A tap water quality pre-detection system

Abstract

The present invention discloses a pre-detection system for tap water quality, belonging to the technical field of water quality detection, which solves the problem that during the detection process, due to the failure to fully discharge stagnant water before sampling, the sample cannot represent daily water use, and ultimately leads to inaccurate water quality safety assessment results. It includes a pipe network basic data acquisition module, a dynamic hydraulic characteristic analysis module, a stagnant water area quantification and correction module, a long-term pollutant enrichment sampling module, a detection data analysis module, and a model construction and optimization module; the pipe network basic data acquisition module is used to obtain the topological structure of the pipe network through high-precision surveying and mapping. The present invention improves the accuracy, stability and traceability ability of water quality detection, constructs a digital twin model of the pipe network, dynamically analyzes the water flow state, quantifies the proportion of the volume of the dead water area, corrects the model parameters by using a variety of technologies, integrates the physical information neural network, realizes high-precision water quality assessment and long-term stable monitoring, and there is no need to drain the water quality of the community water tank, etc. for detection.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality detection, and particularly to a pre-detection system for tap water quality. Background Art

[0002] The tap water pipe network is an important part of the urban water supply system, mainly responsible for delivering the treated tap water to thousands of households and various water-using units. It usually consists of a series of pipes of different sizes, pumping stations, valves, etc. The water coming out of the water plant first enters the large-scale water transmission main pipe, and then through the branch pipe networks at all levels, it gradually spreads to all corners of the city. To ensure stable water supply, the pipe network is equipped with booster pumping stations, water towers and other facilities. At the same time, the setting of valves facilitates the maintenance and emergency repair of the pipes. The normal operation of the tap water pipe network plays a crucial role in the economic development, social stability and people's quality of life of the city, and needs to be regularly detected and maintained to ensure its safe and stable water supply.

[0003] The water quality detection of the tap water pipe network is an important link to ensure water supply safety. The detection mainly targets key indicators such as microorganisms, residual chlorine, turbidity, heavy metals, etc. Sampling is usually carried out at positions such as the outlet of the water plant, the middle of the pipe network and the end of the pipe network to reflect the water quality conditions of different links. The frequency of regular detection is determined according to the water supply scale and risk assessment. Generally, large cities will detect daily or weekly. Modern detection mostly uses advanced instruments, such as portable water quality analyzers, etc., which can quickly and accurately obtain data. Once problems are found, relevant departments will quickly take measures, such as flushing the pipe network, adjusting the water plant process, etc., to ensure that the water supply quality meets the standards and guarantee the safety of residents' water use.

[0004] In some communities, due to the inability to achieve direct tap water supply, secondary water supply facilities for community tap water, that is, secondary water supply points such as community water tanks, are often set up in the community. Since the tap water is not directly supplied to households from the water plant and will be temporarily stored in secondary water supply points such as community water tanks for a period of time, it needs to be regularly detected. However, during the detection process, due to the failure to fully discharge the stagnant water before sampling, the sample cannot represent the daily water use, and finally the water quality safety assessment result is inaccurate.

[0005] Therefore, a pre-detection system for tap water quality is proposed to solve or alleviate the above problems. Summary of the Invention

[0006] The purpose of the present invention is to solve the shortcomings existing in the prior art, and to propose a pre-detection system for tap water quality.

[0007] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0008] A tap water quality pre-detection system, comprising a pipe network basic data acquisition module, a dynamic hydraulic characteristic analysis module, a dead water area quantification and correction module, a pollutant long-term enrichment sampling module, a detection data analysis module, and a model construction and optimization module;

[0009] The pipe network basic data acquisition module is used to obtain the topological structure, node three-dimensional coordinates and pipe physical parameters of the pipe network through high-precision surveying and mapping, and construct a digital twin model including resistance characteristics based on pipe network modeling software;

[0010] The dynamic hydraulic characteristic analysis module is connected to the pipe network basic data acquisition module and is used to monitor the flow data of key nodes in the pipe network in real time, calculate the water flow velocity and Reynolds number to determine the water flow state, and analyze its influence on the mixing ratio of dead water and live water;

