Urban energy-saving and carbon-reducing intelligent operation method and system

Abstract

This invention discloses a city-level intelligent operation method and system for energy conservation and carbon reduction. It constructs a unified energy consumption benchmark based on building type and operating period by collecting energy consumption data from urban buildings and integrating carbon emission data, enabling horizontally comparable evaluation of different buildings. It analyzes the correlation between environmental factors and energy consumption characteristics, identifies the operational controllability of energy consumption, and identifies high-load building nodes through load balancing analysis to implement energy consumption restriction measures and explore effective carbon reduction potential. It establishes conversion rules to standardize carbon reduction potential, generating carbon reduction assessment indicators including potential index, efficiency index, and priority index. It performs spatial positioning and cluster analysis on buildings to identify the collaborative characteristics of regional building clusters and quantify collaborative capabilities, formulating graded carbon reduction operation plans at the regional collaborative level and individual building level, thus achieving accurate identification, scientific evaluation, and intelligent decision-making for urban building carbon reduction.

Description

Technical Field

[0001] This invention relates to the field of smart city energy management technology, and in particular to a city-level energy-saving and carbon-reducing intelligent operation method and system. Background Technology

[0002] Currently, cities have a large number of buildings of various types, with significant differences in their energy consumption characteristics, operating modes, and energy-saving foundations. Existing energy management methods mostly remain at the level of data monitoring and statistics, lacking in-depth analysis of the patterns behind massive amounts of energy consumption data. This makes it difficult to accurately identify which buildings have greater energy-saving potential and which should be prioritized for renovation. This "one-size-fits-all" management model results in scattered investment of energy-saving resources, a lack of focus, and unsatisfactory results.

[0003] Traditional management methods often view the energy consumption of each building in isolation, neglecting the correlation and complementarity between buildings in terms of spatial distribution, functional attributes, and energy consumption timing. While some buildings may have low total energy consumption, they can impact the overall urban load at certain times; conversely, some areas with numerous buildings may fail to achieve large-scale carbon reduction benefits due to a lack of overall planning and coordinated control. Existing methods, when assessing building energy-saving potential, lack quantitative analysis of key factors such as climate adaptability and operational controllability, leading to unscientific carbon reduction targets and unclear implementation paths. Summary of the Invention

[0004] This invention discloses a city-level intelligent operation method and system for energy conservation and carbon reduction. It aims to establish a comparable evaluation system for urban building energy consumption through multi-source data fusion and intelligent analysis technology, identify the internal driving mechanisms and external influencing factors of building energy consumption, accurately locate high-potential carbon reduction targets, and formulate graded and classified carbon reduction operation strategies based on the spatial distribution and collaborative characteristics of buildings, so as to achieve the optimal allocation of urban carbon reduction resources and the maximization of carbon reduction benefits.

[0005] The first aspect of this invention proposes a city-level energy-saving and carbon-reducing intelligent operation method, comprising the following steps:

[0006] Collect building energy consumption data, environmental monitoring data and carbon emission data, perform time-series fusion of the building energy consumption data and the carbon emission data to generate a unified energy consumption benchmark, and extract energy consumption characteristic curves through the unified energy consumption benchmark;

[0007] The energy consumption characteristic curve is subjected to complementary analysis to confirm carbon reduction characteristics. The carbon reduction characteristics are subjected to characteristic curve analysis to determine matching operation periods. The building weight table is determined according to the matching operation periods. The environmental monitoring data is regionally calibrated based on the building weight table to generate environmental impact factors.

[0008] Based on the correlation analysis between the environmental impact factors and the energy consumption characteristic curve, a separation degree is generated. Based on the separation degree, a load fluctuation analysis is performed to generate a load balance coefficient. High-load building nodes in the load balance coefficient are identified to generate a restriction scheme by reverse energy consumption restriction. Effective carbon reduction potential is identified and effective potential data is generated by using the load balance coefficient and the restriction scheme.

[0009] Based on the effective potential data and the carbon emission data, a conversion rule is constructed. The effective potential data is then standardized and converted using the conversion rule to generate standardized carbon reduction data. The standardized carbon reduction data is then correlated with the unified energy consumption benchmark to generate carbon reduction assessment indicators.

[0010] Spatial location analysis is performed on the carbon reduction assessment indicators to generate building location information. Carbon reduction intensity analysis is performed on the building location information to generate synergy coefficients. Multi-level strategy processing is performed based on the synergy coefficients to generate graded carbon reduction operation plans.

[0011] A second aspect of this invention proposes a city-level energy-saving and carbon-reducing intelligent operation system, comprising:

[0012] The data acquisition module is used to collect building energy consumption data, environmental monitoring data and carbon emission data, perform time-series fusion of the building energy consumption data and the carbon emission data to generate a unified energy consumption benchmark, and extract energy consumption characteristic curves through the unified energy consumption benchmark;

[0013] The feature analysis module is used to perform complementary analysis on the energy consumption feature curve to confirm carbon reduction features, perform feature curve analysis on the carbon reduction features to determine matching operation periods, determine a building weight table based on the matching operation periods, and perform regional calibration on the environmental monitoring data based on the building weight table to generate environmental impact factors.

[0014] The potential identification module is used to generate a separation degree by performing correlation analysis between the environmental impact factors and the energy consumption characteristic curve, generate a load balance coefficient by performing load fluctuation analysis based on the separation degree, identify high-load building nodes in the load balance coefficient and generate a restriction scheme by performing reverse energy consumption restriction, and identify effective carbon reduction potential by the load balance coefficient and the restriction scheme to generate effective potential data.

[0015] The data conversion module is used to construct conversion rules based on the effective potential data and the carbon emission data, standardize the effective potential data through the conversion rules to generate standardized carbon reduction data, and perform correlation analysis between the standardized carbon reduction data and the unified energy consumption benchmark to generate carbon reduction assessment indicators.

[0016] The scheme generation module is used to perform spatial positioning analysis on the carbon reduction assessment indicators to generate building location information, perform carbon reduction intensity analysis on the building location information to generate synergy coefficients, and perform multi-level strategy processing based on the synergy coefficients to generate graded carbon reduction operation schemes.

[0017] The beneficial effects of this invention are reflected in the following points: First, by establishing an energy consumption data collection and processing mechanism, building energy consumption data, environmental monitoring data, and carbon emission data are collected, and the building energy consumption data and carbon emission data are fused in a time series. A unified energy consumption benchmark system based on building type and operating period is constructed. This benchmark system considers the inherent energy consumption characteristics of different building types and the dynamic differences in operating periods. Through the calculation and normalization of benchmark energy consumption, horizontal energy consumption comparison across building types is realized, enabling the energy consumption levels of different types of buildings such as commercial buildings, office buildings, and residential buildings to be evaluated and compared under a unified standard. Second, a correlation analysis method between environmental impact factors and energy consumption characteristic curves is introduced. By calculating and separating the quantification of the dependence of building energy consumption on environmental factors, the dependence of energy consumption on environmental factors is quantified, distinguishing between buildings whose energy consumption is mainly driven by the environment and buildings whose energy consumption is mainly controlled by operation. For buildings with high operational controllability, high-load building nodes that aggravate urban load fluctuations are identified through load balance coefficients. Energy consumption reverse restriction schemes based on peak characteristics and restriction intensity levels are formulated for these nodes. Energy consumption reduction measures are implemented during peak load periods, reducing the overall urban load peak and improving the load balance. Finally, a multi-level carbon reduction assessment and strategy formulation system was established. Carbon reduction potential was standardized through conversion rules, and a comprehensive assessment index was generated, including a carbon reduction potential index, a carbon reduction efficiency index, and a carbon reduction priority index, based on the building's actual carbon emission level and priority weights. Spatial location and cluster analysis of buildings were used to identify building clusters in geographically proximate areas with similar carbon reduction characteristics. Based on the distribution of carbon reduction intensity and synergy characteristics within the clusters, a synergy coefficient was calculated, quantifying the feasibility of regional collaborative carbon reduction. For clusters with high synergy coefficients, regional collaborative carbon reduction plans were formulated, achieving economies of scale through centralized resource allocation, unified renovation standards, and coordinated scheduling. For buildings with low synergy coefficients, individual building-level plans were developed for precise implementation. Attached Figure Description

[0018] The accompanying drawings illustrate specific examples of the technical solutions described in this invention and, together with the detailed embodiments, form part of the specification, serving to explain the technical solutions, principles, and effects of this invention.

[0019] Figure 1 This is a flowchart illustrating a city-level energy-saving and carbon-reducing intelligent operation method according to the present invention.

[0020] Figure 2 This is a structural block diagram of a city-level energy-saving and carbon-reducing intelligent operation system according to the present invention. Detailed Implementation

[0021] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0022] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0023] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0024] The technical solutions of the embodiments of this application will be described below.

[0025] like Figure 1 As shown, this embodiment of the invention provides a city-level energy-saving and carbon-reducing intelligent operation method, including the following steps S110-S150:

[0026] Step S110: Collect building energy consumption data, environmental monitoring data and carbon emission data, perform time-series fusion of building energy consumption data and carbon emission data to generate a unified energy consumption benchmark, and extract energy consumption characteristic curves through the unified energy consumption benchmark.

