Method for calculating carbon emissions of a vehicle when passing through a road

Abstract

The application provides a method for calculating carbon emissions when a vehicle passes through a road, including: collecting vehicle passing data, combining preset carbon emission calculation rules, calculating the carbon emissions of the vehicle on the road section or intersection, and storing the calculation results in the data storage unit; based on the vehicle passing data and environmental parameters, generating query results and analysis reports, the query results including carbon emission information of the vehicle in a time period and area, and the analysis reports being used for optimizing traffic flow; through management and protection of carbon emission data, ensuring the integrity and traceability of the data, and providing data backup and recovery mechanisms; using data analysis techniques to analyze the trends and compare the carbon emission data, generating decision suggestions, including traffic signal optimization and route planning schemes.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for calculating carbon emissions when a vehicle passes through a road. Background Technology

[0002] Urban traffic management, as a crucial component of modern urban sustainable development, is directly related to environmental protection and the improvement of residents' quality of life. Carbon emissions in the transportation sector are not only key to environmental governance but also a core consideration in urban planning and policy-making. How to effectively monitor and control carbon emissions in complex traffic environments has become a major challenge that urban managers urgently need to address.

[0003] Currently, although many cities have attempted to assess carbon emissions through traffic data collection and analysis, these methods often have significant limitations. Many schemes rely on limited fixed monitoring points for data acquisition, making it difficult to cover the dynamically changing traffic networks in cities, resulting in an incomplete understanding of actual emissions. Furthermore, existing methods often lack detailed consideration when dealing with individual vehicle differences, failing to accurately reflect the emission characteristics of different types of vehicles on different road sections, thus affecting the reliability of the analysis results.

[0004] A deeper technical challenge lies in achieving real-time and accurate acquisition and matching of vehicle traffic information. Vehicles' operating states on urban roads are constantly changing, involving multiple factors such as speed, road conditions, and vehicle type. These factors interact and collectively determine the accuracy of carbon emission calculations. If specific traffic data for each vehicle cannot be captured in a timely manner, it is difficult to accurately determine its emissions at a particular time and on a specific road segment. For example, during peak hours, the emissions of a heavy truck in a congested area may be far higher than those of a regular passenger car. However, if data collection is not timely or vehicle type identification is inaccurate, its environmental impact may be underestimated. This lag in information acquisition and matching directly leads to biases in carbon emission assessments, thus affecting scientific decision-making in traffic management.

[0005] Therefore, how to obtain vehicle traffic information in real time and accurately calculate emissions based on vehicle type characteristics in a dynamic and complex traffic environment has become a key issue in urban traffic carbon emission management. Summary of the Invention

[0006] This invention provides a method for calculating carbon emissions when a vehicle passes through a road, mainly including:

[0007] By collecting vehicle traffic data and combining it with preset carbon emission calculation rules, the carbon emissions of vehicles at road segments or intersections are calculated, and the calculation results are stored in a data storage unit. Based on vehicle traffic data and environmental parameters, query results and analysis reports are generated. The query results include carbon emission information of vehicles in time periods and regions, and the analysis reports are used to optimize traffic flow. By managing and protecting carbon emission data, the integrity and traceability of the data are ensured, and data backup and recovery mechanisms are provided. Data analysis technology is used to perform trend analysis and comparative analysis on carbon emission data to generate decision-making suggestions, including traffic signal optimization and route planning schemes. Furthermore, the step of calculating vehicle carbon emissions at road segments or intersections by collecting vehicle traffic data and combining it with preset carbon emission calculation rules includes: obtaining vehicle traffic data through interface with traffic monitoring equipment, the vehicle traffic data including vehicle identification, passage time, driving speed, and location information, and matching the vehicle traffic data with preset carbon emission calculation rules; applying corresponding carbon emission calculation rules according to vehicle type and driving status, the driving status including waiting status and congestion status, determining carbon emissions by calculating the vehicle's non-stop running time and speed parameters under different states, and updating the results to the data storage unit. Furthermore, the step of generating query results and analysis reports based on vehicle traffic data and environmental parameters includes: supporting multi-dimensional queries of carbon emission data, the query conditions including time range, vehicle identification, intersection number, and checkpoint number, generating query results including vehicle passage time, average speed, fuel consumption type, and carbon emissions; and generating a trend analysis report by summarizing and comparing carbon emission data for different regions and time periods, the trend analysis report showing the changing patterns of carbon emissions and proposing governance suggestions in conjunction with regional traffic flow data. Furthermore, the application of corresponding carbon emission calculation rules based on vehicle type and driving status includes: applying corresponding calculation parameters for different vehicle types when calculating carbon emissions, including reference displacement, fuel consumption data, speed correction coefficient, and carbon emission coefficient, and reducing calculation errors through a data verification mechanism; supporting custom query conditions when generating query results, including time period, regional range, and vehicle type, and presenting the query results in a visual manner. Furthermore, the management and protection of carbon emission data ensures data integrity and traceability, and provides data backup and recovery mechanisms, including: constructing data storage units to ensure carbon emission data is not lost within a time frame, supporting data classification management and regular backups, and providing a data recovery mechanism; applying data encryption and access control technologies during data management to prevent data leakage and tampering, with access control technologies allocating data access scope according to user permissions and logging and auditing operational behaviors.Furthermore, the use of data analysis technology to perform trend analysis and comparative analysis on carbon emission data to generate decision-making recommendations includes: mining historical records of carbon emission data to identify high-emission areas and time periods, and generating traffic signal timing recommendations, which aim to reduce vehicle waiting time and congestion; combining traffic network layout to perform spatial analysis of carbon emission data and propose traffic route planning recommendations, which include optimizing traffic flow distribution on main roads and promoting green travel modes.