[0011] The dead water area quantification and correction module is linked with the dynamic hydraulic characteristic analysis module. By injecting a tracer into the pipe network and collecting water samples at multiple time periods, the tracer decay curve is fitted to determine the proportion of the dead water area volume and correct the mixing model parameters;

[0012] The pollutant long-term enrichment sampling module is deployed at the end of the pipe network and is used to adsorb heavy metals and organic substances by using a diffusive gradients in thin films device and a solid-phase microextraction fiber respectively, and obtain the time-weighted average concentration of pollutants by analyzing the adsorption amount;

[0013] The detection data analysis module receives and integrates the pipe network topology data, hydraulic characteristic data, tracer data and passive sampling data, performs normalization processing and then inputs them into a physics-informed neural network, and combines the convection-diffusion equation constraint for model training;

[0014] The model construction and optimization module interacts with the detection data analysis module and uses the Bayesian optimization algorithm to dynamically adjust the dead water mixing coefficient, minimize the error between the model prediction value and the laboratory test result, generate a three-dimensional pipe network pollutant distribution map, and intuitively display the concentration and migration law of each node.

[0015] Preferably, the pipe network basic data acquisition module is used to obtain the topological structure, node three-dimensional coordinates and pipe physical parameters of the pipe network through high-precision surveying and mapping, and construct a digital twin model including resistance characteristics based on pipe network modeling software, specifically including the following steps:

[0016] Perform three-dimensional coordinate measurement on the pipe network nodes, record the spatial positions including the starting point, ending point, elbow, tee and valve of the pipe, and collect the material type, nominal diameter and wall thickness parameters of the pipe;

[0017] Based on surveying and mapping data, a digital twin model is constructed using pipeline network modeling software. By inputting the lengths, elevation differences, and local resistance coefficients of each pipe segment, a three-dimensional pipeline network topology diagram including pipeline layouts and resistance characteristics is generated. The local resistance coefficients of pipe fittings are dynamically adjusted according to the actual types to accurately simulate head losses.

[0018] Preferably, the dynamic hydraulic characteristic analysis module is connected to the pipeline network basic data acquisition module and is used to monitor the flow data of key nodes in the pipeline network in real time, calculate the water flow velocity and Reynolds number to determine the water flow state, and analyze its influence on the mixing ratio of stagnant water and flowing water. The specific steps are as follows:

[0019] Install non-intrusive ultrasonic flow meters at key nodes in the pipeline network, continuously monitor the flow data for 72 hours, and generate a minute-level dynamic flow change curve;

[0020] Calculate the average flow velocity of each pipe segment based on the flow data, combine the pipe diameter, fluid density, and dynamic viscosity parameters, and distinguish between laminar flow and turbulent flow states through Reynolds number calculation and mark the high-risk areas of stagnant water.

[0021] Preferably, the stagnant water area quantification and correction module is linked with the dynamic hydraulic characteristic analysis module. By injecting a tracer into the pipeline network and collecting water samples at multiple time periods, fitting the tracer decay curve to determine the proportion of the volume of the stagnant water area, and correcting the parameters of the mixing model. The specific steps are as follows:

[0022] Inject a fluorescent tracer into the pipeline network water tank. At multiple preset time periods after injection, collect the un-discharged water samples at the end and intermediate nodes of the pipeline network, and detect the change of the tracer concentration over time;

[0023] Fit the proportion of the volume of the stagnant water area and the mixing rate constant through the tracer concentration decay curve, combine the total volume parameter of the pipeline network, correct the volume proportion of the stagnant water area in the dynamic mixing model, and optimize the inversion accuracy of the flowing water pollutant concentration.

[0024] Preferably, the long-term pollutant enrichment sampling module is deployed at the end of the pipeline network and is used to adsorb heavy metals and organic matters using a diffusive gradients in thin films device and a solid-phase microextraction fiber respectively, and obtain the time-weighted average concentration of pollutants by analyzing the adsorption amount. The specific steps are as follows:

[0025] Fix the diffusive gradients in thin films device in the straight pipe section at the end of the pipeline network, continuously expose it for 7 days to adsorb heavy metal ions, analyze the resin membrane with nitric acid solution and detect the adsorption amount, and calculate the time-weighted average concentration of heavy metals in the water body;

[0026] Deploy a solid-phase microextraction fiber at the user-end faucet, make it contact the water flow for 30 minutes at the normal household flow rate, adsorb organic matters, and then analyze by thermal desorption and gas chromatography-mass spectrometry to estimate the long-term average concentration of aqueous organic matters.