[0027] Specifically, data on building energy consumption, environmental monitoring, and carbon emissions are collected. Energy monitoring devices are deployed in various types of buildings in the city to collect building energy consumption data in real time. Building energy consumption data includes four basic energy consumption indicators: electricity consumption, water consumption, gas consumption, and centralized heating. The collection frequency is set to once every 15 minutes. Building energy consumption data is recorded according to building type. The peak electricity consumption of commercial buildings is concentrated between 10:00 and 22:00, the peak electricity consumption of office buildings is concentrated between 8:00 and 18:00, and the electricity consumption of residential buildings shows a bi-peak characteristic in the morning and evening. Environmental monitoring equipment is deployed around buildings and in key urban areas to collect environmental monitoring data. Environmental monitoring data includes outdoor temperature, humidity, and solar radiation intensity. The collection frequency of environmental monitoring data is synchronized with that of building energy consumption data. Environmental monitoring data is used to analyze the impact of external environmental conditions on building energy consumption. For every 1°C increase in outdoor temperature, air conditioning energy consumption increases by 3-5% in summer. Carbon emission data is collected through an energy management platform. Carbon emission data includes direct and indirect carbon emissions generated by building energy consumption. Direct carbon emissions come from the direct consumption of fossil fuels such as gas combustion, while indirect carbon emissions come from the carbon emissions at the power generation end corresponding to electricity use. Carbon emission data are converted according to the carbon emission factor published by the state, with the electricity carbon emission factor set at 0.5703 kgCO2 / kWh. The collected building energy consumption data, environmental monitoring data, and carbon emission data are stored uniformly, and the timestamp information of each data is recorded to form a city-level multi-source energy consumption dataset.

[0028] A unified energy consumption benchmark is generated by time-series fusion of building energy consumption and carbon emission data. Time alignment of the two data sets ensures accurate matching of timestamps. Building energy consumption data is measured in actual energy consumption, while carbon emission data is measured in carbon emissions; these different units require unified conversion. Carbon emission data is converted into equivalent energy consumption using the formula E_eq=C / f, where E_eq is equivalent energy consumption, C is carbon emissions, and f is the comprehensive carbon emission factor. The comprehensive carbon emission factor is determined based on the city's energy structure; cities with a higher proportion of electricity use a higher comprehensive carbon emission factor, while those with a higher proportion of clean energy use a lower value. Building energy consumption data is normalized to scale the energy consumption data of different buildings to the same numerical range, eliminating the impact of differences in building size. The normalized building energy consumption data and the converted carbon emission data are weighted and fused using the formula E_base = α × E_norm + (1-α) × E_eq, where E_base is the unified energy consumption benchmark, E_norm is the normalized building energy consumption data, E_eq is the equivalent energy consumption, and α is the weighting coefficient. The weighting coefficient α is dynamically adjusted according to the city's energy conservation and carbon reduction targets. When the city focuses more on carbon reduction targets, the value of α is reduced; when the city focuses more on energy conservation targets, the value of α is increased. The weighting coefficient α for typical cities is 0.6-0.7. Through time-series fusion, the building energy consumption data and carbon emission data are integrated into a unified energy consumption benchmark. This unified energy consumption benchmark reflects both the actual energy consumption of buildings and the carbon emission intensity of the energy consumption process.

[0029] Energy consumption characteristic curves are extracted using a unified energy consumption benchmark. The unified energy consumption benchmark is segmented into 24-hour periods to analyze its intraday temporal variation. Peak and trough periods of energy consumption are identified. Peak periods correspond to times when the unified energy consumption benchmark value is significantly higher than the daily average, while trough periods correspond to times when the value is significantly lower than the daily average. The maximum value of the unified energy consumption benchmark within each peak segment is extracted as peak energy consumption, and the minimum value within each trough segment is extracted as trough energy consumption. Peak and trough energy consumption are key parameters of the energy consumption characteristic curve. The ratio of the difference between peak and trough energy consumption to the total peak energy consumption is calculated, yielding the peak-to-trough difference rate. This rate serves as the core indicator of the energy consumption characteristic curve, reflecting the intraday fluctuation range of building energy consumption. Temperature corrections are applied to peak and trough energy consumption using environmental monitoring data, and outdoor temperature data corresponding to the peak energy consumption time is extracted. Outdoor temperature is a major environmental factor affecting building energy consumption. When the outdoor temperature is within the comfortable range of 18-26℃, air conditioning and heating energy consumption is low. When the temperature exceeds the comfortable range, air conditioning or heating energy consumption rises rapidly. A temperature correction factor is calculated, and the actual peak and valley energy consumption are divided by the temperature correction factor to obtain the corrected energy consumption value, thus eliminating the impact of temperature fluctuations. A daily energy consumption variation curve for the building is plotted with time on the horizontal axis and a unified energy consumption benchmark on the vertical axis. The corrected peak energy consumption, valley energy consumption, and peak-valley difference rate are marked on the curve to construct an energy consumption characteristic curve. This energy consumption characteristic curve fully describes the distribution pattern and variation law of building energy consumption throughout the day.

[0030] Step S120: Perform complementary analysis on the energy consumption characteristic curve to confirm carbon reduction characteristics, perform characteristic curve analysis on the carbon reduction characteristics to determine the matching operation period, determine the building weight table based on the matching operation period, and perform regional calibration on the environmental monitoring data based on the building weight table to generate environmental impact factors.

[0031] Specifically, complementary analysis of energy consumption characteristic curves confirms carbon reduction characteristics. Complementary analysis is performed on the energy consumption characteristic curves of different buildings within the city to identify the complementary relationship between peak and valley energy consumption. This complementary analysis identifies complementary patterns where peak energy consumption in some buildings corresponds to valley energy consumption in others by comparing the energy consumption characteristic curves of different building types during the same time period. The energy consumption curves of commercial buildings peak between 10:00 and 22:00, while the energy consumption curves of office buildings have already entered a declining phase during this period, showing a complementary characteristic between the two types of buildings. The energy consumption curves of residential buildings show double peaks between 7:00 and 9:00 and between 18:00 and 22:00, forming a staggered peak complementarity with the 9:00-18:00 peak of office buildings. The energy consumption intensity of each building at different times is extracted from the energy consumption characteristic curves, and the total urban energy load for each time period is statistically analyzed to determine the fluctuation range of the total load. Based on the results of the complementary analysis, carbon reduction characteristics are confirmed, including staggerable building groups, dispatchable energy consumption types, and optimizeable time periods. Peak-shifting building clusters refer to a collection of buildings that can achieve peak-hour shifts through operational adjustments. Dispatchable energy consumption types refer to energy-consuming equipment types with flexible adjustment capabilities. Optimizable time periods refer to time intervals where carbon emissions can be reduced through complementary optimization.

[0032] Characteristic curve analysis is performed on carbon reduction characteristics to determine matching operation periods. Characteristic curve analysis is conducted on the building clusters that can be staggered in carbon reduction characteristics to extract the peak and off-peak energy consumption periods for each building. The distribution patterns of peak and off-peak periods across different buildings in the time dimension are analyzed to identify sets of peak periods with temporal overlap. When the peak energy consumption periods of multiple buildings are concentrated in the same time interval, this time interval corresponds to the city-level energy consumption peak and is also the period with the greatest pressure for carbon reduction. The dispatchable energy consumption types in carbon reduction characteristics are analyzed to identify the regulation characteristics of each type of energy-consuming equipment. Different types of energy-consuming equipment, such as air conditioning load, lighting load, and production equipment load, have different time-based regulation capabilities and response characteristics. Combining the peak-valley distribution of the building clusters that can be staggered with the regulation capabilities of dispatchable energy consumption types, matching operation periods are determined. Matching operation periods refer to periods that simultaneously meet the following conditions: the energy consumption peaks of multiple buildings overlap within this period; the main energy consumption types within this period have dispatchability capabilities; and the carbon reduction potential of this period exceeds a set threshold. Typical matching operation periods include 10-12 and 14-16 on weekdays, during which the air conditioning load of commercial and office buildings reaches its peak simultaneously. Significant carbon reduction can be achieved by coordinating the air conditioning operation strategies of the two types of buildings.

[0033] In some embodiments, determining the building weight table based on the matched operating period includes: extracting peak-valley time distribution from the matched operating period to generate load contribution; identifying peak values ​​of the load contribution to generate a high-contribution building selection; performing reverse weighting adjustment on the high-contribution building selection to generate adjustment weights; and establishing a building weight table based on the load contribution and the adjustment weights.

[0034] Load contribution is generated by extracting peak and valley time distributions from the matched operation period. Within the matched operation period, energy consumption data for each building is extracted, and the load distribution characteristics of each building during this period are analyzed. The average load and peak load of each building during the matched operation period are statistically analyzed. The average load reflects the building's basic energy consumption level, and the peak load reflects the building's maximum energy intensity. The total energy consumption of each building during the matched operation period is obtained, and the load contribution is calculated using the formula C_i = E_i / E_total, where C_i is the load contribution of the i-th building, E_i is the total energy consumption of the i-th building, and E_total is the city's total energy consumption. The load contribution reflects the degree to which each building contributes to the city-level energy consumption load; buildings with higher load contributions have a greater impact on the city's peak energy consumption. Large commercial complexes can achieve a load contribution of 8-12% during the matched operation period from 10:00 to 12:00, becoming the main contributing buildings to the city's peak energy consumption. Office buildings typically have a load contribution of 3-5% during the matched operation period from 14:00 to 16:00. By calculating the load contribution, the actual contribution of each building to the city's peak energy consumption was quantified, and key buildings for carbon reduction operations were identified.

[0035] For example, the step of extracting peak and valley time distribution from the matched operating period to generate load contribution includes: extracting time period load characteristics from the matched operating period to generate a load feature set; performing time period load intensity analysis on the load feature set to generate a time period intensity distribution; identifying peak periods and valley periods from the time period intensity distribution to generate peak and valley identifiers; and extracting the load proportion of each building based on the peak periods of the peak and valley identifiers to determine the load contribution.

