Carbon emission measuring and calculating method and system based on urban public transport

Abstract

The invention relates to a carbon emission measuring and calculating method and system based on urban public transport. The method comprises the following steps: step 1, constructing an urban public transport carbon emission calculation model; step 2, collecting data related to urban public transport carbon emission; step 3, acquiring public database data; step 4, data cleaning and preprocessing; 5, calculating the carbon emission of the urban public transport: calculating the carbon emission in the urban public transport system according to the data in the step 4, and outputting specific emission data; step 6, carbon emission data aggregation and multi-dimensional analysis; step 7, carbon emission trend prediction; and step 8, strategy optimization. According to the method, the accuracy of carbon emission calculation is improved, the carbon emission condition in the public traffic operation process is reflected in real time, and two scientific carbon emission data supports are provided for traffic management departments, enterprises and the public.

Description

Background Art

[0002] As the global climate change problem becomes increasingly serious, reducing greenhouse gas emissions has become a topic of common concern around the world. As an important way for people to travel in their daily lives, urban public transportation has a direct impact on the city's environmental quality and the control of climate change. However, most urban public transportation carbon emission monitoring and evaluation still rely on manual statistics and rough estimates, lacking accurate calculation models and system support, resulting in low efficiency in carbon emission monitoring and difficulty in providing strong data support for public transportation optimization decisions. Of course, there are also some patented technologies that study carbon emissions from urban transportation, such as:

[0003] Among the existing patents, patent number CN116663773A discloses "a method, system, terminal and storage medium for calculating carbon emissions from urban passenger transportation". Combining the travel characteristics of small cars, rail transit, conventional buses and taxis in urban transportation, and taking into account the total travel volume, travel structure of various modes, travel distance and other travel characteristics, as well as the energy structure of various modes, a comprehensive and relatively high-precision urban transportation carbon emission calculation method is constructed. This patent calculates various types of transportation in urban passenger transportation, including cars, taxis, buses, rail transit, etc., and cannot accurately calculate carbon emission data for specific scenarios of urban public transportation.

[0004] Among the existing patents, patent number CN115759545A discloses "a carbon emission accounting system based on public transportation". The carbon emission accounting system based on public transportation can calculate the carbon emissions of different public transportations and generate written reports, realize the simplicity of carbon emission accounting, provide carbon emission management and carbon trading, provide decision-making support for enterprises, industries, and management departments, and can simulate and warn carbon emission trends, realize intelligent management, and improve management efficiency. The patent describes an information system composed of modules such as carbon emission accounting, information collection, carbon emission analysis, carbon emission management, and carbon trading based on public transportation, but does not describe the method and calculation rules of carbon emission accounting.

[0005] Among the existing patents, patent number CN114707774A discloses "a method and device for predicting carbon emissions based on transportation". This patent is a method for predicting carbon emissions based on transportation. By presetting a transportation carbon emission factor library, the carbon emission factors are calculated by mass balance method to obtain transportation carbon emission factors; the total amount of transportation carbon emissions is determined based on the transportation carbon emission factors; the total amount of transportation carbon emissions is stabilized and corrected to obtain the optimal decision; the transportation modes involved in this patent include internal transportation, external transportation, passenger transportation and freight transportation, and the algorithm for calculating carbon emissions depends on geographical boundary factors, transportation carbon emission chain factors, transportation modes and transportation activity characteristic factors, etc., which are significantly different.

[0006] However, from the above technical analysis, it can be seen that the existing carbon emission calculation methods and monitoring systems still have certain defects, mainly reflected in the lack of carbon emission accounting methods and calculation rules, the inability to accurately calculate carbon emission data for specific scenarios of urban public transportation, and insufficient adaptability to complex traffic environments. These defects may lead to inaccurate carbon emission assessment results, thereby affecting the effectiveness of policy formulation and environmental protection measures. "

[0007] Therefore, there is an urgent need for a carbon emission calculation method and system based on urban public transportation, which can accurately and real-time calculate the carbon emissions during public transportation operations and provide a scientific basis for the optimization and emission reduction of public transportation. Summary of the invention

[0008] The purpose of the present invention is to provide a carbon emission calculation method and system based on urban public transportation, aiming to improve the accuracy of carbon emission calculation, reflect the carbon emission situation in the process of public transportation operation in real time, and provide scientific carbon emission data support for transportation management departments, enterprises and the public.

