Carbon emission control vehicle-mounted gateway and end-cloud cooperative real-time feedback method

Abstract

The invention belongs to the technical field of Internet of Things communication, and discloses a carbon emission control vehicle-mounted gateway and end-cloud cooperative real-time feedback method, which introduces a carbon emission real-time feedback module to realize the function of monitoring the carbon emission of an automobile in real time and visualize data, expands the function of intelligent vehicle-mounted gateway equipment, and improves the real-time performance of the intelligent vehicle-mounted gateway equipment. The intelligent degree of the intelligent vehicle-mounted gateway equipment is improved, and a driver is actively guided to make a contribution to automobile carbon emission reduction. According to the invention, the lightweight carbon emission prediction neural network is used to calculate the carbon emission of the automobile, the requirement on the computing power of the bearing equipment is low, the method can be well deployed on the vehicle-mounted gateway equipment, the calculation time is short, and the method has good real-time feedback. Meanwhile, the carbon emission control vehicle-mounted gateway provided by the invention supports the use of a Beidou satellite positioning system and a 5G mobile communication technology which are independently researched and developed in China, and higher positioning precision and faster and more stable data transmission are realized.

Description

Technical Field

[0001] The present invention belongs to the field of Internet of Things communication technology, and specifically relates to a real-time feedback method for carbon emission monitoring vehicle gateway and end-cloud collaboration. Background Art

[0002] The Internet of Vehicles (IoV) refers to a system network based on in-vehicle networks, inter-vehicle networks, and in-vehicle mobile internet, enabling wireless communication and information exchange between vehicles, roads, pedestrians, and the internet, in accordance with agreed communication protocols and data exchange standards. Its primary purpose is to improve the safety, efficiency, and convenience of road traffic. The vehicle gateway (VG) is a crucial component of automotive electronics systems, serving as a communication bridge between the vehicle's multiple electronic control units (ECUs). With the development of IoV technology, VGs are increasingly integrating more external communication capabilities, connecting vehicles to the internet via cellular networks and other communication technologies, enabling information exchange between vehicles and infrastructure, other vehicles, and even pedestrians.

[0003] In recent years, green and low-carbon development has gained an increasingly important position. As the third largest source of carbon emissions after industry and construction, there is still a long way to go to reduce carbon emissions in the transportation sector. Under such development requirements, it is urgently necessary to give the intelligent transportation system the ability to control carbon emissions.

[0004] Research has shown that current research on in-vehicle gateway devices focuses primarily on improving gateway communication efficiency, quality, and operational stability. Patent Document 1 (CN117857308A) utilizes a redundant design with dual modules and a shared database to ensure the normal operation of the in-vehicle gateway device even if a single module fails, thereby improving the stability and sustainability of the in-vehicle gateway device. Patent Document 2 (CN118381693A) discloses an Ethernet-based in-vehicle gateway device that enables multi-point communication, thus avoiding the high cost of the in-vehicle gateway device. However, existing work often overlooks improving the intelligence level of in-vehicle gateways, resulting in relatively limited functionality and difficulty meeting the carbon emission control requirements of the aforementioned intelligent transportation system. Meanwhile, there has been some work on carbon emission monitoring and management. Patent Document 3 (CN116485602A) discloses a carbon emission management system for the automotive industry to control carbon emissions during vehicle production. Overall, existing carbon emission monitoring methods tend to utilize cloud servers to calculate or verify carbon emission data generated by vehicle operation and then provide feedback to the driver via wireless communication technology. However, most of these works only consider cars driving in an ideal urban road environment. In reality, due to network congestion and weak communication signals, it is difficult for cars to maintain long-term and stable communication with cloud servers. As a result, it is impossible to provide timely reminders and feedback when the car's carbon emission data is abnormal. In addition, communication transmission delays and cloud processing delays further increase the difficulty of real-time feedback. Summary of the Invention

