Automobile exhaust detection method and system based on gas sensor array

Abstract

The invention relates to the technical field of automobile exhaust detection, in particular to an automobile exhaust detection method and system based on a gas sensor array, and aims to analyze the damage of a sensor in the operation process according to the environment of the sensor and the change of detection data in the detection process. According to the method, the damage of the gas sensor is comprehensively evaluated, and the maintenance period is finally determined according to the damage comprehensive evaluation result of the gas sensor, so that the reliability and stability of the gas sensor can be remarkably improved, the maintenance period is dynamically adjusted according to actual abnormal conditions, and the sensor can be always in a good running state; the safety accident risk caused by sensor faults is reduced, the maintenance cost can be effectively reduced, the high cost caused by blind regular maintenance is avoided through accurate arrangement of maintenance work, meanwhile, the production loss caused by shutdown due to sensor faults is reduced, and the win-win situation of economic benefits and equipment performance is achieved.

Description

Technical Field

[0001] This invention relates to the field of automotive exhaust gas detection technology, and in particular to a method and system for automotive exhaust gas detection based on a gas sensor array. Background Technology

[0002] To effectively control vehicle exhaust emissions and reduce their harm to the environment and human health, countries around the world have formulated strict vehicle exhaust emission standards and required automobile manufacturers to install exhaust emission detection systems on vehicles to monitor exhaust emissions in real time and ensure that vehicle exhaust emissions comply with relevant standards. Gas sensors, as the core component of vehicle exhaust emission detection systems, can accurately and in real time detect the content of various components in exhaust gases, providing crucial data support for exhaust emission monitoring and control. However, gas sensors currently used in vehicle exhaust emission detection face many challenges in practical applications. On the one hand, the exhaust emission environment is complex and variable. Exhaust gases not only contain various harmful gases but also involve harsh conditions such as high temperature, high humidity, corrosive gases, airflow impact, and vibrations generated by vehicle operation. These factors can cause varying degrees of damage to gas sensors, leading to sensor performance degradation, increased measurement errors, and even malfunctions, thus affecting the accuracy and reliability of exhaust emission detection. On the other hand, traditional gas sensor maintenance methods typically use fixed standard maintenance cycles. This method cannot be flexibly adjusted according to the actual damage to the sensor, which may lead to problems such as untimely or excessive maintenance. Regular maintenance of sensors with minor damage and relatively stable performance can lead to unnecessary waste of human, material, and time resources, increasing maintenance costs. On the other hand, for sensors with severe damage and rapidly deteriorating performance, fixed maintenance cycles may not be able to respond to their actual maintenance needs in a timely manner, resulting in the sensors not being repaired in time before serious failures occur. This, in turn, affects the normal operation of the exhaust gas detection system and makes it impossible to effectively monitor and control vehicle exhaust emissions.

[0003] Furthermore, most existing vehicle exhaust emission detection methods only focus on the detection results of exhaust gas components, while ignoring the health status of the gas sensors themselves. They lack real-time monitoring and accurate assessment of sensor damage, making it impossible to detect potential sensor problems in a timely manner and difficult to take effective maintenance measures in advance, thereby reducing the reliability and stability of the exhaust emission detection system.

[0004] In view of this, the applicant proposes a method and system for detecting automobile exhaust gases based on a gas sensor array. Summary of the Invention

[0005] In order to overcome the defects and shortcomings of the existing technology, the present invention provides a method and system for detecting automobile exhaust gas based on a gas sensor array.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for detecting automobile exhaust gases based on a gas sensor array, comprising the following steps: S100: Obtain gas sensor detection data, exhaust gas condition data, and corresponding vehicle transportation condition data at the corresponding exhaust gas emission location. S200: By analyzing exhaust gas data and corresponding vehicle transportation data, gas sensor damage anomaly analysis is performed. S300. Perform gas sensor damage analysis during the detection process by analyzing changes in gas sensor detection data. S400. Based on the gas sensor damage anomaly analysis results and the gas sensor damage analysis results during the detection process, perform gas sensor anomaly analysis. S500, based on the gas sensor anomaly analysis results and standard maintenance cycle, analyzes the actual gas sensor maintenance cycle; S600: Perform gas sensor maintenance according to the actual gas sensor maintenance cycle reminder.