[0009] The technical solution adopted by the present invention is:

[0010] A method for calculating carbon emissions based on urban public transportation includes the following steps:

[0011] Step 1: Build a carbon emission calculation model for urban public transportation;

[0012] Step 2: Collect data related to carbon emissions from urban public transportation;

[0013] Step 3, obtaining public database data;

[0014] Step 4: Data cleaning and preprocessing;

[0015] Step 5: Calculate carbon emissions from urban public transportation: Based on the data from step 4, calculate the carbon emissions in the urban public transportation system and output specific emission data;

[0016] Step 6: Carbon emission data aggregation and multi-dimensional analysis;

[0017] Step 7: Carbon emission trend prediction;

[0018] Step 8: Strategy optimization: Based on multi-dimensional analysis of carbon emissions and prediction of carbon emission trends, provide operational optimization suggestions for urban public transportation.

[0019] Preferably, the specific method of step one is as follows:

[0020] Step 1: Calculate the average emission factor E per person-kilometer of registered electric bus travel in the city PKM,p , the calculation formula is as follows:

[0021]

[0022] Where:

[0023] E PKM,p —Average passenger-kilometer emission factor for electric bus travel;

[0024] E cc,j ——Energy consumption of electric buses;

[0025] E cr,j —Average charging efficiency of electric buses;

[0026] EF elec,j —Electricity emission factors;

[0027] —Average start-stop energy consumption of electric bus j;

[0028] —Average number of starts and stops of electric bus j;

[0029] Q j —Annual passenger turnover of electric bus j;

[0030] i—Number of electric bus trips.

[0031] Step 2: Calculate the carbon emission equivalent PE of the city's registered electric buses in kilograms of carbon dioxide (kgCO2) using the following formula:

[0032]

[0033] Where:

[0034] PE—emissions from electric bus trips;

[0035] E PKM,i,j,p —Emission factor of the i-th trip-kilometer of electric bus j;

[0036] PDi,j — passenger’s ith trip mileage;

[0037] P i,j — passenger volume of electric bus j on its i-th trip;

[0038] i—number of trips of electric bus j;

[0039] j is the identification number of the electric bus;

[0040] —Average start-stop energy consumption of electric bus j for the i-th trip;

[0041] k i,j —Number of starts and stops during the i-th trip of electric bus j;

[0042] EF elec,j — Urban electricity emission factors;

[0043] E crp,j —Electric bus charging efficiency, as a function of temperature.

[0044] Preferably, the data related to urban public transportation carbon emissions in step 2 includes vehicle basic data, vehicle energy data, vehicle operation data and vehicle charging data;

[0045] Among them, the basic vehicle data includes vehicle specifications and models, vehicle weight, load capacity, battery capacity, discharge depth, battery cycle number, battery life, pure electric range, power consumption benchmark per 100 kilometers and passenger capacity;

[0046] Vehicle energy data includes vehicle status, charging status, operating mode, vehicle speed, accumulated mileage, voltage and current, battery current storage capacity SOC, energy status, positioning status, and alarm data;

[0047] Driving operation data includes driving route, driving route length, driving frequency, current location, current passenger capacity, historical passenger capacity, historical working days, historical power consumption, and historical mileage;

[0048] Vehicle charging data includes charging station, charging amount, current mileage and charging efficiency.

[0049] Preferably, the public database data includes urban electricity emission factor data and the average one-way distance of urban public transportation.

[0050] Preferably, the data cleaning and preprocessing in step 4 includes:

[0051] Remove outliers: Remove noise or outliers from the original collected data;

[0052] Data filling: For missing or incomplete data, interpolation, mean filling or regression analysis methods are used to fill in the missing or incomplete data to ensure the integrity of the data;

[0053] Data time synchronization: Data from different sources have time differences. Synchronizing data timestamps ensures consistency of data during the analysis process.

[0054] Preferably, step five, calculating the carbon emissions in the urban public transportation system includes calculating the carbon emissions data for each shift, obtaining the cumulative carbon emissions data for the day and the total carbon emissions data for the urban public transportation enterprise for one year.

