Carbon Emission Monitoring Method and System for Urban Water Supply Networks

Abstract

The present application provides a carbon emission monitoring method and system for urban water supply networks, which relates to the technical field of water supply management and includes: obtaining the pipeline length and diameter, determining M water supply paths starting from the water source and ending at the water usage points, and N non-water source nodes; subsequently, conducting carbon emission monitoring and collecting efficiency and emission data; at the same time, introducing an environmental monitoring module to monitor its carbon emission offset effect; analyzing the urban heat island effect, establishing a carbon emission accounting model, dynamically adjusting water supply scheduling, and achieving optimized management. Through the present application, it is possible to solve the problems in the prior art. Due to focusing on single pipeline information, it is difficult to clarify and define the water supply paths and their key nodes. At the same time, the water supply scheduling strategy cannot be adaptively adjusted, resulting in unstable water supply quality and difficult maintenance management, further affecting the efficiency of urban water supply management. It helps to improve the operation efficiency of urban water supply networks, reduce energy consumption and carbon emissions, and ensure the stability and reliability of water supply services.

Description

Technical Field

[0001] This application relates to the technical field of water supply management, and particularly to a carbon emission monitoring method and system for urban water supply networks. Background Art

[0002] With the acceleration of the urbanization process, the growth of urban population and area has led to an increasing demand for water resources. While the urban water supply system meets these demands, it also generates a large amount of energy consumption and carbon emissions. Urban water supply networks are an important part of urban infrastructure, responsible for delivering clean water sources to thousands of households and ensuring the daily water use needs of residents and enterprises. However, due to the extensive, intricate and buried underground distribution of water supply networks, their operating status and management are relatively difficult.

[0003] Currently, due to the emphasis on single pipeline information in the prior art, it is difficult to clarify and define the water supply path and its key nodes. At the same time, the water supply scheduling strategy cannot be adaptively adjusted, resulting in unstable water supply quality and difficult maintenance management, further affecting the efficiency of urban water supply management. Summary of the Invention

[0004] The purpose of this application is to provide a carbon emission monitoring method and system for urban water supply networks, so as to solve the problems in the prior art that due to the emphasis on single pipeline information, it is difficult to clarify and define the water supply path and its key nodes, and at the same time, the water supply scheduling strategy cannot be adaptively adjusted, resulting in unstable water supply quality, difficult maintenance management, and further affecting the efficiency of urban water supply management.

[0005] In view of the above problems, this application provides a carbon emission monitoring method and system for urban water supply networks.

[0006] In a first aspect, the present application provides a carbon emission monitoring method for urban water supply networks, and the carbon emission monitoring method for urban water supply networks is implemented through a carbon emission monitoring system for urban water supply networks. Among them, the carbon emission monitoring method for urban water supply networks includes: based on the urban water supply network corresponding to the target area, obtaining the basic network information, where the basic network information includes the pipe length and pipe diameter; based on the urban water supply network, taking the water source node as the starting point and the water use node as the ending point, determining M water supply paths, and the M water supply paths include N non-water source nodes, and the node types corresponding to the non-water source nodes include pipe segment intersection points, pipe material and diameter change points, and branch pipe connection points; based on the M water supply paths in the urban water supply network, conducting carbon emission monitoring, collecting carbon emission efficiency and carbon emission volume; introducing an environmental monitoring module to obtain environmental monitoring data and integrating green infrastructure, where the environmental monitoring data includes rainfall, and the green infrastructure includes rain gardens and wetlands; based on the green infrastructure in the urban water supply network, conducting carbon emission monitoring, collecting carbon emission offset efficiency and carbon emission offset volume; based on the carbon emission efficiency and carbon emission volume, carbon emission offset efficiency and carbon emission offset volume, combined with the vegetation coverage in the satellite remote sensing data, analyzing the urban heat island effect and establishing a carbon emission accounting model, and dynamically adjusting the water supply scheduling of the urban water supply network, iteratively optimizing to determine the optimal water supply scheduling strategy, and synchronously conducting urban water supply management.

[0007] In a second aspect, the present application also provides a carbon emission monitoring system for urban water supply networks, which is used to execute the carbon emission monitoring method for urban water supply networks as described in the first aspect. Among them, the carbon emission monitoring system for urban water supply networks includes: a basic network information acquisition module, which is used to obtain the basic network information based on the urban water supply network corresponding to the target area, and the basic network information includes the pipe length and pipe diameter; a water supply path determination module, which is used to determine M water supply paths based on the urban water supply network, taking the water source node as the starting point and the water use node as the ending point, and the M water supply paths include N non-water source nodes, and the node types corresponding to the non-water source nodes include pipe segment intersection points, pipe material and diameter change points, and branch pipe connection points; a carbon emission monitoring module, which is used to conduct carbon emission monitoring based on the M water supply paths in the urban water supply network and collect carbon emission efficiency and carbon emission volume; an environmental monitoring data acquisition module, which is used to introduce an environmental monitoring module to obtain environmental monitoring data and integrate green infrastructure, where the environmental monitoring data includes rainfall, and the green infrastructure includes rain gardens and wetlands;

