A three-dimensional fluorescence dynamic monitoring and automated processing system for water quality

Abstract

This invention discloses a three-dimensional fluorescence dynamic monitoring and automated treatment system for water quality, comprising a water sample acquisition module, a three-dimensional fluorescence spectroscopy detection module, a pollution fingerprint database, a spectral analysis unit, a diffusion path simulation module based on a distributed sensor network, and an automated control execution module. The diffusion path simulation module collects spatiotemporal data using water quality spectral sensors, hydrological sensors, and meteorological sensors, and dynamically predicts pollutant migration trajectories by combining a water flow field model constructed using the finite volume method with a first-order kinetic fluorescence decay model. The automated control execution module, based on pollution identification results and trajectory prediction, uses fuzzy logic algorithms to coordinate emergency operations such as gate opening and closing and reagent dosing, achieving full automation from monitoring to treatment. The system completes pollution identification within 10 minutes using a spectral angle mapping algorithm, overcoming the shortcomings of traditional methods such as response lag, lack of dynamic prediction, and automated treatment.

Description

Technical Field

[0001] This invention relates to the field of water quality testing technology, and in particular to a three-dimensional fluorescence dynamic monitoring and automated processing system for water quality. Background Technology

[0002] In the field of water quality monitoring, existing technologies mainly employ chemical analysis, biosensor methods, and online monitoring instruments for water quality assessment. Chemical analysis requires manual sampling and laboratory testing; while offering high accuracy, it suffers from slow response times, complex operation, and high labor costs, making it unsuitable for real-time monitoring of sudden pollution events. Biosensor methods detect pollutants through the specific reactions of bioactive substances; however, their detection range is limited, they are susceptible to environmental interference, and their stability is insufficient. Online monitoring instruments, while capable of real-time data acquisition, primarily focus on measuring conventional physicochemical indicators such as pH, COD, and ammonia nitrogen, lacking the ability to specifically identify pollutants at the molecular level and thus failing to quickly distinguish pollutant types and sources.

[0003] In existing technologies, traditional water quality monitoring systems can only achieve single-point data acquisition, lacking the ability to monitor the entire area based on distributed sensor networks, and cannot capture the spatiotemporal distribution characteristics of pollutants in real time; pollution diffusion prediction mostly relies on static models, without integrating dynamic parameters such as hydrology and meteorology, resulting in large trajectory prediction errors; emergency response relies entirely on manual decision-making, taking several hours from pollution identification to initiation of treatment, making it difficult to cope with sudden pollution events. This invention fills the gap in dynamic prediction and intelligent treatment in existing technologies by using distributed multi-source sensor fusion, multi-physics field coupled modeling, and automated linkage control. Therefore, it proposes a three-dimensional fluorescence dynamic monitoring and automated treatment system for water quality. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a three-dimensional fluorescence dynamic monitoring and automated processing system for water quality, which solves the problems of slow response, weak recognition, low automation, multi-scenario adaptability, and insufficient operation and maintenance in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A three-dimensional fluorescence dynamic monitoring and automated processing system for water quality is characterized by comprising: a water sample acquisition module, a three-dimensional fluorescence spectroscopy detection module, a pollution fingerprint database, a spectral analysis unit, a diffusion path simulation module, and an automated control execution module;

[0007] The water sample acquisition module is used to sample the target water body in real time and transport the water sample to the three-dimensional fluorescence spectroscopy detection module;

[0008] The three-dimensional fluorescence spectroscopy detection module performs excitation-emission wavelength matrix scanning on the water sample to obtain three-dimensional spectral data including fluorescence intensity;

[0009] The pollution fingerprint database pre-stores standard three-dimensional fluorescence fingerprint spectra of common pollutants such as polycyclic aromatic hydrocarbons, pesticides, and PPCPs.

[0010] The spectral analysis unit uses a spectral similarity algorithm to calculate the characteristic angle between the spectrum to be tested and the standard spectrum in the database. When the angle is less than a preset threshold, it automatically identifies the pollution type and level.