[0008] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0009] This invention discloses a method for carbon emission analysis in urban transportation. By accurately collecting and processing vehicle data, it addresses the challenges of difficult carbon emission monitoring, complex data analysis, and insufficient decision support in urban traffic management. These problems are interconnected, forming a logically related challenge: how to achieve real-time, accurate calculation of carbon emissions and provide scientific decision support in a dynamic and complex traffic environment. This invention constructs a comprehensive data collection network, utilizes traffic monitoring equipment to acquire vehicle traffic information in real time, and combines vehicle type identification with carbon emission calculation rules to accurately calculate the emissions of each vehicle on a specific road segment. It also provides multi-dimensional query, trend analysis, and decision suggestion functions to assist traffic management departments in optimizing traffic light timing and managing traffic flow, ultimately effectively reducing carbon emissions. The technical advantage of this invention lies in achieving intelligent management of the entire process from data collection to decision support, significantly improving the scientific nature and efficiency of urban traffic governance, and providing important support for the construction of low-carbon cities. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating a method for calculating carbon emissions when a vehicle passes through a road, according to the present invention.

[0011] Figure 2 This is a schematic diagram illustrating a method for calculating carbon emissions when a vehicle passes through a road, according to the present invention.

[0012] Figure 3 This is another schematic diagram of a method for calculating carbon emissions when a vehicle passes through a road, according to the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0014] Example 1

[0015] This invention provides a method for analyzing carbon emissions from transportation, comprising the following steps:

[0016] By collecting vehicle traffic data and combining it with preset carbon emission calculation rules, the carbon emissions of vehicles on road sections or intersections are calculated, and the calculation results are stored in the data storage unit.

[0017] Based on vehicle traffic data and environmental parameters, query results and analysis reports are generated. The query results include carbon emission information of vehicles in time periods and regions, and the analysis reports are used to optimize traffic flow.

[0018] By managing and protecting carbon emission data, we ensure the integrity and traceability of the data, and provide data backup and recovery mechanisms;

[0019] Data analytics techniques are used to perform trend and comparative analysis on carbon emission data to generate decision recommendations, including traffic signal optimization and route planning schemes.

[0020] Example 2

[0021] The embodiments of the present invention will be described in detail below with reference to specific implementation methods, so as to make the objectives, technical solutions and advantages of the present invention clearer. The embodiments of the present invention provide a method for carbon emission analysis in the urban transportation sector, aiming to achieve accurate calculation and real-time monitoring of the carbon emissions of each vehicle on a specific road segment or intersection through the collection and processing of vehicle data, providing scientific data support for traffic management departments and helping to optimize urban traffic governance.

[0022] like Figure 1 As shown in the figure, this embodiment two provides a method for calculating carbon emissions when a vehicle passes through a road, which may specifically include:

[0023] Step S1: Acquire vehicle traffic data collected by urban traffic monitoring equipment. In one implementation, real-time vehicle traffic information on urban roads is acquired by interfacing with existing traffic monitoring equipment. These devices include cameras, checkpoint systems, and other sensors installed at intersections and key road locations to capture basic vehicle information. Specifically, the collected data includes vehicle license plate numbers, the time of passage through a specific intersection or checkpoint, the vehicle's instantaneous speed at that location, and the vehicle's coordinates. This data forms the basis for subsequent carbon emission calculations, ensuring the real-time nature and comprehensiveness of the data. During the acquisition process, the monitoring equipment continuously records vehicle information at a preset frequency, such as once per second, to ensure data continuity. For example, at major intersections in Baotou City, traffic monitoring cameras are typically installed on traffic light poles or brackets near the intersection, covering multiple directions of the intersection. Through these devices, the traffic conditions of each vehicle during morning and evening rush hours can be obtained. For instance, if a small car with license plate number Meng A12345 passes through an intersection at 8:15 AM at a speed of 10 kilometers per hour, the system will record the relevant information at that moment. It should be noted that data collection is not limited to peak hours but also includes all-day vehicle traffic data to comprehensively understand traffic flow and vehicle behavior. In one possible implementation, data collection also includes monitoring the operational status of equipment to ensure data accuracy and integrity. For example, the system periodically checks whether cameras are working properly and whether there are any blurry images or data transmission interruptions. If a device malfunction is detected, the system automatically generates an alarm message to notify maintenance personnel for timely handling. Furthermore, the collected data is transmitted to the backend server via a secure communication protocol to prevent data tampering or loss during transmission. This method ensures data reliability and lays the foundation for subsequent processing. Step S2: Based on vehicle traffic data, identify vehicle types and match them with corresponding carbon emission calculation rules. Specifically, after obtaining vehicle traffic data, the type of each vehicle needs to be identified to match the corresponding carbon emission calculation rules. Vehicle type identification can be achieved by comparing the license plate number with the vehicle registration database to obtain basic vehicle information, such as whether the vehicle is a small gasoline car, a medium-sized diesel truck, or another type of vehicle. After identification, the system will match the vehicle with the corresponding carbon emission calculation parameters according to a preset calculation rule library. These parameters include the vehicle's reference displacement, fuel consumption per 100 kilometers, speed correction factor, and carbon emission factor, ensuring the relevance and accuracy of the calculations. For example, for a small gasoline car, the system queries the database to find that its displacement is 1.0 liters, falling into the category of displacement less than or equal to 1.1 liters. According to the corresponding parameters in the rule base, this model has a fuel consumption of 5 liters per 100 kilometers in urban driving conditions, a speed correction factor of 0.2, and a carbon emission factor of 2.31 kg per liter of fuel.The system combines these parameters with actual vehicle traffic data for subsequent carbon emission calculations. It's important to note that the rule base covers various vehicle types and operating conditions, including gasoline vehicles, diesel vehicles, and trucks of different engine displacements, ensuring that all types of vehicles can find corresponding calculation bases. In one embodiment, for special vehicle types or vehicles not registered in the database, the system uses default parameters for matching. For example, for a small truck with an unspecified engine displacement, the system will initially determine that it likely belongs to the 3.8-meter-long small truck category based on its vehicle size and appearance, and assign corresponding fuel consumption and carbon emission coefficients. This approach avoids calculation interruptions due to missing data, and further improves matching accuracy through subsequent data correction and manual review. Step S3: Based on vehicle traffic data and the matched carbon emission calculation rules, calculate the carbon emissions of vehicles on specific road segments or intersections. In one implementation, the system combines vehicle traffic data and corresponding calculation rules to calculate the carbon emissions of each vehicle when it stops or passes through a specific road segment or intersection. Specifically, the system analyzes information such as the duration the vehicle remains running at red lights or in traffic jams, as well as its average speed while passing through intersections. It then combines this data with fuel consumption and carbon emission coefficients from a rule base to determine the vehicle's carbon emissions at that location. The calculation process considers the impact of vehicle speed on fuel consumption; for example, lower speeds result in higher fuel consumption and consequently, higher carbon emissions. For instance, at a busy intersection in Baotou City, a small gasoline car waits at a red light for 60 seconds. Based on its running time and idling fuel consumption parameters, the system calculates its fuel consumption during this period to be 0.05 liters, and combined with the carbon emission coefficient, the carbon emissions are calculated to be 0.1155 kilograms. Furthermore, if the vehicle's average speed while passing through the intersection is 15 kilometers per hour, the system adjusts the fuel consumption value according to a speed correction coefficient, further calculating the carbon emissions while passing through the intersection. This meticulous calculation method accurately reflects the vehicle's carbon emissions under different traffic conditions. In one possible implementation, the system may also correct the calculation results to improve accuracy. For example, to address specific traffic conditions at intersections, such as frequent vehicle starts and stops due to prolonged construction, the system introduces an additional correction factor to comprehensively consider the impact of vehicle start-stop frequency on fuel consumption. This correction factor can be derived through historical data analysis, ensuring the calculation results more closely reflect real-world conditions. Furthermore, the system regularly updates parameters in its rule base, adjusting fuel consumption values ​​and coefficients based on the latest fuel consumption standards or carbon emission test data to adapt to changes in vehicle technology and traffic conditions. It's important to note that the calculation process categorizes different time periods and road segments. For instance, carbon emission calculations during peak hours focus more on vehicle idling time, while off-peak hours consider the impact of average vehicle speed on fuel consumption.Through this classification calculation, the system can more accurately reflect the carbon emission characteristics under different traffic scenarios, providing reliable data support for subsequent analysis. In one embodiment, the system also supports continuous tracking of the carbon emissions of specific vehicles. For example, for a medium-sized diesel truck that frequently passes through a certain intersection, the system records its carbon emissions each time it passes through the intersection in the past week, and calculates the average and total. This continuous tracking helps to identify high-emission behavior of certain vehicles on specific road sections, providing traffic management departments with targeted governance basis. At the same time, the system will present the calculation results in a visual way, such as displaying the total carbon emissions of vehicles at a certain intersection at different time periods through charts, making it easier for managers to quickly understand the distribution of carbon emissions. For example, at a checkpoint on a main road in Baotou City, the system recorded that a medium-sized diesel truck with license plate number Meng B45678 passed through the checkpoint in the past three days, with each stop time exceeding 30 seconds and an average speed of less than 5 kilometers per hour. Combining its vehicle model parameters, the system calculated that the carbon emissions of the vehicle each time it passed through were approximately 0.2 kg, and the cumulative emissions over three days were 0.6 kg. By aggregating carbon emission data from these high-frequency passing vehicles, the system can identify vehicles and road