[0005] To address the aforementioned technical issues, the present invention provides a vehicle-mounted gateway for carbon emissions monitoring. By constructing a vehicle-mounted gateway with real-time carbon emissions feedback capabilities, the gateway is fully capable of meeting the requirements of vehicle carbon emissions monitoring, enabling the collection, storage, and forwarding of various driving data, local calculation of carbon emissions data, generation of feedback, and real-time presentation of these data and feedback to the driver when abnormal carbon emissions data is detected. Furthermore, a method for real-time carbon emissions feedback, coordinated by the end-to-end cloud and based on the vehicle-mounted gateway and a cloud server, is provided. The vehicle-mounted gateway is used to provide real-time feedback on carbon emissions and correct driving behavior, while the cloud's computing power is leveraged to accurately analyze the entire driving process using a deep learning model to generate a final driving evaluation.

[0006] The present invention provides the following technical solutions:

[0007] The carbon emission monitoring vehicle gateway includes a power module, an MCU (microcontroller) module, a data acquisition module, a positioning module, a data storage module, a wireless communication module, and a carbon emission real-time feedback module, wherein:

[0008] The power module supplies power to all modules of the vehicle gateway to support the operation of the gateway;

[0009] The MCU module realizes the coordinated control between the various modules of the entire gateway;

[0010] The data acquisition module uses ECUs (electronic control units) to collect various data during vehicle driving and pre-process and prioritize the data;

[0011] The positioning module uses a satellite positioning system to obtain information such as the position of the car in real time;

[0012] The data storage module is used to store all data acquired by the data acquisition module and the positioning module;

[0013] The wireless communication module uses wireless communication technology to send the relevant data acquired by the data acquisition module and the positioning module and the carbon emission data calculated by the carbon emission real-time feedback module to the cloud;

[0014] After obtaining the relevant data collected by the data acquisition module and the positioning module, the carbon emission real-time feedback module calculates the carbon emission data locally and analyzes the driving behavior. When the carbon emission data is abnormal, it can generate corresponding driving suggestions and feedback the above-mentioned relevant data to the driver;

[0015] There is a one-way interface between the MCU module and the data acquisition module to realize the transmission of the collected data to the MCU module; there is a one-way interface between the MCU module and the positioning module to realize the transmission of the collected vehicle positioning and other information to the MCU module; there is a two-way interface between the MCU module and the data storage module to realize the transmission of all data acquired by the data acquisition module and the positioning module to the data storage module and the call of data from the data storage module when needed; there is a one-way interface between the MCU module and the wireless communication module to realize the transmission of the relevant data acquired by the data acquisition module and the positioning module and the carbon emission data calculated by the carbon emission real-time feedback module to the wireless communication module; there is a two-way interface between the MCU module and the carbon emission real-time feedback module to realize the transmission of the relevant data acquired by the data acquisition module and the positioning module to the carbon emission real-time feedback module for calculation and the reception of the calculated carbon emissions.

[0016] As a further limitation of the technical solution of the present invention, the data acquisition module includes a bus communication unit, a data preprocessing unit and a priority sorting unit.

[0017] The bus communication unit uses bus technology to communicate with the ECUs on the vehicle to collect various data;

[0018] The data pre-processing unit implements screening and pre-processing of a large amount of data collected by the bus communication unit;

[0019] The priority sorting unit tags the pre-processed data according to a preset sorting order, and data with a high priority will be processed first when it is subsequently sent and received.

[0020] As a further limitation of the technical solution of the present invention, the carbon emission real-time feedback module includes a carbon emission calculation unit, a carbon emission data storage unit, a driving suggestion generation unit, an electronic display unit and a voice reminder unit:

[0021] The carbon emission calculation unit calculates the carbon emissions of the car within n seconds by constructing a carbon emission prediction neural network;

[0022] The carbon emission data storage unit is used to store all carbon emission data calculated by the automobile carbon emission calculation unit and arrange them in chronological order;

[0023] The driving suggestion generating unit analyzes the driver's driving behavior based on the collected driving data and generates corresponding driving suggestions when the carbon emission data is abnormal;

[0024] The electronic display unit is used to display carbon emissions data and driving suggestions in real time on an electronic screen and provide feedback to the driver;

[0025] When the vehicle's carbon emission data is abnormal, the voice reminder unit emits a buzzing sound to attract the driver's attention and voice broadcasts the current carbon emission data and driving suggestions.

