Method, device and electronic device for monitoring and regulating an aeration system

Abstract

This invention provides a monitoring and regulation method, device, and electronic equipment for an aeration system, relating to the field of wastewater treatment technology. This method, by introducing the forward-looking perception of infrared spectroscopy and electrostatic signals, coupled with pressure and flow data analysis, enables early diagnosis and forward-looking regulation of the health status and flow imbalance of the gas delivery pipeline in the aeration system. It intervenes and regulates before a fault or imbalance occurs, thereby achieving precise, stable, and efficient operation of the aeration system while ensuring treatment effectiveness.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to a method, apparatus, and electronic device for monitoring and regulating an aeration system. Background Technology

[0002] In activated sludge wastewater treatment processes, uniform and efficient aeration of the aerobic tank is the core element for maintaining microbial activity and ensuring biochemical treatment efficiency and energy economy. The aeration system typically consists of a blower, main air ducts, branch ducts, and numerous aeration pipes; its stable flux and uniform distribution directly affect effluent quality and operating costs.

[0003] Currently, mainstream aeration control systems mainly rely on feedback regulation of macroscopic parameters such as traditional thermal mass flow meters and pressure transmitters. This results in sensing lag and diagnostic blind spots: on the one hand, they can only make delayed adjustments after the flow rate or pressure has deviated significantly; on the other hand, they cannot identify early hidden faults before significant changes in macroscopic parameters. The limited monitoring dimensions make it difficult for existing technologies to comprehensively assess the true health status of the aeration system.

[0004] Therefore, how to achieve early warning and accurate monitoring of the gas delivery pipeline flow in the aeration system has become an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a monitoring and regulation method, device, and electronic device for an aeration system, which solves the defects of the prior art in the sensing lag and low monitoring accuracy of the gas delivery pipeline flux in the aeration system, and realizes early diagnosis and proactive regulation of the health status and flow imbalance of the gas delivery pipeline in the aeration system.

[0006] This invention provides a method for monitoring and regulating an aeration system, comprising the following steps: Multimodal data of the gas delivery pipeline in the aeration system are collected; the multimodal data includes infrared spectral data, electrostatic signal data, pressure data, and flow rate data; the gas delivery pipeline includes aeration pipes and aeration tubes; Based on the infrared spectral data, the infrared energy changes and peak wavelength shifts of the gas in the gas delivery pipeline are monitored to obtain information on the changing trends of the gas flow state. When the trend information indicates an abnormality in the gas flow state, the multimodal data is coupled and analyzed to obtain the diagnostic results of the gas delivery pipeline. Based on the diagnostic results, an adjustment command is generated, and the flow rate of the gas delivery pipeline is adjusted based on the adjustment command.

[0007] According to the present invention, a monitoring and adjustment method for an aeration system includes gas flow state change trend information including gas density change trend information and gas temperature change trend information; the step of monitoring the infrared energy change and peak wavelength shift of the gas in the gas delivery pipeline based on the infrared spectral data to obtain gas flow state change trend information includes: The energy attenuation of infrared rays in a specific absorption band in the infrared spectral data is monitored, and the trend of gas density change in the gas delivery pipeline is obtained by inversion based on the Beer-Lamber law. The peak wavelength shift of infrared radiation in the infrared spectral data is monitored, and the trend information of gas temperature change in the gas delivery pipeline is obtained based on Planck's radiation law.

[0008] According to a monitoring and adjustment method for an aeration system provided by the present invention, the step of performing coupled analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline includes: Identify the amount of charge change in the electrostatic signal data and extract the amplitude of the charge change; Under the condition that the first objective is met, the gas delivery pipeline is determined to be an early warning system for flow imbalance; wherein, the first objective includes: The infrared spectral data showed abnormal energy fluctuations in a specific absorption band, and a shift in the peak wavelength of infrared radiation. The magnitude of the charge change exceeds the dynamic baseline threshold determined based on historical steady-state data; The fluctuations in both the pressure data and the flow data remained consistently within their respective normal fluctuation threshold ranges.

[0009] According to the monitoring and adjustment method of an aeration system provided by the present invention, the step of performing coupled analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline further includes: Identify abnormal low-temperature energy distribution regions in the infrared spectral data, and detect electrostatic voltage gradient attenuation regions based on the electrostatic signal data; If the second objective condition is met, it is determined that there is a leak in the gas delivery pipeline, and the leak point is located; wherein, the second objective condition includes: The deviations of the abnormal low temperature energy distribution area, the electrostatic voltage gradient attenuation area, and the local pressure drop abrupt change points identified from the pressure data in the pipeline spatial location are less than the set tolerance range. The traffic data shows a continuous downward trend; The error between the pressure drop amplitude at the local pressure drop abrupt change point and the gas leakage rate determined based on the abnormal low temperature energy distribution region is within a set range.

[0010] According to the monitoring and adjustment method of an aeration system provided by the present invention, the step of performing coupled analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline further includes: The system detects energy accumulation characteristics in the infrared spectral data upstream of the suspected blockage point and analyzes whether the electrostatic signal data shows a continuous upward trend in the target pipeline section; the target pipeline section is the pipeline section spatially related to the energy accumulation characteristics. If the third objective condition is met, it is determined that the aeration pipe in the gas delivery pipeline is partially blocked; wherein, the third objective condition includes: The energy accumulation characteristics and the continuous upward trend of the electrostatic signal are both located within the target pipeline section in space. The pressure data detected that the increase in pressure difference between the inlet and outlet of the target pipeline section exceeded the set pressure difference threshold. The rate of decrease in the traffic data exceeds the set rate of decrease threshold.

[0011] According to the monitoring and adjustment method of an aeration system provided by the present invention, the step of performing coupled analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline further includes: Under the condition that the flow rate data and the pressure data remain stable, the aeration uniformity index in the gas delivery pipeline is determined based on the infrared spectral data, and the spectral characteristic index of the electrostatic signal data is analyzed. Under the condition that the fourth objective condition is met, the aeration tube diaphragm in the gas delivery pipeline is determined to be aged; wherein, the fourth objective condition includes: The difference between the value of the aeration uniformity index and the benchmark value established by the gas delivery pipeline in the initial healthy state or in the previous health assessment cycle is greater than the first set threshold. The difference between the value of the spectral characteristic index and the benchmark value established under the same benchmark state is greater than the second set threshold.

[0012] According to the present invention, a method for monitoring and regulating an aeration system is provided, the method further includes: The collected multimodal data is sent to an AI cloud platform; wherein, the AI ​​cloud platform is equipped with a coupling analysis model, which is used to perform coupling analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline; Receive the diagnostic results of the gas delivery pipeline sent by the AI ​​cloud platform.

[0013] According to the present invention, a method for monitoring and regulating an aeration system, wherein generating a regulation command based on the diagnostic results includes: Based on the type of the diagnostic result, a corresponding adjustment strategy is matched from the strategy library; the strategy library includes the mapping relationship between the diagnostic result and the adjustment target and valve action parameters; Based on the aforementioned regulation strategy, control commands are generated for controlling the regulating valves on the gas delivery pipeline.

[0014] The present invention also provides a monitoring and adjustment device for an aeration system, comprising the following modules: The acquisition module is used to acquire multimodal data of the gas delivery pipeline in the aeration system; the multimodal data includes infrared spectral data, electrostatic signal data, pressure data, and flow rate data; the gas delivery pipeline includes aeration pipes and aeration tubes; The monitoring module is used to monitor the changes in infrared energy and peak wavelength shift of the gas in the gas delivery pipeline based on the infrared spectral data, so as to obtain information on the changing trend of the gas flow state. The coupling analysis module is used to perform coupling analysis on the multimodal data when the trend information indicates that the gas flow state is abnormal, so as to obtain the diagnostic results of the gas delivery pipeline. An adjustment module is used to generate adjustment instructions based on the diagnostic results and adjust the flow rate of the gas delivery pipeline based on the adjustment instructions.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the monitoring and adjustment method of any of the aeration systems described above.