[0008] The offset quantity collection module is used to monitor carbon emissions based on the green infrastructure in the urban water supply network, and collect carbon emission offset efficiency and carbon emission offset quantity; the water supply management module is used to analyze the urban heat island effect and establish a carbon emission accounting model based on carbon emission efficiency, carbon emission quantity, carbon emission offset efficiency and carbon emission offset quantity, combined with the vegetation coverage in satellite remote sensing data, and dynamically adjust the water supply scheduling of the urban water supply network, iteratively optimize to determine the optimal water supply scheduling strategy, and simultaneously conduct urban water supply management.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By obtaining the basic information of the pipe network based on the urban water supply network corresponding to the target area, the basic information of the pipe network includes pipe length and pipe diameter; based on the urban water supply network, taking the water source node as the starting point and the water use node as the ending point, M water supply paths are determined, and the M water supply paths include N non-water source nodes, and the node types corresponding to the non-water source nodes include pipe section intersection points, pipe material and diameter change points, and branch connection points; based on the M water supply paths in the urban water supply network, carbon emission monitoring is carried out, and carbon emission efficiency and carbon emission quantity are collected; an environmental monitoring module is introduced to obtain environmental monitoring data and integrate green infrastructure, where the environmental monitoring data includes rainfall, and the green infrastructure includes rain gardens and wetlands; based on the green infrastructure in the urban water supply network, carbon emission monitoring is carried out, and carbon emission offset efficiency and carbon emission offset quantity are collected; based on carbon emission efficiency, carbon emission quantity, carbon emission offset efficiency and carbon emission offset quantity, combined with the vegetation coverage in satellite remote sensing data, analyze the urban heat island effect and establish a carbon emission accounting model, and dynamically adjust the water supply scheduling of the urban water supply network, iteratively optimize to determine the optimal water supply scheduling strategy, and simultaneously conduct urban water supply management, effectively solving the problems in the prior art that it is difficult to clarify and define the water supply path and its key nodes due to focusing on single pipe information, and at the same time, the water supply scheduling strategy cannot be adaptively adjusted, resulting in unstable water supply quality, difficult maintenance and management, and further affecting the efficiency of urban water supply management, which helps to improve the operation efficiency of the urban water supply network, reduce energy consumption and carbon emissions, and at the same time ensure the stability and reliability of water supply services.

[0011] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically illustrates the specific embodiments of this application. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of this application, nor is it used to limit the scope of this application. Other features of this application will become easy to understand through the following description. Description of the Drawings

[0012] To more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only exemplary. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on the provided drawings.

[0013] Figure 1 It is a schematic flow chart of the carbon emission monitoring method for urban water supply networks of the present application;

[0014] Figure 2 It is a schematic structural diagram of the carbon emission monitoring system for urban water supply networks of the present application.

[0015] Explanation of reference numerals:

[0016] Network basic information acquisition module 11, water supply path determination module 12, carbon emission monitoring module 13, environmental monitoring data acquisition module 14, offset collection module 15, water supply management module 16. Detailed implementation manners

[0017] By providing a carbon emission monitoring method and system for urban water supply networks, the present application solves the problems in the prior art that due to focusing on single pipeline information, it is difficult to clarify and define the water supply path and its key nodes. At the same time, the water supply scheduling strategy cannot be adjusted adaptively, resulting in unstable water supply quality, difficult maintenance and management, and further affecting the efficiency of urban water supply management. It helps to improve the operation efficiency of urban water supply networks, reduce energy consumption and carbon emissions, and at the same time ensure the stability and reliability of water supply services.

[0018] Next, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described here. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application. Additionally, it should be noted that for the sake of description, only the parts related to the present application are shown in the accompanying drawings rather than all of them.

[0019] Embodiment 1, please refer to the attached Figure 1 , the present application provides a carbon emission monitoring method for urban water supply networks. Among them, the carbon emission monitoring method for urban water supply networks is applied to a carbon emission monitoring system for urban water supply networks. The carbon emission monitoring method for urban water supply networks specifically includes the following steps:

[0020] S1: Based on the urban water supply network corresponding to the target area, obtain the basic network information, where the basic network information includes the pipe length and pipe diameter.

[0021] Specifically, the pipe length refers to the actual length of each section of the water supply network, and the pipe length affects the hydraulic loss and the required pumping energy. The pipe diameter refers to the inner diameter size of the pipe, and the pipe diameter directly affects the flow velocity and flow rate of the water. A smaller pipe diameter will increase the water flow velocity, resulting in greater hydraulic loss; while a larger pipe diameter may reduce the hydraulic loss, but may increase the construction and material costs. Use GIS software to integrate the geospatial data of the urban water supply network, including the layout, length, and connection method of the pipes. Use non-destructive detection techniques, such as ground penetrating radar or acoustic detection, to determine the specific location, depth, and material of the pipes.

[0022] S2: Based on the urban water supply network, taking the water source node as the starting point and the water consumption node as the ending point, determine M water supply paths, where the M water supply paths include N non-water source nodes, and the node types corresponding to the non-water source nodes include pipe section intersection points, pipe material and diameter change points, and branch pipe connection points.

