Gasoline engine remote emission monitoring method and system based on OBD and wireless communication

Abstract

The invention discloses a gasoline engine remote emission monitoring method and system based on OBD and wireless communication, and aims to solve the problems of insufficient data acquisition precision, low transmission reliability, poor safety, high power consumption and weak multi-dimensional analysis capability in the prior art. The method comprises four steps of multi-dimensional OBD data acquisition and preprocessing, dual-mode wireless transmission and national secret encryption, multi-module cooperative control and energy consumption optimization, and cloud data processing and multi-dimensional analysis. The system comprises a master control module, a data acquisition module, a wireless transmission module, a positioning module, a security encryption module and a power supply module, and software adopts a layered architecture and FreeRTOS cooperative scheduling. Through dual-mode acquisition, NRF24L01 + SIM900A dual-mode transmission, SM2 national secret encryption and multi-task cooperative scheduling combined with CAN and K lines, emission monitoring with high precision, low delay, high safety and low power consumption is realized, multi-dimensional analysis is supported, the national 6 standard requirements are met, and data support can be provided for vehicle emission pollution traceability and post-processing technology optimization.

Description

Technical Field

[0001] This invention belongs to the field of automotive exhaust emission monitoring technology, specifically relating to a method and system for remote emission monitoring of gasoline engines based on OBD and wireless communication. It is applicable to real-time emission monitoring of light gasoline vehicles meeting China VI emission standards and various gasoline engines under cold start conditions and after-processor heating conditions. Background Technology

[0002] Remote emission management on-board terminals (remote OBD systems) aim to achieve real-time monitoring of vehicle emission data during actual driving, overcoming the shortcomings of traditional bench testing and the discrepancy between emissions from real-world road conditions. The equipping of remote emission management on-board terminals (remote OBD systems) with future light-duty vehicles, including light-duty gasoline vehicles, is an inevitable trend.

[0003] However, existing gasoline engine emission monitoring technologies have significant limitations: First, data acquisition accuracy and coverage are insufficient. Traditional OBD systems mostly rely on a single CAN bus to read conventional emission data, failing to accurately capture the dynamic parameters of after-processing systems (TWC, GPF), and are susceptible to bus interference, resulting in a data packet loss rate exceeding 8%. Second, wireless transmission technology has poor adaptability. Some remote monitoring systems use a single GPRS transmission, with data transmission delays exceeding 30 seconds in remote areas with weak signals. The transmission protocol has not been optimized for real-time emission data, failing to meet the need for rapid source tracing of vehicles exceeding emission standards. Third, data security and reliability are lacking. Existing technologies mostly use RSA encryption algorithms, which suffer from complex key management, low encryption efficiency, and a lack of data integrity verification mechanisms, making them prone to data tampering risks. Fourth, multi-module collaboration is weak. Modules such as GPS positioning, emission data acquisition, and wireless transmission often operate independently without a collaborative scheduling mechanism, leading to excessive system power consumption and an inability to achieve multi-dimensional data correlation analysis.

[0004] In view of this, the present invention is proposed. Summary of the Invention

[0005] To solve the above-mentioned technical problems, the basic concept of the technical solution adopted by the present invention is as follows:

[0006] A method for remote emission monitoring of gasoline engines based on OBD and wireless communication includes the following steps:

[0007] Step S1: Multi-dimensional OBD data acquisition and preprocessing: Based on the OBD-II interface, a dual-mode communication protocol combining CAN bus and K-line is used to acquire engine operating condition data, after-treatment system dynamic parameters, fault codes and historical emission data. Noise reduction is achieved through an algorithm combining sliding window filtering and outlier removal. Timestamp synchronization is used to achieve spatiotemporal matching of multiple data. A temperature compensation module is added to adapt to the 200-250℃ after-treatment preheating condition.

[0008] Step S2: Dual-mode wireless transmission and data encryption: A dual-mode transmission architecture combining NRF and SIM is adopted. The transmission mode is automatically switched according to the signal strength. Secure data transmission is achieved through SM2 elliptic curve encryption algorithm and CRC32 check. A preemptive transmission mechanism is adopted based on data priority.

