Multi-scenario-driven water supply carbon emission accounting system optimization method and system

Abstract

The invention relates to the technical field of carbon emission accounting, and discloses a multi-scenario-driven water supply carbon emission accounting system optimization method and system, and the method comprises the steps: constructing a three-dimensional dynamic entropy weight grading model according to the process topological characteristics of a water supply system, water source heterogeneity and time-space load fluctuation, dividing scenario clusters, and calculating a carbon accounting gradient; collecting heterogeneous carbon source pulse data to generate a dynamic carbon accounting density function and heterogeneous entropy weight deviation; a carbon accounting gradient multi-objective optimization model is constructed and solved, an optimal carbon accounting gradient is obtained, and an optimization result is output; according to the invention, the problem that the existing carbon emission accounting method in the water supply industry is not uniform, inaccurate and not suitable for different situations is solved, and powerful support is provided for urban low-carbon development and energy conservation and emission reduction in the water supply industry.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission accounting, and more specifically, to an optimization method and system for a water supply carbon emission accounting system driven by multiple scenarios. Background Art

[0002] As an important urban infrastructure, carbon emissions in the water supply industry run through multiple links such as raw water intake, water treatment in water plants, and water transmission and distribution. Accurately clarifying the carbon emissions in these links and constructing a scientific accounting system are of great significance for the low-carbon development of cities and energy conservation and emission reduction in the water supply industry. However, the current water supply industry faces many challenges in carbon emission accounting and urgently needs effective solutions.

[0003] Although existing carbon emission accounting-related technologies have been applied in other fields, there are compatibility problems in the water supply industry. For example, a multi-scenario building carbon emission prediction method and device proposed in a Chinese patent application with the publication number CN114723134A mainly focuses on the carbon emission prediction during the building operation period. This method predicts carbon emissions, carbon peak time, and peak value by analyzing the current situation of building energy consumption and carbon emissions and constructing a building LEAP scenario model. However, there are significant differences between the water supply industry and the building industry in terms of energy consumption structure, process flow, etc. The water supply industry involves complex water treatment processes, and its energy consumption not only includes electricity but also involves indirect carbon emissions brought about by the use of chemicals, etc.; while the building industry is mainly the energy consumption of various energy-consuming equipment. Therefore, the method of this existing technology is difficult to be directly applied to the water supply industry, cannot accurately account for the carbon emissions in each link of the water supply process, and cannot meet the requirement of the water supply industry for accurate quantification of carbon emissions in different treatment processes.

[0004] A Chinese patent application with the publication number CN115796340A discloses a carbon emission prediction method and system for multiple urban fields. By obtaining historical terminal energy consumption data, a city energy consumption LEAP model is constructed to calculate the total energy carbon emissions in different fields and the city as a whole. However, this method focuses on the calculation of energy consumption and carbon emissions at the macro level of multiple urban fields and does not deeply consider the impact of the unique water source heterogeneity, process topology characteristics, and spatio-temporal load fluctuations in the water supply industry on carbon emissions. For example, the water quality differences in different water source areas will lead to different energy and chemical consumption in the water treatment process, thereby affecting carbon emissions. However, this existing technology does not analyze such factors unique to the water supply industry and is difficult to achieve refined accounting of carbon emissions in the water supply industry and cannot meet the requirement of accurate accounting of carbon emissions in the water supply industry under different scenarios.

[0005] At present, there is a lack of a unified, accurate, and applicable carbon emission accounting method for the water supply industry in different scenarios. The existing related technologies cannot fully consider the particularity of the water supply industry, making it difficult to comprehensively and accurately account for the carbon emissions of the water supply industry and unable to meet the needs of urban low-carbon development and energy conservation and emission reduction in the water supply industry for accurate carbon emission data. Summary of the Invention

[0006] The present invention is applicable to water supply systems of various scales and different operation modes. Whether it is a large urban water supply network or a small town water supply facility, the carbon emissions during the water supply process can be accurately accounted for by means of this method and system. For example, in urban planning, the water supply facilities can be reasonably arranged, the water supply process can be optimized, and carbon emissions can be reduced according to the accounting results; in the daily operation and management of water supply enterprises, the carbon emission situation can be monitored in real time through this system, and the operation strategy can be adjusted in time to achieve the goal of energy conservation and emission reduction.

[0007] In order to overcome the above defects of the existing technology, the present invention provides an optimization method and system for a water supply carbon emission accounting system driven by multiple scenarios. By constructing a three-dimensional dynamic entropy weight hierarchical model and fully considering the process topology characteristics, water source heterogeneity, and spatio-temporal load fluctuations of the water supply system, refined analysis of water supply carbon emissions in different scenarios is realized. This method can collect heterogeneous carbon source pulse data in real time, generate an accurate dynamic carbon accounting density function, and obtain the optimal carbon accounting gradient by constructing a carbon accounting gradient multi-objective optimization model, thereby outputting an optimized carbon emission accounting result, providing strong support for urban low-carbon development and energy conservation and emission reduction in the water supply industry.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] An optimization method for a water supply carbon emission accounting system driven by multiple scenarios, comprising:

[0010] Construct a three-dimensional dynamic entropy weight hierarchical model according to the process topology characteristics, water source heterogeneity, and spatio-temporal load fluctuations of the water supply system. According to the three-dimensional dynamic entropy weight hierarchical model, divide the accounting system into N scenario clusters and calculate the carbon accounting gradient of each scenario cluster , where N ≥ 3;

[0011] Collect heterogeneous carbon source pulse data of the entire water supply process in real time, map the heterogeneous carbon source pulse data to the N scenario clusters divided by the accounting system, generate a dynamic carbon accounting density function , and calculate the heterogeneous entropy weight deviation ;

[0012] According to the dynamic carbon accounting density function and the heterogeneous entropy weight deviation , construct a carbon accounting gradient multi-objective optimization model; solve the carbon accounting gradient multi-objective optimization model and iteratively optimize the carbon accounting gradient of each scenario cluster , the optimal carbon accounting gradient is obtained , according to the optimal carbon accounting gradient , the optimized results of multi-scenario carbon emission accounting are output.

[0013] Furthermore, the construction of the three-dimensional dynamic entropy weight classification model includes:

[0014] Based on the process topology characteristics of the water supply system, historical power consumption data and historical chemical dosage data of each treatment process are obtained. According to the historical power consumption data and historical chemical dosage data of each treatment process, the process entropy value E is obtained p ;

[0015] Based on the water source heterogeneity of the water supply system, the raw water BOD concentration and the length of the water transmission pipeline network of each water source area are obtained. According to the raw water BOD concentration and the length of the water transmission pipeline network of each water source area, the water source coupling factor α is generated s ;

[0016] Based on the spatio-temporal load fluctuation of the water supply system, the load fluctuation coefficient β is calculated t ;

[0017] According to the process entropy value E p , the water source coupling factor α s and the load fluctuation coefficient β t , a three-dimensional dynamic entropy weight classification model is constructed.

[0018] Furthermore, the obtaining of the process entropy value E p includes:

[0019] According to the historical power consumption data of each treatment process, the power consumption entropy increase rate of each treatment process is calculated. The difference between the power consumption entropy increase rate of each treatment process and the energy consumption entropy increase rate of the reference process is obtained to get the relative entropy increase rate;

[0020] According to the historical chemical dosage data of each treatment process, the total chemical dosage of each treatment process is obtained, and the ratio of the total chemical dosage to the influent water volume is calculated to get the chemical dissipation ratio;

[0021] The relative entropy increase rate and the chemical dissipation ratio are linearly combined to obtain the process entropy value E p .

[0022] Furthermore, the generation of the water source coupling factor α s includes:

[0023] The raw water BOD concentration of each water source area is normalized by min-max to obtain the BOD heterogeneity index; the length of the water transmission pipeline network of each water source area is normalized by min-max to obtain the distance heterogeneity index; the BOD heterogeneity index and the distance heterogeneity index are weighted and summed to obtain the water source coupling factor α s。

[0024] Furthermore, the calculation load fluctuation coefficient β t includes:

[0025] Based on the spatio-temporal load fluctuation of the water supply system, obtain the historical monthly water production data and the historical carbon emission intensity time series data;

[0026] According to the historical monthly water production data, calculate the coefficient of variation of the average monthly water production to obtain the water production fluctuation index;

[0027] Perform Fourier transform on the historical carbon emission intensity time series data, extract the dominant frequency, calculate the peak period, and obtain the carbon emission intensity periodic index;

[0028] Take the square root of the product of the water production fluctuation index and the carbon emission intensity periodic index to obtain the load fluctuation coefficient β t 。

[0029] Furthermore, the construction of the three-dimensional dynamic entropy weight grading model includes:

[0030] Normalize the process entropy value E p , the water source coupling factor α s and the load fluctuation coefficient β t respectively;

[0031] Assign the weight coefficient w p to the normalized process entropy value, assign the weight coefficient w s to the normalized water source coupling factor, and assign the weight coefficient w t to the normalized load fluctuation coefficient; form the entropy weight vector [w p , w s , w t with w p , w s , w t ;

[0032] Construct a three-dimensional dynamic entropy weight grading model from the process entropy value E p , the water source coupling factor α s , the load fluctuation coefficient β t and the entropy weight vector [w p , w s , w t .

[0033] Furthermore, the calculation of the carbon accounting gradient for each scenario cluster includes:

[0034] Calculate the mean of the process entropy value of each scenario cluster , the mean of the water source coupling factor and the mean of the load fluctuation coefficient , multiply , and according to the entropy weight vector [w p , w s , w t in the three-dimensional dynamic entropy weight classification model for weighted calculation to obtain the carbon accounting gradient = , where represents the carbon accounting gradient of the Nth scenario cluster.

[0035] Further, the heterogeneous carbon source pulse data of the whole water supply process includes the real-time power consumption data of each link of the water supply system and the real-time carbon emission factor of the regional power grid, the real-time chemical agent dosing data and the chemical agent transportation distance of the water supply system, the methane emission rate data r d (t) of the sludge anaerobic fermentation in the sludge treatment unit of the sludge treatment link, the methane emission rate data r l (t) of the landfill process in the landfill, the key sludge treatment parameters of the sludge treatment unit and the key landfill parameters of the landfill;

[0036] The generation of the dynamic carbon accounting density function includes:

[0037] Based on the heterogeneous carbon source pulse data of the whole water supply process, obtain the power entropy pulse signal P e , the carbon emission chain correlation matrix Γ c of the chemical agent link, and the carbon emission eddy current coupling coefficient Ω s ; set the fusion weight vector of the scenario cluster, where is the fusion weight of the Nth scenario cluster; based on the fusion weight vector of the scenario cluster, fuse the power entropy pulse signal P e , the carbon emission chain correlation matrix Γ c and the carbon emission eddy current coupling coefficient Ω s to generate the dynamic carbon accounting density function .

[0038] Further, the heterogeneous carbon source pulse data of the whole water supply process includes the real-time power consumption data of each link of the water supply system, the real-time carbon emission factor of the regional power grid, the real-time chemical agent dosing data and the chemical agent transportation distance of the water supply system;

[0039] The obtaining of the power entropy pulse signal P e includes:

[0040] Multiply the real-time power consumption data of each link of the water supply system by the real-time carbon emission factor of the regional power grid to obtain the time series data of the real-time carbon emission intensity of the power energy consumption;

[0041] Perform pulse detection on the real-time carbon emission intensity time series data, extract the power carbon emission pulse events, and construct the power entropy pulse signal P e 。

[0042] Furthermore, the real-time chemical dosing data includes the real-time chemical dosing amount and the chemical concentration;

[0043] The carbon emission chain correlation matrix Γ of the chemical dosing link c includes:

[0044] Organize the real-time chemical dosing amount, chemical concentration, and chemical transportation distance of each chemical into a three-dimensional correlation matrix; quantify each element in the three-dimensional correlation matrix into carbon emission equivalents to generate the carbon emission chain correlation matrix Γ c 。

[0045] Furthermore, the construction of the carbon accounting gradient multi-objective optimization model includes:

[0046] Integrate the dynamic carbon accounting density function to calculate the total carbon emissions ;

[0047] According to the carbon accounting gradient of each scenario cluster and the water supply volume of the water supply unit corresponding to each scenario cluster , calculate the theoretical total carbon emissions of the water supply system ;

[0048] According to and , define the cross-modal verification deviation ;

[0049] Combine the cross-modal verification deviation and the heterogeneous entropy weight deviation to construct a carbon accounting gradient multi-objective optimization model.

