A precise wastewater source tracing sampling device for wastewater treatment

Abstract

This invention relates to the field of environmental monitoring technology and discloses a precise wastewater source tracing sampling device for wastewater treatment. The device includes: a multi-channel optical feature reconstruction unit that compensates for water turbidity and flow velocity shear rate, outputting a reconstructed fluorescence deviation; a cross-modal kinetic energy surge extraction unit that extracts a kinetic energy surge index by fusing transient acoustic pulses from the pipe network with fluid kinetic energy mutation characteristics; a spatiotemporal coherence trigger evaluation unit that generates a joint trigger index for source tracing sampling by combining acoustic-optical coherence bandwidth confidence and physical response delay; and an adaptive sampling closed-loop control unit that dynamically controls the pumping cycle by fusing manhole hydraulic dispersion characteristics with a temporal synchronization compensation mechanism. This invention, through multi-modal signal fusion, effectively overcomes problems such as equipment erroneous triggering and water sample dilution distortion under harsh operating conditions, achieving precise source tracing and high-quality sampling of wastewater discharge events.

Description

Technical Field

[0001] This invention belongs to the field of environmental monitoring technology, and in particular relates to a precise wastewater source tracing sampling device for wastewater treatment. Background Technology

[0002] Accurate source tracing and sampling for sudden sewage discharge from underground pipe networks is crucial for water pollution prevention and control. Currently, existing pipe network monitoring and sampling systems have formed a standardized workstation system. At the sensing layer, the system mainly relies on portable water quality sensors (such as oil fluorescence sensors in water) and optical turbidimeters to obtain single chemical indicators of the water body; at the control and execution layer, edge gateways are generally used in conjunction with single threshold triggering logic, that is, when the water quality indicator exceeds the limit, the sampling pump is directly driven to perform a sampling action for a fixed duration or a fixed volume. To cope with the harsh operating conditions of the pipe network, some existing equipment will introduce a simple linear turbidity compensation algorithm in signal processing, or add a mechanical filter cover to the outside of the sensor to resist impurity interference.

[0003] However, the aforementioned existing technologies have significant limitations under complex real-world conditions: First, the optical sensing end lacks cross-compensation for flow velocity shear and micro-vibration of the pipeline network, making it highly susceptible to false triggering due to water bubbles or probe micro-vibration; second, the system only monitors the lagging end chemical characteristics in isolation, completely ignoring the feedforward physical characteristics such as the surge in fluid kinetic energy and acoustic pulses accompanying the discharge, and lacks cross-modal coherent confidence verification; third, the rigid sampling strategy does not consider the spatial hydraulic dispersion effect of the inspection well and the time delay difference in the response of acoustic and optical signals, which can easily lead to severe dilution and distortion of the extracted water sample by subsequent clean water. Summary of the Invention

[0004] The purpose of this invention is to provide a precise wastewater source tracing sampling device for wastewater treatment, aiming to solve the above-mentioned problems.

[0005] This invention is implemented as follows: a wastewater precise source tracing sampling device for wastewater treatment includes: a multi-channel optical feature reconstruction unit, configured to collect multi-channel fluorescence spectral features of the target water body, perform optical masking attenuation compensation based on real-time turbidity of the water body, and introduce the dynamic ratio of shear rate characterizing the turbulent state of the pipe network for hydrodynamic cross-compensation, thereby outputting the reconstructed fluorescence deviation after filtering out environmental interference; and a cross-modal kinetic energy surge extraction unit, configured to simultaneously capture transient acoustic pulse sequences and hydraulic fluid parameters of the target pipe network, perform cross-modal fusion of the distribution density of abnormal acoustic events and the abrupt change characteristics of fluid kinetic head, and extract the kinetic energy surge characterizing the sewage discharge impact event by comparing with the historical stable period baseline. The system includes: an index; a spatiotemporal coherence triggering evaluation unit, configured to use the kinetic energy surge index as a spatial feedforward early warning signal and the reconstructed fluorescence deviation as a target verification signal; extracting the coherence bandwidth confidence of the acousto-optic heterogeneous signal within the characteristic frequency band to dynamically weight the feedforward sensitivity; introducing the physical response delay between heterogeneous signals for temporal convergence constraints; and calculating and generating the source tracing sampling joint triggering index; and an adaptive sampling closed-loop control unit, configured to respond to the source tracing sampling joint triggering index, fusing the hydraulic dispersion scale characteristics caused by the spatial geometrical change of the target inspection well, and constructing a temporal synchronization compensation mechanism based on the physical response delay and the estimated hydraulic arrival delay to adaptively control the dynamic pumping cycle of the sampling actuator.

[0006] A further technical solution involves the following specific logic for the multi-channel optical feature reconstruction unit to obtain the reconstructed fluorescence deviation: The ratio of the fluorescence intensity of the blue channel to that of the ultraviolet channel is extracted, and the absolute deviation of this ratio from the baseline fluorescence ratio adaptively extracted by the system is calculated. An exponential optical masking compensation factor with a positive correlation is constructed using the ratio of the real-time turbidity of the water body to a reference turbidity threshold. A linear hydrodynamic cross-compensation factor is constructed using the dynamic ratio of the real-time pipe network flow velocity shear rate to a reference pipe network flow velocity shear rate. The absolute deviation, the optical masking compensation factor, and the hydrodynamic cross-compensation factor are then jointly weighted and mapped to obtain the reconstructed fluorescence deviation.

[0007] A further technical solution is that the specific logic of the cross-modal kinetic energy surge extraction unit to obtain the kinetic energy surge index is as follows: extract the incremental ratio of the real-time abnormal sound event count rate relative to the background abnormal sound baseline, and perform logarithmic smoothing on the incremental ratio to construct the acoustic pulse surge weight; extract the dynamic ratio of the fluid kinetic energy head relative to the reference kinetic energy head to construct the hydraulic impact weight; and perform joint correlation mapping between the acoustic pulse surge weight and the hydraulic impact weight to obtain the kinetic energy surge index characterizing the sewage discharge impact intensity.

[0008] A further technical solution involves the following specific logic for the spatiotemporal coherence triggering evaluation unit to obtain the joint triggering index for source tracing sampling: Based on the fusion result of coherence bandwidth confidence and kinetic energy surge index, a feedforward activation weight exhibiting exponential saturation characteristics is constructed; based on the ratio of physical response delay to the maximum tolerance delay threshold, a temporal convergence suppression weight exhibiting exponential decay characteristics is constructed; using the reconstructed fluorescence deviation as the target benchmark, the feedforward activation weight is used to perform forward triggering modulation, and the temporal convergence suppression weight is used to perform inverse convergence constraint, ultimately calculating and obtaining the joint triggering index for source tracing sampling.

[0009] A further technical solution involves obtaining the coherent bandwidth confidence level based on amplitude squared coherence analysis. The specific logic is as follows: within the upper and lower frequency limits of the characteristic frequency band, the square of the cross power spectral density of the acoustic energy signal and the fluorescence deviation signal is calculated, and the ratio of this cross power spectral density to the product of the acoustic energy signal and the fluorescence deviation signal is calculated. The ratio within the upper and lower frequency limits of the characteristic frequency band is then integrated and normalized to obtain the coherent bandwidth confidence level characterizing the dynamic coupling strength of heterogeneous signals.

[0010] A further technical solution is that the timing synchronization compensation mechanism is specifically represented as a pumping synchronization compensation term. The logic for obtaining the pumping synchronization compensation term is as follows: calculate the absolute time deviation between the physical response delay and the estimated hydraulic arrival delay; construct an exponential decay compensation function based on the absolute time deviation using the synchronization reference time constant as the scale standard; when the absolute time deviation approaches zero, the pumping synchronization compensation term approaches the compensation maximum value.

[0011] A further technical solution is that the hydraulic dispersion scale feature is specifically characterized as the geometric spatial abrupt change ratio. The logic for obtaining the geometric spatial abrupt change ratio is as follows: based on the cross-sectional area of ​​the upstream inflow pipe connected to the target inspection well and the equivalent diameter of the upstream inflow pipe, the effective hydraulic characteristic volume of the upstream inflow pipe is calculated; the ratio of the node volume of the target inspection well to the effective hydraulic characteristic volume is calculated, and the ratio is used as the geometric spatial abrupt change ratio characterizing the degree of hydraulic dispersion and dilution of the target inspection well.

[0012] A further technical solution involves the following logic for the adaptive sampling closed-loop control unit to obtain the dynamic suction cycle: Based on the positive combined effect of the joint triggering index and the suction synchronization compensation term, and the negative adjustment effect of the geometric space mutation ratio, a dynamic suction gain coefficient is constructed. This dynamic suction gain coefficient exhibits exponential saturation convergence characteristics as the joint triggering index increases. The dynamic suction gain coefficient is then combined with the maximum additional suction time to obtain the actual additional suction time. Finally, the shortest guaranteed suction time is added to the actual additional suction time to obtain the adaptively adjusted dynamic suction cycle.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0014] 1. The multi-channel optical feature reconstruction unit incorporates optical masking attenuation compensation for water turbidity and cross-compensation for shear rate hydrodynamics, effectively filtering out environmental physical interferences such as water flow turbulence microbubbles, probe micro-vibrations, and water turbidity changes in underground pipe networks. This mechanism acquires high-purity reconstructed fluorescence deviation signals, overcoming the shortcomings of single optical sensing that are prone to false triggering from the source, and ensuring the reliability of chemical feature sensing.

[0015] 2. Establish a cross-modal verification mechanism to improve the accuracy of source tracing triggering. The system innovatively captures transient acoustic pulse sequences and hydraulic fluid parameters simultaneously, using the kinetic energy surge index as a spatial feedforward early warning signal and the reconstructed fluorescence deviation as a target verification signal. Simultaneously, it extracts the coherence bandwidth confidence of the acousto-optic heterogeneous signals and introduces physical response delay for temporal convergence constraints, ensuring that triggering actions occur only when the physical hydraulic impact and chemical pollution characteristics are highly homogeneous, significantly improving the accuracy and reliability of pollution event identification.

[0016] 3. Adaptive closed-loop sampling ensures the authenticity and representativeness of extracted water samples. The system abandons the traditional rigid timed sampling strategy, innovatively integrating the hydraulic dispersion scale characteristics caused by abrupt changes in the spatial geometry of the target inspection well, and constructing a time-series synchronization compensation mechanism based on the physical response delay and the estimated hydraulic arrival delay. Accordingly, the device can adaptively adjust the dynamic suction cycle, ensuring that the sampling action accurately matches the actual arrival and dispersion process of the sewage cloud, avoiding the distortion caused by subsequent dilution of the collected water sample with clean water, and significantly improving the representativeness of the water sample and the accuracy of water quality monitoring results. Attached Figure Description

[0017] Figure 1 The present invention provides an overall architecture and data flow diagram of a wastewater precise source tracing sampling device for wastewater treatment. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] In traditional underground pipe network monitoring and sampling systems, the optical sensing end lacks a cross-compensation mechanism for flow velocity shear and pipe network mechanical micro-vibration, making it prone to false triggering due to water flow bubbles or probe micro-vibrations. The system only monitors lagging end-point chemical characteristics in isolation, lacking coherent confidence verification of feedforward physical characteristics such as the surge in fluid kinetic energy and acoustic pulses during sewage discharge, leading to reduced accuracy in trigger judgment. Furthermore, the rigid sampling strategy fails to consider the spatial hydraulic dispersion effect of inspection wells and the time delay difference in acoustic and optical signal response, causing the extracted water sample to be diluted by subsequent clean water, resulting in loss of representativeness and affecting the effectiveness of pollution source tracing evidence. In particular, optical masking attenuation compensation relies solely on a single turbidity parameter, failing to eliminate interference introduced by turbulent shear effects, while the lack of a cross-modal signal verification mechanism prevents the system from distinguishing between actual sewage discharge events and environmental noise, ultimately causing a mismatch between sampling actions and the spatiotemporal location of sewage plumes.