[0011] The diffusion path simulation module predicts the migration trajectory of pollutants based on spatiotemporal spectral data collected by a distributed sensor network, combined with a water flow field model and a fluorescence decay model.

[0012] The automated control execution module, based on pollution identification results and trajectory prediction, coordinates with the actuator to perform emergency operations such as gate opening and closing and chemical dosing; the actuator includes: a gate control system (electric / hydraulic gate) and a chemical dosing device (metering pump + storage tank).

[0013] The automated control execution module also includes a contingency plan knowledge base, which has a pre-stored 'pollution type-treatment parameter' mapping table.

[0014] Preferably, the water sample collection module includes an automatic sampling pump, a multi-stage filtration assembly, and a self-cleaning mechanism; the automatic sampling pump extracts water samples at a set frequency, the multi-stage filtration assembly removes particulate impurities through a 0.45μm filter membrane, and the self-cleaning mechanism periodically backwashes and cleans the sampling pipeline and filtration unit to avoid cross-contamination.

[0015] Preferably, the three-dimensional fluorescence spectroscopy detection module integrates a pulsed laser exciter, a high-sensitivity CCD detector, a temperature-controlled optical path system, and an automatic calibration module; the pulsed laser exciter emits tunable excitation light, and the CCD detector synchronously acquires fluorescence signals across the entire wavelength range; the temperature-controlled optical path system maintains stable optical path temperature through a semiconductor temperature control element, and the automatic calibration module periodically injects quinine sulfate standard solution for sensitivity verification.

[0016] Preferably, the spectral analysis unit adopts an edge computing architecture, with a built-in preprocessing algorithm module (including baseline correction and scattering subtraction) and a self-learning model; after feature extraction, the preprocessed spectral data is compared with the contamination fingerprint database in real time, and the database feature vector is dynamically optimized based on new sample data.

[0017] Preferably, the distributed sensor network includes:

[0018] Water quality spectral sensors (such as fluorescence probes) are used to acquire three-dimensional fluorescence spectral data of water bodies in real time;

[0019] Hydrological sensors (including Doppler current meters and water level gauges) are used to acquire real-time hydrological parameters such as flow velocity, flow direction, and riverbed topography.

[0020] Meteorological sensors (anemometers, rain gauges) are used to collect environmental data such as wind speed and rainfall.

[0021] The water flow field model is constructed based on the finite volume method, and the water flow motion is simulated by solving the Navier-Stokes equations; the fluorescence decay model adopts the first-order dynamic equation, and dynamically calculates the decay coefficient by combining parameters such as pollutant photon yield and water temperature; the module integrates multi-source data through data fusion algorithms (such as Kalman filtering) to realize the dynamic correction of pollutant migration trajectory.

[0022] Preferably, the actuator includes:

[0023] The gate control system (electric / hydraulic gate) supports remote opening and closing and flow regulation;

[0024] The chemical dosing device (metering pump + storage tank) can automatically add activated carbon, oxidants and other chemicals according to the type of pollution.

[0025] The contingency plan knowledge base pre-stores a mapping table of 'pollution type-treatment parameters' (e.g., benzene series pollution corresponds to an activated carbon dosage of 10 mg / L), and matches the optimal treatment plan through a fuzzy logic algorithm, with an execution delay of ≤30 seconds.

[0026] Preferably, it also includes a data storage module and an encrypted transmission module; the data storage module adopts a distributed architecture to store raw spectral data, analysis results, and trajectory prediction data; the encrypted transmission module uses the AES-256 algorithm to encrypt the data during transmission to ensure data security.

[0027] Preferably, the formula for calculating the feature angle in the spectral similarity algorithm is: ,in Let i be the intensity value at the i-th wavelength point of the spectrum to be measured. The standard fingerprint spectrum corresponds to the intensity value of the wavelength point, and n is the number of spectral sampling points; the system presets multiple pollution level thresholds, and different thresholds correspond to different emergency response levels.