[0026] The present invention also provides the following end-cloud collaborative carbon emission real-time feedback method, which is based on the carbon emission monitoring vehicle gateway and the cloud server and includes the following steps:

[0027] Step S1: Use bus communication technology to communicate with the ECUs on the vehicle and collect vehicle operating parameters, while using satellite positioning technology to obtain information such as the vehicle's position and speed;

[0028] Step S2: The carbon emission real-time feedback module in the vehicle gateway uses the collected relevant data to calculate carbon emissions and analyze driving behavior in real time locally;

[0029] Step S3: The carbon emission real-time feedback module compares the latest carbon emission data with the historical carbon emission data. If the carbon emission data increases abnormally and bad driving behavior is detected, the module provides driving suggestions to the driver to adjust the driving behavior.

[0030] Step S4: The vehicle gateway uses wireless communication technology to send the collected vehicle data, positioning information and locally calculated carbon emission data to the cloud server;

[0031] Step S5: The cloud server uses a deep learning model to further analyze driving data, verify carbon emissions, and evaluate all driving behaviors, summarizing them into a detailed analysis report, which is sent to the driver's mobile terminal at the end of the trip.

[0032] The beneficial effects of the present invention are:

[0033] 1. Compared to traditional in-vehicle gateway architectures, this invention introduces a real-time carbon emissions feedback module that enables real-time monitoring of vehicle carbon emissions, analysis of driving behavior, and data visualization. This expands the functionality of intelligent in-vehicle gateway devices and enhances their intelligence. Ultimately, this system displays carbon emissions data to drivers in real time and provides timely recommendations to regulate driving behavior, thereby reducing vehicle carbon emissions.

[0034] 2. Traditional mathematical calculations for estimating carbon emissions often lack accuracy and have limited application. This invention uses a lightweight carbon emission prediction neural network to calculate vehicle carbon emissions. Because the carbon emission prediction neural network used in this invention has a relatively small number of nodes and calculation parameters, it requires less computing power from the host device and can be easily deployed on the vehicle gateway device. It also has a short calculation time and provides good real-time feedback.

[0035] 3. Compared with existing vehicle carbon emission monitoring methods, this invention proposes to combine local real-time monitoring and cloud-based data analysis to reduce latency and the impact of network fluctuations, helping drivers adjust their driving behavior in real time. At the same time, after the trip, they can view the analysis report generated by the cloud-based deep learning model to further understand their own poor driving behavior and cultivate good driving habits in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a structural block diagram of a carbon emission monitoring vehicle gateway device provided by an embodiment of the present invention;

[0037] Figure 2 is a structural block diagram of a data acquisition module in an embodiment of the present invention;

[0038] Figure 3 This is a structural block diagram of a carbon emission real-time feedback module according to an embodiment of the present invention;

[0039] Figure 4 is a topological structure diagram of a carbon emission prediction neural network according to an embodiment of the present invention;

[0040] Figure 5 This is a block diagram of data transmission between the vehicle gateway, the cloud, and the mobile terminal in an embodiment of the present invention;

[0041] Figure 6 This is a flow chart of the end-cloud collaborative real-time feedback method for carbon emissions in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0043] like Figure 1 As shown, the carbon emission monitoring vehicle gateway of an embodiment of the present invention is composed of the following seven modules: a power module, an MCU (microcontroller) module, a data acquisition module, a positioning module, a data storage module, a carbon emission real-time feedback module and a wireless communication module.

[0044] The power module provides power to all modules of the vehicle-mounted gateway, supporting its operation. The power module uses an appropriate power supply size based on the hardware model of the gateway being assembled. It can use a power adapter with a suitable interface or the interface of the vehicle's onboard diagnostic system for power input. Furthermore, the power module can use a power regulator chip to generate a constant output voltage, ensuring more stable gateway operation.