[0036] Load feature sets are generated by extracting time-period load characteristics from the matched operating periods. The matched operating periods are subdivided into 30-minute intervals, and load characteristics for each building within each sub-period are extracted. Time-period load characteristics include average load and peak load. The average load is the mean load within the time period, and the peak load is the maximum load within the time period. For commercial buildings, the average load is 800kW and the peak load is 950kW between 10:00-10:30, and the average load is 850kW and the peak load is 1000kW between 10:30-11:00, showing a gradually increasing load trend. For office buildings, the average loads during the same time periods are 600kW and 650kW, and the peak loads are 720kW and 780kW, respectively. The load characteristics of all buildings within each sub-period are then aggregated to form a load feature set. This load feature set contains complete load characteristic data for all buildings within the matched operating periods in each sub-period. The load feature set is indexed by building and time period identifiers to facilitate subsequent intensity analysis and peak / valley identification.

[0037] A time-period load intensity analysis is performed on the load feature set to generate a time-period intensity distribution. Based on the load feature set, the total urban load intensity for each sub-period is statistically analyzed. The total urban load intensity is obtained by weighted summation of all building loads within that period, with weights determined based on the carbon emission intensity of each building type; building types with higher carbon emission intensity have higher weights. The temporal changes in total urban load intensity during the matched operation period are analyzed to identify the rising, stable, and declining phases of load intensity. A time-period intensity distribution map is plotted, with the horizontal axis representing the sub-periods and the vertical axis representing the total urban load intensity. The curve illustrates the distribution characteristics of load intensity over time. The time-period intensity distribution reflects the dynamic changes in urban energy consumption load during the matched operation period. During the matching operation period from 10:00 to 12:00, the intensity distribution showed a trend of first rising and then stabilizing. The period from 10:00 to 10:30 was the rising phase, with the total urban load intensity increasing from 45MW to 52MW. The period from 10:30 to 11:30 was the stabilizing phase, with the total load intensity remaining in the range of 52-55MW. The period from 11:30 to 12:00 began to decline, with the total load intensity dropping to 48MW.

[0038] Peak and trough periods are identified from the time-based load intensity distribution to generate peak-trough identifiers. The time-based load intensity distribution curve is analyzed to identify local maximum and local minimum points. The time period corresponding to the local maximum point is the peak period, and the time period corresponding to the local minimum point is the trough period. A peak period refers to a subdivided time period where the city's total load intensity reaches a local peak, and a trough period refers to a subdivided time period where the city's total load intensity is at a local trough. A peak identification threshold is set: when the load intensity of a time period exceeds 10% of the load intensity of an adjacent time period, it is marked as a peak period. A trough identification threshold is set: when the load intensity of a time period is less than 10% of the load intensity of an adjacent time period, it is marked as a trough period. Identification symbols are added to each peak and trough period: peak periods are identified as "P", and trough periods are identified as "V", generating peak-trough identifiers. Peak-trough identifiers are used to clearly distinguish between high-load and low-load periods within a matched operating period. During the matching operation period from 10:00 to 12:00, 10:30 to 11:00 is designated as the peak period "P", during which the city's total load intensity reaches a peak of 54MW. 11:00 to 11:30 is the transition period, and 11:30 to 12:00 is designated as the valley period "V", during which the total load intensity drops to 48MW.

[0039] The load contribution is determined by extracting the load share of each building during peak periods based on peak-valley identifiers. All peak periods marked "P" are selected, and the total load of each building within these peak periods is calculated. Peak period identification is based on the peak-valley identifiers generated in previous steps; only periods marked "P" are included in the statistics, ensuring that the load contribution calculation focuses on the true peak times of urban energy consumption. The load share is calculated using the formula P_i = ΣE_i_peak / ΣE_total_peak, where P_i is the load share of the i-th building, ΣE_i_peak is the total load of that building during all peak periods, and ΣE_total_peak is the total load of the city during all peak periods. The load share is the load contribution, reflecting the actual contribution of each building to the city's peak energy consumption. Buildings with high load contributions play a dominant role in the formation of urban peak energy consumption and are key targets for carbon reduction operations. By quantifying load contribution, the group of buildings with the greatest impact on the city's peak load can be accurately identified, providing a data foundation for subsequent building weight allocation and control strategy formulation.

[0040] Peak load contribution is identified to generate a high-contribution building selection. The load contribution of each building is ranked from highest to lowest, forming a load contribution ranking table. A load contribution threshold is set, typically 3-5%, representing the level of contribution that has a significant impact on the city's peak energy consumption. Buildings with load contributions exceeding the threshold are identified as high-contribution buildings. High-contribution buildings are key targets for urban carbon reduction operations; optimizing their energy use strategies can achieve the most significant carbon reduction effects. The identified high-contribution buildings are compiled to generate a high-contribution building selection. This selection typically includes 10-15% of the buildings in the city, but these buildings contribute 60-70% of the city's peak load. The high-contribution building selection is categorized by building type, with large commercial complexes, Grade A office buildings, and industrial plants being the main high-contribution building types. A dynamic updating mechanism for the high-contribution building selection is established, adjusting the selection members according to seasonal changes and urban development; the composition of high-contribution buildings differs between summer and winter. For example, a city has 500 buildings during the matching operation period. Among them, 65 buildings have a load contribution of more than 3%. These 65 buildings constitute the high-contribution building selection set, and their total load contribution is 68%.

[0041] A reverse weighting adjustment is applied to a selection of high-contribution buildings to generate adjustment weights. The purpose of this adjustment is to appropriately reduce the regulatory intensity on high-contribution buildings when formulating operational strategies, avoiding excessive concentration of regulation and causing some buildings to bear excessive regulatory pressure. The logic of the reverse weighting is that the higher the load contribution of a building, the lower its adjustment weight, achieving a balanced distribution of regulatory pressure. The adjustment weight is calculated using the formula W_adj = 1 - k × C_i, where W_adj is the adjustment weight, k is the weighting coefficient, and C_i is the load contribution. The weighting coefficient k is typically set between 0.3 and 0.5. The value of the weighting coefficient k is determined comprehensively based on the city's carbon reduction goals and the building's willingness to cooperate. Aggressive carbon reduction goals correspond to smaller k values, while lower building cooperation levels use larger k values ​​to reduce regulatory pressure. The adjustment weight for large shopping malls is typically in the range of 0.92-0.97, achieving carbon reduction without affecting commercial operations. The adjustment weight for office buildings is typically between 0.97 and 0.99 to ensure a comfortable working environment. For residential buildings, due to their impact on residents' living comfort, the adjustment weight is set between 0.98 and 1.0. The adjustment weight for public service buildings such as hospitals and schools is prioritized at 0.98 or higher to ensure that basic services are not affected. Through reverse weighting adjustments, the carbon reduction operation strategy is ensured to be evenly distributed across multiple buildings, avoiding an excessive regulatory burden on any single building and improving the feasibility and acceptability of the operation strategy.

[0042] A building weight table is established based on load contribution and adjustment weights. The load contribution and adjustment weights of each building are integrated to create the building weight table. The building weight table includes five fields: building identifier, building type, load contribution, adjustment weight, and operational priority. The building identifier is used to uniquely identify the building, using a unified city coding system to facilitate cross-departmental data sharing. Building type is categorized into commercial, office, and residential, further subdivided into subtypes such as large commercial complexes, Grade A office buildings, and high-rise residential buildings, with different control strategies corresponding to different subtypes. Load contribution is the contribution value calculated in previous steps, the adjustment weight is the weight value after reverse weight reduction, and the operational priority is determined comprehensively based on load contribution and adjustment weight. Operational priority is divided into three levels: Level 1 priority buildings are those with a load contribution exceeding 8%, Level 2 priority buildings are those with a load contribution between 5% and 8%, and Level 3 priority buildings are those with a load contribution between 3% and 5%. The building weight table is presented in tabular form, with each row corresponding to one building and listing the relevant weight parameters for that building. Buildings in the high-contribution building selection are marked with a "priority focus" label in the building weight table, indicating that these buildings are priority targets for carbon reduction operations. The weight table is updated regularly, adjusting the weight parameters of each building according to seasonal changes and holiday characteristics; the weight tables differ between summer and winter. The building weight table is used to guide the formulation of city-level carbon reduction operation strategies, assigning differentiated control targets and adjustment ranges to each building to achieve precise carbon reduction management.

[0043] Environmental impact factors are generated by regionalizing environmental monitoring data based on a building weight table. The table identifies buildings with high weights and their corresponding areas, which are key areas of focus for urban carbon reduction operations. The division of key areas comprehensively considers building density, load contribution, and carbon reduction potential. Spatial clustering methods are used to group geographically adjacent buildings with similar weight parameters into the same key area. Environmental monitoring data is then regionally grouped, assigning each monitoring point to its corresponding key area. A spatial correspondence between environmental monitoring points and buildings is established, using the nearest neighbor allocation principle to associate each building with the nearest environmental monitoring point. Within each key area, the correlation between environmental monitoring data and building energy consumption is analyzed. Correlation analysis is used to identify the most significant environmental factors affecting energy consumption. Building energy consumption in commercial areas shows a strong correlation with outdoor temperature, while the correlation is relatively weaker in office and residential areas. Outdoor temperature, humidity, and solar radiation intensity data for each area are extracted, and environmental impact coefficients are calculated using the formula I_env = ΔE / ΔT, where I_env is the environmental impact coefficient, ΔE is the change in energy consumption, and ΔT is the change in temperature. A 1°C change in outdoor temperature has a 4% impact coefficient on air conditioning energy consumption in commercial areas and a 3% impact coefficient in office areas. The environmental impact coefficients vary across seasons, with larger coefficients during the high-temperature summer and low-temperature winter periods, and smaller coefficients during the spring and autumn transitional seasons. The environmental impact coefficients for each region are normalized to generate environmental impact factors. The normalization process uses a maximum-minimum standardization method to map the environmental impact coefficients to the 0-1 range. The environmental impact factor quantifies the degree of influence of different regional environmental conditions on building energy consumption; a higher environmental impact factor value indicates a more sensitive building energy consumption to environmental changes in that region.

