Urban carbon dioxide emission data management method and system

Abstract

The invention discloses an urban carbon dioxide emission data management method and system, and relates to the technical field of monitoring management. According to the urban carbon dioxide emission data management method, carbon dioxide emission time sequence data and carbon dioxide absorption time sequence data in a set period of a city are obtained, a carbon dioxide comprehensive emission index is obtained through analysis, and judgment and analysis are conducted on the carbon dioxide comprehensive emission index and a preset emission abnormal interval; when the comprehensive carbon dioxide emission index is within a preset emission abnormal interval, it is regarded that urban carbon dioxide emission is abnormal, and emission abnormal management measures are taken. According to the method, the carbon emission data is divided into kinetic energy emission, functional energy emission, energy efficiency emission and ecological carbon absorption indexes, so that the carbon emission data management is more refined, the evaluation of the carbon emission is more scientific and accurate based on a multi-dimensional comprehensive calculation mode, and efficient data support is provided for urban carbon management.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring and management, and specifically to a method and system for managing urban carbon dioxide emission data. Background Art

[0002] With the increasingly serious global climate change problem, carbon emissions have become one of the core issues of concern. As the main greenhouse gas, the continuous growth of carbon dioxide emissions directly leads to problems such as rising global temperatures, frequent extreme weather, and ecological system imbalance. And cities, as the main sources of energy consumption and carbon emissions, bear the important responsibility of reducing greenhouse gas emissions. Therefore, establishing an efficient urban carbon dioxide emission data management system is of great significance for achieving the goals of carbon peak and carbon neutrality.

[0003] Currently, urban carbon dioxide emission management mainly relies on traditional statistical methods and macro models, such as total amount estimation based on energy consumption data or calculation of industry emission coefficients. However, these methods have many limitations. For example, the data is lagged, the spatial resolution is low, and it is difficult to achieve real-time monitoring. In addition, with the acceleration of the urbanization process, the factors involved in carbon dioxide emissions are becoming increasingly complex, including not only traditional industrial production and energy consumption, but also affected by multi-dimensional factors such as traffic flow, infrastructure operation efficiency, and ecological carbon sequestration capacity. Therefore, single-dimensional or static statistical methods are no longer sufficient to meet the needs of refined management of modern urban carbon emissions.

[0004] The prior art, such as a carbon dioxide emission data management method and system disclosed in the invention patent application with the publication number: CN113162028B, includes: monitoring various parameters of carbon dioxide in a thermal power plant, calculating the carbon dioxide emission amount based on the various parameters of carbon dioxide; judging whether the carbon dioxide parameters are abnormal; if the parameters are abnormal, recording and marking them, and performing rounding; if the parameters are normal, recording the data. By monitoring various parameters of carbon dioxide and judging the various parameters of carbon dioxide, and processing the abnormal values, the data deviation is solved, and the economic loss of carbon emissions is reduced.

[0005] Based on the above scheme, it is found that the limitations of the existing technology include at least the following problems: the monitoring and data management methods of the existing technology lack real-time and dynamic characteristics, and mainly rely on periodic manual inspections and static parameter evaluations. This method is difficult to timely capture short-term and sudden abnormal phenomena that occur in the operation of thermal power plants, such as data zeroing, data freezing, and drastic parameter fluctuations. Once these abnormal phenomena occur, there will be obvious deviations between the monitoring data and the actual emissions, which will greatly reduce the accuracy and credibility of carbon emission accounting, and easily cause unreasonable carbon quota allocation and corporate economic losses. In addition, due to the long data processing cycle and single detection method, it is difficult to quickly feedback and correct problems, which makes the overall system unable to cope with complex and changeable actual working conditions, seriously restricting the refinement and intelligence level of carbon emission management. Summary of the invention

[0006] In view of the shortcomings of the existing technology, the present invention provides a method and system for urban carbon dioxide emissions management, which solves the problem that the existing technology relies on periodic manual inspections and static parameter evaluations, making it difficult to timely capture and correct abnormal carbon dioxide emissions from thermal power plants, resulting in data deviations and inaccurate calculations.

[0007] To achieve the above objectives, the first aspect of the present invention provides a method for managing urban carbon dioxide emissions data, comprising the following steps:

[0008] Acquire carbon dioxide emission time series data and carbon dioxide absorption time series data within a set period of the city, wherein the carbon dioxide emission time series data includes energy emission time series data, industrial energy emission time series data, and energy efficiency emission time series data, and pre-process them respectively;

[0009] Comprehensively analyze the pre-processed carbon dioxide emission time series data and carbon dioxide absorption time series data within the city setting period, and obtain the kinetic energy emission time series index, industrial energy emission time series index, energy efficiency emission time series index, and ecological carbon absorption time series index within the city setting period, and conduct comprehensive analysis to obtain the comprehensive carbon dioxide emission index within the city setting period;

[0010] The comprehensive carbon dioxide emission index within the city's set period is judged and analyzed with the preset emission abnormality range. When the comprehensive carbon dioxide emission index within the city's set period is within the preset emission abnormality range, it is regarded as the city's carbon dioxide emissions abnormality, and emission abnormality management measures are taken.

[0011] Furthermore, the specific formula for calculating the comprehensive carbon dioxide emission index within a city’s set period is as follows: Among them, Tp is the comprehensive carbon dioxide emission index within the urban setting period, Dp is the kinetic energy emission time series index within the urban setting period, λ1 is the kinetic energy emission weight coefficient stored in the database, Gp is the industrial energy emission time series index within the urban setting period, λ2 is the industrial energy emission weight coefficient stored in the database, Np is the energy efficiency emission time series index within the urban setting period, λ3 is the energy efficiency emission weight coefficient stored in the database, δ1 is the interaction influence coefficient between kinetic energy and industrial energy stored in the database, δ2 is the interaction influence coefficient between industrial energy and energy efficiency stored in the database, δ3 is the interaction influence coefficient between kinetic energy and energy efficiency stored in the database, and Sx is the ecological carbon absorption time series index within the urban setting period.

[0012] Further, the kinetic energy emission time series data includes traffic flow values, traffic density values, driving speed values, and fuel consumption values of several traffic roads in several time periods within the urban setting period. The specific steps to obtain the kinetic energy emission time series index within the urban setting period are as follows: Comprehensively analyze the traffic flow values and traffic density values of several traffic roads in several time periods within the urban setting period to obtain the traffic flow comprehensive index and traffic density comprehensive index in several time periods within the urban setting period; Comprehensively analyze the driving speed values and fuel consumption values of each vehicle on several traffic roads in several time periods within the urban setting period to obtain the driving speed comprehensive index and fuel consumption comprehensive index in several time periods within the urban setting period; Obtain the road condition index in several time periods within the urban setting period, and comprehensively analyze it in combination with the traffic flow comprehensive index, traffic density comprehensive index, driving speed comprehensive index, and fuel consumption comprehensive index in the corresponding time periods to obtain the kinetic energy emission time series index within the urban setting period.