[0050] A multi-scenario-driven water supply carbon emission accounting system optimization system, which is used to implement the above-mentioned multi-scenario-driven water supply carbon emission accounting system optimization method. The system includes:

[0051] Scenario classification module: According to the process topology characteristics, water source heterogeneity, and spatio-temporal load fluctuations of the water supply system, construct a three-dimensional dynamic entropy weight classification model. According to the three-dimensional dynamic entropy weight classification model, divide the accounting system into N scenario clusters, and calculate the carbon accounting gradient of each scenario cluster , where N≥3;

[0052] Deviation calculation module: Real-time collect the heterogeneous carbon source pulse data of the entire water supply process, map the heterogeneous carbon source pulse data to the N scenario clusters divided by the accounting system, and generate a dynamic carbon accounting density function , and calculate the heterogeneous entropy weight deviation ;

[0053] Carbon accounting optimization module: According to the dynamic carbon accounting density function and the heterogeneous entropy weight deviation , construct a carbon accounting gradient multi-objective optimization model; solve the carbon accounting gradient multi-objective optimization model, and iteratively optimize the carbon accounting gradient of each scenario cluster to obtain the optimal carbon accounting gradient , according to the optimal carbon accounting gradient , output the optimized results of multi-scenario carbon emission accounting.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] The present invention comprehensively considers various factors such as the process topology characteristics of the water supply system, water source heterogeneity, and spatio-temporal load fluctuations, constructs a three-dimensional dynamic entropy weight classification model to divide scenario clusters, and realizes the refined analysis of carbon emissions in different scenarios. By collecting heterogeneous carbon source pulse data in real time, generating a dynamic carbon accounting density function and calculating the heterogeneous entropy weight deviation, it can more accurately reflect the real-time carbon emission situation of the system. Constructing and solving a carbon accounting gradient multi-objective optimization model, the obtained optimal carbon accounting gradient further optimizes the accounting results. This method provides a unified, accurate and scenario-applicable accounting scheme, which helps water supply enterprises and urban managers clearly understand the carbon emission situation, formulate targeted emission reduction strategies, improve energy utilization efficiency, reduce carbon emissions, meet the needs of urban low-carbon development and energy conservation and emission reduction in the water supply industry, and promote the sustainable development of the water supply industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0057] Figure 1 is the principle flow chart of the optimization method for the multi-scenario-driven water supply carbon emission accounting system of the present invention;

[0058] Figure 2 is the method flow chart for obtaining the process entropy value E p in the optimization method for the multi-scenario-driven water supply carbon emission accounting system of the present invention;

[0059] Figure 3 is the method for generating the water source coupling factor α sMethod flowchart;

[0060] Figure 4 For calculating the load fluctuation coefficient β in the optimized method of the multi-scenario-driven water supply carbon emission accounting system of the present invention t Method flowchart;

[0061] Figure 5 For the method flowchart of constructing a three-dimensional dynamic entropy weight grading model in the optimized method of the multi-scenario-driven water supply carbon emission accounting system of the present invention

[0062] Figure 6 For obtaining the power entropy pulse signal P in the optimized method of the multi-scenario-driven water supply carbon emission accounting system of the present invention e Method flowchart;

[0063] Figure 7 For the method flowchart of obtaining the candidate function set in the optimized method of the multi-scenario-driven water supply carbon emission accounting system of the present invention

[0064] Figure 8 For the functional module diagram of the multi-scenario-driven water supply carbon emission accounting system optimization system in the present invention. Detailed implementation manners

[0065] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0066] Embodiment 1

[0067] Please refer to Figure 1 As shown, this embodiment provides an optimized method for a multi-scenario-driven water supply carbon emission accounting system, including:

[0068] Step S1000, based on the process topology characteristics, water source heterogeneity, and spatio-temporal load fluctuations of the water supply system, construct a three-dimensional dynamic entropy weight grading model. According to the three-dimensional dynamic entropy weight grading model, divide the accounting system into N scenario clusters, and calculate the carbon accounting gradient of each scenario cluster , where N≥3;

[0069] Furthermore, step S1000 includes:

[0070] Step S1100, based on the process topology characteristics of the water supply system, obtain the historical power consumption data and historical chemical dosage data of each treatment process. According to the historical power consumption data and historical chemical dosage data of each treatment process, obtain the process entropy value E p;

[0071] Further, as Figure 2 shown, step S1100 includes:

[0072] Step S1110, based on the process topology characteristics of the water supply system, obtain the historical power consumption data and historical chemical dosage data of each treatment process;

[0073] Step S1120, according to the historical power consumption data of each treatment process, calculate the power consumption entropy increase rate of each treatment process, and subtract the power consumption entropy increase rate of each treatment process from that of the reference process to obtain the relative entropy increase rate;

[0074] Step S1130, obtain the total chemical dosage of each treatment process according to the historical chemical dosage data of each treatment process, calculate the ratio of the total chemical dosage to the influent water volume, and obtain the chemical dissipation ratio;

[0075] Step S1140, linearly combine the relative entropy increase rate and the chemical dissipation ratio to obtain the process entropy value E p .

[0076] Specifically, the process topology characteristics of the water supply system refer to the interconnection relationship, layout among the treatment processes in the water supply system, and the position and role of different processes in the entire water supply process. It covers various treatment processes and their combination methods involved in the process from raw water intake to finished water delivery. The process topology characteristics of the water supply system cover various treatment methods such as conventional treatment, advanced treatment, and CO2 dosing process. For different treatment processes, the data acquisition methods are different. Taking the coagulation and sedimentation link in the conventional treatment process as an example, the historical power consumption data can be obtained by installing power metering devices on relevant equipment (such as mixers, water pumps, etc.) and recording their power consumption at different times. The historical chemical dosage data can be obtained from the operation records of the chemical dosing equipment, such as recording the time and dosage of each flocculant addition. These data are the original basis for subsequent analysis of the carbon emission characteristics of each process. Its beneficial effect is to provide a data basis for subsequent quantification of the carbon emissions of each process. There is a lack of a unified and accurate method for carbon emission accounting in the water supply industry, and obtaining these data is a key step in achieving accurate accounting. By accurately grasping the power consumption and chemical dosage of each treatment process, the performance of different processes in terms of carbon emissions can be evaluated more precisely. For example, if the power consumption data of the advanced treatment process of a water plant is relatively high during a certain period, combined with the chemical dosage data, it can be further analyzed whether there is a problem of excessive carbon emissions in this process, providing a direction for subsequent process optimization and carbon emission reduction, thus helping to solve the problem of the accuracy of carbon emission accounting in the water supply industry.

[0077] The power consumption entropy increase rate is an index to measure the degree of disorder in the change of power consumption of each treatment process. When calculating the power consumption entropy increase rate, first, based on the historical power consumption data of each treatment process, analyze the change of power consumption at different time points. Suppose a treatment process has power consumptions of within the time interval . According to the relevant theory of thermodynamic entropy, its entropy increase can be calculated through a specific formula (where is the Boltzmann constant, n' is the total number of energy consumption states of the treatment process, i' is the index of the energy consumption state of the treatment process, is the probability of the process in different energy consumption states, which can be calculated according to the proportion of energy consumption data in the total energy consumption data), and then the power consumption entropy increase rate is obtained. The energy consumption state of the treatment process refers to the different power consumption situations presented by each treatment process during operation in the water supply system. During the operation of the treatment process in the water supply system, its power consumption is not fixed, but will fluctuate due to various factors. Differences in energy consumption at different times, changes in equipment performance, and external environmental factors will all lead to different energy consumption states. Take the operation of the water pump in the conventional treatment process as an example. During the peak water consumption period, to meet the water supply demand, the water pump needs to operate at a higher power, and the power consumption at this time is higher; while during the low water consumption period, the power of the water pump decreases, and the power consumption also decreases accordingly. This forms different energy consumption states. Suppose the water pump consumes 100 degrees of electricity per hour during the peak water consumption period and 50 degrees during the low water consumption period. These two different electricity consumption situations are two different energy consumption states.

[0078] By subtracting the power consumption entropy increase rate of each treatment process from that of the reference process (usually selecting a process with relatively stable and representative energy consumption as the reference), the relative entropy increase rate can be obtained. The larger the relative entropy increase rate, the higher the relative carbon emission intensity of the process. This is because power consumption is closely related to carbon emissions, and high energy consumption is often accompanied by high carbon emissions. By calculating the relative entropy increase rate, the differences in carbon emission intensities of each treatment process relative to the reference process can be intuitively compared. For example, if the relative entropy increase rate of a certain process is high, it indicates that the degree of disorder in the change of its power consumption is large, and there may be problems such as low energy utilization efficiency, resulting in an increase in carbon emissions. The beneficial effect of this step is that it can quickly screen out the processes with relatively high carbon emission intensities, provide important clues for further optimizing the processes and reducing carbon emissions, help solve the problem of evaluating the carbon emission intensity of processes in the carbon emission accounting of the water supply industry, and make subsequent emission reduction measures more targeted.

[0079] The ratio of the total dosage of chemicals to the influent water volume is the chemical dissipation ratio. For example, in a certain treatment process within a certain period, the total dosage of chemicals is M' and the influent water volume is Q', then the chemical dissipation ratio is 。The larger the chemical consumption ratio, the more chemicals are consumed by the process when treating the same amount of water. Since carbon emissions may occur during the production, transportation, and use of chemicals, a larger chemical consumption ratio means a higher carbon emission intensity for the process. Its beneficial effect is to evaluate the carbon emission intensity of the process from the perspective of chemical use. In the carbon emission accounting of the water supply industry, chemical use is an important carbon emission source. By calculating the chemical consumption ratio, it is possible to clearly understand the impact of each treatment process on carbon emissions in terms of chemical use. For example, for the disinfection process of a certain water plant, if its chemical consumption ratio is too high, it is possible to further analyze whether there is excessive chemical dosing, and then reduce carbon emissions by optimizing the chemical dosing amount, etc., which helps to achieve a comprehensive assessment and precise control of the carbon emissions of each process in the water supply system.

[0080] The way of linear combination usually adopts the form of weighted summation, that is, E p = q1×relative entropy increase rate + q2×chemical consumption ratio (where q1 and q2 are the weight coefficients of the relative entropy increase rate and the chemical consumption ratio respectively, and q1 + q2 = 1. The weight coefficients can be determined according to the actual situation through expert experience or machine learning algorithms to balance the contribution degrees of the relative entropy increase rate and the chemical consumption ratio to the process entropy value). In this way, the process entropy value E p comprehensively considers two main carbon emission sources, energy and chemicals. The process entropy value E p can more comprehensively evaluate the carbon emission characteristics of each process. Compared with considering only the power energy consumption or chemical dosing amount separately, it can more accurately reflect the overall carbon emission situation of a treatment process. For example, for a process with a low entropy increase rate of power energy consumption but a high chemical consumption ratio, looking only at the power energy consumption may consider its carbon emissions to be low, but after comprehensively considering chemical use, through the process entropy value E p it can be found that its overall carbon emission intensity is not low, which helps to avoid misjudgment of the process carbon emissions. Secondly, by introducing the process entropy value E p , the contribution differences of different treatment processes to carbon emissions can be quantitatively evaluated. In the water supply system, different treatment processes have different contributions to carbon emissions. By comparing the E p values of each process, it is possible to clarify which processes are the main carbon emission sources, provide a basis for the selection and optimization of the process route, reveal the important impact of the process route selection and optimization on carbon reduction, and thus promote the water supply industry to develop in a low-carbon direction, solving the problems of comprehensively evaluating process carbon emissions and guiding carbon reduction in the carbon emission accounting of the water supply industry.

[0081] Step S1200, based on the water source heterogeneity of the water supply system, obtain the raw water BOD concentration and the length of the water transmission pipeline network of each water source area, and generate the water source coupling factor α according to the raw water BOD concentration and the length of the water transmission pipeline network of each water source area s ;

[0082] Further, as Figure 3 shown, step S1200 includes:

[0083] Step S1210, based on the water source heterogeneity of the water supply system, obtain the raw water BOD concentration and the length of the water transmission pipeline network of each water source site;

[0084] Specifically, water source heterogeneity refers to the differential characteristics of different water source sites in the water supply system in terms of water quality, water transmission distance, etc. The raw water BOD concentration, that is, the concentration of biochemical oxygen demand, reflects the amount of dissolved oxygen consumed during the process of microorganisms decomposing oxidizable organic matter in water under specific conditions. The unit is usually milligrams per liter (mg / L). The higher its value, the more oxidizable organic matter there is in the water and the worse the water quality. The length of the water transmission pipeline network reflects the water transmission distance between the water source site and the water supply area. In actual operation, to obtain the raw water BOD concentration, water quality monitoring points can be set at each water source site, water samples can be regularly collected and sent to the laboratory for testing and analysis, and professional water quality detection equipment and methods can be used to measure the BOD concentration. For example, using the standard dilution method, the water sample is diluted and cultured under specified conditions, and the BOD concentration is calculated by measuring the difference in dissolved oxygen before and after culturing. For the length of the water transmission pipeline network, geographic information system (GIS) technology can be used, combined with the pipeline network layout map of the water supply system, to accurately measure the actual length of the water transmission pipeline from each water source site to the water supply area. The beneficial effect of this step is to provide basic data for subsequent analysis of the impact of water sources on carbon emissions. In the carbon emission accounting of the water supply industry, water sources are one of the important influencing factors. Accurately obtaining the raw water BOD concentration and the length of the water transmission pipeline network helps to understand the water quality status and the difference in water transmission distance of different water source sites. For example, if the raw water BOD concentration of a certain water source site is high, it means that more energy and chemicals may be consumed in the subsequent water treatment process to purify the water quality, thereby increasing carbon emissions; a longer water transmission pipeline network length will lead to an increase in energy consumption during water transmission, which will also increase carbon emissions. Through these data, the carbon emission situation of different water source sites can be evaluated more comprehensively, providing strong support for solving the accuracy problem of carbon emission accounting in the water supply industry.

[0085] Step S1220, perform min-max normalization on the raw water BOD concentration of each water source site to obtain the BOD heterogeneity index;

[0086] Specifically, min-max normalization is a data standardization method aimed at mapping the value range of data to the interval [0, 1], eliminating the differences in dimension and numerical range between different data, and making the data comparable. The normalized raw water BOD concentration is used as the BOD heterogeneity index; the larger the BOD heterogeneity index, the relatively worse the water quality of the water source. Because after normalization, the closer the value is to 1, it indicates that the raw water BOD concentration is relatively high among all water sources, which also means that there is more oxidizable organic matter in the water and the water quality is worse. The beneficial effect of this step is to convert the raw water BOD concentration into a dimensionless index with comparability, facilitating the accurate measurement of the impact of water quality differences in different water sources on carbon emissions in subsequent analyses. In the water supply system, different water qualities will lead to differences in water treatment processes and energy consumption, thereby affecting carbon emissions. Through the BOD heterogeneity index, the potential impact degree of water quality in each water source on carbon emissions can be clearly compared. For example, when classifying scenarios, according to the size of the BOD heterogeneity index, the scenario clusters where water sources with poor water quality are located can be divided into categories with relatively high carbon emission intensities, providing a basis for achieving refined carbon emission accounting and management, and helping to solve the problem of evaluating the impact of water quality in different water sources in carbon emission accounting in the water supply industry.

