Water quality three-dimensional fluorescence dynamic monitoring and automatic processing system

Abstract

The invention discloses a three-dimensional fluorescence dynamic monitoring and automatic processing system for water quality. The system comprises a water sample collection module, a three-dimensional fluorescence spectrum detection module, a pollution fingerprint database, a spectral analysis unit, a diffusion path simulation module based on a distributed sensor network and an automatic control execution module. The diffusion path simulation module is used for acquiring spatio-temporal data through a water quality spectrum sensor, a hydrological sensor and a meteorological sensor, and dynamically predicting a pollutant migration track by combining a water body flow field model constructed by a finite volume method and a first-order dynamic fluorescence attenuation model; the automatic control execution module is based on the pollution recognition result and track prediction, emergency operations such as gate opening and closing and medicament adding are linked through a fuzzy logic algorithm, and full-process automation from monitoring to disposal is achieved. The system completes pollution identification within 10 minutes through a spectral angle mapping algorithm, and the defects that a traditional method lags in response and lacks dynamic prediction and automatic disposal are overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality detection, and particularly to a three-dimensional fluorescence dynamic monitoring and automated processing system for water quality. Background Art

[0002] In the field of water quality monitoring, the existing technologies mainly use chemical analysis methods, biosensor methods, and on-line monitoring instruments for water quality assessment. Chemical analysis methods require manual sampling and then laboratory testing. Although the detection accuracy is relatively high, there are problems such as slow response speed, complex operation, and high labor costs, making it difficult to meet the real-time monitoring requirements of sudden pollution incidents. The biosensor method realizes detection through the specific reaction of biological active substances to pollutants. However, its detection range is limited, it is easily interfered by environmental factors, and its stability is insufficient. Although on-line monitoring instruments can achieve real-time data collection, most of them focus on the determination of conventional physical and chemical indicators such as pH, COD, and ammonia nitrogen, lacking the specific recognition ability at the molecular level of pollutants and unable to quickly distinguish the types and sources of pollutants.

[0003] In the existing technology, traditional water quality monitoring systems can only achieve single-point data collection, lacking the overall monitoring ability based on distributed sensor networks and unable to capture the spatio-temporal distribution characteristics of pollutants in real time; pollution diffusion prediction mostly relies on static models and does not integrate dynamic parameters such as hydrology and meteorology, resulting in large trajectory prediction errors; emergency response completely depends on manual decision-making, and it takes several hours from pollution identification to starting disposal, making it difficult to cope with sudden pollution incidents. The present invention fills the gaps in dynamic prediction and intelligent disposal of the existing technology through *distributed multi-source sensor fusion, multi-physical field coupling modeling, and automated linkage control. Therefore, a three-dimensional fluorescence dynamic monitoring and automated processing system for water quality is proposed. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the existing technology, an embodiment of the present invention provides a three-dimensional fluorescence dynamic monitoring and automated processing system for water quality, which solves the problems of slow response, weak recognition, low automation, insufficient multi-scenario adaptation and operation and maintenance in the existing technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A three-dimensional fluorescence dynamic monitoring and automated processing system for water quality, characterized by comprising: a water sample collection 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 collection module is used for real-time sampling of the target water body and transporting the water sample to the three-dimensional fluorescence spectroscopy detection module;

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

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

[0010] The spectrum analysis unit calculates the characteristic angle between the spectrum to be tested and the standard spectrum in the database through a spectrum similarity algorithm, and automatically identifies the pollution type and level when the angle is less than a preset threshold;

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

[0012] The automatic control execution module links the actuator to execute emergency operations such as gate opening and closing and agent dosing according to the pollution identification results and trajectory prediction; the actuator includes: gate control system (electric / hydraulic gate), agent dosing device (metering pump + drug storage tank);

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

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

[0015] Preferably, the three-dimensional fluorescence spectrum detection module integrates a pulse laser exciter, a high-sensitivity CCD detector, a temperature-controlled optical path system and an automatic calibration module; the pulse laser exciter emits tunable excitation light, and the CCD detector synchronously collects full-band fluorescence signals; the temperature-controlled optical path system maintains the stability of the optical path temperature through a semiconductor temperature control element, and the automatic calibration module regularly injects quinine sulfate standard solution for sensitivity calibration.