[0007] In one implementation of the present invention, step S100 includes the following specific contents: Step 110: Install a gas sensor array in the exhaust pipe of the corresponding vehicle to obtain the content of each component of the vehicle exhaust gas. Obtain the detection status of each component of the exhaust gas during the operation of the gas sensor and the safe range of the corresponding gas sensor. Analyze the damage to the measurement process by the influence of the rate of change of the detected components on the measurement accuracy of the gas sensor. At the same time, conduct a quantitative analysis of the impact of the corrosion effect of the detected components on the sensor. In this case, the gas sensor corresponds one-to-one with the corresponding gas. Step 120: Simultaneously, environmental data at the corresponding exhaust pipe location during vehicle operation is acquired through environmental sensors, including environmental data such as ambient temperature, humidity, corrosive gas concentration, and airflow velocity, as well as vibration conditions such as vibration amplitude and vibration frequency caused by the vehicle. By analyzing the impact of environmental data and vehicle operation on the sensors respectively, the degree of external damage to the sensors by the overall environment is assessed. Step 130: Store the acquired data in the storage module for later use.

[0008] In one implementation of the present invention, the gas sensor damage anomaly analysis in step S200 includes the following specific steps: Step 210: Obtain environmental data inside the exhaust pipe during historical vehicle operation and the average vibration caused by the corresponding vehicle operation. Step 220: Perform an environmental impact anomaly analysis by comparing the environmental data inside the exhaust pipe with the safe range of the corresponding environment that the gas sensor can withstand. The environmental data includes ambient temperature, humidity, corrosive gas concentration, and airflow velocity. The specific steps are as follows: obtain the average standard deviation of the environmental data and the safe range of the corresponding environmental type that the gas sensor can withstand within a set period, set it as the anomaly value of the corresponding environmental type, and perform a weighted summation of the anomaly values ​​of all environmental types to obtain the environmental impact anomaly analysis result. Step 230: Obtain the average vibration caused by vehicle operation. Set the ratio of the average vibration amplitude to the maximum value of the safe vibration amplitude range of the gas sensor as the amplitude anomaly value, and set the ratio of the average vibration frequency to the maximum value of the safe vibration frequency range of the gas sensor as the vibration frequency anomaly value. Multiply the amplitude anomaly value and the vibration frequency anomaly value to obtain the vibration anomaly analysis result. Use the experimentally determined safe vibration amplitude and frequency range as the benchmark, compare the actual vibration data with them, and quantify the degree of vibration impact. The product of amplitude and frequency anomaly values ​​comprehensively considers the synergistic effect of vibration intensity and frequency, and more comprehensively reflects the vibration hazards. Step 240: The environmental impact anomaly analysis results and the vibration anomaly analysis results are weighted and summed to obtain the gas sensor damage anomaly analysis results. The environmental and vibration anomaly analysis results quantify the damage contribution of different factors respectively. The weighted summation ensures that the assessment is more reasonable, conforms to the objective law of the superposition of multiple factors under actual working conditions, and improves the accuracy of prediction.

[0009] In one implementation of the present invention, the gas sensor damage analysis in step S300 specifically includes the following: Step 310: Obtain the changes in the corresponding gas in the vehicle exhaust emissions collected by the corresponding gas sensor, and at the same time obtain the safe range of the gas sensor. Frequent high-concentration impacts can cause irreversible passivation of the electrode catalyst of the electrochemical sensor or changes in the metal oxide lattice structure of the semiconductor sensor, resulting in decreased sensitivity. At the same time, drastic concentration changes may trigger the sensor memory effect, leading to baseline drift. Therefore, it is necessary to analyze the abnormal effects of these factors on the gas sensor. By monitoring the comparison between the actual exposure concentration of the gas sensor and the safe range, it is possible to predict whether the sensor is in an overload risk state and avoid irreversible damage due to long-term over-limit use. Step 320: Obtain the changes in the corresponding gas in the vehicle exhaust emissions and obtain the abnormal concentration changes of the corresponding gas. The abnormal concentration changes of the corresponding gas are calculated as follows: the absolute value of the difference between the concentration value of the corresponding gas at the corresponding moment within the corresponding vehicle driving cycle and the concentration of the corresponding gas at the previous moment, divided by the safe range value of the gas sensor, to obtain the abnormal concentration changes at the corresponding moment. The abnormal concentration changes of the corresponding gas are then averaged over all moments within the corresponding driving cycle. Step 330: Obtain the concentration change anomaly at the corresponding time within the corresponding vehicle driving cycle, and at the same time obtain the ratio of the gas concentration value at the corresponding time to the maximum value of the range to obtain the concentration magnitude anomaly at the corresponding time. Multiply the concentration magnitude anomaly at the corresponding time with the concentration change anomaly to obtain the high concentration impact anomaly at the corresponding time. Average the high concentration impact anomalies at all times within the corresponding driving cycle duration to obtain the high concentration impact anomaly of the corresponding gas. This analysis of the impact of high concentration impact of gas concentration change on the gas sensor, combined with the instantaneous absolute value of concentration and the rate of change, more comprehensively reflects the overall damage potential. Step 340: The corresponding gas concentration change anomalies and the corresponding gas high concentration impact anomalies are weighted and summed to obtain the gas sensor damage analysis results during the detection process.