[0009] Step S3: Multi-module collaborative control and energy consumption optimization: A multi-task scheduling model is built based on FreeRTOS to realize the collaborative work of data acquisition, positioning, encryption, transmission and storage. The system power consumption is reduced through dynamic voltage regulation and idle module hibernation. The GPS module collects location information and associates it with emission data.

[0010] Step S4: Cloud Data Processing and Multi-Dimensional Analysis: The Alibaba Cloud platform parses and stores data through edge computing and cloud storage, performs multi-dimensional analysis based on the correlation model of operating conditions, emissions, and location, and triggers multi-level warnings for exceeding standards.

[0011] As a preferred embodiment of the present invention, the present invention also discloses a remote emission monitoring system for gasoline engines based on OBD and wireless communication, including a hardware architecture and a software architecture:

[0012] The hardware architecture includes a main control module, a data acquisition module, a wireless transmission module, a positioning module, a security encryption module, and a power supply module;

[0013] The software architecture includes a driver layer, a protocol layer, an application layer, and a cloud platform layer. The driver layer develops drivers for multiple peripherals, the protocol layer implements the parsing and encapsulation of various protocols, the application layer implements multi-task collaborative scheduling based on FreeRTOS, and the cloud platform layer develops a monitoring interface and integrates a map API to achieve visualization analysis.

[0014] In a preferred embodiment of the present invention, the sliding window filtering algorithm in step S1 is specifically implemented as follows: The sliding window size is set to N, and for continuously acquired raw data sequences... Filtered data sequence ;

[0015] Where N takes the value of 5-15, and the fluctuation range of the filtered data is ≤3%.

[0016] In a preferred embodiment of the present invention, the outlier removal algorithm in step S1 adopts... The principle, specifically, is: calculate the mean of the original data sequence. and standard deviation If data satisfy These are identified as outliers and removed. After removal, data is supplemented using linear interpolation. The interpolation formula is: in, This is supplementary data after interpolation. This is the timestamp for the collection of the corresponding data.

[0017] In a preferred embodiment of the present invention, the correction formula of the temperature compensation module in step S1 is as follows: ,in, This is the corrected pollutant concentration. The original detection value of the sensor. Here, T is the temperature correction factor, and T is the real-time exhaust temperature. The standard reference temperature is used, and the corrected detection error is ≤5%.

[0018] In a preferred embodiment of the present invention, the determination condition for dual-mode transmission switching in step S2 is as follows: the RSSI value is used to determine whether NRF24L01 is enabled for short-distance transmission when RSSI ≥ -80dBm; when RSSI < -80dBm, the system automatically switches to SIM900A for long-distance transmission and uploads data based on the MQTT protocol.

[0019] In a preferred embodiment of the present invention, the priority allocation of FreeRTOS task scheduling in step 3 satisfies the following: data encryption task priority > wireless transmission task priority > data acquisition task priority > GPS positioning task priority > data storage task priority, and the response time of the highest priority task is ≤1ms.

[0020] In a preferred embodiment of the present invention, the correlation model between operating conditions, emissions, and location in step S4 employs multiple linear regression analysis, and the model expression is:

[0021] Where E represents the emission pollutant concentration, W represents the engine operating parameters (speed / torque), S represents the vehicle location parameters (latitude and longitude derived values), L represents the engine thermal state parameters (temperature), and α, β, and γ are regression coefficients. The error term represents the model fit, and the goodness of fit R² is ≥ 0.85.

[0022] In a preferred embodiment of the present invention, the main control module adopts an STM32H7 series microcontroller with an integrated hardware encryption engine, which is connected to the data acquisition module and the security encryption module respectively through the FSMC interface.

[0023] In a preferred embodiment of the present invention, the data acquisition module includes a CAN bus controller and a K-line protocol conversion chip, wherein the K-line protocol conversion chip adopts a TJA1020 transceiver and is configured with the temperature compensation circuit based on a PN junction sensor; in the wireless transmission module, the NRF short-range transmission unit adopts an nRF52840 chip, and the SIM remote transmission unit adopts a 4GCat.1 communication module based on the Quectel EC200S series.

