Intelligent analysis method and system for external water infiltration based on pipe network microorganisms

Abstract

The invention discloses an intelligent external water infiltration analysis system and method based on pipe network microorganisms, and particularly relates to the technical field of water body microorganism detection.The method comprises the steps that the external water infiltration condition is monitored, and microorganism detection data and fluid dynamic parameters in a pipe network water body are collected; the influence of external water infiltration on microbial metabolism gas production is analyzed in combination with a dynamic model, the change trend of microbubble density is predicted, and the influence of microbubbles caused by external water infiltration on the hydraulic characteristics and operation stability of a pipe network is quantified by using turbulence energy dissipation anomaly analysis and pressure fluctuation asymmetry evaluation. And according to the comprehensive evaluation result, the system generates an operation adjustment signal, and dynamically optimizes pipe network operation parameters including flow velocity, pressure and microbubble suppression measures. The micro-bubble dynamic characteristics caused by external water infiltration can be accurately analyzed, and the operation stability of a pipe network and the water conveying efficiency are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water body microorganism detection, and more specifically, the present invention relates to an intelligent analysis method and system for external water infiltration based on pipe network microorganisms. Background Art

[0002] During the operation of a water supply pipe network, the phenomenon of external water infiltration often occurs. External water infiltration refers to the infiltration of groundwater, surface water, or other external water bodies into the pipe network through pipeline cracks, loose joints, etc. This infiltration may carry organic substances and external microorganisms, changing the water quality and microbial community in the pipe network, further leading to an increase in the microbial metabolic activities in the water body. The microbial metabolic activities in the water body will produce gases, such as hydrogen and methane, and these gases exist in the pipe network in the form of microbubbles. The formation and aggregation of microbubbles may affect the hydraulic characteristics of the pipe network and the operating stability of the pipeline; the existing technology mainly relies on the monitoring of physical and chemical parameters to evaluate the state of the pipe network, but does not conduct a systematic analysis on the dynamic characteristics of microbubbles and their potential impact on the operation of the pipe network.

[0003] In addition, in the existing technology, there is a lack of precise monitoring and dynamic analysis of the formation and distribution characteristics of microbubbles, which will lead to the inability to effectively predict the comprehensive impact of microbubbles on the hydraulic characteristics and operating parameters of the pipe network, and may cause abnormal pipe network pressure, flow velocity disorder, and a decrease in water supply efficiency. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the existing technology, the present invention provides an intelligent analysis method and system for external water infiltration based on pipe network microorganisms to solve the problems raised in the above background art.

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

[0006] An intelligent analysis method for external water infiltration based on pipe network microorganisms, comprising the following steps:

[0007] When it is detected that external water infiltration occurs in the pipe network, obtain the density of microbubbles in the pipe network and determine whether it exceeds a preset microbubble density threshold;

[0008] When the density of microbubbles in the pipe network does not exceed the preset microbubble density threshold, establish a microbial metabolism gas production model through microbial detection data and hydrodynamic parameters to determine whether the possibility of a sharp increase in the microbubble density in the pipe network is too high;

[0009] Based on the judgment results of whether the density of microbubbles in the pipe network exceeds the preset microbubble density threshold and whether the possibility of a sharp increase in the microbubble density in the pipe network is too high, determine whether to generate a microbubble impact analysis signal;

[0010] When generating the microbubble influence analysis signal, evaluate the potential damage degree of microbubble aggregation to hydraulic characteristics and energy dissipation through the structural analysis of the abnormal mode of turbulent energy dissipation;

[0011] When generating the microbubble influence analysis signal, evaluate the influence degree of microbubble formation and distribution on the pressure fluctuation asymmetry through the multi-scale tensor principal component analysis of high-frequency pressure fluctuation data;

[0012] Based on the potential damage degree of microbubble aggregation to hydraulic characteristics and energy dissipation and the influence degree of microbubble formation and distribution on the pressure fluctuation asymmetry, adjust the operation parameters of the pipe network.

[0013] In a preferred embodiment, when it is monitored that external water infiltration occurs in the pipe network, obtain the density of microbubbles in the pipe network and judge whether it exceeds the preset microbubble density threshold, specifically including:

[0014] Install microbubble detection sensors at key nodes of the pipe network to collect microbubble concentration data in the water body;

[0015] Verify the collected microbubble concentration data, eliminate noise and invalid data, and generate an effective microbubble density value;

[0016] Compare the effective microbubble density value with the preset microbubble density threshold to judge whether the density of microbubbles in the pipe network is normal.

[0017] In a preferred embodiment, when the density of microbubbles in the pipe network does not exceed the preset microbubble density threshold, establish a microbial metabolism gas production model based on microbial detection data and hydrodynamic parameters to judge whether the possibility of a sharp increase in the microbubble density in the pipe network is too high, specifically including:

[0018] Analyze the microbial metabolism characteristics based on the microbial detection data, and extract the key factors affecting gas production in combination with the hydrodynamic parameters;

[0019] Adopt the dynamic reaction kinetics modeling method to establish a microbial metabolism gas production model, and correlate the gas generation rate with the environmental conditions;

[0020] Calibrate the parameters of the microbial metabolism gas production model according to the microbial growth rate, gas production rate and mass transfer effect;

[0021] Calculate the change of microbubble density in the future period through the microbial metabolism gas production model, and judge whether the possibility of a sharp increase in the microbubble density in the pipe network is too high;

[0022] Among them, the microbial detection data includes the types, concentrations and distribution characteristics of microorganisms; the hydrodynamic parameters include the flow velocity, pressure and shear stress distribution.

[0023] In a preferred embodiment, based on the judgment results of whether the density of microbubbles in the pipe network exceeds a preset microbubble density threshold and whether the possibility of a sharp increase in the microbubble density in the pipe network is too high, it is judged whether to generate a microbubble impact analysis signal, specifically including:

[0024] When either of the conditions that the density of microbubbles in the pipe network exceeds the preset microbubble density threshold and the possibility of a sharp increase in the microbubble density in the pipe network is too high is met, a microbubble impact analysis signal is generated.

