Coupling carbon reduction optimization method for water supply and drainage system of building

Abstract

The invention relates to the technical field of building engineering, in particular to a coupling carbon reduction optimization method for a building water supply and drainage system. The method comprises the following steps: acquiring operation data of a building drainage system; carrying out pump station working condition abnormity analysis based on the building drainage system operation data, and generating pump station working condition abnormity data; extracting a rainwater recovery path according to the building drainage system operation data; performing rainwater recovery pipe network backflow analysis based on the rainwater recovery path to obtain pipe network backflow data; the pipe network bearing pressure is evaluated according to the pipe network backflow data; performing leakage risk assessment based on the pipe network bearing pressure to obtain a pipe network leakage risk; the pump station start-stop times are counted according to the pump station working condition abnormal data; performing water hammer effect analysis according to the start-stop times of the pump station to obtain water hammer effect data; and according to the water hammer effect data and the pipe network leakage risk, identifying a fragile pipe network to obtain fragile pipe network data. The energy efficiency and carbon emission management of the building drainage system are optimized based on the building engineering technology, and the energy-saving and carbon-reducing efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building engineering, and in particular to a coupled carbon reduction optimization method for a building water supply and drainage system. Background Art

[0002] Traditional carbon reduction optimization usually only focuses on energy saving of pump station equipment or improving water resource recovery rate, ignoring the identification of systematic carbon emission sources and dynamic risk response in the drainage process. It lacks in-depth analysis of the coupling relationship between pipeline structure status, water hammer impact, leakage risk and carbon emissions, and cannot achieve accurate identification and targeted governance of high-risk areas. At the same time, the use of operation data is limited to simple statistics, and a full-chain closed-loop control system from data perception, risk identification to carbon emission assessment to energy-saving strategy execution has not been established. As a result, the energy-saving and carbon reduction effects are limited, the reliability is poor, and the system's adaptive adjustment capabilities are weak, making it difficult to meet the current needs of green building, low-carbon operation and smart water development. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a coupled carbon reduction optimization method for building water supply and drainage systems to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a coupled carbon reduction optimization method for building water supply and drainage systems is provided, comprising the following steps:

[0005] Step S1: Acquire building drainage system operation data; perform pump station operating condition abnormality analysis based on the building drainage system operation data to generate pump station operating condition abnormality data;

[0006] Step S2: Extracting rainwater recycling paths based on building drainage system operation data; performing rainwater recycling pipe network backflow analysis based on the rainwater recycling paths to obtain pipe network backflow data; assessing pipe network bearing pressure based on the pipe network backflow data; and performing leakage risk assessment based on the pipe network bearing pressure to obtain pipe network leakage risk;

[0007] Step S3: Counting the number of pump station starts and stops based on the abnormal operating condition data of the pump station; performing water hammer effect analysis based on the number of pump station starts and stops to obtain water hammer effect data; identifying vulnerable pipeline networks based on the water hammer effect data and pipeline network leakage risks to obtain vulnerable pipeline network data;

[0008] Step S4: Perform carbon emission analysis based on the vulnerable pipe network data to obtain carbon emissions; perform building drainage energy conservation and carbon reduction analysis based on the carbon emissions to obtain energy conservation and carbon reduction data, and upload it to the building water supply and drainage system to execute the building energy conservation and carbon reduction task.

[0009] The present invention realizes dynamic monitoring and abnormal identification of key operating states of building drainage systems by integrating pump station operation status perception, rainwater recycling path analysis, backflow risk assessment, water hammer effect simulation and vulnerable pipe network identification, thereby improving the system's perception of hidden carbon emission sources; by coupling the pump station start-stop frequency with water hammer impact modeling, it realizes the simulation of structural impact caused by water hammer and the marking of high-risk areas, thereby enhancing the drainage system's response capability to sudden abnormal working conditions; by pipe network pressure calculation and leakage risk simulation, it dynamically associates structural strength, water flow load and carbon emissions, filling the existing The defect of insufficient carbon emission identification dimension in the technology is overcome; by classifying and evaluating the carbon emissions of vulnerable pipe sections, the definition of energy consumption and leakage carbon emissions is further refined, which is helpful to identify key governance targets; on this basis, an energy-saving and carbon reduction strategy based on dredging and connection joint optimization is introduced, and the optimization results are fed back to the building system, realizing intelligent collaboration of data-driven, risk-oriented and low-carbon control integration, effectively improving the accuracy and execution efficiency of the drainage system's energy-saving and carbon reduction strategy, overcoming the shortcomings of traditional methods such as feedback lag and regulation lag, and improving system reliability and green operation level.

[0010] Preferably, step S1 is specifically as follows:

[0011] Step S11: Acquire the building drainage system operation data, and extract the pump station vibration data and pump station liquid level data;

[0012] Step S12: performing bearing fault analysis based on the pump station vibration data to obtain bearing fault data;

[0013] Step S13: performing water inlet pipe blockage analysis based on the pump station liquid level data to obtain water inlet pipe blockage data;

[0014] Step S14: Integrate the bearing fault data and the water inlet pipe blockage data to obtain the abnormal operating condition data of the pump station.

[0015] The present invention improves the multi-dimensional monitoring capability of the pump station operation status by introducing a dual-source sensing mechanism of vibration data and liquid level data, helps to accurately capture potential equipment fault signals and abnormal water level change trends, and overcomes the misjudgment and lag problems caused by relying solely on a single operating parameter in traditional methods; through vibration frequency domain analysis and liquid level change rate analysis, high-precision identification of bearing faults and water inlet pipe blockages is achieved, and the core causes of reduced pump station efficiency and increased system energy consumption are identified from the source, thereby enhancing the traceability and pertinence of carbon reduction analysis; further structural fault data and fluidity blockage data are integrated and processed to construct a more logically related pump station operating condition abnormality expression model, which provides a more reliable data basis for subsequent water hammer analysis, fragile pipe network identification and carbon emission assessment, improves the real-time and accuracy of systemic carbon emission identification and dynamic response, and promotes energy conservation and carbon reduction management of building drainage systems from static equipment energy conservation to dynamic system collaboration.

[0016] Preferably, step S12 is specifically as follows:

[0017] Step S121: performing fast Fourier transform on the pump station vibration data to obtain pump station vibration frequency domain data;

[0018] Step S122: performing a bearing fault frequency comparison on the pump station vibration frequency domain data according to a preset bearing fault frequency to obtain the bearing fault frequency;

[0019] Step S123: screening faulty bearings based on the bearing fault frequency, and capturing images of the faulty bearings to obtain images of the faulty bearings;

[0020] Step S124: performing scratch edge detection on the faulty bearing image to obtain scratch edge data; performing bearing wear area identification on the faulty bearing image based on the scratch edge data to obtain a bearing wear image;

[0021] Step S125: Calculate the gradient of the bearing wear image to obtain gradient data; identify the crack direction of the bearing wear image based on the gradient data; perform crack propagation simulation based on the crack direction to obtain crack propagation data;

[0022] Step S126: performing bearing fault prediction based on the crack growth data to obtain bearing fault data.

[0023] By integrating frequency domain analysis and image recognition technology, the present invention realizes a multi-level fault identification mechanism from weak vibration signals to physical damage status. Compared with traditional methods, it can capture bearing anomalies earlier and more accurately and predict their development trends, significantly improving the foresight and accuracy of abnormal pump station operation diagnosis. Fast Fourier transform is used to extract spectral features to effectively identify typical fault frequencies and realize fault pattern recognition; the introduction of image acquisition and scratch edge detection technology enhances the visual perception of bearing surface damage and solves the problem of difficulty in identifying non-structural damage in traditional diagnostic methods; gradient analysis and crack direction extraction are linked to crack propagation simulation, which further provides support for predicting the remaining life of bearings and formulating precise maintenance strategies, breaking the traditional limitation of judging health status based solely on alarm thresholds. Through the full-process spectrum-image-prediction integrated analysis system, the active diagnosis and risk control capabilities of building drainage systems in carbon emission management and control are improved, the accurate perception of energy consumption sources is strengthened, and an intelligent foundation is laid for building a full-link closed-loop energy-saving and carbon reduction mechanism.

[0024] Preferably, step S13 is specifically as follows:

[0025] Step S131: Calculating the pumping station liquid level change rate based on the pumping station liquid level data; extracting the pumping station liquid level rapid change rate of the pumping station liquid level change rate;

[0026] Step S132: marking abnormal pumping stations according to the rapid change rate of the pumping station liquid level, and performing pipeline water inflow simulation based on the abnormal pumping stations to obtain pipeline water inflow data;

[0027] Step S133: Perform water turbidity detection based on the pipeline water inlet data to obtain water turbidity data;

[0028] Step S134: performing metal ion concentration statistics based on the pipeline inlet water data to obtain the metal ion concentration;

[0029] Step S135: performing water inlet pipe corrosion analysis based on the water turbidity data and the metal ion concentration to obtain water inlet pipe corrosion data;

[0030] Step S136: performing a corrosion material accumulation simulation based on the water inlet pipe corrosion data to obtain corrosion material accumulation data;

[0031] Step S137: determining whether the water inlet pipe is blocked by the corrosion material accumulation data according to a preset pipe accumulation blockage threshold value to obtain water inlet pipe blockage data.

[0032] By incorporating pumping station liquid level change rate analysis, this method can rapidly identify abnormal water level fluctuations, providing early warning of potential water inflow obstructions and enhancing the system's dynamic response capabilities. Rapid liquid level fluctuations are used to flag abnormal pumping stations and coupled with simulation analysis to accurately reconstruct the pipeline inflow process, providing a data foundation for tracing blockages. Combining dual-indicator detection of water turbidity and metal ion concentration not only enhances sensitivity to water anomalies but also expands the chemical dimension of corrosion potential. Corrosion analysis further models the accumulation of corrosive substances, dynamically quantifying invisible hidden faults and effectively addressing the delays inherent in traditional physical observation-based judgments. Blockage determination, based on a set blockage threshold, enhances the objectivity and effectiveness of system blockage identification, enabling early identification of high-risk pipeline sections and assisting in optimizing operation and maintenance strategies. This integrated approach, using liquid level anomalies as trigger signals and integrating water quality sensing with corrosion mechanism modeling, establishes a logical closed loop from data changes to blockage determination. This improves the drainage system's perception accuracy and risk intervention capabilities in energy conservation and carbon reduction efforts, effectively promoting the intelligent evolution of green, low-carbon drainage systems.