[0024] Compared with the prior art, the present invention has the following advantages:

[0025] This invention features multi-protocol fusion data acquisition: It solves the problem of incomplete data coverage on a single bus by using dual-mode communication combining CAN and K-line; it employs SM2 encryption algorithm combined with a hardware encryption chip to ensure data transmission and storage security, meeting the encryption requirements of the China VI emission standard; multi-module collaborative scheduling: based on the FreeRTOS task scheduling mechanism, it enables collaborative work of acquisition, transmission, and positioning, reducing system power consumption while achieving multi-dimensional data correlation analysis; engine after-treatment heating condition adaptation: An added temperature compensation module can accurately monitor temperature changes under engine after-treatment heating conditions of 200-250℃, providing technical support for pollution control during the cold start process of gasoline engines.

[0026] The specific embodiments of the present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0027] In the attached diagram:

[0028] Figure 1 This is a flowchart of the dual-mode wireless transmission switching process of the present invention;

[0029] Figure 2 This is an architecture diagram of a gasoline engine remote emission monitoring system based on OBD and wireless communication.

[0030] Figure 3 This is a system architecture diagram of a gasoline engine emission control system. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments will be clearly and completely described below with reference to the accompanying drawings. The following embodiments are used to illustrate the present invention.

[0032] A method for remote emission monitoring of gasoline engines based on OBD and wireless communication includes the following steps:

[0033] Step S1: Multi-dimensional OBD data acquisition and preprocessing: Based on the OBD-II interface, a dual-mode communication protocol combining CAN bus and K-line is used to acquire engine operating condition data, after-treatment system dynamic parameters, fault codes and historical emission data. Noise reduction is achieved through an algorithm combining sliding window filtering and outlier removal. Timestamp synchronization is used to achieve spatiotemporal matching of multiple data. A temperature compensation module is added to adapt to the 200-250℃ after-treatment preheating condition.

[0034] Step S2: Dual-mode wireless transmission and data encryption: A dual-mode transmission architecture combining NRF and SIM is adopted. The transmission mode is automatically switched according to the signal strength. Secure data transmission is achieved through SM2 elliptic curve encryption algorithm and CRC32 check. A preemptive transmission mechanism is adopted based on data priority.

[0035] Step S3: Multi-module collaborative control and energy consumption optimization: A multi-task scheduling model is built based on FreeRTOS to realize the collaborative work of data acquisition, positioning, encryption, transmission and storage. The system power consumption is reduced through dynamic voltage regulation and idle module hibernation. The GPS module collects location information and associates it with emission data.

[0036] Step S4: Cloud Data Processing and Multi-Dimensional Analysis: The Alibaba Cloud platform parses and stores data through edge computing and cloud storage, performs multi-dimensional analysis based on the correlation model of operating conditions, emissions, and location, and triggers multi-level warnings for exceeding standards.

[0037] As a preferred embodiment of the present invention, the present invention also discloses a remote emission monitoring system for gasoline engines based on OBD and wireless communication, including a hardware architecture and a software architecture:

[0038] The hardware architecture includes a main control module, a data acquisition module, a wireless transmission module, a positioning module, a security encryption module, and a power supply module;

[0039] The software architecture includes a driver layer, a protocol layer, an application layer, and a cloud platform layer. The driver layer develops drivers for multiple peripherals, the protocol layer implements the parsing and encapsulation of various protocols, the application layer implements multi-task collaborative scheduling based on FreeRTOS, and the cloud platform layer develops a monitoring interface and integrates a map API to achieve visualization analysis.

[0040] In a preferred embodiment of the present invention, the sliding window filtering algorithm in step S1 is specifically implemented as follows: The sliding window size is set to N, and for continuously acquired raw data sequences... Filtered data sequence ;

[0041] Where N takes the value of 5-15, and the fluctuation range of the filtered data is ≤3%.

[0042] In a preferred embodiment of the present invention, the outlier removal algorithm in step S1 adopts... The principle, specifically, is: calculate the mean of the original data sequence. and standard deviation If data satisfy These are identified as outliers and removed. After removal, data is supplemented using linear interpolation. The interpolation formula is: in, This is supplementary data after interpolation. This is the timestamp for the collection of the corresponding data.