[0010] In one implementation of the present invention, the gas sensor anomaly analysis in step S400 includes the following specific contents: The gas sensor damage anomaly analysis results and the gas sensor damage analysis results during the detection process are obtained. The two are weighted and summed to obtain the gas sensor anomaly analysis result. The gas sensor damage anomaly analysis result is the gas sensor damage caused by external factors, while the gas sensor damage analysis result during the detection process is the gas sensor damage caused by internal factors during the detection process. Although the damage mechanisms of external and internal factors are independent of each other, they will jointly accelerate the sensor failure.

[0011] In one implementation of the present invention, the analysis of the actual gas sensor maintenance cycle in step S500 includes the following specific contents: The gas sensor anomaly analysis result is obtained by dividing it by the set gas sensor anomaly analysis threshold to obtain the sensor anomaly impact coefficient. The standard maintenance cycle is then divided by the sensor anomaly impact coefficient to obtain the actual gas sensor maintenance cycle. There is a close correlation between the degree of gas sensor anomaly and the maintenance cycle. The sensor anomaly analysis result reflects the current abnormal state of the sensor, while the set anomaly analysis threshold is determined based on the sensor's normal operation standards and safety requirements. The anomaly impact coefficient obtained by dividing the two can quantify the degree of impact of sensor anomalies on its normal operation. Since sensor anomalies can accelerate its performance degradation and the probability of failure, the maintenance cycle is adjusted based on the standard maintenance cycle and combined with the anomaly impact coefficient, which conforms to the dynamic relationship between the actual operating state of the sensor and maintenance needs.

[0012] Secondly, the present invention also provides an automotive exhaust gas detection system based on a gas sensor array, comprising: The data acquisition module acquires gas sensor detection data, exhaust gas condition data, and corresponding vehicle transportation condition data at the corresponding exhaust emission location. The damage anomaly analysis module analyzes the damage of gas sensors by analyzing exhaust gas data and corresponding vehicle transportation data. The detection process analysis module analyzes the damage to the gas sensor during the detection process by analyzing changes in the gas sensor detection data. The sensor anomaly analysis module performs gas sensor anomaly analysis based on the gas sensor damage anomaly analysis results and the gas sensor damage analysis results during the detection process. The maintenance cycle analysis module analyzes the actual gas sensor maintenance cycle based on the gas sensor anomaly analysis results and the standard maintenance cycle. The maintenance module reminds users to perform gas sensor maintenance according to the actual gas sensor maintenance cycle.

[0013] Thirdly, the present invention provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a method for detecting automobile exhaust gas based on a gas sensor array by calling the computer program stored in the memory.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method for detecting automobile exhaust gases based on a gas sensor array.

[0015] Compared with the prior art, the present invention has the following advantages and beneficial effects: By analyzing the damage to the sensor's operating environment and the changes in detection data during the detection process, a comprehensive damage assessment of the gas sensor is conducted. Based on this assessment, the maintenance cycle is ultimately determined, significantly improving the reliability and stability of the gas sensor. Dynamically adjusting the maintenance cycle according to actual anomalies ensures the sensor remains in optimal operating condition, reducing the risk of safety accidents caused by sensor malfunctions and effectively lowering maintenance costs. Precisely scheduled maintenance avoids the high costs associated with blindly scheduled maintenance and minimizes production losses due to sensor downtime, achieving a win-win situation for both economic benefits and equipment performance. Attached Figure Description