[0045] The data acquisition module uses the bus to collect various data during the vehicle driving process from the ECUs (electronic control units) on the vehicle, such as vehicle speed, driving time, engine speed, coolant temperature, fuel pressure and consumption, brake pedal status, ABS working status, tire pressure and wheel speed, etc., and pre-processes and prioritizes the data, and forwards the processed data to the MCU module.

[0046] The MCU module realizes the coordinated control between the various modules of the entire gateway. Specifically, the MCU module receives various driving data collected and processed by the data acquisition module, and at the same time receives data such as vehicle positioning and speed collected by the positioning module; after receiving all necessary data, the MCU module sends these data to the data storage module for storage, to the wireless communication module for transmission, and to the carbon emission real-time feedback module for carbon emission calculation; after the carbon emission real-time feedback module obtains the carbon emission data, the MCU module should also have the function of forwarding the carbon emission data to the wireless communication module. Furthermore, when sending and receiving data, the MCU module can give priority to sending more important data based on the priority assigned to the data by the data acquisition module.

[0047] The positioning module uses a satellite positioning system (GPS or Beidou) to obtain the vehicle's location in real time. Specifically, the module acquires and processes positioning data in two steps. First, the module uses an antenna to receive incoming messages containing positioning data in real time. Second, the module parses these messages into readable information such as latitude and longitude, altitude, and speed according to the NMEA protocol, and transmits them to the MCU via the serial port.

[0048] The data storage module is used to store the data forwarded by the MCU module after the data acquisition module and the positioning module have acquired and analyzed it, so as to realize the function of local data storage and provide convenience for the subsequent reuse and repeated analysis of the data.

[0049] The real-time carbon emissions feedback module receives various driving data and estimates the vehicle's carbon emissions using a carbon emissions prediction neural network. It also analyzes current driving behavior based on information such as vehicle speed, engine speed, coolant temperature, and accelerator pedal pressure. By comparing historical carbon emissions data, it provides real-time driving advice and corrects the driver's behavior when carbon emissions increase abnormally.

[0050] The wireless communication module can utilize wireless communication technology to transmit the relevant data acquired and analyzed by the data acquisition module and the positioning module to the cloud. Specifically, the wireless communication module can support at least 4G / 5G wireless communication technology, use AT commands to implement dial-up Internet access, and use sockets to establish a TCP / IP connection with the cloud server for data transmission.

[0051] The carbon emissions monitoring vehicle gateway of an embodiment of the present invention includes a power module, an MCU module, a data acquisition module, a positioning module, a data storage module, a carbon emissions real-time feedback module, and a wireless communication module. These modules provide the vehicle gateway with the functions of vehicle data acquisition, real-time carbon emissions calculation, and driving behavior analysis, enabling timely correction of the driver's driving behavior and reducing carbon emissions during vehicle operation. The data acquisition module utilizes a bus to collect and pre-process various vehicle data, while the carbon emissions real-time feedback module estimates and analyzes carbon emissions locally and provides timely feedback to the driver. This significantly reduces latency compared to calculating carbon emissions in the cloud and then feeding them back to the vehicle, enhancing the timeliness of feedback and the effectiveness of recommendations.

[0052] like Figure 2As shown, in one embodiment of the present invention, the data acquisition module may include a bus communication unit, a data preprocessing unit, and a prioritization unit. The bus communication unit supports communication with ECUs throughout the vehicle using bus technologies such as CAN, LIN, and Ethernet, and collects various data during vehicle driving. Data collection by the bus communication unit can be divided into two steps: the first step is to communicate with the ECUs using bus technology and receive data messages via the serial port. The second step is to interpret the received messages into understandable data such as speed, distance, and fuel consumption based on different bus message protocols and send them to the data preprocessing unit.

[0053] As connected vehicles increasingly integrate more electronic control units (ECUs), their overall electronic and electrical architectures become increasingly complex, leading to a rapidly growing demand for dynamic data collection on the vehicle side. When the amount of data exceeds the bus's transmission capacity, information queues for transmission, increasing transmission latency. If the bus cannot process all the data, packet loss may occur.