[0027] Preferably, the detection data analysis module receives and integrates pipe network topology data, hydraulic characteristic data, tracer data and passive sampling data, performs normalization processing, and then inputs them into a physics-informed neural network. Combining with the constraints of the convection-diffusion equation, model training is carried out, which specifically includes the following steps:

[0028] Integrate pipe network topology data, dynamic flow data, tracer data and passive sampling data, and perform normalization processing to unify the data format;

[0029] Input the processed data into a physics-informed neural network. Combining with the convection, diffusion and adsorption laws of pollutants in the pipe network, train the model through physical equation constraints to predict the pollutant concentration distribution at each node of the pipe network.

[0030] Preferably, the model construction and optimization module interacts with the detection data analysis module and uses the Bayesian optimization algorithm to dynamically adjust the dead water mixing coefficient, minimize the error between the model prediction value and the laboratory test result, and generate a three-dimensional pipe network pollutant distribution map to intuitively display the concentration and migration law of each node, which specifically includes the following steps:

[0031] Use the Bayesian optimization algorithm to dynamically adjust the dead water mixing coefficient, and optimize the parameter configuration of the mixing model by iteratively minimizing the error between the model prediction value and the laboratory standard test result;

[0032] Based on the optimized model, combine the pipe network hydraulic conditions and pollutant migration characteristics to generate a three-dimensional pollutant distribution map to intuitively display the concentration gradient and pollutant diffusion path of each node in the pipe network.

[0033] The present invention has the following beneficial effects:

[0034] The present invention can significantly improve the accuracy of water quality detection, the long-term monitoring stability and the pollution source tracing ability, construct a digital twin model of the pipe network, accurately restore the pipe layout, resistance characteristics and three-dimensional spatial relationship, provide a reliable structural basis for dynamic analysis, monitor the flow data in real time through an ultrasonic flowmeter and calculate the Reynolds number, dynamically identify the water flow state, quantify the proportion of the dead water area volume in combination with the tracer decay experiment, correct the parameters of the mixing model, and eliminate the underestimation or overestimation errors of pollutant concentration caused by water flow stagnation. The DGT and SPME passive sampling technologies capture the time-weighted average concentration of heavy metals and organic substances through long-term adsorption, avoid the influence of instantaneous sampling by water flow fluctuations and uneven pollutant distribution, and truly reflect the long-term trend of water quality. The physical information neural network integrates the pipe network topology, hydraulic parameters and pollutant migration laws, dynamically adjusts the dead water mixing coefficient by using Bayesian optimization, minimizes the deviation between the model prediction value and the laboratory test, generates a three-dimensional pollutant heat map that can accurately locate pollution hot spots, and predicts the pollution development trend, realizes the full-chain linkage from the pipe network structure analysis to the inversion of the spatio-temporal distribution of pollutants, and finally achieves high-precision water quality assessment and long-term stable monitoring, so as to realize the detection of tap water quality without draining water from secondary water supply points such as community water tanks. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0036] Figure 1 It is a schematic structural diagram of the present invention.

[0037] In the figure: 1, pipe network basic data acquisition module; 2, dynamic hydraulic characteristic analysis module; 3, dead water area quantification and correction module; 4, long-term pollutant enrichment sampling module; 5, detection data analysis module; 6, model construction and optimization module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0039] Accordingly, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0040] It should be noted that like reference numerals and letters denote like items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0041] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, or the orientation or positional relationships in which the inventive product is customarily placed during use, or the orientation or positional relationships commonly understood by those skilled in the art. These are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation on the present invention.

[0042] In addition, the terms "first", "second", "third", etc. are only used for descriptive distinction and should not be construed as indicating or implying relative importance.