[0013] Further, the specific formula for calculating the kinetic energy emission time series index within the urban setting period is as follows: Among them, Dp is the kinetic energy emission time series index within the urban setting period, JtL i is the traffic flow comprehensive index in the i-th time period within the urban setting period, ω1 is the flow regulation coefficient stored in the database, DzK i is the road condition index in the i-th time period within the urban setting period, ω2 is the road condition regulation coefficient stored in the database, JtM i is the traffic density comprehensive index in the i-th time period within the urban setting period, ω3 is the density regulation coefficient stored in the database, CsD i is the driving speed comprehensive index in the i-th time period within the urban setting period, ε is the driving speed regulation factor stored in the database, CrX iThe comprehensive fuel consumption index for the i-th time period within the set cycle of the city, ω4 is the fuel consumption adjustment coefficient stored in the database, i = 1, 2, 3, …, i0, and i0 is the number of time periods.

[0014] Furthermore, the energy emission time-series data includes the production volume of each product, raw material consumption, equipment operation time, and equipment load rate of each production facility for several production plants over several time periods. The specific steps to obtain the energy emission time-series index within the set cycle of the city are as follows: Read the production volume of each product and raw material consumption of several production plants over several time periods within the set cycle of the city, and conduct comprehensive analysis respectively to obtain the product production comprehensive index and raw material consumption comprehensive index for several time periods within the set cycle of the city; Read the equipment operation time and equipment load rate of each production facility of several production plants over several time periods within the set cycle of the city, and conduct comprehensive analysis respectively to obtain the equipment operation comprehensive index and equipment load comprehensive index for several time periods within the set cycle of the city.

[0015] Furthermore, the specific formula for calculating the energy emission time-series index within the set cycle of the city is as follows: where Gp is the energy emission time-series index within the set cycle of the city, ScL i is the product production comprehensive index for the i-th time period within the set cycle of the city, χ1 is the production adjustment coefficient stored in the database, SfH i is the equipment load comprehensive index for the i-th time period within the set cycle of the city, χ2 is the load adjustment coefficient stored in the database, SyX i is the equipment operation comprehensive index for the i-th time period within the set cycle of the city, η is the operation adjustment factor stored in the database, YcX i is the raw material consumption comprehensive index for the i-th time period within the set cycle of the city, χ3 is the consumption adjustment coefficient stored in the database, χ4 is the operation adjustment coefficient stored in the database, i = 1, 2, 3, …, i0, and i0 is the number of time periods.

[0016] Furthermore, the energy efficiency emission time-series data includes the power consumption, gas consumption, energy conversion efficiency, and energy loss rate over several time periods. The specific steps to obtain the energy efficiency emission time-series index within the set cycle of the city are as follows: Obtain the energy conversion efficiency and energy loss rate over several time periods within the set cycle of the city and perform preprocessing; Combine the power consumption and gas consumption over several time periods within the set cycle of the city with the corresponding preprocessed energy conversion efficiency and energy loss rate of each time period for comprehensive analysis to obtain the energy efficiency emission time-series index within the set cycle of the city.

[0017] Further, the carbon dioxide absorption timing data includes the greening coverage rate, air quality index, soil humidity, vegetation growth index, and vegetation density of each greening area for several time periods. The specific steps to obtain the ecological carbon absorption timing index within the set cycle of the city are as follows: Read the soil humidity, vegetation growth index, and vegetation density of each greening area for several time periods within the set cycle of the city, and conduct comprehensive analysis respectively to obtain the soil humidity comprehensive index, vegetation growth index comprehensive index, and vegetation density comprehensive index for several time periods within the set cycle of the city; Combine the greening coverage rate and air quality index for several time periods within the set cycle of the city with the soil humidity comprehensive index, vegetation growth index comprehensive index, and vegetation density comprehensive index for the corresponding time periods respectively, and conduct comprehensive analysis to obtain the ecological carbon absorption timing index within the set cycle of the city.

[0018] Further, the specific steps to take emission anomaly management measures are as follows: Compare the kinetic energy emission timing index, industrial energy emission timing index, energy efficiency emission timing index, and ecological carbon absorption timing index within the set cycle of the city with the preset kinetic energy emission anomaly range, industrial energy emission anomaly range, energy efficiency emission anomaly range, and ecological carbon absorption anomaly range respectively for judgment and analysis; If the kinetic energy emission timing index within the set cycle of the city is within the preset kinetic energy emission anomaly range, it is regarded as kinetic energy emission anomaly, and a kinetic energy emission anomaly alarm is sent to the relevant staff; If the industrial energy emission timing index within the set cycle of the city is within the preset industrial energy emission anomaly range, it is regarded as industrial energy emission anomaly, and an industrial energy emission anomaly alarm is sent to the relevant staff; If the energy efficiency emission timing index within the set cycle of the city is within the preset energy efficiency emission anomaly range, it is regarded as energy efficiency emission anomaly, and an energy efficiency emission anomaly alarm is sent to the relevant staff; If the ecological carbon absorption timing index within the set cycle of the city is within the preset ecological carbon absorption anomaly range, it is regarded as ecological carbon absorption anomaly, and an ecological carbon absorption anomaly alarm is sent to the relevant staff.

[0019] The second aspect of the present invention provides a city carbon dioxide emission data management system, including:

[0020] A data acquisition unit, configured to acquire the carbon dioxide emission timing data and carbon dioxide absorption timing data within the set cycle of the city. The carbon dioxide emission timing data includes energy emission timing data, industrial energy emission timing data, and energy efficiency emission timing data, and preprocesses them respectively.

[0021] A timing analysis unit, configured to conduct comprehensive analysis on the preprocessed carbon dioxide emission timing data and carbon dioxide absorption timing data within the set cycle of the city respectively to obtain the kinetic energy emission timing index, industrial energy emission timing index, energy efficiency emission timing index, and ecological carbon absorption timing index within the set cycle of the city.

[0022] A comprehensive analysis unit is used to comprehensively analyze the kinetic energy emission time series index, industrial energy emission time series index, energy efficiency emission time series index, and ecological carbon absorption time series index within a set period of the city, so as to obtain the comprehensive carbon dioxide emission index within the set period of the city.

[0023] A judgment and management unit is used to conduct judgment and analysis on the comprehensive carbon dioxide emission index within the set period of the city and the preset emission abnormal range. When the comprehensive carbon dioxide emission index within the set period of the city is within the preset emission abnormal range, it is regarded as abnormal carbon dioxide emission in the city, and emission abnormal management measures are taken.

[0024] The present invention has the following beneficial effects:

[0025] 1) By obtaining the carbon dioxide emission time series data and carbon dioxide absorption time series data within the set period of the city, and subdividing them into kinetic energy emission, industrial energy emission, energy efficiency emission, and ecological carbon absorption indexes, the carbon emission data management becomes more refined. The calculation of each index combines multiple key parameters. For example, the kinetic energy emission time series index is calculated through factors such as traffic flow, road conditions, driving speed, and fuel consumption. The industrial energy emission time series index is analyzed based on product production volume, equipment load, equipment operation time, and raw material consumption. The energy efficiency emission time series index considers data such as electricity and gas consumption, energy conversion efficiency, and energy loss rate. The ecological carbon absorption time series index combines indicators such as greening coverage rate, vegetation density, and soil humidity. This multi-dimensional comprehensive calculation method makes the evaluation of carbon emissions more scientific and accurate, providing efficient data support for urban carbon management.