[0020] For example, at a monitoring point of a manhole in an underground pipe network in an urban industrial area, when a sudden oily wastewater discharge event occurs, the turbulent water flow generates bubbles that interfere with the optical sensor, and the mechanical vibration of the pipe network causes micro-vibrations in the probe, leading to the failure of the turbidity compensation algorithm and the false triggering of the sampling pump. The system only starts fixed-capacity sampling based on the fluorescence sensor exceeding its limit, but does not verify whether the acoustic pulse signal matches the chemical characteristics, thus failing to eliminate interference events. Furthermore, due to abrupt changes in the geometry of the manhole, the discharge cloud undergoes hydraulic dispersion, and the fixed-duration sampling action starts after the discharge cloud has passed, resulting in the collected water sample being diluted by upstream clean water and failing to reflect the original pollution state. Further, the time delay difference in the acoustic and optical signal response is not corrected, and the kinetic energy surge characteristic and fluorescence deviation are misaligned in time, causing the system to misjudge the clean water section as a discharge event, leading to a continuous decline in the representativeness of the water sample.

[0021] If the above problems are not addressed, false triggering will lead to frequent invalid sampling, continuous deterioration of water sample representativeness, and a broken chain of evidence for pollution source tracing, ultimately making it difficult to obtain accurate pollution source tracing data. Among these issues, unsuppressed optical sensing interference will expand the scope of false triggering, the lack of cross-modal verification will prevent the system from establishing physical consistency criteria for pollution discharge events, and the uncompensated hydraulic dispersion effect will exacerbate the dilution of water samples, thereby causing the pollution source to fail to be located, ultimately making it difficult to obtain accurate pollution source tracing data.

[0022] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0023] like Figure 1 As shown, a wastewater precise source tracing sampling device for wastewater treatment is provided in one embodiment of the present invention, comprising:

[0024] The multi-channel optical feature reconstruction unit is configured to acquire multi-channel fluorescence spectral features of the target water body. It performs optical masking attenuation compensation based on real-time turbidity and introduces the dynamic shear rate ratio, which characterizes the turbulent state of the pipe network flow, for hydrodynamic cross-compensation, thereby outputting the reconstructed fluorescence deviation after filtering out environmental interference. Multi-channel fluorescence spectral features refer to acquiring fluorescence signals generated after excitation of the target water body through multiple specific wavelength channels to obtain comprehensive information on different fluorescent substances in the water. This multi-channel acquisition method can provide richer and more comprehensive water quality chemical fingerprints than single-channel methods. Optical masking attenuation compensation corrects for the scattering and absorption effects of fluorescence signals caused by turbidity due to suspended matter or particulate matter in the water body. By monitoring water turbidity in real time and applying corresponding compensation algorithms, the interference of turbidity on fluorescence intensity measurement can be eliminated, thus obtaining fluorescence signals closer to the true values. The dynamic shear rate ratio is a quantitative indicator of the velocity gradient change within the pipe network flow, used to characterize the turbulent state of the water flow. Comparing it with a reference shear rate reflects the real-time changes in the degree of water flow turbulence. This ratio is introduced for hydrodynamic cross-compensation to eliminate interference from mechanical factors such as water turbulence, bubbles, or probe micro-vibration on the optical signal. Reconstructed fluorescence deviation refers to the degree of difference between the fluorescence signal filtered out of environmental interference and the preset baseline fluorescence signal after optical masking attenuation compensation and hydrodynamic cross-compensation. This deviation can more accurately reflect changes in the concentration of fluorescent substances in the water caused by pollution discharge.

[0025] A cross-modal kinetic energy surge extraction unit is configured to simultaneously capture transient acoustic pulse sequences and hydraulic fluid parameters of the target pipe network. It performs cross-modal fusion of the distribution density of anomalous acoustic events and the abrupt change characteristics of fluid kinetic head, and compares this with historical steady-state baselines to extract the kinetic energy surge index characterizing sewage impact events. Transient acoustic pulse sequences refer to short-duration, high-energy sound waves or vibration signals generated in the pipe network due to sudden events (such as sewage impacts). Capturing these sequences can serve as early physical evidence of the event. Hydraulic fluid parameters are physical quantities describing the state of water flow within the pipe network, such as flow velocity, pressure, and water level. Changes in these parameters can reflect the dynamic hydraulic processes within the pipe network. The distribution density of anomalous acoustic events refers to the frequency and intensity of anomalous acoustic signals exceeding the background noise level within a specific time window. This density can quantify the acoustic characteristics of sewage impact events. The abrupt change characteristics of fluid kinetic head refer to the sudden change in the kinetic energy per unit mass of water flow. Kinetic head is proportional to the square of flow velocity, and its abrupt change usually indicates a sharp change in water velocity or flow rate, representing a direct physical manifestation of sewage shock events. The kinetic head surge index is a comprehensive indicator characterizing the intensity of sewage shock events, extracted by integrating the distribution density of anomalous acoustic events with the abrupt characteristics of fluid kinetic head, and comparing it with historical stable baselines. This index serves as a feedforward early warning signal, providing early physical evidence of sewage events.

[0026] The spatiotemporal coherence triggering evaluation unit is configured to use the kinetic energy surge index as a spatial feedforward early warning signal and the reconstructed fluorescence deviation as a target verification signal. It extracts the coherence bandwidth confidence of the acousto-optic heterogeneous signals within a characteristic frequency band, dynamically weights the feedforward sensitivity, and introduces the physical response delay between heterogeneous signals for temporal convergence constraints. It then calculates and generates a source-tracing sampling joint triggering index. The spatial feedforward early warning signal is a signal that provides spatial location information and issues an early warning in the early stages of an event. It allows the system to initiate an early warning response immediately before the chemical characteristics of the event are fully manifested. The target verification signal is a signal used to verify and confirm the feedforward early warning signal. It typically provides more specific chemical or qualitative information to ensure the accuracy of the warning. The coherence bandwidth confidence is a quantitative indicator of the correlation strength between two heterogeneous signals (e.g., acoustic and optical signals) within a specific characteristic frequency band. A higher confidence indicates that the two signals may originate from the same physical event. The physical response delay is the time difference between two different types of signals (e.g., acoustic surge and fluorescence deviation) triggered by the same physical event when detected by different sensors. This delay is used to verify the homology of heterogeneous signals. The temporal convergence constraint sets an acceptable range for the physical response delay of heterogeneous signals to ensure that they are only considered to originate from the same event when they are sufficiently close in time. The source tracing sampling joint triggering index is a comprehensive decision-making index calculated by considering multiple factors such as the kinetic energy surge index, reconstructed fluorescence deviation, coherence bandwidth confidence, and physical response delay, used to determine whether to initiate source tracing sampling.

[0027] An adaptive sampling closed-loop control unit is configured to respond to a source-tracing sampling joint triggering index, integrate the hydraulic dispersion scale characteristics caused by abrupt changes in the spatial geometry of the target inspection well, and construct a timing synchronization compensation mechanism based on the physical response delay and the estimated hydraulic arrival delay to adaptively control the dynamic suction cycle of the sampling actuator. The hydraulic dispersion scale characteristics caused by abrupt changes in the spatial geometry of the target inspection well refer to the influence of the complex geometry inside the inspection well on the mixing, dilution, and diffusion of contaminant plumes in the water flow. This characteristic quantifies this dispersion effect and is instructive for determining the sampling duration. The estimated hydraulic arrival delay refers to the time required for the contaminant plume to reach the sampling point, estimated in advance based on the distance between the upstream surge event location and the sampling point, as well as the pipeline flow velocity. The timing synchronization compensation mechanism is a mechanism that precisely adjusts the start-up time of the sampling actuator based on the physical response delay and the estimated hydraulic arrival delay. This mechanism aims to ensure that the sampling process is synchronized with the actual arrival time of the contaminant plume, avoiding sampling too early or too late. Adaptive closed-loop control refers to a control strategy that allows the system to dynamically adjust the operating parameters (such as the suction cycle) of the sampling actuator based on real-time monitoring data and feedback information to optimize the sampling effect. The dynamic suction cycle of the sampling actuator refers to the length of time the sampling pump draws water samples each time. This cycle is not fixed but is adaptively adjusted based on real-time operating conditions and calculation results.

[0028] Suppose that the wastewater source tracing sampling device described in this application is deployed inside an inspection well at a monitoring point in an urban underground sewage pipe network. One day, a sudden illegal discharge of industrial wastewater occurs upstream.

[0029] First, when a large amount of industrial wastewater suddenly floods the pipe network, it immediately causes a sharp change in the flow velocity and pressure within the network, accompanied by unique transient acoustic pulses. The acoustic and hydraulic sensors configured in the cross-modal kinetic energy surge extraction unit simultaneously capture these transient acoustic pulse sequences along with hydraulic fluid parameters. This unit performs cross-modal fusion of the distribution density of the abnormal acoustic events detected in real time with the abrupt changes in the fluid kinetic head, and compares this with the historical steady-state baseline of the pipe network under normal operating conditions. From this, the unit extracts a kinetic energy surge index that is significantly higher than the baseline. This index serves as a spatial feedforward early warning signal, rapidly indicating a possible sewage discharge shock event within the pipe network.

[0030] Subsequently, as the industrial wastewater mass flows downstream and gradually reaches the inspection well where the sampling device is located, specific chemicals it contains (e.g., some organic pollutants have fluorescent properties) alter the optical characteristics of the water body. The optical sensors configured in the multi-channel optical feature reconstruction unit acquire the multi-channel fluorescence spectral characteristics of the target water body. Simultaneously, the unit measures the turbidity of the water body in real time and performs optical masking attenuation compensation based on this real-time turbidity to eliminate interference from turbidity on the fluorescence signal. Furthermore, the unit introduces the dynamic shear rate ratio, characterizing the turbulent state of the pipe network water flow, for hydrodynamic cross-compensation to filter out the influence of factors such as water flow turbulence or equipment micro-vibrations on the optical measurements. After these compensation processes, the unit outputs the reconstructed fluorescence deviation after filtering out environmental interference. This deviation serves as a target verification signal, chemically confirming the existence of the discharge event.

[0031] Next, the spatiotemporal coherence triggering evaluation unit receives the kinetic energy surge index output by the cross-modal kinetic energy surge extraction unit and the reconstructed fluorescence deviation output by the multi-channel optical feature reconstruction unit. This unit extracts the coherence bandwidth confidence of the acoustic and optical signals within specific characteristic frequency bands and dynamically weights the feedforward sensitivity accordingly, ensuring that the feedforward warning weight is higher only when the two heterogeneous signals are physically highly correlated. For example, if the acoustic surge signal and the fluorescence deviation signal exhibit high consistency in time and characteristics, the confidence level will be high. Furthermore, this unit introduces the physical response delay between heterogeneous signals for temporal convergence constraints. For instance, by calculating the difference in arrival timestamps between the acoustic surge peak and the fluorescence intensity deviation peak, if this delay is within a preset reasonable range, it further confirms that the two signals originate from the same pollution discharge event. Through these comprehensive evaluations, the unit calculates and generates a source tracing sampling joint triggering index. When this index reaches a preset trigger threshold, the system issues a joint triggering command.