[0033] Preferably, the rainwater recycling pipe network backflow analysis in step S2 includes:

[0034] Calculate the path slope based on the rainwater recovery path;

[0035] Extract the historical maximum rainfall based on the building drainage system operation data;

[0036] Construct a rainwater recycling network model based on the historical maximum rainfall and path slope;

[0037] Conduct rainwater recycling simulation based on the rainwater recycling pipe network model to obtain rainwater recycling data;

[0038] Calculate the water level of the recycling pipe network based on rainwater recycling data;

[0039] Extracting node terrain height based on rainwater recovery data;

[0040] Based on the water level height of the recovery pipe network and the node terrain height, the pipe network backflow analysis is carried out to obtain the pipe network backflow data.

[0041] By introducing path slope calculations, this invention can accurately reflect the gravity-driven capacity of rainwater recycling paths, providing key parameter support for pipe network water flow distribution modeling. Combined with the extraction of historical maximum rainfall, the simulation scenario has adaptability to extreme conditions, enhancing the system's responsiveness to extreme weather such as sudden rainstorms. Constructing and simulating a rainwater recycling network model helps to comprehensively evaluate the flow status and recycling efficiency under different structural conditions, improving the accuracy and practicality of model predictions. By calculating the water level of the recycling network and extracting the elevation of the node, a dynamic comparison of the water level difference inside and outside the network can be achieved, thereby more realistically reflecting the impact of terrain changes on the system's drainage capacity. Combining water level height with terrain height for backflow analysis can effectively identify backflow risks caused by low-lying terrain, unreasonable slope design, or poor drainage, and improve the system's ability to locate and predict potential risks in rainwater recycling paths. The overall process realizes the integrated analysis of structural design, historical climate factors and actual terrain conditions, enhances the stability and safety of the drainage system under extreme working conditions, and lays a foundation for dynamic simulation and risk identification for the precise implementation of subsequent energy-saving and carbon reduction measures.

[0042] Preferably, the assessment of the pipe network bearing pressure in step S2 includes:

[0043] Obtain pipeline network material strength data and pipeline cross-sectional area;

[0044] Calculate the return water velocity based on the backflow data of the pipe network;

[0045] Calculate the return water flow rate based on the pipe cross-sectional area and return water velocity;

[0046] Extract instantaneous backflow rainwater data based on backflow water flow;

[0047] Calculate the maximum instantaneous rain impact force based on instantaneous backflow rain data;

[0048] Calculate pipeline bearing pressure based on pipeline network material strength data;

[0049] Assess the bearing capacity of the pipeline network based on the maximum instantaneous rain impact force and the pipe bearing capacity.

[0050] By acquiring pipeline material strength data and pipeline cross-sectional area, the present invention can establish a direct link between structural performance and water flow load, ensuring the accuracy of the physical basis in subsequent impact assessments. By calculating the backflow velocity and further obtaining the backflow flow rate, the dynamic characteristics of the sudden backflow water body can be accurately quantified, providing core parameter support for rainwater impact assessment. After extracting instantaneous backflow rainwater data, the maximum instantaneous rainwater impact force is calculated, which can simulate the actual impact load on the pipeline structure under sudden strong backflow scenarios and reflect the mechanical response capacity of the system under extreme conditions. Calculating the pipeline bearing pressure in combination with material strength makes the assessment of bearing capacity more targeted and accurate. Furthermore, by comparing the maximum impact force with the bearing pressure, the overall pipeline network bearing capacity can be assessed, which can comprehensively identify potential risk weak areas of the structure and expose the failure points that may occur in the actual operation of the pipeline network in advance. The overall process realizes coupled modeling from hydrodynamic characteristic extraction to structural strength analysis, improving the system's pressure assessment capability and risk response efficiency under complex meteorological or emergency conditions, and providing a reliable basis for subsequent pipeline network structure maintenance optimization and carbon reduction path decision-making.

[0051] Preferably, the leakage risk assessment in step S2 includes:

[0052] Based on the bearing pressure of the pipe network, the pressure pipe shape is divided to obtain the winding section pipe data and the bending section pipe data;

[0053] Perform water flow load simulation based on the winding section pipeline data to obtain the winding section pipeline water flow load data;

[0054] Calculate the stress of the winding section pipeline based on the water flow load data of the winding section pipeline; perform pipeline fatigue limit analysis based on the stress of the winding section pipeline to obtain the fatigue limit data of the winding section pipeline; predict the leakage risk of the pipeline network based on the fatigue limit data of the winding section pipeline to obtain the leakage risk of the winding section pipeline;

[0055] Perform water flow impact simulation based on the curved section pipeline data to obtain water flow impact data; perform deformation analysis of the curved section pipeline based on the water flow impact data to obtain deformation data of the curved section pipeline; predict the leakage risk of the pipeline network based on the deformation data of the curved section pipeline to obtain the leakage risk of the curved section pipeline;

[0056] The leakage risk of the winding section of the pipeline and the leakage risk of the curved section of the pipeline are integrated to obtain the leakage risk of the pipeline network.

[0057] The present invention divides the morphology of pressure-bearing pipes based on the bearing pressure of the pipe network, which can accurately distinguish the response characteristics of different structural features to hydraulic loads, making subsequent simulation analysis more targeted and engineering-adaptable. The water flow load simulation of the winding section pipe combined with stress calculation can carefully reveal the force distribution characteristics of the structure, provide a physical basis for fatigue limit analysis, and thus improve the scientificity and timeliness of leakage risk prediction, and realize dynamic identification of weak points of winding structures. The water flow impact simulation and deformation analysis of the curved section pipe are carried out in conjunction, so that potential leakage problems caused by flexible deformation of the structure can be evaluated in advance, making up for the shortcomings of traditional solutions in local structural analysis. Finally, the leakage risks of the winding section and the curved section are integrated, which helps to build a systematic leakage risk map, realize the zoning and classification management of complex structural pipe networks, and effectively improve the active perception and hierarchical control capabilities of the drainage system. The overall method strengthens the deep coupling of structural mechanics and hydrodynamics, takes into account the dual evaluation dimensions of static strength and dynamic response, and effectively supports the comprehensive deployment of leakage control, resource conservation and carbon emission optimization strategies in the smart drainage system.

[0058] Preferably, step S3 is specifically as follows:

[0059] Step S31: Counting the number of pump station starts and stops based on the abnormal operating condition data of the pump station;

[0060] Step S32: Counting the start and stop frequency of the pump station based on the number of start and stop times of the pump station;

[0061] Step S33: Obtain pipeline diameter data; construct a water hammer effect model based on the pipeline diameter data and the start-stop frequency of the pump station;

[0062] Step S34: performing water hammer pressure simulation according to the water hammer effect model to obtain water hammer pressure data;

[0063] Step S35: Counting the water hammer pressure peak value according to the water hammer pressure data; determining the water hammer effect based on the preset maximum pressure value of the pipeline and the water hammer pressure peak value to obtain water hammer effect data;

[0064] Step S36: Identify vulnerable pipeline networks based on the water hammer effect data and pipeline network leakage risks, and obtain vulnerable pipeline network data.

[0065] By counting the number of pump station starts and stops and the frequency of start and stop, the present invention can accurately evaluate the operation mode and abnormal working conditions of the pump station, thereby providing basic data for water hammer effect analysis. By combining the pipeline diameter data and the start and stop frequency of the pump station, a water hammer effect model is constructed, which makes the simulation of water hammer pressure more precise and can dynamically reflect the water hammer impact characteristics of the pipeline network under different working conditions. Through statistical analysis of the water hammer pressure peak, combined with the maximum pressure value of the pipeline, the potential risk of the water hammer effect can be effectively judged to ensure the safety of the pipeline system during operation. Further, by combining the water hammer effect with the leakage risk of the pipeline network, fragile pipelines can be accurately identified, especially the pipe sections affected by high-frequency start and stop conditions, providing data support for subsequent reinforcement and optimization. This process effectively improves the accuracy and predictive ability of pipeline network management through multi-dimensional data analysis, helps the system to respond to water hammer impact and leakage risks in real time, improves the reliability and intelligence level of the drainage system, and promotes the realization of green building, low-carbon operation and smart water management goals.

[0066] Preferably, step S36 is specifically as follows:

[0067] Step S361: Identify high-pressure areas in the pipe network based on water hammer effect data;

[0068] Step S362: Identify pipeline network areas with high leakage risk based on the pipeline network leakage risk;

[0069] Step S363: marking vulnerable pipeline networks according to high-pressure areas and high-leakage-risk areas of the pipeline network to obtain vulnerable pipeline network data.

[0070] By identifying high-pressure areas in the pipeline network, the present invention can accurately locate the pipe sections that are greatly affected by the water hammer effect, providing a basis for further optimization of the pipeline network. Identifying high leakage risk areas in combination with the leakage risk of the pipeline network helps to find weak links in the pipeline network that are prone to leakage. Through the combined analysis of high-pressure areas and high leakage risk areas, fragile pipelines can be marked to determine which areas are more prone to failure or damage under the dual effects of water hammer effect and leakage risk. This marking not only helps to accurately identify the areas in the pipeline network that are most prone to problems, but also provides data support for subsequent repair, reinforcement and optimization work, effectively reducing the failure rate of the pipeline network system, improving its reliability, and thus achieving a more energy-saving and low-carbon operation mode. The entire process makes the operation and maintenance of the pipeline network more intelligent and refined, enhances the adaptive adjustment capability of the system, and meets the development needs of green buildings and smart water services.

[0071] Preferably, step S4 is specifically as follows:

[0072] Step S41: Calculating the length of the vulnerable pipe section based on the vulnerable pipe network data to obtain the vulnerable pipe section length data; and evaluating the energy consumption carbon emissions based on the vulnerable pipe section length data;

[0073] Step S42: Calculate the leakage rate of the vulnerable pipe section based on the vulnerable pipe network data; and evaluate the leakage carbon emissions according to the leakage rate of the vulnerable pipe section;

[0074] Step S43: Integrate energy consumption carbon emissions and leakage carbon emissions to obtain carbon emissions;

[0075] Step S44: identifying high carbon emission pipeline components based on carbon emissions, and performing pipeline dredging based on the high carbon emission pipeline components to obtain pipeline dredging data;

[0076] Step S45: performing connection joint optimization design based on high carbon emission pipeline components to obtain connection joint optimization data;

[0077] Step S46: Perform energy-saving and carbon-reduction benefit assessment based on pipeline dredging data and connection joint optimization data, obtain energy-saving and carbon-reduction data, and upload it to the building water supply and drainage system to execute the building energy-saving and carbon-reduction task.