[0054] To address the above-mentioned issues, in this embodiment, the data acquisition module introduces a data preprocessing unit to perform data cleaning, signal filtering, and anomaly detection on the parsed data. Specifically, the data preprocessing unit can remove invalid values ​​or erroneous data records through data cleaning, can use filters to remove noise in the signal, and can also mark abnormal data points caused by abnormal conditions such as sensor failures, thereby providing more accurate values ​​for subsequent data analysis.

[0055] Furthermore, to address transmission delays caused by large amounts of data, the data acquisition module incorporates a prioritization unit to prioritize preprocessed data. Data priorities can be pre-determined by those skilled in the art based on the frequency of use of different data in subsequent data processing, or determined based on the data's relevance to carbon emissions analysis. The prioritization unit assigns priority tags to different data according to pre-defined rules, with higher-priority data being sent and received first, ensuring that the carbon emissions real-time feedback module receives data promptly and provides faster feedback.

[0056] like Figure 3 As shown, in one embodiment of the present invention, the carbon emission real-time feedback module is composed of a carbon emission calculation unit, a carbon emission data storage unit, a driving suggestion generation unit, an electronic display unit and a voice reminder unit.

[0057] After receiving the collected data, the carbon emission calculation unit can construct a lightweight carbon emission prediction neural network based on the average vehicle speed, driving distance, and fuel consumption and calculate the vehicle's carbon emissions. The carbon emission calculation unit calculates the carbon emissions at a time interval of n seconds. The specific value is determined by those skilled in the art as needed but must not be less than the calculation time of the carbon emission prediction neural network. Specifically, if Figure 4 As shown, the carbon emission prediction neural network in this embodiment consists of an input layer, a hidden layer, and an output layer, with each layer interconnected by modifiable weights. The input layer uses three neurons, which input the vehicle's distance L traveled in n seconds, the average speed v (which can be calculated by dividing the distance L by the driving time n), and the fuel consumption Gt. The output layer uses one neuron to output the vehicle's carbon emissions in time n. The number of neurons in the hidden layer performs a weighted sum operation on each input. The number of neurons in the hidden layer is calculated based on the number of nodes in the input and output layers. The following empirical formula can be used:

[0058]

[0059] Among them, N h is the number of nodes in each hidden layer, N x is the number of nodes in the input layer, N y is the number of nodes in the output layer, and c is a constant, usually selected in the range of 1 to 10.

[0060] Furthermore, the carbon emission prediction neural network requires training before use. The accuracy of the calculation results and the maximum number of training cycles are determined based on actual needs. The training data can be sourced from relevant data measured during actual driving. 70% of this data is used as the training set, and 30% as the test set. The carbon emission prediction neural network is trained and tested. The interneuron weights are updated using gradient descent, and the activation function uses the Sigmoid function, whose formula is as follows:

[0061]

[0062] Wherein, x represents the input value, which refers to the output value of the hidden layer neurons in the carbon emission prediction neural network.

[0063] The carbon emission calculation unit calculates the carbon emission within n seconds of driving time and sends it to the carbon emission data storage unit for storage, and at the same time sends it to the driving suggestion generation unit.

[0064] The carbon emission data storage unit locally stores the carbon emission data calculated by the carbon emission calculation unit and is capable of storing at least the carbon emission data for the current and previous driving sessions. This historical carbon emission data remains accessible to the driving advice generation unit for easy comparison. Furthermore, upon receiving the latest carbon emission data, the carbon emission data storage unit sends it to the electronic display unit and the voice reminder unit. It also sends the calculated carbon emissions back to the MCU module for forwarding.

[0065] The driving advice generation unit is configured to analyze the driver's driving behavior, particularly identifying various undesirable behaviors that increase vehicle carbon emissions. It also compares carbon emissions and, when carbon emissions increase abnormally, generates corresponding advice that is sent to the electronic display unit and voice reminder unit. Specifically, after receiving status information from the vehicle's ECUs and other necessary data, the driving advice generation unit identifies undesirable driving behaviors based on preset normal vehicle driving standards. If all driving indicators are within a reasonable range, the driving behavior is analyzed as "normal driving." In this embodiment, the preset normal driving standards are as follows: an economical speed of 60-80 km / h; a standard accelerator / brake pedal depression time of less than or equal to 1500 milliseconds; a driving coolant temperature of 40°C; an idle engine speed of 700-900 rpm; a maximum idle time of 120 seconds; a maximum coasting speed of 40 km / h; and a normal tire pressure of 230-250 kPa.