[0023] Specifically, the water source node is the starting point of the water supply network, referring to water source areas such as water treatment plants, reservoirs, or groundwater wells. The water consumption node is the ending point of the water supply network, referring to water demand points such as residential areas, industrial areas, and commercial areas. The M water supply paths are all possible paths from the water source node to the water consumption node. The N non-water source nodes are intermediate nodes on the water supply paths, and these nodes are neither water source nodes nor water consumption nodes, but key points in the network. Including but not limited to pipe section intersection points, where different pipes intersect and the water flow may bifurcate or merge. Pipe material and diameter change points, where the pipe material or diameter changes, which may affect the hydraulic loss and flow velocity. Branch pipe connection points, where the main water supply pipe is connected to the branch pipe, and the water flow may flow to different areas. Use GIS data to construct the topological structure of the network and identify all possible water supply paths. Use hydraulic model software to input the topological structure of the network, pipe attributes, and node information to simulate the water flow dynamics of the network. In the hydraulic model, mark all non-water source nodes and classify them according to their functions as pipe section intersection points, pipe material and diameter change points, and branch pipe connection points. Through the hydraulic model, trace all M water supply paths from the water source node to the water consumption node and record the non-water source nodes on each path.

[0024] S3: Based on the M water supply paths in the urban water supply network, conduct carbon emission monitoring and collect carbon emission efficiency and carbon emissions.

[0025] Specifically, carbon emission monitoring refers to tracking and measuring the greenhouse gas emissions generated by the water supply network during operation due to energy consumption. These emissions mainly come from the electricity use of water pumps and the generation of heat energy. Carbon emission efficiency refers to the carbon emissions generated per unit of water supply. For example, the carbon dioxide equivalent generated per cubic meter of water supply. The higher the efficiency, the lower the carbon emissions per unit of water volume. Carbon emissions refer to the total carbon emissions generated by the water supply network within a certain period of time. Collect the energy consumption data of all water pumps and other related equipment in the water supply network, including the usage amounts of electricity and fuel. Use carbon emission factors to convert the energy consumption data into carbon emissions. For example, the carbon dioxide emissions generated per kilowatt-hour of electricity. Use hydraulic model software to analyze the hydraulic characteristics of M water supply paths, including flow rate, pressure loss, and pumping demand. According to the results of the hydraulic model, combined with the energy consumption data, calculate the carbon emission efficiency of each water supply path. Sum up the carbon emissions of all M water supply paths to obtain the total carbon emissions of the entire water supply network.

[0026] S4: Introduce an environmental monitoring module, obtain environmental monitoring data, and integrate green infrastructure, where the environmental monitoring data includes rainfall, and the green infrastructure includes rain gardens and wetlands.

[0027] Specifically, the environmental monitoring module is sensors and equipment for monitoring environmental parameters, which can provide information about climate, weather, and the hydrological cycle. The environmental monitoring data includes, but is not limited to, rainfall, temperature, humidity, wind speed, etc. Green infrastructure refers to integrating natural or natural-simulated elements into the urban environment to provide ecosystem services, including water filtration, carbon sequestration, and urban cooling. For example, rain gardens and wetlands are green infrastructure. Install devices such as rain gauges, temperature sensors, and humidity sensors in the target area to collect rainfall and other relevant environmental data, and collect and transmit the data in real time or regularly. Use the collected environmental data to analyze its impact on the operation of the urban water supply network, such as the impact of rainfall on water supply demand and the rainwater collection system. Analyze the effects of green infrastructure on reducing runoff pollution, improving rainwater utilization rate, etc.

[0028] S5: Based on the green infrastructure in the urban water supply network, conduct carbon emission monitoring, and collect carbon emission offset efficiency and carbon emission offset amounts.

[0029] Specifically, the carbon emission offset efficiency refers to the ability of green infrastructure to offset a portion of the carbon emissions generated by the water supply network by absorbing carbon dioxide and other greenhouse gases, as well as reducing energy consumption. The carbon emission offset amount refers to the total amount of carbon emissions actually offset by green infrastructure within a specific time period. The effectiveness of green infrastructure in carbon sequestration is evaluated through plant growth monitoring, soil carbon content analysis, and atmospheric carbon concentration measurement. The carbon emissions offset by green infrastructure are calculated based on its carbon sequestration effectiveness and the reduction in energy consumption.

[0030] S6: Based on the carbon emission efficiency and carbon emission amount, the carbon emission offset efficiency and carbon emission offset amount, and combined with the vegetation coverage in satellite remote sensing data, analyze the urban heat island effect, establish a carbon emission accounting model, dynamically adjust the water supply scheduling of the urban water supply network, iteratively optimize to determine the optimal water supply scheduling strategy, and simultaneously conduct urban water supply management.