[0028] Preferably, it also includes a fault diagnosis module; the module monitors the operating parameters of each unit in real time, establishes an anomaly discrimination model by combining historical data, automatically identifies equipment faults and triggers alarms, and generates maintenance suggestion work orders.

[0029] Preferably, it also includes a human-computer interaction interface; the interface displays three-dimensional spectral maps, pollution identification results, trajectory prediction dynamics and equipment status in real time, supports users to set parameters, retrieve historical data and perform manual emergency operations, and realizes visual management and interactive control.

[0030] The technical effects and advantages of the three-dimensional fluorescence dynamic monitoring and automated treatment system for water quality of this invention are as follows:

[0031] 1. This invention calculates the characteristic angle between the spectrum to be tested and the standard spectrum in the pollution fingerprint database using a spectral similarity algorithm (such as spectral angle mapping), enabling automatic identification of pollution type and level within 10 minutes. Compared to traditional water quality monitoring methods that require manual sampling and testing, taking more than 4 hours, this system greatly shortens the pollution response time, enabling timely detection of sudden pollution events and avoiding significant losses caused by pollution spread.

[0032] 2. This invention offers a comprehensive range of pollutant detection. The pollutant fingerprint database pre-stores standard three-dimensional fluorescence fingerprint spectra of common pollutants such as polycyclic aromatic hydrocarbons, pesticides, and PPCPs. Combined with the three-dimensional fluorescence spectroscopy detection module, the system performs excitation-emission wavelength matrix scanning on water samples. This enables the system to not only detect conventional pollutants but also identify unknown pollutants such as novel pesticide intermediates through the self-learning model of the spectral analysis unit. This overcomes the shortcomings of traditional methods that rely on preset detection items and are difficult to detect non-target pollutants.

[0033] 3. This invention, through the diffusion path simulation module based on spatiotemporal spectral data collected by a distributed sensor network, combined with a water flow field model and a fluorescence decay model, and with the addition of meteorological data for dynamic correction, can accurately predict the migration trajectory of pollutants. Compared with the shortcomings of traditional methods that lack real-time prediction capabilities, it has significant advantages.

[0034] 4. The invention features an automated control execution module that retrieves corresponding treatment plans from the contingency plan knowledge base based on the pollution identification results from the spectral analysis unit and the trajectory prediction from the diffusion path simulation module. This module then performs emergency operations such as gate opening and closing and reagent dosing in a coordinated manner, avoiding the problems of long and delayed emergency response chains caused by traditional manual decision-making, and effectively reducing pollution hazards.

[0035] 5. This invention employs a distributed architecture in its data storage module and encrypts data transmission using the AES-256 algorithm to ensure data security and prevent loss; the human-computer interaction interface enables visual management and interactive control, supports remote manual intervention and emergency operations, and ensures stable and reliable system operation; the collaborative work of each module allows this system to be flexibly applied to different scenarios. Attached Figure Description

[0036] Figure 1 This is a block diagram of a three-dimensional fluorescence dynamic monitoring and automated processing system for water quality proposed in this invention. Detailed Implementation

[0037] 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.

[0038] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0039] Example 1

[0040] refer to Figure 1 This embodiment provides a three-dimensional fluorescence dynamic monitoring and automated processing system for water quality, used for monitoring sudden organic pollution in rivers. Specific implementation steps include:

[0041] Implementation scenario: Urban rivers polluted by organic matter.

[0042] Implementation method:

[0043] Water sample collection module: At a set frequency of 3 times / hour, the automatic sampling pump extracts water samples from different points in the upstream of the river. The water samples pass through a multi-stage filtration assembly with 0.45μm filter membranes to remove particulate impurities. The self-cleaning mechanism ensures the cleanliness of the sampling pipeline and avoids cross-contamination. The processed water samples are then transported to the three-dimensional fluorescence spectroscopy detection module.