[0078] By calculating the length of vulnerable pipe sections, the present invention can quantify which areas in the entire pipe network system have higher energy-consuming carbon emissions due to their vulnerability. This provides important data support for evaluating and optimizing the energy efficiency of the pipe network. At the same time, combined with the leakage rate of the vulnerable pipe section, it is possible to identify the source of carbon emissions caused by leakage, thereby comprehensively evaluating the environmental impact caused by water leakage in the pipe network. After integrating energy-consuming carbon emissions and leakage carbon emissions, more targeted measures can be provided for energy conservation and carbon reduction in the pipe network. After identifying high-carbon emission pipeline components based on carbon emission data, the flow resistance can be effectively reduced and energy consumption can be reduced by dredging the pipelines. By optimizing the design of pipe connection joints, the sealing and efficiency of the system can be improved, thereby further reducing the energy loss and leakage of the system. Through the implementation of these measures, the energy efficiency of the building water supply and drainage system can be significantly improved, carbon emissions can be reduced, and it can help to achieve a more intelligent, efficient, and low-carbon pipe network operation model, providing data support and practical effects for the development goals of green buildings, low-carbon and smart water services, and promoting the smooth implementation of energy conservation and carbon reduction tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0080] Figure 1 This is a schematic flow chart of the steps of a coupled carbon reduction optimization method for a building water supply and drainage system according to the present invention;

[0081] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0082] Figure 3 Detailed flowchart of step S12 in the present invention;

[0083] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0084] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of the present invention.

[0085] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0086] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0087] To achieve this, please refer to Figures 1 to 3 The present invention provides a coupled carbon reduction optimization method for a building water supply and drainage system, the method comprising the following steps:

[0088] Step S1: Acquire building drainage system operation data; perform pump station operating condition abnormality analysis based on the building drainage system operation data to generate pump station operating condition abnormality data;

[0089] In this embodiment, the process of obtaining the operating data of the building drainage system first includes collecting the operating parameter data of the pump station, pipeline and rainwater recovery device through the Internet of Things sensor network. The pump station operating data includes the start and stop time of the pump station, operating power, liquid level changes, pressure changes and vibration data, etc., which can be obtained in real time through sensors installed in the pump station. For the collection of the operating data of each pump station, it should be recorded at time intervals (for example, every hour) and transmitted to the central database using the data acquisition system. Next, based on this data, the pump station operating condition is analyzed for abnormalities, and the threshold detection method is used for data cleaning and processing. For example, the liquid level change exceeding ±5% is set as an abnormal range, and the pump station power fluctuation exceeding 10% is set as an abnormal range. These thresholds are used to filter out abnormal data points of all pump stations and generate a pump station operating condition abnormality data report. During this analysis process, the frequency of abnormalities should also be counted to establish the laws and trends of abnormal occurrence and provide data support for subsequent processing.

[0090] Step S2: Extracting rainwater recycling paths based on building drainage system operation data; performing rainwater recycling pipe network backflow analysis based on the rainwater recycling paths to obtain pipe network backflow data; assessing pipe network bearing pressure based on the pipe network backflow data; and performing leakage risk assessment based on the pipe network bearing pressure to obtain pipe network leakage risk;

[0091] In this embodiment, water flow simulation technology is used to input information such as the flow rate, flow rate, and direction of each drainage branch in a building's drainage network into a rainwater recovery network model. This model takes into account the structure and location of rainwater collection devices within the building, such as rooftop drainage pipes and water storage tanks. When extracting the paths, GIS (Geographic Information System) technology can be used to determine which pipes are connected to the rainwater recovery system using spatial and flow data. Backflow analysis is then performed on these pipe paths to assess backflow during the rainwater recovery process. This analysis requires obtaining relevant pipeline pressure data, terrain elevation data, and the water level of the rainwater recovery system to simulate backflow in the pipes under different rainfall intensities. Based on this data, a pressure analysis is performed, setting a maximum pressure value for the pipes (e.g., an upper limit of 0.8 MPa for the pipe pressure). If the water pressure exceeds this value, it indicates that backflow has occurred in the network. Next, using the same pipeline data and combining it with the material properties of the pumping stations and pipes, a static structural analysis is performed to assess the leakage risk of the network. The leakage risk assessment is based on data such as pipe diameter, wall thickness, material, and pressure changes, using stress analysis methods to calculate the probability value of pipeline leakage.

[0092] Step S3: Counting the number of pump station starts and stops based on the abnormal operating condition data of the pump station; performing water hammer effect analysis based on the number of pump station starts and stops to obtain water hammer effect data; identifying vulnerable pipeline networks based on the water hammer effect data and pipeline network leakage risks to obtain vulnerable pipeline network data;

[0093] In this embodiment, the number of starts and stops of each pump station is counted, and the calculation is performed using the pump station start and stop record data. Each start and stop of the pump station can be automatically recorded by the pump station control system, and the number of starts and stops of each pump station in a specified time period (such as monthly or annually) is counted. For pump stations that start and stop frequently, they are screened by calculating the start and stop frequency (for example, pump stations that start and stop more than 10 times a day are considered to have high frequency start and stop). Then, water hammer effect analysis is performed based on these start and stop data. Water hammer effect analysis requires obtaining parameters such as the water flow rate, pipe length, and diameter of the pipeline network, and combining them with the start and stop data of the pump station for calculation. For example, if the start and stop time of a pump station occurs frequently in a short period of time (such as each interval is less than 5 minutes), the water hammer pressure fluctuation caused by the start and stop of this pump station will be large. Using the computational fluid dynamics (CFD) method, based on the pipeline network flow, pressure, and start and stop frequency, the pressure changes caused by the water hammer effect are simulated, and water hammer pressure data is obtained. Through this data, vulnerable pipe sections in the pipeline network that are susceptible to water hammer impact are further identified, and their leakage risk areas are determined.

[0094] Step S4: Perform carbon emission analysis based on the vulnerable pipe network data to obtain carbon emissions; perform building drainage energy conservation and carbon reduction analysis based on the carbon emissions to obtain energy conservation and carbon reduction data, and upload it to the building water supply and drainage system to execute the building energy conservation and carbon reduction task.

[0095] In this embodiment, based on the vulnerable pipe network area, the pipe diameter, material, structure and other information of each area are extracted, and the energy consumption of each vulnerable pipe section is calculated in combination with the specific flow and pressure measurement data. The energy consumption carbon emissions are obtained by calculating the energy consumption and the corresponding carbon emission factors in the process of pumping stations and pipeline water transportation. For example, if a pumping station consumes 10kWh of electricity per hour, and the carbon emission factor of electricity is 0.5kgCO2 / kWh, the carbon emissions of the pumping station per hour are 5kgCO2. Next, the leakage carbon emissions are calculated based on the leakage rate, by calculating the leakage flow of each vulnerable pipe section (such as 10m per hour). 3 Leakage) and energy consumption during water treatment are used to estimate their contribution to carbon emissions. Ultimately, energy-related carbon emissions and leakage-related carbon emissions are combined to generate overall carbon emissions data. Based on this data, an energy-saving and carbon-reduction analysis of the building drainage system is conducted to assess the energy-saving effects of system optimization, such as reducing energy-related carbon emissions by 10% or reducing carbon emissions from leakage by 20%. All results are uploaded to the building energy-saving management system, and corresponding energy-saving and carbon-reduction tasks are implemented to optimize system design and reduce energy waste.

[0096] Preferably, step S1 is specifically as follows:

[0097] Step S11: Acquire the building drainage system operation data, and extract the pump station vibration data and pump station liquid level data;

[0098] In this embodiment, obtaining the operating data of the building drainage system first includes collecting various working parameters of the pump station through an intelligent sensor network. The vibration data of the pump station can be obtained through vibration sensors. The sensors should be installed in key parts of the pump station such as bearings and pump bodies. The collection frequency should be set to once per second to monitor the vibration conditions in real time. Vibration data includes information such as vibration amplitude, frequency, and waveform. The data can be uploaded to the central monitoring system via wireless transmission. Liquid level data is obtained through liquid level sensors. The liquid level sensors should be installed in the water inlet pool or pump room of the pump station to record the height changes of the water level in real time. The collection frequency of liquid level data is set to once per minute to record the water inlet conditions of the pump station in detail. The liquid level and vibration data of the pump station are transmitted to the central data processing platform for subsequent analysis.

[0099] Step S12: performing bearing fault analysis based on the pump station vibration data to obtain bearing fault data;

[0100] In this embodiment, the vibration data collected by each vibration sensor of the pumping station is analyzed in the time domain to calculate the vibration amplitude and frequency per second. The vibration signal is converted into a spectrum diagram by FFT (Fast Fourier Transform), and the main frequency components in the spectrum diagram are analyzed. According to the operating frequency of the equipment and the characteristics of the bearing, a specific threshold is set. For example, if the vibration amplitude of a certain frequency band exceeds 0.1g and the frequency matches the operating frequency of the pumping station, it is judged that there is a bearing fault. The diagnosis of bearing faults can be determined by identifying whether the "bearing frequency" and "frequency multiplication" features appear in the spectrum. The specific fault type (such as wear, looseness, etc.) can be obtained by further analysis of the abnormal frequency in the spectrum diagram.

[0101] Step S13: performing water inlet pipe blockage analysis based on the pump station liquid level data to obtain water inlet pipe blockage data;

[0102] In this embodiment, the time series data of the liquid level of the pump station is obtained, and the normal liquid level change range and fluctuation amplitude are set. For example, under normal circumstances, the liquid level of the pump station fluctuates within the range of ±5%. If the liquid level fluctuation exceeds this range and the frequency of change is low, the water inlet pipe will be blocked. By setting a threshold for liquid level change (for example, a warning value when the liquid level drops by more than 10%), if the rate of change of the liquid level is significantly lower than the normal range and continues for more than the set time (such as more than 30 minutes), the possibility of pipe blockage is determined by this method. Combined with factors such as pipe diameter and pump station flow, the occurrence of the blockage area is further confirmed, and the real-time data of the flow monitoring system is used to verify whether flow abnormalities have occurred in the water inlet pipe.

[0103] Step S14: Integrate the bearing fault data and the water inlet pipe blockage data to obtain the abnormal operating condition data of the pump station.