[0016] The present invention provides a monitoring and regulation method, device, and electronic equipment for an aeration system. This method collects multimodal data from the gas delivery pipeline within the aeration system. This multimodal data includes infrared spectral data, electrostatic signal data, pressure data, and flow rate data. Based on the infrared spectral data, it monitors the changes in infrared energy and peak wavelength shifts of the gas within the pipeline to obtain information on the changing trends of the gas flow state. When the changing trend indicates an abnormality in the gas flow state, it performs coupled analysis on the multimodal data to obtain a diagnostic result for the gas delivery pipeline. Based on the diagnostic result, it generates regulation commands and adjusts the flux of the gas delivery pipeline accordingly. By introducing the forward-looking perception of infrared spectroscopy and electrostatic signals, and coupling them with macroscopic pressure and flow rate data for analysis, this invention achieves early diagnosis and forward-looking regulation of the health status and flow imbalance of the gas delivery pipeline in the aeration system. It can intervene and regulate before a fault or imbalance occurs, thereby ensuring the precise, stable, and efficient operation of the aeration system while maintaining treatment effectiveness. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the monitoring and adjustment method for the aeration system provided by the present invention.

[0019] Figure 2 This is a schematic diagram of the process for accurate monitoring of gas delivery pipeline flow based on infrared and electrostatic multimodal sensing provided by the present invention.

[0020] Figure 3 This is one of the schematic diagrams of the aeration system provided by the present invention.

[0021] Figure 4 This is the second schematic diagram of the aeration system provided by the present invention.

[0022] Figure 5 This is a schematic diagram of the monitoring and adjustment device for the aeration system provided by the present invention.

[0023] Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention.

[0024] Figure label: 1: Thermal mass flow meter for the main aeration pipe; 2: Regulating electric butterfly valve for the main aeration pipe; 3: Thermal mass flow meter for the branch aeration pipe; 4: Regulating electric butterfly valve for the branch aeration pipe; 5: Non-contact electrostatic voltage sensor; 6: Infrared spectral sensing module; 7: Pressure sensor; 8: Data acquisition and main control unit. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0026] The following is combined with Figures 1-6 The present invention describes a monitoring and adjustment method, apparatus, and electronic equipment for an aeration system.

[0027] Figure 1This is a flowchart illustrating the monitoring and adjustment method for the aeration system provided by the present invention, as shown below. Figure 1 As shown, the method includes the following: Step 101: Collect multimodal data of the gas delivery pipeline in the aeration system.

[0028] It should be understood that gas delivery pipelines include the following two functional sections: Main transport section: also known as aeration pipeline, refers to a rigid, large-diameter main pipe, which is responsible for transporting and distributing gas from the gas source over long distances and under high pressure to each aeration area.

[0029] Terminal aeration section: also known as aeration pipe, refers to a flexible branch pipe or device with a diffusion structure, which is responsible for evenly dispersing gas into the water.

[0030] In one embodiment, the aeration tube can be a microporous aeration hose.

[0031] Multimodal data includes infrared spectral data, electrostatic signal data, pressure data, and flow rate data. Infrared spectral data can be acquired by an infrared emitter and receiver assembly installed on the outside of the gas delivery pipeline or within a specific cavity. The emitter emits infrared light of a specific wavelength that passes through the pipe wall or gas, and the receiver detects the transmitted or reflected spectrum. Infrared spectral data can reflect the thermodynamic state of gas molecules within the gas delivery pipeline.

[0032] Electrostatic signal data can be acquired by electrostatic sensors (such as inductive electrodes) installed on or near the inner wall of the gas delivery pipeline. When gas flows at high speed within the pipeline, it generates charge separation through friction with the pipeline wall, dust, or itself, forming a flowing current. Electrostatic signal data can reflect the gas-solid interface friction state and flow stability within the pipeline. It should be understood that the signal strength of the electrostatic signal is related to airflow velocity, turbulence intensity, and particulate matter concentration; the fluctuation frequency and pattern of the electrostatic signal are directly related to microscopic events such as flow state (laminar / turbulent), bubble rupture, and diaphragm vibration frequency. Specific faults (such as leakage or blockage) will produce unique electrostatic fluctuations.

[0033] Pressure data can be acquired by pressure sensors installed on the gas delivery pipeline. Pressure data reflects the overall resistance characteristics and energy loss of the aeration system. It should be understood that in early warning mode, stable pressure is a key indicator of whether infrared and electrostatic anomalies are early-stage rather than global disturbances; in leakage location or blockage warning mode, increased local pressure drop or differential pressure is decisive physical evidence confirming the existence of a fault and quantifying its severity.

[0034] Flow data can be obtained from a thermo-mass flow meter. Flow data reflects the macroscopic results of gas transport in the pipeline. It should be understood that the long-term regulation goal of the system is to maintain stable flow. In the early warning logic, maintaining normal flow data is one of the key conditions for defining an early fault; if the flow has changed significantly, the fault has entered the middle or late stage.

[0035] Collect multimodal data of the gas delivery pipeline in the aeration system. For example, use an infrared spectral sensor to collect spectral data characterizing the vibrational energy level transition states of gas molecules, such as continuous spectral scanning at a sampling frequency of not less than 100 Hz; use an electrostatic sensor to collect charge fluctuation data generated by airflow friction against the pipe wall or internal components; use a pressure sensor to collect pressure data characterizing the resistance characteristics of the aeration system; and use a flow sensor to collect flow data characterizing the gas delivery flux.

[0036] Step 102: Based on the infrared spectral data, monitor the changes in infrared energy and peak wavelength shifts of the gas in the gas delivery pipeline to obtain information on the changing trends of the gas flow state.

[0037] Infrared energy changes are determined by monitoring microscopic perturbations in gas density. It should be understood that when infrared light of a specific wavelength passes through the gas being measured, gas molecules selectively absorb infrared photons that match their vibrational-rotational energy levels, resulting in attenuation of the transmitted or reflected infrared light energy. For example, an infrared spectroscopy sensor continuously emits a beam of infrared light containing a specific absorption band (e.g., characteristic absorption peaks for O2 or N2 in air) and receives the light signal after it penetrates the gas. When the gas flow state within a gas delivery pipeline undergoes microscopic changes, such as localized airflow acceleration due to minor leaks or increased turbulence due to precursors of blockage, it causes small, rapid fluctuations in the local gas molecule number density (i.e., density). Changes in gas density directly alter its absorption intensity of infrared light; a sudden increase in density enhances absorption and reduces the received energy, while a sudden decrease in density has the opposite effect.

[0038] The peak wavelength shift is determined by monitoring microscopic changes in gas temperature. It should be understood that objects above absolute zero emit infrared radiation, and the peak wavelength of their radiation spectrum is inversely proportional to the object's absolute temperature. When the flow state of a gas changes, accompanied by energy conversion, such as adiabatic expansion (e.g., through a tiny leak), the temperature decreases; frictional heating or compression (e.g., blocking an upstream outlet), the temperature increases. These microscopic changes in gas temperature cause a shift in the peak wavelength of its infrared radiation (or its response to a specific band of thermal radiation). As temperature increases, the peak wavelength shifts towards shorter wavelengths (blue shift); as temperature decreases, it shifts towards longer wavelengths (red shift).

[0039] Information on the changing trends of gas flow states refers to a set of dynamic characteristics derived from real-time, high-frequency monitoring data of infrared energy and peak wavelength shifts, which are used to describe how the gas flow state is evolving and how it will develop in the future.

[0040] From infrared spectral data, infrared energy variation characteristics and wavelength shift characteristics of infrared absorption peaks are extracted. For example, characteristic absorption bands of specific gas components are selected, and the integral area, average absorbance, or absorbance value at a specific wavelength point of the characteristic absorption band is calculated over a continuous time period to form an energy time series reflecting changes in gas concentration or density. In continuous spectral data, the wavelength corresponding to the peak position of at least one selected absorption peak is tracked; the change sequence of this peak wavelength over time is recorded, where the shift of the peak wavelength is related to changes in gas pressure, temperature, or gas component ratio within the gas delivery pipeline. Based on the combined analysis of infrared energy variation characteristics and wavelength shift characteristics, the trend of gas flow state changes within the gas delivery pipeline is determined.

[0041] Step 103: When the trend information indicates an abnormality in the gas flow state, perform coupled analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline.

[0042] Based on the trend information, the rate of change of gas density and temperature is calculated, and the correlation coefficient between the two in time is determined. When the rate of change continues to exceed the threshold and the absolute value of the correlation coefficient is greater than the set threshold, a trend signal indicating that the flow state is developing towards instability is generated.