[0026] 2) The data management method of the present invention can not only calculate the comprehensive carbon dioxide emission index, but also establish an emission abnormal monitoring mechanism based on the set period. By comparing the comprehensive carbon dioxide emission index with the preset abnormal range, when the index exceeds the normal range, the system will judge whether the emission is abnormal and take targeted management measures. For example, if the kinetic energy emission time series index exceeds the set threshold, an abnormal kinetic energy emission alarm will be sent to the relevant department. There are also corresponding warning mechanisms for industrial energy emission, energy efficiency emission, and ecological carbon absorption indexes. This intelligent warning mechanism can help managers promptly discover abnormal urban carbon emissions and quickly take countermeasures, such as adjusting traffic flow, optimizing industrial production plans, improving energy use efficiency, increasing greening coverage rate, etc., so as to realize the dynamic optimization management of urban carbon emissions.

[0027] 3) The data management method of the present invention evaluates the contributions of factors such as vegetation, greening coverage rate, and soil humidity to carbon absorption through the ecological carbon absorption time series index. When calculating carbon emissions, the ecological carbon absorption index can be comprehensively analyzed with other emission indexes, making the urban carbon emission management more balanced. For example, in the case of abnormal carbon emissions, part of the carbon emissions can be offset by increasing the greening area, improving air quality, optimizing the ecological environment, etc., and the urban carbon neutralization ability can be improved.

[0028] 4) Through the collaborative work of the data acquisition unit, time series analysis unit, comprehensive analysis unit, and judgment and management unit of the data management system of the present invention, the fully automated acquisition, processing, analysis, and management of urban carbon dioxide emission data can be realized. First, the system can automatically acquire and preprocess the carbon dioxide emission data and absorption data within the set period of the city to ensure the accuracy and consistency of the data. Subsequently, the time series analysis unit performs refined calculations on various types of carbon emission data to generate different types of time series indexes, making the carbon emission characteristics more intuitive and quantifiable. The comprehensive analysis unit further calculates the comprehensive carbon dioxide emission index to provide a precise assessment of the overall carbon emission situation of the city. With the support of the judgment and management unit, the system can monitor the carbon emission status in real time and automatically issue an alarm for abnormal situations, reducing the burden of manual monitoring and improving the emergency response efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flowchart of the method for managing urban carbon dioxide emission data provided by the present invention.

[0030] Figure 2 It is a flowchart of the specific steps for obtaining the kinetic energy emission time series index within the set period of the city in the method for managing urban carbon dioxide emission data provided by the present invention.

[0031] Figure 3 It is a block diagram of the urban carbon dioxide emission data management system provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The principles and features of the present invention will be described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not intended to limit the scope of the present invention.

[0033] Please refer to Figure 1 , the method for managing urban carbon dioxide emission data provided by the embodiment of the present invention includes the following steps:

[0034] Obtain the carbon dioxide emission time series data and carbon dioxide absorption time series data within the set period of the city. The carbon dioxide emission time series data includes energy emission time series data, industrial energy emission time series data, and energy efficiency emission time series data, and preprocess them respectively;

[0035] Comprehensively analyze the pre - processed time - series data of carbon dioxide emissions and carbon dioxide absorption within the set period of the city respectively, obtain the kinetic energy emission time - series index, industrial energy emission time - series index, energy efficiency emission time - series index, and ecological carbon absorption time - series index within the set period of the city, and conduct a comprehensive analysis to obtain the carbon dioxide comprehensive emission index within the set period of the city;

[0036] Judge and analyze the carbon dioxide comprehensive emission index within the set period of the city with the preset emission anomaly range. When the carbon dioxide comprehensive emission index within the set period of the city is within the preset emission anomaly range, it is regarded as abnormal carbon dioxide emissions in the city, and emission anomaly management measures are taken.

[0037] The specific formula for calculating the carbon dioxide comprehensive emission index within the set period of the city is as follows: Among them, Tp is the carbon dioxide comprehensive emission index within the set period of the city, Dp is the kinetic energy emission time - series index within the set period of the city, λ1 is the kinetic energy emission weight coefficient stored in the database, Gp is the industrial energy emission time - series index within the set period of the city, λ2 is the industrial energy emission weight coefficient stored in the database, Np is the energy efficiency emission time - series index within the set period of the city, λ3 is the energy efficiency emission weight coefficient stored in the database, δ1 is the interaction influence coefficient between kinetic energy and industrial energy stored in the database, δ2 is the interaction influence coefficient between industrial energy and energy efficiency stored in the database, δ3 is the interaction influence coefficient between kinetic energy and energy efficiency stored in the database, and Sx is the ecological carbon absorption time - series index within the set period of the city.

[0038] It should be explained that the specific steps for obtaining the kinetic energy emission weight coefficient λ1, industrial energy emission weight coefficient λ2, and energy efficiency emission weight coefficient λ3 stored in the database are as follows: By statistically analyzing the proportions of kinetic energy emissions, industrial energy emissions, and energy efficiency emissions in historical data at different times, and using regression analysis or machine learning models to evaluate the influence degree of each emission source on the total emissions, the calculated weight values are written into the database for direct call during formula calculation.

[0039] The specific steps for obtaining the interaction influence coefficient δ1 between kinetic energy and industrial energy, interaction influence coefficient δ2 between industrial energy and energy efficiency, and interaction influence coefficient δ3 between kinetic energy and energy efficiency stored in the database are as follows: By selecting scenarios where two - by - two emission sources such as kinetic energy and industrial energy, industrial energy and energy efficiency, and kinetic energy and energy efficiency change simultaneously in historical data and conducting multiple regression or covariance analysis, after quantifying the interaction effect intensity between the two, the obtained coefficient values are stored in the database for interaction term calculation in the formula.

[0040] The specific implementation example of calculating the carbon dioxide comprehensive emission index within the set period of the city is as follows. There is the following data:

[0041] The kinetic energy emission timing index within the urban setting period is approximately: 152.378.

[0042] The kinetic energy emission weight coefficient stored in the database is approximately: 0.482.

[0043] The energy emission timing index within the urban setting period is approximately: 98.245.

[0044] The energy emission weight coefficient stored in the database is approximately: 0.329.

[0045] The energy efficiency emission timing index within the urban setting period is approximately: 45.673.

[0046] The energy efficiency emission weight coefficient stored in the database is approximately: 0.189.

[0047] The interaction influence coefficient between kinetic energy and energy stored in the database is approximately: 0.215.

[0048] The interaction influence coefficient between energy and energy efficiency stored in the database is approximately: 0.176.

[0049] The interaction influence coefficient between kinetic energy and energy efficiency stored in the database is approximately: 0.194.