[0032] Finally, in response to the aforementioned joint trigger command, the adaptive sampling closed-loop control unit activates the sampling actuator. This unit first integrates the hydraulic dispersion scale characteristics caused by abrupt changes in the spatial geometry of the target inspection well. For example, if the internal structure of the inspection well is complex, the contaminant cloud will disperse more significantly within it, requiring a longer sampling time to capture the entire cloud. Simultaneously, this unit constructs a timing synchronization compensation mechanism based on the physical response delay and the estimated hydraulic arrival delay to ensure that the sampling pump starts pumping when the contaminant cloud actually arrives at the sampling point and continues for a sufficient time to completely capture the cloud. For example, if the contaminant cloud is estimated to arrive 30 seconds after the trigger command is issued, and the physical response delay shows that the acoustic signal arrives 5 seconds earlier than the optical signal, the system will precisely adjust the sampling start time to ensure that the sampling window accurately matches the actual arrival time window of the contaminant cloud. Thus, this unit adaptively controls the dynamic pumping cycle of the sampling actuator, ensuring that the collected water sample is highly representative and avoiding distortion caused by subsequent dilution with clean water.

[0033] Traditional optical sensing devices, when used in complex pipe network environments, lack cross-compensation for flow velocity shear and pipe network mechanical micro-vibrations, making them highly susceptible to false triggering due to water flow bubbles or probe micro-vibrations. This application's multi-channel optical feature reconstruction unit introduces a dynamic shear rate ratio characterizing the turbulent state of the pipe network flow for hydrodynamic cross-compensation. This effectively filters out environmental interferences such as water flow turbulence, bubble disturbances, and probe micro-vibrations, significantly improving the accuracy of reconstructed fluorescence deviation measurement, thereby avoiding false triggering and ensuring the reliability of chemical feature sensing.

[0034] Furthermore, existing systems only monitor the lagging end-stage chemical characteristics in isolation, completely ignoring the feedforward physical characteristics such as the surge in fluid kinetic energy and acoustic pulses accompanying the discharge, and lacking cross-modal coherent confidence verification, resulting in poor triggering accuracy. The cross-modal kinetic energy surge extraction unit in this application can simultaneously capture transient acoustic pulse sequences and hydraulic fluid parameters, uncovering the feedforward physical characteristics of the discharge event. Further, the spatiotemporal coherent triggering evaluation unit uses the kinetic energy surge index as a spatial feedforward early warning signal, the reconstructed fluorescence deviation as a target verification signal, and extracts the coherent bandwidth confidence of the acousto-optic heterogeneous signals within the characteristic frequency band to dynamically weight the feedforward sensitivity, while introducing the physical response delay between heterogeneous signals for temporal convergence constraints. This multimodal fusion and homogeneous verification mechanism significantly improves the accuracy and reliability of trigger judgment, effectively solving the problem of false triggering caused by single signal triggering.

[0035] Furthermore, traditional, rigid sampling strategies fail to consider the spatial hydraulic dispersion effect of inspection wells and the time delay difference in acoustic and optical signal response. This easily leads to severe dilution and distortion of the extracted water samples by subsequent clean water, making it difficult to obtain accurate pollution source tracing data. The adaptive sampling closed-loop control unit of this application responds to joint trigger commands, integrates the hydraulic dispersion scale characteristics caused by abrupt changes in the spatial geometry of the target inspection well, and constructs a time-series synchronization compensation mechanism based on the physical response time delay and the estimated hydraulic arrival time delay. Therefore, this unit can adaptively adjust the dynamic suction cycle of the sampling execution mechanism, ensuring that the sampling time precisely matches the actual arrival and dispersion process of the pollutant plume. This guarantees that the collected water sample corresponds to the pollutant plume, avoids dilution and distortion of the water sample by subsequent clean water, and significantly improves the representativeness of the water sample.

[0036] In summary, this application achieves accurate source tracing and sampling of sewage discharge events through innovative design in the entire process of signal sensing, trigger judgment, and sampling execution. It overcomes the problems of existing technologies such as easy false triggering under complex working conditions, poor triggering accuracy, and easy distortion of water samples, and provides a more reliable technical means for sewage treatment.

[0037] This application further proposes the following specific logic for obtaining the reconstructed fluorescence deviation using a multi-channel optical feature reconstruction unit: The ratio of the fluorescence intensity of the blue channel to that of the ultraviolet channel is extracted, and the absolute deviation of this ratio from the baseline fluorescence ratio adaptively extracted by the system is calculated; a positively correlated exponential optical masking compensation factor is constructed using the ratio of the real-time turbidity of the water body to a reference turbidity threshold; a linear hydrodynamic cross-compensation factor is constructed using the dynamic ratio of the real-time pipe network flow velocity shear rate to the reference pipe network flow velocity shear rate; the absolute deviation, the optical masking compensation factor, and the hydrodynamic cross-compensation factor are jointly weighted and mapped to obtain the reconstructed fluorescence deviation; the calculation formula for the reconstructed fluorescence deviation is as follows:

[0038]

[0039] in, To reconstruct fluorescence deviation, The fluorescence intensity of the blue light channel. The fluorescence intensity of the ultraviolet channel. Basic fluorescence ratio, This refers to the real-time turbidity of the water body. As a reference turbidity threshold, This refers to the real-time shear rate of the pipeline flow velocity. Reference network flow velocity shear rate. Reconstructed fluorescence deviation. This is a quantitative indicator characterizing the deviation of the fluorescence characteristics of a target water body from the fluorescence characteristics of normal background water body after multiple environmental interference compensations. It aims to provide a more accurate and robust signal of abnormal sewage discharge, overcoming the limitations of traditional fluorescence detection which is susceptible to environmental interference. The model comprehensively considers multiple parameters such as fluorescence intensity ratio, water turbidity, and water flow shear rate. For example, exponential or polynomial functions can be used to construct the compensation term to adapt to the nonlinear characteristics of different types of environmental interference. (Blue channel fluorescence intensity) Ultraviolet (UV) fluorescence intensity refers to the intensity of the fluorescence signal emitted by a fluorescent substance in the blue light band in water at a specific excitation wavelength. It is commonly used to detect organic pollutants in water. It can be measured using a fluorescence sensor or spectrometer equipped with a blue light filter. For example, an LED light source can be used to excite the water, and a photomultiplier tube or CCD detector combined with a narrow-band filter can capture the fluorescence signal in the blue light band. This refers to the intensity of the fluorescence signal emitted by a fluorescent substance in water in the ultraviolet band at a specific excitation wavelength. This channel is often used to detect certain organic compounds or as a background fluorescence reference. Similar to the blue light channel, it can be measured by configuring a fluorescence sensor or spectrometer with an ultraviolet filter. For example, an ultraviolet LED or xenon lamp can be used as the excitation source, and the signal can be acquired by using an ultraviolet-sensitive photodetector combined with an appropriate filter. (Basic fluorescence ratio) This is the average ratio of the fluorescence intensity of the blue channel to the fluorescence intensity of the ultraviolet channel under historically stable, clean water conditions (normal network operation, no sewage discharge events). It represents the inherent fluorescence characteristics of normal, unpolluted water bodies and serves as a benchmark for judging abnormal deviations. It can be obtained by long-term monitoring of water fluorescence data from the pipeline network under conditions without sewage discharge events and by performing statistical analysis on this data. For example, a microprocessor can periodically collect fluorescence intensity data under clean water conditions and dynamically update it using a moving average algorithm or an exponentially weighted average algorithm. Value. It can also cache the continuous multi-channel fluorescence ratio sequence of the target pipeline within a set period (e.g., 24 hours) in real time, using the Laida rule ( (Criteria) Abnormal abrupt changes exceeding the normal fluctuation range are removed, and the lower quartile of the remaining stationary data sequence is used as the base fluorescence ratio for dynamic updates. Real-time turbidity of water bodies. Turbidity is an indicator that measures the degree to which suspended particulate matter in water causes light scattering or absorption, directly affecting the transmission and detection of optical signals. It can be measured in real time using a turbidity sensor; for example, a turbidimeter based on the principle of light scattering can be used to calculate the turbidity value by measuring the intensity of scattered light at a specific angle. (Reference turbidity threshold) This is a pre-set upper limit of turbidity based on environmental water quality standards or practical application needs. It is used to calculate turbidity compensation terms, indicating at what turbidity level significant optical attenuation compensation is required. It can be set according to national or local wastewater discharge standards, surface water environmental quality standards, and other regulations. For example, it can be set as the turbidity limit for a specific discharge outlet in the "Pollutant Discharge Standard for Urban Wastewater Treatment Plants." Environmental water quality standards are a series of water quality indicator limits and requirements formulated by national or local governments to protect the water environment, safeguard human health, and maintain ecological balance. These standards are usually published in the form of regulatory documents, such as the "Surface Water Environmental Quality Standard" and the "Pollutant Discharge Standard for Urban Wastewater Treatment Plants." Real-time pipe network flow velocity shear rate. It is a physical quantity that measures the change in velocity gradient of water flow within a pipe network, reflecting the degree of turbulence and internal friction. It can be calculated indirectly using velocity sensors or pressure sensors; for example, an ultrasonic Doppler current meter can be used to measure the flow velocity at different locations and then calculate the velocity gradient. (Reference pipe network velocity shear rate) This is a physical benchmark quantity characterizing the turbulent characteristics of water flow under normal, stable operation without sewage discharge shocks. It is used to filter out the interference of normal water flow turbulence on the fluorescence signal in the fluid dynamics compensation term. The specific adaptive acquisition logic of this benchmark quantity is as follows: In the initial deployment stage or during a stable operation phase where no abnormal sewage discharge is confirmed, the system continuously collects real-time pipeline flow velocity shear rate data within a preset period (e.g., 72 consecutive hours) to construct a background dataset. To eliminate occasional pipeline pressure fluctuations or sensor noise interference, the system numerically sorts the background dataset and removes extreme values ​​at both ends (e.g., removing the highest and lowest 10% of data) to obtain a valid dataset. Finally, the system extracts the median or mode of this valid dataset and uses it as the dynamically fixed reference pipeline flow velocity shear rate. This mechanism achieves objective calibration of the benchmark value without manual intervention through adaptive data truncation and central tendency extraction.

[0040] The solution in this application provides reconstructed fluorescence deviation. The calculation formula enables fine-grained correction of the fluorescence deviation output by the multi-channel optical feature reconstruction unit. This formula calculates the fluorescence intensity of the blue channel. With UV channel fluorescence intensity The ratio of the two values ​​was used as the basic fluorescence feature, and was adaptively averaged with the basic fluorescence ratio obtained by extracting historical clear water conditions of the pipe network by a microprocessor. By comparing these parameters, a basic term reflecting the degree of deviation from the original fluorescence is formed. Based on this, two key compensation factors are introduced: one based on real-time turbidity of the water body. and reference turbidity threshold Optical masking attenuation compensation term And a shear rate based on real-time pipeline flow velocity and reference pipeline flow velocity shear rate Fluid dynamics cross-compensation term The operating logic of this calculation mechanism is that, firstly, through... The ratio form effectively offsets systematic errors such as sensor aging and gain drift, ensuring the stability of basic fluorescence characteristics. The absolute value of the difference directly quantifies the deviation between the current fluorescence characteristics of the water body and the normal clear water background. Secondly, considering the strong attenuation effect of water turbidity on optical signals, an exponential turbidity compensation term is introduced. This term is characterized by a larger compensation amplitude as turbidity increases, which aligns with the nonlinear law of actual optical attenuation, effectively correcting optical detection errors caused by turbidity. Finally, addressing the interference of fluid dynamics factors such as turbulent water flow and bubble disturbances on the fluorescence signal, a fluid dynamics compensation term constructed using shear rate further eliminates these dynamic interferences, improving the sensitivity and accuracy of fluorescence deviation in response to real sewage discharge events. Through this multi-dimensional, nonlinear compensation mechanism, this scheme, in the multi-channel optical feature reconstruction unit, can transform the original fluorescence signal into a highly pure and accurate reconstructed fluorescence deviation that reflects sewage discharge anomalies. This enables the device to effectively filter out environmental interferences such as water flow bubbles, probe micro-vibrations, and changes in water turbidity when facing complex actual working conditions of underground pipe networks, significantly reducing the false trigger rate and providing a more reliable targeted verification signal for the subsequent spatiotemporal coherent trigger assessment unit, thereby improving the accuracy and reliability of the entire wastewater precision source tracing sampling device.