[0043] Coupling analysis refers to analyzing the temporal correlation between infrared spectral data and electrostatic signal data, and verifying and quantifying the physical consistency of the correlation by combining pressure data and flow data.

[0044] In one embodiment, a coupled analysis model is used to perform coupled analysis on multimodal data to achieve early diagnosis and warning of problems such as flow imbalance, leakage, blockage, and aging of aeration tube diaphragms in gas delivery pipelines. For example, the multimodal data is preprocessed by time-domain and frequency-domain fusion to remove abnormal noise data and extract information such as infrared energy fluctuation characteristics, peak wavelength shift trends, electrostatic voltage fluctuation patterns, and pressure change gradients. The extracted information is then input into the "infrared-electrostatic-pressure-flow" coupled analysis model of the AI ​​cloud analysis platform to obtain the diagnostic results of the gas delivery pipeline output by the coupled analysis model.

[0045] Step 104: Generate adjustment instructions based on the diagnostic results, and adjust the flow rate of the gas delivery pipeline based on the adjustment instructions.

[0046] Based on the type and severity of the abnormality indicated by the diagnostic results, a control command containing the target adjustment amount is generated. This control command is configured to be issued proactively, before any significant deviation in the flow data occurs, to adjust the gas delivery pipeline flow in advance. The adjustment command is then sent to the electrically operated regulating valve installed in the gas delivery pipeline; the valve is driven to change its opening according to the adjustment command, thereby stabilizing the gas flow rate, pressure, and flow field within the gas delivery pipeline within the target range.

[0047] In one embodiment, after executing an adjustment command to change the gas delivery pipeline flux, the adjusted infrared spectral data is acquired, and the stability change of the adjusted infrared spectral data is analyzed. Based on the stability change, the analysis model or the generation logic of the adjustment command is optimized through feedback. For example, the changes in the time-domain statistical characteristics and / or frequency-domain characteristics of the adjusted infrared spectral data are calculated to quantify its stability; a mapping relationship is established between the stability index of the infrared spectral data and the performance index of the aeration system; wherein, the system performance index includes aeration uniformity or energy efficiency; with the goal of improving the system performance index, the control parameters used to generate the adjustment command or the diagnostic threshold in the coupled analysis model are optimized through iterative adjustment.

[0048] The monitoring and regulation method for an aeration system provided in this invention collects multimodal data from the gas delivery pipeline in the aeration system. This multimodal data includes infrared spectral data, electrostatic signal data, pressure data, and flow rate data. Based on the infrared spectral data, the method monitors the changes in infrared energy and peak wavelength shifts of the gas within the gas delivery pipeline to obtain information on the changing trends of the gas flow state. When the changing trend information indicates an abnormality in the gas flow state, the multimodal data is coupled and analyzed to obtain a diagnostic result for the gas delivery pipeline. Based on the diagnostic result, a regulation command is generated, and the flux of the gas delivery pipeline is adjusted based on the regulation command. This invention, by introducing the forward-looking perception of infrared spectroscopy and electrostatic signals, and coupling it with macroscopic pressure and flow rate data for analysis, achieves early diagnosis and forward-looking regulation of the health status and flow imbalance of the gas delivery pipeline in the aeration system. It can intervene and regulate before a fault or imbalance occurs, thereby achieving precise, stable, and efficient operation of the aeration system while ensuring treatment effectiveness.

[0049] Based on the above embodiments, the step of monitoring the infrared energy changes and peak wavelength shifts of the gas in the gas delivery pipeline based on the infrared spectral data to obtain information on the changing trends of the gas flow state includes: The energy attenuation of infrared rays in a specific absorption band in the infrared spectral data is monitored, and the trend of gas density change in the gas delivery pipeline is obtained by inversion based on the Beer-Lamber law. The peak wavelength shift of infrared radiation in the infrared spectral data is monitored, and the trend information of gas temperature change in the gas delivery pipeline is obtained based on Planck's radiation law.

[0050] The information on the changing trends of gas flow states includes the changing trends of gas density and gas temperature. Gas density refers to the average gas density along the transmission path of the infrared beam (i.e., the optical path measured within the gas delivery pipeline), and its changing trend indicates how the average density along this path changes over time, such as remaining stable, increasing, decreasing, periodic pulsation, or random turbulence. Gas temperature refers to the thermodynamic temperature corresponding to the average kinetic energy of gas molecules within the infrared beam detection area, and its changing trend indicates how this temperature value changes over time.

[0051] The energy attenuation of infrared rays in specific absorption bands within infrared spectral data is monitored, and the trend of gas density variation within the gas delivery pipeline is retrieved based on the Beer-Lambert law. For example, tunable diode laser absorption spectroscopy is used to obtain the integrated absorbance by scanning the complete gas molecule absorption lines corresponding to specific absorption bands. These specific absorption bands can be carbon dioxide absorption lines located in the 2.0 μm to 2.5 μm or 4.2 μm to 4.6 μm bands, or water vapor absorption lines located in the 1.5 μm to 1.7 μm band. Then, the trend of gas density variation is retrieved based on the Beer-Lambert law, for example, by calculating using the formula ρ∝A / (S(T)·L), where ρ is the gas density, A is the integrated absorbance, S(T) is the line intensity of the gas molecule absorption line at temperature T, and L is the effective absorption path length.

[0052] The method monitors the peak wavelength shift of infrared radiation in infrared spectral data and, based on Planck's radiation law, obtains information on the temperature change trend of the gas in the gas delivery pipeline. For example, the infrared radiation refers to the self-emitted infrared radiation emitted by the high-temperature gas in the gas delivery pipeline; monitoring the peak wavelength shift involves continuously acquiring the spectrum of self-emitted radiation using an infrared spectrometer and identifying the peak wavelength position of a specific molecular radiation band in the spectrum. The gas temperature is obtained based on Planck's radiation law. For instance, the complete spectrum of self-emitted radiation over a continuous wavelength band is acquired, and the measured spectral intensity distribution is fitted to Planck's blackbody radiation theoretical curves at different temperatures. The temperature corresponding to the best-fit curve is taken as the gas temperature. Specifically, the gas temperature T can be directly calculated by monitoring the shift of the peak wavelength λ_max, where b is the Wien displacement constant, according to Wien's displacement law λ_max = b / T. Finally, the temperature change trend of the gas in the gas delivery pipeline is determined based on the gas temperature.

[0053] By synchronously acquiring density and temperature change trends, this invention can directly correlate and analyze the momentum and energy transfer process of gas flow, thereby enabling more accurate and reliable comprehensive diagnosis and intelligent early warning of key operating conditions such as aeration efficiency, airflow mixing uniformity, and system anomalies, thus improving the refinement and intelligence of process control.

[0054] Based on the above embodiments, the step of performing coupled analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline includes: Identify the amount of charge change in the electrostatic signal data and extract the amplitude of the charge change; Under the condition that the first objective is met, the gas delivery pipeline is determined to be an early warning system for flow imbalance; wherein, the first objective includes: The infrared spectral data showed abnormal energy fluctuations in a specific absorption band, and a shift in the peak wavelength of infrared radiation. The magnitude of the charge change exceeds the dynamic baseline threshold determined based on historical steady-state data; The fluctuations in both the pressure data and the flow data remained consistently within their respective normal fluctuation threshold ranges.

[0055] The change in charge refers to the net change in electrostatic charge caused by dynamic events such as friction, collision, and separation between solid particles (or droplets) in the aeration pipeline and the pipe wall, sensor probe, or each other within a specific time window, as monitored by an electrostatic sensor.

[0056] The induced current signal in the inner wall of the pipe or in the gas is measured by an electrostatic sensor. The induced current signal is integrated within a set time window to calculate the net charge change within that time window. The absolute value of the calculated net charge change, or the peak value of the charge change within that time window, is taken as the amplitude of the charge change.

[0057] Under the condition that the first objective is met, the gas delivery pipeline is determined to be in an early warning state of flow imbalance. This early warning of flow imbalance refers to using the high sensitivity of infrared and electrostatic sensors to identify the initial state of impending flow imbalance in different branches or points within the gas delivery pipeline network before the flow meter detects a clear flow deviation, and then issuing a warning.