[0044] Three-dimensional fluorescence spectroscopy detection module: The integrated pulsed laser exciter emits tunable excitation light in the wavelength range of 225-600nm to irradiate the water sample and excite fluorescent substances; the high-sensitivity CCD detector synchronously collects fluorescence signals across the entire wavelength range to obtain three-dimensional spectral data including fluorescence intensity; the temperature-controlled optical path system stably maintains the optical path temperature at (25±0.5)℃, and the automatic calibration module ensures the sensitivity of the detection.

[0045] Pollution fingerprint database: It contains standard three-dimensional fluorescence fingerprint spectra of common pollutants such as benzene series compounds. The spectra cover the excitation-emission wavelength matrix features under different concentration gradients, providing a basis for comparison for subsequent pollution identification.

[0046] Spectral Analysis Unit: Receives data from the three-dimensional fluorescence spectroscopy detection module, performs baseline correction and scattering subtraction on the original spectrum using the built-in preprocessing algorithm module; calculates the characteristic angle between the spectrum to be measured and the standard spectrum of benzene series compounds in the pollution fingerprint database using the spectral angle mapping algorithm, and obtains an angle of 0.08 rad. Combined with a preset threshold, it automatically identifies the pollution type as benzene series compound pollution and outputs the pollution level.

[0047] The distributed sensor network includes: five edge fluorescence monitoring devices (spaced 1 km apart) deployed along the river to collect excitation-emission spectral data in real time; a Doppler current meter (accuracy ±0.01 m / s) at the upstream hydrological station to provide flow velocity data; and a meteorological station providing real-time wind speed (2 m / s southeast wind). The diffusion path simulation module simulates water flow diffusion using the finite volume method, combined with the benzene series compound light attenuation coefficient (0.05 / h), predicting that pollutants will migrate downstream at a speed of 0.5 m / s and reach the water plant intake after 6 hours. The automated control execution module automatically triggers gate closure (action time 2 minutes) and activated carbon dosing (dosage calculated based on pollution level as 15 mg / L) according to the pre-set knowledge base.

[0048] The diffusion path simulation module is based on spatiotemporal spectral data collected by a distributed sensor network, combined with real-time flow velocity (0.5 m / s), flow direction data and riverbed topography data obtained from hydrological monitoring stations to construct a water flow field model; a fluorescence decay model is constructed based on the photochemical characteristic parameters of benzene series compounds; and the migration trajectory of pollutants is predicted by combining wind speed data provided by the meteorological department, and it is determined that the pollutants will reach the downstream water plant intake in 6 hours.

[0049] The automated control execution module receives the pollution identification results from the spectral analysis unit and the migration trajectory prediction data from the diffusion path simulation module. It then retrieves the corresponding treatment plan for benzene series pollution from the contingency plan knowledge base, sends instructions to the execution mechanism via the wireless communication unit, immediately closes the river gate, and starts the activated carbon adsorption agent dosing device.

[0050] Results: Pollutants were successfully intercepted, and the water quality of the water plant was not affected. It took only 8 minutes from the occurrence of pollution to the system's identification and activation of emergency operations.

[0051] Example 2

[0052] This embodiment provides a three-dimensional fluorescence dynamic monitoring and automated processing system for water quality, used for the implementation of eutrophication early warning in lakes. Specific implementation details include:

[0053] Implementation scenario: Monitoring of algal blooms in lakes during summer.

[0054] Implementation method:

[0055] The water sampling module collects deep water samples every hour;

[0056] The three-dimensional fluorescence spectroscopy detection module captured the characteristic peaks of phycocyanin (excitation / emission: 620 / 650 nm).

[0057] The spectral analysis unit determined through a self-learning model that it was a cyanobacterial bloom, with a pollution level of II.