[0041] As a specific implementation method, reconstructing fluorescence deviation The calculations can be performed by a microprocessor within the multi-channel optical feature reconstruction unit. This microprocessor can be a low-power microcontroller from TI's MSP430 series, whose integrated ADC module can acquire the analog signal output from the fluorescence sensor with high precision. The fluorescence sensor can employ a dual-channel fluorescence probe; for example, one channel can detect the intensity of 450nm blue fluorescence under 365nm ultraviolet excitation. Another channel was used to detect the 340 nm ultraviolet fluorescence intensity under the same excitation. Real-time turbidity of water body This can be obtained using a turbidity sensor based on the principle of infrared scattering. The voltage signal output by this sensor is converted into a digital quantity by an ADC. Real-time pipeline flow velocity shear rate. This can be provided by an ultrasonic Doppler current meter, which estimates the shear rate by measuring the velocity difference between water layers at different depths. During the system initialization phase, the microprocessor continuously monitors the fluorescence data of the pipe network during low-flow, no-sewage periods at night and calculates... The average value is used as the basic fluorescence ratio. This value is stored in non-volatile memory. Reference turbidity threshold. It can be preset to 50 NTU, referencing the shear rate of the pipeline flow velocity. It can be preset to 0.1s⁻ 1 During real-time monitoring, the microprocessor periodically collects data. , , and The instantaneous value is then substituted into the above formula for calculation. For example, when It is 1000mV. It is 500mV. It is 1.8. For 80 NTU, For 50 NTU, It is 0.2s⁻ 1 , It is 0.1s⁻ 1 At that time, the microprocessor will calculate: =2.97. This calculation result is the reconstructed fluorescence deviation at the current moment, and this value will then be passed to the spatiotemporal coherence triggering evaluation unit for further processing.

[0042] Through the above technical solution, this application provides a more accurate and robust reconstructed fluorescence deviation. This solution uses the ratio of blue channel fluorescence intensity to ultraviolet channel fluorescence intensity as a fundamental feature, combined with real-time water turbidity for optical masking attenuation compensation, and introduces shear rate, characterizing the turbulent state of the pipe network, for hydrodynamic cross-compensation, effectively overcoming the limitations of traditional fluorescence detection which is susceptible to environmental interference. Specifically, the use of the dual-channel fluorescence ratio effectively eliminates systematic errors such as sensor drift, improving the stability of the fundamental feature; exponential turbidity compensation accurately corrects the nonlinear attenuation effect of water turbidity on the optical signal; and the introduction of shear rate effectively corrects fluorescence feature shifts caused by hydrodynamic factors such as water turbulence and bubble disturbance. Therefore, this solution significantly improves the accuracy of reconstructed fluorescence deviation in identifying real abnormal sewage discharge states, greatly reduces false triggering caused by environmental interference, and provides a highly reliable targeted verification signal for precise sewage source tracing sampling devices, thereby ensuring the accuracy and effectiveness of subsequent sampling decisions.

[0043] This application further proposes the following specific logic for the cross-modal kinetic energy surge extraction unit to obtain the kinetic energy surge index: Extracting the incremental ratio of the real-time abnormal acoustic event count rate relative to the background abnormal acoustic baseline, and performing logarithmic smoothing on the incremental ratio to construct an acoustic pulse surge weight; extracting the dynamic ratio of the fluid kinetic head relative to the reference kinetic head to construct a hydraulic impact weight; and jointly mapping the acoustic pulse surge weight and the hydraulic impact weight to obtain the kinetic energy surge index characterizing the intensity of the sewage discharge impact; the calculation formula for the kinetic energy surge index is as follows:

[0044]

[0045] in, The kinetic energy surge index, This represents the real-time abnormal sound event count rate. As the background anomalous sound baseline, For fluid kinetic energy head, The baseline kinetic head. Real-time abnormal acoustic event count rate. This refers to the frequency of abnormal acoustic events detected in real-time within the target pipe network. Abnormal acoustic events typically refer to acoustic signals that significantly deviate from normal water flow noise patterns, such as transient pulses or persistent abnormal noises caused by contaminant impacts, bubble bursts, or solid particle collisions. Its function is to capture the immediate physical response of sewage impact events through acoustic analysis. Implementation methods can include: continuously collecting acoustic signals within the pipe network by installing high-sensitivity acoustic sensors inside or outside the network; subsequently, using digital signal processing techniques, such as short-time Fourier transform, wavelet analysis, or machine learning-based anomaly detection algorithms, to analyze the collected acoustic signals in real-time, identifying and counting abnormal acoustic events exceeding preset thresholds or patterns; or, using statistical methods, such as calculating the instantaneous energy, kurtosis, or skewness of the acoustic signal, where a significant deviation in these characteristics within a short period is identified as an abnormal acoustic event and counted. Background abnormal acoustic baseline. This refers to the average or typical frequency of abnormal acoustic events occurring under normal and stable operating conditions of the pipeline network. It provides a reference benchmark for the real-time abnormal acoustic event count rate, distinguishing normal background noise fluctuations from actual sewage impact events. Implementation methods can include: continuously monitoring the real-time abnormal acoustic event count rate over a period of time during the initial system deployment or when the pipeline network is in a known clean and stable operating state, and calculating its long-term average or establishing a dynamic baseline using methods such as moving average or exponential smoothing. This baseline will be updated slowly to adapt to long-term changes in the pipeline network environment, but is not sensitive to short-term drastic fluctuations; alternatively, an empirical background abnormal acoustic baseline can be pre-set based on parameters such as different pipeline types, pipe diameters, and flow velocities through experiments or historical data analysis, and fine-tuned in practical applications. (Fluid kinetic head) This refers to the kinetic energy per unit weight of a fluid, directly reflecting the velocity state of water flow within a pipe network. Sewage impacts are typically accompanied by sudden changes in water velocity or pressure; therefore, the fluid kinetic head can characterize sewage events from a hydraulic perspective. Its role is to provide hydraulic dimensions of sewage impact characteristics. Implementation methods can include: real-time measurement of water flow velocity using flow velocity sensors installed within the pipe network. Then, according to the definition formula of fluid kinetic energy head... (in The calculation can be performed using gravity acceleration; alternatively, pressure sensors installed on the pipeline network can be used to monitor changes in pipeline pressure in real time. When sewage impact occurs in the pipeline network, it usually causes local pressure fluctuations or transient pressure increases. By establishing a hydraulic model between pressure and flow velocity, the measured pressure data is converted into flow velocity, and then the fluid kinetic head is calculated. (Reference kinetic head) This refers to the average or typical value of the fluid kinetic head under normal and stable operation of the pipeline network. It provides a reference benchmark for real-time fluid kinetic head, used to quantify the degree of deviation of sewage shock from the hydraulic state. Implementation methods may include: continuously collecting fluid kinetic head data during normal pipeline network operation and calculating its long-term average or median as the benchmark kinetic head. This baseline can be periodically updated based on seasonal or diurnal flow rate variations in the pipeline network; alternatively, a theoretical benchmark kinetic head can be pre-calculated and set based on parameters such as the pipeline network's design flow rate and pipe diameter, combined with empirical formulas or hydraulic models.

[0046] This application's cross-modal kinetic energy surge extraction unit quantifies the intensity of sewage discharge impact events by fusing two heterogeneous signals: acoustic and hydraulic. Specifically, the unit first simultaneously captures transient acoustic pulse sequences and hydraulic fluid parameters of the target pipe network. After acquiring this raw data, the kinetic energy surge index is calculated to characterize the sewage discharge impact event. The formula for calculating the kinetic energy surge index is to logarithmically process the ratio of the real-time anomalous acoustic event count rate to the background anomalous acoustic baseline, and then multiply it by the ratio of the fluid kinetic head to the reference kinetic head. The ingenuity of this calculation method lies in its full utilization of the physical characteristic that sewage discharge impact events often simultaneously trigger acoustic anomalies and hydraulic kinetic energy mutations. When sewage discharge occurs, it usually produces unique acoustic characteristics, resulting in a real-time anomalous acoustic event count rate that is significantly higher than the background anomalous acoustic baseline; simultaneously, the water injection brought about by sewage discharge also changes the fluid kinetic energy state within the pipe network, causing the fluid kinetic head to deviate from the reference kinetic head. By multiplicatively fusing information from these two dimensions, this scheme can achieve mutual verification and enhancement of cross-modal characteristics. Specifically, taking the natural logarithm of the ratio of the real-time abnormal acoustic event count rate to the background abnormal acoustic baseline effectively amplifies the acoustic anomaly signal caused by real sewage discharge impacts while suppressing slight fluctuations in background noise, ensuring the sensitivity and robustness of the acoustic features. Multiplying this logarithmic result by the ratio of fluid kinetic head to the reference kinetic head further introduces evidence from the hydraulic dimension. The kinetic surge index only shows a high value when both acoustic and hydraulic aspects show significant anomalies, thus avoiding misjudgments caused by a single signal anomaly. For example, if there is only acoustic noise without hydraulic changes, or only hydraulic fluctuations without acoustic anomalies, the index will not increase significantly, thus effectively distinguishing real sewage discharge impact events from other interfering factors. This cross-modal fusion mechanism enables the extracted kinetic surge index to more accurately and reliably characterize sewage discharge impact events, significantly improving the ability to identify real sewage discharge events from complex backgrounds and solving the problem of misjudgment and missed judgments that are prone to occur when relying on a single dimension.

[0047] As a specific implementation, the cross-modal kinetic energy surge extraction unit can be configured as follows: one or more high-frequency hydrophones and an ultrasonic Doppler velocimeter are installed at key locations in the target pipeline network. The hydrophones continuously monitor acoustic signals within the pipeline network, and their output signals are sent to an embedded microprocessor after passing through a front-end amplifier and an analog-to-digital converter. The microprocessor runs a real-time acoustic event detection algorithm. For example, by setting a dynamic threshold, when the instantaneous energy of the acoustic signal continuously exceeds the threshold for a certain period of time within a specific frequency band, it is determined as an abnormal acoustic event. The microprocessor counts the number of abnormal acoustic events per unit time in real time to obtain the real-time abnormal acoustic event count rate. Meanwhile, the microprocessor maintains a long-running moving average as a baseline for background anomalous sound. This baseline is updated slowly during normal operation of the pipe network. The ultrasonic Doppler velocimeter measures the water flow velocity within the pipe network in real time. After receiving the velocity data, the microprocessor applies the fluid kinetic head formula... Calculate real-time fluid kinetic head Similarly, the microprocessor also maintains a long-term moving average as a reference kinetic head. Once acquired , , and Based on these four parameters, the microprocessor will calculate the kinetic energy surge index in real time according to the formula for calculating the kinetic energy surge index. For example, when a large number of bursting bubbles and a sudden increase in water flow velocity are detected in the pipe network, It will increase significantly. It will also increase significantly, thus leading to the calculated The rapid rise indicates a possible sewage shock event.