[0058] Under the first target condition, abnormal energy fluctuations are manifested as the standard deviation of the instantaneous absorbance of a specific absorption band exceeding a preset percentage of its historical steady-state mean within a preset time window; peak wavelength shifts are manifested as the shift in the peak wavelength of infrared radiation exceeding a preset multiple of the spectrometer resolution within the same time window. The infrared spectral data showing abnormal energy fluctuations in a specific absorption band, and the shift in the peak wavelength of infrared radiation, indicate that the local thermodynamic state (density and temperature) of the gas within the gas delivery pipeline has undergone rapid and correlated microscopic changes, and these changes do not originate from a uniform, overall alteration.

[0059] In the first target condition, if the amplitude of the charge change exceeds the dynamic baseline threshold determined based on historical steady-state data, it indicates that the motion state of solid particles (or droplets) within the gas delivery pipeline changes from stable, uniform flow to a violent, unsteady state of collision, friction, or oscillation. In one embodiment, the dynamic baseline threshold is determined as follows: within the historical steady-state data period, the moving average (μ) and moving standard deviation (σ) of the charge change amplitude are calculated using a sliding time window; the dynamic baseline threshold (Threshold_dynamic) is set as: Threshold_dynamic = μ + k × σ, where k is a constant determined based on a preset false alarm rate tolerance. It should be understood that historical steady-state data refers to the set of multi-source sensor data collected and used to establish subsequent early warning benchmarks during the period when the aeration system is in normal, balanced operation.

[0060] Under the first target condition, the fluctuations in pressure data and flow rate data remained within their respective normal fluctuation thresholds, indicating that from the macroscopic and overall hydraulic perspective of the entire aeration system, mass transport and momentum balance have not been disrupted, and the system is still in steady-state operation.

[0061] It should be understood that infrared spectral data (energy decay and peak wavelength shift) directly and sensitively reflect subtle changes in gas molecule density and temperature, which are the earliest physical precursors to changes in gas flow state (flow velocity, pressure). When the amplitude of charge change exceeds a threshold, it indicates that the motion state of fluid clusters or particles within the gas delivery pipeline has shifted from steady state to unstable state, revealing microscopic turbulence that foreshadows changes in the flow field structure. Normal pressure and flow data indicate that the macroscopic hydraulic balance of the pipeline network has not yet been disrupted, and traditional monitoring methods cannot detect it. Therefore, meeting the first objective condition indicates that the microscopic physical process driving flow imbalance has begun, but the macroscopic effects of the imbalance are not yet reflected in traditional instruments, thus achieving early warning of flow imbalance.

[0062] This invention achieves high-confidence early warning of flow imbalance in aeration systems through the coordinated judgment of four-dimensional data: infrared, electrostatic, pressure, and flow rate. This improves the accuracy and reliability of the warning, thereby avoiding unplanned downtime and ensuring stable and efficient system operation.

[0063] Based on the above embodiments, the step of performing coupled analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline further includes: Identify abnormal low-temperature energy distribution regions in the infrared spectral data, and detect electrostatic voltage gradient attenuation regions based on the electrostatic signal data; If the second objective condition is met, it is determined that there is a leak in the gas delivery pipeline, and the leak point is located; wherein, the second objective condition includes: The deviations of the abnormal low temperature energy distribution area, the electrostatic voltage gradient attenuation area, and the local pressure drop abrupt change points identified from the pressure data in the pipeline spatial location are less than the set tolerance range. The traffic data shows a continuous downward trend; The error between the pressure drop amplitude at the local pressure drop abrupt change point and the gas leakage rate determined based on the abnormal low temperature energy distribution region is within a set range.

[0064] An abnormally low temperature energy distribution area refers to a continuous spatial range in the temperature field data obtained by infrared thermal imaging scanning (or multi-point infrared thermography arranged along the gas delivery pipeline) of the outer wall or near-field space of a gas delivery pipeline, where one or more temperatures are significantly lower than those of the surrounding normal pipe wall area. It should be understood that when a gas leak occurs in a gas delivery pipeline, the high-pressure gas expands rapidly at the leak point, absorbing a large amount of heat. This causes a sharp drop in the surface temperature of the metal pipe wall or insulation layer near the leak point, thus forming a negative temperature anomaly area on the infrared image.

[0065] The electrostatic voltage gradient attenuation region refers to a section where the electrostatic voltage amplitude abnormally drops sharply along the axial direction of the gas delivery pipeline, as detected by measuring the electrostatic field distribution generated by the friction of flowing dry gas and particles using an array of electrostatic sensors deployed axially on or inside the pipeline. It should be understood that stable gas-solid flow within the gas delivery pipeline creates a relatively stable electrostatic field distribution. When a leak occurs in the pipeline, the leak point becomes a channel for charge leakage or neutralization, disrupting the original electrostatic balance and causing an attenuation of the electrostatic induced voltage downstream of the leak point.

[0066] A local pressure drop abrupt point refers to a spatial location identified through pressure signal analysis (such as wavelet transform and abrupt change detection algorithms) based on data collected by a distributed array of high-frequency pressure sensors along the gas delivery pipeline. At this point, the fluid pressure within the gas delivery pipeline experiences an abnormally sharp drop along the flow direction. It should be understood that, according to fluid mechanics, when gas flows through a leak in the gas delivery pipeline, the sudden expansion of the local flow area generates additional local resistance loss. This loss is far greater than the friction loss along a normal straight pipe section, thus creating a significant pressure drop at the leak point.

[0067] Identify abnormal low-temperature energy distribution areas in infrared spectral data. For example, continuously acquire two-dimensional thermal radiation distribution images of the outer surface of the gas delivery pipeline by using an infrared thermal imager that moves or is fixedly deployed along the axial direction of the gas delivery pipeline; compare the acquired thermal radiation image data with a baseline thermal image library of the same gas delivery pipeline under steady-state conditions without leakage, pixel by pixel or by region; and identify areas where the temperature value is lower than the baseline temperature threshold and the number of spatially consecutive pixels exceeds the minimum cluster threshold using image difference and clustering algorithms, thus determining these areas as abnormal low-temperature energy distribution areas.

[0068] Electrostatic voltage gradient attenuation zones are detected based on electrostatic signal data. For example, an array of electrostatic sensors, evenly spaced along the axial direction of a gas delivery pipeline, synchronously measures the electrostatic voltage signal on the inner wall or near-field space of the pipeline. The axial electrostatic voltage gradient between adjacent sensor measurement points is calculated, where the gradient value is the difference or ratio between the voltage at the downstream measurement point and the voltage at the upstream measurement point. The real-time calculated voltage gradient value is compared with a voltage gradient reference band established based on historical steady-state data. An axial pipe segment where the voltage gradient between multiple consecutive measurement points is consistently lower than the lower limit of the reference band is identified, and this segment is determined to be an electrostatic voltage gradient attenuation zone.

[0069] Under the condition that the second objective is met, a leak is determined to exist in the gas delivery pipeline, and the leak point is located. It should be understood that the negative temperature anomaly region, the electrostatic field attenuation region, and the abrupt change point in the pressure signal in the infrared thermographic image originate from different physical mechanisms (gas expansion absorbing heat, charge loss, and a sudden increase in local resistance, respectively), but they overlap spatially, ruling out the possibility of false alarms from a single sensor or environmental interference. Simultaneously, the continuous decrease in flow rate provides macroscopic evidence of a leak, while the error verification between the pressure drop and the theoretical leakage rate completes the final closed-loop verification based on the principles of mass conservation and fluid mechanics. Therefore, through a multi-level fusion decision mechanism, the system can deterministically identify and locate the actual leak.

[0070] In the second target condition, the deviations of the abnormal low temperature energy distribution area, the electrostatic voltage gradient attenuation area, and the local pressure drop mutation point identified from the pressure data in the pipeline spatial location are less than the set tolerance range. This indicates that the leak point has been cross-verified and accurately located in physical space by three independent sensing principles, eliminating false alarms caused by occasional interference or sensor failure.

[0071] In one embodiment, identifying local pressure drop abrupt change points from pressure data includes: synchronously collecting pressure time-series data at each measuring point using a high-frequency dynamic pressure sensor array evenly spaced along the axial direction of the gas delivery pipeline; performing wavelet transform or differential calculation on the pressure time-series data to extract the pressure gradient distribution curve along the axial direction of the gas delivery pipeline; identifying abnormally steep drop sections in the pressure gradient distribution curve where the gradient value exceeds a preset abrupt change threshold and the spatial span is less than a preset length threshold; and determining the spatial location point with the largest absolute value of the pressure gradient within the abnormally steep drop section as a local pressure drop abrupt change point.