[0104] In this embodiment, when integrating bearing fault data and water inlet pipe blockage data, it is first necessary to perform time synchronization processing on the two types of data to ensure the timeliness of the data. For bearing fault data, if the diagnosis results show that there is an abnormal frequency or vibration amplitude exceeding the set threshold, it will be marked as a risk point for bearing failure. At the same time, if the liquid level data analysis results show that the pump station liquid level exceeds the normal fluctuation range and meets the standard for pipeline blockage, it will be marked as a water inlet pipe blockage risk. These two risk data are integrated to form the pump station operating condition abnormality data. At this time, the pump station operating condition abnormality data can be uploaded to the monitoring platform through the data warehouse system, and the abnormal situation can be displayed in real time for management personnel to conduct subsequent analysis and decision-making.

[0105] Preferably, step S12 is specifically as follows:

[0106] Step S121: performing fast Fourier transform on the pump station vibration data to obtain pump station vibration frequency domain data;

[0107] In this embodiment, the vibration data of the pumping station is collected in real time by vibration sensors installed in various key components of the pumping station (such as bearings and pump bodies). Each vibration sensor is set to collect data once per second and record the time domain data of the vibration signal. The collected time domain data is input into the fast Fourier transform (FFT) algorithm for processing. The purpose of FFT is to convert the time domain signal into a frequency domain signal in order to analyze the frequency components of the vibration. Specifically, the vibration signal is first windowed, and then the FFT algorithm is applied to calculate the various frequency components. The obtained frequency domain data includes vibration amplitude and phase data of multiple frequencies. Each frequency point represents a different vibration characteristic. The data will contain complete spectrum information from low frequency to high frequency. The sampling frequency should be set to at least twice the maximum frequency of the vibration signal to ensure the accuracy of the spectrum data.

[0108] Step S122: performing a bearing fault frequency comparison on the pump station vibration frequency domain data according to a preset bearing fault frequency to obtain the bearing fault frequency;

[0109] In this embodiment, a preset bearing fault frequency range is set based on the operating frequency characteristics of the pump station bearings. The bearing fault frequency generally includes the normal operating frequency and specific frequency components caused by bearing wear or damage, such as the natural frequency of the bearing itself or its multiples. The preset bearing fault frequency range is usually composed of the main frequency, harmonic frequency and side frequency of the bearing. For example, the bearing fault frequency is set to the range of 50Hz to 500Hz. These frequency ranges are usually related to the vibration characteristics after the bearing is damaged. The pump station vibration frequency domain data converted by FFT is compared with the preset bearing fault frequency, the amplitude of each frequency point is calculated, and the frequency points overlapping with the fault frequency range are marked. If the amplitude of certain frequency components in the frequency domain data significantly exceeds the normal operating range (for example, the amplitude is greater than 0.2g), the frequency segment is determined to be a signal of bearing fault.

[0110] Step S123: screening faulty bearings based on the bearing fault frequency, and capturing images of the faulty bearings to obtain images of the faulty bearings;

[0111] In this embodiment, through further analysis of the frequency domain data, the frequency segment that coincides with the set bearing fault frequency is screened out. On this basis, the specific time period and bearing position where the fault occurs are obtained, and the abnormal data points in the vibration sensor data are recorded. If it is found that the amplitude of the vibration signal exceeds the threshold at these frequency points (for example, the vibration amplitude exceeds 0.3g), further analysis and processing are performed on these data points. Next, an image is collected at the location where the fault occurs, and a high-resolution infrared or visible light camera is used to photograph the bearing to ensure that the captured image clearly shows the details of the bearing, especially the areas of wear or cracks. When collecting images, appropriate lighting conditions are used and the shooting angle is kept fixed to ensure image consistency.

[0112] Step S124: performing scratch edge detection on the faulty bearing image to obtain scratch edge data; performing bearing wear area identification on the faulty bearing image based on the scratch edge data to obtain a bearing wear image;

[0113] In this embodiment, the image of the faulty bearing is subjected to scratch edge detection using common image processing algorithms, such as the Canny edge detection algorithm. First, the image data is converted into a single-channel image through grayscale to simplify processing. The Canny algorithm is then applied to extract edge features in the image by setting a low threshold (such as 30) and a high threshold (such as 100) to identify the scratch edges on the bearing surface. After edge detection, the edge data obtained includes the positions of the starting and ending points of each scratch, as well as its shape and length. These edge data are further used to identify the wear area of ​​the faulty bearing. By setting a threshold, if the scratch area in the image exceeds a certain standard (such as the scratch area exceeds 1cm 2), the area is considered as the wear area and marked out to obtain the bearing wear image for subsequent analysis.

[0114] Step S125: Calculate the gradient of the bearing wear image to obtain gradient data; identify the crack direction of the bearing wear image based on the gradient data; perform crack propagation simulation based on the crack direction to obtain crack propagation data;

[0115] In this embodiment, the gradient calculation is performed on the bearing wear image using the gradient operator in image processing, such as the Sobel operator. First, the bearing wear image is converted into a grayscale image, and then the gradient value of each pixel in the image is calculated using the Sobel operator. The calculated gradient data reflects the area with the most significant changes in the image (i.e., the edge of the wear area). Based on these gradient values, the directional information of the wear area is extracted. By analyzing the gradient direction, the main expansion direction of the crack can be identified. For example, if the gradient changes greatly in a certain direction, it can be determined that the crack expands along this direction. Based on this directional information, the crack expansion simulation is further carried out. During the simulation process, the mechanical properties of the material (such as the yield strength and tensile strength of the bearing material) and the initial size of the crack are used, and common crack expansion models, such as the Paris law, are applied to simulate the crack expansion process in the bearing.

[0116] Step S126: performing bearing fault prediction based on the crack growth data to obtain bearing fault data.

[0117] In this embodiment, the speed and direction of crack propagation are analyzed, and the crack growth in the future is predicted based on the data obtained from the crack propagation model. The fatigue limit of the material and the crack propagation law are used to calculate the time point when the bearing will completely fail. Specifically, the critical crack size at which the bearing will fail is estimated by the crack area growth rate and the crack propagation direction. If the length or area of ​​the crack reaches a certain set threshold (for example, the crack length exceeds 3mm), it is predicted that the bearing will fail. By comprehensively considering the effects of crack propagation and material fatigue, early warning data for bearing failure is obtained, and relevant reports are generated for reference in subsequent maintenance and replacement work.

[0118] Preferably, step S13 is specifically as follows:

[0119] Step S131: Calculating the pumping station liquid level change rate based on the pumping station liquid level data; extracting the pumping station liquid level rapid change rate of the pumping station liquid level change rate;

[0120] In this embodiment, the sampling frequency of the liquid level sensor is set to once per second, and the collected data records the changes in the liquid level of the pumping station over time. First, the liquid level change rate is calculated based on the pumping station liquid level data. The liquid level change rate is obtained by calculating the difference between two adjacent liquid level data and dividing it by the time interval. The formula is: Liquid level change rate = (current liquid level - previous liquid level) / time interval. The time interval is generally set to 1 second. Next, the rapidly changing part of the pumping station liquid level change rate is extracted, and a threshold is usually set. For example, when the liquid level change rate is greater than 0.5m / s, it is marked as a rapid change. The rapid change rate of the pumping station liquid level can be obtained by comparing the set liquid level change rate threshold with the real-time data. Rapid liquid level changes generally indicate an abnormal state of the pumping station.

[0121] Step S132: marking abnormal pumping stations according to the rapid change rate of the pumping station liquid level, and performing pipeline water inflow simulation based on the abnormal pumping stations to obtain pipeline water inflow data;

[0122] In this embodiment, the pump station is marked as abnormal based on the rapid change data of the liquid level. When the rate of change of the liquid level exceeds a preset threshold (for example, the rate of change of the liquid level is greater than 0.5m / s or the liquid level fluctuation exceeds 0.3m), it is determined that the pump station is in an abnormal state. The pump station marked as abnormal will be further checked for its operation in subsequent analysis. Subsequently, the abnormal state information of the pump station is used to simulate the pipeline water inflow. The simulation uses the known pipeline structure and water flow characteristics, combined with the water inflow flow and water flow velocity of the pump station, to construct a flow model of water in the pipeline. The pipeline water inflow simulation is dynamically adjusted according to the rate of change of the liquid level and the operating conditions of the pump station, calculates the water flow change in the pipeline, and generates pipeline water inflow data. These data contain information such as water flow, flow velocity, and pressure.

[0123] Step S133: Perform water turbidity detection based on the pipeline water inlet data to obtain water turbidity data;

[0124] In this embodiment, water turbidity is measured using a turbidity sensor installed at the water inlet, which records the concentration of suspended particulate matter in the water in real time. This sensor uses scattered light technology to measure the degree of light scattering caused by particulate matter in the water. A larger value indicates more turbid water. Specifically, the sensor outputs a numerical value representing the turbidity of the water in NTUs (Nephelometric Turbidity Units). A turbidity greater than 10 NTU indicates poor water quality and the presence of high levels of suspended matter. Water turbidity data needs to be continuously tracked and recorded for subsequent analysis.

[0125] Step S134: performing metal ion concentration statistics based on the pipeline inlet water data to obtain the metal ion concentration;

[0126] In this embodiment, metal ion concentrations are detected in real time using a chemical analysis sensor. The sensor uses electrochemical methods to detect the type and concentration of metal ions in water. Common metal ions include iron, copper, lead, and manganese. This process involves placing a sensor at the entrance of the water inlet pipe to sample. The sensor outputs the concentration value of each metal ion in mg / L. The concentration of metal ions varies depending on factors such as water flow rate, source water quality, and pipe material. The concentration of each metal ion is calculated based on the sensor's voltage output and a calibration curve. By collecting and analyzing statistical data, metal ion concentration information is obtained.

[0127] Step S135: performing water inlet pipe corrosion analysis based on the water turbidity data and the metal ion concentration to obtain water inlet pipe corrosion data;

[0128] In the present embodiment, based on turbidity data and metal ion concentration data, a water quality corrosivity assessment model is established. The model predicts corrosion rate by referring to a standardized water quality corrosion evaluation method, combining indicators such as metal ion concentration (such as the concentration of iron, copper, lead) and the pH value of water. The corrosion rate can be calculated by consulting a standard corrosion rate formula (such as according to the NACE standard). When the metal ion concentration exceeds a threshold value (such as an iron concentration greater than 10 mg / L), the corrosivity in the water increases, thereby affecting the degree of corrosion of the pipeline. By calculating the corrosion rate, the corrosion risk of the pipeline is obtained.