[0043] In a preferred embodiment of the present invention, the correction formula of the temperature compensation module in step S1 is as follows: ,in, This is the corrected pollutant concentration. The original detection value of the sensor. Here, T is the temperature correction factor, and T is the real-time exhaust temperature. The standard reference temperature is used, and the corrected detection error is ≤5%.

[0044] In a preferred embodiment of the present invention, the determination condition for dual-mode transmission switching in step S2 is as follows: the RSSI value is used to determine whether NRF24L01 is enabled for short-distance transmission when RSSI ≥ -80dBm; when RSSI < -80dBm, the system automatically switches to SIM900A for long-distance transmission and uploads data based on the MQTT protocol.

[0045] In a preferred embodiment of the present invention, the priority allocation of FreeRTOS task scheduling in step 3 satisfies the following: data encryption task priority > wireless transmission task priority > data acquisition task priority > GPS positioning task priority > data storage task priority, and the response time of the highest priority task is ≤1ms.

[0046] In a preferred embodiment of the present invention, the correlation model between operating conditions, emissions, and location in step S4 employs multiple linear regression analysis, and the model expression is:

[0047] Where E represents the emission pollutant concentration, W represents the engine operating parameters (speed / torque), S represents the vehicle location parameters (latitude and longitude derived values), L represents the engine thermal state parameters (temperature), and α, β, and γ are regression coefficients. The error term represents the model fit, and the goodness of fit R² is ≥ 0.85.

[0048] In a preferred embodiment of the present invention, the main control module adopts an STM32H7 series microcontroller with an integrated hardware encryption engine, which is connected to the data acquisition module and the security encryption module respectively through the FSMC interface.

[0049] In a preferred embodiment of the present invention, the data acquisition module includes a CAN bus controller and a K-line protocol conversion chip, wherein the K-line protocol conversion chip adopts a TJA1020 transceiver and is configured with the temperature compensation circuit based on a PN junction sensor; in the wireless transmission module, the NRF short-range transmission unit adopts an nRF52840 chip, and the SIM remote transmission unit adopts a 4GCat.1 communication module based on the Quectel EC200S series.

[0050] Test Example 1: Monitoring of Gasoline Engine Emissions on Urban Roads

[0051] Experimental conditions: One light-duty gasoline vehicle (1.5L turbocharged engine) was selected for 24-hour continuous monitoring on congested urban roads (speed 0-40km / h) and expressways (speed 60-80km / h);

[0052] The system monitors vehicle speed (km / h), engine speed (r / min), throttle valve absolute position (%), actual boost pressure (kPa), fuel flow rate (g / s), air flow rate (kg / h), vehicle VIN code, front oxygen sensor signal voltage (V), rear oxygen sensor signal voltage (V), ignition advance angle (°), ambient temperature (°C), intake air temperature (°C), atmospheric pressure (kPa), malfunction indicator light status (on / off), fault code information, and NOx concentration (sensor detection value or model prediction value). The status of the malfunction indicator light is used to determine whether vehicle emissions exceed standards. If the malfunction indicator light is on while the vehicle is in motion, emissions are considered excessive; if the malfunction indicator light is off while the vehicle is in motion, emissions are considered within standards. Actual monitoring results show that the emissions of the selected light-duty gasoline vehicle tested did not exceed standards. The NOx emission rate is calculated based on the monitored NOx concentration and exhaust flow (fuel flow rate + air flow rate) data.

[0053] Table 1. Average values ​​of core parameters for gasoline engine emission monitoring in urban roads, Example 1.