[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; the preprocessed spectral data is compared with the pollution fingerprint database in real time after feature extraction, and the database feature vector is dynamically optimized according to the new sample data.

[0017] Preferably, the distributed sensor network comprises:

[0018] Water quality spectral sensor (such as fluorescence probe), used to collect 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 obtain real-time hydrological parameters such as flow velocity, flow direction, and riverbed topography;

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

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

[0022] Preferably, the actuator includes:

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

[0024] A chemical dosing device (metering pump + chemical storage tank) that can automatically dose chemicals such as activated carbon and oxidants according to the type of pollution;

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

[0026] Preferably, it further includes a data storage module and an encrypted transmission module; the data storage module adopts a distributed architecture to store the original spectral data, analysis results, and trajectory prediction data; the encrypted transmission module encrypts the data transmission through the AES-256 algorithm to ensure data security.

[0027] Preferably, the characteristic angle calculation formula of the spectral similarity algorithm is: , where is the intensity value of the i-th wavelength point of the spectral to be measured, 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 multi-level pollution level thresholds, and different thresholds correspond to different emergency response levels.

[0028] Preferably, it further includes a fault diagnosis module; the module automatically identifies equipment faults and triggers alarms by real-time monitoring the operating parameters of each unit and establishing an abnormal discrimination model in combination with historical data, and at the same time generates a maintenance suggestion work order.

[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 water quality three-dimensional fluorescence dynamic monitoring and automatic processing system of the present invention are as follows:

[0031] 1. This invention can automatically identify the type and level of pollution within 10 minutes by calculating the characteristic angle between the spectrum to be tested and the standard spectrum in the pollution fingerprint database through a spectral similarity algorithm (such as spectral angle mapping). Compared with the traditional water quality monitoring method that requires manual sampling and inspection and takes more than 4 hours, this system greatly shortens the pollution response time, can detect sudden pollution incidents in a timely manner, and avoid significant losses caused by the spread of pollution.

[0032] 2. This invention has a comprehensive pollutant detection range. The pollution fingerprint database pre-stores standard three-dimensional fluorescence fingerprints of common pollutants such as polycyclic aromatic hydrocarbons, pesticides, PPCPs, etc., and combines the three-dimensional fluorescence spectrum detection module to perform excitation-emission wavelength matrix scanning on water samples. The system can not only detect conventional pollutants, but also identify unknown pollutants such as new pesticide intermediates through the self-learning model of the spectral analysis unit, overcoming the defects 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, is based on the spatiotemporal spectral data collected by the distributed sensor network, combined with the water flow field model and the fluorescence attenuation model, and connected to the meteorological data for dynamic correction. It can accurately predict the migration trajectory of pollutants, which has significant advantages over the traditional methods that lack real-time prediction capabilities.

[0034] 4. The invention, the automated control execution module, retrieves the corresponding treatment plan from the plan knowledge base according to the pollution identification results of the spectral analysis unit and the trajectory prediction of the diffusion path simulation module, and jointly executes emergency operations such as gate opening and closing and agent addition, avoiding the problem of long and delayed emergency response chains initiated by traditional manual decision-making, and effectively reducing pollution hazards.

[0035] 5. This invention adopts a distributed architecture through the data storage module and encrypts the data through the AES-256 algorithm to ensure data security and prevent loss; the human-computer interaction interface realizes visual management and interactive control, supports remote manual intervention emergency operations, and ensures stable and reliable operation of the system; the collaborative work of various modules allows the system to be flexibly applied to different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a module block diagram of a water quality three-dimensional fluorescence dynamic monitoring and automatic processing system proposed by the present invention. Detailed implementation manners

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0038] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, the elements defined by the statement "including..." do not exclude the existence of additional identical elements in the process, method, article or device including the said elements.

[0039] Embodiment 1

[0040] Reference Figure 1 , this embodiment provides a three-dimensional fluorescence dynamic monitoring and automatic processing system for water quality, which is used for the implementation of monitoring sudden organic pollution in rivers. The specific implementation steps include:

[0041] Implementation scenario: An urban river polluted by organic matter.