[0055] Preferably, the carbon emission data aggregation and multi-dimensional analysis in step 6 includes:

[0056] Carbon emission data and classification analysis: Classify and summarize carbon emission data by region, vehicle type, time period, and route;

[0057] Carbon emission hotspot analysis: Through visualization, areas or road sections with high carbon emissions can be identified, thereby providing key focus areas for traffic management and environmental protection departments to take improvement measures;

[0058] Analysis of the impact of climate and seasonal factors: By analyzing carbon emission data, the impact of climate change on carbon emissions can be identified.

[0059] Preferably, in step seven, carbon emission trend prediction: using time series analysis technology to predict future emission trends based on historical carbon emission data.

[0060] A carbon emission measurement system based on urban public transportation, comprising:

[0061] Model building module, used to build a carbon emission calculation model for urban public transportation;

[0062] The data collection module related to carbon emissions of urban public transportation is used to obtain data related to carbon emissions of urban public transportation, including vehicle basic data, vehicle energy data, driving operation data and vehicle charging data;

[0063] Public database data acquisition module, used to obtain urban electricity emission factor data and the average one-way distance of urban public transportation;

[0064] Data cleaning and preprocessing module, used to preprocess the raw data;

[0065] An urban public transport carbon emission calculation module, used to calculate the carbon emission in the urban public transport system based on the carbon emission calculation parameters;

[0066] Carbon emission data aggregation and multi-dimensional analysis module: used to classify and aggregate carbon emission data and conduct multi-dimensional analysis;

[0067] Carbon emission trend prediction module, used to predict future emission trends;

[0068] Strategy Optimization Module: Used to provide operational optimization suggestions for urban public transportation.

[0069] The present invention has at least the following beneficial effects:

[0070] 1. The carbon emission calculation method of the present invention improves the accuracy of carbon emission calculation: when calculating vehicle carbon emissions, existing patents combine fuel vehicles, new energy cars, public transportation, and even rail transportation into one method, and the accuracy of the calculation is difficult to guarantee; the present invention provides more scientific and accurate carbon emission data support for urban public transportation through precise data collection and a comprehensive emission calculation model, avoiding the errors caused by traditional manual estimation.

[0071] 2. Improved the real-time calculation of carbon emissions. Most existing patents are based on a unified average value for calculation, which requires a time period. This method collects real-time operation data of vehicles and can calculate carbon emissions in real time based on the data, helping transportation departments to promptly discover and deal with high emission problems and optimize transportation operations.

[0072] 3. The present invention collects basic vehicle data, energy data, operation data, charging data, etc. in real time, and can realize real-time and accurate output of vehicle carbon emission data through a rigorous calculation process, thereby solving the problem of real-time calculation accuracy of carbon emission data of ground public transportation. At the same time, the present invention conducts in-depth analysis of carbon emission data, providing powerful data support for competent authorities to formulate low-carbon policies and promote green travel, and providing the public with more environmentally friendly travel options.

[0073] 4. The present invention not only optimizes the existing operating model through systematic calculation and data analysis technology, but also can predict and schedule according to future travel trends, thereby improving the carbon emission management level of public transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0075] Figure 1 A structural block diagram of a carbon emission calculation system based on urban public transportation provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0076] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0077] As a possible embodiment of the present invention, the carbon emission calculation method based on urban public transportation includes the following steps:

[0078] Step 1: Build a carbon emission calculation model for urban public transportation. The specific method is as follows:

[0079] Step 1: Calculate the average emission factor E per person-kilometer of registered electric bus travel in the city PKM,p , the unit is kilograms of carbon dioxide per person kilometer (kgCO2 / PKM), and the calculation formula is as follows:

[0080]

[0081] Where:

[0082] E PKM,p —Average emission factor per person-kilometer for electric bus travel, in kilograms of carbon dioxide per person-kilometer (kgCO2 / PKM);

[0083] E cc,j ——Energy consumption of electric bus j, in kilowatt-hours (kW h);

[0084] E cr,j —Average charging efficiency of electric buses, expressed as a percentage;

[0085] EF elec,j —Electricity emission factor, in kilograms of carbon dioxide per kilowatt-hour (kgCO2 / kWh);

[0086] —Average start-stop energy consumption of electric bus j, in kilowatt-hours (kW·h);

[0087] —Average number of starts and stops of electric bus j, in times;

[0088] Q j —Annual passenger turnover of electric bus j, in person-kilometers (PKM);

[0089] i is the number of trips of the electric bus, in times; j is the identification number of the electric bus;