[0044] Step S130: Based on the correlation analysis between environmental impact factors and energy consumption characteristic curves, a separation degree is generated. Based on the separation degree, a load fluctuation analysis is performed to generate a load balance coefficient. High-load building nodes in the load balance coefficient are identified, and energy consumption is reversed to generate a limiting scheme. Effective carbon reduction potential is identified through the load balance coefficient and the limiting scheme to generate effective potential data.

[0045] In some embodiments, the step of generating a separation degree based on the correlation analysis between the environmental impact factors and the energy consumption characteristic curve includes: extracting factor weights from the environmental impact factors to generate a factor weight set; performing a coupling degree analysis between the energy consumption characteristic curve and the factor weight set to generate a coupling degree index; identifying independence features from the coupling degree index to generate an independence identifier; and determining the separation degree based on the independence identifier.

[0046] Factor weights are extracted from environmental impact factors to generate a factor weight set. The influence weights of each environmental parameter within the environmental impact factors are extracted. These parameters include outdoor temperature, humidity, and solar radiation intensity. The contribution of each environmental parameter to building energy consumption is analyzed, identifying dominant and secondary influencing parameters. For commercial buildings, outdoor temperature is the dominant influencing parameter, typically accounting for 60-70% of the influence weight, while humidity and solar radiation intensity are secondary influencing parameters, each accounting for 15-20%. For office buildings, outdoor temperature has an influence weight of 50-60%, solar radiation intensity has an influence weight of 20-30%, and humidity has an influence weight of 15-20%. The weight coefficients of each environmental parameter are calculated using multiple regression analysis. The regression model is E = w_T×T + w_H×H + w_R×R + ε, where E is energy consumption, T is temperature, H is humidity, R is solar radiation intensity, w_T, w_H, and w_R are the weight coefficients of each parameter, and ε is the error term. The weight coefficients in the regression model are extracted and normalized so that the sum of all weights is 1. The normalized weight coefficients are then aggregated to generate a factor weight set. This factor weight set contains each environmental parameter and its corresponding weight coefficient, reflecting the relative importance of each environmental parameter among the environmental impact factors.

[0047] A coupling degree index is generated by performing coupling analysis between the energy consumption characteristic curve and the factor weight set. This analysis identifies the coupling relationship between the energy consumption characteristic curve and various environmental parameters. The coupling degree analysis is achieved by calculating the correlation between the energy consumption characteristic curve and the weighted environmental parameter sequence. First, the environmental parameter sequences are weighted according to the factor weight set to obtain the weighted environmental parameter sequence E_env = Σ(w_i × P_i), where w_i is the weight coefficient of the i-th environmental parameter, and P_i is the time series of the i-th environmental parameter. Then, the correlation coefficient between the energy consumption characteristic curve and the weighted environmental parameter sequence is calculated, r = Cov(E_curve, E_env) / (σ_curve × σ_env), where E_curve is the energy consumption characteristic curve, and σ_curve and σ_env are the standard deviations of the energy consumption characteristic curve and the weighted environmental parameter sequence, respectively. The absolute value of the correlation coefficient is the coupling degree index, which ranges from 0 to 1. A higher coupling degree index indicates a stronger coupling between the energy consumption characteristic curve and the environmental parameters. The correlation coefficient between the energy consumption characteristic curve and the weighted environmental parameter sequence of a commercial building is 0.82, and the coupling degree index is 0.82, indicating that the building's energy consumption is highly coupled with environmental factors. The coupling degree index of an office building is 0.45, indicating that energy consumption is moderately coupled with environmental factors.

[0048] Independence characteristics are identified from coupling degree indices to generate independence labels. Independence characteristics refer to the energy consumption characteristic curve being unaffected or minimally affected by environmental parameters. An independence threshold is set, typically between 0.3 and 0.4, indicating that when the coupling degree index is below this threshold, energy consumption is considered to have independence characteristics. The coupling degree index of each building is assessed. When the coupling degree index is below the independence threshold, the building is marked as "independent," indicating that its energy consumption is mainly determined by internal operational factors. When the coupling degree index is above the independence threshold, the building is marked as "dependent," indicating that its energy consumption is significantly affected by environmental factors. An independence label is generated, consisting of three fields: building identifier, coupling degree index, and independence type. The independence label is used to distinguish the energy consumption driving mechanisms of different building types, providing a basis for subsequent separation degree calculations and carbon reduction strategy formulation. For example, a data center has a coupling degree index of 0.25, below the independence threshold of 0.3, and is marked as "independent," indicating that the data center's energy consumption is mainly determined by server load rather than ambient temperature. A residential building has a coupling index of 0.75, which is marked as "dependent", indicating that the energy consumption of the building is significantly affected by the outdoor temperature.

[0049] Separation degree is determined based on independence indicators. The separation degree of each building is determined based on independence indicators, quantifying the degree of separation between energy consumption characteristic curves and environmental impact factors. For buildings labeled "independent," the separation degree is 1 minus the coupling degree index, indicating a high separation degree. For buildings labeled "dependent," the separation degree is also 1 minus the coupling degree index, but a lower separation degree. The separation degree formula is S=1-C, where S is the separation degree and C is the coupling degree index. The separation degree ranges from 0 to 1. A separation degree close to 1 indicates a high degree of separation between energy consumption and the environment, with energy consumption primarily driven by internal operational factors. A separation degree close to 0 indicates a high degree of coupling between energy consumption and the environment, with energy consumption primarily driven by environmental factors. A data center has a coupling degree index of 0.25 and a separation degree of 0.75, indicating that the building's energy consumption has high operational controllability. A residential building has a coupling degree index of 0.75 and a separation degree of 0.25, indicating that the building's energy consumption is dominated by environmental factors, resulting in low operational controllability. Separation degree provides an important basis for the formulation of carbon reduction strategies. High separation degree buildings are the priority targets for carbon reduction operations, while low separation degree buildings need to be combined with environmental control measures to implement carbon reduction.

[0050] Load fluctuation analysis is performed based on separation degree to generate a load balance coefficient. Buildings are categorized according to separation degree into environment-dependent buildings and operation-driven buildings. Environment-dependent buildings have lower separation degree, and their energy consumption fluctuations are mainly driven by environmental factors such as outdoor temperature and humidity. Operation-driven buildings have higher separation degree, and their energy consumption fluctuations are mainly driven by operational factors such as internal equipment operation and personnel activity. Load fluctuation analysis is performed on the energy consumption characteristic curves of each type of building, and the load fluctuation amplitude of each building during the matched operation period is statistically analyzed. The load fluctuation amplitude is determined by the ratio of the difference between the maximum and minimum load values ​​to the average load. The synchronicity between the load fluctuation of each building and the fluctuation of the city's total load is analyzed to identify buildings that exacerbate and mitigate urban load fluctuations. The load balance coefficient is calculated using the formula B = 1 - |L_i - L_avg| / L_avg, where B is the load balance coefficient, Li is the load fluctuation amplitude of the building, and L_avg is the average load fluctuation amplitude of the city. The load balance coefficient reflects the contribution of building load to the urban load balance. Building load fluctuations with a high load balance coefficient are close to the urban average level, contributing positively to the urban load balance. Building load fluctuations with a low load balance coefficient deviate from the urban average level, exacerbating the urban load imbalance.

[0051] In some embodiments, the step of identifying high-load building nodes in the load balance coefficient and generating a restriction scheme by reverse energy consumption restriction includes: identifying high-load building nodes from the load balance coefficient to generate a node set; extracting peak energy consumption characteristics of the node set to generate peak characteristic data; determining the restriction intensity level based on the peak characteristic data to generate a level configuration; and generating a restriction scheme based on the level configuration.

[0052] High-load building nodes are identified from load balance coefficients to generate a node set. First, the load balance coefficients are sorted from lowest to highest. A high-load identification threshold is set, typically between 0.6 and 0.7, indicating that buildings with load balance coefficients below this threshold are high-load building nodes. Buildings with load balance coefficients below the threshold are identified because their load fluctuations deviate significantly from the city's average level, negatively impacting the city's load balance. The load fluctuation amplitude of these buildings is statistically analyzed, and buildings with load fluctuation amplitudes exceeding 30% are further screened as high-load building nodes. High-load building nodes possess both low load balance coefficients and large load fluctuation amplitudes, making them the main buildings exacerbating peak urban energy consumption. The identified high-load building nodes are then aggregated to generate a node set. The node set includes information such as the building identifier, load balance coefficient, load fluctuation amplitude, and building type for each high-load building node. In one city, 35 high-load building nodes were identified during a matched operating period. These buildings all had load balance coefficients below 0.65 and load fluctuation amplitudes exceeding 35%, forming the node set.

[0053] Peak energy consumption features are extracted from the node set to generate peak feature data. Peak energy consumption features for each building in the node set are extracted, including peak energy consumption and peak duration. Peak energy consumption is the maximum energy consumption of a building during the matched operating period, and peak duration is the length of time the energy consumption remains near the peak level. The area near the peak level is defined as 90-100% of the peak energy consumption; when a building's energy consumption is within this range, it is considered to be in peak condition. For a commercial complex, a peak energy consumption of 1200kW and a peak duration of 45 minutes indicate that the building's energy consumption reached 1200kW during the matched operating period and remained within the 1080-1200kW range for 45 minutes. For an office building, the peak energy consumption is 450kW and the peak duration is 30 minutes. For a large shopping mall, the peak energy consumption is 950kW and the peak duration is 60 minutes. The numerical distribution of peak features for each building is analyzed to identify differences in peak energy consumption and peak duration. The peak characteristic data of each building are aggregated to generate a peak characteristic dataset. This peak characteristic dataset provides the basic data support for subsequent determination of the restriction intensity level.