[0025] In a preferred embodiment, through the structural analysis of the abnormal mode of turbulent energy dissipation, the potential damage degree of microbubble aggregation to hydraulic characteristics and energy dissipation is evaluated, specifically including:

[0026] Obtain the turbulent energy dissipation data of the microbubble aggregation area through computational fluid dynamics simulation;

[0027] Use a sparse regression model based on the decomposition of turbulent structures to decompose the turbulent energy dissipation data into a normal energy dissipation mode and an abnormal energy dissipation mode;

[0028] Through the analysis of the abnormal mode of turbulent energy dissipation, quantify the local turbulent energy dissipation characteristics of the microbubble aggregation area;

[0029] According to the abnormal energy dissipation mode of the microbubble aggregation area, calculate the turbulent energy dissipation abnormal index to quantify the potential damage degree of microbubble aggregation to hydraulic characteristics and energy dissipation.

[0030] In a preferred embodiment, through the multi-scale tensor principal component analysis of high-frequency pressure fluctuation data, the influence degree of microbubble formation and distribution on the pressure fluctuation asymmetry is evaluated, specifically including:

[0031] Collect pressure fluctuation data in the microbubble aggregation area and record its time series distribution in real time;

[0032] Construct a multi-scale tensor model for the pressure fluctuation data according to the spatial coordinates, time series and frequency components to characterize the multi-dimensional distribution characteristics of the pressure fluctuation;

[0033] Adopt the principal component analysis method to perform dimensionality reduction processing on the multi-scale tensor and extract the principal component vector describing the pressure fluctuation characteristics;

[0034] According to the principal component vector, analyze the spatial asymmetry characteristics of the pressure fluctuation, and calculate the pressure fluctuation asymmetry index to quantify the influence degree of microbubble formation and distribution on the pressure fluctuation asymmetry.

[0035] In a preferred embodiment, based on the potential damage degree of microbubble aggregation to hydraulic characteristics and energy dissipation, and the influence degree of microbubble formation and distribution on the pressure fluctuation asymmetry, the operation parameters of the pipe network are adjusted in real time, specifically including:

[0036] Normalize the turbulent energy dissipation anomaly index and the pressure fluctuation asymmetry index, assign weight coefficients to the normalized turbulent energy dissipation anomaly index and the pressure fluctuation asymmetry index respectively, and calculate the microbubble pipe network influence coefficient;

[0037] Set the microbubble pipe network influence threshold, and compare the microbubble pipe network influence coefficient with the microbubble pipe network influence threshold:

[0038] When the microbubble pipe network influence coefficient is greater than the microbubble pipe network influence threshold, it is determined to generate an adjustment signal for the operation parameters of the pipe network; when the microbubble pipe network influence coefficient is less than or equal to the microbubble pipe network influence threshold, it is determined to generate a signal that the operation parameters of the pipe network do not need to be adjusted;

[0039] Based on the adjustment signal of the operation parameters of the pipe network and the microbubble pipe network influence coefficient, adjust the operation parameters of the pipe network, including adjusting the flow rate, optimizing the pressure, regional shunt control, suppressing microbubble aggregation, and real-time monitoring.

[0040] On the other hand, the present invention provides an intelligent analysis system for external water infiltration based on pipe network microorganisms, including a bubble density detection module, a metabolic gas production prediction module, an influence signal generation module, an energy dissipation evaluation module, a pressure fluctuation analysis module, and a pipe network parameter adjustment module;

[0041] Bubble density detection module: When it is detected that there is external water infiltration in the pipe network, obtain the density of microbubbles in the pipe network and judge whether it exceeds the preset microbubble density threshold;

[0042] Metabolic gas production prediction module: When the density of microbubbles in the pipe network does not exceed the preset microbubble density threshold, establish a microbial metabolic gas production model through microbial detection data and hydrodynamic parameters to judge whether the possibility of a sharp increase in the density of microbubbles in the pipe network is too high;

[0043] Influence signal generation module: Based on the judgment results of whether the density of microbubbles in the pipe network exceeds the preset microbubble density threshold and whether the possibility of a sharp increase in the density of microbubbles in the pipe network is too high, judge whether to generate a microbubble influence analysis signal;

[0044] Energy dissipation evaluation module: When a microbubble influence analysis signal is generated, evaluate the potential damage degree of microbubble aggregation to hydraulic characteristics and energy dissipation through the structural analysis of the abnormal mode of turbulent energy dissipation;

[0045] Pressure Fluctuation Analysis Module: When generating the microbubble influence analysis signal, it evaluates the influence degree of microbubble formation and distribution on the asymmetry of pressure fluctuation through multi-scale tensor principal component analysis of high-frequency pressure fluctuation data;

[0046] Pipe Network Parameter Adjustment Module: Based on the potential damage degree of microbubble aggregation to hydraulic characteristics and energy dissipation and the influence degree of microbubble formation and distribution on the asymmetry of pressure fluctuation, it adjusts the operating parameters of the pipe network.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. Aiming at the problem of microbial metabolic gas production caused by external water infiltration, the present invention uses real-time detection and data analysis technologies to accurately obtain the types, concentrations and distribution characteristics of microorganisms, and combines fluid dynamics parameters to establish a microbial metabolic gas production model, effectively predicting the possibility of a sharp increase in microbubble density. By setting a microbubble density threshold and dynamically analyzing the influence of external water infiltration, it can timely capture potential pipe network anomalies, providing a scientific basis for subsequent adjustment of pipe network operating parameters. Compared with the prior art, it can achieve precise monitoring and prediction for external water infiltration and microbubble dynamic characteristics, significantly improving the stability of pipe network operation and water supply efficiency.