[0054] Monitoring section Average vehicle speed (km / h) Average engine speed (r / min) Average fuel flow rate (g / s) Average airflow (kg / h) NOx emission rate (mg / s) Fault indicator status Do emissions meet standards? Urban congested road sections 25 1600 1.5 71 2.2 Extinguish yes Urban expressway 70 1900 1.7 84 3.5 Extinguish yes

[0055] Example 2: Collecting IUPR (In-Use Performance Ratio) data from 10 in-use gasoline vehicles

[0056] The system collects data on vehicle start-up counts, OBD monitoring completion counts, catalytic converter, oxygen sensor, etc., including numerator and denominator data for the secondary oxygen sensor, EGR system, VVT system, secondary air system, and evaporative system (if leak monitoring is present). The ignition cycle counter records the number of ignition cycles the vehicle experiences. The ignition cycle counter increments no more than once per operating cycle. The numerator counter of the monitored system indicates the number of times the monitoring conditions set for that monitoring function were met during vehicle operation. Generally, monitoring is only performed when all monitoring conditions specified by the manufacturer are met. Unless there is a reasonable technical reason, the numerator counter should not increase more than once per operating cycle. The denominator counter indicates the number of times a specific monitoring function met its specified monitoring conditions. If the above conditions are met during the operating cycle, the denominator counter generally increases according to relevant regulations.

[0057] The on-board data acquisition terminal collects vehicle parameters via the CAN bus and stores the data locally. Simultaneously, it transmits the data to the data management platform in real time. The received IUPR data is monitored and processed in real time. To quantify the monitoring frequency of emission-related components by the OBD system and ensure its full operation, regulations require the IUPR to output ignition cycle counters and general denominator counters, as well as numerator and denominator counters for monitoring items such as the catalytic converter, oxygen sensor / exhaust gas sensor, evaporator system, EGR system, VVT system, secondary air system, rear oxygen sensor, fuel system, and particulate filter. IUPR is the frequency at which diagnostic functions operate during routine vehicle use; it is a long-term statistical ratio. IUPR-related regulations require the following: ① The average IUPRM value must be greater than or equal to 0.1; ② More than 50% of vehicles must have an IUPRM value greater than or equal to 0.1.

[0058] Test results show that the maximum IUPR value of the EGR of the test vehicles was 4.52, and the minimum was 0.12, both exceeding the standard limit of 0.1. The average value was 1.76, meaning that the EGR of all test vehicles met the requirements, and the EGR monitoring frequency of most vehicles was much higher than the limit requirement. The boundary conditions for the molecular counter of EGR monitoring are set by the manufacturer, hence the differences in IUPR values ​​between different brands of vehicles.

[0059] The maximum IUPR of the catalytic converter in the test vehicle was 1.89, and the minimum was 0.12. The average IUPRM of the entire fleet was 0.68, which is greater than 0.1 and meets the regulatory limit requirements.

[0060] The maximum IUPR value of the oxygen sensor in the test vehicles was 4.7, and the minimum was 0.08. One out of ten vehicles had an IUPR value less than the limit of 0.1. This may be due to the manufacturer's molecular monitoring conditions not matching the actual road conditions in my country. The average IUPR value of the entire fleet was 0.84, which is greater than 0.1 and meets the regulatory limit.

[0061] The maximum value of the fuel evaporation system IUPR of the test vehicle was 3.09, and the minimum value was 0.14. The average value of the IUPRM of the entire fleet was 1.02, which is greater than 0.1 and meets the regulatory limit requirements.

[0062] The maximum IUPR of the VVT ​​system of the test vehicle was 2.83, and the minimum was 0.13. The average IUPRM of the entire fleet was 0.94, which is greater than 0.1 and meets the regulatory limit requirements.

[0063] By statistically analyzing the average values ​​of various IUPR data of the test vehicle, it can be seen that the average IUPR values ​​of the catalytic converter, oxygen sensor, EGR, fuel evaporation system and VVT system monitoring items are all much greater than the regulatory requirement of 0.1.

[0064] Table 2 Summary of IUPR data from 210 in-use gasoline vehicles in Experiment Example 2

[0065] Monitoring Projects Maximum value Minimum value average value Number of vehicles meeting regulatory requirements (IUPRM≥0.1) Number of vehicles not meeting regulatory requirements Compliance rate EGR system 4.52 0.12 1.76 10 0 100% Catalytic converter 1.89 0.12 0.68 10 0 100% Oxygen sensor 4.7 0.08 0.84 9 1 96% Evaporation system 3.09 0.14 1.02 10 0 100% VVT system 2.83 0.13 0.94 10 0 100%

[0066] Example 3: Estimating vehicle mileage and fuel consumption based on test data

[0067] Test vehicles were selected and continuously monitored for 24 hours on congested urban roads (speed 0-40 km / h), expressways (speed 60-80 km / h), and highways. Data was monitored including vehicle speed (km / h), engine speed (r / min), fuel flow rate (g / s), air flow rate (kg / h), vehicle VIN code, ambient temperature (°C), intake air temperature (°C), and atmospheric pressure (kPa), with data transmitted and received every second. The vehicle's mileage and fuel consumption for that driving range can be calculated from the data, and the fuel consumption per 100 kilometers can be calculated to evaluate its economy.