sections with high carbon emissions, providing important references for subsequent traffic optimization. In one possible implementation, the system also handles abnormal data during the calculation process. For example, if a vehicle's traffic data shows an abnormally high or low speed, clearly inconsistent with actual traffic conditions, the system will automatically mark the data as abnormal and supplement it with historical averages or data from surrounding vehicles. This anomaly handling mechanism effectively avoids calculation biases caused by data errors, ensuring the stability of carbon emission calculations. It should be noted that carbon emission calculations are not limited to a single intersection or checkpoint but can be extended to an entire road or region. For example, the system can calculate the total carbon emissions of all vehicles on a main road in a day, or analyze the carbon emission distribution of all intersections within an administrative region. Through this multi-layered calculation approach, the system can provide traffic management departments with comprehensive data support from micro to macro levels, helping to formulate more scientific traffic governance strategies. In one embodiment, the system also supports the classification and statistical analysis of carbon emissions from different types of vehicles. For example, at an intersection near a commercial area in Baotou City, the system counted 300 small gasoline cars passing through the intersection between 9:00 AM and 10:00 AM, with an average carbon emission of 0.1 kg per vehicle; and 50 medium-sized diesel trucks, with an average carbon emission of 0.3 kg per vehicle. Through this categorized statistical analysis, the system can clearly show the contribution ratio of different vehicle types to total carbon emissions, providing a basis for the formulation of targeted emission reduction measures. For instance, further analysis of the statistics for the aforementioned commercial area intersection revealed that although medium-sized diesel trucks accounted for a smaller number of vehicles, they contributed nearly 40% of the total carbon emissions.This finding suggests that traffic management departments can restrict truck passage times at this intersection or guide trucks to choose alternative routes, thereby effectively reducing the total carbon emissions in the area. This classification-based statistical analysis helps managers quickly identify high-emission sources and formulate precise control measures. In one possible implementation, the system also supports real-time updates of carbon emission calculation results. For example, when traffic conditions at an intersection change, such as traffic light timing adjustments leading to shorter vehicle dwell times, the system will recalculate carbon emissions based on the latest traffic data and update the results to the database in real time. This real-time update mechanism ensures the timeliness of the data, providing traffic management departments with the latest reference information. It should be noted that the accuracy of carbon emission calculations can be further improved by introducing more dimensions of data. For example, the system can incorporate the impact of weather conditions on vehicle fuel consumption, appropriately increasing fuel consumption values ​​in rainy or snowy weather because vehicle resistance increases on slippery surfaces. Through this multi-dimensional data fusion, the system can more comprehensively reflect the actual situation of vehicle carbon emissions. In one embodiment, the system also supports retrospective calculations of historical data. For example, based on vehicle traffic data from a certain intersection in Baotou City over the past month, the system can recalculate the carbon emissions of each vehicle and generate a detailed report. This retrospective calculation helps traffic management departments understand the long-term trend of carbon emissions, providing data support for setting annual emission reduction targets. Simultaneously, the system compares the retrospective calculation results with real-time calculation results to analyze the changes in carbon emissions before and after the implementation of traffic management measures, in order to evaluate the effectiveness of the measures. For instance, after implementing traffic light timing optimization, the system, through retrospective calculation, found that the average vehicle dwell time at a certain intersection decreased from 50 seconds to 30 seconds, with a corresponding reduction in carbon emissions of approximately 20%. This comparative analysis can intuitively reflect the actual effect of traffic optimization, providing an important basis for subsequent adjustments to management measures. In one possible implementation, the system also supports batch processing of carbon emission calculation results. For example, based on vehicle traffic data from all intersections in Baotou City over a certain time period, the system can calculate the carbon emissions of all vehicles at once and generate a summary report. This batch processing method can significantly improve data processing efficiency and meet the needs of large-scale data analysis. Simultaneously, the system stores the batch processing results in a database for easy subsequent querying and retrieval. It's important to note that the ultimate goal of carbon emission calculation is to provide decision support for traffic management departments. Therefore, after calculation, the system automatically generates various types of analysis reports, such as carbon emission statistics reports categorized by time period, intersection, and vehicle type. These reports help managers quickly understand the carbon emission status of the city's transportation sector, providing a scientific basis for formulating emission reduction policies. In one embodiment, the system also supports anomaly detection in carbon emission calculation results.For example, if carbon emissions at an intersection suddenly increase significantly within a certain time period, the system will automatically generate an alarm and analyze possible causes, such as prolonged congestion due to a traffic accident. Through this anomaly detection mechanism, the system can promptly identify potential traffic problems, providing managers with a basis for rapid response. For instance, at an intersection in Baotou City, the system detected carbon emissions between 8:00 AM and 9:00 AM on a certain day that were 50% higher than usual. Further analysis revealed that the average vehicle dwell time during this period reached 80 seconds, far exceeding the usual 40 seconds. Combining this with traffic monitoring footage, the system determined that the congestion was likely caused by a minor traffic accident at the intersection, thus prompting the traffic management department to dispatch personnel for timely handling. This anomaly detection and analysis capability can effectively improve the response speed of traffic management. In one possible implementation, the system also supports multi-dimensional cross-analysis of carbon emission calculation results. For example, the system can analyze the changes in carbon emissions at an intersection under different time periods and weather conditions to identify the main factors affecting carbon emissions. Through this cross-analysis, the system can provide traffic management departments with deeper insights, helping to formulate more precise emission reduction measures. It is important to note that data security is paramount throughout the entire carbon emission calculation process. The system encrypts and stores vehicle traffic data and calculation results to prevent data leakage or tampering. Simultaneously, the system restricts access to sensitive data based on user permission settings, ensuring that only authorized personnel can view and manipulate the relevant data. This security mechanism effectively protects vehicle owner privacy and the data security of traffic management departments. In one embodiment, the system also supports the sharing and export of carbon emission calculation results. For example, traffic management departments can export carbon emission statistics for a specific intersection into a tabular format for sharing with other departments or agencies to jointly develop emission reduction strategies. Furthermore, the system supports diverse data export formats, such as generating charts or reports to meet the needs of different scenarios. For instance, when the Baotou City traffic management department and environmental protection department jointly conduct emission reduction projects, the system can export carbon emission data from major intersections throughout the city into a visual chart, clearly showing the carbon emission distribution at each intersection. Through this data sharing method, the two departments can collaborate more efficiently and develop more comprehensive emission reduction plans. In one possible implementation, the system also supports long-term storage of carbon emission calculation results. The system will