[0049] 2. Based on the comprehensive influence characteristics of microbubbles, through the analysis of abnormal turbulent energy dissipation and the evaluation of pressure fluctuation asymmetry, it quantifies the potential influence degree of microbubbles on the hydraulic characteristics and operating state of the pipe network, uses the microbubble pipe network influence coefficient to comprehensively evaluate the combined action of turbulence and pressure fluctuation, and combines the operating parameter adjustment strategy to dynamically optimize the flow rate, pressure and microbubble suppression measures of the pipe network. In a data-driven manner, it realizes precise control of pipe network operation under the condition of external water infiltration, effectively reducing the risk of pressure anomalies and equipment loss caused by microbubble aggregation, and at the same time extending the service life of the pipe network. Description of the Drawings

[0050] Figure 1 It is a schematic diagram of an intelligent analysis method for external water infiltration based on pipe network microorganisms of the present invention;

[0051] Figure 2 It is a schematic diagram of the structure of an intelligent analysis system for external water infiltration based on pipe network microorganisms of the present invention. Detailed Embodiments

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] Example 1

[0054] Figure 1 A schematic diagram of an intelligent analysis method for external water infiltration based on pipe network microorganisms of the present invention is given. The intelligent analysis method for external water infiltration includes the following steps:

[0055] When external water infiltration in the pipe network is detected, obtain the density of microbubbles in the pipe network and determine whether it exceeds a preset microbubble density threshold.

[0056] When the density of microbubbles in the pipe network does not exceed the preset microbubble density threshold, establish a microbial metabolism gas production model based on microbial detection data and hydrodynamic parameters to determine whether the possibility of a sharp increase in the microbubble density in the pipe network is too high.

[0057] When the density of microbubbles in the pipe network exceeds the preset microbubble density threshold or the possibility of a sharp increase in the microbubble density in the pipe network is high:

[0058] Evaluate the potential damage degree of microbubble aggregation to hydraulic characteristics and energy dissipation through the structural analysis of the abnormal mode of turbulent energy dissipation;

[0059] Evaluate the influence degree of microbubble formation and distribution on the asymmetry of pressure fluctuation through the multi-scale tensor principal component analysis of high-frequency pressure fluctuation data.

[0060] Based on the potential damage degree of microbubble aggregation to hydraulic characteristics and energy dissipation and the influence degree of microbubble formation and distribution on the asymmetry of pressure fluctuation, adjust the operating parameters of the pipe network.

[0061] When external water infiltration in the pipe network is detected, obtain the density of microbubbles in the pipe network and determine whether it exceeds a preset microbubble density threshold, specifically including:

[0062] Install microbubble detection sensors at key nodes of the pipe network to collect microbubble concentration data in the water body. Specifically, the key nodes of the pipe network include: according to the operating characteristics of the pipe network fluid, select key nodes where microbubble aggregation may occur, such as pressure fluctuation areas, pipe bends, or positions with obvious velocity changes; combine the pipe network design drawing and actual operating conditions to determine the specific positions most suitable for installing microbubble detection sensors to ensure the representativeness of sampling.

[0063] In the present invention, the microbubble detection sensor uses an optical microbubble detection sensor with high sensitivity, which can accurately detect the concentration change of microbubbles in the pipe network water body and has anti-interference ability to reduce the influence of fluid impurities. Install sensors at key nodes of the pipe network to ensure that it is parallel to the water flow and close to the pipe wall to avoid reading errors caused by installation angle problems.

[0064] The frequency of microbubble data acquisition is set to multiple times per second in the microbubble detection sensor to capture the dynamic changes in microbubble concentration.

[0065] Among them, monitoring of external water infiltration in the pipeline network can be achieved through comprehensive monitoring devices installed at key nodes of the pipeline network. These monitoring devices include flow sensors, water quality sensors and water level monitoring equipment; flow sensors are used to monitor abnormal changes in flow in the pipeline network. For example, when normal water demand remains unchanged, a sudden increase in flow may indicate external water infiltration; water quality sensors such as conductivity, dissolved oxygen, and turbidity detectors are used to detect changes in chemical parameters of water bodies in the pipeline network. For example, an increase in conductivity may reflect groundwater or surface water infiltration; water level monitoring equipment monitors the water level of the pipeline network in real time and compares it with historical data or preset standard values. When an abnormal increase in water level is found, it may also indicate external water infiltration; the above sensors collect data in real time and upload it to the central control system. The multi-source data collected by the above monitoring devices are fused and comprehensively judged through a dynamic threshold analysis algorithm to ensure rapid response and accurate positioning of external water infiltration; the system can further trigger subsequent microbubble detection processes to achieve closed-loop management from external water infiltration to pipeline network operation adjustment.

[0066] To verify the collected microbubble concentration data, it is necessary to remove noise and invalid data to generate a valid microbubble density value:

[0067] The microbubble concentration data collected by the microbubble detection sensor is imported into the data processing module for preprocessing: the environmental interference that the microbubble detection sensor may be subjected to, such as temperature changes and electromagnetic interference, is preliminarily filtered, and noise data with abnormal frequency is eliminated using a noise filtering algorithm such as a low-pass filter.

[0068] Time series analysis technology is used to perform dynamic trend modeling on the pre-processed microbubble concentration data to identify and eliminate abnormal data points that obviously deviate from the normal range; when the data fluctuation exceeds a certain amplitude, such as more than three times the standard deviation, the data will be marked as invalid data.

[0069] The microbubble concentration data with abnormal data points removed is dynamically smoothed to reduce the acquisition error caused by transient fluctuations of the fluid; the dynamically smoothed microbubble concentration data is standardized according to the operating status of the pipeline network such as flow rate and pressure to generate effective microbubble density values ​​with consistent units.

[0070] Compare the effective microbubble density value with the preset microbubble density threshold to determine whether the density of microbubbles in the pipe network is normal:

[0071] According to the safety requirements of pipeline network operation and historical data analysis, a preset microbubble density threshold is set, and the specific value can be determined based on the experimental data of the impact of microbubbles on pipeline fluid.

[0072] Compare the effective microbubble density value with the preset microbubble density threshold in real time. If the effective microbubble density value is greater than the preset microbubble density threshold, mark this key node as "abnormal state"; otherwise, mark it as "normal state". Here, the key node being in the "abnormal state" indicates that the density of microbubbles at this key node in the pipe network is abnormal.

[0073] When the density of microbubbles in the pipe network does not exceed the preset microbubble density threshold, establish a microbial metabolic gas production model based on microbial detection data and hydrodynamic parameters to determine whether the possibility of a sharp increase in the microbubble density in the pipe network is too high. Specifically, it includes:

[0074] The microbial detection data includes the types, concentrations, and distribution characteristics of microorganisms.

