of the pollutant, according to the water quality sensor data of the alarm point. The system includes: a peak matching degree for at least one corresponding discharge outlet; a correlation coefficient determination module, used to determine the pollutant correlation coefficient within a preset time window based on at least one water quality sensor data of pollutants at the alarm point and at least one water quality sensor data of pollutants at at least one discharge outlet; a suspicion degree determination module, used to determine the suspicion degree of pollutant discharge for any discharge outlet based on the pollutant correlation coefficient, peak matching degree, historical violation records, and preset discharge outlet weight; and a suspected discharge outlet determination module, used to determine at least one suspected discharge outlet monitoring point among at least one discharge outlet monitoring point based on the suspected discharge outlet monitoring point, and send at least one suspected discharge outlet monitoring point to the user so that the user can conduct on-site verification based on the suspected discharge outlet monitoring point.

[0074] For a description of the features in the embodiments corresponding to the water quality monitoring device, please refer to the relevant descriptions in the embodiments corresponding to the water quality monitoring method, which will not be repeated here.

[0075] like Figure 8 The diagram shown is a structural schematic of a water quality monitoring system provided in an embodiment of this application. The water quality monitoring system includes: at least one quantum dot spectral sensor, a water quality monitoring and analysis module, and a user interaction module. A quantum dot spectrometer is used to acquire water quality sensing data of at least one pollutant at a monitoring point and send the water quality sensing data to a water quality monitoring and analysis module; wherein, the quantum dot spectrometer is deployed at the monitoring point; The water quality monitoring and analysis module receives water quality sensing data from the quantum dot spectral sensor. For any pollutant at a monitoring point, it determines the corresponding water quality monitoring level based on the water quality sensing data. If the water quality monitoring level indicates abnormal water quality, the monitoring point is designated as an alarm point. Based on the alarm time, preset water flow velocity, preset error value, distance to at least one upstream discharge monitoring point, and peak discharge time of the pollutant, the module determines the peak matching degree of the discharge point. Within a preset time window, it determines the pollutant correlation coefficient based on at least one water quality sensing data of the pollutant at the alarm point and at least one water quality sensing data of the pollutant at at least one discharge point. Based on the pollutant correlation coefficient, peak matching degree, historical violation records, and discharge point weight, the module determines the pollutant discharge suspicion level. Based on the pollutant discharge suspicion level, at least one suspected discharge monitoring point is identified among at least one discharge monitoring point, and this suspected discharge monitoring point is sent to the user interaction module. The user interaction module is used to push alarm information to users, provide at least one suspected discharge outlet monitoring point and corresponding navigation information, and provide a port for filling in handling information.

[0076] For a description of the features in the embodiments corresponding to the water quality monitoring system, please refer to the relevant descriptions in the embodiments corresponding to the water quality monitoring method, which will not be repeated here.

[0077] Embodiments of this application also provide an electronic device, such as... Figure 9 The diagram shown is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, including a processor 10 and a memory 20. The memory 20 stores a computer program, and the processor 10 is configured to run the computer program to execute the steps in any of the above-described water quality monitoring method embodiments.

[0078] Embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above-described water quality monitoring method embodiments when it is run.

[0079] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0080] Embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above-described water quality monitoring method embodiments.

[0081] Embodiments of this application also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in any of the above-described water quality monitoring method embodiments.

[0082] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0083] The foregoing has provided a detailed description of a water quality monitoring method, apparatus, system, electronic device, and storage medium provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only intended to aid in understanding the method and core ideas of this application. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A water quality monitoring method, characterized in that, The method includes: For any given monitoring point, water quality sensor data for at least one pollutant is acquired via sensors; wherein, the monitoring points include discharge outlet monitoring points and preset water flow monitoring points; For any pollutant at the monitoring point, the corresponding water quality monitoring level is determined based on the water quality sensor data corresponding to the pollutant. If the water quality monitoring level indicates abnormal water quality, the monitoring point is determined as an alarm point; Based on the alarm time, preset water flow velocity, preset error value, and distance of at least one discharge monitoring point upstream of the alarm point and the peak emission time of the pollutant, the peak matching degree of at least one discharge point corresponding to the at least one discharge monitoring point is determined. Within a preset time window, a pollutant correlation coefficient is determined based on at least one water quality sensor data of the pollutant at the alarm point and at least one water quality sensor data of the pollutant at the at least one discharge outlet. For any of the aforementioned discharge outlets, the pollutant emission suspicion level is determined based on the pollutant correlation coefficient, peak matching degree, historical violation records, and preset discharge outlet weights of the discharge outlet; Based on the suspected pollutant emission level, at least one suspected emission monitoring point is identified among the at least one emission monitoring point, and the at least one suspected emission monitoring point is sent to the user so that the user can conduct on-site verification based on the suspected emission monitoring point.

2. The water quality monitoring method according to claim 1, characterized in that, The step of determining the peak matching degree of at least one discharge outlet corresponding to the at least one discharge outlet monitoring point based on the alarm time, preset water flow velocity, preset error value, distance of at least one discharge outlet monitoring point upstream of the alarm point, and peak discharge time of the pollutant, includes: The theoretical transmission time is determined based on the preset water flow velocity and the distance to at least one discharge outlet monitoring point upstream of the alarm point; Based on the alarm time, preset error value, and theoretical transmission time corresponding to the water quality sensor data of the alarm point, and the peak emission time of pollutants at at least one discharge monitoring point upstream of the alarm point, the peak matching degree of at least one discharge point corresponding to at least one discharge monitoring point is determined.