[0050] Sx is the ecological carbon absorption timing index within the urban setting period, approximately: 78.592.

[0051] Substitute the above data into the specific formula for calculating the comprehensive carbon dioxide emission index within the urban setting period, and we get:

[0052] The comprehensive carbon dioxide emission index within the urban setting period = (0.482 × 152.378) + (0.329 × 98.245) + (0.189 × 45.673) + [(152.378 × 98.245)^0.215 + (98.245 × 45.673)^0.176 + (152.378 × 45.673)^0.194] - 78.592 ≈ 53.668.

[0053] Specifically, such as Figure 2As shown, the kinetic energy emission time series data includes traffic flow values, traffic density values, driving speed values, and fuel consumption values of several traffic roads in several time periods (e.g., five minutes), and the specific steps to obtain the kinetic energy emission time series index within the urban set period are as follows: Conduct comprehensive analysis on the traffic flow values and traffic density values of several traffic roads in several time periods within the urban set period (i.e., conduct mean value analysis on the traffic flow values and traffic density values of several roads respectively), and obtain the traffic flow comprehensive index and traffic density comprehensive index in several time periods within the urban set period; Conduct comprehensive analysis on the driving speed values and fuel consumption values of each vehicle on several traffic roads in several time periods within the urban set period (i.e., conduct mean value analysis on the driving speed values and fuel consumption values of each vehicle on several traffic roads respectively), and obtain the driving speed comprehensive index and fuel consumption comprehensive index in several time periods within the urban set period; Obtain the road condition index in several time periods within the urban set period, and conduct comprehensive analysis by combining the traffic flow comprehensive index, traffic density comprehensive index, driving speed comprehensive index, and fuel consumption comprehensive index in the corresponding time periods respectively, so as to obtain the kinetic energy emission time series index within the urban set period.

[0054] Among them, the traffic flow value refers to the number of vehicles passing through the traffic road, which can be measured and obtained through devices such as traffic monitoring cameras, road surface sensors, and GPS.

[0055] The traffic density value refers to the number of vehicles on the traffic road, which can be measured and obtained through in-vehicle GPS systems and road surface sensors.

[0056] The driving speed value of the vehicle refers to the distance traveled by the vehicle per unit time, which can be measured and obtained through GPS devices and road monitoring systems.

[0057] The fuel consumption value of the vehicle refers to the speed at which the vehicle consumes fuel during driving, which can be measured and obtained through the vehicle's fuel gauge and OBD (on-board diagnostic system).

[0058] The road condition index is an indicator reflecting the impact of road quality on emissions, and its specific acquisition steps are as follows:

[0059] Obtain the road surface quality of several traffic roads (indicating the surface flatness and wear degree of the road, which can be used to evaluate the flatness and cracks of the road by using ground sensors, laser scanners, or drone images), traffic signal efficiency (indicating the duration, response time of traffic lights, and the matching degree of traffic flow, which can be used to monitor and analyze the matching efficiency of the response time of traffic lights and traffic flow by a traffic management system), road surface friction coefficient (indicating the value of road surface friction, which can be measured and obtained by using friction test equipment), traffic smoothness (indicating the congestion degree of the road and its impact on traffic flow, which can be used to calculate traffic flow and parking time by a traffic flow monitoring system and GPS data), and perform standardization processing (i.e., remove the unit);

[0060] For each traffic road, perform weighted analysis based on the standardized road surface quality, traffic signal efficiency, road surface friction coefficient, and traffic smoothness respectively. After weighted processing, perform mean analysis on several traffic roads to obtain the road condition index.

[0061] The specific formula for calculating the kinetic energy emission time series index within the urban set period is as follows: Among them, Dp is the kinetic energy emission time series index within the urban set period, JtL i is the traffic flow comprehensive index in the i-th time period within the urban set period, ω1 is the flow regulation coefficient stored in the database, DzK i is the road condition index in the i-th time period within the urban set period, ω2 is the road condition regulation coefficient stored in the database, JtM i is the traffic density comprehensive index in the i-th time period within the urban set period, ω3 is the density regulation coefficient stored in the database, CsD i is the driving speed comprehensive index in the i-th time period within the urban set period, ε is the driving speed regulation factor stored in the database, used to prevent the denominator from being 0, CrX i is the fuel consumption comprehensive index in the i-th time period within the urban set period, ω4 is the fuel consumption regulation coefficient stored in the database, i = 1, 2, 3,..., i0, and i0 is the number of time periods.

[0062] It should be noted that the specific steps for obtaining the traffic regulation coefficient ω1, road condition regulation coefficient ω2, density regulation coefficient ω3, and fuel consumption regulation coefficient ω4 stored in the database are as follows: First, collect basic information such as traffic flow, road condition quality, vehicle density, and fuel consumption at each time period from multi-source data such as traffic monitoring systems and road surface sensors, and perform data cleaning, outlier removal, and feature extraction. Then, use regression analysis or machine learning algorithms to quantify the correlation between these parameters and the kinetic energy emission time series. Finally, determine the values of each regulation coefficient according to the model output results and write them into the database for direct invocation during formula calculation.

[0063] In this implementation plan, multi-dimensional data such as traffic flow, traffic density, vehicle driving speed, and fuel consumption are used, combined with the road condition index, to comprehensively analyze carbon emissions from multiple perspectives. This fine-grained data processing method ensures that carbon emission assessment does not rely on a single factor, but comprehensively considers complex variables such as road usage, traffic flow, and vehicle energy consumption, improving the accuracy of emission assessment. Secondly, this method uses standardization and weighted analysis to calculate the road condition index, fully considering the impacts of road surface quality, traffic signal efficiency, road surface friction coefficient, and traffic flow on emissions, quantifying the role of road conditions in carbon emissions. In addition, this method optimizes through key parameters such as the traffic regulation coefficient, density regulation coefficient, and fuel consumption regulation coefficient stored in the database, combined with machine learning algorithms, enabling the calculation model to continuously adapt to the traffic characteristics of different cities and different time periods, improving the applicability and prediction ability of the model. Especially, using regression analysis or machine learning techniques to quantify the correlation between traffic data and emission indices makes the setting of regulation coefficients more scientific and reasonable, avoiding errors caused by subjective experience. This method not only improves the intelligent level of emission assessment but also provides decision-making support for traffic managers, such as optimizing signal light duration, adjusting traffic flow direction, and improving road maintenance, thus reducing carbon emissions while improving traffic efficiency.

[0064] Specifically, the industrial energy emission time series data includes the production volume, raw material consumption of each product of several production plants in several time periods, as well as the equipment operation time and equipment load rate of each production equipment. The specific steps to obtain the industrial energy emission time series index within the urban set period are as follows: Read the production volume and raw material consumption of each product of several production plants in several time periods within the urban set period, and conduct comprehensive analysis (i.e., mean analysis) respectively to obtain the product production comprehensive index and raw material consumption comprehensive index in several time periods within the urban set period; Read the equipment operation time and equipment load rate of each production equipment of several production plants in several time periods within the urban set period and conduct comprehensive analysis (i.e., mean analysis) respectively to obtain the equipment operation comprehensive index and equipment load comprehensive index in several time periods within the urban set period.