[0031] Specifically, the urban heat island effect refers to the phenomenon that the temperature in the central urban area is higher than that in the surrounding rural areas, which is caused by the absorption and retention of heat by artificial structures such as buildings and roads, and the reduction of green spaces. The carbon emission accounting model is a mathematical model used to calculate and evaluate the carbon emissions of urban water supply networks, which takes into account the energy consumption of water supply networks, the carbon offset effect of green infrastructure, and other relevant factors. Dynamic adjustment of water supply scheduling refers to adjusting the operation strategy of water supply networks according to real-time or recent data, such as water demand, energy price, carbon emission efficiency, and offset volume, in order to optimize energy use and carbon emissions. Iterative optimization finds the optimal operation mode by repeatedly calculating and comparing different water supply scheduling strategies. Combine data such as carbon emission efficiency, carbon emission volume, carbon emission offset efficiency, and carbon emission offset volume with satellite remote sensing data to analyze the impact of vegetation coverage on the urban heat island effect. Using the data analysis results, establish a carbon emission accounting model, which should be able to take into account the complexity and dynamic changes of water supply networks. Collect historical data, including historical water use data, historical environmental monitoring data, historical official website operation data, and green infrastructure data. Specifically, historical water use data is used to predict future water demand; environmental monitoring data, including temperature, humidity, rainfall, etc., is used to analyze the urban heat island effect; pipe network operation parameters, such as pump working time and frequency, are used to analyze energy consumption; green infrastructure data, such as the location and function of rain gardens and wetlands, is used to analyze the carbon offset effect. The model integrates the seasonal autoregressive integrated moving average model and multi-criteria decision analysis; the seasonal autoregressive integrated moving average model is used to evaluate the impact of different factors on carbon emissions; multi-criteria decision analysis is used to predict seasonal water demand. Clean and standardize the collected historical data, use the processed data to train the model, and verify the prediction ability of the model through methods such as cross-validation. Continuously adjust and optimize the model parameters according to the actual operation situation. Use the model to predict future water demand and analyze the influencing factors of carbon emissions. According to the model output, dynamically adjust the water supply scheduling strategy to reduce carbon emissions. The dynamic adjustment of water supply scheduling is based on real-time data to optimize the operation of pumps. Real-time monitor the operation status and environmental parameters of the water supply network to provide real-time data for the model. According to the model output and real-time data, dynamically adjust the operation time, frequency, and intensity of pumps to optimize energy consumption and carbon emissions. By simulating different water supply scheduling strategies, compare their carbon emission and energy consumption results, and gradually iteratively find the optimal strategy.

[0032] Furthermore, step S2 of this application further includes:

[0033] Based on historical water use data, taking the standard season as the starting point, identify the water use peak periods in different seasons; based on the water use peak periods in different seasons, evaluate the environmental fitness of multiple water supply scheduling strategies, and take the water supply scheduling strategy with the highest environmental fitness as the optimal water supply scheduling strategy.

[0034] Specifically, the peak water usage period refers to the period in a day or a year when the water demand reaches its highest value. These peak periods may be related to seasons, weather, holidays, or daily activity patterns, such as morning and evening rush hours. The environmental adaptability refers to the degree to which the water supply scheduling strategy minimizes the environmental impact while meeting the water demand. A strategy with high environmental adaptability can effectively reduce energy consumption and carbon emissions while maintaining the stability and reliability of the water supply. Collect and analyze historical water usage data to identify the peak water usage periods and water usage patterns in different seasons, such as spring, summer, autumn, and winter. Based on the historical data, use statistical or machine learning methods to predict the water demand in future seasons in order to better prepare the water supply scheduling strategy. Develop multiple water supply scheduling strategies, considering different pump operation modes, water storage tank usage, water source allocation, etc. Simulate and evaluate each strategy, considering its impact on energy consumption, carbon emissions, water pressure stability, and water supply reliability. Compare the environmental adaptability of different strategies and select the strategy with the highest adaptability as the optimal water supply scheduling strategy.

[0035] Furthermore, this application also includes:

[0036] Deploy Internet of Things sensors at key positions on the M water supply paths based on the peak water usage periods in different seasons. The Internet of Things sensors use wireless communication protocols to transmit carbon emission data in real time. Analyze the correlation between the peak water usage period and environmental conditions based on the carbon emission data collected by the Internet of Things sensors. Based on the correlation between the peak water usage period and environmental conditions, conduct reinforcement learning with the goal of enhancing the carbon emission offset effect of the green infrastructure.

[0037] Specifically, Internet of Things (IoT) sensors can monitor and transmit data on key parameters such as energy consumption, water quality, flow rate, and pressure, thereby providing real-time carbon emission information. A wireless communication protocol is a communication method that does not require a physical connection and allows sensors to transmit data to a central monitoring system through a network. Correlation analysis refers to analyzing the statistical relationship between high water demand during peak water usage periods, such as at noon in summer, and environmental conditions, such as temperature, humidity, wind speed, etc. Reinforcement learning is a machine learning method that maximizes a certain reward signal by continuously experimenting and adjusting strategies. In this embodiment, it is to enhance the carbon emission offset effect of green infrastructure. Install IoT sensors at key positions in the M water supply paths, such as pumping stations, pipe network intersections, water storage facilities, etc. Configure the sensors to use wireless communication protocols, such as LoRa, NB-IoT, Wi-Fi, etc., to ensure real-time transmission of carbon emission data to the monitoring center. Collect sensor data and environmental monitoring data, and use statistical or data mining techniques to analyze the correlation between peak water usage periods and environmental conditions. Based on the analysis results, establish a reinforcement learning model to optimize the layout and operation of green infrastructure and enhance its carbon offset effect. By simulating different green infrastructure configurations and operation strategies, evaluate their impact on the carbon emission offset effect and iteratively optimize the strategies. And continuously monitor the performance and environmental impact of green infrastructure and adjust the strategies according to the feedback.