[0075] According to the water flow direction in the pipe network and the possible distribution characteristics of microorganisms, select appropriate sampling points, such as the intersections of the main pipe and branch pipes, and areas with obvious pressure fluctuations; detect the types, concentrations, and distribution characteristics of microorganisms in the water body through optical detection and biochemical reaction techniques.

[0076] Among them, when classifying and detecting the types of microorganisms, it is necessary to identify the types of microorganisms according to their biofluorescence characteristics and record the trend of their concentration changes; when measuring the distribution characteristics of microorganisms, use multi-point sampling combined with fluid flow direction analysis to generate a distribution map.

[0077] The hydrodynamic parameters include flow velocity, pressure, and shear stress distribution.

[0078] Install hydrodynamic parameter detection devices at the key nodes of the pipe network, including flow meters, pressure sensors, and shear stress sensors, to ensure that the devices are consistent with the pipe flow direction;

[0079] Flow velocity acquisition: Obtain the flow velocity of the water body in the pipe network in real time through a high-frequency flow meter and record its temporal variation.

[0080] Pressure acquisition: Measure the real-time pressure distribution of the water body in the pipe network through a pressure sensor, especially in areas prone to pressure fluctuations.

[0081] Shear stress distribution acquisition: Monitor the change of shear stress near the inner wall of the pipe through a shear stress sensor to obtain its spatial distribution characteristics.

[0082] Perform synchronous time marking on the microbial detection data and hydrodynamic parameters.

[0083] When collecting microbial detection data and hydrodynamic parameters, add a synchronous timestamp to each group of data to ensure the time consistency of multi-source data in subsequent analysis.

[0084] Analyze the microbial metabolic characteristics based on microbial detection data, and extract the key factors affecting gas production by combining hydrodynamic parameters:

[0085] Use the microbial detection data to classify and analyze the metabolic characteristics of the main microbial species in the pipe network water body, including the types of metabolic pathways and the types of metabolic products; analyze the metabolic rates of different microbial species, such as the correlation between glucose consumption rate and gas production rate. Convert these characteristics into parameters for subsequent modeling.

[0086] Use hydrodynamic data to analyze the influence of environmental factors on microbial metabolism, including the influence of flow rate on mass transfer effect, the interference of pressure change on microbial activity, etc.; extract the key parameters that have a significant impact on microbial metabolism, such as the relationship between shear stress and oxygen transfer rate.

[0087] Use a multiple linear regression model to analyze the relationship between microbial metabolic characteristics and hydrodynamic parameters, and screen out the key factors that have a significant impact on gas production.

[0088] According to the screened key factors, such as microbial concentration, maximum specific growth rate of microorganisms, substrate concentration, flow rate, and local oxygen concentration, etc., and assign values to them to establish a microbial metabolic gas production model using the dynamic reaction kinetics modeling method, and correlate the gas production rate with environmental conditions:

[0089] Assume that microbial metabolism follows the Monod kinetic law, and the gas production rate is related to microbial concentration, substrate concentration, and environmental conditions such as oxygen concentration; assume that hydrodynamic conditions indirectly affect the microbial metabolic rate by influencing the mass transfer effect.

[0090] For example, a microbial metabolic gas production model can be established based on microbial concentration, maximum specific growth rate of microorganisms, and substrate concentration, expressed as:

[0091]

[0092] where R gas is the microbial metabolic gas production model, used to represent the gas production rate; μ max represents the maximum specific growth rate of microorganisms; C S represents the substrate concentration; K s represents the half-saturation constant, that is, the value when the substrate concentration reaches half of the maximum growth rate; X represents the microbial concentration; α T represents the temperature correction factor, which corrects the gas production rate according to the actual environmental temperature.

[0093] According to the hydrodynamic parameters, establish the correlation formula between shear stress, flow rate, and oxygen mass transfer rate: where represents the oxygen mass transfer rate; β represents the proportionality constant between the flow velocity and the mass transfer coefficient; u represents the flow velocity; n is the power exponent of the flow velocity, which is determined according to the experimental data.

[0094] Calibrate the parameters of the microbial metabolism gas production model according to the microbial growth rate, gas production rate, and mass transfer effect:

[0095] Microbial growth rate calibration: Use experimental data to obtain the microbial growth rate curve at different substrate concentrations, and fit the maximum specific growth rate and half-saturation constant in the microbial metabolism gas production model;

[0096] Gas production rate calibration: Under the condition of known hydrodynamic parameters, measure the actual gas generation rate, and adjust the proportionality constant in the model to ensure that the microbial metabolism gas production model can accurately predict the gas generation amount;

[0097] Mass transfer effect calibration: Optimize the proportionality constant and power exponent in the oxygen mass transfer rate formula according to the experimental measurement data of shear stress and oxygen concentration.

[0098] Calculate the change in microbubble density in the future period through the microbial metabolism gas production model, and judge whether the possibility of a sharp increase in microbubble density in the pipe network is too high:

[0099] Combined with the gas generation rate and the pipe network operation parameters, calculate the change in microbubble density in the future period: ΔC B =R gas ·Δt - R removal ; where, ΔC B represents the change in microbubble concentration per unit time, Δt represents the time interval, and R removal represents the natural elimination rate of microbubbles.

[0100] Compare the change in microbubble concentration per unit time with the preset surge threshold:

[0101] When the change in microbubble concentration per unit time is greater than the preset surge threshold, it is determined that the possibility of a sharp increase in microbubble density in the pipe network is high, indicating that the bubble generation rate in the pipe network is too fast, which may cause risks such as local flow velocity disorder, increased pressure fluctuation, and decreased fluid transportation efficiency;

[0102] When the change in microbubble concentration per unit time is less than or equal to the preset surge threshold, it is determined that the possibility of a sharp increase in microbubble density in the pipe network is low, indicating that the bubble generation rate in the pipe network is within the safe range, the hydraulic characteristics and operating pressure are in a stable state, and it will not have a significant impact on the operation of the pipe network.

[0103] Among them, the preset surge threshold is the critical value of the change in microbubble concentration determined according to the requirements of the safe operation of the pipe network and historical data analysis, reflecting the upper safety limit of the change in the microbubble concentration in the pipe network per unit time. Exceeding this value may affect the operational stability of the pipe network.