[0054] For example, the step of determining the limit intensity level and generating the level configuration based on the peak feature data includes: performing peak classification on the peak feature data to identify high peak groups; extracting the peak duration of the high peak groups to generate duration data; upgrading the level of the high peak groups whose duration data exceeds the standard to generate upgraded peak groups; and setting the limit intensity level and generating the level configuration based on the upgraded peak groups.

[0055] Peak energy consumption in the peak characteristic data is classified into high, medium, and low levels. A classification standard is set: peak energy consumption greater than 800kW is high level, 400-800kW is medium level, and less than 400kW is low level. Buildings belonging to the high level are identified; these buildings have higher peak energy consumption and contribute significantly to the city's peak energy consumption. These high-level buildings are aggregated to generate a peak energy consumption group. The peak energy consumption group includes all buildings with peak energy consumption greater than 800kW; these buildings are key targets for reverse energy consumption control. In a city's node set, there are 35 high-load building nodes. After peak energy consumption classification, 12 of these buildings have peak energy consumption greater than 800kW and are identified as part of the peak energy consumption group. The building types in the peak energy consumption group are mainly large commercial complexes, shopping malls, and convention centers, whose air conditioning and lighting loads reach their maximum during peak hours.

[0056] Extract the peak duration from the peak energy consumption group to generate duration data. Extract the peak duration for each building in the peak energy consumption group. Peak duration is the length of time a building's energy consumption remains near its peak level. Near the peak level is defined as the range of 90-100% of peak energy consumption; when a building's energy consumption is within this range, it is considered to be in peak condition. Calculate the cumulative duration of peak condition for each building during the matched operating period. For example, a commercial complex has a peak energy consumption of 1200kW. When energy consumption is maintained within the range of 1080-1200kW, it is considered to be in peak condition, and the cumulative duration of this state is calculated to be 75 minutes. A shopping mall has a peak duration of 50 minutes, and an exhibition center has a peak duration of 90 minutes. Summarize the peak durations of each building to generate duration data. The duration data includes two fields: building identifier and peak duration.

[0057] For peak data exceeding the limit, the peak duration group is upgraded to generate an upgraded peak group. A standard threshold for peak duration is set, typically between 45-60 minutes, indicating that peak duration within this range is normal. Buildings whose peak duration exceeds the standard threshold are identified; these buildings not only have high peak energy consumption but also long peak duration, resulting in a greater negative impact on urban load balance. These buildings are upgraded, meaning their restriction intensity level is increased, increasing the reduction target. Buildings originally designated as Level 1 restrictions are upgraded to Level 1 Enhanced Restriction if their peak duration exceeds the limit. Buildings originally designated as Level 2 restrictions are upgraded to Level 1 restrictions if their peak duration exceeds the limit. A commercial complex with a peak duration of 75 minutes, exceeding the standard threshold of 60 minutes, is upgraded from Level 1 to Level 1 Enhanced Restriction. A convention center with a peak duration of 90 minutes is also upgraded to Level 1 Enhanced Restriction. The upgraded buildings are then regrouped to generate an upgraded peak group. The upgraded peak group includes the upgraded buildings and their new restriction intensity level.

[0058] Based on the upgraded peak energy group, a level configuration is generated by setting the restriction intensity level. The restriction intensity level and reduction target for each building are set according to the upgraded peak energy group. Level 1 enhanced restriction requires a reduction of 20-25% in peak energy consumption, Level 1 restriction requires a reduction of 15-20%, Level 2 restriction requires a reduction of 10-15%, and Level 3 restriction requires a reduction of 5-10%. For buildings in the upgraded peak energy group, the corresponding reduction target is set according to their restriction intensity level. A commercial complex is set to Level 1 enhanced restriction with a reduction target of 22%, meaning it needs to reduce energy consumption from 1200kW to 936kW during peak hours. A shopping mall is set to Level 1 restriction with a reduction target of 18%, requiring a reduction of peak energy consumption from 950kW to 779kW. A convention center is set to Level 1 enhanced restriction with a reduction target of 23%, requiring a reduction of peak energy consumption to 924kW. The restriction intensity level and reduction target for each building are integrated to generate a level configuration table. The rating configuration table includes five fields: building identification, peak energy consumption, peak duration, limit intensity level, and reduction target, providing clear parameter basis for the formulation of subsequent restriction schemes.

[0059] Restriction plans are generated based on the level configuration. These plans specify the concrete measures and implementation methods for each building. Differentiated restrictions are developed based on building type and available resource type. For commercial buildings, restrictions include raising air conditioning temperature by 2-3°C, reducing lighting brightness by 20-30%, and shutting down equipment in non-business areas. For office buildings, restrictions include raising air conditioning temperature by 1-2°C, centralized use of meeting rooms, and turning off non-essential lighting. The combination of restrictions is determined based on the restriction intensity level; Level 1 enhanced restrictions require multiple restrictions to be implemented simultaneously, while Level 2 and 3 restrictions allow for the selection of some restrictions. Restriction periods are determined, corresponding to matching operating hours, typically 10-12 am and 2-4 pm on weekdays. An implementation process for the restriction plan is established, including four stages: advance notification, real-time monitoring, effect evaluation, and adjustment and optimization. The advance notification stage requires sending a notification to building management 24 hours before the implementation of restrictions, explaining the restriction periods and requirements. The real-time monitoring stage requires continuous monitoring of building energy consumption during the implementation of restrictions to ensure the achievement of reduction targets. The effectiveness evaluation phase requires calculating the actual reduction after the restrictive measures end to assess the effectiveness of the implementation plan. The adjustment and optimization phase involves optimizing and adjusting the restrictive plan based on the effectiveness evaluation results to improve the accuracy of subsequent implementation.

[0060] Effective carbon reduction potential is identified and effective potential data is generated by using load balance coefficients and restrictive schemes. The effective carbon reduction potential of a city is identified through load balance coefficients and restrictive schemes; effective carbon reduction potential refers to the amount of carbon emission reduction that can be achieved by implementing restrictive schemes. Based on the reduction targets for each building in the restrictive schemes, the energy consumption reduction for each building is calculated. Energy consumption reduction ΔE = E_peak × R_cut, where E_peak is the peak energy consumption and R_cut is the reduction ratio. The energy consumption reduction is converted to carbon emission reduction using the formula ΔC = ΔE × f_carbon, where ΔC is the carbon emission reduction and f_carbon is the carbon emission factor. The carbon emission reductions of all buildings implementing restrictive schemes are summarized to obtain the city-level effective carbon reduction potential. In one city, restrictive schemes were implemented on 35 high-load building nodes, resulting in a total energy consumption reduction of 8500 kWh and a corresponding carbon emission reduction of 4850 kg CO2. The distribution characteristics of effective carbon reduction potential are analyzed to identify the building types and time periods that contribute the most to carbon reduction. Commercial buildings contribute 55% of the total carbon reduction potential, office buildings 30%, and other building types 15%. The carbon reduction potential is 45% between 10:00 and 12:00 and 55% between 14:00 and 16:00. Effective potential data is generated, including the city's total carbon reduction potential, the distribution of carbon reduction potential by building type, and the distribution of carbon reduction potential by time period.

[0061] Step S140: Construct conversion rules based on effective potential data and carbon emission data; standardize and convert the effective potential data using the conversion rules to generate standardized carbon reduction data; and perform correlation analysis between the standardized carbon reduction data and a unified energy consumption benchmark to generate carbon reduction assessment indicators.

[0062] Specifically, conversion rules are constructed based on effective potential data and carbon emission data. These rules are used to uniformly convert the carbon reduction potential of different building types into comparable assessment data. Effective potential data includes the city's total carbon reduction potential, the distribution of carbon reduction potential for each building type, and the distribution of carbon reduction potential for each time period. Carbon emission data includes the actual carbon emissions and carbon emission intensity of each building. The construction of conversion rules first extracts the statistical characteristics of carbon reduction potential per unit area for each building type from the effective potential data, and simultaneously extracts the statistical characteristics of carbon emission intensity for each building type from the carbon emission data. A mapping relationship between carbon reduction potential and carbon emission intensity is established through regression analysis. When a building's carbon emission intensity is higher than the type average, the conversion rules assign a higher weight coefficient to its carbon reduction potential. The correlation between the two is analyzed to identify the corresponding patterns between carbon reduction potential and actual carbon emissions. High-carbon-emission buildings typically have larger carbon reduction potential, but implementation is also more difficult. The three core elements of the conversion rule are determined based on the mapping relationship: the baseline parameters of the conversion rule include the carbon emission baseline value and the conversion coefficient. The carbon emission baseline value is determined according to the building type: 150 kg CO2 / m² for commercial buildings, 100 kg CO2 / m² for office buildings, and 80 kg CO2 / m² for residential buildings. The conversion coefficient, as the core algorithm of the conversion rule, is calculated using the formula K = C_actual / C_base, where K is the conversion coefficient, C_actual is the actual carbon emission of the building, and C_base is the carbon emission baseline value. The conversion rule adjusts the weights through the conversion coefficient. When the actual carbon emission of a commercial building is higher than the baseline value, its conversion coefficient is greater than 1. During the standardized conversion, the conversion rule amplifies the weight of its carbon reduction potential, reflecting the necessity of carbon reduction for high-carbon emission buildings.