[0038] Furthermore, this application also includes:

[0039] Obtain the pipe network operation parameters, where the pipe network operation parameters include the working time and frequency of the water pump; obtain the current water demand, and combine the historical water usage data to predict the water demand at the next time series node; at N non-water source nodes in the M water supply paths, use the current water demand as the starting point and the water demand at the next time series node as the ending point, and perform grid search verification in combination with the pipe network operation parameters to obtain the multiple water supply scheduling strategies.

[0040] Specifically, the pipeline network operation parameters include the working hours and frequencies of the water pumps. The current water demand refers to the actual water consumption at a specific time point or during a specific time period. The predicted water demand refers to the estimation of the water demand for a future period based on historical water consumption data and other relevant factors, such as season, weather, holidays, etc. Grid search verification involves systematically searching all possible combinations within a specific parameter space to find the optimal or near-optimal solution. By installing monitoring devices, the operation parameters such as the working hours and frequencies of the water pumps are obtained in real time. The current water demand data is obtained in real time through water metering devices. Time series analysis, machine learning, or other prediction models are used, combined with historical water consumption data and environmental factors, to predict the water demand at the next time series node. On the N non-water source nodes of the M water supply paths, different starting and ending points of water demand are set, and combined with the pipeline network operation parameters, a grid search algorithm is used to evaluate multiple water supply scheduling strategies. Each strategy is evaluated, considering factors such as its ability to meet water demand, energy consumption, and carbon emissions, and the optimal strategy is selected.

[0041] Furthermore, this application also includes:

[0042] Multi-criteria decision analysis is adopted to evaluate the impact of the urban heat island effect on the urban water supply pipeline network and obtain the first decision impact factor; multi-criteria decision analysis is adopted to evaluate the impact of climate change in different seasons on the urban water supply pipeline network and obtain the second decision impact factor; based on the first decision impact factor and the second decision impact factor, the carbon emission offset effect of the green infrastructure is evaluated.

[0043] Specifically, the urban heat island effect is a phenomenon where the temperature in the central urban area is higher than that in the surrounding rural areas, which has a direct impact on the operation of the urban water supply pipeline network, such as increasing energy consumption and reducing the efficiency of the water supply system. Climate change in different seasons affects water demand, energy consumption, and the stability of the water supply system. Decision impact factors These are important factors that affect decision-making, such as energy consumption, carbon emissions, hydraulic losses, system reliability, etc. Data on the urban heat island effect, including temperature differences, heat energy distribution, etc., is collected and analyzed. MCDA is used to evaluate the impact of the urban heat island effect on the water supply pipeline network, determine which factors are most affected, and use these factors as the first decision impact factor. Data on climate change in different seasons, including temperature changes, precipitation, etc., is collected and analyzed. MCDA is used to evaluate the impact of climate change on the water supply pipeline network, determine which factors are most affected, and use these factors as the second decision impact factor. Data on green infrastructure, including carbon sequestration capacity, water treatment effect, etc., is collected and analyzed. MCDA is used to evaluate the carbon emission offset effect of green infrastructure, considering the first and second decision impact factors.

[0044] Furthermore, this application also includes:

[0045] In the GIS system, import the green infrastructure and emission offset efficiency, establish an urban microclimate model, and add the first decision-making influencing factor and the second decision-making influencing factor; in the urban microclimate model, combine the multiple water supply scheduling strategies for multiple iterations, and evaluate the contribution degree of the reduction in energy demand of the urban water supply network; perform scale transformation on the contribution degrees of multiple energy demand reductions to obtain the carbon emission offset effect of the green infrastructure.

[0046] Specifically, a GIS system is a computer system used for collecting, storing, analyzing, and managing geospatial data. The green infrastructure and emission offset efficiency refer to the data on green infrastructure such as rain gardens, wetlands, etc. imported into the GIS system and their efficiency in reducing carbon emissions. An urban microclimate model is a model that simulates the internal climate conditions of a city, including parameters such as temperature, humidity, wind speed, etc., and how they change over time and space. The decision-making influencing factors are the key factors affecting the operation of the water supply network and carbon emissions, such as the urban heat island effect, climate change, etc. Import the geographical information of the green infrastructure and the emission offset efficiency data into the GIS system. Add the first and second decision-making influencing factors, such as the intensity of the urban heat island effect, the trend of climate change, etc. Use the spatial analysis tools in the GIS system to establish an urban microclimate model to simulate the internal climate conditions of the city. In the urban microclimate model, combine multiple water supply scheduling strategies for multiple iterations. Evaluate the contribution degrees of different strategies to the reduction in energy demand and their impact on the carbon emission offset effect. Perform scale transformation on the contribution degrees of multiple energy demand reductions for comparison with the carbon emission offset effect of the green infrastructure. Analyze the carbon emission offset effects of different water supply scheduling strategies and green infrastructure to determine the best combination and optimal strategy.