[0129] Step S136: performing a corrosion material accumulation simulation based on the water inlet pipe corrosion data to obtain corrosion material accumulation data;

[0130] In this embodiment, the simulation of corrosive material accumulation is based on the deposits generated by corrosion within the pipeline. By analyzing the influent water quality, the velocity and pressure changes of the water flow within the pipeline, a fluid dynamics model is used to simulate the accumulation of corrosive materials (such as rust and mineral deposits) within the pipeline. The simulation considers factors such as water velocity, pipe inner diameter, fluid viscosity, and dissolved oxygen concentration in the water, which influence the adhesion and accumulation of deposits. The amount of corrosion product deposited is calculated based on the corrosion rate. The simulation results show the distribution of corrosive material accumulation at each point within the pipeline, including information such as the amount and location of accumulation.

[0131] Step S137: determining whether the water inlet pipe is blocked by the corrosion material accumulation data according to a preset pipe accumulation blockage threshold value to obtain water inlet pipe blockage data.

[0132] In this embodiment, the water inlet pipe blockage is determined based on the corrosion material accumulation data according to the preset pipeline accumulation blockage threshold. The preset pipeline accumulation blockage threshold is set according to the material, diameter and maximum flow rate of the pipeline. For example, when the accumulated material in the pipeline reaches 30% of the inner diameter of the pipeline, it can be considered that there is a risk of blockage. First, the accumulation amount of each pipeline section is analyzed and compared with the blockage threshold of the pipeline section. If the accumulation amount exceeds the threshold (such as the accumulation accounts for 30% of the inner diameter of the pipeline), the pipeline section is determined to be blocked and the water inlet pipe blockage data is generated. This data includes information such as the location of the blocked pipeline section, the accumulation amount and the severity of the blockage, which is used for subsequent pipeline maintenance and repair decisions.

[0133] Preferably, the rainwater recycling pipe network backflow analysis in step S2 includes:

[0134] Calculate the path slope based on the rainwater recovery path;

[0135] In this embodiment, the slope calculation of the rainwater recovery path needs to be performed through the terrain data of the building drainage system. The terrain data can be obtained through elevation measurement or digital elevation model (DEM), and is usually processed using GIS (Geographic Information System) tools. First, the starting point and end point coordinates of the rainwater recovery path are extracted from the design drawing of the building drainage system. Using the elevation data, calculate the changes in the horizontal and vertical directions of the path. The slope of the path can be calculated using the formula: slope = (starting point elevation - end point elevation) / horizontal distance. Assuming that the starting point elevation is 10 meters, the end point elevation is 5 meters, and the path length is 50 meters, then the slope = (10-5) / 50 = 0.1, indicating that the slope of the path is 10%.

[0136] Extract the historical maximum rainfall based on the building drainage system operation data;

[0137] In this embodiment, data on historical maximum rainfall is typically obtained from a weather station or sensors in a rainwater collection system. The rainfall data provided by the weather station should include hourly or minute-by-minute rainfall records. Maximum rainfall is extracted based on the time series data of rainfall. During implementation, historical meteorological data is consulted to identify the highest historical rainfall value. Assume that the historical maximum rainfall is 100 mm / h, representing the maximum rainfall in a given hour. This data will be used as a design parameter in the subsequent rainwater harvesting network model.

[0138] Construct a rainwater recycling network model based on the historical maximum rainfall and path slope;

[0139] In this embodiment, based on the historical maximum rainfall and the path slope, a mathematical model of the flow and water level changes of the rainwater recovery network is established through a hydraulic model. Using the principles of hydraulics, the Manning formula (or a similar formula) is applied to calculate the flow rate of water in the recovery network. The Manning formula is: Q = (1 / n) * A * R^(2 / 3) * S^(1 / 2), where Q is the flow rate, n is the Manning roughness coefficient, A is the flow area, R is the hydraulic radius of the water flow, and S is the slope. By calculating the flow rate under the historical maximum rainfall and combining it with the actual slope of the recovery network, the flow model of the recovery network is obtained, and then a hydraulic simulation model of the rainwater recovery network is constructed. The model calculation results include the flow rate and pressure distribution of each pipe section, which are used for subsequent simulation analysis.

[0140] Conduct rainwater recycling simulation based on the rainwater recycling pipe network model to obtain rainwater recycling data;

[0141] In this embodiment, a rainwater recycling network model is established, and historical maximum rainfall and path slope data are input to perform a rainwater recycling simulation. During the simulation, the water flow, water level, and pressure at each node in the network are calculated using a time-stepping method. The model considers factors such as precipitation, pipe cross-sectional dimensions, pipe material, and slope, and distributes the historical maximum rainfall to each pipe according to flow distribution rules. The simulation results include hydrological information such as water level, flow velocity, and pressure at each node. The simulation software can be hydraulic analysis software such as EPANET or other software specifically designed for urban drainage system simulation.

[0142] Calculate the water level of the recycling pipe network based on rainwater recycling data;

[0143] In this embodiment, the water level at each node in the recycling network is calculated based on the recycling network data obtained from the rainwater recycling simulation. The water level is calculated by inferring the flow and pressure distribution at each node in the network. In the rainwater recycling simulation, the flow rate and pressure at each node are calculated based on the flow distribution and path slope. Then, the water level change is derived based on hydraulic relationships. For each node, the water level can be calculated using the formula: water level = base height + (pressure / ρg), where ρ is the density of water, g is the acceleration due to gravity, and pressure is the pressure value at the node.

[0144] Extracting node terrain height based on rainwater recovery data;

[0145] In this embodiment, the node elevation can be obtained from the design drawing or elevation data of the building drainage system. Specifically, the location elevation of each node is extracted from the GIS system using a digital elevation model (DEM). The elevation data of each node can be extracted using a spatial analysis tool such as ArcGIS. If the node is on a slope, the elevation of the node will be extracted based on the elevation data of that location. For example, the elevation of a certain node is 8 meters.

[0146] Based on the water level height of the recovery pipe network and the node terrain height, the pipe network backflow analysis is carried out to obtain the pipe network backflow data.

[0147] In this embodiment, the backflow analysis of the pipe network is performed by comparing the water level height of the recycling pipe network and the elevation of the node. If the water level height of the recycling pipe network exceeds the elevation of the node, it indicates that backflow has occurred at the node. The criterion for judging backflow is: when the water level height of the recycling pipe network is greater than the elevation of the node, it is judged that backflow has occurred. Specifically, assuming that the water level height of a node is 9 meters and the elevation of the node is 8 meters, there is a risk of backflow at this node. The backflow data will include information such as the node location where the backflow occurs, the difference between the water level height and the elevation, and subsequent treatment measures will be taken based on this information, such as strengthening the anti-backflow design of the drainage pipe.

[0148] Preferably, the assessment of the pipe network bearing pressure in step S2 includes:

[0149] Obtain pipeline network material strength data and pipeline cross-sectional area;

[0150] In this embodiment, the pipe network material strength data is usually obtained from the technical manual of the pipe manufacturer, or determined by the pipe material standard and relevant test data. Common pipe network materials include steel pipes, PVC pipes, cast iron pipes, etc., and the strength parameters of different materials are also different. For example, the tensile strength of steel pipes is 400MPa, while that of PVC pipes is 45MPa. The cross-sectional area of ​​the pipe is calculated by the diameter of the pipe. For circular pipes, the cross-sectional area A can be calculated by the formula A = π*(D / 2) 2 Calculate, where D is the inner diameter of the pipe. For example, if the inner diameter of the pipe is 0.3 meters, then the cross-sectional area A = π*(0.3 / 2) 2 ≈0.071m 2 .

[0151] Calculate the return water velocity based on the backflow data of the pipe network;

[0152] In this embodiment, the calculation of the return water velocity is based on the backflow data of the pipe network, especially the change in the return water level. The return water velocity can be calculated by the flow-velocity relationship. For example, assuming that the water level height of the backflow pipe is 10 meters and the slope of the pipe is 0.02, the flow velocity is calculated using the Manning formula. The flow velocity formula is: V = (1 / n) * R^(2 / 3) * S^(1 / 2), where n is the Manning roughness coefficient, R is the hydraulic radius of the water flow, and S is the slope. Assuming that the Manning roughness coefficient n = 0.013, the water flow radius R = 0.15 meters, and the slope S = 0.02, the return water velocity is calculated. The unit of flow velocity is m / s, and the return water velocity is obtained after calculation.

[0153] Calculate the return water flow rate based on the pipe cross-sectional area and return water velocity;

[0154] In this embodiment, the return water flow rate Q can be calculated by the formula Q = A * V, where A is the pipe cross-sectional area and V is the return water velocity. In the implementation process, the pipe cross-sectional area value obtained in step S1 is multiplied by the return water velocity calculated in step S2 to obtain the flow rate. For example, if the pipe cross-sectional area A = 0.071m 2 , return water velocity V = 2m / s, then the return water flow Q = 0.071*2 = 0.142m 3 / s. This flow rate value indicates the amount of water flowing through the pipe per unit time.

[0155] Extract instantaneous backflow rainwater data based on backflow water flow;

[0156] In this embodiment, the instantaneous backflow rainwater data is extracted based on the change in the backflow water flow rate, which is usually monitored in real time by sensors in the rainwater recycling system. The real-time flow data measured by the sensors can be combined with historical precipitation data to calculate the instantaneous backflow rainwater volume. By multiplying the backflow water flow rate Q by the time period t, the volume of the instantaneous backflow rainwater is obtained: V = Q * t. Assume that the backflow water flow rate in a 30-minute time period is 0.142m 3 / s, then the instantaneous backflow rainwater volume is V = 0.142*30*60 = 255.6m 3 This data reflects the amount of backflow rainwater in a short period of time.

[0157] Calculate the maximum instantaneous rain impact force based on instantaneous backflow rain data;

[0158] In this embodiment, the calculation of the maximum instantaneous rainwater impact force depends on the backflow water flow rate and pipe characteristics, and is usually evaluated by the momentum change of the water flow. The impact force can be calculated using the formula F = Δp / Δt, where Δp is the momentum change of the water flow and Δt is the time change. Momentum change Δp = ρ*V*A*(V_final-V_initial), where ρ is the density of water (usually 1000kg / m 3), where V is the flow velocity, A is the cross-sectional area of ​​the pipe, and V_final and V_initial are the values ​​before and after the flow velocity change, respectively. In this step, assuming a large instantaneous flow velocity change, the maximum instantaneous impact force can be calculated from the change in momentum of the water flow.

[0159] Calculate pipeline bearing pressure based on pipeline network material strength data;

[0160] In this embodiment, the pressure bearing capacity of the pipeline is determined by the material strength of the pipeline, the external pressure, and the structural design of the pipeline. First, the compressive strength of the pipeline material is used to determine the pressure bearing capacity. For example, for a steel pipe, assuming its compressive strength is 400 MPa, the pressure bearing capacity P of the pipeline can be calculated using the formula P = σ * A, where σ is the compressive strength of the material and A is the cross-sectional area of ​​the pipeline. Assuming the cross-sectional area of ​​the steel pipe is A = 0.071m 2 , compressive strength σ = 400MPa (or 400,000,000Pa), then the bearing pressure P = 400,000,000*0.071 = 28,400,000Pa (i.e. 28.4MPa).