[0063] In some embodiments, the step of standardizing the effective potential data to generate standardized carbon reduction data using the conversion rules includes: determining a baseline threshold based on the conversion rules to generate a threshold reference; comparing the effective potential data with the threshold reference to generate a potential classification result; prioritizing high-potential items in the potential classification result to generate an enhancement configuration; and performing data conversion on the potential classification result according to the enhancement configuration to generate standardized carbon reduction data.

[0064] A threshold reference is generated based on conversion rules to determine baseline thresholds. The threshold reference is used to classify carbon reduction potential into different levels, setting tiered thresholds according to the baseline carbon reduction potential values ​​for each building type. The baseline carbon reduction potential value is obtained by converting the carbon emission baseline value from the conversion rules using the formula P_base = C_base × η, where P_base is the baseline carbon reduction potential value, C_base is the carbon emission baseline value in the conversion rules, and η is an energy efficiency improvement coefficient, approximately 0.2. For commercial buildings, the carbon emission baseline value of 150 kg CO2 / m² in the conversion rules is converted to a carbon reduction potential baseline value of 30 kg CO2 / m². The high-potential threshold in the threshold reference is set at 120% of the baseline value, i.e., 36 kg CO2 / m². This ratio ensures that the identification standard for high-potential buildings can both screen out truly potential buildings and avoid overlooking key buildings due to excessively high standards. The medium-potential threshold range is set at 80-120% of the baseline value, covering most buildings with conventional carbon reduction potential. This range considers the differences between buildings while maintaining the rationality of the tiered classification. The low carbon reduction potential threshold is below 80% of the baseline value, corresponding to buildings with limited room for carbon reduction. The baseline value for carbon reduction potential for office buildings is 20 kg CO2 / m², and the threshold reference sets the thresholds for each level in the same proportion to ensure consistency in the classification standards for different building types. The baseline value for carbon reduction potential for residential buildings is 15 kg CO2 / m², and the threshold reference also follows the same proportional relationship.

[0065] The effective carbon reduction potential data is compared with threshold references to generate potential classification results. The comparison process first identifies the building type, then retrieves the corresponding classification threshold from the threshold reference, and finally compares the building's carbon reduction potential value with the threshold to determine its classification. Taking a commercial complex as an example, when its carbon reduction potential reaches 42 kg CO2 / m², the high-potential threshold of 36 kg CO2 / m² for commercial buildings is retrieved from the threshold reference. Since the actual potential significantly exceeds the threshold, the building is determined to be a high-potential building and marked as "high" in the potential classification results. Such buildings should be the focus of urban carbon reduction operations. For commercial buildings with medium carbon reduction potential, the comparison with the threshold reference falls into the medium-potential range, and the building is marked as "medium" in the potential classification results. Carbon reduction measures for such buildings can be gradually implemented according to resource availability. For office buildings, the corresponding threshold in the threshold reference is retrieved for classification. If the carbon reduction potential falls into the medium-potential range, the building is marked as a medium-potential building. The distribution of carbon reduction potential results shows that, although high-potential buildings account for only 17% of the total number of buildings assessed in a certain city, they contribute nearly half of the city's total carbon reduction potential. This distribution characteristic indicates that prioritizing carbon reduction in key buildings can achieve twice the result with half the effort. Medium-potential buildings account for the largest proportion and are an important supplementary force in urban carbon reduction. Although low-potential buildings have limited carbon reduction potential, they can still contribute to carbon reduction through refined management.

[0066] The process involves prioritizing high-potential items from the potential grading results to generate enhancement configurations. High-potential items are identified from the potential grading results, and enhancement configurations are established to prioritize these buildings. The core of the enhancement configuration is analyzing the carbon reduction potential composition of high-potential items, identifying the main contributing sources, and determining their priority levels accordingly. Taking a commercial complex as an example, a detailed breakdown analysis of its carbon reduction potential reveals that air conditioning system optimization is the main contributing source, while lighting system renovation and energy management also contribute to some extent but are relatively minor. The enhancement configuration sets priority levels based on the potential of the main contributing sources. When air conditioning optimization potential dominates, the enhancement configuration sets the building's priority to Level 1, indicating that carbon reduction renovations of the air conditioning system should be implemented with the highest priority, ensuring that carbon reduction resources are invested in the area with the greatest effect. When lighting renovation potential is significant, the priority is set to Level 2, indicating that the renovation will proceed only after the Level 1 priority buildings have been renovated. The priority weight in the configuration is calculated using the formula W_priority=1+α×(P-P_threshold) / P_threshold, where W_priority is the priority weight, α is the enhancement coefficient ranging from 0.3 to 0.5, P is the building's carbon reduction potential, and P_threshold is the high-potential threshold. The design logic of this formula is that the greater the carbon reduction potential exceeds the threshold, the higher the priority weight. This method amplifies the score of high-potential buildings during standardization conversion, ensuring these buildings receive priority in subsequent resource allocation and policy support.

[0067] Based on the potential grading results of the upgraded configuration, standardized carbon reduction data is generated through data transformation. This standardized carbon reduction data is calculated using the formula S_standard = P × K × W_priority, where S_standard is the standardized carbon reduction score, P is the carbon reduction potential, K is the conversion coefficient, and W_priority is the priority weight. This formula implements a triple weighting to ensure the scientific nature of the score: the first weight, P, reflects the theoretical carbon reduction potential of the building; the greater the carbon reduction potential, the higher the base score. The second weight, K, reflects the actual carbon emission level of the building; the higher the carbon emission, the larger the conversion coefficient, indicating a stronger necessity for carbon reduction. The third weight, W_priority, reflects the implementation priority of the building; high-potential buildings receive higher weights to reflect their priority status. Taking a commercial complex as an example, it has high carbon reduction potential, and its actual carbon emissions are also higher than the benchmark level, resulting in a conversion coefficient greater than 1. As a high-potential building, it also receives additional weight from the upgraded configuration. After triple weighting, the score in the standardized carbon reduction data is significantly higher than the benchmark level, clearly indicating that this building should be a key target for urban carbon reduction control. For office buildings with medium carbon reduction potential, their actual carbon emissions are lower than the baseline level, resulting in a conversion factor of less than 1. As buildings with medium carbon reduction potential, they do not receive additional priority weighting. Their scores in standardized carbon reduction data are at a medium level, reflecting the building's conventional position in the city's carbon reduction system.

[0068] In some embodiments, the step of performing correlation analysis between the standardized carbon reduction data and the unified energy consumption benchmark to generate carbon reduction assessment indicators includes: comparing the standardized carbon reduction data and the unified energy consumption benchmark to generate a difference distribution; identifying high-benchmark, low-carbon-reduction buildings from the difference distribution to generate a problem building set; adjusting the assessment values ​​of the problem building set to generate a corrected difference distribution; and constructing carbon reduction assessment indicators based on the corrected difference distribution.

[0069] A difference distribution is generated by comparing standardized carbon reduction data with a unified energy consumption benchmark. This difference distribution reflects the degree of matching between a building's energy consumption level and its carbon reduction potential. Theoretically, buildings with higher unified energy consumption benchmarks should have higher carbon reduction potential, and the difference should be close to zero. A positive difference indicates that the energy consumption level is higher than the carbon reduction potential level, potentially indicating an assessment mismatch, requiring verification of whether the building has undergone energy-saving renovations. A negative difference indicates that the carbon reduction potential is higher than the energy consumption level, suggesting that although the building's energy consumption is not high, there is still significant room for carbon reduction, and these buildings have higher carbon reduction implementation efficiency. For example, an office building's unified energy consumption benchmark normalized value is at a lower-middle level, but its score in the standardized carbon reduction data is even lower, resulting in a positive difference. Investigation revealed that the building had already implemented some energy-saving renovations, compressing the potential for further carbon reduction. The difference distribution analysis shows that the differences for most buildings are concentrated within the normal range, indicating a basic match between energy consumption and carbon reduction potential. A small number of buildings have differences exceeding the normal range, requiring focused verification. The difference distribution can quickly identify the entities needing investigation.

[0070] A problem building set was generated by identifying high-benchmark, low-carbon-reduction buildings from the difference distribution. The identification criteria for the problem building set were set with two conditions: the difference exceeded a set threshold and the unified energy consumption benchmark was at a high level, avoiding misclassification of low-energy-consumption buildings as problem buildings. For example, an office building had a high normalized value in its unified energy consumption benchmark, but its score in the standardized carbon reduction data was low, with the difference significantly exceeding the identification threshold, thus being included in the problem building set. On-site investigation revealed that the building had undergone frequency conversion renovation of its air conditioning system and LED lighting renovation a few years ago. The unified energy consumption benchmark used current energy consumption data and did not consider the historical context of these renovations, leading to an underestimation of its carbon reduction potential. Another industrial building was also included in the problem building set because production equipment dominated its energy consumption structure, and the carbon reduction potential of production equipment was less constrained by the process flow. In a certain city's building assessment, the problem building set included a certain proportion of buildings. These buildings were screened out through the problem set identification, providing clear targets for subsequent assessment corrections.

[0071] The evaluation values ​​of problematic buildings are adjusted downwards to generate a corrected difference distribution. The correction principle is to select the correction method based on the cause of the problem. For buildings that have implemented energy-saving renovations, the energy consumption benchmark is adjusted downwards; for buildings with special energy consumption structures, the carbon reduction potential is adjusted upwards. Taking the aforementioned office building that has completed energy-saving renovations as an example, the energy consumption benchmark is adjusted downwards, with the adjustment range based on the actual energy-saving effect of the renovation measures. The frequency conversion renovation of air conditioning and the LED lighting renovation reduced the overall energy consumption of the building, so the unified energy consumption benchmark was appropriately lowered. After the correction, the normalized value of the unified energy consumption benchmark is lower, the score in the standardized carbon reduction data remains unchanged, and the difference in the difference distribution of this building is significantly reduced, more reasonably reflecting the actual carbon reduction space of the building after energy-saving renovations. For industrial buildings with special energy consumption structures, considering that although the space for conventional energy-saving measures is limited, there is still carbon reduction potential through process optimization, the score in the standardized carbon reduction data is adjusted upwards. In the corrected difference distribution, the number of buildings with differences exceeding the abnormal threshold is significantly reduced, the concentration of the difference distribution is improved, and the rationality of the overall assessment is improved.