[0047] Furthermore, this application also includes:

[0048] Fit the seasonal autoregressive integrated moving average formula:

[0049] , where is the autoregressive coefficient, p is the order of the autoregressive term, B is the backshift operator, , s is the length of the seasonal cycle, D is the order of seasonal differencing, is the observed value at time point t, is the moving average coefficient, q is the order of the moving average term, is the moving average part, is the seasonal autoregressive coefficient, w is the order of the seasonal autoregressive term, is the error term of the seasonal cycle, is the seasonal autoregressive part; substituting into the seasonal autoregressive integrated moving average formula to quantify the linear correlation between the peak water consumption period and environmental conditions.

[0050] Specifically, use the fitted model to predict future water consumption and consider changes in environmental conditions. The linear correlation between the peak water consumption period and environmental conditions can be quantified, and how environmental conditions affect water consumption can be understood.

[0051] In summary, the carbon emission monitoring method for urban water supply networks provided by this application has the following technical effects:

[0052] By obtaining the basic network information based on the urban water supply network corresponding to the target area, the basic network information includes pipeline length and pipeline diameter; based on the urban water supply network, taking the water source node as the starting point and the water consumption node as the ending point, M water supply paths are determined, and the M water supply paths include N non-water source nodes, and the node types corresponding to the non-water source nodes include pipe section intersection points, pipe material and diameter change points, and branch connection points; based on the M water supply paths in the urban water supply network, carbon emission monitoring is carried out to collect carbon emission efficiency and carbon emissions; an environmental monitoring module is introduced to obtain environmental monitoring data and integrate green infrastructure, where the environmental monitoring data includes rainfall, and the green infrastructure includes rain gardens and wetlands; based on the green infrastructure in the urban water supply network, carbon emission monitoring is carried out to collect carbon emission offset efficiency and carbon emission offset amounts; based on the carbon emission efficiency and carbon emissions, carbon emission offset efficiency and carbon emission offset amounts, combined with the vegetation coverage in the satellite remote sensing data, analyze the urban heat island effect and establish a carbon emission accounting model, and dynamically adjust the water supply scheduling of the urban water supply network, iteratively optimize to determine the optimal water supply scheduling strategy, and synchronously carry out urban water supply management, effectively solving the problems in the prior art that it is difficult to clarify and define the water supply paths and their key nodes due to focusing on single pipeline information, and at the same time, the water supply scheduling strategy cannot be adaptively adjusted, resulting in unstable water supply quality, difficult maintenance and management, and further affecting the efficiency of urban water supply management, which helps to improve the operation efficiency of urban water supply networks, reduce energy consumption and carbon emissions, and ensure the stability and reliability of water supply services.

[0053] Embodiment 2, based on the carbon emission monitoring method for urban water supply networks in the foregoing embodiment with the same inventive concept, this application also provides a carbon emission monitoring system for urban water supply networks. Please refer to the appendix Figure 2 The carbon emission monitoring system for urban water supply networks includes:

[0054] The basic network information acquisition module 11 is used to obtain the basic network information based on the urban water supply network corresponding to the target area, and the basic network information includes pipeline length and pipeline diameter.

[0055] A water supply path determination module 12, which is used to determine M water supply paths based on the urban water supply network, with the water source node as the starting point and the water consumption node as the ending point. The M water supply paths include N non-water source nodes, and the node types corresponding to the non-water source nodes include pipe intersection points, pipe material and diameter change points, and branch connection points.

[0056] A carbon emission monitoring module 13, which is used to conduct carbon emission monitoring based on the M water supply paths in the urban water supply network, and collect carbon emission efficiency and carbon emission amounts.

[0057] An environmental monitoring data acquisition module 14, which is used to introduce an environmental monitoring module group to obtain environmental monitoring data and integrate green infrastructure. Among them, the environmental monitoring data includes rainfall, and the green infrastructure includes rain gardens and wetlands.

[0058] An offset amount collection module 15, which is used to conduct carbon emission monitoring based on the green infrastructure in the urban water supply network, and collect carbon emission offset efficiency and carbon emission offset amounts.

[0059] A water supply management module 16, which is used to analyze the urban heat island effect and establish a carbon emission accounting model based on carbon emission efficiency and carbon emission amounts, carbon emission offset efficiency and carbon emission offset amounts, combined with the vegetation coverage in satellite remote sensing data, and dynamically adjust the water supply scheduling of the urban water supply network, iteratively optimize to determine the optimal water supply scheduling strategy, and synchronously conduct urban water supply management.

[0060] Furthermore, the water supply path determination module 12 in the carbon emission monitoring system for urban water supply networks is further used for:

[0061] Based on historical water consumption data, taking the standard season as the entry point, identify the water consumption peak periods in different seasons; based on the water consumption peak periods in different seasons, evaluate the environmental adaptability of multiple water supply scheduling strategies, and take the water supply scheduling strategy with the highest environmental adaptability as the optimal water supply scheduling strategy.