[0043] In the description of the present invention, it should also be noted that unless otherwise clearly specified and limited, the terms "set", "install", "connect", "couple" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0044] A tap water quality pre-detection system, as Figure 1 shown, includes a pipe network basic data acquisition module 1, a dynamic hydraulic characteristic analysis module 2, a stagnant water area quantification and correction module 3, a pollutant long-term enrichment sampling module 4, a detection data analysis module 5, and a model construction and optimization module 6;

[0045] Among them, the pipe network basic data acquisition module 1 is used to obtain the topological structure, node three-dimensional coordinates and pipe physical parameters of the pipe network through high-precision surveying and mapping, and construct a digital twin model including resistance characteristics based on pipe network modeling software;

[0046] Specifically: Measure the three-dimensional coordinates of the pipe network nodes, record the spatial positions including the starting point, ending point, elbows, tees, and valves of the pipes, and collect the material type, nominal diameter, and wall thickness parameters of the pipes;

[0047] Based on the surveying and mapping data, use pipe network modeling software to construct a digital twin model, input the lengths, elevation differences, and local resistance coefficients of each pipe section, and generate a three-dimensional pipe network topology diagram including the pipe layout and resistance characteristics. The local resistance coefficients of the pipe fittings are dynamically adjusted according to the actual types to accurately simulate the head loss;

[0048] Among them, the dynamic hydraulic characteristic analysis module 2 is connected to the pipe network basic data collection module 1 and is used to monitor the flow data of the key nodes of the pipe network in real time, calculate the water flow velocity and Reynolds number to determine the water flow state, and analyze its influence on the mixing ratio of stagnant water and flowing water;

[0049] Specifically: Install non-intrusive ultrasonic flowmeters at the key nodes of the pipe network, continuously monitor the flow data for 72 hours and generate a minute-level dynamic flow change curve;

[0050] Calculate the average flow velocity of each pipe section according to the flow data, combine the pipe diameter, fluid density, and dynamic viscosity parameters, and distinguish between laminar flow and turbulent flow states through Reynolds number calculation and mark the high-risk areas of stagnant water.

[0051] Among them, the stagnant water area quantification and correction module 3 is linked with the dynamic hydraulic characteristic analysis module 2. By injecting tracers into the pipe network and collecting water samples at multiple time intervals, fit the tracer decay curve to determine the proportion of the stagnant water area volume and correct the parameters of the mixing model;

[0052] Specifically:

[0053] Inject fluorescent tracers into the pipe network water tank. At multiple preset time intervals after injection, collect the non-discharged water samples at the end and intermediate nodes of the pipe network, and detect the change of tracer concentration over time;

[0054] Fit the proportion of the stagnant water area volume and the mixing rate constant through the tracer concentration decay curve, combine the total volume parameter of the pipe network, correct the volume proportion of the stagnant water area in the dynamic mixing model, and optimize the inversion accuracy of the flowing water pollutant concentration.

[0055] Among them, the long-term pollutant enrichment sampling module 4 is deployed at the end of the pipe network and is used to adsorb heavy metals and organic compounds by using diffusion gradient thin film devices and solid-phase microextraction fibers respectively, and obtain the time-weighted average concentration of pollutants by analyzing the adsorption amount;

[0056] Specifically:

[0057] Fix the diffusive gradient thin-film device at the straight pipe section at the end of the pipe network, continuously expose it for 7 days to adsorb heavy metal ions, desorb the resin film with nitric acid solution and detect the adsorption amount, and calculate the time-weighted average concentration of heavy metals in the water body;

[0058] Deploy a solid-phase microextraction fiber at the user-end faucet, make it contact the water flow for 30 minutes at the normal household flow rate, analyze it by thermal desorption and gas chromatography-mass spectrometry after adsorbing organic matter, and estimate the long-term average concentration of aqueous organic matter.

[0059] Among them, the detection data analysis module 5 receives and integrates the pipe network topology data, hydraulic characteristic data, tracer data and passive sampling data, performs normalization processing and then inputs them into the physics-informed neural network, and combines the convection-diffusion equation constraints for model training;

[0060] Specifically:

[0061] Integrate the pipe network topology data, dynamic flow data, tracer data and passive sampling data, and perform normalization processing to unify the data format;

[0062] Input the processed data into the physics-informed neural network, combine the convection, diffusion and adsorption laws of pollutants in the pipe network, and train the model through physical equation constraints to predict the pollutant concentration distribution at each node of the pipe network.