[0048] Through the above technical solution, this application effectively solves the problems of misjudgment and missed judgment caused by the lack of multimodal fusion in traditional methods when identifying sewage impact events. Specifically, this solution constructs a kinetic energy surge index by multiplicatively fusing the abnormal acoustic event count rate with the fluid kinetic head. This fusion mechanism ensures that the index only increases significantly when the sewage impact simultaneously causes significant anomalies in both acoustic and hydraulic aspects, thereby greatly improving the accuracy and reliability of sewage impact event identification. Logarithmic processing of the real-time abnormal acoustic event count rate makes the index effectively suppress small background noise fluctuations while being more sensitive to changes in acoustic characteristics caused by genuine sewage impacts. Combining the ratio of fluid kinetic head further eliminates false alarms caused by single-modal anomalies, enabling the system to more accurately distinguish genuine sewage impact events from complex pipe network backgrounds. Furthermore, by introducing a background abnormal sound baseline and a reference kinetic head, this scheme can adaptively adjust to different pipe network environments and normal operating conditions, ensuring that sewage discharge shocks deviating from normal conditions can be accurately identified under various operating conditions, providing highly reliable feedforward early warning signals for subsequent source tracing sampling.

[0049] This application further proposes the following specific logic for obtaining the source sampling joint triggering index using a spatiotemporal coherent triggering evaluation unit: Based on the fusion result of coherent bandwidth confidence and kinetic energy surge index, a feedforward activation weight exhibiting exponential saturation characteristics is constructed; based on the ratio of physical response delay to the maximum tolerance delay threshold, a temporal convergence suppression weight exhibiting exponential decay characteristics is constructed; using the reconstructed fluorescence deviation as the target reference quantity, the feedforward activation weight is used for forward triggering modulation, and the temporal convergence suppression weight is used for inverse convergence constraint, finally calculating and obtaining the source sampling joint triggering index; the calculation formula for the source sampling joint triggering index is as follows:

[0050]

[0051] in, For source tracing, the sampling joint trigger index, For coherent bandwidth confidence. The kinetic energy surge index, To reconstruct fluorescence deviation, For physical response delay, The maximum tolerable time delay threshold is the time convergence constraint limit by which the characterization device determines heterogeneous mutation signals as non-homogeneous events. Source tracing sampling joint triggering index. It is a key indicator used to comprehensively assess the probability and intensity of pollution discharge incidents; its value directly determines whether sampling action is triggered. This index provides a quantitative and reliable decision-making basis by integrating information from multiple dimensions. (Coherence bandwidth confidence score) This quantifies the correlation between acoustic energy signals and fluorescence deviation signals within a specific frequency band, reflecting whether the two heterogeneous signals originate from the same event. One approach is to perform Fourier transforms on the acoustic and fluorescence signals, calculate their cross-power spectral density and their respective self-power spectral density, obtain the coherence function, and then perform integral averaging within the characteristic frequency band. Another approach is to use wavelet coherence analysis to accommodate the characteristics of non-stationary signals. Kinetic energy surge index It is an indicator characterizing the kinetic energy surge of a sewage discharge shock event. It can be obtained by analyzing the distribution density of transient acoustic pulse sequences in the pipe network and the abrupt changes in hydraulic fluid parameters (such as flow velocity and pressure), and comparing them with historical stable baselines. For example, it can be calculated by statistically analyzing the frequency of abnormal acoustic events per unit time and combining this with real-time changes in fluid kinetic head. (Reconstructed fluorescence deviation) This is a quantitative indicator of the deviation of water body fluorescence characteristics from the baseline level after environmental interference compensation. It is obtained by acquiring the fluorescence spectral characteristics of the target water body through a multi-channel optical sensor, performing optical masking attenuation compensation based on real-time turbidity, and simultaneously introducing the dynamic ratio of shear rate, which characterizes the turbulent state of the pipe network water flow, for hydrodynamic cross-compensation, thereby obtaining the fluorescence deviation after filtering out environmental interference. Physical response delay. This refers to the time difference between the peak of the acoustic surge and the deviation of the fluorescence intensity from its peak, reflecting the time required for a pollutant to propagate from the physical impact point to the optical detection point. This time delay can be acquired in real time by recording the occurrence times of anomalous peaks in the acoustic and optical signals using high-precision timestamps and calculating their difference. Alternatively, a cross-correlation algorithm can be used to analyze the time series of the two signals to determine the optimal time lag. Maximum tolerable time delay threshold. This is a preset time limit used to determine whether acoustic and optical anomalies belong to the same sewage discharge event. If the physical response delay... If the values ​​exceed this threshold, the two abnormal events are considered to be from different sources. This threshold can be set based on the actual length of the pipeline network, flow velocity, hydraulic model simulation results, or historical experience data; for example, the maximum tolerable time delay threshold. The calculation logic is based on the dynamic calculation of the hydraulic boundary parameters of the target pipeline network and the physical deployment space of each sensor in the device. ;in, This refers to the actual physical installation spacing between the acoustic sensor and the multi-channel optical sensor along the pipeline axis. Design the minimum guaranteed flow velocity for the target pipeline network during the dry season. This is a fixed delay compensation constant for system hardware communication and signal processing.

[0052] The proposed solution uses a quantitative formula to fuse multi-dimensional anomaly features to obtain a unified joint triggering index. This method can accurately determine sewage discharge triggers by combining multiple attributes of heterogeneous acoustic and optical signals, effectively solving the problem of false judgments and triggers. Overall, this calculation method integrates information from multiple dimensions, including feedforward warning strength, signal coherence, water quality anomaly verification, and temporal synchronization constraints, into a single trigger index. It balances the forward-looking nature of feedforward warnings with the accuracy of water quality verification, while also incorporating homology constraints. The resulting trigger index accurately reflects the probability and intensity of sewage discharge events, providing a reliable basis for subsequent sampling and control. Specifically, the first term in the formula... Based on coherence bandwidth confidence and kinetic surge index The calculations show that multiplying the two results in a transformation that introduces the kinetic energy surge index. The resulting feedforward warning intensity information is combined with the coherent bandwidth confidence level. The coherence of the reflected acoustic and optical signals is considered; only acoustic anomalies with high coherence will affect the trigger index. It produces a high positive contribution and effectively filters out irrelevant, independent acoustic anomalies. This transformation method is chosen to normalize parameters with varying distributions to a reasonable contribution range, preventing a single parameter from significantly impacting the overall results. The reconstructed fluorescence deviation is introduced. As a multiplicative term, the reconstructed fluorescence deviation is... These are optical anomalies in water quality that have been freed from environmental interference. Incorporating them into the calculation as a targeted verification step ensures that triggering judgments are based on actual water quality anomalies. This avoids misjudgments caused by relying solely on physical characteristics to trigger sampling, aligning with the logic of early warning followed by verification and improving the accuracy of the judgment. (Physical response delay is also included.) and maximum tolerable delay threshold exponential decay term An exponential decay penalty is applied to abnormal combinations with delays exceeding the tolerance range. This aligns with the physical law that the audio-visual anomalies in pollution events originating from the same source should have an abnormal delay within a certain range. This achieves timing convergence constraints, effectively filtering out abnormal signal combinations from different sources and further reducing the probability of false triggering. (Physical response delay) By calculating the timestamp difference between the two signal peaks in real time, it can adapt to the actual operating conditions of different pipeline networks, ensuring that the delay constraint always conforms to the current actual situation, and the maximum tolerable delay threshold. The criteria for judging non-homogeneous events have been clarified, providing clear and enforceable standards for time series convergence constraints.

[0053] The following is a concrete example to illustrate this. Suppose that during a certain monitoring, the cross-modal kinetic energy surge extraction unit detects a significant surge in fluid kinetic energy within the pipeline network, and calculates the kinetic energy surge index. The value was 0.7. Simultaneously, the multi-channel optical feature reconstruction unit detected a significant deviation in the water fluorescence characteristics, with the reconstructed fluorescence deviation being [value missing]. The confidence level was 0.6. Further analysis by the spatiotemporal coherence triggering evaluation unit revealed that the acoustic and fluorescence signals exhibited high coherence within the characteristic frequency band, and the coherence bandwidth confidence level was calculated. The value is 0.8. Furthermore, the time difference between the peak of the acoustic flux surge and the deviation of the fluorescence intensity from the peak, i.e., the physical response delay, is also considered. The maximum tolerable latency is 8 seconds. This is the system's preset maximum tolerance latency threshold. The duration is 15 seconds. Based on this real-time data, the source tracing sampling joint triggering index is... It can be calculated as follows: =0.151. If the system's sampling trigger threshold is set to 0.1, then the calculated value is... If the value (0.151) is higher than the threshold, the system will issue a sampling command. Conversely, if the time delay of acoustic and optical anomalies is too large, for example... If the time is 20 seconds, then the exponential decay term... =0.264, which will significantly reduce The numerical value may fall below the trigger threshold, thus avoiding missampling caused by non-same-source events.

[0054] Through the above technical solution, this application introduces coherent bandwidth confidence. This ensures that the joint triggering index for source tracing sampling is only applied when there is a high correlation between the acousto-optic heterogeneous signals. This makes a significant contribution, effectively filtering out false triggers caused by independent noise or unrelated events. Simultaneously, by introducing physical response delay... and maximum tolerable delay threshold This mechanism imposes strict constraints on the physical response timing of heterogeneous signals and penalizes event combinations with delays exceeding reasonable limits, significantly reducing the possibility of misclassifying non-homogeneous abnormal signals as pollution discharge events. This multi-dimensional and collaborative evaluation mechanism enables the joint triggering index of source tracing sampling. It can more accurately reflect the probability and intensity of actual sewage discharge events, providing a highly reliable triggering basis for the subsequent adaptive sampling closed-loop control unit, and significantly improving the accuracy and reliability of precise sewage source tracing sampling.