[0161] Assess the bearing capacity of the pipeline network based on the maximum instantaneous rain impact force and the pipe bearing capacity.

[0162] In this embodiment, when evaluating the bearing capacity of the pipe network, it is first necessary to compare the maximum instantaneous rainwater impact force with the bearing capacity of the pipe. If the impact force exceeds the bearing capacity of the pipe, it will cause the pipe to rupture or be damaged. Assume that the maximum instantaneous rainwater impact force calculated in step S5 is 500,000N, and the bearing capacity of the pipe is 28.4MPa (i.e. 28,400,000N / m 2 ), the two must be compared. If the impact force is less than the bearing pressure, the pipeline network can withstand it. If the impact force is greater than the bearing pressure, pipeline reinforcement or replacement is necessary. This calculation provides a basis for the safety and reliability of the pipeline network, and thus provides reference data for pipeline optimization.

[0163] Preferably, the leakage risk assessment in step S2 includes:

[0164] Based on the bearing pressure of the pipe network, the pressure pipe shape is divided to obtain the winding section pipe data and the bending section pipe data;

[0165] In this embodiment, the pipeline is divided into winding sections and curved sections according to the bearing pressure of the pipeline network and the morphological characteristics of the pipeline. The morphological division of the pressure-bearing pipeline is based on the geometric shape, stress characteristics and pressure distribution of the pipeline. Winding section pipelines usually refer to pipelines with a surrounding shape. The stress on these pipelines is relatively uniform and the pressure distribution is relatively stable. Curved section pipelines refer to curved pipelines with uneven stress and stress concentration at the bends. In implementation, it is first necessary to obtain the morphological parameters of the pipeline, such as the diameter, length, bending angle, etc. of the pipeline, and calculate and analyze these data and the bearing pressure of the pipeline to obtain the specific positions and data of the winding section and the curved section.

[0166] Perform water flow load simulation based on the winding section pipeline data to obtain the winding section pipeline water flow load data;

[0167] In this embodiment, the water flow load simulation can be performed by calculating the flow velocity, flow rate and stress state of the fluid in the pipeline. When performing water flow load simulation, it is necessary to first set parameters such as the inner diameter of the pipeline, the flow velocity, flow rate and pipeline material of the fluid. A common simulation method is CFD (computational fluid dynamics) simulation, which uses CFD software (such as ANSYS Fluent) to simulate water flow. According to the water flow velocity and flow rate in the pipeline, combined with the viscosity, density and friction coefficient of the fluid, the load effect of the fluid on the pipeline is calculated. For example, for a winding section pipeline with an inner diameter of 0.3 meters, the flow velocity is 2m / s and the flow rate is 0.142m 3 / s, the water flow load can be obtained through simulation and output as specific load data.

[0168] Calculate the stress of the winding section pipeline based on the water flow load data of the winding section pipeline; perform pipeline fatigue limit analysis based on the stress of the winding section pipeline to obtain the fatigue limit data of the winding section pipeline; predict the leakage risk of the pipeline network based on the fatigue limit data of the winding section pipeline to obtain the leakage risk of the winding section pipeline;

[0169] In this embodiment, the stress calculation is based on the water flow load data of the winding section pipe. The stress of the pipe can be calculated using the classic thin-walled pipe formula: σ = (P*D) / (2*t), where P is the water flow pressure, D is the outer diameter of the pipe, and t is the wall thickness of the pipe. Assuming that the outer diameter of the winding section pipe is 0.3 meters, the wall thickness is 0.02 meters, and the water flow pressure is 500kPa, the stress of the pipe is: σ = (500*10 3*0.3) / (2*0.02)=3750kPa. This stress value can serve as the basis for subsequent fatigue limit analysis. Pipeline fatigue limit analysis is based on stress amplitude and loading times. During implementation, fatigue life must first be calculated based on the relationship between cyclic stress and loading times. The commonly used material fatigue limit formula is the SN curve, which is the relationship between stress and fatigue cycle life. Assuming that the stress fluctuation caused by water pressure in the wound section of the pipeline is 3000kPa, fatigue limit data can be obtained based on the material's SN curve (for example, the SN curve of a steel pipe). If the pipeline material is steel, the fatigue limit is typically 50MPa. Based on this data, combined with the actual operating environment and loading cycles of the pipeline, the fatigue limit of the pipeline can be calculated. Pipeline network leakage risk prediction is based on the fatigue limit data of the wound section of the pipeline. When the cyclic stress of the pipeline exceeds its fatigue limit, fatigue failure will occur in the pipeline, leading to leakage. Based on the fatigue limit and actual operating conditions, combined with stress concentration, a fatigue damage model can be used for prediction. In practice, assuming the fatigue limit is 50 MPa and the actual stress fluctuation is 55 MPa, the pipeline fatigue damage factor will exceed 1, indicating a risk of pipeline leakage. This method can be used to predict the leakage risk of the wound section of the pipeline.

[0170] Perform water flow impact simulation based on the curved section pipeline data to obtain water flow impact data; perform deformation analysis of the curved section pipeline based on the water flow impact data to obtain deformation data of the curved section pipeline; predict the leakage risk of the pipeline network based on the deformation data of the curved section pipeline to obtain the leakage risk of the curved section pipeline;

[0171] In this embodiment, the simulation of water flow impact in the curved section of the pipeline is based on the instantaneous velocity change of the fluid and the geometric characteristics of the pipeline. When performing the simulation, the water hammer effect simulation technology is used to calculate the impact of the instantaneous change in flow velocity on the pipeline. A common method is to use the water hammer effect equation: Δp = ρ*a*L, where Δp is the pressure change, ρ is the fluid density, a is the acceleration of the water flow, and L is the length of the pipeline. Through CFD simulation, the pressure fluctuations caused by water flow impact at different flow rates are calculated, and impact data are obtained. These data provide input for subsequent deformation analysis. The deformation analysis of the curved section of the pipeline is based on the water flow impact data, and the finite element analysis (FEA) method is usually used. The deformation analysis calculates the deformation of the curved section of the pipeline under the action of water flow impact, taking into account the material, geometry and instantaneous changes of the water flow impact of the pipeline. For example, assuming the impact force is 1000N and the elastic modulus of the pipeline is 2*10 11Pa, the wall thickness of the pipe is 0.02 meters and the outer diameter is 0.3 meters. The maximum deformation of the pipe can be calculated through finite element analysis. The deformation data will serve as the basis for leakage risk analysis. The leakage risk prediction of the pipeline is based on its deformation data. Generally, the leakage risk can be predicted by calculating the maximum deformation and deformation frequency of the pipeline, combined with the location of stress concentration. If the deformation of the pipeline exceeds the allowable deformation range of the pipeline, or the pipeline cracks or becomes locally unstable, the risk of leakage will increase. Assuming that the deformation of the pipeline exceeds 1mm in the deformation analysis, it can be judged that the leakage risk of the curved section of the pipeline is high, and the safety of the pipeline can be further evaluated.

[0172] The leakage risk of the winding section of the pipeline and the leakage risk of the curved section of the pipeline are integrated to obtain the leakage risk of the pipeline network.

[0173] In this embodiment, the leakage risk of the entire pipeline network can be obtained by integrating the leakage risks of the winding section and the curved section of the pipeline. The integration of leakage risk takes into account the independent risks of each section of the pipeline and their combined impact. The weighted average method is usually used to integrate risk data. Assuming that the leakage risk of the winding section is 0.3 and the leakage risk of the curved section is 0.6, the leakage risk of the integrated pipeline network can be calculated by weighted average: overall leakage risk = (0.3*winding section pipeline length + 0.6*curved section pipeline length) / (winding section pipeline length +curved section pipeline length). This method can be used to obtain the overall leakage risk of the pipeline network, providing a basis for pipeline network maintenance and optimization.

[0174] Preferably, step S3 is specifically as follows:

[0175] Step S31: Counting the number of pump station starts and stops based on the abnormal operating condition data of the pump station;

[0176] In this embodiment, based on the abnormal working condition data of the pump station, the timestamp of each start and stop and the operating status of the pump station are first extracted. Each time the pump station changes from a stopped state to an operating state, or from an operating state to a stopped state, a start and stop event is recorded. The specific operation includes analyzing the pump station control system log to find the time point of each pump station state change. For the statistics of the number of starts and stops, a time window can be set to filter abnormal start and stop situations. For example, if more than 5 starts and stops occur within a certain hour, it is recorded as an abnormal situation. The statistics of the number of starts and stops are based on the time series analysis of the working condition data. The required data include timestamps, status identifiers (start and stop status) and operation records.

[0177] Step S32: Counting the start and stop frequency of the pump station based on the number of start and stop times of the pump station;

[0178] In this embodiment, the statistics of the start-stop frequency are calculated based on the ratio of the number of starts and stops of the pump station to the observation time period. The number of starts and stops of the pump station in a predetermined time interval (for example, 24 hours) is calculated. Next, the start-stop frequency is obtained by dividing the number of starts and stops by the length of the observation time period. For example, if the pump station starts and stops 12 times within 24 hours, the start-stop frequency is 12 times / 24 hours = 0.5 times / hour. The calculation of the start-stop frequency can be performed by analyzing the control system log of the pump station, and the time unit can be adjusted according to demand, such as calculating the frequency in hours or days.

[0179] Step S33: Obtain pipeline diameter data; construct a water hammer effect model based on the pipeline diameter data and the start-stop frequency of the pump station;

[0180] In this embodiment, the pipe diameter data is usually obtained through pipe design documents, installation records or measuring tools, and the pipe diameter is a key parameter of the water flow channel. Specifically, the actual diameter value of the pipe can be collected through design drawings or sensors. The unit of the pipe diameter is meter (m). The start and stop frequency of the pump station is combined with the pipe diameter to establish a water hammer effect model. The construction of the water hammer effect model is based on the start and stop frequency of the pump station and the geometric characteristics of the pipe (such as diameter, length, etc.) and the flow rate of the fluid and other data. The commonly used water hammer effect model is modeled by a one-dimensional water hammer equation, and the formula is: ΔP = ρ*c*ΔV, where ΔP is the pressure change, ρ is the fluid density, c is the speed of sound, and ΔV is the flow rate change. This model calculates the impact of pressure fluctuations on the pipeline.