[0065] The specific formula for calculating the industrial energy emission time series index within the urban set period is as follows: Among them, Gp is the industrial energy emission time series index within the urban set period, ScL i is the product production comprehensive index in the i-th time period within the urban set period, χ1 is the production adjustment coefficient stored in the database, SfH i is the equipment load comprehensive index in the i-th time period within the urban set period, χ2 is the load adjustment coefficient stored in the database, SyX i is the equipment operation comprehensive index in the i-th time period within the urban set period, η is the operation adjustment factor stored in the database, which is used to prevent the denominator from being 0, YcX i is the raw material consumption comprehensive index in the i-th time period within the urban set period, χ3 is the consumption adjustment coefficient stored in the database, χ4 is the operation adjustment coefficient stored in the database, i = 1, 2, 3, …, i0, and i0 is the number of time periods.

[0066] It should be explained that the specific acquisition steps of the production adjustment coefficient χ1, load adjustment coefficient χ2, consumption adjustment coefficient χ3, and operation adjustment coefficient χ4 stored in the database are as follows: Collect the original data such as production volume, equipment load, energy consumption, and operation duration of each time period through multi-source channels such as the industrial production management system, equipment monitoring platform, and energy consumption monitoring system, and clean, de-duplicate, and remove outliers from them. Subsequently, use regression analysis or machine learning models to evaluate the correlation between production volume and emissions, the coupling effect between equipment load and emissions, the sensitivity of consumption to emissions, and the impact intensity of operation duration on emissions. Finally, write the numerical values of each adjustment coefficient calculated according to the model output results into the database for direct use when the formula is called.

[0067] In this implementation plan, by collecting key data such as the production volume, raw material consumption, equipment operation time, and equipment load rate of multiple production plants in each time period, the comprehensiveness and accuracy of carbon emission assessment are ensured. Through mean analysis, the comprehensive index of product production, the comprehensive index of raw material consumption, the comprehensive index of equipment operation, and the comprehensive index of equipment load are calculated, enabling the standardization of data from different factories and different time periods and improving the comparability of data. In addition, this method fully considers the impact of various factors in the industrial production process on carbon emissions, especially how changes in equipment load and operation time affect overall carbon emissions, making the calculation of the energy emission time series index more scientific and applicable. Secondly, this method uses the production adjustment coefficient, load adjustment coefficient, consumption adjustment coefficient, and operation adjustment coefficient stored in the database, enabling the carbon emission calculation model to be optimized based on historical data and quantifying the impact degree of each parameter on emissions through regression analysis or machine learning techniques. For example, the model can automatically evaluate the positive correlation between production volume and carbon emissions, the non-linear impact of equipment load on carbon emissions, and the sensitivity of raw material consumption to emissions, making the formulation of adjustment coefficients more accurate. In addition, this method ensures the authenticity and timeliness of carbon emission data through multi-source data acquisition means such as industrial production management systems, equipment monitoring platforms, and energy consumption monitoring systems, and improves data quality through data cleaning, deduplication, and outlier removal. This method can not only be used for real-time monitoring of energy emissions but also for predicting future carbon emission trends.

[0068] Specifically, the energy efficiency emission time series data includes the electricity consumption, gas consumption, energy conversion efficiency, and energy loss rate for several time periods. The specific steps to obtain the energy efficiency emission time series index within the city's set cycle are as follows: Obtain the energy conversion efficiency and energy loss rate for several time periods within the city's set cycle and perform preprocessing; comprehensively analyze the electricity consumption and gas consumption for several time periods within the city's set cycle in combination with the corresponding preprocessed energy conversion efficiency and energy loss rate for each time period to obtain the energy efficiency emission time series index within the city's set cycle.

[0069] Among them, the electricity consumption refers to the total amount of electricity consumed by the city, which directly affects carbon dioxide emissions and can be measured and obtained through an electricity monitoring system or an electric meter.

[0070] The gas consumption refers to the total amount of gas consumed by the city, which directly affects carbon dioxide emissions and can be measured and obtained through a gas meter or a gas supply system.

[0071] The energy conversion efficiency refers to the energy conversion and utilization efficiency in the city. A higher energy conversion efficiency means that more useful output can be obtained with the same electricity and gas consumption, and the emissions are relatively lower. It can be measured and obtained through an energy management system or an equipment monitoring system.

[0072] The energy loss rate refers to the proportion of energy lost during transmission and conversion. A higher loss rate means more energy waste and usually higher emissions, which can be measured and obtained through an energy monitoring system.

[0073] Among them, the specific formula for calculating the energy efficiency emission time series index within the city's set cycle is as follows: Among them, Np is the energy efficiency emission time series index within the city's set cycle, DqX i is the electricity consumption in the i-th time period within the city's set cycle, RqX i is the gas consumption in the i-th time period within the city's set cycle, NzH i is the energy conversion efficiency of the i-th time period after pretreatment within the city's set cycle, ψ is the energy conversion adjustment factor stored in the database, used to prevent the denominator from being 0, NsS i is the energy loss rate of the i-th time period after pretreatment within the city's set cycle, θ is the energy loss adjustment coefficient stored in the database, i = 1, 2, 3,..., i0, and i0 is the number of time periods.

[0074] It should be explained that the specific steps for obtaining the energy loss adjustment coefficient θ stored in the database are as follows: First, collect the original data of energy input, output, and loss in each time period from the energy consumption monitoring system and equipment sensors, and summarize and mark them in combination with the loss links in pipeline transportation, power generation, power distribution, or industrial processes. Then, quantify the influence intensity of energy on emissions during transmission, conversion, and end-use through regression analysis or machine learning methods. Finally, write the energy loss adjustment coefficient calculated by the model into the database for direct use when the formula is called.

[0075] In this implementation plan, the emissions during the energy usage process are comprehensively evaluated through four key indicators: power consumption, gas consumption, energy conversion efficiency, and energy loss rate. The power and gas consumption directly determine the total carbon emissions, while the energy conversion efficiency and energy loss rate determine the impact of unit energy consumption on the environment. A higher energy conversion efficiency means that the same amount of energy consumption can generate more effective output, thereby reducing carbon emissions. A higher energy loss rate represents greater energy waste and higher carbon emissions. The introduction of this method ensures that the carbon emissions calculation not only focuses on the absolute value of energy consumption but also considers the energy usage efficiency, making the evaluation more comprehensive. Secondly, by preprocessing the energy conversion efficiency and energy loss rate, the data becomes more stable, eliminating the calculation errors caused by short-term fluctuations and improving the reliability of the emissions assessment. In addition, relying on the energy loss adjustment coefficients stored in the database, this method can dynamically adjust the impact of different energy types, different industrial processes, and different energy transmission links on carbon emissions during the calculation process. The energy input, output, and loss data for each period are obtained through the energy consumption monitoring system and equipment sensors, and regression analysis or machine learning methods are used for the quantitative analysis of energy loss, making the acquisition of the adjustment coefficients more scientific and reasonable, and avoiding the deviations that may be caused by relying on empirical parameter settings in traditional methods.