[0063] Among them, the model construction and optimization module 6 interacts with the detection data analysis module 5 and uses the Bayesian optimization algorithm to dynamically adjust the dead water mixing coefficient, minimize the error between the model prediction value and the laboratory test result, generate a three-dimensional pipe network pollutant distribution map, and intuitively display the concentration and migration law of each node.

[0064] Specifically:

[0065] Use the Bayesian optimization algorithm to dynamically adjust the dead water mixing coefficient, minimize the error between the model prediction value and the laboratory standard test result through iteration, and optimize the parameter configuration of the mixing model;

[0066] Based on the optimized model, combine the pipe network hydraulic conditions and pollutant migration characteristics to generate a three-dimensional pollutant distribution map, and intuitively display the concentration gradient of each node and the pollutant diffusion path in the pipe network.

[0067] When the system is working, first, a RTK surveying instrument is used to measure the three-dimensional coordinates of the nodes in the pipe network with centimeter-level accuracy, including the spatial coordinates of key positions such as the starting point, ending point, elbows, tees, and valves of the pipeline. At the same time, the material type, nominal diameter, and wall thickness parameters of the pipeline are recorded. These data are input through pipe network modeling software, such as EPANET or WaterGEMS, and combined with the pipe segment length, elevation difference, and local resistance coefficients. For example, the valve resistance coefficient is set to 16 - 20, and the elbow resistance coefficient is set to 0.75, to construct a digital twin model that includes the pipeline layout, resistance characteristics, and hydraulic parameters, providing a basic framework for subsequent analysis.

[0068] Subsequently, the dynamic hydraulic characteristics analysis module 2 deploys non-intrusive ultrasonic flowmeters at key nodes in the pipe network, specifically at positions such as the main water supply pipe and the user inlet pipe. The flow velocity is monitored in real time through the time-difference method principle, and the instantaneous flow data within 72 hours is continuously recorded to generate a water consumption change curve. Based on the flow data, the average flow velocity and Reynolds number are calculated to determine the flow state as laminar flow, turbulent flow, or transitional flow, and the mixing ratio of stagnant water and flowing water under different flow states is analyzed. For example, in the turbulent flow state, the violent mixing of the water flow leads to a faster diffusion of pollutants in the dead water area, while in the laminar flow state, the pollutants in the dead water area have a longer residence time. These analysis results provide support for the dynamic hydraulic conditions of the subsequent model.

[0069] Next, the stagnant water area quantification and calibration module 3 precisely calibrates the stagnant water areas in the pipe network through tracer experiments. The system injects rhodamine WT fluorescent tracer into the secondary water supply tank, and the initial concentration is set to 1 ppb. Subsequently, at multiple preset time periods after injection, such as 0 minutes, 10 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, 8 hours, 12 hours, 24 hours, etc., water samples that have not been discharged are collected at the end and intermediate nodes of the pipe network. The change in tracer concentration is detected using a fluorescence spectrophotometer. By fitting the concentration decay curve and combining with the exponential decay model, the volume ratio of the dead water area and the mixing rate constant are calculated. If the tracer concentration drops to 30% of the initial value within 24 hours, it indicates that the volume of the dead water area accounts for 40% of the total volume of the pipe network. These parameters are absorbed by the dynamic mixing model to correct the back-calculation algorithm for the concentration of pollutants in flowing water, ensuring that the subsequent detection results are closer to the true values.

[0070] Meanwhile, the long-term pollutant enrichment sampling module 4 installs a diffusive gradients in thin films device at the straight pipe section at the end of the pipe network. The Chelex-100 resin membrane inside adsorbs heavy metal ions in the water body through Fick's diffusion law. After the diffusive gradients in thin films device is continuously exposed for 7 days, the Chelex-100 resin membrane is taken out, and the adsorbed heavy metals are desorbed with nitric acid solution. The metal concentration in the desorbed solution is detected by inductively coupled plasma mass spectrometry, and the time-weighted average concentration of heavy metals in the water body is calculated by combining the exposure time and diffusion coefficient.