[0055] This application further proposes obtaining the coherent bandwidth confidence level based on amplitude squared coherence analysis. The specific logic is as follows: Within the upper and lower frequency limits of the characteristic frequency band, the square of the cross-power spectral density of the acoustic energy signal and the fluorescence deviation signal is calculated, and this square is then compared with the product of the self-power spectral density of the acoustic energy signal and the fluorescence deviation signal. The ratio within the upper and lower frequency limits of the characteristic frequency band is then integrated and normalized to obtain the coherent bandwidth confidence level characterizing the dynamic coupling strength of heterogeneous signals. The formula for calculating the coherent bandwidth confidence level is as follows:

[0056]

[0057] in, For coherent bandwidth confidence. This is the upper limit frequency of the characteristic frequency band. This is the lower limit frequency of the characteristic frequency band. The cross-power spectral density of the acoustic energy signal and the fluorescence deviation signal. The self-power spectral density of the acoustic energy signal. The autopower spectral density of the fluorescence deviation signal. Coherence bandwidth confidence level. This is an indicator used to quantify the correlation between two signals within a specific frequency range. It reflects the synchronicity and consistency of the acoustic energy signal and the fluorescence deviation signal in the frequency domain, and is crucial for assessing whether these two heterogeneous signals originate from the same pollution discharge event. The upper frequency limit of the characteristic band. and lower limit frequency A specific frequency range was jointly defined, within which signal changes were considered strongly correlated with sewage discharge events. By setting this frequency band, noise and interference signals unrelated to sewage discharge could be effectively filtered out, allowing calculations to focus on valid information. The setting of this characteristic frequency band can be based on the analysis of historical sewage discharge event data to identify which frequency ranges acoustic and fluorescence signals exhibit strong correlations or characteristic changes, thus allowing for empirical setting; alternatively, it can be achieved through machine learning algorithms to dynamically learn and adjust the optimal characteristic frequency band range, such as a lower limit frequency, based on different pipe network environments and sewage discharge types. Set as the cutoff frequency to filter out the background noise of the normal steady-state water flow in the pipe network, and set the upper limit frequency. The upper limit of the acoustic broadband resonance is set to cover the transient opening of the drain valve and the impact of the high-pressure jet on the pipe wall. The cross-power spectral density of the acoustic energy signal and the fluorescence deviation signal. This describes the interrelationship and common energy distribution of two different signals in the frequency domain. It reveals the correlation between heterogeneous signals at different frequency components, serving as a core basis for determining whether they change synchronously or originate from the same source. The cross-power spectral density can be obtained by performing Fourier transforms on the acoustic energy signal and the fluorescence deviation signal respectively, obtaining their spectra, then multiplying the spectrum of one signal by the conjugate of the spectrum of the other signal, and finally averaging the results. The self-power spectral density of the acoustic energy signal... The self-power spectral density of the fluorescence deviation signal These describe the energy distribution of a single signal in the frequency domain. They are used to normalize the cross-power spectral density to eliminate the influence of the energy difference between the two signals on the coherence calculation, so that the coherence bandwidth confidence can objectively reflect the correlation between signals, rather than their absolute energy. The self-power spectral density can be obtained by performing a Fourier transform on the signal to obtain its spectrum, then multiplying the spectrum by its own conjugate, and then averaging.

[0058] The solution in this application defines the confidence level of the coherent bandwidth. The specific calculation method can accurately quantify the coherence between the acoustic energy signal and the fluorescence deviation signal, two heterogeneous signals, providing accurate and reliable parameters for the subsequent calculation of the joint triggering index for source tracing sampling. This calculation process uses the upper frequency limit of the characteristic frequency band. and lower limit frequency By defining the integration interval and selecting the effective frequency range corresponding to the changes in the sewage discharge signal, noise interference from irrelevant frequency bands can be filtered out, preventing fluctuations in irrelevant signals from affecting the calculation results and ensuring the relevance of the results. The cross-power spectral density of the acoustic energy signal and the fluorescence deviation signal is introduced. This can accurately reflect the correlation of two heterogeneous signals at different frequency components, demonstrating the degree of their synchronous changes, which is the core basis for determining whether two signals originate from the same source. Simultaneously, the self-power spectral density of the acoustic energy signal is introduced. The self-power spectral density of the fluorescence deviation signal This is used to normalize the cross-power spectrum results, eliminating calculation biases caused by the different energy bases of acoustic and fluorescence signals, resulting in more objective and accurate coherence levels. The final coherence bandwidth confidence score is... This average representation of the coherence of two signals within the entire characteristic frequency band can comprehensively reflect whether the acoustic and optical signals originate from the same pollution discharge event. It provides accurate basic parameters for subsequent dynamic weighted feedforward sensitivity calculation and joint triggering index calculation, effectively eliminating non-homogeneous interference and improving the reliability of trigger determination. This calculation method is closely integrated with the signals acquired by the multi-channel optical feature reconstruction unit and the cross-modal kinetic energy surge extraction unit, enabling the spatiotemporal coherence triggering evaluation unit to more accurately determine the authenticity of pollution discharge events, thereby avoiding false triggering or missed triggering and significantly improving the intelligence and reliability of the entire source tracing sampling device.

[0059] As a specific implementation method, in practical applications, the multi-channel optical feature reconstruction unit can continuously acquire the multi-channel fluorescence spectral features of the target water body and output the reconstructed fluorescence deviation. Simultaneously, the cross-modal kinetic energy surge extraction unit synchronously captures the transient acoustic pulse sequence and hydraulic fluid parameters of the target pipeline network, and extracts the kinetic energy surge index. After receiving these signals, the spatiotemporal coherence-triggered evaluation unit first preprocesses the acoustic energy signal and fluorescence deviation signal, for example, by performing bandpass filtering to remove high-frequency and low-frequency noise. Then, it transforms the time-domain signal to the frequency domain using a Fast Fourier Transform (FFT) to calculate the autopower spectral density of the acoustic energy signal. The autopower spectral density of the fluorescence deviation signal and their cross-power spectral density The upper limit frequency of the characteristic frequency band. and lower limit frequency The frequency range can be preset to, for example, 50Hz to 500Hz, which typically covers the typical acoustic vibrations and fluorescence signal fluctuations caused by sewage discharge impact events. Then, these power spectral densities are substituted into the above formula, and numerical integration methods (such as the trapezoidal rule or Simpson's rule) are used to... arrive The integral is calculated within the characteristic frequency band, and the coherence bandwidth confidence score is finally obtained. For example, digital signal processors (DSPs) or embedded microcontrollers can be used to perform these complex signal processing and computational tasks, ensuring real-time performance and accuracy.

[0060] Through the above technical solution, this application clarifies the calculation method of coherence bandwidth confidence, thereby accurately quantifying the degree of coherence between acoustic energy signals and fluorescence deviation signals. This enables the device to effectively distinguish between synchronous signal changes caused by real sewage discharge events and asynchronous signal changes caused by environmental interference, significantly improving the accuracy of source tracing sampling trigger determination. This method avoids false triggering or missed triggering problems caused by inaccurate signal homology judgment, ensuring the reliability of subsequent source tracing sampling joint triggering index, and providing a more accurate and reliable sampling basis for sewage treatment.

[0061] This application further proposes a timing synchronization compensation mechanism specifically represented as a pumping synchronization compensation term. The logic for obtaining the pumping synchronization compensation term is as follows: calculate the absolute time deviation between the physical response delay and the estimated hydraulic arrival delay; construct an exponential decay compensation function based on the absolute time deviation using the synchronization reference time constant as the scale standard; when the absolute time deviation approaches zero, the pumping synchronization compensation term approaches the compensation maximum value; the calculation formula for the pumping synchronization compensation term is:

[0062]

[0063] in, This is a suction synchronization compensation term. For physical response delay, To estimate the time delay of hydraulic arrival, The time constant is used as the synchronization reference. This scheme quantifies the timing synchronization compensation mechanism into a suction synchronization compensation term. The compensation value, which can be used to adjust the suction cycle, is calculated based on the deviation between the physical response delay and the estimated hydraulic arrival delay. This provides a quantifiable basis for adjusting the dynamic suction cycle, correcting sampling timing errors caused by timing deviations, ensuring precise targeting of the sewage discharge front, and avoiding distortion of the collected water samples. Specifically, the suction synchronization compensation term... This is a dimensionless numerical value used to quantify the degree of synchronization between the current sampling time and the actual arrival time of the discharge front. The value of this compensation term typically ranges from 0 to 1; a larger value indicates better synchronization, and vice versa. This compensation term can be used as a weighting factor or adjustment parameter, directly affecting the adaptive sampling closed-loop control unit's decision on the dynamic suction cycle of the sampling actuator. Physical response delay. This refers to the time interval between the detection of an abnormal acoustic event (such as an acoustic pulse sequence) by the cross-modal kinetic energy surge extraction unit and the detection of a significant change in water fluorescence deviation by the multi-channel optical feature reconstruction unit. This time delay reflects the actual propagation and response time of a pollution shock event from physical surge to the manifestation of chemical features. The physical response time delay can be obtained by real-time monitoring of the peaks or feature points of the acoustic and optical signals and calculating their timestamp differences. Alternatively, it can be accurately obtained by performing cross-correlation analysis on the acoustic energy signal and the fluorescence deviation signal to determine the time lag with the greatest correlation between the two. (Estimated hydraulic arrival time delay) This refers to the estimated time required for the sewage front to reach the sampling point, based on the distance between the upstream surge event location and the sampling point, as well as the flow velocity within the pipeline network. This time delay is a prediction value based on fluid dynamics principles. The estimated hydraulic arrival time delay can be calculated by dividing the distance from the upstream surge event location to the sampling point by the real-time or average flow velocity of the pipeline network. The upstream surge event location can be determined by triangulation using multiple acoustic sensors, or inferred from a pre-defined pipeline network topology and the event occurrence area; the pipeline flow velocity can be measured in real-time by dedicated flow sensors, or estimated using a pipeline network hydraulic model. Alternatively, a digital twin model of the pipeline network can be used, combined with real-time monitored pressure and flow data, to simulate and calculate the transport path and velocity of pollutants within the pipeline network, thereby accurately predicting the arrival time of the sewage front. Synchronization reference time constant. This is a configurable parameter used to normalize the absolute deviation between the physical response delay and the estimated hydraulic arrival delay, thereby adjusting the sensitivity of the pumping synchronization compensation term to time deviations. The synchronization reference time constant... Based on the inherent hydraulic retention characteristics of the target inspection well, its value selection logic is as follows: based on the nodal volume of the target inspection well. The ratio of the target pipeline's long-term average flow rate to the target pipeline network's flow rate is used as the synchronization reference time constant. The theoretical characteristic time is used to characterize the dispersion effect caused by the fluid passing through this spatial node.

[0064] The proposed solution quantifies the timing synchronization compensation mechanism into a suction synchronization compensation term. This transforms the impact of timing deviations on sampling timing into a standardized value, facilitating the direct integration of this compensation value into the calculation of the dynamic suction cycle. This enables adaptive adjustment of the suction action, replacing the rigid fixed-cycle sampling method. The calculation process uses the absolute deviation between the physical response delay and the estimated hydraulic arrival delay as core variables. The physical response delay is the inherent response time difference of the acoustic-optical heterogeneous signals, while the estimated hydraulic arrival delay is the estimated time for the sewage front to travel from the upstream surge location to the sampling point. The deviation between these two variables accurately reflects the error between the actual signal triggering timing and the estimated sewage arrival timing. Based on this calculation, the required adjustment range for the sampling timing can be accurately obtained, ensuring that the compensation result truly reflects the current level of timing synchronization. The compensation term is calculated using a negative exponential function, which aligns with the influence of deviation on synchronization. Smaller deviations result in larger compensation terms, indicating higher synchronization at the current sampling time. Conversely, larger deviations lead to smaller compensation terms, indicating lower synchronization. This approach directly reflects the impact of temporal synchronization on sampling effectiveness and adapts to subsequent pumping cycle adjustments. Introducing a synchronization reference time constant as a normalization standard normalizes the deviation amplitude, ensuring that the calculated compensation terms have a unified measurement standard across underground pipe networks of different sizes and flow velocities. This adaptability to various pipe network conditions enhances the versatility of the solution.

[0065] The following is a concrete example to illustrate this. Suppose that in an underground pipe network, a cross-modal kinetic energy surge extraction unit is... The system can detect surges in sewage discharge upstream in real time and predict the location of the discharge front based on pipeline flow velocity and distance. The estimated hydraulic arrival time is 1 second to 1 second. The duration is 120 seconds. However, the multi-channel optical feature reconstruction unit... The peak value of the water fluorescence deviation was detected only after seconds, indicating that the actual arrival time of the pollutants was... Seconds, at which point the physical response delay occurs. Set the synchronization reference time constant to 115 seconds. The duration is 10 seconds. According to the calculation formula in this application, the suction synchronization compensation term... It can be calculated as follows: =0.6065. This calculated value of 0.6065 for the suction synchronization compensation term will then be used by the adaptive sampling closed-loop control unit to adjust the dynamic suction cycle of the sampling actuator. For example, if this value is below a certain preset threshold, the system can determine that the current synchronization is poor, thereby extending the suction time or fine-tuning the suction start time to ensure that even with timing deviations, the sewage front can be captured as much as possible. Conversely, if... A value close to 1 indicates good synchronization, allowing for more precise, shorter suction cycles.