[0087] Step S1230, perform min-max normalization on the pipeline network lengths of each water source to obtain the distance heterogeneity index;

[0088] Specifically, the normalized pipeline network length is used as the distance heterogeneity index. The larger the distance heterogeneity index, the relatively longer the water conveyance distance of the water source. Because the closer the value is to 1 after normalization, it indicates that the pipeline network length is relatively long among all water sources. The beneficial effect of this step is to convert the pipeline network length into a unified and comparable index, highlighting the impact of differences in water conveyance distances in different water sources on carbon emissions. The length of the water conveyance distance is directly related to the energy consumption during water conveyance. A longer water conveyance distance requires more power to maintain water transportation, thus increasing carbon emissions. Through the distance heterogeneity index, the carbon emission differences caused by the water conveyance distances of each water source can be intuitively compared. During the scenario classification process, according to the distance heterogeneity index, the scenario clusters where water sources with long water conveyance distances are located can be classified into categories with relatively high carbon emission intensities, providing data support for accurate carbon emission accounting and formulating targeted emission reduction strategies, and helping to solve the problem of evaluating the impact of water conveyance distance in carbon emission accounting in the water supply industry.

[0089] Step S1240, perform weighted summation of the BOD heterogeneity index and the distance heterogeneity index to obtain the water source coupling factor α s 。

[0090] Specifically, the water source coupling factor α sBy comprehensively considering two key factors, water quality and water conveyance distance, it can more comprehensively and accurately reflect the comprehensive impact of water source areas on carbon emissions. Compared with considering water quality or water conveyance distance alone, α s can more precisely evaluate the carbon emissions of different water source areas. For example, for a water source area with poor water quality but short water conveyance distance and a water source area with good water quality but long water conveyance distance, through α s the carbon emission intensity of them can be comprehensively judged, avoiding the one-sidedness of single-factor evaluation. Secondly, α s provides a criterion for scenario classification from the water source dimension. When constructing a three-dimensional dynamic entropy weight classification model, α s as an important indicator, can be used to divide water supply units with similar water source characteristics into the same scenario cluster, realizing refined scenario classification, making carbon accounting more in line with the actual situation, helping to solve the problem of inaccurate scenario classification in carbon emission accounting of the water supply industry, and providing strong support for subsequent carbon accounting and formulation of emission reduction measures.

[0091] Step S1300, calculate the load fluctuation coefficient β based on the spatio-temporal load fluctuation of the water supply system t ;

[0092] Furthermore, as shown in Figure 4 Step S1300 includes:

[0093] Step S1310, obtain the historical monthly water production data and the historical carbon emission intensity time series data based on the spatio-temporal load fluctuation of the water supply system; the spatio-temporal load fluctuation includes the monthly average water production and the peak period of carbon emission intensity;

[0094] Specifically, the spatio-temporal load fluctuations cover aspects such as the monthly average water production volume and the peak period of carbon emission intensity. To obtain historical monthly water production data, water volume measurement devices installed in the water supply system can be used. These devices can record the water production volume for each month. For example, intelligent water meters are installed on the outlet pipes of water treatment plants, and the change in the reading recorded each month is the water production volume for that month. Historical time-series data of carbon emission intensity can be obtained by installing carbon emission monitoring devices at various links in the water supply system. For example, at energy-consuming devices (such as water pumps, motors, etc.), combined with energy consumption data and corresponding carbon emission factors, the carbon emission intensity is calculated and recorded in chronological order. The purpose of this step is to comprehensively understand the load changes of the water supply system in the time dimension and the corresponding carbon emissions. Its beneficial effect is to provide data support for accurately analyzing the dynamic characteristics of the water supply system. In the current situation where there is a lack of a unified and accurate carbon emission accounting method for the water supply industry, these data are the basis for subsequent calculations and analyses. By analyzing historical monthly water production data and time-series data of carbon emission intensity, it is possible to discover the load differences of the water supply system in different months and the changing patterns of carbon emissions. For example, it is found that the water consumption is larger in summer, the water production volume increases, and at the same time, the carbon emission intensity may also increase due to the high-load operation of equipment. These patterns help to deeply understand the carbon emission behavior of the water supply system, provide a basis for formulating targeted carbon accounting strategies and emission reduction measures, solve the problem of collecting time-dimensional data in carbon emission accounting for the water supply industry, and make subsequent analyses and calculations more accurate and targeted.

[0095] Step S1320: Calculate the coefficient of variation of the monthly average water production volume based on the historical monthly water production data to obtain the water production volume fluctuation index.

[0096] Specifically, the coefficient of variation is a statistic that measures the degree of dispersion of data. It eliminates the influence of data dimensions and can more accurately compare the fluctuations of different data sets. For the calculation of the coefficient of variation of the monthly average water production volume, the monthly average water production volume needs to be calculated first. Assume that the water production volumes of a water supply system from January to December are , then the monthly average water production volume . Then calculate the standard deviation of the water production volume . The coefficient of variation of the monthly average water production volume , this coefficient of variation is the water production fluctuation index. The larger the water production fluctuation index, the stronger the dynamic change of the water supply load. For example, if the water production fluctuation index of a water supply system is 0.3 in a year, while that of another water supply system is 0.5, then the dynamic change of the water supply load of the latter is more intense, meaning that the difference in water production between different months is greater. The purpose of this step is to quantify the degree of dynamic change of the water supply load. Its beneficial effect is to provide a quantitative index for evaluating the stability of the water supply system and potential changes in carbon emissions. The fluctuation of the water supply load will affect the operation efficiency of equipment and energy consumption, and thus affect carbon emissions. Through the water production fluctuation index, the change of the water supply load can be intuitively understood. When the water production fluctuation index is high, it indicates that the load of the water supply system is unstable, which may lead to frequent adjustment of the operation state of the equipment, increasing energy consumption and carbon emissions. This helps water supply enterprises to timely discover the unstable factors in the system and take corresponding measures for optimization, such as adjusting the equipment operation strategy or optimizing the water supply dispatching, so as to reduce carbon emissions, solve the problem of quantitative evaluation of the dynamic change of the water supply load in the carbon emission accounting of the water supply industry, and provide an important basis for more accurate carbon emission accounting in the follow-up.

[0097] Step S1330: Perform Fourier transform on the historical carbon emission intensity time series data, extract the dominant frequency, calculate the peak period, and obtain the carbon emission intensity periodicity index;

[0098] Specifically, the Fourier transform is a mathematical transform that can convert a time-domain signal into a frequency-domain signal and reveal the distribution of different frequency components in the signal. For the historical carbon emission intensity time series data, through the Fourier transform, the carbon emission intensity data is converted from the time domain to the frequency domain to obtain the spectrum. In the spectrum, the frequency component with a larger amplitude is the dominant frequency of the signal; the peak period refers to the time interval when the carbon emission intensity appears at the peak. When calculating the peak period, first calculate the period according to the dominant frequency , and this period is the peak period of the carbon emission intensity. The carbon emission intensity periodicity index can be obtained by performing a certain standardization process on the peak period. For example, if a reference period is set, the carbon emission intensity periodicity index 。The larger the periodicity index, the stronger the burstiness of carbon emissions. For example, if the carbon emission intensity periodicity index of one water supply system is 3 and that of another is 2, the system with an index of 3 has stronger carbon emission burstiness, that is, the change in its carbon emission intensity at the peak moment is more significant. The purpose of this step is to analyze the periodic characteristics of carbon emission intensity. Its beneficial effect is to help understand the temporal distribution law of carbon emissions. In a water supply system, the periodic change of carbon emission intensity is related to various factors, such as seasonal water demand changes, equipment maintenance cycles, etc. By extracting the dominant frequency and calculating the peak period to obtain the carbon emission intensity periodicity index, these laws can be clearly grasped. For example, it is found that in a certain water supply system during the peak electricity consumption period in summer, due to the high-load operation of equipment, the carbon emission intensity shows obvious periodic changes and the periodicity index is relatively high. This is of great significance for reasonably arranging equipment maintenance time and optimizing energy use strategies. It can carry out equipment maintenance during the low peak period according to the periodic change of carbon emissions, reduce the equipment failure risk during high carbon emission periods, and at the same time provide more accurate time dimension information for carbon accounting, solve the problem of periodic analysis of carbon emissions in the carbon accounting of the water supply industry, and make carbon accounting more in line with the actual situation.

[0099] Step S1340, take the square root of the product of the water production fluctuation index and the carbon emission intensity periodicity index to obtain the load fluctuation coefficient β t 。

[0100] Specifically, β t The larger it is, the stronger the spatio-temporal dynamics of carbon emissions, and a higher accounting frequency and time resolution are required. Because β t comprehensively reflects the dynamic changes of the water supply load (reflected by the water production fluctuation index) and the periodic changes of carbon emissions (reflected by the carbon emission intensity periodicity index). When the value of β t is relatively large, it indicates that both the load change and the carbon emission change of the water supply system in the time dimension are relatively drastic. The traditional low-frequency accounting method is difficult to accurately reflect its carbon emission situation, so a higher accounting frequency and finer time resolution are needed to capture these changes. The purpose of this step is to comprehensively consider the dynamic characteristics of the water supply load and carbon emissions and construct an index that can comprehensively reflect the spatio-temporal load fluctuations. Its beneficial effect is to provide a time dimension criterion for scenario classification, enabling carbon accounting to better match the dynamic change requirements of the water supply system. When constructing a three-dimensional dynamic entropy weight classification model, the load fluctuation coefficient β t as an important index can divide water supply units with similar spatio-temporal load fluctuation characteristics into the same scenario cluster. For example, for β tFor scenario clusters with larger values, higher accounting frequencies and more refined accounting methods can be adopted to improve the accuracy of carbon accounting. This helps to address the issues of inaccurate scenario division and mismatch between accounting methods and actual situations in carbon emissions accounting for the water supply industry, improves the adaptability and accuracy of the carbon accounting system, and provides stronger support for energy conservation, emission reduction, and low-carbon development in the water supply industry.

[0101] Step S1400, based on the process entropy value E p , the water source coupling factor α s and the load fluctuation coefficient β t , construct a three-dimensional dynamic entropy weight classification model;

[0102] Furthermore, as Figure 5 shown, step S1400 includes:

[0103] Step S1410, perform normalization processing on the process entropy value E p , the water source coupling factor α s and the load fluctuation coefficient β t respectively;

[0104] Step S1420, assign a weight coefficient w p to the normalized process entropy value, assign a weight coefficient w s to the normalized water source coupling factor, and assign a weight coefficient w t to the normalized load fluctuation coefficient; Let w p , w s , w t constitute the entropy weight vector [w p , w s , w t ;

[0105] Step S1430, from the process entropy value E p , the water source coupling factor α s , the load fluctuation coefficient β t and the entropy weight vector [w p , w s , w t , construct a three-dimensional dynamic entropy weight classification model.

[0106] Specifically, for the process entropy value E p , the water source coupling factor α s and the load fluctuation coefficient β tNormalization is carried out separately with the aim of laying a foundation for the subsequent construction of the entropy weight vector and the three-dimensional dynamic entropy weight grading model. When constructing the three-dimensional dynamic entropy weight grading model, after unifying the dimension and numerical range, the influence degrees of different indicators on carbon emission intensity can be compared and analyzed on the same scale. For example, when determining the weights of each indicator, the influence of some indicators will not be over-amplified or under-amplified due to differences in dimension and numerical range, ensuring the scientificity and accuracy of the model. When dividing the scenarios of carbon emissions in the water supply system, the normalized data can more reasonably reflect the relative importance of each factor, making the division results more in line with the actual situation. This helps to solve the problem of inaccurate analysis caused by differences in data dimension and value in the carbon emission accounting of the water supply industry, and improves the accuracy of subsequent scenario division and carbon accounting.

[0107] Weight coefficient w p , w s , w t Used to characterize the relative contribution degrees of different indicators (process entropy value E p , water source coupling factor α s and load fluctuation coefficient β t ) on carbon emission intensity. The determination of these weight coefficients can be based on expert experience or machine learning algorithms. Taking the expert experience method as an example, experts will evaluate each indicator according to the actual operation conditions of the water supply system, historical data, and in-depth understanding of the influence of each factor on carbon emission intensity, so as to determine the corresponding weight coefficients. In a certain water supply system, if the process link has a more critical impact on carbon emissions, experts may assign a relatively high weight coefficient w p to the process entropy value E p ; if the water quality and water conveyance distance of the water source have a significant impact on carbon emissions, the weight coefficient w s of the water source coupling factor α s will be relatively large. Machine learning algorithms determine the weight coefficients that can best reflect the relationship between each indicator and carbon emission intensity through learning and analyzing a large amount of historical data. The purpose of step S1420 is to quantify the influence degrees of each indicator on carbon emission intensity and construct an entropy weight vector, so as to comprehensively consider each factor in the subsequent model. Its beneficial effect is that it can flexibly adjust the importance of each indicator according to the characteristics and actual situation of the water supply system. By reasonably setting the weight coefficients, the roles of different factors in carbon emissions can be more accurately reflected. For example, in a water supply system mainly based on advanced treatment processes and with relatively concentrated water sources, by adjusting the weight coefficients to highlight the influence of the process entropy value on carbon emission intensity, the carbon emissions can be more accurately evaluated. This is of great significance for solving the problem of comprehensive evaluation of multiple factors in the carbon emission accounting of the water supply industry, making the carbon accounting results more in line with the actual situation, and providing a more reliable basis for formulating targeted emission reduction strategies.

[0108] Step S1430 constructs a three-dimensional dynamic entropy weight classification model based on the previously obtained normalized data and entropy weight vector. This model takes the process entropy value E p , the water source coupling factor α s , and the load fluctuation coefficient β t as three dimensions, and weights and synthesizes the data of these three dimensions through the entropy weight vector. During the construction process, the data of each dimension is multiplied by the corresponding weight coefficient and then accumulated to obtain a value that comprehensively reflects the multi-dimensional heterogeneity of the carbon emissions of the water supply system. The purpose of this step is to construct a model that can comprehensively consider the three core influencing factors of process, water source, and load to achieve multi-dimensional characterization and scenario division of the carbon emissions of the water supply system. Its beneficial effects are significant. First, through this model, the heterogeneity of the carbon emissions of the water supply system can be analyzed comprehensively and systematically. Different water supply units have differences in terms of process, water source, and load. The three-dimensional dynamic entropy weight classification model can combine these differences to more accurately reflect the carbon emission characteristics of each water supply unit. Second, based on this model for scenario division, water supply units with similar carbon emission characteristics can be divided into the same scenario cluster, realizing refined and hierarchical scenario division. This helps to formulate more targeted carbon accounting methods and emission reduction strategies according to the characteristics of different scenario clusters, improve the accuracy and effectiveness of carbon accounting, provide strong support for energy conservation and emission reduction in the water supply industry, and solve the problems of inaccurate scenario division and inability to fully consider the influence of multiple factors in the carbon emission accounting of the water supply industry.