[0058] The diffusion path simulation module predicts that algae will cover 50% of the lake surface within 3 days;

[0059] The automated control execution module activates the ecological floating island linkage device, releasing algae-removing microbial agents.

[0060] Results: Algae diffusion rate decreased by 60%, and chlorophyll a concentration decreased by 45% within 72 hours.

[0061] Example 3

[0062] This embodiment provides a three-dimensional fluorescence dynamic monitoring and automated treatment system for water quality, used for the implementation of industrial wastewater discharge supervision. Specific implementation details include:

[0063] Implementation scenario: Real-time monitoring of wastewater discharge outlets in chemical industrial parks.

[0064] Implementation method:

[0065] The water sampling module performs high-frequency sampling at 6 times per hour;

[0066] Three-dimensional fluorescence spectroscopy revealed the characteristics of polycyclic aromatic hydrocarbon (PAH) complex pollution.

[0067] The spectral analysis unit, combined with historical data, identified a new unknown pollutant (angle 0.12 rad) and triggered an early warning.

[0068] The diffusion path simulation module predicts that if wastewater is directly discharged, it will pollute the surrounding farmland.

[0069] The automated control execution module works in conjunction with the park's wastewater treatment system to intercept wastewater and initiate advanced treatment procedures.

[0070] Implementation results: Farmland pollution was avoided, and the unknown pollutants were confirmed by the laboratory to be novel pesticide intermediates.

[0071] Example 4

[0072] This embodiment provides a three-dimensional fluorescence dynamic monitoring and automated processing system for water quality, used for long-term monitoring of drinking water sources. Specific implementation details include:

[0073] Implementation scenario: Routine monitoring of drinking water sources in reservoirs.

[0074] Implementation method:

[0075] The water sampling module automatically samples water every morning at dawn.

[0076] The spectral analysis unit, through long-term data comparison, found that the fluorescence peak intensity of humic substances continued to rise (the biogenic index BIX increased from 0.8 to 1.2), indicating a potential risk of eutrophication.

[0077] The diffusion path simulation module predicts risk areas, and the automated control execution module pre-dispenses biological enzymes to regulate water quality.

[0078] Results: The water quality at the water source remained stable at Class II standard, and no algal blooms occurred.

[0079] Example 5

[0080] This embodiment provides a three-dimensional fluorescence dynamic monitoring and automated processing system for water quality, used for cross-regional watershed collaborative monitoring. Specific implementation details include:

[0081] Implementation scenario: Joint prevention and control of pollution in upstream and downstream areas of cross-provincial river basins.

[0082] Implementation method:

[0083] Pesticide contamination was detected at the upstream monitoring point (angle 0.13 rad), and the data was simultaneously transmitted to the three downstream monitoring points via a data encryption transmission module.

[0084] Downstream monitoring points initiated emergency responses in advance and adjusted the water plant's pretreatment process.

[0085] The diffusion path simulation module integrates hydrological data across regions to generate a basin-wide prediction map of pollutant migration.

[0086] Implementation results: Downstream water plants have increased the pesticide residue removal rate to 90% through pretreatment, ensuring water supply safety.

[0087] Comparative Example 1

[0088] This comparative example provides traditional water quality monitoring methods, and the specific implementation details include:

[0089] Implementation scenario: The same river benzene series leakage event as in Example 1, using traditional chemical analysis methods (gas chromatography-mass spectrometry GC-MS).

[0090] Implementation method:

[0091] Manual sampling followed by laboratory analysis took 4 hours.

[0092] The testing items include routine indicators such as COD and ammonia nitrogen, but do not specifically test for benzene series compounds;

[0093] The anomaly was only discovered through other channels two hours after the pollutants had spread, and by the time the emergency response was activated, the waters 2 kilometers downstream had already been polluted.

[0094] Results: Benzene levels at the water plant intake exceeded the standard by 3 times, and emergency repairs caused a 12-hour water supply disruption in the surrounding area.