[0181] Step S34: performing water hammer pressure simulation according to the water hammer effect model to obtain water hammer pressure data;

[0182] In this embodiment, the water hammer pressure simulation obtains the pressure data generated by the water hammer by simulating the instantaneous change in flow rate caused by the start and stop of the pump station. Based on the aforementioned water hammer effect model, the pipe size, flow rate, fluid characteristics and the start and stop frequency of the pump station are input into the model. The simulation tool can be professional fluid dynamics software such as ANSYS Fluent or OpenFOAM for simulation. In the simulation, it is assumed that the initial velocity of the water flow is 2m / s and the fluid density is 1000kg / m 3 The inner diameter of the pipeline is 0.3 meters, and the pump station starts and stops once per hour. Calculate the pressure fluctuation after each start and stop. For example, a water hammer simulation can determine the maximum water hammer pressure peak to be 500 kPa. Based on this data, a water hammer pressure curve can be generated for the entire process.

[0183] Step S35: Counting the water hammer pressure peak value according to the water hammer pressure data; determining the water hammer effect based on the preset maximum pressure value of the pipeline and the water hammer pressure peak value to obtain water hammer effect data;

[0184] In this embodiment, the statistical water hammer pressure peak value needs to extract the maximum value of the water hammer pressure from the simulation results. For example, in the water hammer simulation, the maximum value in the pressure change curve obtained is 600kPa, which is the water hammer pressure peak value. Next, the maximum pressure bearing value of the pipeline (usually determined by the pipeline material and design standards) is used for comparison, assuming that the maximum pressure bearing value of the pipeline is 400kPa. If the water hammer pressure peak value exceeds the pressure bearing value, it means that the pipeline is damaged or a serious water hammer effect occurs. In this step, the water hammer effect is determined based on the comparison of the set maximum pressure bearing value of the pipeline (such as 400kPa) and the water hammer pressure peak value of the simulation result (such as 600kPa). If the peak value exceeds the pressure bearing value, it is determined that the pipeline is at risk of water hammer effect.

[0185] Step S36: Identify vulnerable pipeline networks based on the water hammer effect data and pipeline network leakage risks, and obtain vulnerable pipeline network data.

[0186] In this embodiment, the identification of fragile pipelines is based on water hammer effect data and leakage risk data of the pipelines. The water hammer effect data provides the pressure bearing capacity of the pipeline under the action of water hammer, while the leakage risk data is based on the leakage points or aging conditions that occur in the pipeline during long-term operation. During the implementation process, the pipeline segments that have experienced a pressure greater than the bearing value are first screened out from the water hammer effect data, and then these pipeline segments are risk assessed through leakage risk analysis (for example, based on fatigue analysis or corrosion simulation). If the pressure peak caused by the water hammer effect exceeds the maximum pressure bearing capacity of the pipeline, the pipeline segment is considered fragile. In addition, combined with factors such as the material, aging degree and pressure cycle of the pipeline, the fragile pipeline segment can be further evaluated and marked. For example, for a pipeline segment whose pressure peak exceeds the pressure bearing capacity of the pipeline, if the pipeline segment has a high leakage risk in the past six months, it is marked as a fragile pipeline.

[0187] Preferably, step S36 is specifically as follows:

[0188] Step S361: Identify high-pressure areas in the pipe network based on water hammer effect data;

[0189] In this embodiment, the water hammer effect data includes the instantaneous pressure fluctuations generated at each node and segment in the pipeline network during startup and shutdown operations. Based on this data, peak pressure statistics can be compiled for each region in the pipeline network to determine which regions experienced high water hammer pressure. This includes extracting the pressure data for each segment from the water hammer simulation results and calculating the maximum water hammer pressure for each node or segment. For example, if the peak pressure in a pipeline segment reaches 600 kPa, while the system's maximum pressure tolerance is 400 kPa, this region is considered a high-pressure region. High-pressure regions are identified when the peak pressure exceeds a preset threshold (e.g., 400 kPa), marking these regions as high-pressure regions in the pipeline network. Parameters used include peak pressure, pipeline design pressure tolerance, and simulation data. The water hammer effect data includes the instantaneous pressure fluctuations generated at each node and segment in the pipeline network during startup and shutdown operations. Based on this data, peak pressure statistics can be compiled for each region in the pipeline network to determine which regions experienced high water hammer pressure. This includes extracting the pressure data for each segment from the water hammer simulation results and calculating the maximum water hammer pressure for each node or segment. For example, if the peak pressure in a section of pipe reaches 600kPa, while the system's maximum pressure rating is 400kPa, that area is considered a high-pressure zone. High-pressure areas are identified when the peak pressure exceeds a preset threshold (e.g., 400kPa), marking these areas as high-pressure areas in the pipe network. Parameters used include peak pressure, pipeline design pressure rating, and simulation data.

[0190] Step S362: Identify pipeline network areas with high leakage risk based on the pipeline network leakage risk;

[0191] In this embodiment, leakage risk data is usually evaluated through information such as historical maintenance records, pipeline materials, pipeline aging degree, and pressure cycles. In order to identify high leakage risk areas, leakage risk data of each pipe section is first extracted from the health monitoring system of the pipeline network. These data include historical leakage point locations, repair records, pipeline materials and service life, etc. The leakage risk scoring model usually combines these factors to evaluate the leakage risk of each pipe section. If the leakage risk score of a pipe section exceeds a set threshold (such as 0.8), the area is marked as a high leakage risk area. This process also needs to consider factors such as the corrosion condition, fatigue damage and physical aging of the pipeline. The data extraction and analysis of leakage risk can rely on pipeline network monitoring equipment (such as flow meters, pressure sensors) and regular physical inspection reports.

[0192] Step S363: marking vulnerable pipeline networks according to high-pressure areas and high-leakage-risk areas of the pipeline network to obtain vulnerable pipeline network data.

[0193] In this embodiment, the marking of fragile pipelines is based on a comprehensive assessment of high-pressure areas and high-leakage risk areas. In this step, the high-pressure area data is first cross-checked with the high-leakage risk area data. If a certain pipeline network area belongs to both a high-pressure area and a high-leakage risk area, then the area is considered to be a fragile area. The specific operations include: marking each pipe section in the pipeline network. If the pressure peak of the pipe section exceeds the pressure bearing value in the water hammer effect analysis, and its risk score in the leakage risk analysis is also higher than the set threshold (such as 0.8), then the pipe section is marked as a fragile pipeline network. The data involved in this process include: pressure peak, pipe section leakage risk score, pipe section material and aging data, etc. All data are processed and marked through a dedicated pipeline management platform. The fragile pipeline network data finally obtained can be used as a key area for subsequent pipeline network maintenance and optimization.

[0194] Preferably, step S4 is specifically as follows:

[0195] Step S41: Calculating the length of the vulnerable pipe section based on the vulnerable pipe network data to obtain the vulnerable pipe section length data; and evaluating the energy consumption carbon emissions based on the vulnerable pipe section length data;

[0196] In this embodiment, based on the fragile pipeline network data, the length of each fragile pipe section is extracted, and the total length of all fragile pipe sections is calculated. The specific operation is: extract the geometric information of all fragile pipe sections from the pipeline network management database, such as the starting and ending positions of the pipeline and the length of the pipe section. The lengths of all fragile pipe sections are summed up to obtain the total length data of the fragile pipe sections. Based on the length data of the fragile pipe sections, the calculation of energy-related carbon emissions involves calculating the energy consumption of the pumping station. The energy efficiency calculation model is used to evaluate the energy-related carbon emissions of the fragile pipe section area through parameters such as the start and stop frequency of the pumping station, the length of the pipe section, and the flow rate of the fluid transported by the pipeline. The evaluation formula may include: carbon emissions = (pump station power consumption × operating time) × carbon emission factor, where the power consumption of the pumping station can be calculated by the flow rate, pressure and pipeline length, and the carbon emission factor is a standard value (such as 0.8kgCO2 / kWh).

[0197] Step S42: Calculate the leakage rate of the vulnerable pipe section based on the vulnerable pipe network data; and evaluate the leakage carbon emissions according to the leakage rate of the vulnerable pipe section;

[0198] In this embodiment, the leakage rate of the vulnerable pipe section can be calculated by the pressure and flow data of the real-time monitoring system, as well as the historical leakage records. First, the water flow and pressure data of each pipe section are obtained from the pipe network monitoring system, the abnormal changes therein are analyzed, and the leakage amount is obtained by calculating the difference between the flow and pressure. The specific operations include: calculating the difference between the expected flow and the actual flow of the pipe section, and then estimating it through the leakage rate formula based on factors such as the pipe material and the length of the pipe section. The leakage rate calculation formula is: leakage rate = (leakage amount / total flow) × 100%. The leakage carbon emissions are calculated based on the leakage rate and the pipe network flow. The formula is: leakage carbon emissions = (leakage amount × operating time × carbon emission factor), where the carbon emission factor is the energy consumption standard for water resource transportation, usually set to 0.5kgCO2 / m 3 .

[0199] Step S43: Integrate energy consumption carbon emissions and leakage carbon emissions to obtain carbon emissions;

[0200] In this embodiment, energy-related carbon emissions and leakage-related carbon emissions are integrated to calculate the overall carbon emissions. By adding the two types of carbon emission data obtained in steps S41 and S42, the carbon emissions of the entire pipeline network are obtained. The specific operation is: carbon emissions = energy-related carbon emissions + leakage-related carbon emissions. Energy-related carbon emissions come from the power consumption of the pumping station and the pipeline operation data, while leakage-related carbon emissions come from the calculation of pipeline leakage. When integrating the data, it is necessary to confirm whether the carbon emission factors used are consistent and to check the calculation method. The output of this step is the total carbon emissions data of the entire pipeline network within the specified period.

[0201] Step S44: identifying high carbon emission pipeline components based on carbon emissions, and performing pipeline dredging based on the high carbon emission pipeline components to obtain pipeline dredging data;

[0202] In this embodiment, by sorting the carbon emissions of each pipeline component, a group of pipeline components whose emissions exceed a set threshold (such as a pipe section that accounts for 80% of the total emissions) are selected. For these high-carbon emission pipeline components, dredging operations are performed. The dredging process includes: using pipeline dredging equipment (such as pneumatic dredging, mechanical scrapers, etc.) to clean the high-carbon emission pipe sections. After each dredging, the pipeline dredging status, such as the flow rate after cleaning, pressure recovery, and time taken, is recorded. These data constitute the pipeline dredging data. The data after dredging can be used to evaluate the cleaning effect, thereby reducing energy consumption and carbon emissions caused by leakage.