[0109] Step S1500 divides the accounting system into N scenario clusters according to the three-dimensional dynamic entropy weight classification model and calculates the carbon accounting gradient of each scenario cluster, where N≥3.

[0110] Further, step S1500 includes:

[0111] Step S1510 uses the process entropy value E p , the water source coupling factor α s , and the load fluctuation coefficient β t in the three-dimensional dynamic entropy weight classification model as three dimensions to perform three-dimensional space mapping on each water supply unit of the water supply system to form a sample point set;

[0112] Step S1520 performs fuzzy C-means clustering on the sample point set to obtain N scenario clusters;

[0113] Step S1530 calculates the mean of the process entropy values, the mean of the water source coupling factors, and the mean of the load fluctuation coefficients of each scenario cluster, and uses , and According to the entropy weight vector [w p , w s , w t in the three-dimensional dynamic entropy weight classification model, weighted calculation is carried out to obtain the carbon accounting gradient = , where represents the carbon accounting gradient of the Nth scenario cluster.

[0114] Specifically, each water supply unit in step S1510 refers to the part of the water supply system that has an independent water supply function or has obvious differences in carbon emission characteristics, such as different water plants, different water supply areas, etc. Taking the water supply system of a certain city as an example, it includes three water plants A, B, and C. Each water plant has its own characteristics in terms of process, water source, and load. These three water plants can be regarded as three different water supply units. For each water supply unit, its process entropy value E p , water source coupling factor α s , and load fluctuation coefficient β t are used as coordinate values in the three-dimensional space, so as to determine a point in the three-dimensional space. The points corresponding to all water supply units are gathered together to form a sample point set. For example, the process entropy value E p of water plant A is 0.7, the water source coupling factor α s is 0.5, and the load fluctuation coefficient β t is 0.6. Then in the three-dimensional space, the point coordinates corresponding to water plant A are (0.7, 0.5, 0.6). After determining the points corresponding to all water plants in the water supply system of this city, the sample point set is obtained. The purpose of this step is to present the carbon emission-related characteristics of each water supply unit in the water supply system in the form of three-dimensional space points, providing a data basis for subsequent clustering analysis. Its beneficial effect is that it can intuitively display the distribution of each water supply unit in the three-dimensional space, facilitating the discovery of similarities and differences between different water supply units. By observing the distribution of the sample point set, it can be preliminarily judged which water supply units have similar carbon emission characteristics in terms of process, water source, and load, providing an intuitive basis for subsequent scenario division. This helps to solve the problems of visualizing and preliminarily classifying the carbon emission characteristics of water supply units in the carbon emission accounting of the water supply industry, making the subsequent scenario division more targeted and reasonable.

[0115] Fuzzy C - means clustering is a commonly used clustering algorithm. Its principle is to determine the optimal cluster centers and the membership degrees of each sample point to each cluster center by minimizing the objective function. In this step, fuzzy C - means clustering is performed on the sample point set obtained in step S1510, and the sample points are divided into N scenario clusters (N≥3). The N obtained cluster centers determine the N scenario clusters, and each sample point is divided into the corresponding scenario cluster according to its membership degree. During the clustering process, the closer the distance between each sample point and the cluster center, the higher the membership degree of the sample point to the cluster center. For example, assume N = 4, and fuzzy C - means clustering is performed on the sample point set of a certain water supply system. After multiple iterative calculations, the objective function converges, and 4 cluster centers C1, C2, C3, and C4 are obtained. The sample point x'1 has the highest membership degree to C1, so x'1 is divided into the scenario cluster centered on C1. In this way, all sample points are divided into 4 scenario clusters to complete the scenario division. The purpose of step S1520 is to perform clustering according to the carbon emission characteristics of water supply units, group water supply units with similar characteristics into the same scenario cluster, and achieve scenario grading. Its beneficial effect is that it can classify the complex water supply system according to carbon emission characteristics, making subsequent carbon accounting and management more efficient. Different scenario clusters have different carbon emission characteristics. For each scenario cluster, more suitable carbon accounting methods and emission reduction strategies can be formulated. For example, for a scenario cluster mainly with high - energy - consuming processes, key attention can be paid to process optimization to reduce carbon emissions; for a scenario cluster with poor water quality at the water source and long water conveyance distance, optimizing water source selection or improving water conveyance methods can be considered. This helps to solve the problems of unclear scenario division and inability to manage targeted in carbon emission accounting in the water supply industry, and improves the accuracy of carbon accounting and the effectiveness of emission reduction measures.

[0116] The purpose of step S1530 is to quantify the carbon emission intensity of each scenario cluster, providing a key indicator for subsequent carbon accounting and emission reduction analysis. Its beneficial effect lies in that by calculating the carbon accounting gradient, the carbon emission intensity level of each scenario cluster can be clearly understood. The larger the carbon accounting gradient, the higher the carbon emission intensity level of the scenario cluster, and a finer accounting granularity is required in subsequent carbon accounting. For example, for a scenario cluster with a high carbon accounting gradient, more accurate carbon emission calculation methods and stricter monitoring means can be adopted to ensure the accuracy of carbon emission accounting. At the same time, this also helps to formulate differentiated emission reduction strategies according to the carbon accounting gradients of different scenario clusters, focusing on emission reduction for scenario clusters with high carbon emission intensity, improving the emission reduction efficiency, solving the problems of quantitative assessment of carbon emission intensity and targeted management for different scenario clusters in carbon emission accounting in the water supply industry, and providing strong support for achieving the carbon emission reduction goal of the water supply industry.

[0117] Step S2000: Collect the heterogeneous carbon source pulse data of the entire water supply process in real time, map the heterogeneous carbon source pulse data to N scenario clusters divided by the accounting system, generate a dynamic carbon accounting density function , and calculate the heterogeneous entropy weight deviation ; The heterogeneous carbon source pulse data of the entire water supply process includes the real-time power consumption data of each link of the water supply system and the real-time carbon emission factor of the regional power grid, the real-time chemical agent dosing data of the water supply system and the chemical agent transportation distance, and the methane emission rate data r d (t) of the sludge anaerobic fermentation in the sludge treatment unit of the sludge treatment link, the methane emission rate data r l (t) of the landfill process in the landfill, the key sludge treatment parameters of the sludge treatment unit, and the key landfill parameters of the landfill;

[0118] Furthermore, step S2000 includes:

[0119] Step S2100: Collect the real-time power consumption data of each link of the water supply system and the real-time carbon emission factor of the regional power grid, and calculate the real-time carbon emission intensity time series data based on the real-time power consumption data of each link of the water supply system and the real-time carbon emission factor of the regional power grid to obtain the power entropy pulse signal P e ;

[0120] Furthermore, as Figure 6 shown, step S2100 includes:

[0121] Step S2110: Collect the real-time power consumption data of each link of the water supply system;

[0122] Step S2120: Obtain the real-time carbon emission factor of the regional power grid;

[0123] Step S2130: Multiply the real-time power consumption data of each link of the water supply system by the real-time carbon emission factor of the regional power grid to obtain the real-time carbon emission intensity time series data of the power energy consumption;

[0124] Step S2140: Perform pulse detection on the real-time carbon emission intensity time series data, extract the power carbon emission pulse events, and construct the power entropy pulse signal P e .

[0125] Specifically, the water supply system covers multiple links such as water intake, water transmission and distribution, and water quality treatment, all of which consume electricity during operation. For example, the water pumps in the water intake link require electricity to drive the extraction of raw water; during the water transmission and distribution process, the operation of the equipment in the pressurization pumping station depends on electricity; in the water quality treatment link, various types of equipment involved in processes such as coagulation, sedimentation, filtration, and disinfection also consume electricity. In order to collect these real-time data, it is necessary to deploy on-line power consumption monitoring devices on relevant equipment. These devices can be smart meters, power monitoring sensors, etc., which can record the power consumption of the equipment in real time and transmit the data to the data collection center through wired or wireless communication technologies. In the current context of the lack of a unified and accurate carbon emission accounting method for the water supply industry, obtaining real-time power consumption data for each link is the key to achieving accurate accounting. By accurately grasping the power consumption situation of each link, the relationship between energy consumption and carbon emissions can be evaluated more precisely. For example, if the power consumption of the water quality treatment link in a certain water plant suddenly increases during a certain period, through subsequent calculations, the change in carbon emissions in this link can be further analyzed, providing a basis for judging whether there is energy waste or equipment abnormalities in this link, thus helping to solve the problem of the accuracy of carbon emission accounting in the water supply industry.

[0126] The real-time carbon emission factor of the regional power grid reflects the amount of carbon emissions generated by the regional power grid per unit of electricity consumption. This factor is not a fixed value and will fluctuate with factors such as the energy structure of the power grid (such as the proportion of different energy power generation such as thermal power generation, hydropower generation, and wind power generation), the level of power generation technology, and the operation efficiency of power generation equipment. The way to obtain the real-time carbon emission factor is usually to conduct real-time data docking through the data interface of the regional power grid, or obtain real-time updated data from relevant energy management departments and professional data platforms. For example, the energy management departments in some regions will monitor the power generation volume and corresponding carbon emissions of various power generation methods in the power grid in real time, and after calculation and collation, provide the real-time carbon emission factor data of the power grid in this region to the outside world. Step S2120 is to establish a direct connection between the power consumption and carbon emissions of the water supply system. When calculating the carbon emissions of the water supply system, it is not enough to only know the power consumption data. It is also necessary to combine the carbon emission factor of the regional power grid to determine the carbon emissions corresponding to these power consumption. For example, in a regional power grid dominated by thermal power generation, its carbon emission factor is relatively high, so when the water supply system consumes the same amount of electricity, the carbon emissions generated are relatively large; while in a regional power grid with a relatively high proportion of clean energy, the carbon emission factor is low, and the carbon emissions corresponding to the same power consumption are also small. Accurately obtaining the real-time carbon emission factor helps to accurately calculate the carbon emissions of the water supply system, improve the accuracy of carbon emission accounting, and solve the problem of inaccurate accounting caused by the inability to accurately associate power consumption with carbon emissions in the carbon emission accounting of the water supply industry.

[0127] After obtaining the real-time power consumption data of each link in the water supply system and the real-time carbon emission factor of the regional power grid, multiplying the two can obtain the time series data of the real-time carbon emission intensity of power energy consumption. Step S2130 converts the power consumption data into carbon emission intensity data with practical significance, providing direct data support for subsequent analysis of the dynamic changes in the carbon emissions of the water supply system. By generating the time series data of the real-time carbon emission intensity, the changes in the carbon emission intensity of the water supply system at different time points and different links can be intuitively understood. For example, analyzing these data may find that during the peak water consumption period in summer, due to the high-load operation of water supply equipment, the power consumption increases, and at the same time, the carbon emission factor of the regional power grid increases due to the change in the energy structure caused by the increased electricity demand, resulting in a significant increase in the real-time carbon emission intensity of the water supply system. This information helps water supply enterprises to timely discover the peak periods and key links of carbon emissions, providing a basis for formulating targeted emission reduction strategies, and solving the problem of monitoring the dynamic changes in carbon emissions in the carbon emission accounting of the water supply industry.

[0128] Pulse detection is a signal processing technology used to identify and extract the pulse characteristics in a signal. For the time series data of the real-time carbon emission intensity, by setting appropriate thresholds and detection algorithms, it is possible to determine which parts of the data constitute pulse events. For example, when the carbon emission intensity at a certain moment is significantly higher than the average level of the period before and after it and exceeds the pre-set threshold, the moment and a section of data with obvious change characteristics near it can be identified as an electric power carbon emission pulse event. Then, based on these detected pulse events, the electric power entropy pulse signal P is constructed. e . During the construction process, the pulse amplitude reflects the magnitude of the carbon emission intensity. The larger the pulse amplitude, the higher the carbon emission intensity at that moment; the pulse frequency reflects the dynamic change characteristics of the carbon emissions. The higher the pulse frequency, the more frequent the changes in carbon emissions. The electric power entropy pulse signal P e can depict the dynamic carbon footprint of the energy consumption of the water supply system, sense the suddenness and intermittency of carbon emissions, and enrich the description of the time series characteristics of carbon emissions. For example, in a certain link of the water supply system, there may be a sudden increase in power consumption due to equipment failure, which in turn causes an instantaneous increase in the carbon emission intensity. This sudden change will be manifested as a pulse with a relatively large amplitude in the electric power entropy pulse signal P e . By analyzing the electric power entropy pulse signal P e , water supply enterprises can timely discover these abnormal carbon emission changes and take corresponding measures, such as checking the equipment operation status, optimizing the equipment scheduling, etc., to reduce carbon emissions. At the same time, this signal provides important basic data for the subsequent construction of the dynamic carbon accounting density function, helping to achieve the refined accounting of the carbon emissions of the water supply system and solving the problem of insufficient description of the dynamic characteristics of carbon emissions in the carbon emission accounting of the water supply industry.