[0095] Compared with Comparative Example 1, the water quality dynamic monitoring and automated processing system based on three-dimensional fluorescent fingerprint tracing used in Examples 1-5 in response to sudden water pollution incidents stands in stark contrast to the traditional water quality monitoring method in Comparative Example 1.

[0096] Response speed and timeliness: In Example 1, a sudden benzene series leakage occurred in the river. The system, leveraging its high-frequency automatic sampling by the water sampling module, rapid data acquisition by the three-dimensional fluorescence spectroscopy detection module, and second-level spectral angle calculation by the spectral analysis unit, identified the pollution type within 8 minutes. The diffusion path simulation module, combined with real-time hydrological data, accurately predicted that the pollutants would reach the downstream water plant in 6 hours, and the automated control execution module immediately initiated interception and adsorption measures. In contrast, Comparative Example 1 used manual sampling and gas chromatography-mass spectrometry (GC-MS), which took 4 hours from sampling to results. By the time pollution was detected, it had already diffused for 2 hours, causing a 12-hour water outage at the downstream water plant. This demonstrates the significant lag in response of the traditional method.

[0097] Detection Capability and Accuracy: In Example 2, when monitoring lake eutrophication, the spectral analysis unit, through a self-learning model, could keenly capture the characteristic fluorescence peaks of phycocyanin and accurately determine the level of cyanobacterial blooms. In Example 3, in monitoring wastewater in a chemical industrial park, it could not only identify polycyclic aromatic hydrocarbons but also issue early warnings for unknown pollutants with an angle of 0.12 rad. In contrast, Comparative Example 1 only detected conventional indicators such as COD and ammonia nitrogen. Faced with sudden benzene series pollution, it was completely unable to identify it due to the lack of targeted detection, leading to uncontrolled pollution.

[0098] Prediction and prevention capabilities: Example 4, targeting drinking water sources, uses long-term spectral data trend analysis to predict the risk of eutrophication caused by changes in humus; Example 5, achieving cross-regional watershed collaborative monitoring, allows downstream water plant processes to be adjusted in advance based on data transmitted by the system after pesticide pollution is detected upstream. Traditional methods, lacking real-time prediction models, can only respond passively based on experience after pollution occurs and causes impact, making early prevention and control difficult.

[0099] Operation Mode and Cost: The systems in Examples 1-5 achieve unattended online monitoring, automating the entire process of water sample collection, testing, analysis, and execution, significantly reducing labor and sampling costs. Comparative Example 1 relies on frequent manual sampling and laboratory testing, resulting in high labor and time costs and low efficiency.

[0100] A comparison of Examples 1-5 shows that although Examples 1-5 all use the same water quality monitoring system, they have different focuses in terms of application scenarios, monitoring priorities and treatment strategies.

[0101] Application scenario differences: Example 1 focuses on sudden organic pollution in rivers, emphasizing rapid identification and interception; Example 2 targets lake ecological problems, focusing on monitoring algal blooms; Example 3 is used for industrial wastewater discharge supervision, emphasizing dual detection of known and unknown pollutants; Example 4 focuses on long-term trend monitoring of drinking water sources; Example 5 achieves collaborative prevention and control across regions and river basins.

[0102] The monitoring focuses are different: Examples 1 and 3 focus on the accurate identification of specific pollutants (benzene series, polycyclic aromatic hydrocarbons); Examples 2 and 4 pay more attention to the ecological trends reflected by changes in spectral characteristics, such as phycocyanin peaks and humic fluorescence intensity; Example 5 emphasizes the integration and sharing of multi-regional data to achieve joint prevention and control.

[0103] The system employs diverse treatment strategies: Example 1 utilizes gate interception and chemical adsorption; Example 2 releases microbial agents to regulate the ecosystem; Example 3 integrates with the park's wastewater treatment system for deep purification; Example 4 pre-emptively introduces biological enzymes to prevent water quality deterioration; Example 5 achieves pollution diversion and treatment through process adjustments at upstream and downstream water plants. These differences demonstrate the system's flexible adaptability to various scenarios, enabling it to handle sudden pollution events, maintain long-term ecosystems, and achieve regional collaborative governance, showcasing its strong functional scalability and practicality.