[0066] Furthermore, after receiving the latest carbon emission data of the vehicle, the driving suggestion generation unit compares it with the historical carbon emission data in the carbon emission data storage unit. If the carbon emission Ce2 in the current time n seconds far exceeds the emission Ce1 in the previous time period, for example, Ce2 is more than 10 times Ce1, it can be determined that the vehicle's carbon emissions in the past n seconds are abnormal. Subsequently, the driving suggestion generation unit gives a preset driving suggestion based on the detected type of bad driving behavior and sends it to the electronic display unit and voice reminder unit. The following table refers to the preset normal driving standards in this embodiment to give some types of bad driving behaviors, judgment criteria and reasonable driving suggestions. It is worth noting that the bad driving behaviors listed in the table do not cover all possible erroneous behaviors in actual driving. Those skilled in the art should add or delete according to actual needs.

[0067] Table 1 shows the criteria for judging bad driving behavior and corresponding driving suggestions

[0068]

[0069] The electronic display unit is at least capable of displaying the carbon emissions of the car calculated by the carbon emissions calculation unit and the driving advice generated by the driving advice generation unit on an electronic screen, and providing real-time feedback to the driver. Furthermore, in order to allow the driver to have a more intuitive feeling of the data changes, the electronic display unit is capable of simultaneously displaying a portion of the historical carbon emissions and the driving distance and fuel consumption in the corresponding time period. The voice reminder unit is at least capable of emitting a short beep when the carbon emissions data increases abnormally and voice broadcasting the latest carbon emissions and driving advice at this time, so as to attract the driver's attention and adjust the driving behavior in time. Among them, the carbon emissions are sent by the carbon emissions data storage unit, and the driving advice is sent by the driving advice generation unit.

[0070] In summary, the carbon emission real-time feedback module in this embodiment incorporates a carbon emission prediction neural network to calculate carbon emissions. Because this carbon emission prediction neural network has strong nonlinear mapping capabilities, high self-learning and adaptive capabilities, and supports multi-parameter input, it is particularly suitable for nonlinear fitting between multiple operating parameters and vehicle carbon emissions. Furthermore, the carbon emission real-time feedback module workflow can be divided into two scenarios. First, during normal vehicle driving, the vehicle's carbon emissions are relatively stable, the driving advice generation unit does not generate driving advice, and the electronic display unit only displays current and partial historical carbon emissions data. Second, during vehicle driving, the vehicle's carbon emissions data abnormally increase due to improper driving behavior. The driving advice generation unit identifies the improper driving behavior and generates corresponding driving advice. The electronic display unit not only displays the carbon emissions but also the driving advice, and the voice reminder unit also broadcasts this information simultaneously. Through the electronic display and voice announcement, the driver can see the changes in the vehicle's carbon emissions in real time during driving and promptly correct any improper driving behavior, thereby achieving the goal of reducing carbon emissions during vehicle driving.

[0071] The above analysis demonstrates that this invention improves and innovates on the traditional in-vehicle gateway architecture, enabling local, real-time monitoring of vehicle carbon emissions while driving, helping drivers adjust their driving behavior in a timely manner. However, due to the limited computing power of vehicle-side equipment, it is difficult to impose more complex requirements on lightweight neural networks while ensuring that the carbon emission calculation data closely matches actual emissions. As a preferred solution, this can be supplemented by integrating cloud servers to further mine and analyze data features from the entire driving process. After the drive, a detailed analysis report is generated and fed back to the driver, helping them better understand the relationship between driving behavior and vehicle carbon emissions. Figure 4 It demonstrates the data transmission process between the vehicle gateway, the cloud, and the user's mobile terminal.