[0068] Table 3. Statistics of vehicle mileage and fuel consumption on different road sections in Experiment Example 3

[0069] Monitoring section Monitoring duration (h) Total mileage (km) Average vehicle speed (km / h) Cumulative fuel consumption (L) Fuel consumption per 100 kilometers (L / 100km) Ambient temperature (°C) Inlet air temperature (°C) Urban congested road sections 8 180 22.5 16.2 9.0 28 32 Urban expressway 10 650 65 45.5 7.0 28 30 highway section 6 540 90 32.4 6.0 26 28

[0070] Table 4. Detailed Test Table of Dual-Mode Transmission and Encryption Performance in Example 3

[0071] Test Project Test conditions Test Results Improved effect compared to traditional technology NRF24L01 Short-Range Transmission RSSI ≥ -80dBm, transmitting 1000 data entries Latency ≤ 10ms, packet loss rate 0% Latency reduced by 40%, packet loss rate reduced to 0 SIM900A Long-Distance Transmission RSSI < -80dBm, transmitting 1000 data entries Delay ≤ 25s, success rate 99.8% Success rate increased by 5%, delay reduced by 15 seconds SM2 encryption time Single NOx concentration data (16 bytes) Encryption time ≤ 8ms, decryption time ≤ 3ms Encryption efficiency improved by 30% CRC32 check 1000 encrypted data transmissions 100% success rate in verification. No data tampering, improved reliability Preemptive transmission mechanism (for data exceeding limits) Simulated NOx concentration exceeds the standard (>50 mg / s) Priority transmission delay ≤3ms The response speed has been improved by 60% beyond the standard.

[0072] The engine used in this design is a 1.5-liter turbocharged gasoline engine. Figure 3As shown, the gasoline engine aftertreatment system adopts the EHC+TWC+DPF+pSCR technology route that meets China VI and above emission standards. Therefore, it is also necessary to add corresponding sensors to support this closed-loop system.

[0073] (I) Experimental Materials and Equipment

[0074] Experimental subject: A 1.5L turbocharged gasoline engine sedan (aftertreatment system adopts EHC+TWC+DPF+pSCR technology route), adapted to China VI emission standards, with emission control system as follows: Figure 3 As shown;

[0075] A method and system for remote emission monitoring of gasoline engines based on OBD and wireless communication, such as Figure 2 As shown. Core equipment: STM32F103ZET6 development board, TJA1050 CAN transceiver, SIM900AGPRS module, L76-LBGPS module, Huada HC32F460 encryption chip, LabVIEW 2021 monitoring platform, Alibaba Cloud IoT platform;

[0076] Testing tools: INCA software (ECU data reading), STARS software (bench data synchronization), and Serial Hunter V3.1.

[0077] (II) Experimental Procedure

[0078] 1. System Setup and Initialization

[0079] Hardware connections: The OBD interface connects to CAN-H, CAN-L, and K lines; the main control chip STM32F103ZET6 connects to NRF24L01 via SPI; UART3 connects to SIM900A; UART2 connects to the GPS module; and I2C connects to the encryption chip.

[0080] Software initialization: Write a FreeRTOS task scheduler in MDKKeil5, configure the CAN filter, UART baud rate, SPI transmission rate, and initialize SM2 encryption parameters.

[0081] 2. Data Acquisition and Transmission Test

[0082] Targeted data acquisition: When the gasoline engine is started, the CAN bus reads the upstream temperature of TWC, DPF differential pressure, and engine speed in real time, and reads fault codes on the K line. The data is filtered and synchronized by timestamp.