[0104] Compared to traditional water quality monitoring methods, this system achieves a comprehensive breakthrough in response speed, detection accuracy, predictive and control capabilities, and operating costs. Its automated, real-time monitoring mode reduces pollution identification time from hours to minutes, and can accurately detect unknown pollutants. Combined with dynamic prediction models, it allows for proactive control measures, preventing pollution spread and significant losses caused by the lag in traditional methods. Simultaneously, the unattended operation mode significantly reduces labor and time costs, improving monitoring efficiency.

[0105] Examples 1-5 cover diverse scenarios including sudden river pollution, lake ecological early warning, industrial emission monitoring, drinking water source maintenance, and cross-regional watershed collaboration. The system can adjust its monitoring focus and processing strategies according to different scenario requirements. For example, it emphasizes rapid interception for sudden pollution, focuses on trend prediction for ecological problems, and achieves joint prevention and control for regional pollution, demonstrating strong adaptability and practicality, and providing efficient and accurate solutions for various water quality monitoring needs.

[0106] The above embodiments can be implemented in whole or in part by software, hardware, firmware or other arbitrary combinations. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0107] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. 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.

[0108] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0109] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

[0110] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A three-dimensional fluorescence dynamic monitoring and automated processing system for water quality, characterized in that, include: The system includes a water sample acquisition module, a three-dimensional fluorescence spectroscopy detection module, a pollution fingerprint database, a spectral analysis unit, a diffusion path simulation module, and an automated control execution module. The water sample acquisition module is used to sample the target water body in real time and transport the water sample to the three-dimensional fluorescence spectroscopy detection module; The three-dimensional fluorescence spectroscopy detection module performs excitation-emission wavelength matrix scanning on the water sample to obtain three-dimensional spectral data including fluorescence intensity; The pollution fingerprint database pre-stores standard three-dimensional fluorescence fingerprint spectra of common pollutants such as polycyclic aromatic hydrocarbons, pesticides, and PPCPs. The spectral analysis unit uses a spectral similarity algorithm to calculate the characteristic angle between the spectrum to be tested and the standard spectrum in the database. When the angle is less than a preset threshold, it automatically identifies the pollution type and level. The diffusion path simulation module predicts the migration trajectory of pollutants based on spatiotemporal spectral data collected by a distributed sensor network, combined with a water flow field model and a fluorescence decay model. The automated control execution module, based on pollution identification results and trajectory prediction, coordinates with the execution mechanism to perform emergency operations such as gate opening and closing and chemical dosing. The execution mechanism includes a gate control system and a chemical dosing device. The gate control system covers two core gate types: electric gates and hydraulic gates. The chemical dosing device includes a metering pump and a chemical storage tank. The automated control execution module also includes a contingency plan knowledge base, which has a pre-stored 'pollution type-treatment parameter' mapping table.

2. The three-dimensional fluorescence dynamic monitoring and automated processing system for water quality as described in claim 1, characterized in that, The water sample collection module includes an automatic sampling pump, a multi-stage filtration assembly, and a self-cleaning mechanism. The automatic sampling pump extracts water samples at a set frequency, the multi-stage filtration assembly removes particulate impurities through a 0.45μm filter membrane, and the self-cleaning mechanism periodically backwashes and cleans the sampling pipeline and filtration unit to avoid cross-contamination.

3. The three-dimensional fluorescence dynamic monitoring and automated treatment system for water quality as described in claim 1, wherein... The feature is that the three-dimensional fluorescence spectroscopy detection module integrates a pulsed laser exciter, a high-sensitivity CCD detector, a temperature-controlled optical path system, and an automatic calibration module; the pulsed laser exciter emits tunable excitation light, and the CCD detector synchronously acquires fluorescence signals across the entire wavelength range; the temperature-controlled optical path system maintains stable optical path temperature through a semiconductor temperature control element, and the automatic calibration module periodically injects quinine sulfate standard solution for sensitivity verification.