[0076] Specifically, the carbon dioxide absorption time-series data includes the greening coverage rate, air quality index, and soil humidity, vegetation growth index, and vegetation density of each greening area for several time periods. The specific steps to obtain the ecological carbon absorption time-series index within the urban set period are as follows: Read the soil humidity, vegetation growth index, and vegetation density of each greening area for several time periods within the urban set period, and conduct comprehensive analysis (i.e., mean analysis) respectively to obtain the soil humidity comprehensive index, vegetation growth index comprehensive index, and vegetation density comprehensive index for several time periods within the urban set period; Combine the greening coverage rate and air quality index for several time periods within the urban set period with the corresponding soil humidity comprehensive index, vegetation growth index comprehensive index, and vegetation density comprehensive index for each time period respectively for comprehensive analysis to obtain the ecological carbon absorption time-series index within the urban set period.

[0077] Among them, the greening coverage rate refers to the proportion of the urban greening area to the total area. The higher the greening coverage rate, the stronger the carbon absorption ability usually is. It can be measured and obtained through remote sensing technology (analyzing satellite images or aerial photography data to obtain the urban greening coverage rate) and geographic information system (using GIS software combined with urban planning data to calculate the greening coverage rate).

[0078] The Air Quality Index refers to the air quality of a city, which reflects the impact of pollution on the ecosystem. When the air quality is poor, the carbon absorption capacity of vegetation may be inhibited. It can be measured through an air quality monitoring system (usually multiple air quality monitoring stations are set up in a city, and these stations will monitor and record the concentrations of major pollutants in the air, such as PM2.5, PM10, CO, NO2, SO2, etc., so as to calculate the air quality index).

[0079] Soil moisture refers to the water content in the soil, which affects the growth of plants and their carbon absorption capacity. The higher the humidity, the more vigorous the plant growth usually is, and the greater the carbon absorption. It can be measured through soil moisture sensors (humidity sensors deployed in different soil layers, such as electromagnetic induction, time domain reflectometry, etc., to monitor soil moisture in real time), and meteorological stations (some meteorological stations are also equipped with soil moisture monitoring instruments to provide relevant soil moisture data).

[0080] The Vegetation Growth Index refers to the growth status of vegetation, which reflects the health status of vegetation and its carbon absorption capacity. It can be measured through remote sensing data (using images taken by satellites or drones, and calculating the growth of vegetation through vegetation indices, such as NDVI, Normalized Difference Vegetation Index), and satellite imagery (regularly taken satellite imagery, and extracting the vegetation growth index through image processing technology).

[0081] Vegetation density refers to the number of plants per unit area. The higher the vegetation density, the stronger the carbon absorption capacity. It can be analyzed through remote sensing data (through remote sensing technologies, such as high-resolution satellite images, LiDAR and other lidar technologies, to estimate the vegetation density in a specific area), and on-site surveys (conducting field surveys in urban green spaces, forests and other areas to count the number and types of vegetation per unit area).

[0082] Among them, the specific formula for calculating the ecological carbon absorption time series index within the set cycle of the city is as follows: Among them, Sx is the ecological carbon absorption time series index within the set cycle of the city, LfG i is the greening coverage rate in the i-th time period within the set cycle of the city, ZmD i is the comprehensive vegetation density index in the i-th time period within the set cycle of the city, SzB i is the comprehensive vegetation growth index in the i-th time period within the set cycle of the city, TrS i is the comprehensive soil moisture index in the i-th time period within the set cycle of the city, KqZ i is the air quality index in the i-th time period within the set cycle of the city, is the air quality adjustment coefficient stored in the database, i = 1, 2, 3,..., i0, and i0 is the number of time periods.

[0083] It should be noted that the specific steps for obtaining the air quality adjustment coefficient stored in the database are as follows: First, collect the pollutant concentrations, AQI indicators, and meteorological data within each time period from the urban air quality monitoring system and the meteorological monitoring platform, and perform cleaning, deduplication, and outlier removal. Then, use regression analysis or machine learning methods to quantify the impact intensity of air quality on the ecological carbon absorption capacity or ecological indicators. Finally, write the adjustment coefficient output by the model into the database for direct call during formula calculation.

[0084] In this implementation plan, multiple ecological factors such as greening coverage rate, vegetation growth index, vegetation density, soil humidity, and air quality index are comprehensively considered to ensure a comprehensive assessment of the carbon absorption capacity of the ecosystem. Among them, the greening coverage rate and vegetation density directly determine the potential of vegetation carbon absorption, the soil humidity affects the growth status of vegetation, and the air quality index measures the potential inhibitory effect of environmental pollution on the ecosystem. This multi-variable collaborative analysis method makes the carbon absorption calculation more accurate and reliable, and can dynamically reflect the ecological carbon sink capacity of different cities or different time periods. Secondly, this method uses a variety of technical means such as remote sensing technology, GIS system, soil humidity sensors, and air quality monitoring stations to obtain data, ensuring the real-time and accuracy of the data, and avoiding the errors caused by the traditional method relying on a single measurement method. In addition, this method stores the air quality adjustment coefficient through the database, and combines regression analysis or machine learning models to quantify the impact of air pollution on carbon absorption, making the carbon absorption assessment not just a static measurement, but an intelligent prediction based on dynamic changes. This method can be used for urban planning, such as optimizing the greening layout, improving the quality of urban forests, and improving soil conditions, so as to enhance the carbon absorption capacity. At the same time, this method can also help environmental managers formulate precise emission reduction and ecological restoration strategies, such as adjusting the greening plan when the air quality deteriorates to enhance the adaptability of the ecosystem. Therefore, this method not only improves the refinement level of urban carbon management, but also provides a scientific basis for the global carbon neutralization strategy, enabling cities to find the best balance point between carbon emission control and ecological restoration.

[0085] Specifically, the specific steps for taking emission anomaly management measures are as follows: Judge and analyze the kinetic energy emission time series index, industrial energy emission time series index, energy efficiency emission time series index, and ecological carbon absorption time series index within the set cycle of the city, respectively, with the preset kinetic energy emission anomaly interval, industrial energy emission anomaly interval, energy efficiency emission anomaly interval, and ecological carbon absorption anomaly interval;

[0086] If the kinetic energy emission time series index within the city's set period is within the preset abnormal range of kinetic energy emissions, it is regarded as abnormal kinetic energy emissions, and an abnormal kinetic energy emissions alarm is sent to relevant staff members. At the same time, management suggestions for kinetic energy emissions are sent, including optimizing traffic flow control (adjusting signal light duration, optimizing the green wave band, improving road traffic efficiency), restricting high-emission vehicles (restricting the passage of old vehicles during high-emission periods or areas, and encouraging the use of new energy vehicles), increasing the utilization rate of public transportation (increasing bus frequencies, optimizing routes, promoting shared travel services), and improving road infrastructure (repairing potholed roads to reduce increased fuel consumption caused by poor road conditions);