[0072] A carbon reduction assessment index is constructed based on the adjusted difference distribution. This index comprises three dimensions: a carbon reduction potential index, a carbon reduction efficiency index, and a carbon reduction priority index. The carbon reduction potential index is obtained by multiplying the score in standardized carbon reduction data by a potential weight. The potential weight is determined based on the difficulty of carbon reduction for each building type; commercial buildings are given higher potential weights due to the diversity of energy-consuming equipment, while residential buildings are given lower potential weights because their energy consumption behavior is greatly influenced by residents' habits. The carbon reduction efficiency index is obtained by dividing the carbon reduction potential by a unified energy consumption benchmark. A higher index indicates a greater amount of carbon reduction achievable per unit of energy consumption and a higher input-output ratio for carbon reduction. The carbon reduction priority index is calculated using the formula I_priority = I_potential × I_efficiency × (1 - |D_adjusted| / 100), where I_priority is the carbon reduction priority index, I_potential is the carbon reduction potential index, I_efficiency is the carbon reduction efficiency index, and D_adjusted is the adjusted difference. This formula ensures the scientific nature of priority determination through a comprehensive assessment across three dimensions: high potential reflects significant carbon reduction space, high efficiency reflects a high input-output ratio, and low difference reflects strong assessment reliability. Buildings are ranked according to the carbon reduction priority index, and the comprehensive rating is divided into four levels: A, B, C, and D. Level A buildings possess the characteristics of significant carbon reduction space, high implementation efficiency, and reliable assessment, and should be the primary targets for urban carbon reduction operations.

[0073] Step S150: Spatial location analysis is performed on the carbon reduction assessment indicators to generate building location information; carbon reduction intensity analysis is performed on the building location information to generate synergy coefficients; and multi-level strategy processing is performed based on the synergy coefficients to generate a graded carbon reduction operation plan.

[0074] Specifically, spatial location analysis is performed on carbon reduction assessment indicators to generate building location information. This analysis links the carbon reduction assessment results of each building to its geographical location, forming building location information. Building location information includes the building's geographical coordinates, its region, and its carbon reduction assessment level. Spatial location analysis can identify the spatial distribution characteristics of the city's carbon reduction potential. The carbon reduction priority index for each building is extracted from the carbon reduction assessment indicators and linked to the building's geographical location information. Geographical location information is represented by the building's latitude and longitude coordinates, and the region is determined according to the city's administrative divisions or functional zones. The spatial location information of the buildings is visualized using a geographic information system (GIS). The location of each building is marked on a city map, and its carbon reduction priority level is indicated by color: Level A buildings are marked with a dark color to indicate the highest priority, and Level D buildings are marked with a light color to indicate the next highest priority. Spatial analysis of building location information can identify hotspots and cold spots in carbon reduction potential. Hotspots, which concentrate a large number of high-priority buildings, should be the focus of concentrated resources for urban carbon reduction operations. High-priority buildings in cold spots are scattered and require different implementation strategies.

[0075] In some embodiments, the step of performing carbon reduction intensity analysis on the building location information to generate synergy coefficients includes: spatially clustering the building location information to identify regional building clusters; extracting the carbon reduction intensity distribution of the regional building clusters to generate intensity distribution data; identifying carbon reduction synergy features based on the intensity distribution data to generate a synergy feature set; and quantifying the synergy feature set to generate synergy coefficients.

[0076] Spatial clustering is used to identify regional building clusters based on building location information. The spatial clustering method is based on the similarity of geographical distance and carbon reduction priority levels between buildings. When multiple buildings are spatially adjacent and have similar carbon reduction priorities, they are grouped into the same regional building cluster. Taking a commercial district in a city as an example, this area contains multiple commercial complexes and shopping centers. These buildings are all marked as high-priority buildings in the building location information and are geographically adjacent. Through spatial clustering, they are identified as a high-potential cluster in the commercial district. The identification of regional building clusters uses a density clustering algorithm, which can automatically identify areas with dense buildings and similar characteristics. This algorithm does not require pre-setting the number of clusters, and the clustering results more closely reflect the actual spatial distribution of the city. The clustering process considers the geographical distance and similarity of carbon reduction priorities between buildings. Two buildings that are close to each other and have the same or similar priority levels are more likely to be grouped into the same cluster. For a residential area, although there are many buildings and they are geographically adjacent, most of the residential buildings have low carbon reduction priorities and are scattered. Spatial clustering identifies this area as a low-priority, scattered cluster, and such clusters are not considered key areas for carbon reduction operations.

[0077] Carbon reduction intensity distribution data is generated by extracting the carbon reduction intensity distribution of building clusters in a region. Carbon reduction intensity is obtained by dividing a building's carbon reduction potential by its area, reflecting the carbon reduction capacity per unit area. Higher carbon reduction intensity indicates a greater carbon reduction density for that building. The intensity distribution data statistically analyzes the distribution characteristics of carbon reduction intensity within the regional building cluster, including average carbon reduction intensity, variance of intensity, and extreme values ​​of intensity. Taking a high-potential cluster in a commercial area as an example, this cluster contains more than ten commercial buildings. Intensity distribution data shows that these buildings generally have high carbon reduction intensity, with the average carbon reduction intensity significantly higher than the overall urban level, indicating that the area has a strong concentrated carbon reduction capacity. A small variance in intensity indicates that the carbon reduction intensity of buildings within the cluster is relatively similar, suggesting that the difficulty and effectiveness of carbon reduction implementation are similar, making a unified carbon reduction strategy suitable. For an industrial park cluster, the intensity distribution data shows a large variance in carbon reduction intensity. The high carbon reduction intensity of some buildings mainly stems from the potential for retrofitting production equipment, while the lower carbon reduction intensity of some buildings is due to completed energy-saving retrofits. This distribution characteristic suggests that this cluster requires a differentiated carbon reduction strategy.

[0078] Based on intensity distribution data, a collaborative feature set is generated by identifying carbon reduction synergy characteristics. Carbon reduction synergy characteristics refer to the ability of multiple buildings within a region to cooperate, share resources, or coordinate scheduling during carbon reduction implementation. The identification of the collaborative feature set first analyzes the similarity features in the intensity distribution data. When the carbon reduction intensity of multiple buildings within a cluster is similar, it indicates that their carbon reduction needs and implementation conditions are similar, and a batch-based carbon reduction retrofit scheme can be adopted to reduce implementation costs. Taking a high-potential cluster in a commercial area as an example, the commercial buildings in this cluster have similar carbon reduction intensities, and their main carbon reduction potential comes from the optimization of air conditioning systems. The collaborative feature set identifies that this cluster has the characteristic of "equipment retrofit synergy," which can be achieved through centralized equipment procurement, unified technical standards, and shared retrofit experience. The collaborative feature set also identifies time-based synergy characteristics. When the peak energy consumption periods of multiple buildings within a cluster overlap, it indicates that these buildings have synergy in load regulation, and a regional-level load reduction strategy can be adopted. The collaborative feature set also includes energy supply synergy characteristics. When buildings within a cluster share the same energy supply system or are located within the same energy supply network coverage area, a regional-level energy optimization strategy can be adopted.

[0079] Synergy coefficients are generated by quantifying the synergy feature set. The synergy feature set is then analyzed to transform the synergy features into quantitative synergy coefficients. These coefficients reflect the feasibility and potential benefits of collaborative carbon reduction among buildings within a regional building cluster. Higher coefficients indicate that the cluster is more suitable for a regional collaborative carbon reduction strategy. The synergy coefficient C_syn = w_equip × S_equip + w_time × S_time + w_energy × S_energy, where S_equip is the synergy degree in terms of equipment, S_time is the synergy degree in terms of time period, and S_energy is the synergy degree in terms of energy. w_equip, w_time, and w_energy are the weighting coefficients for each dimension. Equipment-level synergy is quantified by the similarity of carbon reduction intensity among buildings within the cluster. Higher similarity indicates closer carbon reduction needs and stronger feasibility for a unified retrofit plan. Time-period-level synergy is quantified by the overlap of peak energy consumption periods among buildings within the cluster. Higher overlap indicates potential for collaborative regulation among these buildings during the same time period. Energy-level synergy is quantified by the degree to which buildings within the cluster share energy infrastructure. Higher sharing indicates greater potential for regional energy optimization. Clusters with high synergy coefficients should prioritize regional collaborative carbon reduction strategies, achieving economies of scale through centralized resource allocation, unified technical standards, and coordinated scheduling.

[0080] A tiered carbon reduction operation plan is generated through multi-level strategy processing based on the synergy coefficient. This plan comprises two levels: a regional synergy level plan and a building-level plan, with the choice of plan based on the synergy coefficient. For regional building clusters with high synergy coefficients, a regional synergy level plan is adopted, treating the cluster as a whole for carbon reduction planning and implementation. Taking a high-potential commercial cluster as an example, the regional synergy level plan establishes unified standards for air conditioning system upgrades, organizes centralized procurement of high-efficiency air conditioning equipment within the cluster to reduce equipment costs, and standardizes upgrade times and technical solutions to reduce redundant investment. The regional synergy level plan also develops cluster-level energy dispatch strategies, uniformly implementing load reduction measures for all buildings within the cluster during peak urban load periods, improving the load reduction effect through regional-level coordinated control. For regional building clusters or scattered individual buildings with low synergy coefficients, a building-level plan is adopted, which develops personalized carbon reduction measures for each building based on its specific circumstances. The tiered carbon reduction operation plan achieves optimized allocation of carbon reduction resources through multi-level strategy processing; high-synergy clusters improve efficiency through regional collaboration, while low-synergy buildings ensure effectiveness through precise policy implementation.