[0042] Implementation manner:

[0043] Water sample collection module: According to the set frequency of 3 times per hour, the automatic sampling pump extracts water samples from different positions upstream of the river. The water samples pass through a 0.45μm filter membrane of a multi-stage filtration component in sequence to remove particulate impurities. The self-cleaning mechanism ensures the cleanliness of the sampling pipeline and avoids cross-contamination. The treated water samples are 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 to excite fluorescent substances; the high-sensitivity CCD detector synchronously collects the full-band fluorescence signal 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) °C, and the automatic calibration module ensures the sensitivity of the detection.

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

[0046] Spectral analysis unit: Receives the data transmitted by the three-dimensional fluorescence spectroscopy detection module, and uses the built-in preprocessing algorithm module to perform baseline correction and scattering deduction on the original spectrum; calculates the characteristic angle between the spectrum to be measured and the standard spectrum of benzene series compounds in the pollution fingerprint database through the spectral angle mapping algorithm, obtaining an angle of 0.08 rad, and automatically identifies the pollution type as benzene series compound pollution in combination with the preset threshold, and outputs the pollution level.

[0047] The distributed sensor network includes: 5 edge fluorescence monitoring devices arranged along the river (at an interval of 1 km), which collect excitation-emission spectral data in real time; a Doppler current meter (accuracy ±0.01 m / s) at the upstream hydrological station provides flow velocity data; the meteorological station accesses the real-time wind speed (2 m / s southeast wind). The diffusion path simulation module simulates the water flow diffusion by the finite volume method, and combines the light attenuation coefficient of benzene series compounds (0.05 / h) to predict that the pollutant migrates downstream at a speed of 0.5 m / s and reaches the water intake of the water plant after 6 hours. The automatic control execution module triggers the gate closing (action time 2 minutes) and activated carbon dosing (dose calculated according to the pollution level as 15 mg / L) according to the pre-plan knowledge base.

[0048] Diffusion path simulation module: Based on the spatio-temporal spectral data collected by the distributed sensor network, combines the real-time flow velocity (0.5 m / s), flow direction data obtained from the hydrological monitoring station and the riverbed terrain data to construct a water body flow field model; constructs a fluorescence attenuation model based on the photochemical characteristic parameters of benzene series compounds; comprehensively considers the wind speed data provided by the meteorological department to predict the pollutant migration trajectory and judges that the pollutant will reach the water intake of the downstream water plant after 6 hours.

[0049] Automatic control execution module: After receiving the pollution identification result of the spectral analysis unit and the migration trajectory prediction data of the diffusion path simulation module, retrieves the treatment plan corresponding to benzene series compound pollution from the pre-plan knowledge base, and sends instructions to the execution mechanism through the wireless communication unit to immediately close the river gate and start the activated carbon adsorption agent dosing device.

[0050] Implementation effect: The pollutant is successfully intercepted, and the water quality of the water plant is not affected. It only takes 8 minutes from the occurrence of pollution to the system identification and the start of emergency operations.

[0051] Example 2

[0052] This example provides a three-dimensional fluorescence dynamic monitoring and automatic processing system for water quality, which is used for the implementation of lake eutrophication early warning. The specific implementation content includes:

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

[0054] Implementation method:

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

[0056] The three-dimensional fluorescence spectrum detection module captures the characteristic peak of phycocyanin (excitation / emission: 620 / 650nm);

[0057] The spectrum analysis unit judged that it was a blue algae outbreak through the self-learning model, and the pollution level was Level 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 to release algaecidal microbial agents.

[0060] Implementation effect: The algae diffusion rate was reduced by 60% and the chlorophyll a concentration decreased by 45% within 72 hours.

[0061] Example 3

[0062] This embodiment provides a water quality three-dimensional fluorescence dynamic monitoring and automatic processing system for the implementation of industrial wastewater discharge supervision. The specific implementation content includes:

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

[0064] Implementation method:

[0065] The water sample collection module samples at a high frequency of 6 times per hour;

[0066] Three-dimensional fluorescence spectrum detection shows the complex pollution characteristics of PAHs;

[0067] The spectral analysis unit combines historical data to identify new unknown pollutants (angle 0.12rad) and trigger an early warning;

[0068] The diffusion path simulation module predicts that wastewater will pollute surrounding farmland if discharged directly;

[0069] The automated control execution module links the park’s sewage treatment system to intercept wastewater and initiate deep treatment procedures.