[0083] Dual-mode transmission test: For short distances, data is transmitted via NRF24L01; for long distances, data is switched to SIM900A and uploaded to Alibaba Cloud based on the MQTT protocol.

[0084] Encryption verification: The uploaded NOx concentration data is encrypted using SM2, which increases the data length by 30%. The decryption time is ≤5ms, and the data is verified to be tamper-proof using CRC32.

[0085] 3. Multi-dimensional analysis and early warning testing

[0086] GPS-Emissions Association: GPS collects vehicle location and associates it with emissions fault monitoring to display whether the vehicle's emissions exceed the standard, monitors the status of fault indicators, and monitors whether fault codes are recorded.

[0087] Gasoline engine close-coupled catalytic converter heating condition monitoring: During cold start, the gasoline engine close-coupled catalytic converter is heated to 200-250℃ to improve TWC conversion efficiency. The system monitors the heating circuit and temperature. If a fault occurs, an early warning is triggered (local audible and visual alarm + cloud push).

[0088] Power consumption test: Before the engine starts, the current and voltage of the gasoline engine's tight-coupled catalytic converter heating circuit are monitored to check the heater power consumption and whether the time to reach the target temperature meets the control requirements.

[0089] (III) Experimental Results

[0090] In this embodiment, the system achieves the following performance metrics:

[0091] Data acquisition: The post-processing system acquires 12 types of parameters (6 more than traditional technologies), with a detection error of ≤3%;

[0092] Wireless transmission: short-range latency ≤10ms, long-range success rate ≥99.5%;

[0093] Data security: SM2 encryption takes ≤10ms, and the key protection level meets the China VI standard;

[0094] Power consumption: Standby power consumption ≤150mW, operating power consumption ≤300mW;

[0095] Multi-dimensional analysis: It can generate reports on the correlation between location, emissions and operating conditions, with an early warning response time of ≤10s.

[0096] Performance indicators Test Results Data collection The post-treatment system collects 12 types of parameters, with a detection error ≤3% and a NOx detection limit of 1 ppm. wireless transmission Short-distance latency ≤10ms, packet loss rate 0%; long-distance latency ≤30s, transmission success rate ≥99.5%. Data security SM2 encryption takes ≤10ms, key protection level meets China VI standard, and CRC32 checksum success rate is 100%. System power consumption Standby power consumption ≤150mW, operating power consumption ≤300mW Collaborative Analysis It can generate a "location-emission-operating condition" related report, with GPS positioning accuracy ≤10m. Early warning response The response time for exceeding the standard warning is ≤10 seconds, and it supports multi-level warnings in both local and cloud environments.

[0097] Experimental results show that the present invention can achieve high-precision, low-latency, and high-security remote monitoring of gasoline engine emission data, meeting the regulatory requirements of the China VI emission standard, and providing reliable data support for emission pollution source tracing and after-treatment technology optimization.

Claims

1. A method and system for remote emission monitoring of gasoline engines based on OBD and wireless communication, characterized in that, Includes the following steps: Step S1: Multi-dimensional OBD data acquisition and preprocessing: Based on the OBD-II interface, a dual-mode communication protocol combining CAN bus and K-line is used to acquire engine operating condition data, after-treatment system dynamic parameters, fault codes and historical emission data. Noise reduction is achieved through an algorithm combining sliding window filtering and outlier removal. Timestamp synchronization is used to achieve spatiotemporal matching of multiple data. A gasoline engine after-treatment temperature compensation module is added to adapt to the 200-250℃ after-treatment preheating condition. Step S2: Dual-mode wireless transmission and data encryption: A dual-mode transmission architecture combining NRF and SIM is adopted. The transmission mode is automatically switched according to the signal strength. Secure data transmission is achieved through SM2 elliptic curve encryption algorithm and CRC32 check. A preemptive transmission mechanism is adopted based on data priority. Step S3: Multi-module collaborative control and energy consumption optimization: A multi-task scheduling model is built based on FreeRTOS to realize the collaborative work of data acquisition, positioning, encryption, transmission and storage. The system power consumption is reduced through dynamic voltage regulation and idle module hibernation. The GPS module collects location information and associates it with emission data. Step S4: Cloud Data Processing and Multi-Dimensional Analysis: The Alibaba Cloud platform parses and stores data through edge computing and cloud storage, performs multi-dimensional analysis based on the correlation model of operating conditions, emissions, and location, and triggers multi-level warnings for exceeding standards.