[0072] In the second objective condition, the traffic data showed a continuous downward trend, indicating that there was a continuous quality loss in the system. This confirmed the consequences of the leakage from a macroscopic perspective and formed a logical closed loop of phenomenon-result with the location information.

[0073] In the second target condition, the error between the pressure drop amplitude at the local pressure drop abrupt point and the gas leakage rate determined based on the abnormal low temperature energy distribution area is within the set range, indicating that the observed hydraulic phenomenon (pressure drop) and the physical quantity (leakage rate) inferred from the thermodynamic phenomenon (low temperature area) conform to the laws of physics, thus completing the final verification in principle.

[0074] This invention achieves precise location of gas delivery pipeline leaks by integrating spatial and temporal information from four types of data: infrared, electrostatic, pressure, and flow. It also shortens maintenance time, reduces false alarm rates, avoids unnecessary downtime, and improves the safety and operational efficiency of the aeration system.

[0075] Based on the above embodiments, the step of performing coupled analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline further includes: The energy accumulation characteristics appearing upstream of the suspected blockage point in the infrared spectral data are detected, and the electrostatic signal data is analyzed to see if there is a continuous upward trend in the target pipeline section. If the third objective condition is met, it is determined that the aeration pipe in the gas delivery pipeline is partially blocked; wherein, the third objective condition includes: The energy accumulation characteristics and the continuous upward trend of the electrostatic signal are both located within the target pipeline section in space. The pressure data detected that the increase in pressure difference between the inlet and outlet of the target pipeline section exceeded the set pressure difference threshold. The rate of decrease in the traffic data exceeds the set rate of decrease threshold.

[0076] It should be understood that energy accumulation characteristics refer to the abnormal patterns of infrared radiation signals with specific spatiotemporal evolution patterns identified in the upstream and adjacent areas of suspected blockage points when monitoring gas delivery pipelines using infrared spectroscopy.

[0077] The target pipeline section is the pipeline section that is spatially related to the energy accumulation characteristics.

[0078] The system detects energy accumulation characteristics upstream of suspected blockage points in infrared spectral data. For example, it preprocesses the raw transmitted light intensity signal or calculated absorptivity signal of a specific absorption band in the infrared spectral data to obtain the target time-series signal. Using the suspected blockage point location as a reference, a fixed-length monitoring time window is set upstream of the airflow source direction. Within the monitoring time window, at least one of the following statistical or dynamic characteristics of the target time-series signal is calculated: the local trend slope of the signal, calculated through linear fitting; the signal volatility index, obtained by calculating the standard deviation or variance of the data within the window; and the energy accumulation index of the signal, obtained by calculating the integral or cumulative sum of the signal value over time. The calculated characteristics are compared with the normal range of corresponding characteristics established based on historical unblocked steady-state data. When the characteristics continuously exceed the normal range, it is determined that energy accumulation characteristics exist in the pipeline section corresponding to the monitoring window.

[0079] The analysis determines whether the electrostatic signal data in the target pipeline section exhibits a continuous upward trend. For example, it acquires raw voltage or current signals from at least one electrostatic sensor deployed within the target pipeline section; preprocesses the raw signals, including bandpass filtering to extract characteristic frequency band signals related to particle flow and moving average filtering to suppress impulse noise; calculates the trend intensity index of the preprocessed signal, and compares the calculated trend intensity index with a trend determination threshold; when the trend intensity index exceeds the trend determination threshold and remains there for more than a preset duration, it is determined that the electrostatic signal data in the target pipeline section exhibits a continuous upward trend.

[0080] Under the condition that the third objective is met, a partial blockage in the gas delivery pipeline is determined. It should be understood that the formation of a blockage causes the upstream fluid's kinetic energy to be converted into pressure energy, manifested as an energy accumulation characteristic in the infrared absorption signal due to increased gas density and weakened turbulence. Simultaneously, intensified particle collisions cause a continuous rise in the electrostatic signal. The pressure difference across the blockage point increases significantly due to increased flow resistance, directly confirming the obstruction. The overall system mass flux continuously decreases due to this local resistance, with the rate of flow decrease meeting the target. These three signals (upstream state accumulation, sudden increase in local pressure difference, and overall flow attenuation) are logically consistent in space and time, thus confirming a partial blockage in the gas delivery pipeline.

[0081] In the third target condition, the energy accumulation characteristics and the continuous upward trend of the electrostatic signal are both located within the target pipeline section in space, indicating that gas stagnation and particle activity occurred simultaneously upstream of the same suspected blockage point. The two are spatially co-located, indicating that this is the source area of ​​restricted flow.

[0082] In the third target condition, the pressure data detected that the increase in pressure difference between the inlet and outlet of the target pipeline section exceeded the set pressure difference threshold, indicating that the section generated a significantly increased abnormal flow resistance, directly confirming the existence of blockage or throttling effect.

[0083] In the third objective condition, if the rate of decrease in flow data exceeds the set rate of decrease threshold, it indicates that the consequences caused by the local high-resistivity section have developed to the point of affecting the quality transmission capacity of the entire system, and the rate of deterioration has reached a level that requires an alarm.

[0084] This invention enables early and accurate diagnosis of local blockages in gas delivery pipelines through temporal and spatial correlation analysis of infrared, electrostatic, pressure, and flow data. This provides key decision-making support for predictive maintenance and precise intervention, effectively preventing system downtime and efficiency losses caused by the expansion of blockages.

[0085] Based on the above embodiments, the step of performing coupled analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline further includes: Under the condition that the flow rate data and the pressure data remain stable, the aeration uniformity index in the gas delivery pipeline is determined based on the infrared spectral data, and the spectral characteristic index of the electrostatic signal data is analyzed. Under the condition that the fourth objective condition is met, the aeration tube diaphragm in the gas delivery pipeline is determined to be aged; wherein, the fourth objective condition includes: The difference between the value of the aeration uniformity index and the benchmark value established by the gas delivery pipeline in the initial healthy state or in the previous health assessment cycle is greater than the first set threshold. The difference between the value of the spectral characteristic index and the benchmark value established under the same benchmark state is greater than the second set threshold.

[0086] It should be understood that the aeration uniformity index is used to quantify the uniformity of gas release or distribution along the length of the gas delivery pipeline or at different locations on the plane of the aeration tank. It reflects the operational consistency of the aeration pipe group (or different orifices of a single aeration pipe).

[0087] Spectral characteristic indicators are used to quantify the frequency structure characteristics of electrostatic noise signals excited by the dynamic process of bubble generation, detachment, rise, and collapse from a frequency domain perspective. They reflect the dynamic stability of the aeration process and the statistical characteristics of the bubble swarm.

[0088] Infrared spectral data is used to determine the aeration uniformity index in the gas delivery pipeline. For example, at least three spatially known infrared spectral monitoring points are set up along the length of the gas delivery pipeline or on the surface of the aeration tank; the infrared transmission intensity signal of a specific absorption band at each monitoring point or the instantaneous value of the calculated gas concentration is acquired simultaneously; within a set evaluation time window, the average value of the gas concentration data at each monitoring point is calculated, and the spatial standard deviation or coefficient of variation of the average concentration at all monitoring points is calculated; the calculated spatial standard deviation or coefficient of variation is used directly or after normalization as the aeration uniformity index.

[0089] Analyze the spectral characteristics of electrostatic signal data. For example, preprocess the raw electrostatic signal collected within the target pipeline section, including bandpass filtering to retain the frequency bands related to bubble dynamics and removing DC components and high-frequency noise; perform a fast Fourier transform on the preprocessed signal segment (length corresponding to the preset analysis time) to calculate its power spectral density; extract at least one of the following quantization parameters from the power spectral density as spectral characteristic indicators: main peak frequency, characteristic band energy ratio, spectral centroid frequency, and spectral flatness.