[0129] Step S2200: Obtain the real-time chemical dosing data and chemical transportation distance of the water supply system, and based on the real-time chemical dosing data and chemical transportation distance of the water supply system, obtain the carbon emission chain correlation matrix Γ of the chemical link c ;

[0130] Further, as Figure 7 shown, step S2200 includes:

[0131] Step S2210: Obtain the real-time chemical dosing data and chemical transportation distance, where the real-time chemical dosing data includes the real-time chemical dosing amount and chemical concentration;

[0132] Step S2220: Organize the real-time chemical dosing amount, chemical concentration, and chemical transportation distance of each chemical into a three-dimensional correlation matrix;

[0133] Step S2230: Quantify each element in the three-dimensional correlation matrix into carbon emission equivalents to generate the carbon emission chain correlation matrix Γ c 。

[0134] Specifically, the purposes of steps S2210 and S2220 are to collect real-time data related to chemical agent use in the water supply system, which serve as the basis for subsequent analysis of carbon emissions in the chemical agent link. The real-time chemical agent dosing data cover the real-time dosing amounts and concentrations of various chemical agents, and the chemical agent transportation distance records the distance information from the chemical agent production site to the usage point in the water supply system. Obtaining these data is crucial for accurately assessing carbon emissions during chemical agent use. In actual operation, the real-time chemical agent dosing data can be obtained with the help of an automatic chemical agent dosing system. This system usually has precise metering devices that can monitor and record parameters such as the dosing amounts and concentrations of various chemical agents in real time. For example, in the flocculation treatment process of a certain water plant, the specific dosing amount and concentration of the flocculant added each time can be accurately known through the automatic chemical agent dosing system. For the chemical agent transportation distance, it can be updated by connecting in real time with the logistics information system provided by the chemical agent supplier. The supplier's logistics system can track the transportation trajectory of the chemical agent in real time, thereby obtaining accurate transportation distance data. Suppose the disinfectant used by a certain water plant is provided by a specific supplier. By docking with the supplier's logistics information system, the water plant can obtain the transportation distance information of the disinfectant in real time. The beneficial effect of this step is to provide accurate data support for quantifying carbon emissions in the chemical agent use link. In the carbon emission accounting of the water supply industry, chemical agent use is an important carbon emission source. Accurate chemical agent dosing data and transportation distance data help accurately calculate the carbon emissions of chemical agents during production, transportation, and use. Through these data, the relationship between the chemical agent use situation and carbon emissions at different times, for different chemical agents, and in different treatment processes can be discovered. For example, if the dosing amount of a certain chemical agent in a certain water plant significantly increases during a certain period, combined with the transportation distance data, the impact of this change on carbon emissions can be further analyzed, providing a basis for optimizing the chemical agent use strategy and reducing carbon emissions, and solving the problem of inaccurate data acquisition in the chemical agent use link in the carbon emission accounting of the water supply industry.

[0135] Step S2220: Integrate the data obtained in Step S2210 to construct a three-dimensional correlation matrix. The three dimensions of the matrix are chemical type, treatment process, and time series. Such a three-dimensional structure can comprehensively display the usage of chemicals in the water supply system. For example, at a certain moment, for a certain chemical type such as a coagulant, in the coagulation and sedimentation treatment process of the water plant, its dosage is X' kg, the concentration is Y'%, and the transportation distance is Z km. These data constitute an element in the matrix. By filling in the relevant data of different chemicals, different treatment processes, and different time points into the matrix, a complete three-dimensional correlation matrix can be formed. The purpose of this step is to present the scattered data in a structured manner for subsequent systematic analysis of the chemical usage. Its beneficial effects are reflected in many aspects. First, this structured data organization method can clearly show the usage differences of different chemicals in different treatment processes and at different times. Through the analysis of the matrix, it can be intuitively seen which chemicals have a large usage volume in which processes and time periods, facilitating the targeted optimization of chemical usage. Second, it provides an orderly data basis for subsequent quantification of carbon emissions. When converting these data into carbon emission equivalents, the structure of the three-dimensional correlation matrix helps to accurately calculate the carbon emissions in different situations, improving the accuracy of carbon emission accounting. For example, by analyzing the matrix, it can be found that during the high-temperature period in summer, the usage of a certain disinfectant in the disinfection process increases. Combining information such as the transportation distance, the carbon emission change caused by chemical usage in the disinfection process during this period can be more accurately evaluated, solving the problem of chaotic data organization and unfavorable analysis in carbon emission accounting in the water supply industry.

[0136] In Step S2230, each element in the three-dimensional correlation matrix is converted into the corresponding carbon emission equivalent to generate a carbon emission chain correlation matrix Γ c . The quantification process is calculated based on factors such as the carbon emission factors of different chemicals and the transportation distance. For the carbon emissions in the chemical production process, the carbon emission factors are determined according to the production processes of different chemicals. For example, in the production process of a certain flocculant, each kilogram of production generates M'' kg of carbon dioxide emissions as the carbon emission factor for chemical production. For the carbon emissions in the transportation process, they are calculated according to the carbon emission coefficient of the transportation distance and transportation method. Assuming road transportation, each kilometer of transportation per ton of chemical generates N'' kg of carbon dioxide emissions as the transportation carbon emission coefficient. If an element represents the addition of A'' kg of a certain chemical with a concentration of B% at a certain treatment process and a certain moment, and the transportation distance is C'' km, then the carbon emission equivalent corresponding to this element is: the carbon emissions in chemical production (A'' × M'') plus the carbon emissions in the transportation process (A'' × N'' × C''). Calculating all the elements in the three-dimensional correlation matrix in this way can obtain the carbon emission chain correlation matrix Γ c . The carbon emission chain correlation matrix Γc It reflects the carbon emission correlation of various agents at different links and different time periods. Through this matrix, it is possible to clearly see the carbon emission contributions of different agents in each link of the water supply system and their mutual influences. For example, through the analysis of the matrix, it can be found that in the treatment process of a certain water plant, the use of coagulants not only generates carbon emissions in the coagulation link, but also, due to its transportation process and its impact on subsequent treatment links, is associated with the carbon emissions of other agents. This association may be manifested as follows: due to the change in the dosage of coagulants, the dosage of agents in the subsequent disinfection link is affected, and thus indirectly affects the carbon emissions in the disinfection link. On the one hand, this step realizes the transformation from agent usage data to carbon emission data, closely linking agent usage with carbon emissions, making the carbon emission accounting in the water supply industry more comprehensive and accurate. By quantifying the carbon emission equivalent of each element, it is possible to accurately evaluate the specific contribution of agent usage to carbon emissions, providing an accurate basis for formulating emission reduction measures. On the other hand, the carbon emission chain correlation matrix Γ c reveals the network characteristics of carbon emission transfer, which helps to discover the chain reaction of carbon emissions in the water supply system. For example, if the dosage of agents in a certain link changes, through the matrix, the impact of this change on the carbon emissions in other links can be analyzed, so as to take measures in advance for optimization to achieve the goal of carbon emission reduction, solving the problem of insufficient quantification and correlation analysis of carbon emissions in the agent usage link in the carbon emission accounting of the water supply industry.

[0137] Step S2300: Obtain the methane emission rate data r d (t) of sludge anaerobic fermentation in the sludge treatment unit of the sludge treatment link and the methane emission rate data r l (t) of the landfill process in the landfill site, as well as the key sludge treatment parameters of the sludge treatment unit and the key landfill parameters of the landfill site, and calculate the carbon emission eddy coupling coefficient Ω s ;

[0138] The purpose of step S2300 is to obtain the relevant data of the sludge treatment and landfill processes through a series of data collection, calculation and analysis means, and calculate the carbon emission eddy coupling coefficient Ω s to quantitatively describe the carbon emission correlation characteristics of these two processes and provide a key basis for the whole-process accounting and control of sludge carbon emissions.

[0139] Furthermore, step S2300 includes:

[0140] Step S2310: Install on-line methane monitoring equipment in the sludge treatment unit and the landfill site respectively to collect the methane emission rate data r d (t) of sludge anaerobic fermentation and the methane emission rate data r l (t) of the landfill process in real time;

[0141] Specifically, the methane emission rate refers to the volume or mass of methane gas emitted by the sludge treatment unit or landfill per unit time, which is the directly measurable raw data. In actual operation, in order to achieve real-time collection, professional on-line methane monitoring equipment needs to be installed in the sludge treatment unit and landfill respectively. These devices can continuously monitor the methane emissions and transmit the data to the data collection system in real time. For example, in the anaerobic fermentation tank of a sewage treatment plant, a high-precision methane sensor is installed, which can accurately measure the volume of methane gas emitted from the fermentation tank per hour; in the corresponding landfill, similar equipment is also installed to monitor the methane emissions during the landfill process. In the carbon emission accounting of the water supply industry, the methane emissions from the sludge treatment and landfill processes are important carbon emission sources. Accurately obtaining the real-time emission rate data can timely grasp the dynamic changes of carbon emissions in these two links. Through the analysis of these data, the laws of methane emissions can be found. For example, in the initial stage of anaerobic fermentation of sludge, the methane emission rate may increase with the enhancement of microbial activity and then gradually stabilize or decline; the methane emissions from landfills will show specific trends affected by factors such as landfill time and landfill volume. The discovery of these laws helps to deeply understand the generation mechanism of carbon emissions, provides a strong basis for formulating targeted emission reduction measures, and solves the problem of obtaining real-time data for the sludge treatment and landfill links in the carbon emission accounting of the water supply industry.

[0142] Step S2320, obtain the key sludge treatment parameters of the sludge treatment unit and the key landfill parameters of the landfill;

[0143] Specifically, the key parameters for sludge treatment include the feed rate, residence time, operating temperature, etc. of the sludge treatment unit. The feed rate is directly related to the total amount of substances participating in the reaction. A larger feed rate may lead to more methane production. The residence time affects the degree of decomposition of organic matter by microorganisms. The longer the residence time, the more thoroughly the organic matter decomposes, and the methane production may change. The operating temperature has a significant impact on the activity of microorganisms. An appropriate temperature can promote the growth and metabolism of microorganisms, thereby affecting the amount of methane produced. The key parameters for landfilling include the landfilling volume, landfilling depth, covering method, etc. The larger the landfilling volume, the more potential decomposable organic matter there is, and the greater the possible amount of methane produced. The landfilling depth will affect environmental factors such as the oxygen content and temperature in the landfill, and thus affect the rate and total amount of methane production. The covering method will affect the escape path and rate of methane. The beneficial effect of this step is to provide necessary parameter information for accurately calculating and analyzing carbon emissions. These key parameters play a crucial role in calculating the methane emission flux and studying the methane emission law of landfills. Taking the calculation of methane emission flux as an example, the feed rate is one of the important bases for calculation. By accurately obtaining these parameters, the carbon emissions of the sludge treatment and landfilling processes can be more accurately evaluated, providing data support for formulating reasonable emission reduction strategies in the future, and solving the problem of obtaining key parameters affecting carbon emissions in the carbon emission accounting of the water supply industry.

[0144] Step S2330, based on the methane emission rate data r d (t) of the sludge treatment unit and the key parameters of sludge treatment, calculate the methane emission flux F d (t) of the sludge treatment unit to obtain the time series data of the emission flux;

[0145] Specifically, in this step, based on the principle of material conservation and the empirical formula of methane production rate, the methane emission rate data is converted into emission flux data. The methane emission flux represents the ratio of the volume or mass of methane gas emitted by the sludge treatment unit per unit time to the feed sludge volume, and its calculation formula is , where is the feed rate of the sludge treatment unit. The empirical formula for methane production rate estimates the methane production rate of sludge based on sludge properties (such as organic matter content, biodegradability, etc.) and operating conditions (such as temperature, residence time, etc.), and then calculates the methane emission flux. Through this step, the methane emission rate data is converted into emission flux data, which is convenient for comparative analysis with the emission flux of the landfill. Since the emission flux takes into account the feed rate factor, it can more accurately reflect the methane emissions per unit feed rate, making the carbon emission situations comparable among different sludge treatment units and with landfills. Its beneficial effect is to provide a more scientific indicator for evaluating the carbon emission efficiency of sludge treatment units. By analyzing the time series data of the emission flux, the change of the carbon emission efficiency of the sludge treatment unit at different times can be intuitively understood. If the emission flux suddenly increases during a certain period, it may mean that there are abnormalities in the sludge treatment process, such as enhanced microbial activity or changed feed composition, etc., and it is necessary to further analyze the reasons and take corresponding measures. This helps to optimize the sludge treatment process, improve energy utilization efficiency, reduce carbon emissions, and solves the problem of evaluating the carbon emission efficiency of sludge treatment units in the carbon emission accounting of the water supply industry.

[0146] Step S2340, based on the methane emission rate data r l (t) of the landfill and landfill key parameters, fit the time series model of the methane emission flux of the landfill to obtain the time series data of the landfill emission flux;

[0147] Specifically, the methane emission of the landfill has certain hysteresis and attenuation laws. In this step, the methane generation attenuation model is used to fit the time series model of the methane emission flux of the landfill. The commonly used first-order exponential attenuation model is , where is the initial emission flux, k is the attenuation coefficient, and t is the landfill time. Using the methane emission rate data r l (t) and key parameters such as landfill volume and landfill time, calibrate the model parameters and by the method of curve fitting. For example, collect the methane emission rate data of a landfill at different landfill times for a period of time, combine parameters such as landfill volume, and use professional data analysis software or mathematical methods for curve fitting to determine the initial emission flux and the attenuation coefficient values, so as to obtain the landfill emission flux The time series model. This model can be used to predict and extrapolate the long-term methane emissions of landfills. Through the analysis of the model, the changing trend of landfill methane emissions over time can be understood, and the management and emission reduction measures of landfills can be planned in advance. Its beneficial effect is to provide a powerful tool for the carbon emission management of landfills. By predicting the future methane emissions of landfills, landfill managers can arrange corresponding emission reduction equipment and measures in advance, such as installing methane collection and utilization devices to convert methane into energy and reduce its direct emissions into the atmosphere. At the same time, this also helps to evaluate the carbon emission situation of landfills throughout their life cycle, provides data support for urban carbon emission planning and management, and solves the problem of predicting the long-term carbon emissions of landfills in the carbon emission accounting of the water supply industry.

[0148] Step S2350, for the methane emission flux F of the sludge treatment unit d (t) and the landfill methane emission flux Perform cross-correlation analysis, calculate the cross-correlation coefficient ρ(τ) and time-delay constant τ0 of F d (t) and to obtain the carbon emission eddy current coupling coefficient Ω s .