[0104] The absolute value of the microbubble density reflects the current risk level, while the likelihood of density surge indicates the trend of future risks. By simultaneously evaluating these two dimensions, the logic can cover the current and future possible risk scenarios. Taking either the condition that the density of microbubbles in the pipe network exceeds the preset microbubble density threshold or the high likelihood of microbubble density surge in the pipe network as the microbubble impact analysis signal, it triggers subsequent in-depth analysis and control measures to ensure the safety and stability of the pipe network operation, helps avoid irrelevant analysis, and improves the efficiency and accuracy of the intelligent analysis system for external water infiltration.

[0105] Through the structural analysis of the abnormal pattern of turbulent energy dissipation, evaluate the potential damage degree of microbubble aggregation to hydraulic characteristics and energy dissipation, specifically including:

[0106] Obtain the turbulent energy dissipation data of the microbubble aggregation area through computational fluid dynamics simulation:

[0107] Based on computational fluid dynamics software, construct a three-dimensional simulation model of the pipe network, and the model needs to include the fluid domain, boundary conditions, and physical parameter settings; set the boundary conditions, including the inlet flow rate, outlet pressure, and pipe wall smoothness, to ensure that the simulation results are consistent with the actual operating conditions;

[0108] Input the microbubble aggregation area into the three-dimensional simulation model of the pipe network as the initial condition, and specify the bubble concentration distribution range and flow velocity distribution.

[0109] Among them, the microbubble aggregation area is the area corresponding to the key nodes that generate the microbubble impact analysis signal. The microbubble aggregation area refers to the spatial range corresponding to the nodes in the pipe network where the microbubble density exceeds the preset microbubble density threshold or the likelihood of microbubble density surge is too high. The microbubble aggregation area indicates the potential high-density aggregation of microbubbles around these nodes. The range of the microbubble aggregation area is not only directly affected by the microbubble density but may also expand dynamically due to the action of fluid dynamics parameters such as flow velocity and shear stress.

[0110] Enable turbulent simulation in the three-dimensional simulation model of the pipe network, and use the Reynolds-Averaged Navier-Stokes (RANS) method to calculate the turbulent energy dissipation rate:

[0111]

[0112] Among them, ∈ represents the turbulent energy dissipation rate, which represents the attenuation rate of turbulent energy per unit volume; v represents the dynamic viscosity of the fluid, used to quantify the internal friction characteristics of the fluid; a, b, and c respectively represent the velocity components of the fluid in the x, y, and z directions; and respectively represent the gradients of the velocity components a, b, and c in the x, y, and z spatial directions; x, y, and z respectively represent the horizontal axis, the vertical axis, and the depth axis.

[0113] Export the turbulent energy dissipation rate data obtained from the simulation and mark them according to the spatial coordinates of the microbubble aggregation region to ensure that the specific region corresponding to the data can be located in the subsequent steps.

[0114] Using a sparse regression model based on turbulent structure decomposition, decompose the turbulent energy dissipation data into a normal energy dissipation mode and an abnormal energy dissipation mode:

[0115] The normal energy dissipation mode is defined as the reference state of the turbulent energy dissipation in the pipe network without the influence of microbubbles, and the abnormal energy dissipation mode is the additional dissipation caused by the aggregation of microbubbles.

[0116] Use the sparse regression method to establish a dissipation mode decomposition model, and its mathematical expression is: E(x, y, z) = E normal (x, y, z) + E abnormal (x, y, z); where E(x, y, z) is the total energy dissipation distribution function, E normal (x, y, z) represents the energy dissipation distribution function of the normal mode, and E abnormal (x, y, z) represents the energy dissipation distribution function of the abnormal mode.

[0117] Use the least squares method to optimize the parameters of the dissipation mode decomposition model, fit the simulation data into a combination of the normal mode and the abnormal mode, and ensure that the distribution of the abnormal mode is concentrated in the microbubble aggregation region through sparse constraints.

[0118] Through the analysis of the abnormal mode of turbulent energy dissipation, further quantify the local turbulent energy dissipation characteristics of the microbubble aggregation region. The local turbulent energy dissipation characteristics can be quantified by the following formula: Δ∈ local =∫ V E abnormal (x, y, z)dV; where Δ∈ local represents the total amount of local abnormal energy dissipation, V represents the volume of the microbubble aggregation region, and dV is the integral volume element.

[0119] Mark the dissipation characteristic data in the microbubble aggregation region to generate a distribution map corresponding to the spatial coordinates to provide support for subsequent evaluation.

[0120] According to the abnormal energy dissipation pattern in the microbubble aggregation region, calculate the abnormal index of turbulent energy dissipation to quantify the potential damage degree of microbubble aggregation to hydraulic characteristics and energy dissipation:

[0121] The abnormal index of turbulent energy dissipation represents the contribution ratio of the abnormal dissipation caused by microbubble aggregation to the total dissipation, and its expression is: Among them, C abnormal represents the abnormal index of turbulent energy dissipation, and ∫ V E(x, y, z)dV represents the total energy dissipation in the microbubble aggregation region.

[0122] The larger the abnormal index of turbulent energy dissipation, the greater the potential damage degree of microbubble aggregation to hydraulic characteristics and energy dissipation, indicating that the influence of microbubble aggregation on turbulent energy dissipation is more significant, the resulting local turbulence anomaly is stronger, and thus the potential damage degree to hydraulic characteristics is higher. This may lead to an imbalance in the flow velocity distribution and an increase in pressure fluctuations in the pipe network, further affecting the fluid transportation efficiency and system stability.

[0123] Through multi-scale tensor principal component analysis of high-frequency pressure fluctuation data, evaluate the influence degree of microbubble formation and distribution on the asymmetry of pressure fluctuations, specifically including:

[0124] Collect pressure fluctuation data in the microbubble aggregation region and record its time series distribution in real time:

[0125] Install high-frequency pressure sensors in the microbubble aggregation region, and the layout positions need to consider the regions with concentrated pressure fluctuations, such as pipe bends and regions with significant flow velocity changes.

[0126] Collect pressure data in the microbubble aggregation region in real time, record the time series of the pressure value changing with time, and mark the collected data according to the time sequence and spatial position to ensure the consistency of the data and regional positioning.