[0062] Furthermore, the water supply path determination module 12 in the carbon emission monitoring system for urban water supply networks is further used for:

[0063] Based on the water consumption peak periods in different seasons, deploy Internet of Things sensors at key positions on the M water supply paths. The Internet of Things sensors use wireless communication protocols to transmit carbon emission data in real time; based on the carbon emission data collected by the Internet of Things sensors, analyze the correlation between the water consumption peak periods and environmental conditions; based on the correlation between the water consumption peak periods and environmental conditions, conduct reinforcement learning with the goal of enhancing the carbon emission offset effect of the green infrastructure.

[0064] Further, the water supply path determination module 12 in the carbon emission monitoring system for urban water supply networks is further configured to:

[0065] Obtain the pipe network operation parameters, where the pipe network operation parameters include the working time and frequency of the water pump; obtain the current water demand, and combine the historical water use data to predict the water demand at the next time series node; at N non-water source nodes among the M water supply paths, use the current water demand as the starting point and the water demand at the next time series node as the ending point, and perform grid search verification in combination with the pipe network operation parameters to obtain the multiple water supply scheduling strategies.

[0066] Further, the water supply path determination module 12 in the carbon emission monitoring system for urban water supply networks is further configured to:

[0067] Adopt multi-criteria decision analysis to evaluate the impact of the urban heat island effect on the urban water supply network and obtain the first decision impact factor; adopt multi-criteria decision analysis to evaluate the impact of climate change in different seasons on the urban water supply network and obtain the second decision impact factor; based on the first decision impact factor and the second decision impact factor, evaluate the carbon emission offset effect of the green infrastructure.

[0068] Further, the water supply path determination module 12 in the carbon emission monitoring system for urban water supply networks is further configured to:

[0069] In the GIS system, import the green infrastructure and the emission offset efficiency, establish an urban microclimate model, and add the first decision impact factor and the second decision impact factor; in the urban microclimate model, perform multiple iterations in combination with the multiple water supply scheduling strategies, and evaluate the contribution degree of the reduction in the energy demand of the urban water supply network; perform scale transformation on the contribution degrees of multiple energy demand reductions to obtain the carbon emission offset effect of the green infrastructure.

[0070] Further, the water supply path determination module 12 in the carbon emission monitoring system for urban water supply networks is further configured to:

[0071] Fit the seasonal autoregressive integrated moving average formula:

[0072] , where, is the autoregressive coefficient, p is the order of the autoregressive term, B is the backshift operator, , s is the length of the seasonal cycle, D is the order of seasonal differencing, is the observed value at time point t, is the moving average coefficient, q is the order of the moving average term, is the moving average part, is the seasonal autoregressive coefficient, w is the order of the seasonal autoregressive term, is the error term of the seasonal cycle, is the seasonal autoregressive part; substituting into the said seasonal autoregressive integrated moving average formula to quantify the linear correlation between the peak water consumption period and environmental conditions.

[0073] The various embodiments in this specification are described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The foregoing Figure 1 The carbon emission monitoring method and specific examples for urban water supply networks in Embodiment 1 are equally applicable to the carbon emission monitoring system for urban water supply networks in this embodiment. Through the detailed description of the carbon emission monitoring method for urban water supply networks above, those skilled in the art can clearly know the carbon emission monitoring system for urban water supply networks in this embodiment. Therefore, for the sake of simplicity of the specification, it will not be elaborated here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For the relevant parts, please refer to the description in the method section.