[0016] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the overall process structure of an embodiment of the method of the present invention; Figure 2 This is a schematic diagram of the S200 step flow structure of an embodiment of the method of the present invention; Figure 3 This is a schematic diagram of the module composition structure of an embodiment of the system of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0018] Please see Figure 1 , Figure 1 This is a schematic diagram of the overall process of a vehicle exhaust gas detection method based on a gas sensor array provided by an embodiment of the present invention, which specifically includes the following steps: S100: Obtain gas sensor detection data, exhaust gas condition data, and corresponding vehicle transportation condition data at the corresponding exhaust gas emission location. In this embodiment, step S100 includes the following specific contents: Step 110: Install a gas sensor array in the exhaust pipe of the corresponding vehicle to obtain the content of each component of the vehicle exhaust gas. Obtain the detection status of each component of the exhaust gas during the operation of the gas sensor and the safe range of the corresponding gas sensor. Analyze the damage to the measurement process by the influence of the rate of change of the detected components on the measurement accuracy of the gas sensor. At the same time, conduct a quantitative analysis of the impact of the corrosion effect of the detected components on the sensor. In this case, the gas sensor corresponds one-to-one with the corresponding gas. Step 120: Simultaneously, environmental data at the corresponding exhaust pipe location during vehicle operation is acquired through environmental sensors, including environmental data such as ambient temperature, humidity, corrosive gas concentration, and airflow velocity, as well as vibration conditions such as vibration amplitude and vibration frequency caused by the vehicle. By analyzing the impact of environmental data and vehicle operation on the sensors respectively, the degree of external damage to the sensors by the overall environment is assessed. Step 130: Store the acquired data in the storage module for later use; S200: By analyzing exhaust gas data and corresponding vehicle transportation data, gas sensor damage anomaly analysis is performed. In this embodiment, as Figure 2 As shown, the gas sensor damage anomaly analysis in step S200 includes the following specific steps: Step 210: Obtain environmental data inside the exhaust pipe under historical vehicle operating conditions and the average vibration caused by the corresponding vehicle operation. Historical data can reflect the environmental parameters and vibration characteristics of the vehicle under different operating conditions (such as idling, acceleration, high-speed driving, etc.). These data are important references for evaluating the health status of the sensors. Step 220: Perform an environmental impact anomaly analysis by comparing the environmental data within the exhaust pipe with the safe range of the corresponding environment that the gas sensor can withstand. The environmental data includes ambient temperature, humidity, corrosive gas concentration, and airflow velocity. Specifically, the steps are as follows: obtain the average standard deviation of the environmental data and the safe range of the corresponding environmental type that the gas sensor can withstand within a set period, and set this as the anomaly value for that environmental type. Then, sum the weighted anomalies of all environmental types to obtain the environmental impact anomaly analysis result. It should be noted that the weights of each environmental type are obtained experimentally. Specifically, under the same environment, only one type of environmental data is changed to obtain the corresponding environmental data's impact on the gas sensor. The average impact on lifespan (e.g., a one-degree increase in temperature reduces lifespan by two days) is calculated by dividing the average impact of the corresponding environmental data by the sum of the average impacts of all environmental data to obtain the weight of the corresponding environmental data. Simultaneously, the safe range that the gas sensor can withstand in the corresponding environment is obtained experimentally. The standard deviation is calculated as the deviation of the corresponding value from the median of the corresponding range divided by the value of the corresponding range. This method can quantitatively assess the cumulative damage to the sensor caused by environmental factors (such as temperature, humidity, corrosive gases, and airflow). The safe range of environmental parameters is determined experimentally, and the calculation of outliers (standard deviation mean method) ensures sensitivity to environmental fluctuations. Furthermore, the weight allocation is based on experimental data, making the anomaly analysis more consistent with actual impacts and improving the accuracy of the assessment. For example, the effects of various environmental factors on sensors are specifically illustrated here. The mechanism of temperature influence: drastic temperature fluctuations cause inconsistent expansion and contraction of the internal metal / ceramic materials of the sensor, leading to solder joint cracking or coating peeling; the output signal of electrochemical sensors is related to the reaction temperature (Arrhenius equation), for example: high temperature (>150°C): accelerates electrolyte evaporation (such as the failure of NDIR sensors with liquid electrolytes), and increased thermal noise causes signal baseline drift (such as the nonlinear