[0127] Use the low-pass filtering method to remove the high-frequency noise in the pressure fluctuation data, retain the effective pressure fluctuation characteristics, and perform normalization processing on the collected time series data to convert the data at different positions into a comparable unified scale.

[0128] Construct a multi-scale tensor model for the pressure fluctuation data according to the spatial coordinates, time series, and frequency components to characterize the multi-dimensional distribution characteristics of the pressure fluctuations:

[0129] The pressure fluctuation data has three dimensions: spatial coordinates, time series, and frequency components. Construct a third-order tensor model, where each element in the third-order tensor model represents the pressure fluctuation amplitude at a specific spatial position, time point, and frequency component, with the unit of Pascal.

[0130] Convert the time series data into frequency domain data through fast Fourier transform to obtain the pressure fluctuation frequency components at each spatial position.

[0131] Combine the frequency domain data with the spatial coordinates to form a complete multi-scale tensor model T(i, j, k), where i represents the spatial position index, j represents the time index, and k represents the frequency index.

[0132] Use the principal component analysis method to perform dimensionality reduction on the multi-scale tensor and extract the principal component vectors describing the pressure fluctuation characteristics:

[0133] Principal component analysis is used to extract the principal components from the multi-scale tensor that can best describe the pressure fluctuation characteristics, reducing the data dimension while retaining the key features;

[0134] Use the principal component analysis algorithm to calculate the covariance matrix of T(i, j, k) and solve for the eigenvalues and eigenvectors: C = T T ·T, C·V = λ·V; where C represents the covariance matrix; T represents the two-dimensional matrix form after tensor expansion; V represents the eigenvector, dimensionless; λ represents the eigenvalue, reflecting the importance of the eigenvector; T represents the transpose symbol.

[0135] Select several eigenvectors with the largest eigenvalues as the principal component vectors to describe the main characteristics of the pressure fluctuation.

[0136] Analyze the spatial asymmetry characteristics of the pressure fluctuation based on the principal component vectors, and calculate the pressure fluctuation asymmetry index to quantify the influence degree of microbubble formation and distribution on the pressure fluctuation asymmetry:

[0137] Use the principal component vectors to calculate the pressure fluctuation amplitude differences at each spatial position and extract the asymmetry characteristics. Calculate the pressure fluctuation asymmetry index, and its expression is: where I asymmetry represents the pressure fluctuation asymmetry index, P i represents the pressure fluctuation amplitude at the i-th spatial position, P avg represents the average pressure fluctuation amplitude at all positions, and N represents the total number of spatial positions.

[0138] The larger the pressure fluctuation asymmetry index, the greater the influence degree of microbubble formation and distribution on the pressure fluctuation asymmetry, indicating that the microbubble formation and distribution have a more significant impact on the spatial distribution of the pressure fluctuation, and the non-uniformity of the pressure fluctuation is stronger. This shows that microbubbles may cause local pressure anomalies and changes in hydrodynamic characteristics, thus having a greater potential impact on the hydraulic stability of the pipe network.

[0139] Among them, the amplitude of pressure fluctuation is obtained through principal component vector analysis. Specifically, the principal component vector extracts the main change patterns of pressure fluctuation data, and these patterns contain significant features of pressure fluctuation in terms of spatial position. By combining the data in the multi-scale tensor with the principal component vector, the amplitude of pressure fluctuation at each spatial position can be calculated. This amplitude reflects the characteristic magnitude of pressure fluctuation at a specific position.

[0140] Among them, by analyzing the difference between the amplitude of pressure fluctuation at all spatial positions and the overall average amplitude of pressure fluctuation, the asymmetry feature of pressure fluctuation can be extracted. The more significant the difference in the amplitude of pressure fluctuation, the stronger the asymmetry of pressure fluctuation, which can further be used to quantify the influence degree of microbubble formation and distribution on the pressure fluctuation pattern.

[0141] Based on the potential damage degree of microbubble aggregation to hydraulic characteristics and energy dissipation and the influence degree of microbubble formation and distribution on the pressure fluctuation asymmetry, the operating parameters of the pipe network are adjusted in real time, specifically including:

[0142] Normalize the abnormal index of turbulent energy dissipation and the asymmetry index of pressure fluctuation, assign weight coefficients to the normalized abnormal index of turbulent energy dissipation and the asymmetry index of pressure fluctuation respectively, and calculate the microbubble pipe network influence coefficient, whose expression is: I total =w1·C abnormal +w2·I asymmetry ; where, I total represents the microbubble pipe network influence coefficient, w1 and w2 are the weight coefficients of the abnormal index of turbulent energy dissipation and the asymmetry index of pressure fluctuation respectively, and both w1 and w2 are greater than 0.

[0143] Among them, the weight coefficients of the abnormal index of turbulent energy dissipation and the asymmetry index of pressure fluctuation respectively represent their relative importance to the safe operation of the pipe network. The setting of the weight coefficients needs to be combined with specific application scenarios. For example, when the pipe network operation is more sensitive to abnormal energy dissipation, the weight coefficient of the abnormal index of turbulent energy dissipation is higher; while in the scenario sensitive to abnormal pressure fluctuation, the weight coefficient of the asymmetry index of pressure fluctuation needs to be appropriately increased. The sum of the weight coefficients of the abnormal index of turbulent energy dissipation and the asymmetry index of pressure fluctuation is 1, and the specific value is optimized and determined through experimental data or operation experience to ensure the scientific nature of the evaluation and the accuracy of the adjustment.

[0144] The larger the microbubble pipe network influence coefficient, the stronger the comprehensive influence degree of microbubbles on the pipe network, including the intensification of the combined action on turbulent energy dissipation and pressure fluctuation asymmetry, which indicates that the pipe network operation may be disturbed by greater pressure fluctuation and hydraulic characteristic changes, and it is necessary to adjust the operation parameters in time to ensure the stability and safety of the system.

[0145] Set the influence threshold of the microbubble pipe network, and compare the microbubble pipe network influence coefficient with the microbubble pipe network influence threshold:

[0146] When the microbubble pipe network influence coefficient is greater than the microbubble pipe network influence threshold, it is determined that an operating parameter adjustment signal is generated.