[0090] Step 2: Calculate the carbon emission equivalent PE of the city's registered electric buses in kilograms of carbon dioxide (kgCO2) using the following formula:

[0091]

[0092] Where:

[0093] PE—electric bus travel emissions, in kilograms of carbon dioxide (kgCO2);

[0094] E PKM,i,j,p —Emission factor of the i-th trip passenger-kilometer of electric bus j, in kilograms of carbon dioxide per passenger-kilometer (kgCO2 / PKM);

[0095] PD i,j —The passenger’s trip mileage for the i-th time (corrected equivalent mileage), in kilometers (km);

[0096] P i,j —The passenger volume of the i-th trip of electric bus j, in person-times;

[0097] i—number of trips of electric bus j, in times;

[0098] j—identification number of the electric bus;

[0099] —The average start-stop energy consumption of electric bus j during its i-th trip, in kilowatt-hours (kW h);

[0100] k i,j —Number of starts and stops during the i-th trip of electric bus j, in times;

[0101] EF elec,j — Urban electricity emission factor, in kilograms of carbon dioxide per kilowatt-hour (kgCO2 / kW·h);

[0102] E crp,j —Electric bus charging efficiency, as a function of temperature, expressed in percentage.

[0103] Among them, the passenger's i-th trip mileage PD i,j The calculation process of (corrected equivalent mileage) is as follows:

[0104] The actual travel distance D of the passenger i Conduct preliminary standardization, taking into account basic factors such as vehicle type and road conditions:

[0105] D i,standard =D i ×f traffic ×f road ×f driving;

[0106] in:

[0107] f traffic — Traffic condition correction factor (such as congestion), usually > 1 (higher in congestion);

[0108] f road —Road condition correction factors (such as highway, urban area, mountainous area), obtained based on empirical data;

[0109] f driving — Driving behavior correction factors (such as sudden braking, high-speed driving).

[0110] Carbon emission correction factor f emission Calculation

[0111] Modify the carbon emission factor according to the type of transportation (such as electric vehicles, gasoline vehicles, buses, etc.).

[0112] For electric vehicles: emission =Electricity consumption×grid carbon intensity.

[0113] Load correction factor f load Calculation

[0114] According to the actual load L i To make a correction, the greater the load, the shorter the equivalent mileage will generally be.

[0115] f load =1+k×L i ;

[0116] Among them, k is an empirical correction coefficient; it is a coefficient determined according to specific circumstances (such as vehicle type, fuel consumption characteristics, etc.), and is usually a positive value, indicating that the heavier the load, the greater the energy efficiency correction coefficient.

[0117] Actual load L i It usually refers to the actual load of a vehicle during a trip, including the number of passengers, the weight of cargo, etc. The definition and calculation method of load may vary for different types of vehicles.

[0118] Maximum vehicle load capacity

[0119] For some vehicles, the load can also refer to the vehicle's maximum design load capacity. In particular, for public transportation vehicles (such as buses and trains), the load is sometimes compared to its design load capacity to assess the ratio of the current load to the maximum design load.

[0120] Calculation method: L i = Actual load (number of passengers or weight) / maximum carrying capacity (number of passengers or weight).

[0121] For example, if the maximum carrying capacity of a bus is 60 people, and there are 45 people on board, and k = 0.1, then: L i =45 / 60=0.75;

[0122] f load =1+k×L i =1+0.1×0.75=1.075;

[0123] This method can express the relative degree of vehicle load and is usually used to evaluate the operating efficiency and comfort of a vehicle over a period of time.

[0124] In modern intelligent transportation systems, load data can also be dynamically acquired through real-time monitoring technologies (such as vehicle-mounted sensors, smart ticketing systems, etc.). For example, using smart card swiping systems and vehicle-mounted monitoring equipment (such as GPS, sensors, etc.), the number of passengers or vehicle weight in a certain period of time can be calculated and monitored in real time, thereby obtaining accurate load data.

[0125] The final calculation of equivalent mileage is based on the above factors, and the equivalent mileage PD i,j It can be obtained by the following formula:

[0126]

[0127] Step 2: Collect data related to carbon emissions from urban public transportation; collect basic vehicle data, vehicle energy data, driving operation data, and vehicle charging data of public transportation vehicles in real time through sensors, GPS, vehicle monitoring systems and other equipment;

[0128] Basic vehicle data includes vehicle specifications and models, vehicle weight, load capacity, battery capacity, discharge depth, battery cycle number, battery life, pure electric range, power consumption per 100 kilometers and passenger capacity;

[0129] Vehicle energy data includes vehicle status, charging status, operating mode, vehicle speed, accumulated mileage, voltage and current, battery current storage capacity SOC, energy status, positioning status, alarm data, etc.