[0070] Implementation effect: Avoid farmland pollution, and unknown pollutants are confirmed as new pesticide intermediates by laboratories.

[0071] Example 4

[0072] This embodiment provides a water quality three-dimensional fluorescence dynamic monitoring and automatic processing system for implementing long-term monitoring of drinking water sources. The specific implementation content includes:

[0073] Implementation scenario: daily monitoring of the drinking water source area of the reservoir.

[0074] Implementation method:

[0075] The water sample collection module automatically samples at midnight every day;

[0076] Through long-term data comparison, the spectral analysis unit finds that the intensity of the humus-like fluorescence peak continues to rise (the biological index BIX rises from 0.8 to 1.2), indicating a potential eutrophication risk;

[0077] The diffusion path simulation module predicts the risk area, and the automatic control execution module puts in biological enzyme preparations in advance to adjust the water quality.

[0078] Implementation effect: The water quality of the water source area is stably maintained at Class II standard, and no algal bloom occurs.

[0079] Example 5

[0080] This example provides a three-dimensional fluorescence dynamic monitoring and automatic processing system for water quality, which is used for the implementation of cross-regional basin collaborative monitoring. The specific implementation content includes:

[0081] Implementation scenario: joint prevention and control of pollution at the upper and lower reaches of the cross-provincial river basin.

[0082] Implementation method:

[0083] Pesticide pollution is detected at the upstream monitoring point (the included angle is 0.13 rad), and it is synchronized to 3 downstream monitoring points through the data encryption transmission module;

[0084] The downstream monitoring points initiate an emergency response in advance and adjust the pretreatment process of the water plant;

[0085] The diffusion path simulation module integrates hydrological data across regions and generates a whole-basin prediction map of pollutant migration.

[0086] Implementation effect: The downstream water plant improves the pesticide residue removal rate to 90% through pretreatment, ensuring the safety of water supply.

[0087] Comparative example 1

[0088] This comparative example provides a traditional water quality monitoring method. The specific implementation content includes:

[0089] Implementation scenario: For the benzene series leakage incident in the same river as in Example 1, the traditional chemical analysis method (gas chromatography-mass spectrometry GC-MS) is used.

[0090] Implementation method:

[0091] After manual sampling, it is sent to the laboratory for analysis, which takes 4 hours;

[0092] The detection items include conventional indicators such as COD and ammonia nitrogen, but benzene series compounds are not specifically detected;

[0093] The abnormal situation was discovered through other channels 2 hours after the pollutant spread, and 2 kilometers of the downstream water area had been polluted when the emergency response was initiated.

[0094] Implementation effect: The benzene series compounds at the water intake of the water plant exceeded the standard by 3 times, and the suspension of water supply for emergency repair caused the water supply interruption in the surrounding area for 12 hours.

[0095] Compared with Examples 1 - 5 and Comparative Example 1, in dealing with sudden water quality pollution incidents, the water quality dynamic monitoring and automated processing system based on three-dimensional fluorescence fingerprint tracing adopted in Examples 1 - 5 forms a sharp contrast with the traditional water quality monitoring method in Comparative Example 1.

[0096] Response speed and timeliness: In Example 1, when there was a sudden leakage of benzene series compounds in the river, the system, relying on functions such as high-frequency automatic sampling by the water sample collection module, rapid data acquisition by the three-dimensional fluorescence spectroscopy detection module, and second-level calculation of the spectral angle by the spectral analysis unit, identified the pollution type within 8 minutes; the diffusion path simulation module combined with real-time hydrological data to accurately predict that the pollutants would reach the downstream water plant after 6 hours, and the automated control execution module immediately initiated interception and adsorption measures. In contrast, Comparative Example 1 used manual sampling and submission for gas chromatography - mass spectrometry (GC - MS), which took 4 hours from sampling to obtaining results. When the pollution was discovered, the pollutants had spread for 2 hours, resulting in a 12 - hour water supply interruption in the downstream water plant. It can be seen that the traditional method has a serious lag in response.