2. The gasoline engine remote emission monitoring system based on OBD and wireless communication according to claim 1, characterized in that, Including hardware architecture and software architecture: The hardware architecture includes a main control module, a data acquisition module, a wireless transmission module, a positioning module, a security encryption module, and a power supply module; The software architecture includes a driver layer, a protocol layer, an application layer, and a cloud platform layer. The driver layer develops drivers for multiple peripherals, the protocol layer implements the parsing and encapsulation of various protocols, the application layer implements multi-task collaborative scheduling based on FreeRTOS, and the cloud platform layer develops a monitoring interface and integrates a map API to achieve visualization analysis.

3. The method for remote emission monitoring of gasoline engines based on OBD and wireless communication according to claim 1, characterized in that, The specific implementation of the sliding window filtering algorithm in step S1 is as follows: Set the sliding window size to N, for continuously acquired raw data sequences... Filtered data sequence ; Where N takes the value of 5-15, and the fluctuation range of the filtered data is ≤3%.

4. The method for remote emission monitoring of gasoline engines based on OBD and wireless communication according to claim 1, characterized in that, The outlier removal algorithm in step S1 adopts The principle, specifically, is: calculate the mean of the original data sequence. and standard deviation If data satisfy These are identified as outliers and removed. After removal, data is supplemented using linear interpolation. The interpolation formula is: in, This is supplementary data after interpolation. This is the timestamp for the collection of the corresponding data.

5. The method for remote emission monitoring of gasoline engines based on OBD and wireless communication according to claim 1, characterized in that, The correction formula for the temperature compensation module in step S1 is as follows: ,in, The corrected pollutant concentration, The original detection value of the sensor. Here, T is the temperature correction factor, and T is the real-time exhaust temperature. The standard reference temperature is used, and the corrected detection error is ≤5%.

6. The method for remote emission monitoring of gasoline engines based on OBD and wireless communication according to claim 1, characterized in that, The condition for switching between dual-mode transmission in step S2 is as follows: the RSSI value is used to determine whether the NRF24L01 is enabled for short-distance transmission when RSSI ≥ -80dBm, and when RSSI < -80dBm, the SIM900A is automatically switched to for long-distance transmission and data is uploaded based on the MQTT protocol.

7. The method and system for remote emission monitoring of gasoline engines based on OBD and wireless communication according to claim 1, characterized in that, In step 3, the priority allocation of FreeRTOS task scheduling satisfies the following order: data encryption task priority > wireless transmission task priority > data acquisition task priority > GPS positioning task priority > data storage task priority, with the highest priority task response time ≤ 1ms.

8. The method and system for remote emission monitoring of gasoline engines based on OBD and wireless communication according to claim 1, characterized in that, In step S4, the correlation model between operating conditions, emissions, and location uses multiple linear regression analysis. The model expression is as follows: Where E represents the emission pollutant concentration, W represents the engine operating parameters (speed / torque), S represents the vehicle location parameters (latitude and longitude derived values), L represents the engine temperature parameters (temperature), and α, β, and γ are regression coefficients. The error term represents the model fit, and the goodness of fit R² is ≥ 0.

85.

9. A remote emission monitoring system for gasoline engines based on OBD and wireless communication according to claim 2, characterized in that, The main control module uses an STM32H7 series microcontroller with an integrated hardware encryption engine, which is connected to the data acquisition module and the security encryption module through the FSMC interface.

10. A gasoline engine remote emission monitoring system based on OBD and wireless communication according to claim 2, wherein the data acquisition module includes a CAN bus controller and a K-line protocol conversion chip, wherein the K-line protocol conversion chip adopts a TJA1020 transceiver and is configured with the temperature compensation circuit based on a PN junction sensor; in the wireless transmission module, the NRF short-range transmission unit adopts an nRF52840 chip, and the SIM remote transmission unit adopts a 4GCat.1 communication module based on the Quectel EC200S series.

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