[0072] Based on the carbon emission monitoring vehicle gateway device and cloud server provided by the present invention, such as Figure 5 As shown, the present invention also provides a method for real-time feedback of carbon emissions in a terminal-cloud collaborative manner, comprising the following steps:

[0073] Step S1: Use bus communication technology to communicate with the ECUs on the vehicle and collect vehicle operating parameters, while using satellite positioning technology to obtain information such as the vehicle's position and speed;

[0074] Step S2: The carbon emission real-time feedback module in the vehicle gateway uses the collected relevant data to calculate carbon emissions and analyze driving behavior in real time locally;

[0075] Step S3: The carbon emission real-time feedback module compares the latest carbon emission data with the historical carbon emission data. If the carbon emission data increases abnormally and bad driving behavior is detected, the module provides driving suggestions to the driver to adjust the driving behavior.

[0076] Step S4: The vehicle gateway uses wireless communication technology to send the collected vehicle data, positioning information and locally calculated carbon emission data to the cloud server;

[0077] Step S5: The cloud server uses a deep learning model to further analyze driving data, verify carbon emissions, and evaluate all driving behaviors, summarizing them into a detailed analysis report, which is sent to the driver's mobile terminal at the end of the trip.

[0078] Specifically, steps S1 to S4 are all completed on the carbon emission monitoring vehicle gateway device. Step S1 collects relevant data during vehicle driving through the data acquisition module and positioning module; steps S2 and S3 are completed by the carbon emission real-time feedback module, which completes the calculation of carbon emissions, analysis of driving behavior, generation of driving suggestions, and feedback of relevant data on the local vehicle. Through the above three steps, it is possible to monitor vehicle carbon emission data locally in real time and promptly remind the driver when he or she exhibits bad driving behavior, avoiding the problem of poor feedback real-time due to network fluctuations and other problems, and ensuring the effectiveness of driving suggestions.

[0079] Furthermore, due to the computing power limitations of the electronic equipment on the vehicle side, it is difficult to conduct further research on the driving data and carbon emission data locally. Step S4 sends all relevant data of this driving and the locally calculated carbon emissions to the cloud, and the cloud server performs a deeper analysis. For example, the cloud server can use a deep convolutional neural network to further calculate the carbon emissions, quantitatively analyze the impact of different driving behaviors on carbon emissions, or use ViT (Vision Transformer) to analyze the video data of the dashcam and give more reasonable driving suggestions based on the characteristics of the specific driving environment. By feeding back these more specific and effective analyses to the driver, the driver will be able to have a clearer understanding of his or her own driving behavior, understand the potential correct driving choices, and cultivate more scientific and greener driving habits in future driving.

[0080] The foregoing description is merely a preferred embodiment of the present invention and is not intended to further limit the present invention. Persons skilled in the art will readily appreciate that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the present invention. Any equivalent variations made within the scope of the present invention and the accompanying drawings are considered within the scope of protection of the present invention.

Claims

1. A carbon emission control vehicle gateway, characterized by: It includes power module, microcontroller MCU module, data acquisition module, positioning module, data storage module, wireless communication module and carbon emission real-time feedback module; The power module supplies power to each module of the vehicle gateway to support the operation of the gateway; The MCU module realizes the coordinated control among the modules of the entire gateway; The data acquisition module uses the electronic control units ECUs to collect various data during vehicle driving, and pre-processes and prioritizes the data; The positioning module uses the satellite positioning system to obtain the position of the car in real time and transmits it to the MCU module; The data storage module is used to store all data acquired by the data acquisition module and the positioning module; The wireless communication module uses wireless communication technology to send the relevant data acquired by the data acquisition module and the positioning module and the carbon emission data calculated by the carbon emission real-time feedback module to the cloud; After the carbon emission real-time feedback module obtains the relevant data collected by the data collection module and the positioning module, it calculates the carbon emission data locally and analyzes the driving behavior. When the carbon emission data is abnormal, it can generate corresponding driving suggestions and can feed back the above relevant data to the driver; There is a unidirectional interface between the MCU module and the data acquisition module to transmit the collected data to the MCU module; there is a unidirectional interface between the MCU module and the positioning module to transmit the collected vehicle positioning and other information to the MCU module; there is a bidirectional interface between the MCU module and the data storage module to transmit all the data acquired by the data acquisition module and the positioning module to the data storage module and to call data from the data storage module when needed; there is a unidirectional interface between the MCU module and the wireless communication module to send the relevant data acquired by the data acquisition module and the positioning module and the carbon emission data calculated by the carbon emission real-time feedback module to the wireless communication module; there is a bidirectional interface between the MCU module and the carbon emission real-time feedback module to transmit the relevant data acquired by the data acquisition module and the positioning module to the carbon emission real-time feedback module for calculation and receiving the calculated carbon emissions; The data acquisition module includes a bus communication unit, a data preprocessing unit and a priority sorting unit.