[0066] Through the above technical solution, this application provides a quantifiable timing synchronization compensation mechanism, namely, a suction synchronization compensation term, which can accurately reflect the deviation between the physical response delay and the estimated hydraulic arrival delay. This compensation term provides a data-driven basis for the adaptive sampling closed-loop control unit to adjust the dynamic suction cycle, effectively correcting the problem of inaccurate sampling timing caused by the difference in acoustic and optical signal response delays. This ensures that the sampling action can be accurately aimed at the sewage front, significantly reducing the risk of the collected water sample being diluted or distorted by subsequent clean water, thereby improving the accuracy and reliability of source tracing sampling.

[0067] This application further proposes that the hydraulic dispersion scale characteristic be specifically characterized as the geometric spatial abrupt change ratio. The logic for obtaining the geometric spatial abrupt change ratio is as follows: based on the cross-sectional area and equivalent diameter of the upstream inflow pipe connected to the target manhole, the effective hydraulic characteristic volume of the upstream inflow pipe is calculated; the ratio of the nodal volume of the target manhole to the effective hydraulic characteristic volume is calculated, and this ratio is used as the geometric spatial abrupt change ratio characterizing the degree of hydraulic dispersion and dilution of the target manhole; the formula for calculating the geometric spatial abrupt change ratio is:

[0068]

[0069] in, The geometrical space mutation ratio, The node volume of the target inspection well. The cross-sectional area of ​​the upstream inflow pipeline connected to the target manhole. This is the equivalent diameter of the upstream confluence pipe. The hydraulic dispersion scale characteristic refers to the degree to which pollutant concentration changes spatially and temporally after sewage enters the manhole of the pipe network due to factors such as mixing, dilution, and velocity changes within the manhole. This characteristic is one of the key factors affecting sampling accuracy, especially during sewage impact events. Ignoring its impact may result in water samples that do not accurately reflect the concentration of the sewage source. Geometric spatial abrupt change ratio. It is an index that quantifies the hydraulic dispersion scale characteristics. It characterizes the dilution and diffusion effects of sewage within the manhole using the geometric parameters of the manhole and upstream pipeline. The nodal volume of the target manhole... This refers to the effective volume of water that can be contained within the target inspection well. This volume can be obtained in various ways. For example, it can be precisely calculated based on the inspection well's engineering design drawings or CAD model, or its three-dimensional geometric data can be obtained through on-site laser scanning or ultrasonic measurement techniques to calculate its internal volume. The cross-sectional area of ​​the upstream inflow pipe connected to the target inspection well is also mentioned. This refers to the cross-sectional area of ​​the upstream pipeline before the sewage enters the target inspection well. This cross-sectional area can be obtained by consulting standards or direct measurement based on the pipeline's material, shape, and dimensions (e.g., the diameter of a circular pipeline, the side length of a rectangular pipeline), or it can be calculated using data from flow velocity or pressure sensors inside the pipeline. The equivalent diameter of the upstream confluence pipeline... The equivalent diameter refers to the diameter when a non-circular pipe is equivalent to a circular pipe, or directly to the actual diameter of a circular pipe. For a circular pipe, the equivalent diameter is its inner diameter; for a non-circular pipe, the equivalent diameter can be calculated using the relationship between its hydraulic radius and cross-sectional area. For example, it can be calculated using hydraulic formulas based on the pipe's geometry and dimensions, or obtained by consulting relevant engineering handbooks.

[0070] This scheme quantifies the hydraulic dispersion scale characteristics that would otherwise require fusion. By combining the actual geometric parameters of the target manhole and the connected upstream inflow pipe, a quantitative index that can be directly used for sampling parameter calculation is obtained. This geometric spatial abrupt change ratio accurately reflects the degree of hydraulic dispersion under different spatial geometric structures, providing an accurate basis for subsequent dynamic sampling cycle calculations and solving the problem of the inability to quantify dispersion effects. The hydraulic dispersion scale characteristics are characterized as a geometric spatial abrupt change ratio because the degree of diffusion and dilution after sewage enters the manhole is primarily determined by the spatial geometric relationship between the manhole itself and the upstream inflow pipe. Using a ratio can adapt to pipe network nodes of different sizes, exhibiting good versatility. In the calculation formula, the node volume of the target manhole is used as the numerator because the larger the node volume, the higher the degree of dilution of sewage by the original water body under the same sewage inflow, and the stronger the hydraulic dispersion effect. Placing it in the numerator accurately reflects this trend, consistent with actual hydraulic change patterns. The product of the cross-sectional area and equivalent diameter of the upstream inflow pipe connected to the target manhole is used as the denominator because this product accurately reflects the overall flow space of the upstream inflow pipe. The larger the flow space of the upstream pipe, the greater the sewage flow rate entering the manhole under the same sewage impact conditions. The dispersion effect at the manhole is directly related to this parameter. Multiplying the two as the denominator can accurately quantify the influence of the upstream pipe's geometric characteristics on the hydraulic dispersion of the manhole, making the final quantitative result more consistent with actual working conditions. The resulting geometric space mutation ratio can accurately quantify the degree of hydraulic dispersion at the target manhole and can be directly substituted into the subsequent dynamic pumping cycle calculation process, providing an accurate basis for the adaptive adjustment of the sampling cycle. By integrating this geometric space mutation ratio into the adaptive sampling closed-loop control unit, the device can dynamically adjust the pumping cycle of the sampling actuator according to the actual hydraulic dispersion situation of the manhole, thereby ensuring that representative, undiluted sewage samples can be collected under different pipe network geometric conditions.

[0071] One specific implementation method is as follows: First, by consulting the as-built drawings of the target inspection well, obtain its internal geometric dimensions, such as the diameter and depth of the well shaft and the dimensions of the bottom sump, and then calculate the nodal volume of the target inspection well. For example, if the manhole is circular, its nodal volume can be approximated as the combined volume of a cylinder and a frustum. Next, determine the main upstream inflow pipe connected to the manhole. Obtain the inner diameter or cross-sectional dimensions of this upstream pipe through on-site surveys or by consulting pipeline network data. For example, if the upstream pipe is a circular pipe with an inner diameter of D, then its cross-sectional area... for Equivalent diameter That is, D. If it is a rectangular pipe, calculate the cross-sectional area based on its width and height, and then calculate the equivalent diameter based on hydraulic principles. Finally, the calculated value is... , and Substitute into the formula for calculating the geometric spatial mutation ratio The geometric spatial abrupt change ratio of the inspection well can then be obtained. This calculation result is then transmitted to the adaptive sampling closed-loop control unit as one of the key parameters for adjusting the dynamic pumping cycle.

[0072] Through the above technical solution, this application can accurately quantify the influence of the spatial geometry of the inspection well on the dispersion and dilution of sewage samples, providing a precise calculation basis for adaptively adjusting the sampling and suction cycle. This effectively avoids the problem of diluted and distorted water samples caused by the dispersion effect due to incorrect quantification of the geometric space, thereby ensuring that the collected water samples can truly reflect the sewage discharge situation.

[0073] This application further proposes the following specific logic for the adaptive sampling closed-loop control unit to obtain the dynamic suction cycle: Based on the positive joint effect of the joint triggering index and the suction synchronization compensation term, and the negative adjustment effect of the geometric space mutation ratio, a dynamic suction gain coefficient is constructed, and the dynamic suction gain coefficient exhibits exponential saturation convergence characteristics as the joint triggering index increases; the dynamic suction gain coefficient is then combined with the maximum additional suction time to obtain the actual additional suction time, and the shortest guaranteed suction time is added to the actual additional suction time to finally obtain the adaptively adjusted dynamic suction cycle; the calculation formula for the dynamic suction cycle is as follows:

[0074]

[0075] in, This refers to a dynamic suction cycle. To ensure the shortest possible suction time, This is the maximum additional suction time. For joint trigger index, The geometrical spatial abrupt change ratio representing the characteristics of hydraulic dispersion scale. This is a suction synchronization compensation term. Dynamic suction cycle. This refers to the duration of the suction action performed by the sampling actuator during a single sampling event. Its function is to adaptively adjust the sampling duration according to actual operating conditions to ensure that representative and undiluted wastewater samples are obtained. This cycle can be calculated by the device's internal microprocessor or controller based on a preset algorithm and real-time monitoring data, and then instruct the sampling pump or suction mechanism to operate for this duration. Minimum guaranteed suction time. This is the basic aspiration time set to ensure that each sampling yields at least the minimum volume of water sample required for subsequent laboratory analysis or on-site testing. This time is calculated and set based on the minimum required volume of water sample, combined with the rated flow rate of the sampling pump or the aspiration rate of the aspiration mechanism. For example, if the testing requires at least 50 ml of water sample, and the sampling pump flow rate is 10 ml / s, then... It can be set to 5 seconds. Another setting method is to determine the minimum pumping time required to obtain a valid sample under the most unfavorable hydraulic conditions based on historical data statistics. Maximum additional pumping time. This refers to the maximum allowable additional aspiration time based on the minimum guaranteed aspiration time, depending on actual working conditions. This time can be set according to the maximum remaining capacity threshold of the sampler (such as a sampling bottle or sampling bag) to avoid overflow or waste of sampling capacity due to excessive aspiration time. For example, if the total capacity of the sampler is 1000ml, with 200ml reserved as a safety margin, and The corresponding sampling volume is 100ml, then The corresponding maximum additional sampling volume is 700 ml, which is then calculated based on the sampling pump flow rate. Furthermore, a reasonable upper limit can be set based on a comprehensive consideration of the system's sampling efficiency and energy consumption. Joint Triggering Index This is a comprehensive indicator characterizing the confidence level and urgency of a pollution discharge event. The index can be calculated by a spatiotemporally coherent triggering assessment unit; a higher value indicates a greater likelihood of the pollution discharge event and a stronger impact, thus requiring a longer sampling period. Geometric spatial abrupt change ratio This is a parameter characterizing the hydraulic dispersion scale of the target inspection well. This ratio can be calculated by the adaptive sampling closed-loop control unit fusing the hydraulic dispersion scale characteristics caused by the spatial geometric abrupt changes in the target inspection well. A smaller ratio generally indicates a more significant mixing and dilution effect within the inspection well, resulting in a larger dispersion range of the sewage clump within the well, thus requiring a longer pumping time to capture a representative sewage sample. (Pumping synchronization compensation term) This parameter is used to correct the degree of matching between the sampling timing and the physical response delay of a sewage discharge event. This compensation term can be calculated by the adaptive sampling closed-loop control unit based on a timing synchronization compensation mechanism constructed from the physical response delay and the estimated hydraulic arrival delay. A larger value indicates a closer match between the sampling timing and the actual arrival time of the sewage discharge event, thus enabling more effective capture of high-concentration sewage samples, and potentially allowing for a more appropriate extension of the aspiration time to obtain a more sufficient sample.