[0097] Detection ability and accuracy: When monitoring lake eutrophication in Example 2, the spectral analysis unit can sensitively capture the characteristic fluorescence peak of phycocyanin through a self - learning model and accurately judge the level of cyanobacteria bloom; in Example 3, in the wastewater monitoring of a chemical industrial park, it can not only identify polycyclic aromatic hydrocarbons but also issue early warnings for unknown pollutants with an included angle of 0.12 rad. Looking at Comparative Example 1, it only detects conventional indicators such as COD and ammonia nitrogen. In the face of sudden benzene series compound pollution, due to the lack of specific detection, it is completely unable to identify, resulting in the out - of - control of pollution.

[0098] Prediction and prevention and control ability: In Example 4 for drinking water sources, the system predicts the eutrophication risk caused by the change of humus through long - term spectral data trend analysis; in Example 5, cross - regional basin collaborative monitoring is achieved. After the discovery of pesticide pollution in the upstream, the downstream adjusts the water treatment plant process in advance based on the data transmitted by the system. The traditional method lacks a real - time prediction model and can only rely on experience to passively respond after the pollution occurs and causes impacts, making it difficult to prevent and control in advance.

[0099] Operation mode and cost: The systems of Examples 1-5 realize unattended online monitoring, and automate the entire process of water sample collection, testing, analysis, and execution, greatly reducing labor and sampling costs. Comparative Example 1 relies on frequent manual sampling and laboratory testing, which is costly in terms of manpower and time, and inefficient.

[0100] Comparison between Examples 1-5: Although Examples 1-5 all use the same water quality monitoring system, they have different emphases on application scenarios, monitoring priorities and treatment strategies.

[0101] Differences in application scenarios: Example 1 focuses on sudden organic pollution in rivers, emphasizing rapid identification and interception; Example 2 targets lake ecological issues, focusing on monitoring algae outbreaks; 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 realizes coordinated prevention and control across regional 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 humus fluorescence intensity; Example 5 emphasizes multi-regional data integration and sharing to achieve joint prevention and control.

[0103] The treatment strategies are diverse: Example 1 uses gate interception and agent adsorption; Example 2 releases microbial agents to regulate ecology; Example 3 links the park sewage treatment system for deep purification; Example 4 puts in biological enzymes in advance to prevent water quality deterioration; Example 5 achieves pollution diversion treatment by adjusting the upstream and downstream water plant processes. These differences reflect the flexible adaptability of the system in different scenarios, which can not only deal with sudden pollution, but also carry out long-term ecological maintenance, and realize regional collaborative governance, showing strong functional scalability and practicality.

[0104] Compared with traditional water quality monitoring methods, this system has achieved comprehensive breakthroughs in response speed, detection accuracy, predictive prevention and control capabilities, and operating costs. Its automated, real-time monitoring mode shortens pollution identification time from hours to minutes, and can accurately capture unknown pollutants. Combined with dynamic prediction models, prevention and control measures can be taken in advance to avoid the spread of pollution and significant losses caused by the lag of traditional methods. At the same time, the unattended operation mode greatly reduces manpower and time costs and improves monitoring efficiency.

[0105] Examples 1-5 cover multiple scenarios such as sudden river pollution, lake ecological early warning, industrial emission supervision, drinking water source maintenance, and cross-regional river basin collaboration. The system can adjust the monitoring focus and treatment strategies according to different scenario requirements. For example, for sudden pollution, it focuses on rapid interception; for ecological problems, it pays attention to trend prediction; for regional pollution, it realizes joint prevention and control. It shows strong adaptability and practicality, 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 any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0107] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