store the carbon emission data for each vehicle for at least two years to ensure data integrity and traceability. Simultaneously, the system will regularly back up the data to prevent data loss due to hardware failure or human error. This long-term storage mechanism can provide historical data support for traffic management departments, meeting the needs of long-term analysis and planning. It should be noted that during the implementation of carbon emission calculation, the system will continuously collect user feedback.For example, if traffic management departments find discrepancies between the calculated results and actual conditions at certain intersections when using the system, the system will record this feedback and adjust the calculation rules or parameters based on actual data. Through this continuous optimization, the system can continuously improve its calculation accuracy and adapt to different traffic scenarios and needs. In one embodiment, the system also supports personalized customization of carbon emission calculation results. For example, traffic management departments can set specific calculation dimensions or statistical periods according to their own needs, such as calculating only the carbon emissions of a certain type of vehicle within a specific time period, or focusing only on the carbon emissions at certain key intersections. Through this personalized customization function, the system can better meet the needs of different users and improve the relevance and practicality of the data. For example, when carrying out a special action to reduce emissions from heavy-duty trucks in Baotou City, the system can calculate only the carbon emissions of heavy-duty trucks at all checkpoints in the city according to the needs of the management department and generate a special report. This customized calculation method can help managers quickly obtain the required data and improve decision-making efficiency. In one possible implementation, the system also supports the prediction function of carbon emission calculation results. For example, based on historical data and current traffic flow, the system can predict the carbon emission trend of a certain intersection within a future time period. This predictive function can provide traffic management departments with a reference for formulating countermeasures in advance, such as adjusting traffic light timings or increasing police force to manage traffic when a significant increase in carbon emissions during peak hours is predicted. It should be noted that the system also focuses on optimizing the user experience during the carbon emission calculation process. For example, the system interface is designed to be simple and intuitive, allowing users to quickly get started. At the same time, the system provides detailed user guides and online help functions to ensure that users can get timely solutions to any problems encountered during use. In this way, the system can effectively improve user satisfaction. In one embodiment, the system also supports multi-terminal display of carbon emission calculation results. For example, users can access the system through computers, mobile phones, or other devices to view real-time carbon emission data and analysis reports. This multi-terminal support function can meet the needs of different scenarios; for example, traffic management personnel can view data on computers in the office or monitor the intersection situation in real time on their mobile phones. For example, when temporary traffic control measures are implemented at an intersection in Baotou City, on-site staff can log into the system via their mobile phones to view the current carbon emission data for that intersection and decide whether further adjustments to the control measures are needed based on the data. This multi-terminal display method significantly improves the convenience of data acquisition and provides timely support for on-site decision-making. In one possible implementation, the system also supports the automatic archiving of carbon emission calculation results. For example, the system will automatically classify and store the calculation results according to dimensions such as time, region, or vehicle type, and generate corresponding tags for easy retrieval and access by users later.Through this automatic archiving function, the system can effectively improve data management efficiency and reduce the workload of manual operations. It should be noted that during the carbon emission calculation process, the system also emphasizes seamless integration with existing traffic management systems. For example, the system can interface with existing traffic light control systems to automatically adjust traffic light timings based on carbon emission data, reducing vehicle dwell time. This integration method can fully leverage the system's capabilities and enhance the intelligence level of traffic management. In one embodiment, the system also supports dynamic updates to the carbon emission calculation results. For example, when vehicle traffic data at an intersection changes, the system automatically recalculates the carbon emissions for that intersection and updates the latest results to the user interface. Through this dynamic update mechanism, the system ensures that users always have access to the latest data, avoiding decision-making errors caused by data lag. For example, after implementing temporary traffic control measures at an intersection in Baotou City, the system detected that the average vehicle transit time decreased from 60 seconds to 40 seconds, and the corresponding carbon emissions also decreased. The system will immediately update the carbon emission data for that intersection and highlight the trend in the user interface to alert the user. This dynamic update function effectively improves the timeliness of data, providing support for real-time decision-making. In one possible implementation, the system also supports in-depth analysis of carbon emission calculation results. For example, the system can analyze the specific time periods and vehicle types with high carbon emissions at a certain intersection to identify the main causes of high emissions, such as whether unreasonable traffic light timing leads to long waiting times. Through this in-depth analysis function, the system can provide traffic management departments with more detailed analysis results, helping to formulate more precise governance measures. It should be noted that the implementation methods of steps S1 to S3 above cover the complete process from vehicle traffic data collection to carbon emission calculation. Through these steps, the system can achieve accurate calculation of carbon emissions for each vehicle on a specific road segment or intersection, laying a solid foundation for subsequent queries, analysis, and decision support. Subsequent steps will further focus on data query and analysis functions, so please stay tuned. Step S4 provides diversified query functions based on vehicle carbon emission data. In one implementation, the system has developed various query functions based on the previously calculated vehicle carbon emission data to meet the data acquisition needs of traffic management departments in different scenarios. Specifically, the query function covers multiple aspects, including vehicle movement statistics, intersection vehicle information, and checkpoint vehicle information, ensuring users can quickly obtain the data they need. The query operation is implemented through a user-friendly interface, allowing users to filter by time range, location number, or vehicle identifier, and the system will return the corresponding query results in a short time.For example, when the Baotou City traffic management department needs to understand the carbon emissions of vehicles at a specific intersection within a certain period, users can select the specific intersection number and time period through the system interface, such as 8:00 AM to 9:00 AM. The system will quickly list information such as the carbon emissions, average speed, and passage time of all vehicles passing through that intersection during that time period. This query method helps managers quickly grasp the traffic and emissions situation in a specific area, providing a basis for subsequent decision-making. In one possible implementation, the system supports multi-condition combined queries to improve query flexibility. For example, users can simultaneously set three conditions: time range, vehicle type, and intersection number, to query the carbon emission data of a certain type of vehicle passing through a specific intersection within a specific time period. The system will extract the data that meets the requirements from the database based on the user-set conditions and display the results in the form of tables or charts. This multi-condition query function can meet the data needs in complex scenarios and improve the accuracy of data acquisition. See also. Figure 2 To provide diverse query functions based on vehicle carbon emission data, the specific execution steps are as follows:

[0024] Step S41, the specific implementation of the traffic statistics query function. Specifically, the traffic statistics query function allows users to query detailed traffic and carbon emission information for each vehicle recorded by each traffic monitoring device within any time range. Users can obtain various data, including vehicle passage time, average speed, fuel type, fuel consumption, and carbon emissions, by inputting conditions such as time period, license plate number, or monitoring device number. The query results support sorting by different conditions, such as sorting by carbon emissions from high to low, so that users can quickly identify high-emission vehicles. For example, monitoring devices installed on a main road in Baotou City record a large amount of vehicle traffic data. The traffic management department wants to understand the carbon emissions of all small gasoline cars passing through this road section on a certain day. The user inputs the time range from 0:00 to 24:00 of the day in the system interface and selects the vehicle type as small gasoline cars. The system will list the vehicles that meet the conditions, including each vehicle's license plate number, passage time, average speed, and carbon emissions. Through this query, managers can quickly understand the emission distribution of a specific vehicle type. In one embodiment, the system also supports the export function of the traffic statistics query results. For example, after a user queries vehicle traffic data for a specific road segment within a certain time period, the results can be exported as a spreadsheet file for convenient subsequent analysis and archiving. This export function supports multiple formats, ensuring that users can choose the appropriate presentation method according to their needs. Simultaneously, the system encrypts the exported data to prevent unauthorized access during transmission or storage. Step S42: Specific implementation of the intersection vehicle query function. In one implementation, the intersection vehicle query function is used to query vehicle traffic and carbon emission related data for each intersection in the city within any given time period. Users can select a specific intersection number and time range, and the system will return information such as the total number of vehicles passing through the intersection within the specified time period, average passage time, average speed, total carbon emissions, and average vehicle fuel consumption. This data helps managers understand the traffic pressure and emission status of different intersections. For example, at a busy intersection in Baotou City, the traffic management department wants to understand the carbon emissions during the morning rush hour. Users select the intersection number in the system interface and set the time range to 7:00 AM to 9:00 AM. The system will display the total number of vehicles passing through the intersection during that time period: 1200; average passage time: 45 seconds; average speed: 12 kilometers per hour; total carbon emissions: 150 kilograms; and average fuel consumption, categorized by fuel type: 0.12 liters per gasoline vehicle and 0.18 liters per diesel vehicle. This detailed data display provides managers with comprehensive intersection traffic information. In one possible implementation, the system also supports comparative analysis of intersection vehicle query results. For example, users can select multiple intersections and query their carbon emission data within the same time period; the system will then display the differences in carbon emissions between the intersections in chart form.Through this comparison, managers can quickly identify intersections with high carbon emissions, analyze the causes, and take targeted measures, such as adjusting traffic light timings to reduce vehicle dwell time. Step S43 involves the specific implementation of the checkpoint vehicle query function. Specifically, the checkpoint vehicle query function allows users to statistically analyze the number of vehicles passing through and their carbon emissions by checkpoint number. Users can query based on conditions such as checkpoint number and time range. The system will return the total number of vehicles passing through the specified checkpoint during that time period, the total carbon emissions, and statistical data categorized by vehicle type. This query function is particularly suitable for analyzing traffic and emissions on urban arterial roads or key road sections. For example, at a checkpoint on a main road in Baotou City, the traffic management department wants to understand the vehicle traffic and emissions during the weekend. The user selects the checkpoint number in the system interface and sets the time range to Saturday 0:00 to Sunday 24:00. The system will display that the total number of vehicles passing through the checkpoint during that time period is 5,000, with a total carbon emission of 600 kg, of which heavy trucks contributed approximately 350 kg of carbon emissions. Through this query, managers can clearly understand the emission composition of checkpoints, providing data support for formulating traffic restriction policies. In one embodiment, the system also supports trend analysis of checkpoint vehicle query results. For example, users can query the daily carbon emission changes of a certain checkpoint over the past month, and the system will display the emission fluctuation trend in the form of a line graph. Through this analysis, managers can discover abnormally high emissions in certain time periods or on certain dates, and then combine this with other data to analyze the reasons, such as whether it is due to increased traffic flow during holidays. Step S5: Store and manage vehicle carbon emission data. In one embodiment, the system will store and manage the calculated vehicle carbon emission data for a long time to ensure data integrity and security. Specifically, the system uses a high-performance database system to store the passage data and carbon emission data of each vehicle for at least two years to meet the needs of historical data query and analysis. At the same time, the system provides convenient data management tools to facilitate users in maintaining and operating the data. For example, in the system of the Baotou City Traffic Management Department, the carbon emission data of all vehicles are stored in a dedicated database. The system will classify and store the data according to dimensions such as time, intersection number, and vehicle type to ensure that users can quickly find data under specific conditions. Simultaneously, the system regularly checks the database's storage space and operational status to ensure data storage stability. In one possible implementation, the system also supports data backup and recovery functions. For example, the system automatically backs up the database daily, storing the backup files on a separate server to prevent data loss due to primary server failure. In the event of data corruption or loss, the system can quickly restore the data from the backup files, ensuring the platform's normal operation. This backup mechanism effectively enhances data security. See also... Figure 3The specific execution operations of step S5 above include 51-53;