[0090] Under the condition of meeting the fourth objective, the aging of the aeration tube diaphragm in the gas delivery pipeline is determined. It should be understood that diaphragm aging (such as pore blockage, loss of elasticity, or tearing) directly leads to uneven gas output, which is quantified by infrared spectral data as a deterioration in the aeration uniformity index. Simultaneously, aging alters the inherent frequency and stability of bubble generation and detachment, which is quantified by electrostatic signal spectral analysis as a shift in spectral characteristic indicators. When these two independent indicators, reflecting uneven results and process instability respectively, deviate simultaneously from their healthy historical baseline, single-indicator anomalies caused solely by external flow field fluctuations or sensor drift are ruled out. Therefore, it can be determined that the physical properties of the aeration tube diaphragm itself have undergone irreversible degradation, thus achieving early diagnosis of the aging state.

[0091] In the fourth objective condition, if the difference between the value of the aeration uniformity index and the baseline value established by the aeration pipe in the initial healthy state or in the previous health assessment cycle is greater than the first set threshold, it indicates that the spatial distribution uniformity of gas in the aeration tank or gas delivery pipeline has deteriorated significantly and unacceptably, exceeding the self-fluctuation range allowed for normal system operation.

[0092] In the fourth objective condition, if the difference between the value of the spectral characteristic index and the benchmark value established under the same benchmark state is greater than the second set threshold, it indicates that the characteristic frequency or energy distribution pattern of the microscopic dynamic process of bubble generation and detachment has undergone fundamental changes.

[0093] This invention achieves early, accurate, and fundamental diagnosis of the aging state of aeration tube membranes by simultaneously evaluating the synergistic degradation of macroscopic gas distribution uniformity and microscopic bubble generation dynamic spectrum under stable operating conditions.

[0094] Based on the above embodiments, the monitoring and adjustment method of the aeration system further includes: The collected multimodal data is sent to an AI cloud platform; wherein, the AI ​​cloud platform is equipped with a coupling analysis model, which is used to perform coupling analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline; Receive the diagnostic results of the gas delivery pipeline sent by the AI ​​cloud platform.

[0095] The aeration system packages multimodal data from different sensors according to a unified time base and aligns it with timestamps to form structured data packets. Through the industrial IoT gateway, the structured data packets are asynchronously and in real time published to the specified topic or interface of the AI ​​cloud platform using MQTT (Message Queuing Telemetry Transport) or HTTP (Hypertext Transfer Protocol) protocols.

[0096] The AI ​​cloud platform uses a coupled analysis model to perform coupled analysis on multimodal data, obtains diagnostic results for the gas delivery pipeline, and sends these results to the aeration system. The aeration system then receives the diagnostic results for the gas delivery pipeline from the AI ​​cloud platform.

[0097] In one embodiment, when the coupling analysis model is based on a deep neural network, it includes: a multi-branch feature extraction sub-network, which is used to process infrared and electrostatic time-series features respectively; an attention mechanism fusion layer, which is used to calculate the dynamic weights between different modal features and perform feature weighted concatenation; and a fully connected classification sub-network, which is used to output diagnostic results based on the fused features.

[0098] This invention achieves high-precision and intelligent diagnosis of the gas delivery pipeline status by transmitting complex multimodal data (infrared, electrostatic, pressure, flow) to the cloud and centrally processing it using a dedicated coupled analysis model deployed in the cloud.

[0099] Based on the above embodiments, generating adjustment instructions based on the diagnostic results includes: Based on the type of the diagnostic result, a corresponding adjustment strategy is matched from the strategy library; Based on the aforementioned regulation strategy, control commands are generated for controlling the regulating valves on the gas delivery pipeline.

[0100] The strategy library includes mapping relationships between diagnostic results and adjustment targets, as well as valve action parameters. The adjustment target is a specific, quantifiable description of the ideal operating condition that the system should achieve. Within the strategy library, the mapping relationship between diagnostic results and adjustment targets can include: a diagnostic result (periodic pulsation of gas density, amplitude exceeding the threshold) corresponding to an adjustment target (stabilizing the gas supply field and reducing the pulsation amplitude below the threshold); a diagnostic result (gas temperature showing a decreasing trend and inversely related to density changes) corresponding to an adjustment target (preventing condensation and raising and stabilizing the temperature above the dew point); and a diagnostic result (two-dimensional temperature field image showing high-temperature zone shift) corresponding to an adjustment target (balancing heat distribution and homogenizing the temperature field).

[0101] The real-time diagnostic results are matched against predefined diagnostic conditions in the strategy library. When multiple rules are met, the highest priority adjustment strategy is selected according to preset priority or weight. Alternatively, the strategy library is a machine learning model trained on historical data. Matching the corresponding adjustment strategy involves inputting the real-time diagnostic results into the model, which then outputs recommended adjustment targets and valve action parameters.

[0102] The regulation strategy can be a multi-valve coordinated regulation strategy, and the valve action parameters can include the opening combination and timing action scheme of the main gas supply valve, branch valve and return valve.

[0103] Based on the regulation strategy, control commands are generated for controlling the regulating valves on the gas delivery pipeline. For example, the target valve opening or target flow rate value in the regulation strategy is compared with the current valve opening or measured flow rate value, and the real-time valve drive signal is calculated using a proportional-integral-derivative control algorithm.

[0104] In one embodiment, when generating the valve drive signal, a feedforward compensation amount is introduced. This feedforward compensation amount is calculated based on real-time monitoring data of changes in the frequency of the upstream blower or the main pipe pressure, and is used to preemptively offset the influence of external disturbances on the gas state inside the pipeline.

[0105] This invention achieves closed-loop intelligent control of the aeration process by automatically matching real-time diagnostic results to a preset strategy library and generating precise control commands.

[0106] To further explain the monitoring and adjustment method of the aeration system proposed in this invention, please refer to the following embodiments.

[0107] refer to Figure 2 , Figure 2 This is a schematic diagram of the process for accurate monitoring of gas delivery pipeline flux based on infrared and electrostatic multimodal sensing provided by the present invention. The method includes: Step 1: Multimodal data acquisition. The infrared spectroscopy sensor, electrostatic sensor, pressure transmitter, and calorimeter located in the gas delivery pipeline work synchronously to acquire raw data of the four physical quantities in real time.

[0108] Step 2: Edge-side data aggregation and storage. All sensor data is sent to the main controller, which is responsible for initial time-stamp alignment, filtering, and formatting of the data, and then storing it locally.

[0109] Step 3: Local Monitoring and Data Upload. The main control unit sends the processed data packets to the industrial control computer. The industrial control computer mainly performs two functions: displaying data, curves, and alarm status in real time through a graphical human-machine interface; and acting as a communication gateway, uploading the aggregated multimodal data to the cloud-based AI analysis platform.

[0110] Step 4: Cloud-based Intelligent Analysis and Decision-Making. After receiving the data, the cloud platform invokes the "infrared-electrostatic-pressure-flow" coupled analysis model to perform health diagnosis (imbalance, leakage, blockage, aging). Based on the diagnosis results, it generates the optimal adjustment command (such as the target valve opening) from the strategy library.

[0111] Step 5: Decision Command Downlink and Forwarding. The cloud platform sends the generated adjustment commands back to the industrial control computers in the field. The industrial control computers, acting as command relay stations, reliably forward the commands to the main control computer.

[0112] Step 6: Command Execution and Adjustment. The main controller parses the received commands, converts them into industrial standard control signals, and drives the electric butterfly valve to perform precise opening adjustments, thereby changing the pipeline flow rate.

[0113] Step 7: Closed-loop feedback. After the adjustment is completed, the system automatically starts a new round of data acquisition, focusing on monitoring indicators such as the stability of the adjusted infrared spectral signal, and uploads this data to the cloud again to evaluate the adjustment effect and optimize the model, thus forming an adaptive intelligent closed loop of "perception-analysis-decision-execution-optimization".

[0114] refer to Figures 3-4 The aeration system mainly includes: Thermal mass flow meter 1 for aeration main pipe: detects the air flow in the main pipe and is arranged above the water in the biological treatment tank.

[0115] 2. Regulating electric butterfly valve for aeration main pipe: controls and regulates the total air flow of the aeration pipe and is arranged above water in the biological treatment tank.

[0116] 3. Thermal mass flow meter for aeration manifold: Detects air flow in aeration manifold and is installed above the water in the biochemical tank.

[0117] 4. Regulating electric butterfly valves for aeration branch pipes: Control and regulate the airflow of the aeration branch pipes. When aeration is uneven in each branch pipe, the electric butterfly valves of each branch pipe will adjust in tandem to achieve uniform aeration in all branch pipes; shut off when gas leakage exceeds the preset value. Alarm will be triggered on the main control unit. Surface arrangement in the biological treatment tank.