[0149] Specifically, cross-correlation analysis is a method for studying the correlation between two time series data. First, it is necessary to obtain the time series data of the methane emission flux F d (t) of the sludge treatment unit and the landfill methane emission flux . For example, within a certain period of time, record the values of F d (t) and at regular time intervals (such as every hour, every day, etc.) to form two corresponding time series. In this embodiment, based on the cross-correlation function in statistics, calculate the cross-correlation coefficient ρ(τ) and time-delay constant τ0 of F d (t) and . The cross-correlation coefficient represents the correlation magnitude when the landfill emission flux lags behind the sludge treatment unit emission flux by time . The time-delay constant τ0 is the time difference when ρ(τ) reaches the maximum value, reflecting the lagging impact time of sludge treatment emissions on landfill emissions. After calculating a series of cross-correlation coefficients ρ(τ), by observing the curve of ρ(τ) changing with τ, find the τ value that makes ρ(τ) reach the maximum value, and this value is the time-delay constant τ0. For example, if when τ = 3 days, ρ(τ) reaches the maximum value, then the time-delay constant τ0 = 3 days. This means that the landfill emission flux F l (t) lags behind the sludge treatment unit emission flux F dAt 3 days [(t)], the correlation between the two is the strongest.

[0150] Carbon emission eddy current coupling coefficient is defined as , where is the reference starting time of the sludge treatment and landfill processes (i.e., the t0-th day starting from the beginning of the process, t0 ≥ 0). is obtained from the previous cross-correlation analysis. When the lag time is τ0, the methane emission flux F d (t) of the sludge treatment unit and the methane emission flux F l (t) of the landfill. It reflects the strength of the correlation between these two emission fluxes at a time lag of τ0. represents the methane emission flux of the landfill at the moment of the reference starting time t0 plus the time lag constant τ0. This value is used to measure the magnitude of the methane emission flux of the landfill at the lag moment with the strongest correlation with the emission flux F d (t) of the sludge treatment unit. For example, if t0 represents the 10th day after the start of the sludge treatment and landfill processes and τ0 = 3 days, then F l (t0 + τ0) is the methane emission flux of the landfill on the 13th day. It is used in the formula to reflect the actual emission level of the landfill emission flux after a specific lag time and participates in the calculation with F d (t0) to reflect the influence of the proportional relationship between the two on the carbon emission eddy current coupling coefficient. represents the methane emission flux of the sludge treatment unit at the reference starting time t0. It is the emission flux of the sludge treatment unit at a specific starting moment and participates in the calculation of the carbon emission eddy current coupling coefficient as a reference value. For example, in the above example, F d (t0) is the methane emission flux of the sludge treatment unit on the 10th day. By comparing and operating with F l (t0 + τ0), the correlation strength and transfer law between the sludge treatment process and the landfill emissions can be quantified.

[0151] Ω s synthesizes the correlation and proportional relationship between the sludge treatment emissions and the landfill emissions. The larger the value, the stronger the impact of the sludge treatment process on the landfill carbon emissions and the more obvious the transfer amplification effect. By introducing the carbon emission eddy current coupling coefficient Ω s , the correlation strength and transfer law between the sludge treatment link and the landfill carbon emissions can be quantitatively characterized. This helps to reveal the amplification effect of sludge carbon emissions on the entire carbon emission chain and provides a basis for optimizing the sludge treatment process and landfill management. For example, if Ω sA larger value indicates that the carbon emissions in the sludge treatment process have a greater impact on the landfill. It is advisable to prioritize optimizing the sludge treatment process to reduce the organic matter content entering the landfill, thereby reducing the carbon emissions of the landfill, achieving carbon emission reduction and efficiency improvement, and solving the problem of quantitative analysis of the correlation between sludge treatment and landfill carbon emissions in the carbon emission accounting of the water supply industry.

[0152] Step S2400, set the fusion weight vector of the scenario clusters , where is the fusion weight of the Nth scenario cluster; based on the fusion weight vector of the scenario clusters, fuse the power entropy pulse signal P e , the carbon emission chain correlation matrix Γ c and the carbon emission eddy current coupling coefficient Ω s to generate the dynamic carbon accounting density function , and calculate the heterogeneous entropy weight deviation .

[0153] The setting of the fusion weight vector W of the scenario clusters is not arbitrary but requires comprehensive consideration of multiple factors. For example, it can be determined according to the carbon accounting gradient of each scenario cluster, the number of water supply units covered by the scenario cluster, and the contribution degree of each scenario cluster to the overall carbon emissions. If a scenario cluster has a higher carbon accounting gradient, it means that the carbon emission intensity of the water supply units within this scenario cluster is relatively large and has a greater impact on the overall carbon emissions. Then, when setting the weight, the corresponding weight value can be appropriately increased. According to the characteristics of different scenario clusters, reasonably allocate the weights of each scenario cluster in the overall accounting to make the accounting results more accurately reflect the actual carbon emissions. Through precise weight setting, the impact of high-carbon emission scenario clusters can be highlighted, avoiding ignoring important carbon emission sources during the accounting process, which helps to solve the problem of unreasonable weight allocation among different scenario clusters in the carbon emission accounting of the water supply industry and improve the accuracy and reliability of the accounting.

[0154] The mathematical form of the dynamic carbon accounting density function is:

[0155]

[0156] where represents the value of the dynamic carbon accounting density function at position x, that is, the comprehensive carbon emission intensity index at this position.

[0157] represents the spatial position of the water supply system. In practical applications, its specific value can be determined through a geographic information system (GIS) or the distribution of monitoring points in the water supply system. Its significance lies in clarifying the specific position for calculating the carbon emission density, so as to accurately depict the carbon emissions of different spatial positions in the water supply system.

[0158] Indicates the fusion weight of the ith scenario cluster, which reflects the relative importance of the ith scenario cluster in the calculation of the entire dynamic carbon accounting density function. The greater the weight, the greater the impact of the scenario cluster on the final result.

[0159] Indicates the number of scenario clusters.

[0160] Indicates the local mean of the power entropy pulse signal at position x, reflecting the dynamic change characteristics of the power carbon emission intensity at this position.

[0161] Indicates the local mean of the carbon emission chain correlation matrix at position x, reflecting the correlation characteristics of carbon emissions in links such as chemical agent dosing at this position.

[0162] Indicates the local mean of the sludge carbon emission eddy current coupling coefficient at position x, reflecting the coupling characteristics of carbon emissions during the sludge treatment and landfill processes at this position.

[0163] Indicates the trace operation of the matrix, that is, the sum of the elements on the main diagonal of the matrix, which is used to transform the carbon emission chain correlation matrix

[0164] Indicates taking the maximum value at each position, which is used to normalize the power entropy pulse signal , the trace of the carbon emission chain correlation matrix , and the sludge carbon emission eddy current coupling coefficient

[0165] When increases, if other terms remain unchanged, the contribution of the corresponding scenario cluster to the function value increases, will increase accordingly; When any value in increases and the ratio of it to the corresponding maximum value increases, it will also cause

[0166] Heterogeneous entropy weight deviation to increase, and vice versa. This indicates that when the intensity or correlation of each carbon source at the corresponding position increases, the value of the dynamic carbon accounting density function at this position will increase, reflecting a more significant carbon emission situation. This formula fuses multi-source heterogeneous carbon source data such as power, chemical consumption, and sludge, comprehensively reflecting the carbon emission situation of the water supply system at different spatial positions, and achieving integrated accounting on spatio-temporal multi-scales. By comprehensively considering the contributions of each scenario cluster and the characteristics of different carbon sources, the dynamic adaptability and robustness of the accounting paradigm are improved, which helps to accurately evaluate the carbon emissions of the water supply system and provide a scientific basis for formulating targeted emission reduction strategies.The calculation method is as follows:

[0167] According to the fusion weight vector of the scenario clusters , calculate the arithmetic mean of the fusion weights of each scenario cluster , according to and , calculate the heterogeneous entropy weight deviation .

[0168]

[0169] Among them, represents the arithmetic mean of the fusion weights of each scenario cluster, reflects the heterogeneity of the entropy weights of each scenario cluster, The larger it is, the worse the adaptability of the fusion algorithm to different scenarios, and further optimization is required.

[0170] When the fusion weights of each scenario cluster differ greatly, the value of will increase, and the numerator increases. When the denominator changes relatively little, will increase, indicating that the adaptability of the fusion algorithm to different scenarios is worse; on the contrary, when the fusion weights of each scenario cluster are closer, the smaller it is, the better the adaptability of the fusion algorithm. This formula is used to evaluate the rationality of the fusion weight vector W of the scenario clusters and reflects the heterogeneity of the entropy weights of each scenario cluster. By calculating the heterogeneous entropy weight deviation, it is possible to judge the adaptability of the current fusion algorithm to different scenarios. The smaller the deviation, the more reasonable the fusion weight distribution, the better the adaptability of the fusion algorithm to different scenarios, and the more reliable the accounting result.

[0171] Step S3000, according to the dynamic carbon accounting density function and the heterogeneous entropy weight deviation , construct a carbon accounting gradient multi-objective optimization model; solve the carbon accounting gradient multi-objective optimization model, and iteratively optimize the carbon accounting gradient of each scenario cluster to obtain the optimal carbon accounting gradient , according to the optimal carbon accounting gradient , output the optimized result of multi-scenario carbon emission accounting.

[0172] Furthermore, step S3000 includes:

[0173] Step S3100, according to the dynamic carbon accounting density function and the heterogeneous entropy weight deviation , construct a carbon accounting gradient multi-objective optimization model;

[0174] Furthermore, step S3100 includes:

[0175] Step S3110, integrate the dynamic carbon accounting density function to calculate the total carbon emissions ;

[0176] Specifically, the dynamic carbon accounting density function integrates multi-source carbon data such as electricity, chemicals, and sludge in the water supply system, reflecting the carbon emissions of the water supply system at spatial location x. x represents a specific spatial location in the water supply system, covering the areas where raw water is taken, water is treated in the water plant, and water is distributed. Integrate this function over the spatial range X of the water supply system. The formula is ; The integration operation here is a mathematical means that accumulates the carbon emission densities at different spatial locations x to obtain the overall carbon emission level of the entire water supply system .

[0177] For example, if the water supply system is divided into multiple small blocks, each small block has a corresponding value. Through integral calculation, the carbon emissions of these small blocks can be summarized to obtain the total carbon emissions of the entire water supply system. The purpose of this step is to quantify the total carbon emissions of the entire water supply system, providing an overall indicator for subsequent evaluation and analysis. Its beneficial effect is that in the absence of a unified and accurate accounting method, it can comprehensively and accurately measure the carbon emission scale of the water supply system. This is crucial for the urban low-carbon development plan. For example, urban managers can clearly understand the proportion of the water supply industry in the total urban carbon emissions based on the value, and thus formulate more reasonable carbon emission targets and emission reduction strategies. At the same time, it also provides a clear quantitative basis for the energy conservation and emission reduction work of the water supply industry itself, helps to evaluate the carbon emission changes of different water supply systems in different periods, and then specifically improves the process and optimizes management, solving the problem of inaccurate quantification of the total carbon emissions in the carbon emission accounting of the water supply industry.

[0178] Step S3120, calculate the theoretical total carbon emissions of the water supply system according to the carbon accounting gradient of each scenario cluster and the water supply volume of the water supply unit corresponding to each scenario cluster ;

[0179] Specifically, the carbon accounting gradient of each scenario cluster was determined during the previous division of scenario clusters based on factors such as process entropy value, water source coupling factor, and load fluctuation coefficient. It reflects the carbon emission difference per unit water supply under different scenario clusters. Denote the carbon accounting gradient of the $i$-th scenario cluster, where $i$ represents different scenario cluster numbers, ranging from 1 to $N$, and $N$ is the number of scenario clusters with $N\geq3$. The water supply volume of the water supply unit corresponding to each scenario cluster is obtained by monitoring and counting the actual water supply of each unit in the water supply system. The formula for calculating the total theoretical carbon emissions is , where denotes the water supply volume of the water supply unit corresponding to the $i$-th scenario cluster; the meaning of this formula is to multiply the carbon accounting gradient of each scenario cluster by the water supply volume of the corresponding water supply unit, and then sum up the calculation results of all scenario clusters to obtain the estimated value of the theoretical carbon emissions based on scenario classification . The purpose of this step is to estimate the total carbon emissions of the water supply system based on the scenario classification method, taking into account the impact of the differences in water supply units under different scenarios on carbon emissions. Its beneficial effect is that it can analyze the contribution of different scenarios to the total carbon emissions in more detail. Since the carbon accounting gradients of different scenario clusters are different, reflecting the carbon emission characteristics under different processes, water sources, loads, etc., by combining the water supply volume to calculate the total theoretical carbon emissions, it can be determined which scenario clusters are the main carbon emission sources. For example, if it is found through calculation that a certain scenario cluster has a relatively high carbon accounting gradient and a large water supply volume, then this scenario cluster is the key object of concern for carbon emissions, and specific emission reduction measures can be formulated according to its characteristics, which helps to improve the refinement of carbon emission accounting and solves the problem that the carbon emission accounting in the water supply industry cannot consider scenario differences for estimation

[0180] Step S3130, according to and , define the cross-modal verification deviation ;

[0181] Specifically, the cross-modal verification deviation $\varepsilon$ is defined by the formula , which characterizes the deviation degree between the two carbon accounting modes of dynamic accounting and scenario classification The smaller it is, the more consistent the two modalities are, and the higher the reliability of the accounting system. The purpose of this step is to measure the difference between two different accounting methods, so as to evaluate the reliability of the accounting system. Its beneficial effect is to provide a quantitative index for judging the accuracy of the accounting system. The smaller ε is, the closer the actual accounting result based on the dynamic carbon accounting density function is to the theoretical estimation result based on scenario classification, which means that the consistency of the accounting system under different accounting methods is higher and the accounting result is more reliable. For example, if ε always remains at a low level during multiple accounting processes, it indicates that the accounting system can relatively accurately reflect the carbon emissions of the water supply system; on the contrary, if ε is large, it indicates that there is a large difference between the two accounting modalities, and there may be problems such as unreasonable scenario division and inaccurate parameter calculation, and the accounting system needs to be optimized. This helps to improve the credibility of carbon emissions accounting in the water supply industry and solves the problem of lack of a quantitative standard for evaluating the reliability of the accounting system.