change of the resistance value of MOS semiconductor sensors with temperature). The mechanism of humidity's influence: The concentration of KOH electrolyte in electrochemical sensors (such as CO sensors) decreases after absorbing water, leading to a decrease in output current; high humidity (>90%RH) accelerates the electrochemical corrosion of metal electrodes (such as Pt, Au), especially when SO2 is present, forming an acidic liquid film; water vapor absorbs infrared spectra (such as the 2.7μm band) in NDIR sensors, causing cross-interference with target gases (such as CO2 at 4.26μm); The mechanisms by which corrosive gases affect the sensor: SO2 / NOx reacts with the Pt catalyst in the electrochemical sensor to form PtS / PtO, covering the active sites; silicon compounds decompose into SiO2, clogging the filter of the optical sensor (such as hexamethyldisiloxane in automobile exhaust); material embrittlement: corrosive gases accelerate the corrosion of metal leads, causing electrical short circuits; it should be noted that different corrosive gases have different effects on the sensor's lifespan, so the effects of different gases need to be obtained experimentally. Mechanism of airflow velocity influence: High-speed airflow (>5m / s): Disrupts the steady-state diffusion layer of electrochemical sensors, resulting in lower concentration measurements (e.g., oxygen sensors have an error of ±15% in turbulent flow). High-speed airflow carries carbon particles that scour the optical sensor window, causing a decrease in light transmittance (e.g., PM concentration increases sharply when a diesel vehicle's DPF fails). Step 230: Obtain the average vibration caused by vehicle operation. Set the ratio of the average vibration amplitude to the maximum value of the safe vibration amplitude range of the gas sensor as the amplitude anomaly value, and set the ratio of the average vibration frequency to the maximum value of the safe vibration frequency range of the gas sensor as the vibration frequency anomaly value. Multiply the amplitude anomaly value and the vibration frequency anomaly value to obtain the vibration anomaly analysis result. Use the experimentally determined safe vibration amplitude and frequency range as the benchmark, and compare the actual vibration data with them (by calculating the ratio) to quantify the degree of vibration impact. The product of amplitude and frequency anomaly values ​​comprehensively considers the synergistic effect of vibration intensity and frequency, and more comprehensively reflects the vibration hazards. For example, the effects of vibration on sensors are illustrated here: High-frequency vibration (such as engine vibration, 50–2000 Hz) causes fatigue fracture of brittle solder joints (such as gold wire bonding points) inside the sensor, especially when the material toughness decreases at high temperatures; vibration acceleration (>5g) causes microcracks in the gas-permeable membrane (such as PTFE membrane) of electrochemical sensors or the window lens of optical sensors, resulting in abnormal gas permeability or optical path deviation; vibration causes carbon deposits or catalyst particles to fall off inside the sensor, blocking the gas path or contaminating the electrodes (such as electrode carbon deposits in PM sensors). Step 240: The environmental impact anomaly analysis results and the vibration anomaly analysis results are weighted and summed to obtain the gas sensor damage anomaly analysis results. The environmental and vibration anomaly analysis results quantify the damage contribution of different factors respectively. The weighted summation ensures that the assessment is more reasonable, conforms to the objective law of the superposition of multiple factors under actual working conditions, and improves the accuracy of prediction. For example, through numerous experiments, gas sensors are tested for extended periods under varying environmental conditions (such as different temperatures, humidity levels, corrosive gas concentrations, and airflow velocity combinations) and vibration conditions (different amplitudes and frequencies), and the damage to the sensors is recorded. Based on the experimental results, the degree of influence of environmental factors and vibration factors on sensor damage is analyzed, thereby determining their weights in a weighted summation. For instance, if the experiment finds that environmental factors account for 70% of the sensor damage and vibration factors account for 30%, then the weight of the environmental impact anomaly analysis result is 0.7, and the weight of the vibration anomaly analysis result is 0.3. S300. Perform gas sensor damage analysis during the detection process by analyzing changes in gas sensor detection data. In this embodiment, the damage analysis of the gas sensor during the detection process in step S300 specifically includes the following: Step 310: Obtain the changes in the corresponding gas in the vehicle exhaust emissions collected by the corresponding gas sensor, and at the same time obtain the safe range of the gas sensor. Frequent high-concentration impacts can cause irreversible passivation of the electrode catalyst (such as Pt) of the electrochemical sensor, or changes in the metal oxide lattice structure of the semiconductor sensor, resulting in decreased sensitivity. At the same time, drastic concentration changes may trigger the sensor memory effect (such as the adsorption of gas molecules by the optical window of the infrared sensor), resulting in baseline drift. Therefore, it is necessary to analyze the abnormal effects of these factors on the gas sensor. By monitoring the comparison between the actual exposure concentration of the gas sensor and