[0071] At the user-side faucet, the solid-phase microextraction fiber adsorbs organic matter with a polydimethylsiloxane coating. The solid-phase microextraction fiber contacts the water flow for 30 minutes at a household water flow rate of 0.5 - 1 L / min, and then the adsorbed organic matter is released into the gas chromatography-mass spectrometry analyzer for analysis by thermal desorption. The long-term average concentration of aqueous organic matter is deduced based on the partition coefficient, effectively avoiding the contingency of instantaneous sampling.

[0072] The detection data analysis module 5 receives the raw data from each module, including the pipe network topology, dynamic flow monitoring results, tracer decay curves, and the detection values of DGT and SPME. It normalizes these multi-source data. Specifically, it converts the flow data into a unified time series format and standardizes the concentration data to milligrams per liter. The processed data is input into a physics-informed neural network. The network architecture includes 3 - 4 hidden layers, with 64 - 128 neurons in each layer. The input parameters cover the pipe network node coordinates, time, flow rate, and pollutant types, and the output is the predicted pollutant concentration at each node. During the training process, the neural network not only minimizes the error between the predicted value and the measured data but also embeds the physical constraints of the convection-diffusion equation, such as the relationship between the rate of pollutant migration with the water flow and the diffusion intensity, ensuring that the model conforms to both the data pattern and physical laws.

[0073] The model construction and optimization module 6 further dynamically adjusts the dead water mixing coefficient through the Bayesian optimization algorithm. For example, the mixing coefficient is initially set to 0.5, and by iteratively calculating the root mean square error between the model predicted value and the laboratory standard test result, the coefficient is gradually optimized to 0.35, making the model more suitable for the actual pipe network conditions.

[0074] Finally, the system generates a three-dimensional pipe network pollutant distribution map, visually showing the concentration gradients of heavy metals and organic matter at each node in the form of a heat map, and marking the main diffusion paths of pollutants, such as accumulation towards the end along the water supply main road, and predicting the pollution trend in combination with historical data.

[0075] The pipe network model of the basic data acquisition module provides structural support for hydraulic analysis. The flow data of the hydraulic characteristic analysis module drives the calibration process of the tracer experiment. The calibrated dead water parameters optimize the interpretation accuracy of passive sampling data. The detection data feeds back to optimize the model after neural network training, ensuring that the detection results truly reflect the daily water quality and fundamentally improving the reliability of the secondary water supply safety assessment.

[0076] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A tap water quality pre-detection system, characterized in that, It includes a pipeline network basic data acquisition module (1), a dynamic hydraulic characteristic analysis module (2), a dead water area quantification and correction module (3), a long-term pollutant enrichment sampling module (4), a detection data analysis module (5), and a model construction and optimization module (6); The pipeline network basic data acquisition module (1) is used to obtain the topological structure, node three-dimensional coordinates and pipeline physical parameters of the pipeline network through high-precision surveying, and build a digital twin model including resistance characteristics based on pipeline network modeling software; The dynamic hydraulic characteristic analysis module (2) is connected to the pipeline network basic data acquisition module (1) and is used to monitor the flow data of key nodes of the pipeline network in real time, calculate the water flow velocity and Reynolds number to determine the water flow state, and analyze its influence on the mixing ratio of dead water and live water. Install non-intrusive ultrasonic flow meters at key nodes of the pipeline network, continuously monitor the flow data for a certain period of time and generate a minute-level dynamic flow change curve; calculate the average flow velocity of each pipe section according to the flow data, combine the pipe diameter, fluid density and dynamic viscosity parameters, and distinguish laminar flow and turbulent flow states through Reynolds number calculation and mark high-risk dead water areas; The dead water area quantification and correction module (3) is linked with the dynamic hydraulic characteristic analysis module (2). By injecting a tracer into the pipeline network and collecting water samples at multiple time periods, fit the tracer decay curve to determine the proportion of the dead water area volume and correct the mixed model parameters; inject a fluorescent tracer into the pipeline network water tank, and collect non-discharged water samples at the end and intermediate nodes of the pipeline network at multiple preset time periods after injection to detect the change of tracer concentration over time; fit the proportion of the dead water area volume and the mixing rate constant through the tracer concentration decay curve, and combine the pipeline network total volume parameter to correct the volume ratio of the dead water area in the dynamic mixing model and optimize the inversion accuracy of the live water pollutant concentration; The long-term pollutant enrichment sampling module (4) is deployed at the end of the pipeline network and is used to adsorb heavy metals and organic matter by using a diffusive gradients in thin films device and a solid-phase microextraction fiber respectively, and obtain the time-weighted average concentration of pollutants by analyzing the adsorption amount; The detection data analysis module (5) receives and integrates the pipeline network topology data, hydraulic characteristic data, tracer data and passive sampling data, performs normalization processing and then inputs it into a physical information neural network, and conducts model training in combination with the convection-diffusion equation constraint; The model construction and optimization module (6) interacts with the detection data analysis module (5) and uses the Bayesian optimization algorithm to dynamically adjust the dead water mixing coefficient, minimize the error between the model prediction value and the laboratory test result, generate a three-dimensional pipeline network pollutant distribution map, and intuitively display the concentration and migration law of each node; 2. The pre-detection system for tap water quality according to claim 1, wherein The pipeline network basic data acquisition module (1) is used to obtain the topological structure, node three-dimensional coordinates and pipeline physical parameters of the pipeline network through high-precision surveying, and build a digital twin model including resistance characteristics based on pipeline network modeling software. The specific steps are as follows: Measure the three-dimensional coordinates of the pipeline network nodes, record the spatial positions including the starting point, ending point, elbow, tee and valve of the pipeline, and collect the material type, nominal diameter and wall thickness parameters of the pipeline; Based on surveying and mapping data, a digital twin model is constructed using pipeline network modeling software. By inputting the lengths, elevation differences, and local resistance coefficients of each pipe segment, a three-dimensional pipeline network topology map including pipeline layout and resistance characteristics is generated. The local resistance coefficients of pipe fittings are dynamically adjusted according to the actual type to accurately simulate the head loss.