[0130] Driving operation data includes driving route, driving route length, driving frequency, current location, current passenger capacity, historical passenger capacity, historical working days, historical power consumption, and historical mileage;

[0131] Vehicle charging data includes charging station, charging amount, current mileage and charging efficiency.

[0132] In order to obtain more accurate data related to carbon emissions, the system can further deepen the data collection module and enhance support for various sensors and devices, such as:

[0133] Multi-level data collection: In addition to traditional GPS, power consumption, vehicle speed and other data, the system can integrate more sensors, such as: vehicle-mounted environmental monitoring sensors, which can monitor the impact of external climate change on carbon emissions in real time by combining data such as air quality, temperature and humidity inside and outside the vehicle.

[0134] Deploy traffic flow sensors: Deploy intelligent traffic sensors at key roads, intersections and other locations to capture traffic flow and congestion, and provide more accurate upstream and downstream data for carbon emission calculations.

[0135] Electric bus and hydrogen bus monitoring: The carbon emission characteristics of electric buses and hydrogen buses are different from those of traditional fossil fuel vehicles. Therefore, a dedicated monitoring system can be used to track their energy efficiency and emission characteristics in real time to ensure the accuracy of emission calculations.

[0136] Integration of big data and IoT technology: Various hardware facilities in the urban public transportation system, including vehicles, stations, traffic lights, road monitoring cameras, etc., are integrated through IoT technology to ensure real-time updating and efficient transmission of various data, and enhance the overall data acquisition capability of the system.

[0137] Step three: Obtain public database data such as urban electricity emission factor data and average one-way distance of urban public transportation.

[0138] Step 4: Data cleaning and preprocessing:

[0139] Removing outliers: The original collected data may contain some noise or outliers, such as data deviation caused by sensor failure. The data analysis module will first clean the data and remove the data that does not conform to the norm.

[0140] Data filling: For missing or incomplete data, the analysis module can use interpolation, mean filling, regression analysis and other methods to fill in the missing or incomplete data to ensure the integrity of the data.

[0141] Time synchronization: Since data from different sources (such as traffic management centers, vehicle-mounted equipment, etc.) may have time differences, the system needs to synchronize and align data timestamps to ensure consistency in the data during the analysis process.

[0142] Step 5, calculate the carbon emissions of urban public transportation: Based on the calculation model of step 1 and the data of step 4, calculate the carbon emissions of each shift in the urban public transportation system, obtain the cumulative carbon emissions data for the day, and calculate the total carbon emissions data of the company for one year, and output the specific emission data.

[0143] Step 6: Carbon emission data aggregation and multi-dimensional analysis, including:

[0144] Carbon emission data and classification analysis: Classify and summarize carbon emission data by region, vehicle type, time period, and route. For example, the system can count the emissions of different types of public transportation in a certain area, or analyze the difference in carbon emissions between peak hours and non-peak hours;

[0145] Carbon emission hotspot analysis: Through map visualization, areas or road sections with high carbon emissions can be identified, thereby providing key focus areas for traffic management and environmental protection departments to take improvement measures;

[0146] Analysis of the impact of climate and seasonal factors: By analyzing carbon emission data, the impact of climate change (such as temperature and humidity changes) on carbon emissions can be identified. For example, in cold seasons, the energy efficiency of electric buses may be lower, resulting in increased carbon emissions, and the system can predict and remind.

[0147] Energy efficiency assessment: In addition to carbon emissions analysis, the system can also evaluate the energy efficiency of different modes of transportation. For example, which type of vehicle has lower carbon emissions and higher energy efficiency among different types of vehicles on the same route, thereby providing optimization suggestions for public transportation companies.

[0148] Step 7. Carbon emission trend prediction: Prediction model based on time series analysis: The data analysis module can use time series analysis technology (such as ARIMA model, LATM deep learning model, XGBoost algorithm, etc.) to predict future emission trends based on historical carbon emission data, helping governments and operating agencies to make environmental protection plans in advance.