[0025] Step S51, Specific Implementation of Data Storage. Specifically, the data storage function is implemented through a reliable database system to ensure that vehicle carbon emission data and related information can be stored long-term. The system assigns a unique identifier to each vehicle's passage record and carbon emission amount for easy subsequent querying and retrieval. Simultaneously, the system compresses the stored data to reduce storage space usage and improve data read / write efficiency. For example, in the database of the Baotou City Traffic Management Department, each vehicle passage record includes fields such as license plate number, passage time, intersection number, speed, and carbon emission amount. The system generates a unique number for each record to ensure that data is not duplicated or lost. Furthermore, the system regularly optimizes the database, cleaning up invalid data and freeing up storage space to ensure database operating efficiency. In one embodiment, the system also supports hierarchical management of stored data. For example, the system stores data from the most recent month in high-performance storage devices for fast access, while older data is stored in lower-cost storage devices for long-term preservation. Through this hierarchical storage method, the system can reduce storage costs while ensuring data access speed. Step S52, Specific Implementation of Data Backup and Recovery. In one implementation, the system periodically backs up vehicle carbon emission data to ensure rapid data recovery in case of unforeseen circumstances. The backup process is automated, and backup files are stored in multiple physical locations to prevent data loss due to single device failure. Simultaneously, the system provides data recovery tools for users to quickly restore data when needed. For example, in the system of the Baotou City Traffic Management Department, data backup is automatically performed every morning, and backup files are stored simultaneously on a local server and a remote server. If the local server fails, users can restore data through the backup files on the remote server, ensuring uninterrupted system operation. The system also records operation logs for each backup, allowing users to view backup status and historical records. In one possible implementation, the system also supports incremental backup. For example, the system only backs up data added or modified since the last backup, rather than backing up all data each time. This incremental backup method significantly reduces the time and storage space required for backups, improving backup efficiency. Meanwhile, the system performs full backups periodically to ensure data integrity. Step S53: Specific implementation of data management. Specifically, the data management function provides users with various operation tools such as data import, export, deletion, and modification, facilitating data maintenance by the traffic management department. Users can perform these operations through the system interface. The system records detailed information for each operation to ensure data traceability. At the same time, the system strictly controls access permissions to prevent unauthorized users from modifying or deleting data.For example, when the Baotou City traffic management department needs to update vehicle traffic data at a certain intersection, users can import new data files through the system interface. The system will automatically verify the file format and content to ensure the accuracy of the imported data. After the import is complete, the system will generate an operation log, recording information such as the import time, operator, and file content, facilitating subsequent auditing. Simultaneously, if any data errors are found, users can modify them through the system interface, and the modified data will be automatically updated in the database. In one embodiment, the system also supports batch operations on data. For example, users can delete invalid data within a certain time period at once, or batch export carbon emission data from multiple intersections. Through this batch operation function, the system can effectively improve the efficiency of data management and reduce the workload of manual operations. At the same time, the system will generate detailed reports for each batch operation, making it convenient for users to verify the operation results. Step S6: Analyze vehicle carbon emission data and support traffic governance decisions. In one implementation, the system provides decision support to the traffic management department through in-depth analysis of vehicle carbon emission data. Specifically, the system can perform trend analysis and comparative analysis of carbon emission data in different time periods and regions, identify high-emission areas and time periods, and propose suggestions for optimizing traffic management. These analytical results can help managers formulate scientific and reasonable governance measures to reduce carbon emissions in the transportation sector. For example, when the Baotou City traffic management department used the system to analyze the city's carbon emission data, it found that carbon emissions at certain intersections were significantly higher during morning and evening rush hours than at other times. The system further analyzed the reasons for the high emissions at these intersections, such as unreasonable traffic light timing leading to long vehicle wait times, and proposed suggestions for adjusting traffic light cycles. Through this analysis and suggestions, managers can optimize traffic management in a targeted manner to reduce carbon emissions. In one possible implementation, the system also supports the visualization of the analysis results. For example, the system can display the distribution of carbon emissions at various intersections throughout the city through a heat map, with the intensity of the color indicating the level of emissions, allowing users to intuitively understand the location and extent of high-emission areas. Simultaneously, the system supports dynamic display functionality, allowing users to select different time periods to view changes in carbon emissions. This visualization method helps managers quickly grasp data characteristics and improve decision-making efficiency. Step S61, the specific implementation of trend analysis. Specifically, the trend analysis function is used to analyze changes in vehicle carbon emission data across different time periods or regions. The system analyzes carbon emission trends based on the user-selected time or region range and displays the results in chart form. This analysis allows users to understand long-term patterns in carbon emissions, providing a reference for developing emission reduction plans. For example, if the Baotou City transportation management department wants to understand the city's carbon emissions over the past year, users can select the past 12 months in the system interface. The system will then generate a line graph showing the monthly trend of total carbon emissions.By analyzing charts, users discovered that carbon emissions are significantly higher in winter months than in other seasons, possibly due to increased vehicle fuel consumption caused by cold weather. Based on this finding, administrators can strengthen traffic management and reduce vehicle idling time during winter. In one embodiment, the system also supports predictive functions for trend analysis results. For example, based on carbon emission change data from the past year, the system can predict carbon emission trends for the next few months and mark potential peak periods. Through this prediction, administrators can develop countermeasures in advance, such as increasing traffic police presence during predicted peak periods to ensure smooth traffic flow and reduce unnecessary carbon emissions. Step S62, specific implementation of comparative analysis. In one implementation, the comparative analysis function is used to compare carbon emission data between different intersections or checkpoints to help users identify high-emission areas and road sections. Users can select multiple intersections or checkpoints, and the system will statistically analyze their carbon emissions, vehicle throughput, and other data, displaying the comparison results in bar charts or tables. Through this comparison, administrators can quickly locate areas requiring focused management. For example, when the Baotou City traffic management department compared carbon emission data from 10 major intersections throughout the city, it found that the carbon emissions from two intersections were significantly higher than those from the others. Further system analysis revealed that the average dwell time at these two intersections was relatively long, possibly due to inappropriate traffic light timing settings causing congestion. Through this comparative analysis, administrators can prioritize optimizing these two intersections by adjusting traffic light cycles to reduce vehicle waiting times. In one possible implementation, the system also supports in-depth analysis of the comparative results. For example, the system can analyze the specific vehicle composition at high-emission intersections to determine if the increased emissions are due to the concentrated passage of a particular type of vehicle. If heavy trucks are found to be the primary cause, administrators can consider implementing truck restrictions at the intersection or guiding trucks to choose alternative routes. Step S63: Specific Implementation of Decision Recommendations. Specifically, the decision recommendation function provides traffic management departments with specific suggestions for optimizing traffic management through the analysis of carbon emission data. Based on the analysis results and the actual situation of traffic management, the system will propose suggestions including traffic light timing adjustments, traffic flow control, and traffic route planning. These suggestions can help administrators formulate scientific and reasonable governance measures to reduce carbon emissions in the transportation sector. For example, analysis at an intersection in Baotou City revealed that carbon emissions were high during the morning rush hour, and the average vehicle dwell time exceeded 60 seconds. Based on this situation, the system will suggest extending the green light time at the intersection by 10 seconds and shortening the red light time in adjacent directions to reduce vehicle waiting time. By implementing this suggestion, the traffic flow efficiency at the intersection is improved, and carbon emissions are reduced accordingly. In one embodiment, the system also supports simulation testing of the decision-making suggestions. For example, after proposing to adjust the traffic light timing, the system can simulate the changes in traffic flow and carbon emissions after the adjustment for managers' reference.If the simulation results show a significant reduction in carbon emissions after the adjustment, managers can confidently implement the recommendation; if the simulation results are unsatisfactory, the system will further adjust the recommendation until a suitable solution is found. This simulation testing function can effectively reduce decision-making risks and improve the success rate of governance measures. Step S7: Continuously monitor and optimize system functions. In one implementation, the system continuously optimizes its vehicle carbon emission data monitoring and analysis functions to ensure that the system can adapt to the ever-changing traffic environment and user needs. Specifically, the system regularly collects user feedback and actual operating data, analyzes potential problems in the system's calculation, query, and analysis processes, and improves its functions and performance. Simultaneously, the system updates calculation rules and analysis logic based on the latest traffic policies and emission reduction standards to ensure data accuracy and usability. For example, after using the system for a period of time, the Baotou City traffic management department found that the carbon emission calculation results at certain intersections deviated from the actual situation. The system records this feedback information and adjusts the calculation parameters based on actual traffic data, such as updating vehicle fuel consumption values ​​or speed correction coefficients, to improve calculation accuracy. Through this continuous optimization, the system can continuously improve data quality and meet the needs of managers. In one possible implementation, the system also supports the development and integration of new functions. For example, when a traffic management department requests the addition of a specific type of query function, the system development team will design and implement the function according to the requirements and seamlessly integrate it into the existing system. In this way, the system can continuously expand its functional scope to adapt to different application scenarios. Step S71, specific implementation of data monitoring and feedback collection. Specifically, the system will continuously monitor the quality of vehicle traffic data and carbon emission data, and promptly detect data anomalies or system operation problems. At the same time, the system will collect user feedback from traffic management departments to understand the shortcomings of the system in actual application. This feedback information will serve as an important basis for system optimization. For example, when the Baotou City traffic management department used the system, it was found that vehicle traffic data at a certain intersection was frequently missing, affecting the accuracy of carbon emission calculation. The system will record this problem and analyze the reasons for the data loss, such as whether it was due to monitoring equipment failure causing data collection failure. Based on the analysis results, the system will prompt maintenance personnel to check the equipment and complete the missing data to ensure data integrity. In one embodiment, the system also supports the classified management of user feedback. For example, the system categorizes user feedback according to problem type, such as data accuracy issues, query speed issues, or interface operation issues, and prioritizes handling issues that affect the core functions of the system. Through this categorization management method, the system can efficiently process feedback and improve user satisfaction. Step S72, specific implementation of system function optimization. In one implementation, the system optimizes functions and performance based on monitoring results and user feedback.For example, the system will regularly update the database index structure to improve data query speed; or optimize the carbon emission calculation logic to reduce calculation errors. Through these optimization measures, the system can continuously improve operational efficiency and data quality. For example, after the Baotou City traffic management department raised the issue of long query response time, the system development team optimized the database query logic, reduced unnecessary data scanning operations, and added a caching mechanism to temporarily store frequently used query results, thus speeding up the response time. After optimization, the system's query response time was shortened from 5 seconds to less than 2 seconds, significantly improving the user experience. In one possible implementation, the system also supports the evaluation of optimization effects. For example, after completing a function optimization, the system will compare the operational data before and after optimization, such as query response time or calculation accuracy, to evaluate the actual effect of the optimization measures. If the effect does not meet expectations, the system will further adjust the optimization plan until it meets user needs. This evaluation mechanism can ensure the effectiveness of the optimization work. Step S73, specific implementation of system function expansion. Specifically, the system will develop and integrate new functions according to the new needs of the traffic management department. For example, when administrators request a more detailed regional analysis of carbon emission data, the system will develop corresponding functional modules to support carbon emission statistics by administrative region or custom region. Through this functional expansion, the system can continuously meet new application needs. For instance, when the Baotou City traffic management department wants to analyze the distribution of carbon emissions across different administrative regions, the system development team will add a regional segmentation function. Users can select a specific administrative region in the system interface to view the total carbon emissions and distribution of all intersections and checkpoints within that region. This functional expansion allows administrators to gain a more comprehensive understanding of the city's carbon emission situation, providing data support for the formulation of regional emission reduction policies. In one embodiment, the system also supports integration with other traffic management tools. For example, the system can interface with a traffic light control system to automatically adjust traffic light timings based on carbon emission data, reducing vehicle dwell time. Through this integration, the system can further enhance the intelligence level of traffic management and provide more support for reducing carbon emissions.