[0203] Step S45: performing connection joint optimization design based on high carbon emission pipeline components to obtain connection joint optimization data;

[0204] In this example, relevant information is extracted from pipeline components with high carbon emissions, focusing on the design of pipeline connection joints. Joints are the weak link in the pipeline system and are often the main source of leakage and energy loss. An optimized design is performed by analyzing factors such as joint materials, connection methods, and sealing performance. The optimized design involves using more corrosion-resistant materials, tighter joint structures, or improved sealing methods. Technologies used in this step include CAD design software and fluid dynamics simulation to ensure that the joint optimization solution meets the pressure and flow requirements of the pipeline network and reduces the risk of leakage. The final output is the optimized design data for the connection joint.

[0205] Step S46: Perform energy-saving and carbon-reduction benefit assessment based on pipeline dredging data and connection joint optimization data, obtain energy-saving and carbon-reduction data, and upload it to the building water supply and drainage system to execute the building energy-saving and carbon-reduction task.

[0206] In this embodiment, by comparing the energy consumption and leakage data of the pipeline before and after dredging, the energy saved and the reduced carbon emissions are calculated. Secondly, based on the design data of the connection joints after optimization, the improvement in the operating efficiency of the pipeline network is evaluated, and the corresponding carbon emission reduction is calculated. The calculation formula for energy saving and carbon reduction data can be: energy saving and carbon reduction = (energy consumption saved after dredging + energy consumption reduction after optimizing the joints) × carbon emission factor. Finally, the energy saving and carbon reduction data obtained will be uploaded to the building water supply and drainage system through the interface for use in executing the building energy saving and carbon reduction tasks.

[0207] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0208] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A coupled carbon reduction optimization method for building water supply and drainage systems, characterized in that: The following steps are involved: Step S1: Acquire building drainage system operation data; perform pump station operating condition abnormality analysis based on the building drainage system operation data to generate pump station operating condition abnormality data; Step S2: Extracting rainwater recycling paths based on building drainage system operation data; performing rainwater recycling pipe network backflow analysis based on the rainwater recycling paths to obtain pipe network backflow data; assessing pipe network bearing pressure based on the pipe network backflow data; and performing leakage risk assessment based on the pipe network bearing pressure to obtain pipe network leakage risk; Step S3: Counting the number of pump station starts and stops based on the abnormal operating condition data of the pump station; performing water hammer effect analysis based on the number of pump station starts and stops to obtain water hammer effect data; identifying vulnerable pipeline networks based on the water hammer effect data and pipeline network leakage risks to obtain vulnerable pipeline network data; Step S4: Perform carbon emission analysis based on the vulnerable pipe network data to obtain carbon emissions; perform building drainage energy conservation and carbon reduction analysis based on the carbon emissions to obtain energy conservation and carbon reduction data, and upload it to the building water supply and drainage system to execute the building energy conservation and carbon reduction task.

2. The coupled carbon reduction optimization method for building water supply and drainage systems according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: Acquire the building drainage system operation data, and extract the pump station vibration data and pump station liquid level data; Step S12: performing bearing fault analysis based on the pump station vibration data to obtain bearing fault data; Step S13: performing water inlet pipe blockage analysis based on the pump station liquid level data to obtain water inlet pipe blockage data; Step S14: Integrate the bearing fault data and the water inlet pipe blockage data to obtain the abnormal operating condition data of the pump station.

3. The coupled carbon reduction optimization method for building water supply and drainage systems according to claim 2 is characterized in that: Step S12 is specifically as follows: Step S121: performing fast Fourier transform on the pump station vibration data to obtain pump station vibration frequency domain data; Step S122: performing a bearing fault frequency comparison on the pump station vibration frequency domain data according to a preset bearing fault frequency to obtain the bearing fault frequency; Step S123: screening faulty bearings based on the bearing fault frequency, and capturing images of the faulty bearings to obtain images of the faulty bearings; Step S124: performing scratch edge detection on the faulty bearing image to obtain scratch edge data; performing bearing wear area identification on the faulty bearing image based on the scratch edge data to obtain a bearing wear image; Step S125: Calculate the gradient of the bearing wear image to obtain gradient data; identify the crack direction of the bearing wear image based on the gradient data; perform crack propagation simulation based on the crack direction to obtain crack propagation data; Step S126: performing bearing fault prediction based on the crack growth data to obtain bearing fault data.

4. The coupled carbon reduction optimization method for building water supply and drainage systems according to claim 2 is characterized in that: Step S13 is specifically as follows: Step S131: Calculating the pumping station liquid level change rate based on the pumping station liquid level data; extracting the pumping station liquid level rapid change rate of the pumping station liquid level change rate; Step S132: marking abnormal pumping stations according to the rapid change rate of the pumping station liquid level, and performing pipeline water inflow simulation based on the abnormal pumping stations to obtain pipeline water inflow data; Step S133: Perform water turbidity detection based on the pipeline water inlet data to obtain water turbidity data; Step S134: performing metal ion concentration statistics based on the pipeline inlet water data to obtain the metal ion concentration; Step S135: performing water inlet pipe corrosion analysis based on the water turbidity data and the metal ion concentration to obtain water inlet pipe corrosion data; Step S136: performing a corrosion material accumulation simulation based on the water inlet pipe corrosion data to obtain corrosion material accumulation data; Step S137: determining whether the water inlet pipe is blocked by the corrosion material accumulation data according to a preset pipe accumulation blockage threshold value to obtain water inlet pipe blockage data.

5. The coupled carbon reduction optimization method for building water supply and drainage systems according to claim 1 is characterized in that: The backflow analysis of the rainwater recycling network in step S2 includes: Calculate the path slope based on the rainwater recovery path; Extract the historical maximum rainfall based on the building drainage system operation data; Construct a rainwater recycling network model based on the historical maximum rainfall and path slope; Conduct rainwater recycling simulation based on the rainwater recycling pipe network model to obtain rainwater recycling data; Calculate the water level of the recycling pipe network based on rainwater recycling data; Extracting node terrain height based on rainwater recovery data; Based on the water level height of the recovery pipe network and the node terrain height, the pipe network backflow analysis is carried out to obtain the pipe network backflow data.

6. The coupled carbon reduction optimization method for building water supply and drainage systems according to claim 1 is characterized in that: The evaluation of the pipe network bearing pressure in step S2 includes: Obtain pipeline network material strength data and pipeline cross-sectional area; Calculate the return water velocity based on the backflow data of the pipe network; Calculate the return water flow rate based on the pipe cross-sectional area and return water velocity; Extract instantaneous backflow rainwater data based on backflow water flow; Calculate the maximum instantaneous rain impact force based on instantaneous backflow rain data; Calculate pipeline bearing pressure based on pipeline network material strength data; Assess the bearing capacity of the pipeline network based on the maximum instantaneous rain impact force and the pipe bearing capacity.

7. The coupled carbon reduction optimization method for building water supply and drainage systems according to claim 1 is characterized in that: The leakage risk assessment in step S2 includes: Based on the bearing pressure of the pipe network, the pressure pipe shape is divided to obtain the winding section pipe data and the bending section pipe data; Perform water flow load simulation based on the winding section pipeline data to obtain the winding section pipeline water flow load data; Calculate the stress of the winding section pipeline based on the water flow load data of the winding section pipeline; perform pipeline fatigue limit analysis based on the stress of the winding section pipeline to obtain the fatigue limit data of the winding section pipeline; predict the leakage risk of the pipeline network based on the fatigue limit data of the winding section pipeline to obtain the leakage risk of the winding section pipeline; Perform water flow impact simulation based on the curved section pipeline data to obtain water flow impact data; perform deformation analysis of the curved section pipeline based on the water flow impact data to obtain deformation data of the curved section pipeline; predict the leakage risk of the pipeline network based on the deformation data of the curved section pipeline to obtain the leakage risk of the curved section pipeline; The leakage risk of the winding section of the pipeline and the leakage risk of the curved section of the pipeline are integrated to obtain the leakage risk of the pipeline network.

8. The coupled carbon reduction optimization method for building water supply and drainage systems according to claim 1 is characterized in that: Step S3 is specifically as follows: Step S31: Counting the number of pump station starts and stops based on the abnormal operating condition data of the pump station; Step S32: Counting the start and stop frequency of the pump station based on the number of start and stop times of the pump station; Step S33: Obtain pipeline diameter data; construct a water hammer effect model based on the pipeline diameter data and the start-stop frequency of the pump station; Step S34: performing water hammer pressure simulation according to the water hammer effect model to obtain water hammer pressure data; Step S35: Counting the water hammer pressure peak value according to the water hammer pressure data; determining the water hammer effect based on the preset maximum pressure value of the pipeline and the water hammer pressure peak value to obtain water hammer effect data; Step S36: Identify vulnerable pipeline networks based on the water hammer effect data and pipeline network leakage risks, and obtain vulnerable pipeline network data.

9. The coupled carbon reduction optimization method for building water supply and drainage systems according to claim 8 is characterized in that: Step S36 is specifically as follows: Step S361: Identify high-pressure areas in the pipe network based on water hammer effect data; Step S362: Identify pipeline network areas with high leakage risk based on the pipeline network leakage risk; Step S363: marking vulnerable pipeline networks according to high-pressure areas and high-leakage-risk areas of the pipeline network to obtain vulnerable pipeline network data.

10. The coupled carbon reduction optimization method for building water supply and drainage systems according to claim 1 is characterized in that: Step S4 is specifically as follows: Step S41: Calculating the length of the vulnerable pipe section based on the vulnerable pipe network data to obtain the vulnerable pipe section length data; and evaluating the energy consumption carbon emissions based on the vulnerable pipe section length data; Step S42: Calculate the leakage rate of the vulnerable pipe section based on the vulnerable pipe network data; and evaluate the leakage carbon emissions according to the leakage rate of the vulnerable pipe section; Step S43: Integrate energy consumption carbon emissions and leakage carbon emissions to obtain carbon emissions; Step S44: identifying high carbon emission pipeline components based on carbon emissions, and performing pipeline dredging based on the high carbon emission pipeline components to obtain pipeline dredging data; Step S45: performing connection joint optimization design based on high carbon emission pipeline components to obtain connection joint optimization data; Step S46: Perform energy-saving and carbon-reduction benefit assessment based on pipeline dredging data and connection joint optimization data, obtain energy-saving and carbon-reduction data, and upload it to the building water supply and drainage system to execute the building energy-saving and carbon-reduction task.

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