[0087] If the industrial energy emission time series index within the city's set period is within the preset abnormal range of industrial energy emissions, it is regarded as abnormal industrial energy emissions, and an abnormal industrial energy emissions alarm is sent to relevant staff members. At the same time, management suggestions for industrial energy emissions are sent, including optimizing industrial production scheduling (reasonably arranging the operation time of high-energy-consuming equipment to avoid high-emission time periods), enhancing equipment energy efficiency management (promoting high-efficiency energy-saving equipment and improving the automation and intelligent level of production lines), strengthening raw material optimization (reducing the use of high-carbon raw materials and increasing the proportion of renewable materials), and waste gas treatment and waste heat recovery (strengthening pollution emission control and encouraging enterprises to use waste heat recovery to reduce energy waste);

[0088] If the energy efficiency emission time series index within the city's set period is within the preset abnormal range of energy efficiency emissions, it is regarded as abnormal energy efficiency emissions, and an abnormal energy efficiency emissions alarm is sent to relevant staff members. At the same time, management suggestions for energy efficiency emissions are sent, including optimizing power and gas scheduling (increasing the proportion of clean energy use and reducing fossil energy consumption), reducing energy transmission losses (improving the maintenance level of power grids and gas pipelines to reduce losses during energy transmission), encouraging intelligent energy-saving systems (promoting smart grids and energy consumption monitoring platforms to improve the energy utilization efficiency of end-users), and implementing energy-saving policies (encouraging enterprises to implement energy-saving certifications and strengthening the supervision of high-energy-consuming units);

[0089] If the ecological carbon absorption time series index within the city's set period is within the preset abnormal range of ecological carbon absorption, it is regarded as abnormal ecological carbon absorption, and an abnormal ecological carbon absorption alarm is sent to relevant staff members. At the same time, management suggestions for ecological carbon absorption are sent, including increasing the urban greening rate (increasing green space area, optimizing vegetation configuration, and improving the carbon sequestration capacity of the ecosystem), improving air quality (controlling pollution source emissions, such as reducing industrial waste gas and vehicle exhaust, to improve the air quality index), enhancing soil carbon sequestration capacity (optimizing green space soil management, increasing soil moisture, and improving the vegetation growth rate), and strengthening vegetation protection and restoration (preventing the destruction of green spaces, strengthening ecological compensation measures, and increasing vegetation density and health);

[0090] In this implementation, through systematic data analysis, anomalies in multiple aspects such as kinetic energy emissions, industrial energy emissions, energy efficiency emissions, and ecological carbon absorption are automatically identified, and early warnings are sent to relevant management personnel to improve the anomaly response speed. Secondly, this method not only provides an alarm mechanism but also puts forward targeted management suggestions in combination with various emission scenarios. For example, when kinetic energy emissions are abnormal, it is recommended to optimize traffic flow and promote new energy vehicles; when industrial energy emissions are abnormal, measures such as optimizing production scheduling and improving equipment energy efficiency are proposed. These measures can help urban managers quickly adopt response strategies and effectively reduce carbon emissions. In addition, for abnormal energy efficiency emissions, this method proposes means such as optimizing energy dispatching, reducing transmission losses, and promoting intelligent energy-saving technologies to ensure the improvement of urban energy utilization efficiency. For abnormal ecological carbon absorption, this method provides comprehensive ecological optimization measures such as urban greening, air quality improvement, and soil management to strengthen the urban carbon neutralization ability. Overall, this management mechanism not only realizes the dynamic regulation of urban carbon emissions.

[0091] Please refer to Figure 3 , an embodiment of the present invention provides an urban carbon dioxide emission data management system, including:

[0092] A data acquisition unit for acquiring the carbon dioxide emission time series data and carbon dioxide absorption time series data within a set period of the city. The carbon dioxide emission time series data includes energy emission time series data, industrial energy emission time series data, and energy efficiency emission time series data, and preprocessing is performed on each of them;

[0093] A time series analysis unit for comprehensively analyzing the preprocessed carbon dioxide emission time series data and carbon dioxide absorption time series data within a set period of the city to obtain the kinetic energy emission time series index, industrial energy emission time series index, energy efficiency emission time series index, and ecological carbon absorption time series index within a set period of the city;

[0094] A comprehensive analysis unit for comprehensively analyzing the kinetic energy emission time series index, industrial energy emission time series index, energy efficiency emission time series index, and ecological carbon absorption time series index within a set period of the city to obtain the carbon dioxide comprehensive emission index within a set period of the city;

[0095] A judgment and management unit for judging and analyzing the carbon dioxide comprehensive emission index within a set period of the city with a preset emission anomaly range, and when the carbon dioxide comprehensive emission index within a set period of the city is within the preset emission anomaly range, it is regarded as abnormal urban carbon dioxide emissions, and emission anomaly management measures are taken.

[0096] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention.

[0097] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for managing urban carbon dioxide emissions data, characterized in that: The following steps are involved: Acquire carbon dioxide emission time series data and carbon dioxide absorption time series data within a set period of the city, wherein the carbon dioxide emission time series data includes energy emission time series data, industrial energy emission time series data, and energy efficiency emission time series data, and pre-process them respectively; Comprehensively analyze the pre-processed carbon dioxide emission time series data and carbon dioxide absorption time series data within the city setting period, and obtain the kinetic energy emission time series index, industrial energy emission time series index, energy efficiency emission time series index, and ecological carbon absorption time series index within the city setting period, and conduct comprehensive analysis to obtain the comprehensive carbon dioxide emission index within the city setting period; The comprehensive carbon dioxide emission index within the city's set period is judged and analyzed with the preset emission abnormality range. When the comprehensive carbon dioxide emission index within the city's set period is within the preset emission abnormality range, it is regarded as the city's carbon dioxide emissions abnormality, and emission abnormality management measures are taken.

2. The urban carbon dioxide emission data management method according to claim 1, characterized in that: The specific formula for calculating the comprehensive carbon dioxide emission index within a city’s set period is as follows: Among them, Tp, Dp, Gp, Np, and Sx are respectively the comprehensive carbon dioxide emission index, kinetic energy emission time series index, industrial energy emission time series index, energy efficiency emission time series index, and ecological carbon absorption time series index within the set period of the city; λ1, λ2, and λ3 are respectively the kinetic energy emission weight coefficient, industrial energy emission weight coefficient, and energy efficiency emission weight coefficient stored in the database; δ1, δ2, and δ3 are respectively the kinetic energy and industrial energy interaction coefficient, industrial energy and energy efficiency interaction coefficient, and kinetic energy and energy efficiency interaction coefficient stored in the database.