[0147] Based on the operating parameter adjustment signal and the microbubble pipe network influence coefficient, adjust the operating parameters of the pipe network, specifically including:

[0148] Adjust the flow rate: If the microbubble pipe network influence coefficient is too large, reduce the flow rate in the key area of the pipe network to reduce turbulent energy dissipation and pressure fluctuation asymmetry;

[0149] Optimize the pressure: Adjust the operating pressure in the relevant area of the pipe network to avoid further bubble aggregation caused by too high local pressure;

[0150] Regional shunt regulation: Through the pipe network shunt control device, guide part of the fluid to the low-influence area to reduce the load in the high-risk area;

[0151] Suppress microbubble aggregation: Add antifoaming agent or implement other microbubble suppression measures;

[0152] Real-time monitoring: After the parameters are adjusted, continuously collect data to re-evaluate the microbubble pipe network influence coefficient to ensure the effectiveness of the adjustment.

[0153] When the microbubble pipe network influence coefficient is less than or equal to the microbubble pipe network influence threshold, it is determined that an operating parameter adjustment signal is not required.

[0154] Among them, the microbubble pipe network influence threshold is a comprehensive reference value set according to the pipe network operation safety standard and historical data analysis, and is used to measure whether the comprehensive influence degree of microbubbles on the pipe network exceeds the allowable range.

[0155] Embodiment 2

[0156] The difference between Embodiment 2 and Embodiment 1 of the present invention is that this embodiment introduces an intelligent analysis system for external water infiltration based on pipe network microorganisms.

[0157] Figure 2 The structural schematic diagram of an intelligent analysis system for external water infiltration based on pipe network microorganisms of the present invention is given. The external water infiltration intelligent analysis system includes: a bubble density detection module, a metabolic gas production prediction module, an influence signal generation module, an energy dissipation evaluation module, a pressure fluctuation analysis module, and a pipe network parameter adjustment module.

[0158] Bubble density detection module: When it is detected that there is external water infiltration in the pipe network, obtain the density of microbubbles in the pipe network and determine whether it exceeds the preset microbubble density threshold.

[0159] Metabolic gas production prediction module: When the density of microbubbles in the pipe network does not exceed the preset microbubble density threshold, a microbial metabolic gas production model is established based on microbial detection data and hydrodynamic parameters to determine whether the possibility of a sharp increase in the microbubble density in the pipe network is too high.

[0160] Influence signal generation module: Based on the judgment results of whether the density of microbubbles in the pipe network exceeds the preset microbubble density threshold and whether the possibility of a sharp increase in the microbubble density in the pipe network is too high, it is determined whether to generate a microbubble influence analysis signal.

[0161] Energy dissipation assessment module: When a microbubble influence analysis signal is generated, the potential damage degree of microbubble aggregation to hydraulic characteristics and energy dissipation is evaluated through the structural analysis of the abnormal mode of turbulent energy dissipation.

[0162] Pressure fluctuation analysis module: When a microbubble influence analysis signal is generated, the influence degree of microbubble formation and distribution on the asymmetry of pressure fluctuation is evaluated through the multi-scale tensor principal component analysis of high-frequency pressure fluctuation data.

[0163] Pipe network parameter adjustment module: Based on the potential damage degree of microbubble aggregation to hydraulic characteristics and energy dissipation and the influence degree of microbubble formation and distribution on the asymmetry of pressure fluctuation, the operating parameters of the pipe network are adjusted.

[0164] The above formulas are all dimensionless and take their numerical calculations. The formula is a formula obtained by collecting a large amount of data for software simulation to approximate the real situation. The preset parameters and threshold selection in the formula are set by technicians in the field according to the actual situation.

[0165] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0166] Those of ordinary skill in the art will realize that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0167] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0168] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings, direct couplings, or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0169] The module described as a separation component may or may not be physically separated. The component shown as a module may or may not be a physical module. It may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0170] In addition, in each embodiment of the present application, each functional module may be integrated in a processing module, may exist separately as individual physical modules, or two or more modules may be integrated in one module.

[0171] If the function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or a part of this 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 for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0172] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0173] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent analysis method for external water infiltration based on pipe network microorganisms, characterized in that: The following steps are involved: When external water infiltration is detected in the pipe network, the density of microbubbles in the pipe network is obtained, and it is determined whether it exceeds a preset microbubble density threshold; When the density of microbubbles in the pipe network does not exceed the preset microbubble density threshold, a microbial metabolic gas production model is established through microbial detection data and fluid dynamics parameters to determine whether the possibility of a surge in microbubble density in the pipe network is too high; Based on the judgment result of whether the density of microbubbles in the pipe network exceeds a preset microbubble density threshold and whether the possibility of a surge in the density of microbubbles in the pipe network is too great, judging whether to generate a microbubble impact analysis signal; When the microbubble impact analysis signal is generated, the potential damage of microbubble aggregation to hydraulic characteristics and energy dissipation is evaluated through structural analysis of the abnormal pattern of turbulent energy dissipation; When generating microbubble impact analysis signals, the influence of microbubble formation and distribution on the asymmetry of pressure fluctuations is evaluated by multi-scale tensor principal component analysis of high-frequency pressure fluctuation data; Based on the potential damage of microbubble aggregation to hydraulic characteristics and energy dissipation and the influence of microbubble formation and distribution on the asymmetry of pressure fluctuations, the operating parameters of the pipeline network are adjusted.

2. According to claim 1, the method for intelligent analysis of external water infiltration based on pipe network microorganisms is characterized in that: When external water infiltration is detected in the pipe network, the density of microbubbles in the pipe network is obtained, and it is determined whether it exceeds the preset microbubble density threshold, including: Install microbubble detection sensors at key nodes of the pipe network to collect microbubble concentration data in the water; Verify the collected microbubble concentration data, remove noise and invalid data, and generate a valid microbubble density value; The effective microbubble density value is compared with the preset microbubble density threshold to determine whether the density of microbubbles in the pipe network is normal.