[0182] Step S3140, combining cross-modal verification deviation and heterogeneous entropy weight deviation , to construct a carbon accounting gradient multi-objective optimization model.

[0183]

[0184] Among them, J is the carbon accounting gradient multi-objective optimization function, which is the objective to be minimized; is the weight coefficient of the cross-modal verification deviation , is the weight coefficient of the heterogeneous entropy weight deviation , and are used to adjust the relative importance of the two deviations in the optimization objective; is the total water supply of the water supply system, and the constraint condition ensures that the sum of the water supplies of each scenario cluster is equal to the total water supply. This is based on the principle of mass conservation to ensure the rationality of the water supply during the calculation process; ensures that the carbon accounting gradient is non-negative, because the carbon accounting gradient represents the carbon emissions per unit of water supply and cannot be negative in practical significance. The values of the weight coefficients α and β will affect the focus of optimization. For example, in practical applications, if more attention is paid to the consistency between the dynamic accounting and the scenario classification accounting results, then the value of α can be appropriately increased to make the optimization process focus more on reducing the cross-modal verification deviation ε; if more attention is paid to the adaptability of the fusion algorithm to different scenarios, that is, it is hoped that the heterogeneous entropy weight deviation is smaller, then the value of β can be increased. When actually determining α and β, the most suitable value combination for a specific water supply system can be found through multiple experiments, expert experience, or machine learning algorithms, etc.

[0185] The purpose of this model is to find the optimal carbon accounting gradient by minimizing the cross-modal verification bias and heterogeneous entropy weight bias, so as to balance the consistency and difference between dynamic accounting and scenario-based verification and improve the overall performance of the accounting system. Its beneficial effect is that it comprehensively considers the two important deviations in the accounting process, so that the optimized carbon accounting gradient is more in line with the actual situation. For example, in the actual water supply system, after multiple iterative optimizations, when J reaches a small value, the optimal carbon accounting gradient obtained can make the dynamic accounting and scenario-based accounting results closer, and the fusion algorithm is more adaptable to different scenarios, thereby achieving more accurate carbon emission accounting. This helps to solve the problems of unreasonable carbon accounting gradients and the need to improve the performance of the accounting system in carbon emission accounting in the water supply industry, and provide more reliable technical support for achieving low-carbon development in the water supply industry.

[0186] Step S3200, solving the carbon accounting gradient multi-objective optimization model, iteratively optimizing the carbon accounting gradient of each scenario cluster ;

[0187] Further, step S3200 includes:

[0188] Step S3210: Initialize the carbon accounting gradient of each scenario cluster , let the iteration number variable , set the maximum number of iterations and convergence threshold ;

[0189] Specifically, the iteration number variable Initialized to 0, which means the optimization process starts counting from the initial state. Maximum number of iterations It is a pre-set value used to limit the number of iterations. This is because in actual calculations, the iteration process may not be able to proceed indefinitely due to various reasons (such as computing resource limitations, algorithm convergence characteristics, etc.). Setting this value can ensure that the algorithm ends within a reasonable time and resource range. The convergence threshold δ is the standard for judging whether the iteration has converged. When the change in the cross-modal verification deviation ε obtained from two adjacent iterations is less than the convergence threshold δ, it is considered that the iteration process has converged, that is, a relatively stable result that meets the accuracy requirements has been achieved. The purpose of this step is to make initial preparations for the iterative optimization process and determine the starting conditions and termination rules. Its beneficial effect is that it provides a clear operating framework for the entire optimization algorithm. By reasonably setting the initial value and restriction conditions, the operating range and accuracy of the optimization process can be controlled to avoid the algorithm from falling into an infinite loop or producing results that do not meet actual needs. In the carbon emission accounting of the water supply industry, this helps to improve the calculation efficiency and the reliability of the results, solves the problem of lack of effective control and unclear starting conditions in the optimization process, and lays the foundation for subsequent accurate optimization of the carbon accounting gradient.

[0190] Step S3220: Fix the carbon accounting gradients of other scenario clusters and optimize the carbon accounting gradient of the th scenario cluster. ;

[0191] Let:

[0192]

[0193] In this formula, represents the carbon accounting gradient value of the i-th scenario cluster at the s-th iteration, which is the carbon accounting gradient state of this scenario cluster under the current iteration;

[0194] represents the carbon accounting gradient value of the i-th scenario cluster at the (s + 1)-th iteration, which is the result after this optimization update.

[0195] is the learning rate, which is a parameter controlling the optimization step size. It determines the adjustment amplitude of the carbon accounting gradient in each iteration. If η is too large, it may cause the optimization process to jump back and forth near the optimal solution and fail to converge; if it is too small, the optimization process will become very slow and increase the calculation time.

[0196] is the multi-objective optimization function of the carbon accounting gradient The partial derivative of reflects the influence degree of changing on the objective function J under the current state. By calculating the partial derivative, it can be determined in which direction to adjust to make the objective function J decrease faster.

[0197] By continuously adjusting the carbon accounting gradients of each scenario cluster, the objective function J gradually decreases, thus approaching the optimal solution. Its beneficial effect is to provide a specific calculation method for optimizing the carbon accounting gradient. In the carbon emission accounting of the water supply industry, through this method, the carbon accounting gradient can be gradually improved, making the dynamic accounting more consistent with the scenario classification verification and improving the accuracy of the accounting system. Each iteration is adjusted based on the current carbon accounting gradient and the change rate of the objective function, which can fully consider the mutual relationship between each scenario cluster and its impact on the overall accounting result, and solves the problem of how to optimize the carbon accounting gradient to improve the performance of the accounting system.

[0198] Step S3230: Loop and execute Step S3220 until the carbon accounting gradients of all scenario clusters are optimized, and let ;

[0199] Specifically, by continuously repeating the execution of step S3220, the carbon accounting gradient of each scenario cluster is optimized in sequence. In each iteration, the carbon accounting gradients of other scenario clusters are fixed, and only the carbon accounting gradient of the currently selected scenario cluster is updated until all scenario clusters have completed one optimization, and then the iteration number variable s is incremented by 1. This process continues until the termination condition in step S3240 is met. For example, for a water supply system with 5 scenario clusters, in the first iteration, the carbon accounting gradients of scenario clusters 1, 3, 4, and 5 are fixed first, and the carbon accounting gradient of scenario cluster 2 is optimized; then the other 4 scenario clusters are fixed, and the carbon accounting gradient of scenario cluster 1 is optimized, and so on. After completing one optimization of all 5 scenario clusters, s changes from 0 to 1. Then, the second iteration is carried out, repeating the above process, and continuously adjusting the carbon accounting gradients of each scenario cluster. The purpose of this step is to gradually optimize the carbon accounting gradients of all scenario clusters through multiple iterations to achieve the overall optimal effect. Its beneficial effect lies in ensuring the comprehensiveness and systematicness of the optimization process. In the carbon emission accounting of the water supply industry, due to the mutual correlation and influence between scenario clusters, optimizing a single scenario cluster alone may not achieve the overall optimum. Through the way of cyclic iteration, the situations of each scenario cluster can be comprehensively considered, the carbon accounting gradient can be continuously improved, making the accounting system more in line with the actual carbon emission situation, improving the accuracy and reliability of the accounting, and solving the problem of comprehensive optimization of carbon accounting gradients in multiple scenarios.

[0200] Step S3240, calculate the cross-modal verification deviation after optimization , if or , then execute step S3250; otherwise, execute step S3220; where represents the cross-modal verification deviation after the s-th iteration optimization, represents the cross-modal verification deviation after the (s + 1)-th iteration optimization;

[0201] Specifically, first substitute the optimized carbon accounting gradients of each scenario cluster obtained in the -th iteration into the formula of step S3120 to calculate the optimized theoretical total carbon emissions , and substitute into the calculation formula of the cross-modal verification deviation in step S3130 to obtain the cross-modal verification deviation after the -th iteration optimization, and then compare with the obtained in the previous iteration, and calculate the absolute value of their difference If this difference is less than the convergence threshold δ, it indicates that the iterative process has converged, and the optimized carbon accounting gradient has made the dynamic accounting and the scenario - level accounting results close enough, meeting the expected accuracy requirements; or when the number of iterations s is greater than the maximum number of iterations then, regardless of whether it has converged, stop the iterative process and execute step S3250. If the above two conditions are not met, return to step S3220 to continue the iterative optimization. The purpose of this step is to determine whether the iterative optimization process has achieved the expected effect and decide whether to continue the iteration. Its beneficial effect is to ensure that the optimization process ends within a certain accuracy requirement or within a reasonable number of calculations. In the carbon emission accounting of the water supply industry, it avoids unnecessary waste of computing resources and at the same time ensures that the finally obtained carbon accounting gradient can make the accounting system have high reliability. By monitoring the cross - modal verification deviation and comparing it with the convergence threshold, the quality of the optimization result can be effectively controlled, solving the problems of how to determine the termination condition of the optimization process and ensuring the accuracy of the optimization result.

[0202] Step S3250, output the optimal carbon accounting gradient , where represents the optimal carbon accounting gradient of the i - th scenario cluster.

[0203] Specifically, after the previous iterative optimization process, when the termination conditions in step S3240 are met, the carbon accounting gradients of each scenario cluster obtained at this time are the optimal carbon accounting gradients. These optimal carbon accounting gradients are the results obtained through iterative optimization on the basis of comprehensively considering factors such as the dynamic carbon accounting density function, heterogeneous entropy - weight deviation, and cross - modal verification deviation. They can balance the consistency and difference between dynamic accounting and scenario - level verification and are the key achievements of the optimization of the entire multi - scenario carbon emission accounting system. The purpose of this step is to output the optimized carbon accounting gradient and provide the final optimization result for multi - scenario carbon emission accounting. Its beneficial effect is to provide more accurate parameters for the carbon emission accounting of the water supply industry. Based on these optimal carbon accounting gradients, the carbon emission situation under different scenarios can be evaluated more accurately, providing strong support for formulating targeted emission reduction strategies. For example, when formulating emission reduction measures, according to the optimal carbon accounting gradients of different scenario clusters, key attention can be paid to the scenario clusters with higher carbon emission intensity, and more stringent emission reduction measures can be taken, thus achieving the energy - saving and emission - reduction goals of the water supply industry and solving the problems of inaccurate carbon accounting gradients and inability to meet actual needs in the carbon emission accounting of the water supply industry.

[0204] Step S3300, based on the optimal carbon accounting gradients of each scenario cluster, output the multi - scenario carbon emission accounting optimization result.

[0205] Specifically, the optimal carbon accounting gradient is determined after comprehensively considering multiple factors such as the process topology characteristics of the water supply system, water source heterogeneity, spatiotemporal load fluctuations, as well as multiple indicators such as dynamic carbon accounting density function and heterogeneous entropy weight deviation. They can more accurately reflect the carbon emissions per unit water supply under different scenarios, and are an accurate quantification of the carbon emission characteristics of the water supply system. Taking a water supply system containing three scenario clusters as an example, assuming that after iterative optimization, the optimal carbon accounting gradient of scenario cluster 1 (Unit: tons of CO2 / m3 of water supply), optimal carbon accounting gradient for scenario cluster 2 , optimal carbon accounting gradient for scenario cluster 3 These values show that under scenario cluster 1, carbon emissions per cubic meter of water supply are about 0.4 tons; the carbon emissions of scenario cluster 2 are relatively low, with 0.3 tons of carbon dioxide produced per cubic meter of water supply; and the carbon emission intensity of scenario cluster 3 is higher, with 0.5 tons of carbon dioxide produced per cubic meter of water supply.

[0206] The multi-scenario carbon emission accounting optimization results based on these optimal carbon accounting gradient outputs contain more accurate carbon emission accounting data under different scenario clusters. This result can provide multi-faceted support for carbon emission management in the water supply industry:

[0207] Accurately assess carbon emissions: By multiplying the optimal carbon accounting gradient of each scenario cluster by the water supply of the corresponding water supply unit, the carbon emissions of each scenario cluster can be accurately calculated, and then the total carbon emissions of the entire water supply system under different scenarios can be summarized. This enables water supply companies and relevant management departments to clearly understand the carbon emissions under different operating conditions. For example, for scenario clusters with high carbon accounting gradients and large water supply, they can be clearly identified as key control areas for carbon emissions, and resources can be concentrated on the formulation and implementation of energy-saving and emission reduction measures.

[0208] Guide the formulation of emission reduction strategies: formulate emission reduction strategies in a targeted manner according to the carbon emission characteristics of different scenario clusters. For scenario clusters with higher carbon accounting gradients, we can deeply analyze the reasons for their high emissions, such as whether it is due to high energy consumption caused by a specific process, or whether the poor water quality at the water source increases carbon emissions during the treatment process. Then, take corresponding measures to address these reasons, such as optimizing the process, improving water source treatment methods, etc., to achieve precise emission reduction. For example, if it is found that the high carbon emissions of a scenario cluster are mainly due to the low energy efficiency of a certain treatment process, resources can be invested to upgrade the process and reduce its carbon emission intensity.

[0209] Support for low-carbon development planning: Provide a scientific basis for the low-carbon development planning of cities or regions. City managers can rationally plan the layout and operation mode of the water supply system according to the optimized results of multi-scenario carbon emission accounting. For example, for areas with high carbon emission intensity, the allocation of water supply units can be considered adjusted to reduce the water supply volume in high-emission areas, or cleaner water sources can be introduced to overall reduce the carbon emissions of the water supply industry and promote the transformation of the city towards low-carbon development.

[0210] The optimized results of multi-scenario carbon emission accounting output in step S3300 solve the problem of the current lack of a unified, accurate, and applicable carbon emission accounting method for the water supply industry in different scenarios. By providing accurate carbon emission data and targeted emission reduction basis, it helps to meet the needs of urban low-carbon development and energy conservation and emission reduction in the water supply industry. At the same time, this result also provides a solid data foundation and decision-making support for further studying the influencing factors of carbon emissions in the water supply system, optimizing the accounting method, and evaluating the emission reduction effect, etc., and promotes the sustainable development of the water supply industry in the context of addressing climate change.