[0081] To implement the above-described method embodiments, a city-level energy-saving and carbon-reducing intelligent operation method is proposed to achieve the corresponding functional and technical effects. See also... Figure 2 , Figure 2 This diagram illustrates a structural block diagram of a city-level energy-saving and carbon-reducing intelligent operation system 200 provided in an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. The city-level energy-saving and carbon-reducing intelligent operation system 200 provided in this embodiment includes:

[0082] Data acquisition module 201 is used to collect building energy consumption data, environmental monitoring data and carbon emission data, perform time-series fusion of the building energy consumption data and the carbon emission data to generate a unified energy consumption benchmark, and extract energy consumption characteristic curves through the unified energy consumption benchmark;

[0083] Feature analysis module 202 is used to perform complementary analysis on the energy consumption feature curve to confirm carbon reduction characteristics, perform feature curve analysis on the carbon reduction characteristics to determine matching operation periods, determine a building weight table based on the matching operation periods, and perform regional calibration on the environmental monitoring data based on the building weight table to generate environmental impact factors.

[0084] Potential identification module 203 is used to perform correlation analysis between the environmental impact factors and the energy consumption characteristic curve to generate a separation degree, perform load fluctuation analysis based on the separation degree to generate a load balance coefficient, identify high-load building nodes in the load balance coefficient to generate a restriction scheme for energy consumption reverse restriction, and identify effective carbon reduction potential through the load balance coefficient and the restriction scheme to generate effective potential data.

[0085] The data conversion module 204 is used to construct conversion rules based on the effective potential data and the carbon emission data, to standardize and convert the effective potential data through the conversion rules to generate standardized carbon reduction data, and to perform correlation analysis between the standardized carbon reduction data and the unified energy consumption benchmark to generate carbon reduction assessment indicators.

[0086] The scheme generation module 205 is used to perform spatial positioning analysis on the carbon reduction assessment indicators to generate building location information, perform carbon reduction intensity analysis on the building location information to generate synergy coefficients, and perform multi-level strategy processing based on the synergy coefficients to generate graded carbon reduction operation schemes.

[0087] The aforementioned city-level energy-saving and carbon-reducing intelligent operation system 200 can implement one of the city-level energy-saving and carbon-reducing intelligent operation methods described in the above method embodiments. The options in the above method embodiments are also applicable to this embodiment and will not be detailed here. The remaining content of this application's embodiments can be referred to the content of the above method embodiments, and will not be repeated in this embodiment.

[0088] The above embodiments are not an exhaustive list based on the present invention, and there may be many other embodiments not listed. Any substitutions and improvements made without departing from the concept of the present invention are within the protection scope of the present invention.

Claims

1. A city-level intelligent operation method for energy conservation and carbon reduction, characterized in that, include: Collect building energy consumption data, environmental monitoring data and carbon emission data, perform time-series fusion of the building energy consumption data and the carbon emission data to generate a unified energy consumption benchmark, and extract energy consumption characteristic curves through the unified energy consumption benchmark; Complementary analysis is performed on the energy consumption characteristic curve to confirm carbon reduction characteristics. Characteristic curve analysis is performed on the carbon reduction characteristics to determine matching operation periods. A building weight table is determined based on the matching operation periods. Based on the building weight table, regional calibration is performed on the environmental monitoring data to generate environmental impact factors. Based on the correlation analysis between the environmental impact factors and the energy consumption characteristic curve, a separation degree is generated. Based on the separation degree, a load fluctuation analysis is performed to generate a load balance coefficient. High-load building nodes in the load balance coefficient are identified to generate a restriction scheme by reverse energy consumption restriction. Effective carbon reduction potential is identified and effective potential data is generated by using the load balance coefficient and the restriction scheme. Based on the effective potential data and the carbon emission data, a conversion rule is constructed. The effective potential data is then standardized and converted using the conversion rule to generate standardized carbon reduction data. The standardized carbon reduction data is then correlated with the unified energy consumption benchmark to generate carbon reduction assessment indicators. Spatial location analysis is performed on the carbon reduction assessment indicators to generate building location information. Carbon reduction intensity analysis is performed on the building location information to generate synergy coefficients. Multi-level strategy processing is performed based on the synergy coefficients to generate graded carbon reduction operation plans.

2. The method according to claim 1, characterized in that, The step of determining the building weight table based on the matched operating period includes: The load contribution is generated by extracting the peak and valley time distribution from the matched operating periods. Peak values ​​of the load contribution are identified to generate a set of high-contribution buildings; The high-contribution building selection is subjected to reverse weight reduction adjustment to generate adjustment weights; A building weight table is established based on the load contribution and the adjustment weight.

3. The method according to claim 1, characterized in that, The step of generating a separation degree based on the correlation analysis between the environmental impact factors and the energy consumption characteristic curve includes: Factor weights are extracted from the environmental impact factors to generate a factor weight set; The coupling degree index is generated by performing coupling degree analysis between the energy consumption characteristic curve and the factor weight set. Independence identifiers are generated by identifying independence features from the coupling degree index; The degree of separation is determined based on the independence identifier.

4. The method according to claim 1, characterized in that, The process of identifying high-load building nodes in the load balance coefficient and generating a reverse energy consumption limit scheme includes: A node set is generated by identifying high-load building nodes from the load balance coefficient; Extract the peak energy consumption features of the node set to generate peak feature data; The limit intensity level generation level configuration is determined based on the peak characteristic data; A restriction scheme is generated based on the aforementioned level configuration.

5. The method according to claim 1, characterized in that, The process of standardizing the effective potential data using the transformation rules to generate standardized carbon reduction data includes: Based on the conversion rules, a baseline threshold is determined to generate a threshold reference; The effective potential data is compared with the threshold reference to generate potential grading results; Prioritize and upgrade the high-potential items in the potential classification results to generate upgrade configurations; Based on the improved configuration, the potential classification results are converted to generate standardized carbon reduction data.

6. The method according to claim 1, characterized in that, The step of generating carbon reduction assessment indicators by correlating the standardized carbon reduction data with the unified energy consumption benchmark includes: The standardized carbon reduction data is compared with the unified energy consumption benchmark to generate a difference distribution; Identify a set of problematic buildings with high baseline low carbon reduction from the difference distribution; The evaluation values ​​of the problematic building set are adjusted downwards to generate a corrected difference distribution. A carbon reduction assessment index is constructed based on the corrected difference distribution.

7. The method according to claim 1, characterized in that, The step of performing carbon reduction intensity analysis on the building location information to generate a synergy coefficient includes: Spatial clustering is performed on the building location information to identify regional building clusters; Extract the carbon reduction intensity distribution of the building clusters in the region to generate intensity distribution data; Based on the intensity distribution data, carbon reduction synergistic features are identified and a synergistic feature set is generated; The collaborative feature set is subjected to collaborative quantification to generate collaborative coefficients.

8. The method according to claim 2, characterized in that, The step of extracting peak-valley time distribution from the matched operating time period to generate load contribution includes: A load feature set is generated by extracting time-period load features from the matched operating periods; Perform time-period load intensity analysis on the load feature set to generate time-period intensity distribution; Peak and trough periods are identified from the intensity distribution over the time periods to generate peak and trough identifiers; Based on the peak period identified by the peak and valley markers, the load contribution of each building is determined by extracting its load percentage.

9. The method according to claim 4, characterized in that, The step of determining the limit intensity level generation level configuration based on the peak characteristic data includes: The peak feature data is used to identify high-peak groups through peak classification. Extract the peak duration of the peak value group to generate duration data; The peak value groups with excessive duration data are upgraded to generate upgraded peak value groups; Based on the upgraded peak group setting, a limit intensity level is generated to configure the level.

10. A city-level energy-saving and carbon-reducing intelligent operation system, characterized in that, include: The data acquisition module is used to collect building energy consumption data, environmental monitoring data and carbon emission data, perform time-series fusion of the building energy consumption data and the carbon emission data to generate a unified energy consumption benchmark, and extract energy consumption characteristic curves through the unified energy consumption benchmark; The feature analysis module is used to perform complementary analysis on the energy consumption feature curve to confirm carbon reduction features, perform feature curve analysis on the carbon reduction features to determine matching operation periods, determine a building weight table based on the matching operation periods, and perform regional calibration on the environmental monitoring data based on the building weight table to generate environmental impact factors. The potential identification module is used to generate a separation degree by performing correlation analysis between the environmental impact factors and the energy consumption characteristic curve, generate a load balance coefficient by performing load fluctuation analysis based on the separation degree, identify high-load building nodes in the load balance coefficient and generate a restriction scheme by performing reverse energy consumption restriction, and identify effective carbon reduction potential by the load balance coefficient and the restriction scheme to generate effective potential data. The data conversion module is used to construct conversion rules based on the effective potential data and the carbon emission data, standardize the effective potential data through the conversion rules to generate standardized carbon reduction data, and perform correlation analysis between the standardized carbon reduction data and the unified energy consumption benchmark to generate carbon reduction assessment indicators. The scheme generation module is used to perform spatial positioning analysis on the carbon reduction assessment indicators to generate building location information, perform carbon reduction intensity analysis on the building location information to generate synergy coefficients, and perform multi-level strategy processing based on the synergy coefficients to generate graded carbon reduction operation schemes.

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