2. A carbon emission control vehicle gateway according to claim 1, characterized in that: The carbon emission real-time feedback module includes a carbon emission calculation unit, a carbon emission data storage unit, a driving suggestion generation unit, an electronic display unit and a voice reminder unit: The carbon emission calculation unit is used to calculate the carbon emission of the car within n seconds by constructing a carbon emission prediction neural network; The carbon emission data storage unit is used to store all carbon emission data calculated by the automobile carbon emission calculation unit and arrange them in chronological order; The driving suggestion generating unit analyzes the driver's driving behavior based on the collected driving data and generates corresponding driving suggestions when the carbon emission data is abnormal; The electronic display unit is used to display data such as carbon emissions and driving suggestions in real time on an electronic screen and provide feedback to the driver; When the vehicle carbon emission data is abnormal, the voice reminder unit emits a buzzing sound to attract the driver's attention and voice broadcasts the current carbon emission and driving suggestions.

3. The carbon emission control vehicle gateway according to claim 1 is characterized in that: The data acquisition module includes a bus communication unit, a data preprocessing unit and a priority sorting unit; The bus communication unit uses bus technology to communicate with ECUs on the vehicle to collect various data; The data preprocessing unit implements screening and preprocessing of a large amount of data collected by the bus communication unit; The priority sorting unit tags the pre-processed data according to a preset sorting order, and the data with a high priority will be processed first during subsequent transmission and reception.

4. A carbon emission control vehicle gateway according to claim 3, characterized in that: The data preprocessing unit performs data cleaning, signal filtering and anomaly detection on the parsed data, removes invalid values ​​or erroneous data records through data cleaning, uses filters to remove noise in the signal, and marks abnormal data points caused by abnormal conditions such as sensor failure, so as to provide more accurate values ​​for subsequent data analysis.

5. The carbon emission control vehicle gateway according to claim 1 is characterized in that: The positioning module acquires and processes the positioning data; first, the positioning module uses the antenna to receive the message containing the positioning related data in real time; secondly, the positioning module parses the message into readable latitude and longitude, altitude and speed information according to the NMEA protocol, and sends it to the MCU module through the serial port.

6. A real-time feedback method for end-cloud collaboration based on the operation of the carbon emission control vehicle gateway according to any one of claims 1 to 5, characterized in that: The following steps are involved: Step S1: The data acquisition module uses bus communication technology to communicate with the ECUs on the vehicle and combines with the positioning module to collect driving data; Step S2: The carbon emission real-time feedback module in the vehicle gateway uses the collected relevant data to calculate the carbon emission in real time locally and analyze the driving behavior; Step S3: The carbon emission real-time feedback module compares the latest carbon emission data with the historical carbon emission data. If the carbon emission data increases abnormally and bad driving behavior is detected, the driver is given driving suggestions to adjust the driving behavior; Step S4: The vehicle gateway uses wireless communication technology to send the collected vehicle data, positioning information and locally calculated carbon emission data to the cloud server; Step S5: The cloud server uses a deep learning model to further analyze driving data, verify carbon emissions, and evaluate all driving behaviors, summarizing them into a detailed analysis report, which is sent to the driver's mobile terminal at the end of the driving.

Images