3. The water quality monitoring method according to claim 1, characterized in that, The step of determining the peak matching degree of at least one discharge outlet corresponding to at least one discharge outlet monitoring point based on the alarm time, preset error value, and theoretical transmission time corresponding to the water quality sensor data of the alarm point, and the peak emission time of pollutants at at least one discharge outlet monitoring point upstream of the alarm point, includes: The peak matching degree is determined based on the following formula: in, Indicates peak matching degree. This indicates the alarm time corresponding to the water quality sensor data at the alarm point. This indicates the peak emission time of pollutants from at least one discharge monitoring point upstream of the alarm point. Indicates the theoretical transmission time. This indicates the preset error value.

4. The water quality monitoring method according to claim 1, characterized in that, Within a preset time window, determining the pollutant correlation coefficient based on at least one water quality sensor data of the pollutant at the alarm point and at least one water quality sensor data of the pollutant at the at least one discharge outlet includes: The correlation coefficient of pollutants is determined based on the following formula: in, Represents the correlation coefficient of pollutants. Indicates the alarm point is at the 1st Water quality sensor data for the pollutants described in minutes Indicates the outlet is at the Water quality sensor data for the pollutants described in minutes This indicates the average concentration of the pollutant at the alarm point within the time window. This indicates the average concentration of the pollutant at the discharge outlet within the time window. Indicates the duration of the preset time window.

5. The water quality monitoring method according to claim 1, characterized in that, For any of the aforementioned discharge outlets, the pollutant emission suspicion level is determined based on the pollutant correlation coefficient, peak matching degree, historical violation records, and preset discharge outlet weights, including: The suspicion level of pollutant emissions is determined based on the following formula: in, Indicates the sequence number is The suspected pollutant emission level of the discharge outlet Represents the correlation coefficient of pollutants. Indicates peak matching degree. Indicates historical illegal records, Indicates the sequence number is The weight of the outlet. , , and This indicates the preset weighting coefficient. .

6. The water quality monitoring method according to claim 1, characterized in that, For any pollutant at the monitoring point, the corresponding water quality monitoring level is determined based on the water quality sensor data corresponding to the pollutant, including: For any pollutant at the monitoring point, a first statistical data point and a second statistical data point are determined based on the historical sensor data corresponding to the pollutant in the current time interval; wherein, the first statistical data point represents the average concentration of the pollutant in the current time interval, and the first statistical data point represents the degree of concentration fluctuation of the pollutant in the current time interval. Based on the first and second statistical data, the concentration fluctuation range is determined; Based on the current sensor data and concentration fluctuation range corresponding to the pollutant, the corresponding water quality monitoring level is determined; wherein, the water quality sensor data corresponding to the pollutant includes historical sensor data and current sensor data.

7. A water quality testing device, characterized in that, The device includes: The acquisition module is used to acquire water quality sensor data of at least one pollutant for any given monitoring point through a sensor; wherein the monitoring points include discharge outlet monitoring points and preset water flow monitoring points; The level determination module is used to determine the corresponding water quality monitoring level for any pollutant at the monitoring point based on the water quality sensor data corresponding to the pollutant. The alarm point determination module is used to determine the monitoring point as an alarm point when the water quality monitoring level indicates abnormal water quality. The peak matching degree determination module is used to determine the peak matching degree of at least one discharge point corresponding to the at least one discharge point based on the alarm time, preset water flow velocity, preset error value, distance of at least one discharge point monitoring point upstream of the alarm point, and peak discharge time of the pollutant, according to the water quality sensor data corresponding to the alarm point. The correlation coefficient determination module is used to determine the pollutant correlation coefficient within a preset time window based on at least one water quality sensor data of the pollutant at the alarm point and at least one water quality sensor data of the pollutant at the at least one discharge outlet. The suspicion determination module is used to determine the suspicion of pollutant emission for any of the discharge outlets based on the pollutant correlation coefficient, peak matching degree, historical violation records and preset discharge outlet weights of the discharge outlets. The suspected discharge outlet determination module is used to determine at least one suspected discharge outlet monitoring point among the at least one discharge outlet monitoring points based on the suspected pollutant emission level, and send the at least one suspected discharge outlet monitoring point to the user so that the user can conduct on-site verification based on the suspected discharge outlet monitoring point.

8. A water quality monitoring system, characterized in that, The system includes: at least one quantum dot spectral sensor, a water quality monitoring and analysis module, and a user interaction module; The quantum dot spectral sensor is used to acquire water quality sensing data of at least one pollutant at the monitoring point and send the water quality sensing data to the water quality monitoring and analysis module; wherein, the quantum dot spectral sensor is deployed at the monitoring point; The water quality monitoring and analysis module receives water quality sensing data sent by the quantum dot spectral sensor. For any pollutant at the monitoring point, it determines the corresponding water quality monitoring level based on the water quality sensing data corresponding to the pollutant. If the water quality monitoring level indicates abnormal water quality, the monitoring point is designated as an alarm point. Based on the alarm time, preset water flow velocity, preset error value, distance to at least one discharge outlet monitoring point upstream of the alarm point, and the peak emission time of the pollutant, the module determines the peak matching degree corresponding to the discharge outlet. Within a preset time window, it determines the pollutant correlation coefficient based on at least one water quality sensing data of the pollutant at the alarm point and at least one water quality sensing data of the pollutant at the at least one discharge outlet. Based on the pollutant correlation coefficient, peak matching degree, historical violation records, and discharge outlet weight, the module determines the pollutant emission suspicion degree. Based on the pollutant emission suspicion degree, at least one suspected discharge outlet monitoring point is identified among the at least one discharge outlet monitoring points, and the at least one suspected discharge outlet monitoring point is sent to the user interaction module. The user interaction module is used to push alarm information to users, provide the at least one suspected discharge outlet monitoring point and corresponding navigation information, and provide a port for filling in handling information.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the water quality monitoring method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the water quality monitoring method as described in any one of claims 1 to 6.

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