3. The water quality pre-detection system for tap water according to claim 1, characterized in that, The long-term enrichment sampling module (4) for pollutants is deployed at the end of the pipeline network and is used to adsorb heavy metals and organic substances using a diffusive gradients in thin films device and a solid-phase microextraction fiber respectively. The time-weighted average concentration of pollutants is obtained by analyzing the adsorption amount, and specifically includes the following steps: Fix the diffusive gradients in thin films device on the straight pipe segment at the end of the pipeline network, continuously expose it for 7 days to adsorb heavy metal ions, analyze the resin membrane with nitric acid solution and detect the adsorption amount, and calculate the time-weighted average concentration of heavy metals in the water body; Deploy the solid-phase microextraction fiber at the user-end faucet, make it contact the water flow for 30 minutes at the normal household flow rate, adsorb organic substances, and then analyze by thermal desorption and gas chromatography-mass spectrometry to estimate the long-term average concentration of aqueous organic substances.

4. A tap water quality pre-detection system according to claim 1, characterized in that, The detection data analysis module (5) receives and integrates the pipeline network topology data, hydraulic characteristic data, tracer data, and passive sampling data, performs normalization processing and then inputs it into a physics-informed neural network, and combines the convection-diffusion equation constraints for model training, and specifically includes the following steps: Integrate the pipeline network topology data, dynamic flow data, tracer data, and passive sampling data, and perform normalization processing to unify the data format; Input the processed data into a physics-informed neural network, combine the convection, diffusion, and adsorption laws of pollutants in the pipeline network, and train the model through physical equation constraints to predict the pollutant concentration distribution at each node of the pipeline network.

5. A tap water quality pre-detection system according to claim 1, characterized in that, The model construction and optimization module (6) interacts with the detection data analysis module (5) and uses the Bayesian optimization algorithm to dynamically adjust the dead water mixing coefficient, minimize the error between the model prediction value and the laboratory test result, and generate a three-dimensional pipeline network pollutant distribution map, intuitively showing the concentration and migration law of each node, and specifically includes the following steps: Use the Bayesian optimization algorithm to dynamically adjust the dead water mixing coefficient, and minimize the error between the model prediction value and the laboratory standard test result through iteration to optimize the parameter configuration of the mixing model; Based on the optimized model, combine the pipeline network hydraulic conditions and pollutant migration characteristics to generate a three-dimensional pollutant distribution map, intuitively showing the concentration gradient and pollutant diffusion path of each node in the pipeline network.

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