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

[0109] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0110] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in 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: Water sample collection module, three-dimensional fluorescence spectrum detection module, pollution fingerprint database, spectrum analysis unit, diffusion path simulation module and automatic control execution module; The water sample collection module is used to sample the target water body in real time and transport the water sample to the three-dimensional fluorescence spectrum detection module; The three-dimensional fluorescence spectrum detection module performs excitation-emission wavelength matrix scanning on the water sample to obtain three-dimensional spectrum data including fluorescence intensity; The pollution fingerprint database pre-stores standard three-dimensional fluorescent fingerprints of common pollutants such as polycyclic aromatic hydrocarbons, pesticides, and PPCPs; The spectrum analysis unit calculates the characteristic angle between the spectrum to be tested and the standard spectrum in the database through a spectrum similarity algorithm, and automatically identifies the pollution type and level when the angle is less than a preset threshold; The diffusion path simulation module predicts the migration trajectory of pollutants based on the spatiotemporal spectral data collected by the distributed sensor network, combined with the water flow field model and the fluorescence attenuation model; The automatic control execution module links the actuator to execute emergency operations such as gate opening and closing and agent dosing according to the pollution identification results and trajectory prediction; the actuator includes: gate control system (electric / hydraulic gate), agent dosing device (metering pump + drug storage tank); The automatic control execution module also includes a plan knowledge base, which pre-stores a 'pollution type-treatment parameter' mapping table.

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

3. The three-dimensional fluorescence dynamic monitoring and automatic processing system for water quality according to claim 1, characterized in that The three-dimensional fluorescence spectrum detection module integrates a pulse laser exciter, a high-sensitivity CCD detector, a temperature-controlled optical path system and an automatic calibration module; the pulse laser exciter emits tunable excitation light, and the CCD detector synchronously collects full-band fluorescence signals; the temperature-controlled optical path system maintains the stability of the optical path temperature through a semiconductor temperature control element, and the automatic calibration module regularly injects quinine sulfate standard solution for sensitivity verification.

4. The three-dimensional fluorescence dynamic monitoring and automatic processing system for water quality according to claim 1, wherein 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; the preprocessed spectral data is extracted and compared with the pollution fingerprint database in real time, and the database feature vector is dynamically optimized according to the new sample data.

5. The three-dimensional fluorescence dynamic monitoring and automatic processing system for water quality according to claim 1, wherein, The distributed sensor network comprises: Water quality spectral sensor (such as fluorescence probe), used to collect three-dimensional fluorescence spectral data of water bodies in real time; Hydrological sensors (including Doppler current meters and water level meters) are used to obtain real-time hydrological parameters such as flow velocity, flow direction, and riverbed topography; Meteorological sensors (anemometer, rain gauge) 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 movement is simulated by solving the Navier-Stokes equation. The fluorescence attenuation model adopts the first-order kinetic equation, and dynamically calculates the attenuation coefficient by combining the light quantum yield of the pollutant, water temperature and other parameters. The module integrates multi-source data through data fusion algorithms (such as Kalman filtering) to realize dynamic correction of the migration trajectory of pollutants.

6. The three-dimensional fluorescence dynamic monitoring and automatic processing system for water quality according to claim 1, characterized in that The actuator includes: A gate control system (electric / hydraulic gate), supporting remote opening / closing and flow regulation; A chemical dosing device (metering pump + chemical storage tank), which can automatically dose chemicals such as activated carbon and oxidants according to the type of pollution; The pre - stored knowledge base of the plan stores a mapping table of 'pollution type - treatment parameters' (for example, benzene series pollution corresponds to an activated carbon dosing amount of 10 mg / L), matches the optimal treatment plan through a fuzzy logic algorithm, and the execution delay ≤ 30 seconds.

7. The three-dimensional fluorescence dynamic monitoring and automatic treatment system for water quality according to 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 original spectral data, analysis results, and trajectory prediction data; the encrypted transmission module encrypts data transmission through the AES - 256 algorithm to ensure data security.

8. The three-dimensional fluorescence dynamic monitoring and automatic treatment system for water quality according to claim 1, wherein The feature angle calculation formula of the spectral similarity algorithm is as follows: , where is the intensity value of the i-th wavelength point of the spectrum to be measured, 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 multi-level pollution level thresholds, and different thresholds correspond to different emergency response levels.

9. The three-dimensional fluorescence dynamic monitoring and automatic treatment system for water quality according to claim 1, characterized in that It also includes a fault diagnosis module; the module automatically identifies equipment failures and triggers alarms by real - time monitoring the operating parameters of each unit, combining historical data to establish an abnormal discrimination model, and generates maintenance suggestion work orders at the same time.

10. The three-dimensional fluorescence dynamic monitoring and automatic processing system for water quality according to claim 1, characterized in that, It also includes a human - machine interaction interface; the interface displays 3D 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, realizing visual management and interactive control.

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