3. The urban carbon dioxide emission data management method according to claim 1, characterized in that: The kinetic energy emission time series data includes the traffic flow values, traffic density values, and the driving speed and fuel consumption values ​​of several traffic roads in several time periods. The specific steps for obtaining the kinetic energy emission time series index within the city setting period are as follows: The traffic flow values ​​and traffic density values ​​of several traffic roads in several time periods within the city's set cycle are analyzed comprehensively to obtain the comprehensive traffic flow index and traffic density comprehensive index of several time periods within the city's set cycle; Comprehensively analyze the driving speed value and fuel consumption value of each vehicle on a number of traffic roads in a number of time periods within a set period of the city, and obtain the driving speed comprehensive index and fuel consumption comprehensive index of a number of time periods within the set period of the city; The road condition index of several time periods within the city setting cycle is obtained, and the comprehensive traffic flow index, traffic density index, driving speed index and fuel consumption index of the corresponding time periods are combined for comprehensive analysis to obtain the kinetic energy emission time series index within the city setting cycle.

4. The urban carbon dioxide emission data management method according to claim 3 is characterized in that: The specific formula for calculating the kinetic energy emission time series index within a city’s set period is as follows: Where Dp is the time series index of kinetic energy emission within the city’s set period, JtL i ,DzK i , JtM i , CsD i ,CrX i They are the comprehensive traffic flow index, road condition index, traffic density index, driving speed index and fuel consumption index of the i-th time period within the set period of the city, ω1, ω2, ω3 and ω4 are the flow adjustment coefficient, road condition adjustment coefficient, density adjustment coefficient and fuel consumption adjustment coefficient stored in the database, ε is the driving speed adjustment factor stored in the database, i=1, 2, 3, …, i0, where i0 is the number of time periods.

5. The urban carbon dioxide emission data management method according to claim 1, characterized in that: The industrial energy emission time series data includes the production volume of each product, the raw material consumption, and the equipment operation time and equipment load rate of each production equipment of several production plants in several time periods. The specific steps for obtaining the industrial energy emission time series index within the city setting period are as follows: Read the production volume and raw material consumption of each product of several production factories in several time periods within the city setting cycle, and conduct comprehensive analysis respectively to obtain the comprehensive index of product production and comprehensive index of raw material consumption in several time periods within the city setting cycle; The equipment operation time and equipment load rate of each production equipment of several production plants in several time periods within the city setting cycle are read and comprehensively analyzed respectively to obtain the equipment operation comprehensive index and equipment load comprehensive index of several time periods within the city setting cycle.

6. The urban carbon dioxide emission data management method according to claim 5, characterized in that: The specific formula for calculating the time series index of industrial energy emissions within a city’s set period is as follows: Among them, Gp is ​​the time series index of industrial energy emissions within the city’s set period, ScL i , SfH i , SyX i ,YcX i They are respectively the comprehensive index of product production, comprehensive index of equipment load, comprehensive index of equipment operation and comprehensive index of raw material consumption in the i-th time period within the set cycle of the city; χ1, χ2, χ3 and χ4 are respectively the production adjustment coefficient, load adjustment coefficient, consumption adjustment coefficient and operation adjustment coefficient stored in the database; η is the operation adjustment factor stored in the database; i=1, 2, 3,…, i0, where i0 is the number of time periods.

7. The urban carbon dioxide emission data management method according to claim 1, characterized in that: The energy efficiency emission time series data includes electricity consumption, gas consumption, energy conversion efficiency, and energy loss rate in several time periods. The specific steps for obtaining the energy efficiency emission time series index within the city setting period are as follows: Obtain the energy conversion efficiency and energy loss rate of several time periods within the city's set cycle and perform preprocessing; The electricity consumption and gas consumption in several time periods within the city's set cycle are comprehensively analyzed together with the energy conversion efficiency and energy loss rate of the corresponding time periods after preprocessing to obtain the energy efficiency emission time series index within the city's set cycle.

8. The urban carbon dioxide emission data management method according to claim 1, characterized in that: The carbon dioxide absorption time series data includes the green coverage rate, air quality index, soil moisture, vegetation growth index, and vegetation density of each green area in several time periods. The specific steps for obtaining the ecological carbon absorption time series index within the city setting period are as follows: Read the soil moisture, vegetation growth index, and vegetation density of each greening area in several time periods within the city's set cycle, and conduct comprehensive analysis to obtain the soil moisture comprehensive index, vegetation growth index comprehensive index, and vegetation density comprehensive index for several time periods within the city's set cycle; The green coverage rate and air quality index of several time periods within the city's set cycle are comprehensively analyzed together with the soil moisture comprehensive index, vegetation growth index comprehensive index, and vegetation density comprehensive index of the corresponding time periods to obtain the ecological carbon absorption time series index within the city's set cycle.

9. The urban carbon dioxide emission data management method according to claim 1, characterized in that: The specific steps for taking measures to manage abnormal emissions are as follows: The kinetic energy emission time series index, industrial energy emission time series index, energy efficiency emission time series index, and ecological carbon absorption time series index within the city's set period are respectively compared with the preset kinetic energy emission abnormal interval, industrial energy emission abnormal interval, energy efficiency emission abnormal interval, and ecological carbon absorption abnormal interval; If the kinetic energy emission time series index within the city's set period is within the preset kinetic energy emission abnormal range, it will be regarded as kinetic energy emission abnormality, and a kinetic energy emission abnormality alarm will be sent to relevant staff; If the time series index of industrial energy emissions within the city's set period is within the preset industrial energy emissions abnormal range, it will be regarded as industrial energy emissions abnormal, and an industrial energy emissions abnormality alarm will be sent to relevant staff; If the energy efficiency emission time series index within the city's set period is within the preset energy efficiency emission abnormal range, it will be regarded as energy efficiency emission abnormality, and an energy efficiency emission abnormality alarm will be sent to relevant staff; If the ecological carbon absorption time series index within the city's set period is within the preset ecological carbon absorption abnormal range, it will be regarded as an ecological carbon absorption abnormality, and an ecological carbon absorption abnormality alarm will be sent to relevant staff.

10. A city carbon dioxide emission data management system, applying the city carbon dioxide emission data management method according to any one of claims 1 to 9, characterized in that: include: A data acquisition unit, used to acquire carbon dioxide emission time series data and carbon dioxide absorption time series data within a set period of the city, wherein the carbon dioxide emission time series data includes energy emission time series data, industrial energy emission time series data, and energy efficiency emission time series data, and pre-process them respectively; A time series analysis unit is used to comprehensively analyze the pre-processed carbon dioxide emission time series data and carbon dioxide absorption time series data within the city setting period, and obtain the kinetic energy emission time series index, industrial energy emission time series index, energy efficiency emission time series index, and ecological carbon absorption time series index within the city setting period; A comprehensive analysis unit is used to conduct a comprehensive analysis of the kinetic energy emission time series index, industrial energy emission time series index, energy efficiency emission time series index, and ecological carbon absorption time series index within a city setting period to obtain a comprehensive carbon dioxide emission index within the city setting period; The judgment management unit is used to judge and analyze the comprehensive carbon dioxide emission index within the city's set period and the preset emission abnormality interval. When the comprehensive carbon dioxide emission index within the city's set period is within the preset emission abnormality interval, it is regarded as the city's carbon dioxide emission abnormality, and emission abnormality management measures are taken.

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