[0118] Non-contact electrostatic voltage sensor 5: Installed on the outside of the aeration pipe, with a ring electrode, not in contact with the pipe, above the liquid level in the biological tank; the greater the air flow, the greater the friction, the greater the electrostatic field, and the greater the electrostatic voltage. The air flow is adjusted according to the preset electrostatic voltage of the main controller.

[0119] Infrared spectral sensing module 6: Installed in the middle of the aeration pipe, with connecting fittings installed inside the pipe. It senses changes in airflow based on changes in the spectral waveform.

[0120] Pressure sensor 7: Installed at the end of the aeration pipe, underwater in the biological treatment tank. It determines the change in airflow at the end by measuring pressure changes.

[0121] Data Acquisition and Main Control Unit 8: Acquires data and issues execution commands to the electric butterfly valves; uploads data to the AI ​​cloud analysis platform via wired or wireless means. The AI ​​cloud analysis platform issues control commands to the data acquisition and main control unit, and then the main control unit outputs a 4-20mA analog signal to control the opening degree of the electric butterfly valves in each branch.

[0122] This invention applies infrared spectral sensing technology to gas delivery pipeline flow monitoring, combining it with electrostatic monitoring to form a unique dual non-contact sensing scheme. Simultaneously, based on a quantitative evaluation method for the adjustment effect of infrared signal stability, a collaborative processing architecture of edge computing and cloud intelligence is constructed, balancing real-time performance and analytical depth. Furthermore, multimodal sensing technology is applied to a comprehensive assessment of the health status of the aeration network, enabling early diagnosis and proactive adjustment of the health status and flow imbalance of the gas delivery pipeline in the aeration system. This allows for intervention and adjustment before faults or imbalances occur, thereby ensuring accurate, stable, and efficient operation of the aeration system while guaranteeing treatment effectiveness.

[0123] The monitoring and adjustment device for the aeration system provided by the present invention is described below. The monitoring and adjustment device for the aeration system described below can be referred to in correspondence with the monitoring and adjustment method for the aeration system described above.

[0124] refer to Figure 5 The monitoring and adjustment device for the aeration system provided by the present invention includes: a data acquisition module 501, a monitoring module 502, a coupling analysis module 503, and an adjustment module 504.

[0125] The acquisition module 501 is used to acquire multimodal data of the gas delivery pipeline in the aeration system; the multimodal data includes infrared spectral data, electrostatic signal data, pressure data and flow data; the gas delivery pipeline includes aeration pipes and aeration tubes; Monitoring module 502 is used to monitor the changes in infrared energy and peak wavelength shift of the gas in the gas delivery pipeline based on the infrared spectral data, so as to obtain information on the changing trend of the gas flow state. The coupling analysis module 503 is used to perform coupling analysis on the multimodal data when the trend information indicates that the gas flow state is abnormal, so as to obtain the diagnostic results of the gas delivery pipeline. The adjustment module 504 is used to generate adjustment instructions based on the diagnostic results and adjust the flow rate of the gas delivery pipeline based on the adjustment instructions.

[0126] The monitoring and regulation device for an aeration system provided in this invention collects multimodal data from the gas delivery pipeline in the aeration system. This multimodal data includes infrared spectral data, electrostatic signal data, pressure data, and flow rate data. Based on the infrared spectral data, it monitors the changes in infrared energy and peak wavelength shifts of the gas within the pipeline to obtain information on the changing trends of the gas flow state. When the changing trend indicates an abnormality in the gas flow state, it performs coupled analysis on the multimodal data to obtain a diagnostic result for the gas delivery pipeline. Based on the diagnostic result, it generates regulation commands and adjusts the flux of the gas delivery pipeline accordingly. This invention, by introducing the forward-looking perception of infrared spectroscopy and electrostatic signals and coupling them with macroscopic pressure and flow rate data, achieves early diagnosis and forward-looking regulation of the health status and flow imbalance of the gas delivery pipeline in the aeration system. It can intervene and regulate before a fault or imbalance occurs, thereby ensuring the precise, stable, and efficient operation of the aeration system while maintaining treatment effectiveness.

[0127] In one embodiment, the gas flow state change trend information includes gas density change trend information and gas temperature change trend information; the monitoring module 502 is further configured to: The energy attenuation of infrared rays in a specific absorption band in the infrared spectral data is monitored, and the trend of gas density change in the gas delivery pipeline is obtained by inversion based on the Beer-Lamber law. The peak wavelength shift of infrared radiation in the infrared spectral data is monitored, and the trend information of gas temperature change in the gas delivery pipeline is obtained based on Planck's radiation law.

[0128] In one embodiment, the coupling analysis module 503 is further configured to: Identify the amount of charge change in the electrostatic signal data and extract the amplitude of the charge change; Under the condition that the first objective is met, the gas delivery pipeline is determined to be an early warning system for flow imbalance; wherein, the first objective includes: The infrared spectral data showed abnormal energy fluctuations in a specific absorption band, and a shift in the peak wavelength of infrared radiation. The magnitude of the charge change exceeds the dynamic baseline threshold determined based on historical steady-state data; The fluctuations in both the pressure data and the flow data remained consistently within their respective normal fluctuation threshold ranges.

[0129] In one embodiment, the coupling analysis module 503 is further configured to: Identify abnormal low-temperature energy distribution regions in the infrared spectral data, and detect electrostatic voltage gradient attenuation regions based on the electrostatic signal data; If the second objective condition is met, it is determined that there is a leak in the gas delivery pipeline, and the leak point is located; wherein, the second objective condition includes: The deviations of the abnormal low temperature energy distribution area, the electrostatic voltage gradient attenuation area, and the local pressure drop abrupt change points identified from the pressure data in the pipeline spatial location are less than the set tolerance range. The traffic data shows a continuous downward trend; The error between the pressure drop amplitude at the local pressure drop abrupt change point and the gas leakage rate determined based on the abnormal low temperature energy distribution region is within a set range.

[0130] In one embodiment, the coupling analysis module 503 is further configured to: The system detects energy accumulation characteristics in the infrared spectral data upstream of the suspected blockage point and analyzes whether the electrostatic signal data shows a continuous upward trend in the target pipeline section; the target pipeline section is the pipeline section spatially related to the energy accumulation characteristics. If the third objective condition is met, it is determined that the aeration pipe in the gas delivery pipeline is partially blocked; wherein, the third objective condition includes: The energy accumulation characteristics and the continuous upward trend of the electrostatic signal are both located within the target pipeline section in space. The pressure data detected that the increase in pressure difference between the inlet and outlet of the target pipeline section exceeded the set pressure difference threshold. The rate of decrease in the traffic data exceeds the set rate of decrease threshold.

[0131] In one embodiment, the coupling analysis module 503 is further configured to: Under the condition that the flow rate data and the pressure data remain stable, the aeration uniformity index in the gas delivery pipeline is determined based on the infrared spectral data, and the spectral characteristic index of the electrostatic signal data is analyzed. Under the condition that the fourth objective is met, the aerator diaphragm in the gas delivery pipeline is determined to be aged; wherein, the fourth objective includes: The difference between the value of the aeration uniformity index and the benchmark value established by the aerator in the initial healthy state or in the previous health assessment cycle is greater than the first set threshold. The difference between the value of the spectral characteristic index and the benchmark value established under the same benchmark state is greater than the second set threshold.

[0132] In one embodiment, the coupling analysis module 503 is further configured to: The collected multimodal data is sent to an AI cloud platform; wherein, the AI ​​cloud platform is equipped with a coupling analysis model, which is used to perform coupling analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline; Receive the diagnostic results of the gas delivery pipeline sent by the AI ​​cloud platform.

[0133] In one embodiment, the adjustment module 504 is further configured to: Based on the type of the diagnostic result, a corresponding adjustment strategy is matched from the strategy library; the strategy library includes the mapping relationship between the diagnostic result and the adjustment target and valve action parameters; Based on the aforementioned regulation strategy, control commands are generated for controlling the regulating valves on the gas delivery pipeline.