4. The three-dimensional fluorescence dynamic monitoring and automated processing system for water quality as described in claim 1, characterized in that, The spectral analysis unit adopts an edge computing architecture and has a built-in preprocessing algorithm module and a self-learning model. The preprocessing algorithm module includes baseline correction and scattering subtraction. After feature extraction, the preprocessed spectral data is compared with the pollution fingerprint database in real time, and the database feature vector is dynamically optimized based on new sample data.

5. The three-dimensional fluorescence dynamic monitoring and automated processing system for water quality as described in claim 1, characterized in that, The distributed sensor network includes: Water quality spectral sensors are used to acquire three-dimensional fluorescence spectral data of water bodies in real time, and fluorescence probes are one of their common types. Hydrological sensors include: Doppler current meters and water level gauges, and are used to acquire real-time hydrological parameters such as flow velocity, flow direction, and riverbed topography; Meteorological sensors include anemometers and rain gauges, and are used to collect environmental data such as wind speed and rainfall. The water flow field model is constructed based on the finite volume method, and the water flow motion is simulated by solving the Navier-Stokes equations. The fluorescence decay model adopts the first-order dynamic equation and dynamically calculates the decay coefficient by combining parameters such as pollutant photon yield and water temperature. The module integrates multi-source data with the help of data fusion algorithms, among which Kalman filtering is a commonly used data fusion algorithm, so as to realize the dynamic correction of pollutant migration trajectory.

6. The three-dimensional fluorescence dynamic monitoring and automated processing system for water quality as described in claim 1, characterized in that, The implementing mechanism includes: The gate control system includes two types: electric valves and hydraulic valves, and supports remote opening and closing and flow regulation. The reagent dosing device includes a metering pump and a storage tank, and can automatically add activated carbon, oxidants and other reagents according to the type of pollution. The contingency plan knowledge base pre-stores a mapping table of "pollution type - treatment parameters". The treatment parameter corresponding to benzene series pollution is 10 mg / L of activated carbon. The optimal treatment plan is matched by fuzzy logic algorithm, and the execution delay is ≤30 seconds.

7. The three-dimensional fluorescence dynamic monitoring and automated processing system for water quality as described in claim 1, characterized in that, It also includes a data storage module and an encrypted transmission module; the data storage module adopts a distributed architecture to store raw spectral data, analysis results and trajectory prediction data; the encrypted transmission module uses the AES-256 algorithm to encrypt the data during transmission to ensure data security.

8. The three-dimensional fluorescence dynamic monitoring and automated processing system for water quality as described in claim 1, characterized in that, The formula for calculating the feature angle in the spectral similarity algorithm is as follows: Where xi is the intensity value of the i-th wavelength point of the spectrum to be measured, yi is the intensity value of the corresponding wavelength point of the standard fingerprint spectrum, and n is the number of spectral sampling points; the system presets multiple pollution level thresholds, and different thresholds correspond to different emergency response levels.

9. The three-dimensional fluorescence dynamic monitoring and automated processing system for water quality as described in claim 1, characterized in that, It also includes a fault diagnosis module; the module monitors the operating parameters of each unit in real time, combines historical data to establish an anomaly discrimination model, automatically identifies equipment faults and triggers alarms, and generates maintenance suggestion work orders.

10. The three-dimensional fluorescence dynamic monitoring and automated processing system for water quality as described in claim 1, characterized in that, It also includes a human-computer interaction interface; the interface displays three-dimensional spectral maps, pollution identification results, trajectory prediction dynamics and equipment status in real time, and supports users to set parameters, retrieve historical data and perform manual emergency operations, so as to realize visual management and interactive control.

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