[0074] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0075] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A method for monitoring carbon emissions in an urban water supply network, characterized in that: include: Based on the urban water supply network corresponding to the target area, basic information of the network is obtained, wherein the basic information of the network includes the length and diameter of the pipeline; Based on the urban water supply network, with the water source node as the starting point and the water use node as the end point, M water supply paths are determined, wherein the M water supply paths include N non-water source nodes, and the node types corresponding to the non-water source nodes include pipe segment intersection points, pipe diameter change points, and branch pipe connection points; Based on the M water supply paths in the urban water supply network, carbon emission monitoring is performed to collect carbon emission efficiency and carbon emission amount; Introducing an environmental monitoring module to obtain environmental monitoring data and integrate green infrastructure, wherein the environmental monitoring data includes rainfall, and the green infrastructure includes rain gardens and wetlands; Based on the green infrastructure in the urban water supply network, carbon emission monitoring is carried out to collect carbon emission offset efficiency and carbon emission offset amount; Based on carbon emission efficiency and carbon emission amount, carbon emission offset efficiency and carbon emission offset amount, combined with vegetation coverage in satellite remote sensing data, the urban heat island effect is analyzed and a carbon emission accounting model is established. The water supply scheduling of the urban water supply network is dynamically adjusted, and the optimal water supply scheduling strategy is determined by iterative optimization, and urban water supply management is carried out simultaneously. Based on the M water supply paths in the urban water supply network, carbon emission monitoring is performed, further comprising: Based on historical water use data, the peak water use periods in different seasons are identified with the standard season as the starting point; Based on the peak water consumption periods in different seasons, the environmental adaptability of multiple water supply scheduling strategies is evaluated, and the water supply scheduling strategy with the highest environmental adaptability is taken as the optimal water supply scheduling strategy; Based on the peak water consumption periods in different seasons, the environmental adaptability of multiple water supply scheduling strategies is evaluated, including: Based on the peak water consumption periods in different seasons, IoT sensors are deployed at key locations of the M water supply routes, and the IoT sensors transmit carbon emission data in real time using wireless communication protocols; Analyze the correlation between peak water use and environmental conditions based on the carbon emission data collected by the IoT sensors; Based on the correlation between peak water use and environmental conditions, reinforcement learning is performed to enhance the carbon emission offset effect of the green infrastructure; Based on the carbon emission data collected by the IoT sensor, the autoregressive integrated moving average algorithm is used to analyze the correlation between the peak water use period and environmental conditions, including: Fitting seasonal autoregressive integrated moving average formula: Among them, φ i is the autoregressive coefficient, p is the order of the autoregressive term, B is the backward operator, and B i X t =X t-i , s is the length of the seasonal cycle, D is the seasonal difference order, X t is the observed value at time point t, θ j is the sliding average coefficient, q is the order of the sliding average term, is the sliding average part, Φ k is the seasonal autoregressive coefficient, w is the order of the seasonal autoregressive term, ε t-s is the error term of the seasonal cycle, is the seasonal autoregressive part; Substituting into the seasonal autoregressive integrated moving average formula, the linear correlation between peak water use and environmental conditions was quantified.

2. The carbon emission monitoring method for urban water supply network according to claim 1, characterized in that: Conduct reinforcement learning with the goal of enhancing the carbon emission offset effect of the green infrastructure, including: Obtaining pipe network operation parameters, wherein the pipe network operation parameters include water pump working time and frequency; Obtaining the current water demand and, in combination with the historical water demand data, predicting the water demand at the next time series node; At the N non-water source nodes in the M water supply paths, the current water demand is taken as the starting point and the water demand of the next time node is taken as the end point, and grid search verification is performed in combination with the pipe network operation parameters to obtain the multiple water supply scheduling strategies.

3. The carbon emission monitoring method for urban water supply network according to claim 2, characterized in that: include: Using multi-criteria decision analysis, evaluate the impact of the urban heat island effect on the urban water supply network, and obtain the first decision influencing factor; Using multi-criteria decision analysis, evaluate the impact of climate change in different seasons on the urban water supply network, and obtain the second decision-making influencing factor; Based on the first decision influencing factor and the second decision influencing factor, the carbon emission offsetting effect of the green infrastructure is evaluated.

4. The carbon emission monitoring method for urban water supply network according to claim 3, characterized in that: Based on the first decision-making influencing factor and the second decision-making influencing factor, the carbon emission offset effect of the green infrastructure is evaluated, including: In the GIS system, the green infrastructure and emission offset efficiency are imported, an urban microclimate model is established, and the first decision-making influencing factor and the second decision-making influencing factor are added; In the urban microclimate model, multiple iterations are performed in combination with the multiple water supply scheduling strategies, and the contribution of the energy demand reduction of the urban water supply network is evaluated; The contribution of multiple energy demand reduction amounts is scaled and converted to obtain the carbon emission offset effect of the green infrastructure.

5. A carbon emission monitoring system for an urban water supply network, characterized in that: The steps for implementing the carbon emission monitoring method for a city water supply network according to any one of claims 1 to 4, the carbon emission monitoring system for a city water supply network comprising: A pipe network basic information acquisition module is used to acquire pipe network basic information based on the urban water supply pipe network corresponding to the target area, wherein the pipe network basic information includes pipe length and pipe diameter; A water supply path determination module is used to determine M water supply paths based on the urban water supply network, with the water source node as the starting point and the water use node as the end point, wherein the M water supply paths include N non-water source nodes, and the node types corresponding to the non-water source nodes include pipe segment intersection points, pipe diameter change points, and branch pipe connection points; A carbon emission monitoring module, used to monitor carbon emissions based on the M water supply paths in the urban water supply network, and collect carbon emission efficiency and carbon emissions; An environmental monitoring data acquisition module is used to introduce an environmental monitoring module, acquire environmental monitoring data, and integrate green infrastructure, wherein the environmental monitoring data includes rainfall, and the green infrastructure includes rain gardens and wetlands; An offset amount collection module is used to monitor carbon emissions based on the green infrastructure in the urban water supply network and collect carbon emission offset efficiency and carbon emission offset amount; The water supply management module is used to analyze the urban heat island effect and establish a carbon emission accounting model based on carbon emission efficiency and carbon emission amount, carbon emission offset efficiency and carbon emission offset amount, combined with vegetation coverage in satellite remote sensing data, and dynamically adjust the water supply scheduling of the urban water supply network, iteratively optimize and determine the optimal water supply scheduling strategy, and simultaneously perform urban water supply management.

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