3. The method for intelligent analysis of external water infiltration based on pipe network microorganisms according to claim 2 is characterized in that: When the density of microbubbles in the pipe network does not exceed the preset microbubble density threshold, a microbial metabolic gas production model is established based on microbial detection data and fluid dynamics parameters to determine whether the possibility of a surge in microbubble density in the pipe network is too high, including: Analyze microbial metabolic characteristics based on microbial detection data, and extract key factors affecting gas production in combination with fluid dynamics parameters; Dynamic reaction kinetics modeling was used to establish a microbial metabolic gas production model and correlate the gas generation rate with environmental conditions; The parameters of the microbial metabolic gas production model were calibrated according to the microbial growth rate, gas production rate and mass transfer effect; The microbial metabolism gas production model is used to calculate the change in microbubble density in the future period and determine whether the possibility of a surge in microbubble density in the pipe network is too high; Among them, microbial detection data include microbial types, concentrations and their distribution characteristics; fluid dynamics parameters include flow rate, pressure and shear stress distribution.

4. The method for intelligent analysis of external water infiltration based on pipe network microorganisms according to claim 3 is characterized in that: Based on the judgment result of whether the density of microbubbles in the pipe network exceeds the preset microbubble density threshold and whether the possibility of a surge in the density of microbubbles in the pipe network is too great, it is judged whether to generate a microbubble impact analysis signal, specifically including: When any one of the conditions that the density of microbubbles in the pipe network exceeds a preset microbubble density threshold and the possibility of a surge in the density of microbubbles in the pipe network is too great is met, a microbubble impact analysis signal is generated.

5. The method for intelligent analysis of external water infiltration based on pipe network microorganisms according to claim 4 is characterized in that: Through structural analysis of abnormal patterns of turbulent energy dissipation, the potential damage of microbubble aggregation to hydraulic characteristics and energy dissipation is evaluated, including: The turbulent energy dissipation data of the microbubble aggregation area is obtained through computational fluid dynamics simulation; The turbulent energy dissipation data are decomposed into normal energy dissipation mode and abnormal energy dissipation mode using a sparse regression model based on turbulent structure decomposition. By analyzing the abnormal patterns of turbulent energy dissipation, the local turbulent energy dissipation characteristics in the microbubble aggregation area are quantified; According to the abnormal energy dissipation pattern in the microbubble aggregation area, the turbulent energy dissipation anomaly index is calculated to quantify the potential damage of microbubble aggregation to hydraulic characteristics and energy dissipation.

6. The method for intelligent analysis of external water infiltration based on pipe network microorganisms according to claim 5 is characterized in that: The multi-scale tensor principal component analysis of high-frequency pressure fluctuation data is used to evaluate the influence of microbubble formation and distribution on the asymmetry of pressure fluctuations, including: Collect pressure fluctuation data in the microbubble gathering area and record its time series distribution in real time; A multi-scale tensor model is constructed based on the pressure fluctuation data according to spatial coordinates, time series and frequency components to characterize the multi-dimensional distribution characteristics of pressure fluctuations. The principal component analysis method is used to reduce the dimension of the multi-scale tensor and extract the principal component vector that describes the pressure fluctuation characteristics; The spatial asymmetric characteristics of pressure fluctuations were analyzed based on principal component vectors, and the pressure fluctuation asymmetry index was calculated to quantify the influence of microbubble formation and distribution on the pressure fluctuation asymmetry.

7. The method for intelligent analysis of external water infiltration based on pipe network microorganisms according to claim 6 is characterized in that: Based on the potential damage of microbubble aggregation to hydraulic characteristics and energy dissipation and the influence of microbubble formation and distribution on pressure fluctuation asymmetry, the operation parameters of the pipeline network are adjusted in real time, including: The turbulence energy dissipation anomaly index and the pressure fluctuation asymmetry index are normalized, and the normalized turbulence energy dissipation anomaly index and the pressure fluctuation asymmetry index are respectively assigned weight coefficients to calculate the microbubble pipe network influence coefficient; Set the microbubble network influence threshold and compare the microbubble network influence coefficient with the microbubble network influence threshold: When the microbubble network influence coefficient is greater than the microbubble network influence threshold, it is determined that an adjustment signal for the network operation parameters is generated; when the microbubble network influence coefficient is less than or equal to the microbubble network influence threshold, it is determined that a signal that the network operation parameters do not need to be adjusted is generated; Based on the adjustment signal of the pipeline network operating parameters and the microbubble network influence coefficient, the operating parameters of the pipeline network are adjusted, including adjusting the flow rate, optimizing the pressure, regional diversion regulation, inhibiting the aggregation of microbubbles and real-time monitoring.

8. An intelligent analysis system for external water infiltration based on pipe network microorganisms, used to implement an intelligent analysis method for external water infiltration based on pipe network microorganisms according to any one of claims 1 to 7, characterized in that: It includes bubble density detection module, metabolic gas production prediction module, impact signal generation module, energy dissipation assessment module, pressure fluctuation analysis module and pipe network parameter adjustment module; Bubble density detection module: when external water infiltration is detected in the pipe network, the density of microbubbles in the pipe network is obtained, and it is determined whether it exceeds the preset microbubble density threshold; Metabolic gas production prediction module: When the density of microbubbles in the pipe network does not exceed the preset microbubble density threshold, a microbial metabolic gas production model is established through microbial detection data and fluid dynamics parameters to determine whether the possibility of a surge in microbubble density in the pipe network is too high; Impact signal generation module: determines whether to generate a microbubble impact analysis signal based on the judgment result of whether the density of microbubbles in the pipe network exceeds a preset microbubble density threshold and whether the possibility of a surge in the density of microbubbles in the pipe network is too great; Energy dissipation assessment module: When the microbubble impact analysis signal is generated, the potential damage of microbubble aggregation to hydraulic characteristics and energy dissipation is evaluated through structural analysis of the abnormal pattern of turbulent energy dissipation; Pressure Fluctuation Analysis Module: When generating microbubble impact analysis signals, the multi-scale tensor principal component analysis of high-frequency pressure fluctuation data is used to evaluate the impact of microbubble formation and distribution on the asymmetry of pressure fluctuations. Pipeline network parameter adjustment module: Based on the potential damage of microbubble aggregation to hydraulic characteristics and energy dissipation and the influence of microbubble formation and distribution on the asymmetry of pressure fluctuations, the operating parameters of the pipeline network are adjusted.

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