[0134] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6As shown, the electronic device may include a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions in the memory 630 to execute a monitoring and adjustment method for the aeration system. This method includes: acquiring multimodal data of the gas delivery pipeline in the aeration system; the multimodal data includes infrared spectral data, electrostatic signal data, pressure data, and flow data; the gas delivery pipeline includes aeration pipes and aeration tubes; based on the infrared spectral data, monitoring the infrared energy changes and peak wavelength shifts of the gas in the gas delivery pipeline to obtain information on the changing trend of the gas flow state; when the changing trend information indicates an abnormality in the gas flow state, performing coupled analysis on the multimodal data to obtain a diagnostic result for the gas delivery pipeline; generating an adjustment command based on the diagnostic result, and adjusting the flux of the gas delivery pipeline based on the adjustment command.

[0135] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0136] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the monitoring and regulation methods for the aeration system provided by the above methods. The method includes: collecting multimodal data of the gas delivery pipeline in the aeration system; the multimodal data includes infrared spectral data, electrostatic signal data, pressure data, and flow data; the gas delivery pipeline includes aeration pipes and aeration tubes; based on the infrared spectral data, monitoring the infrared energy changes and peak wavelength shifts of the gas in the gas delivery pipeline to obtain information on the changing trend of the gas flow state; when the changing trend information indicates that the gas flow state is abnormal, performing coupled analysis on the multimodal data to obtain a diagnostic result of the gas delivery pipeline; generating a regulation command based on the diagnostic result, and regulating the flux of the gas delivery pipeline based on the regulation command.

[0137] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a monitoring and regulation method for an aeration system provided by the methods described above. This method includes: collecting multimodal data of a gas delivery pipeline in the aeration system; the multimodal data includes infrared spectral data, electrostatic signal data, pressure data, and flow rate data; the gas delivery pipeline includes an aeration pipe and an aeration tube; based on the infrared spectral data, monitoring the infrared energy changes and peak wavelength shifts of the gas within the gas delivery pipeline to obtain information on the changing trend of the gas flow state; when the changing trend information indicates an abnormality in the gas flow state, performing coupled analysis on the multimodal data to obtain a diagnostic result for the gas delivery pipeline; generating a regulation command based on the diagnostic result, and regulating the flux of the gas delivery pipeline based on the regulation command.

[0138] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0139] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring and regulating an aeration system, characterized in that, include: Multimodal data of the gas delivery pipeline in the aeration system are collected; the multimodal data includes infrared spectral data, electrostatic signal data, pressure data, and flow rate data; the gas delivery pipeline includes aeration pipes and aeration tubes; Based on the infrared spectral data, the infrared energy changes and peak wavelength shifts of the gas in the gas delivery pipeline are monitored to obtain information on the changing trends of the gas flow state. When the trend information indicates an abnormality in the gas flow state, the multimodal data is coupled and analyzed to obtain the diagnostic results of the gas delivery pipeline. Based on the diagnostic results, an adjustment command is generated, and the flow rate of the gas delivery pipeline is adjusted based on the adjustment command.

2. The monitoring and adjustment method for the aeration system according to claim 1, characterized in that, The gas flow state change trend information includes gas density change trend information and gas temperature change trend information; the step of monitoring the infrared energy change and peak wavelength shift of the gas in the gas delivery pipeline based on the infrared spectral data to obtain gas flow state change trend information includes: The energy attenuation of infrared rays in a specific absorption band in the infrared spectral data is monitored, and the trend of gas density change in the gas delivery pipeline is obtained by inversion based on the Beer-Lamber law. The peak wavelength shift of infrared radiation in the infrared spectral data is monitored, and the trend information of gas temperature change in the gas delivery pipeline is obtained based on Planck's radiation law.

3. The monitoring and adjustment method for the aeration system according to claim 1, characterized in that, The coupled analysis of the multimodal data to obtain the diagnostic results of the gas delivery pipeline includes: Identify the amount of charge change in the electrostatic signal data and extract the amplitude of the charge change; Under the condition that the first objective is met, the gas delivery pipeline is determined to be an early warning system for flow imbalance; wherein, the first objective includes: The infrared spectral data showed abnormal energy fluctuations in a specific absorption band, and a shift in the peak wavelength of infrared radiation. The magnitude of the charge change exceeds the dynamic baseline threshold determined based on historical steady-state data; The fluctuations in both the pressure data and the flow data remained consistently within their respective normal fluctuation threshold ranges.

4. The monitoring and adjustment method for the aeration system according to claim 1, characterized in that, The step of performing coupled analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline also includes: Identify abnormal low-temperature energy distribution regions in the infrared spectral data, and detect electrostatic voltage gradient attenuation regions based on the electrostatic signal data; If the second objective condition is met, it is determined that there is a leak in the gas delivery pipeline, and the leak point is located; wherein, the second objective condition includes: The deviations of the abnormal low temperature energy distribution area, the electrostatic voltage gradient attenuation area, and the local pressure drop abrupt change points identified from the pressure data in the pipeline spatial location are less than the set tolerance range. The traffic data shows a continuous downward trend; The error between the pressure drop amplitude at the local pressure drop abrupt change point and the gas leakage rate determined based on the abnormal low temperature energy distribution region is within a set range.

5. The monitoring and adjustment method for the aeration system according to claim 1, characterized in that, The step of performing coupled analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline also includes: The system detects energy accumulation characteristics in the infrared spectral data upstream of the suspected blockage point and analyzes whether the electrostatic signal data shows a continuous upward trend in the target pipeline section; the target pipeline section is the pipeline section spatially related to the energy accumulation characteristics. If the third objective condition is met, it is determined that the aeration pipe in the gas delivery pipeline is partially blocked; wherein, the third objective condition includes: The energy accumulation characteristics and the continuous upward trend of the electrostatic signal are both located within the target pipeline section in space. The pressure data detected that the increase in pressure difference between the inlet and outlet of the target pipeline section exceeded the set pressure difference threshold. The rate of decrease in the traffic data exceeds the set rate of decrease threshold.

6. The monitoring and adjustment method for the aeration system according to claim 1, characterized in that, The step of performing coupled analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline also includes: Under the condition that the flow rate data and the pressure data remain stable, the aeration uniformity index in the gas delivery pipeline is determined based on the infrared spectral data, and the spectral characteristic index of the electrostatic signal data is analyzed. Under the condition that the fourth objective condition is met, the aeration tube diaphragm in the gas delivery pipeline is determined to be aged; wherein, the fourth objective condition includes: The difference between the value of the aeration uniformity index and the benchmark value established by the gas delivery pipeline in the initial healthy state or in the previous health assessment cycle is greater than the first set threshold. The difference between the value of the spectral characteristic index and the benchmark value established under the same benchmark state is greater than the second set threshold.

7. The monitoring and adjustment method for the aeration system according to claim 1, characterized in that, The monitoring and adjustment method for the aeration system also includes: The collected multimodal data is sent to an AI cloud platform; wherein, the AI ​​cloud platform is equipped with a coupling analysis model, which is used to perform coupling analysis on the multimodal data to obtain the diagnostic results of the gas delivery pipeline; Receive the diagnostic results of the gas delivery pipeline sent by the AI ​​cloud platform.

8. The monitoring and adjustment method for the aeration system according to claim 1, characterized in that, The generation of adjustment instructions based on the diagnostic results includes: Based on the type of the diagnostic result, a corresponding adjustment strategy is matched from the strategy library; the strategy library includes the mapping relationship between the diagnostic result and the adjustment target and valve action parameters; Based on the aforementioned regulation strategy, control commands are generated for controlling the regulating valves on the gas delivery pipeline.

9. A monitoring and adjustment device for an aeration system, characterized in that, include: The acquisition module is used to acquire multimodal data of the gas delivery pipeline in the aeration system; the multimodal data includes infrared spectral data, electrostatic signal data, pressure data, and flow rate data; the gas delivery pipeline includes aeration pipes and aeration tubes; The monitoring module is used to monitor the changes in infrared energy and peak wavelength shift of the gas in the gas delivery pipeline based on the infrared spectral data, so as to obtain information on the changing trend of the gas flow state. The coupling analysis module is used to perform coupling analysis on the multimodal data when the trend information indicates that the gas flow state is abnormal, so as to obtain the diagnostic results of the gas delivery pipeline. An adjustment module is used to generate adjustment instructions based on the diagnostic results and adjust the flow rate of the gas delivery pipeline based on the adjustment instructions.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the monitoring and adjustment method of the aeration system as described in any one of claims 1 to 8.

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