[0076] This application's solution achieves adaptive closed-loop control of the sampling actuator's suction action by introducing a calculation formula for the dynamic suction cycle. This calculation formula ensures the shortest possible suction time. Based on this, and using an exponential function term to dynamically adjust the additional suction time, the final dynamic suction cycle is determined. Among them, the shortest guaranteed suction time The settings are designed to ensure that the minimum water sample volume required for subsequent testing can be obtained under any circumstances, avoiding the inability to conduct effective source tracing analysis due to insufficient sampling volume. Maximum additional suction time. This provides the system with flexible adjustment space while limiting the total sampling time and preventing waste of sampling capacity. The ingenious aspect of this scheme lies in its joint triggering exponent. Geometric spatial mutation ratio And suction synchronization compensation item Organically integrated into the parameters of the exponential function, they jointly influence the determination of the additional suction duration. Jointly triggering the exponential function. Provided by the spatiotemporal coherence triggering assessment unit, this reflects the confidence level of the pollution event. A higher value indicates a more realistic and intense pollution event, and the system will tend to extend the pumping time to obtain a more representative sample. Geometric spatial mutation ratio. The degree of hydraulic dispersion of sewage within the inspection well is characterized by fusing the spatial geometric features of the target inspection well using an adaptive sampling closed-loop control unit. A lower value means the sewage has a larger dispersion range within the inspection well, and the water sample dilution effect may be more significant. Therefore, a longer pumping time is needed to capture a sewage sample of sufficient concentration. (Pumping synchronization compensation term) Also calculated by the adaptive sampling closed-loop control unit, it measures the degree of matching between the actual sampling timing and the arrival timing of the sewage discharge event. When A higher value indicates that the sampling timing is highly synchronized with the arrival of sewage discharge, resulting in the highest sample concentration. The system can then confidently extend the suction time to obtain a more sufficient high-concentration sample. Through this exponential function integration, the system can smoothly and non-linearly adjust the additional suction time based on the confidence level of the sewage discharge event, the hydraulic dispersion characteristics of the inspection well, and the accuracy of the sampling timing. This dynamic adjustment mechanism allows the sampling cycle to be highly adaptable to different pipeline operating conditions and the characteristics of sewage discharge events, effectively avoiding the sample dilution distortion or insufficient sampling problems caused by traditional fixed-duration sampling strategies. This scheme, working in conjunction with a multi-channel optical feature reconstruction unit, a cross-modal kinetic energy surge extraction unit, a spatiotemporal coherence triggering evaluation unit, and an adaptive sampling closed-loop control unit, jointly constructs a complete closed-loop system from sewage discharge event perception, identification, evaluation to final accurate sampling, significantly improving the accuracy and effectiveness of sewage source tracing sampling.

[0077] The following example illustrates the dynamic suction cycle. The calculation can be performed as follows. Assume that in a certain pollution discharge event, the system calculates the joint triggering index through the spatiotemporal coherence triggering evaluation unit. A value of 0.8 indicates a high confidence level in the sewage discharge event. Simultaneously, the adaptive sampling closed-loop control unit calculates the geometric spatial abrupt change ratio based on the geometric characteristics of the target inspection well. A value of 0.5 suggests the presence of a hydraulic dispersion effect within the inspection well. Furthermore, by calculating the arrival timestamps of the acoustic flow surge peak and the fluorescence intensity deviation peak in real time, and combining this with the estimated hydraulic arrival delay, the adaptive sampling closed-loop control unit derives the suction synchronization compensation term. A value of 0.9 indicates a high degree of coincidence between the current sampling timing and the actual arrival time of the sewage discharge event. Based on this, a pre-set minimum guaranteed suction time is determined. It can be 5 seconds to ensure at least 50ml of sample is obtained. Maximum additional aspiration time. The time can be set to 20 seconds to provide sufficient adjustment range without exceeding the sampler's capacity. Substituting these parameters into the calculation formula for the dynamic suction cycle, the sampling actuator will ultimately operate according to a dynamic suction cycle of approximately 20.26 seconds. In this way, the system can precisely adjust the suction duration based on real-time monitored sewage event characteristics, pipeline hydraulic conditions, and sampling timing, thereby obtaining high-quality, representative sewage samples.

[0078] Through the above technical solution, this application effectively solves the problem of unreasonable sampling duration settings in traditional sampling strategies by providing a quantitative calculation method for dynamic sampling cycles. This solution can adaptively adjust the sampling duration based on the confidence level of the sewage event, the hydraulic dispersion characteristics of the target inspection well, and the temporal synchronization matching results. This avoids the inability to obtain sufficient qualified water samples to meet testing requirements due to insufficient sampling duration, and also avoids the waste of sampling capacity caused by excessive sampling duration. Through this dynamic adjustment mechanism, this solution can adapt to the actual working conditions of different inspection well hydraulic dispersion conditions and signal response delay deviations, significantly reducing the risk of the collected water samples being diluted and distorted by subsequent clean water, ensuring the representativeness and validity of the water samples, thus meeting the stringent requirements of accurate sewage source tracing sampling. The synergistic effect of this solution with the multi-channel optical feature reconstruction unit, the cross-modal kinetic energy surge extraction unit, the spatiotemporal coherence triggering evaluation unit, and the adaptive sampling closed-loop control unit makes the entire source tracing sampling process more accurate and efficient.

[0079] 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, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A precise wastewater source tracing sampling device for wastewater treatment, characterized in that, include: The multi-channel optical feature reconstruction unit is configured to collect multi-channel fluorescence spectral features of the target water body, perform optical masking attenuation compensation based on the real-time turbidity of the water body, and introduce the dynamic ratio of shear rate, which characterizes the turbulent state of the pipe network water flow, to perform hydrodynamic cross-compensation, thereby outputting the reconstructed fluorescence deviation after filtering out environmental interference. The cross-modal kinetic energy surge extraction unit is configured to simultaneously capture transient acoustic pulse sequences and hydraulic fluid parameters of the target pipe network, perform cross-modal fusion of the distribution density of abnormal acoustic events and the abrupt change characteristics of fluid kinetic head, and extract the kinetic energy surge index characterizing the sewage discharge shock event by comparing it with the historical steady-state baseline. The spatiotemporal coherence triggering evaluation unit is configured to use the kinetic energy surge index as a spatial feedforward early warning signal, the reconstructed fluorescence deviation as a target verification signal, extract the coherence bandwidth confidence of the acousto-optic heterogeneous signal in the characteristic frequency band to dynamically weight the feedforward sensitivity, and introduce the physical response delay between heterogeneous signals for time-series convergence constraints, and calculate and generate the source tracing sampling joint triggering index. An adaptive sampling closed-loop control unit is configured to respond to the source-tracing sampling joint triggering index, integrate the hydraulic dispersion scale characteristics caused by the spatial geometric change of the target inspection well, and construct a timing synchronization compensation mechanism based on the physical response delay and the estimated hydraulic arrival delay, so as to adaptively control the dynamic pumping cycle of the sampling actuator.

2. The wastewater precise source tracing sampling device for wastewater treatment according to claim 1, characterized in that, The specific logic for the multi-channel optical feature reconstruction unit to obtain the reconstructed fluorescence deviation is as follows: Extract the ratio of fluorescence intensity in the blue channel to that in the ultraviolet channel, and calculate the absolute deviation of this ratio from the baseline fluorescence ratio extracted adaptively by the system; By using the ratio of real-time turbidity of water to a reference turbidity threshold, an exponential optical masking compensation factor that is positively correlated is constructed. A linear fluid dynamics cross-compensation factor is constructed by using the dynamic ratio of the real-time pipeline flow velocity shear rate to the reference pipeline flow velocity shear rate; The reconstructed fluorescence deviation is obtained by performing a joint weighted mapping of the absolute deviation, the optical masking compensation factor, and the hydrodynamic cross-compensation factor.

3. The wastewater precise source tracing sampling device for wastewater treatment according to claim 1, characterized in that, The specific logic for the cross-modal kinetic energy surge extraction unit to obtain the kinetic energy surge index is as follows: Extract the incremental percentage of the real-time abnormal sound event count rate relative to the background abnormal sound baseline, and perform logarithmic smoothing on the incremental percentage to construct the acoustic impulse surge weight. Extract the dynamic ratio of fluid kinetic head to the reference kinetic head to construct the hydraulic impact weight; By jointly mapping the acoustic pulse surge weights and the hydraulic impact weights, the kinetic energy surge index, which characterizes the intensity of sewage discharge impact, is obtained.

4. The wastewater precise source tracing sampling device for wastewater treatment according to any one of claims 1 to 3, characterized in that, The specific logic for the spatiotemporal coherence triggering evaluation unit to obtain the source tracing sampling joint triggering index is as follows: Based on the fusion results of coherent bandwidth confidence and kinetic energy surge index, a feedforward activation weight with exponential saturation characteristics is constructed. Based on the ratio of physical response delay to the maximum tolerance delay threshold, a temporal convergence suppression weight with exponential decay characteristics is constructed. Using the reconstructed fluorescence deviation as the target reference quantity, the forward activation weight is used to perform positive triggering modulation, and the temporal convergence suppression weight is used to perform inverse convergence constraint, and finally the source sampling joint triggering index is calculated and obtained.

5. The wastewater precise source tracing sampling device for wastewater treatment according to claim 1, characterized in that, The coherence bandwidth confidence level is obtained based on amplitude squared coherence analysis, and the specific logic is as follows: Within the upper and lower frequency limits of the characteristic frequency band, calculate the square of the cross power spectral density of the acoustic energy signal and the fluorescence deviation signal, and then perform a ratio operation on the product of the acoustic energy signal and the fluorescence deviation signal. The ratio within the upper and lower frequency limits of the characteristic frequency band is integrated and normalized with frequency bandwidth to obtain the coherent bandwidth confidence level that characterizes the dynamic coupling strength of heterogeneous signals.

6. The wastewater precise source tracing sampling device for wastewater treatment according to claim 1, characterized in that, The timing synchronization compensation mechanism is specifically represented by a sampling synchronization compensation term, and the logic for obtaining the sampling synchronization compensation term is as follows: Calculate the absolute time deviation between the physical response delay and the estimated hydraulic arrival delay; Using the synchronization reference time constant as a scale standard, an exponential decay compensation function based on the absolute time deviation is constructed; when the absolute time deviation approaches zero, the suction synchronization compensation term approaches the compensation maximum value.

7. The wastewater precise source tracing sampling device for wastewater treatment according to claim 1, characterized in that, The hydraulic dispersion scale feature is specifically characterized as the geometric spatial abrupt change ratio, and the logic for obtaining the geometric spatial abrupt change ratio is as follows: The effective hydraulic characteristic volume of the upstream inflow pipe is calculated based on the cross-sectional area of ​​the upstream inflow pipe connected to the target inspection well and the equivalent diameter of the upstream inflow pipe. The ratio of the node volume of the target inspection well to the effective hydraulic characteristic volume is calculated, and the ratio is used as the geometrical spatial abrupt change ratio characterizing the degree of hydraulic dispersion and dilution of the target inspection well.

8. The wastewater precise source tracing sampling device for wastewater treatment according to any one of claims 6 and 7, characterized in that, The specific logic for the adaptive sampling closed-loop control unit to obtain the dynamic suction cycle is as follows: Based on the positive joint effect of the joint triggering index and the suction synchronization compensation term, and the negative adjustment effect of the geometric space mutation ratio, a dynamic suction gain coefficient is constructed. The dynamic suction gain coefficient exhibits an exponential saturation convergence characteristic as the joint triggering index increases. The actual additional suction time is obtained by combining the dynamic suction gain coefficient with the maximum additional suction time, and the minimum guaranteed suction time is added to the actual additional suction time to finally obtain the adaptively adjusted dynamic suction cycle.

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