[0149] Step 8: Carbon emissions reporting and visualization:

[0150] Dynamic report generation: Based on the real-time data and historical trends of carbon emissions, the analysis module will generate a detailed emission report, which can display carbon emission data in different dimensions (by time, region, vehicle type, etc.).

[0151] Charts and visualization tools: Through data visualization, such as bar charts, line charts, heat maps, pie charts, etc., users can intuitively understand the carbon emissions. For example, it can show the carbon emissions comparison of different models, emission trends in different time periods, and emission differences between regions.

[0152] Alarm and warning function: When carbon emissions exceed the set threshold, the system can trigger an alarm to remind users to take timely measures. The alarm mechanism can be based on the excessive emissions of a single vehicle, or based on the total emissions of the entire fleet or a certain area exceeding the limit.

[0153] Step 9, strategy optimization: Based on multi-dimensional analysis of carbon emissions and prediction of carbon emission trends, provide operational optimization suggestions for urban public transportation.

[0154] This carbon emission measurement system based on urban public transportation includes:

[0155] Model building module, used to build urban public transportation carbon emission calculation model.

[0156] The data collection module related to carbon emissions from urban public transportation is used to obtain data related to carbon emissions from urban public transportation, including vehicle basic data, vehicle energy data, driving operation data and vehicle charging data.

[0157] The public database data acquisition module is used to obtain urban electricity emission factor data and the average one-way distance of urban public transportation.

[0158] Data cleaning and preprocessing module is used to preprocess the raw data.

[0159] An urban public transport carbon emission calculation module, used to calculate the carbon emission in the urban public transport system based on the carbon emission calculation parameters;

[0160] Carbon emission data aggregation and multi-dimensional analysis module: used to classify and aggregate carbon emission data and conduct multi-dimensional analysis;

[0161] The carbon emission trend prediction module is used to predict future emission trends.

[0162] Interface display module: provides users with a graphical interface to facilitate users to view real-time carbon emission data, historical emission trends, carbon emission comparison of transportation tools and other information, and supports alarm function to prompt when carbon emissions exceed the set threshold.

[0163] Strategy Optimization Module: Used to provide operational optimization suggestions for urban public transportation: Through in-depth analysis of carbon emission data, support government departments to formulate more reasonable environmental protection policies. For example, some regions may need to raise the emission standards of public transportation vehicles, or promote electric buses to reduce overall emissions. Based on the multi-dimensional analysis of carbon emissions, provide operational optimization suggestions for public transportation companies, such as reducing carbon emissions by adjusting departure frequency, optimizing route planning, and choosing lower-emission vehicles.

[0164] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0165] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0166] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0167] The electronic device according to this embodiment of the present invention is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0168] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one storage device mentioned above, and a bus connecting different system components (including storage devices and processors).

[0169] The storage stores program codes, which can be executed by the processor, so that the processor executes the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0170] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).

[0171] The storage may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0172] The bus may represent one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0173] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device (e.g., routers, modems, etc.) that enables the electronic device to communicate with one or more other computing devices. Such communication may be performed through an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter. The network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0174] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0175] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0176] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0177] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0178] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0179] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Python, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0180] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0181] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0182] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for calculating carbon emissions based on urban public transportation, characterized in that: The steps include: Step 1: Build a carbon emission calculation model for urban public transportation; Step 2: Collect data related to carbon emissions from urban public transportation; Step 3, obtaining public database data; Step 4: Data cleaning and preprocessing; Step 5: Calculate carbon emissions from urban public transportation: Based on the data from step 4, calculate the carbon emissions in the urban public transportation system and output specific emission data; Step 6: Carbon emission data aggregation and multi-dimensional analysis; Step 7: Carbon emission trend prediction; Step 8: Strategy optimization: Based on multi-dimensional analysis of carbon emissions and prediction of carbon emission trends, provide operational optimization suggestions for urban public transportation.