[0026] If the technical solution of this application involves personal information, the product using this solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If sensitive personal information is involved, the user's separate consent has been obtained before processing, and the "express consent" requirement is met. For example, a clear sign is placed at the collection device such as a camera to inform the user that they have entered the collection area, and the user's voluntary entry is considered as consent; or the processing device clearly indicates the processing rules and obtains authorization through pop-up windows or by asking the user to upload information themselves. The personal information processing rules include the processor, the purpose of processing, the processing method, and the types of personal information.

[0027] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for calculating carbon emissions when a vehicle passes through a road, characterized in that, include: By collecting vehicle traffic data and combining it with preset carbon emission calculation rules, the carbon emissions of vehicles on road sections or intersections are calculated, and the calculation results are stored in the data storage unit. Based on vehicle traffic data and environmental parameters, query results and analysis reports are generated. The query results include carbon emission information of vehicles in different time periods and regions, and the analysis reports are used to optimize traffic flow. By managing and protecting carbon emission data, the integrity and traceability of the data are ensured, and data backup and recovery mechanisms are provided. Data analysis techniques are used to perform trend analysis and comparative analysis on carbon emission data to generate decision recommendations, including traffic signal optimization and route planning schemes.

2. The method for calculating carbon emissions when a vehicle passes through a road as described in claim 1, characterized in that, The process of collecting vehicle traffic data and combining it with preset carbon emission calculation rules to calculate the carbon emissions of vehicles on road segments or intersections includes: obtaining vehicle traffic data by interfacing with traffic monitoring equipment, the vehicle traffic data including vehicle identification, passage time, driving speed and location information, and matching the vehicle traffic data with preset carbon emission calculation rules; applying corresponding carbon emission calculation rules according to vehicle type and driving status, the driving status including waiting status and congestion status, determining the carbon emissions by calculating the vehicle's non-stop running time and speed parameters under different statuses, and updating the results to the data storage unit.

3. The method for calculating carbon emissions when a vehicle passes through a road as described in claim 1, characterized in that, The system generates query results and analysis reports based on vehicle traffic data and environmental parameters, including: supporting multi-dimensional queries of carbon emission data, with query conditions including time range, vehicle identification, intersection number, and checkpoint number, generating query results containing vehicle passage time, average speed, fuel consumption type, and carbon emission amount; and generating trend analysis reports by summarizing and comparing carbon emission data for different regions and time periods, showing the changing patterns of carbon emissions, and proposing governance suggestions in conjunction with regional traffic flow data.

4. The method for calculating carbon emissions when a vehicle passes through a road as described in claim 2, characterized in that, The process of applying corresponding carbon emission calculation rules based on vehicle type and driving status includes: applying corresponding calculation parameters for different vehicle types when calculating carbon emissions, including reference displacement, fuel consumption data, speed correction coefficient, and carbon emission coefficient, and reducing calculation errors through a data verification mechanism; and supporting custom query conditions when generating query results, including time period, regional range, and vehicle type, and presenting the query results in a visual manner.

5. The method for calculating carbon emissions when a vehicle passes through a road as described in claim 1, characterized in that, The method of managing and protecting carbon emission data to ensure data integrity and traceability, and providing data backup and recovery mechanisms, includes: constructing data storage units to ensure that carbon emission data is not lost within a time frame; these data storage units support data classification management and regular backups, and provide data recovery mechanisms; and applying data encryption and access control technologies during data management to prevent data leakage and tampering; the access control technologies allocate data access scope according to user permissions and log and audit operational behaviors.

6. The method for calculating carbon emissions when a vehicle passes through a road as described in claim 1, characterized in that, The method of using data analysis technology to perform trend and comparative analysis on carbon emission data and generate decision-making recommendations includes: identifying high-emission areas and time periods by mining historical carbon emission data, and generating traffic signal timing recommendations, which aim to reduce vehicle waiting time and congestion; and performing spatial analysis of carbon emission data in conjunction with traffic network layout to propose traffic route planning recommendations, which include optimizing traffic flow distribution on main roads and promoting green travel modes.

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