[0211] Embodiment 2

[0212] On the basis of Embodiment 1, this embodiment provides an optimized system for the water supply carbon emission accounting system driven by multiple scenarios, as Figure 8 shown, including:

[0213] Scenario classification module: According to the process topology characteristics, water source heterogeneity, and spatio-temporal load fluctuations of the water supply system, construct a three-dimensional dynamic entropy weight classification model. According to the three-dimensional dynamic entropy weight classification model, divide the accounting system into N scenario clusters, and calculate the carbon accounting gradient of each scenario cluster , where N≥3;

[0214] Deviation calculation module: Real-time collect heterogeneous carbon source pulse data of the entire water supply process, map the heterogeneous carbon source pulse data to the N scenario clusters divided by the accounting system, and generate a dynamic carbon accounting density function , and calculate the heterogeneous entropy weight deviation ;

[0215] Carbon accounting optimization module: According to the dynamic carbon accounting density function and the heterogeneous entropy weight deviation , construct a multi-objective optimization model for carbon accounting gradient; Solve the multi-objective optimization model for carbon accounting gradient, and iteratively optimize the carbon accounting gradient of each scenario cluster to obtain the optimal carbon accounting gradient . According to the optimal carbon accounting gradient , output the optimized results of multi-scenario carbon emission accounting.

[0216] In the scenario classification module, the step of dividing the accounting system into N scenario clusters and calculating the carbon accounting gradient of each scenario cluster Including:

[0217] Step S1510: Using the process entropy value E p , water source coupling factor α s and load fluctuation coefficient β t as three dimensions, perform three-dimensional space mapping on each water supply unit of the water supply system to form a sample point set;

[0218] Step S1520: Perform fuzzy C-means clustering on the sample point set to obtain N scenario clusters;

[0219] Step S1530: Calculate the mean of the process entropy value, the mean of the water source coupling factor, and the mean of the load fluctuation coefficient for each scenario cluster. Then, , and are weighted and calculated according to the entropy weight vector [w p , w s , w t in the three-dimensional dynamic entropy weight classification model to obtain the carbon accounting gradient = , where represents the carbon accounting gradient of the Nth scenario cluster.

[0220] In the deviation calculation module, the generation of the dynamic carbon accounting density function includes:

[0221] Step S2100: Collect the real-time power consumption data of each link of the water supply system and the real-time carbon emission factor of the regional power grid. According to the real-time power consumption data of each link of the water supply system and the real-time carbon emission factor of the regional power grid, calculate the real-time carbon emission intensity time series data to obtain the power entropy pulse signal P e ;

[0222] Step S2200: Obtain the real-time chemical agent dosing data and chemical agent transportation distance of the water supply system. According to the real-time chemical agent dosing data and chemical agent transportation distance of the water supply system, obtain the carbon emission chain correlation matrix Γ c of the chemical agent link;

[0223] Step S2300: Obtain the methane emission rate data r d (t) of the sludge anaerobic fermentation of the sludge treatment unit in the sludge treatment link, the methane emission rate data r l (t) of the landfill process in the landfill site, the key sludge treatment parameters of the sludge treatment unit and the key landfill parameters of the landfill site, and calculate the carbon emission eddy current coupling coefficient Ω s of the sludge treatment and landfill processes;

[0224] Step S2400, set the fusion weight vector of the scenario clusters , where is the fusion weight of the Nth scenario cluster; based on the fusion weight vector of the scenario clusters , fuse the power entropy pulse signal P e , the carbon emission chain correlation matrix Γ c and the carbon emission eddy current coupling coefficient Ω s to generate the dynamic carbon accounting density function , and calculate the heterogeneous entropy weight deviation .

[0225] In the carbon accounting optimization module, solving the carbon accounting gradient multi-objective optimization model and iteratively optimizing the carbon accounting gradient of each scenario cluster includes:

[0226] Step S3210, initialize the carbon accounting gradient of each scenario cluster , let the iteration number variable , set the maximum number of iterations and the convergence threshold ;

[0227] Step S3220, fix the carbon accounting gradients of other scenario clusters, and optimize the carbon accounting gradient of the th scenario cluster;

[0228] Step S3230, loop and execute Step S3220 until the carbon accounting gradients of all scenario clusters are optimized, and let ;

[0229] Step S3240, calculate the optimized cross-modal verification deviation , if or , then execute Step S3250; otherwise, execute Step S3220; where represents the cross-modal verification deviation after the s-th iterative optimization, represents the cross-modal verification deviation after the (s + 1)-th iterative optimization;

[0230] Step S3250, output the optimal carbon accounting gradient , where represents the optimal carbon accounting gradient of the i-th scenario cluster.

[0231] The methods and systems of the present application can be implemented in many ways. For example, the methods and systems of the present application can be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present application are not limited to the order specifically described above, unless otherwise specifically stated.

[0232] In addition, parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive elaboration.

[0233] As described above in the specific embodiments, the objectives, technical solutions, and beneficial effects of the present invention have been further described in detail. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An optimization method for a water supply carbon emission accounting system driven by multiple scenarios, characterized in that, The method includes: Construct a three-dimensional dynamic entropy weight classification model according to the process topology characteristics of the water supply system, the heterogeneity of water sources, and the spatio-temporal load fluctuations. According to the three-dimensional dynamic entropy weight classification model, divide the accounting system into N scenario clusters, and calculate the carbon accounting gradient of each scenario cluster , where N ≥ 3; Real-time collect heterogeneous carbon source pulse data of the entire water supply process, map the heterogeneous carbon source pulse data to N scenario clusters divided by the accounting system, and generate a dynamic carbon accounting density function , and calculate the heterogeneous entropy weight deviation ; According to the dynamic carbon accounting density function and the heterogeneous entropy weight deviation , a carbon accounting gradient multi-objective optimization model is constructed; the carbon accounting gradient multi-objective optimization model is solved, and the carbon accounting gradient of each scenario cluster is iteratively optimized to obtain the optimal carbon accounting gradient . According to the optimal carbon accounting gradient , the optimized results of multi-scenario carbon emission accounting are output.

2. The optimized method for calculating the carbon emissions of water supply driven by multiple scenarios according to claim 1, characterized in that The construction of the three-dimensional dynamic entropy weight grading model includes: Based on the process topology characteristics of the water supply system, obtain the historical power consumption data and historical chemical dosage data of each treatment process. According to the historical power consumption data and historical chemical dosage data of each treatment process, obtain the process entropy value E p ; Based on the heterogeneity of water sources in the water supply system, obtain the raw water BOD concentration and the length of the water transmission pipeline network of each water source site, and generate the water source coupling factor α according to the raw water BOD concentration and the length of the water transmission pipeline network of each water source site s ; Calculate the load fluctuation coefficient β based on the spatio-temporal load fluctuations of the water supply system t ; According to the process entropy value E p , the water source coupling factor α s and the load fluctuation coefficient β t , a three-dimensional dynamic entropy weight classification model is constructed.

3. The optimization method for the multi-scenario-driven water supply carbon emission accounting system according to claim 2, wherein, The obtained process entropy value E p includes: According to the historical power consumption data of each treatment process, calculate the power consumption entropy increase rate of each treatment process, and subtract the power consumption entropy increase rate of each treatment process from that of the benchmark process to obtain the relative entropy increase rate; Obtain the total chemical agent dosage of each treatment process according to the historical chemical agent dosage data of each treatment process, and calculate the ratio of the total chemical agent dosage to the water inflow to obtain the chemical agent dissipation ratio; The relative entropy increase rate and the agent dissipation ratio are linearly combined to obtain the process entropy value E p .

4. The optimization method of the multi-scenario-driven water supply carbon emission accounting system according to claim 2, characterized in that, The generated water source coupling factor α s includes: The BOD concentration of the raw water at each water source is normalized by min-max to obtain the BOD heterogeneity index; the length of the water transmission pipeline network at each water source is normalized by min-max to obtain the distance heterogeneity index; the BOD heterogeneity index and the distance heterogeneity index are weighted and summed to obtain the water source coupling factor α s .

5. The optimization method of the multi-scenario-driven water supply carbon emission accounting system according to claim 2, wherein The calculated load fluctuation coefficient β t includes: Based on the spatio-temporal load fluctuation of the water supply system, obtain the historical monthly water production data and the historical carbon emission intensity time series data; According to the historical monthly water production data, calculate the coefficient of variation of the monthly average water production to obtain the water production fluctuation index; Perform Fourier transform on the historical carbon emission intensity time series data, extract the dominant frequency, and calculate the peak period to obtain the carbon emission intensity periodicity index; Take the square root of the product of the water production fluctuation index and the carbon emission intensity periodic index to obtain the load fluctuation coefficient β t .

6. The optimization method of the multi-scenario-driven water supply carbon emission accounting system according to claim 2, wherein, The construction of the three-dimensional dynamic entropy weight grading model includes: For the process entropy value E p , the water source coupling factor α s and the load fluctuation coefficient β t are respectively normalized; Assign a weight coefficient w to the normalized process entropy value p and assign a weight coefficient w to the normalized water source coupling factor s and assign a weight coefficient w to the normalized load fluctuation coefficient t ; Let w p , w s , and w t form an entropy weight vector [w p , w s , w t ; From the process entropy value E p , water source coupling factor α s , load fluctuation coefficient β t and entropy weight vector [w p , w s , w t , a three-dimensional dynamic entropy weight classification model is constructed.

7. The method for optimizing the water supply carbon emission accounting system driven by multiple scenarios according to claim 6, characterized in that Calculating the carbon accounting gradient for each scenario cluster including: Calculate the mean value of the process entropy of each scenario cluster , the mean value of the water source coupling factor and the mean value of the load fluctuation coefficient , and use , and to perform weighted calculations according to the entropy weight vector [w p , w s , w t in the three-dimensional dynamic entropy weight classification model to obtain the carbon accounting gradient of each scenario cluster = , where represents the carbon accounting gradient of the Nth scenario cluster.

8. The optimization method of the multi-scenario-driven water supply carbon emission accounting system according to claim 1, characterized in that The heterogeneous carbon source pulse data of the entire water supply process includes the real-time power consumption data of each link of the water supply system and the real-time carbon emission factor of the regional power grid, the real-time chemical agent dosing data and the chemical agent transportation distance of the water supply system, and the methane emission rate data r d (t) of the anaerobic fermentation of sludge in the sludge treatment unit during the sludge treatment process, and the methane emission rate data r l (t) during the landfill process of the landfill, the key sludge treatment parameters of the sludge treatment unit, and the key landfill parameters of the landfill; The generation of the dynamic carbon accounting density function includes: Based on the heterogeneous carbon source pulse data of the entire water supply process, the power entropy pulse signal P is obtained e , the carbon emission chain correlation matrix Γ of the chemical agent link c and the carbon emission eddy current coupling coefficient Ω of the sludge treatment and landfill process s ; set the fusion weight vector of the scenario cluster , where is the fusion weight of the Nth scenario cluster; based on the fusion weight vector of the scenario cluster , fuse the power entropy pulse signal P e , the carbon emission chain correlation matrix Γ c and the carbon emission eddy current coupling coefficient Ω s , and generate the dynamic carbon accounting density function .

9. The optimization method of the multi-scenario-driven water supply carbon emission accounting system according to claim 8, characterized in that The heterogeneous carbon source pulse data of the entire water supply process includes the real-time power consumption data of each link of the water supply system, the real-time carbon emission factor of the regional power grid, the real-time chemical agent dosage data of the water supply system, and the chemical agent transportation distance; The obtained power entropy pulse signal P e includes: Multiply the real-time power consumption data of each link of the water supply system by the real-time carbon emission factor of the regional power grid to obtain the real-time carbon emission intensity time series data of the power consumption; Perform pulse detection on the real-time carbon emission intensity time series data, extract the power carbon emission pulse events, and construct the power entropy pulse signal P e .

10. The optimization method of the multi-scenario-driven water supply carbon emission accounting system according to claim 9, characterized in that The real-time chemical agent dosage data includes the real-time chemical agent dosage and the chemical agent concentration; The carbon emission chain correlation matrix Γ of the pharmaceutical process c including: Organize the real-time chemical agent dosage, chemical agent concentration, and chemical agent transportation distance of each chemical agent into a three-dimensional correlation matrix; Quantify each element in the three-dimensional association matrix into carbon emission equivalents to generate a carbon emission chain association matrix Γ c .

11. The optimized method for calculating the carbon emissions of water supply driven by multiple scenarios according to claim 1, characterized in that, The construction of the carbon accounting gradient multi-objective optimization model includes: Integrate the dynamic carbon accounting density function to calculate the total carbon emissions ; According to the carbon accounting gradient of each scenario cluster and the water supply volume of the water supply unit corresponding to each scenario cluster , calculate the total theoretical carbon emissions of the water supply system ; According to and , define the cross-modal verification deviation ; Combined with cross-modal verification deviation and heterogeneous entropy weight deviation , a carbon accounting gradient multi-objective optimization model is constructed.

12. An optimization system for a multi-scenario-driven water supply carbon emission accounting system, which is used to implement the multi-scenario-driven water supply carbon emission accounting system optimization method described in any one of claims 1-11, characterized in that, The system includes: Scenario Classification Module: Construct a three-dimensional dynamic entropy weight classification model based on the process topology characteristics of the water supply system, water source heterogeneity, and spatio-temporal load fluctuations. According to the three-dimensional dynamic entropy weight classification model, divide the accounting system into N scenario clusters, and calculate the carbon accounting gradient of each scenario cluster , where N ≥ 3; Deviation calculation module: Collect heterogeneous carbon source pulse data of the entire water supply process in real time, map the heterogeneous carbon source pulse data to N scenario clusters divided by the accounting system, and generate a dynamic carbon accounting density function , and calculate the heterogeneous entropy weight deviation ; Carbon accounting optimization module: According to the dynamic carbon accounting density function and the heterogeneous entropy weight deviation , construct a carbon accounting gradient multi-objective optimization model; Solve the carbon accounting gradient multi-objective optimization model and iteratively optimize the carbon accounting gradient of each scenario cluster to obtain the optimal carbon accounting gradient , and according to the optimal carbon accounting gradient , output the optimized results of multi-scenario carbon emission accounting.

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