2. The carbon emission calculation method based on urban public transportation according to claim 1 is characterized in that: The specific method of step one is as follows: Step 1: Calculate the average emission factor E per person-kilometer of registered electric bus travel in the city PKM,p , the calculation formula is as follows: Where: E PKM,p —Average passenger-kilometer emission factor for electric bus travel; E cc,j ——Energy consumption of electric buses; E cr,j —Average charging efficiency of electric buses; EF elec,j —Electricity emission factors; —Average start-stop energy consumption of electric bus j; —Average number of starts and stops of electric bus j; Q j —Annual passenger turnover of electric bus j; i—number of electric bus trips; Step 2: Calculate the carbon emission equivalent PE of the city’s registered electric buses using the following formula: Where: PE—emissions from electric bus trips; E PKM,i,j,p —Emission factor of the i-th trip-kilometer of electric bus j; PD i,j — passenger’s ith trip mileage; P i,j — passenger volume of electric bus j on its i-th trip; i—number of trips of electric bus j; j—identification number of the electric bus; —Average start-stop energy consumption of electric bus j for the i-th trip; k i,j —Number of starts and stops during the i-th trip of electric bus j; EF elec,j — Urban electricity emission factors; E crp,j —Electric bus charging efficiency, as a function of temperature.

3. The carbon emission calculation method based on urban public transportation according to claim 1 is characterized in that: The data related to urban public transportation carbon emissions in step 2 include vehicle basic data, vehicle energy data, driving operation data and vehicle charging data; Among them, the basic vehicle data includes vehicle specifications and models, vehicle weight, load capacity, battery capacity, discharge depth, battery cycle number, battery life, pure electric range, power consumption benchmark per 100 kilometers and passenger capacity; Vehicle energy data includes vehicle status, charging status, operating mode, vehicle speed, accumulated mileage, voltage and current, battery current storage capacity SOC, energy status, positioning status, alarm data, etc. Driving operation data includes driving route, driving route length, driving frequency, current location, current passenger capacity, historical passenger capacity, historical working days, historical power consumption, and historical mileage; Vehicle charging data includes charging station, charging amount, current mileage and charging efficiency.

4. The carbon emission calculation method based on urban public transportation according to claim 1 is characterized in that: The public database data include urban electricity emission factor data and the average one-way distance of urban public transportation.

5. The carbon emission calculation method based on urban public transportation according to claim 1 is characterized in that: Data cleaning and preprocessing in step 4 include: Remove outliers: Remove noise or outliers from the original collected data; Data filling: For missing or incomplete data, interpolation, mean filling or regression analysis methods are used to fill in the missing or incomplete data to ensure the integrity of the data; Data time synchronization: Data from different sources have time differences. Synchronizing data timestamps ensures consistency of data during the analysis process.

6. The carbon emission calculation method based on urban public transportation according to claim 1 is characterized in that: Step five, calculate the carbon emissions in the urban public transportation system, including calculating the carbon emissions data for each shift, obtaining the cumulative carbon emissions data for the day and the total carbon emissions data for the city's public transportation enterprises in one year.

7. The carbon emission calculation method based on urban public transportation according to claim 1 is characterized by: The carbon emission data aggregation and multi-dimensional analysis in step 6 include: Carbon emission data and classification analysis: Classify and summarize carbon emission data by region, vehicle type, time period, and route; Carbon emission hotspot analysis: Through visualization, areas or road sections with high carbon emissions can be identified, thereby providing key focus areas for traffic management and environmental protection departments to take improvement measures; Analysis of the impact of climate and seasonal factors: By analyzing carbon emission data, the impact of climate change on carbon emissions can be identified.

8. The carbon emission calculation method based on urban public transportation according to claim 1 is characterized by: Step 7: Carbon emission trend prediction: Use time series analysis technology to predict future emission trends based on historical carbon emission data.

9. A carbon emission calculation system based on urban public transportation, characterized in that: include: Model building module, used to build a carbon emission calculation model for urban public transportation; The data collection module related to carbon emissions of urban public transportation is used to obtain data related to carbon emissions of urban public transportation, including vehicle basic data, vehicle energy data, driving operation data and vehicle charging data; Public database data acquisition module, used to obtain urban electricity emission factor data and the average one-way distance of urban public transportation; Data cleaning and preprocessing module, used to preprocess the raw data; An urban public transport carbon emission calculation module, used to calculate the carbon emission in the urban public transport system based on the carbon emission calculation parameters; Carbon emission data aggregation and multi-dimensional analysis module: used to classify and aggregate carbon emission data and conduct multi-dimensional analysis; Carbon emission trend prediction module, used to predict future emission trends; Strategy Optimization Module: Used to provide operational optimization suggestions for urban public transportation.

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