the safe range, it is possible to predict whether the sensor is in an overload risk state and avoid irreversible damage caused by long-term over-limit use. Step 320: Obtain the changes in the corresponding gases in vehicle exhaust emissions and obtain the abnormal concentration changes of the corresponding gases. The abnormal concentration changes of the corresponding gases are calculated as follows: the absolute value of the difference between the concentration value of the corresponding gas at the corresponding moment within the corresponding vehicle driving cycle and the concentration of the corresponding gas at the previous moment, divided by the safe range of the gas sensor, to obtain the abnormal concentration change at the corresponding moment. The abnormal concentration changes of the corresponding gases are averaged over all moments within the corresponding driving cycle to obtain the abnormal concentration change of the corresponding gases. This analyzes the impact of the drastic change in gas concentration on the gas sensor. By calculating the normalized rate of change of concentration, the unit difference between different gas ranges is eliminated, cross-sensor comparability is achieved, the drastic fluctuation is dynamically assessed, and transient high-slope changes are identified. Step 330: Obtain the concentration change anomaly at the corresponding time within the corresponding vehicle driving cycle, and simultaneously obtain the ratio of the gas concentration value at the corresponding time to the maximum range value to obtain the concentration magnitude anomaly at the corresponding time. Multiply the concentration magnitude anomaly at the corresponding time with the concentration change anomaly to obtain the high concentration impact anomaly at the corresponding time. Average the high concentration impact anomalies at all times within the corresponding driving cycle duration to obtain the high concentration impact anomaly of the corresponding gas. This analysis of the impact of high concentration impacts on gas concentration changes on the gas sensor, combined with the instantaneous absolute value of concentration (risk of approaching the upper limit of the range) and the rate of change (impact of steep rise / fall), more comprehensively reflects the overall damage potential. The product operation (concentration magnitude anomaly × change anomaly) can amplify the weight of extreme conditions (such as second-level jumps from 0 to 90% of the range). Step 340: The obtained abnormal concentration changes of the corresponding gas and the abnormal high concentration shock of the corresponding gas are weighted and summed to obtain the damage analysis results of the gas sensor in the detection process. The weighted sum is adapted to different sensor characteristics (e.g., electrochemical sensors are more susceptible to rapid changes, while semiconductor sensors are more susceptible to high absolute values). The weighting coefficients can be determined by principal component analysis (PCA). For example, experiments show that in the degradation of CO sensor, the fluctuation frequency accounts for 58%, and the absolute concentration accounts for 42%. S400. Based on the gas sensor damage anomaly analysis results and the gas sensor damage analysis results during the detection process, perform gas sensor anomaly analysis. In this embodiment, the gas sensor anomaly analysis in step S400 includes the following specific contents: The gas sensor damage anomaly analysis results and the gas sensor damage analysis results during the detection process are obtained. The two are weighted and summed to obtain the gas sensor anomaly analysis result. The gas sensor damage anomaly analysis result is the gas sensor damage caused by external factors, while the gas sensor damage analysis result during the detection process is the gas sensor damage caused by internal factors during the detection process. Although the damage mechanisms of external and internal factors are independent of each other, they will jointly accelerate the sensor failure. For example, the weights in this step are obtained by conducting long-term actual operation monitoring of gas sensors in multiple vehicles, recording sensor damage, environmental data, exhaust emission data, etc. The actual impact of external factors (analysis results of gas sensor damage) and internal factors (analysis results of gas sensor damage during the detection process) on sensor damage is analyzed, and the weights of the two are determined based on the analysis results. S500, based on the gas sensor anomaly analysis results and standard maintenance cycle, analyzes the actual gas sensor maintenance cycle; In this embodiment, the analysis of the actual gas sensor maintenance cycle in step S500 includes the following specific details: The gas sensor anomaly analysis result is obtained by dividing it by a set gas sensor anomaly analysis threshold to obtain the sensor anomaly impact coefficient. The standard maintenance cycle is then divided by this coefficient to obtain the actual gas sensor maintenance cycle. There is a close correlation between the degree of gas sensor anomaly and the maintenance cycle. The sensor anomaly analysis result reflects the current abnormal state of the sensor, while the set anomaly analysis threshold is determined based on the sensor's normal operating standards and safety requirements. The anomaly impact coefficient, obtained by dividing the two, quantifies the degree to which sensor anomalies affect its normal operation. Since sensor anomalies accelerate performance degradation and increase the probability of failure, adjusting the maintenance cycle based on the standard maintenance cycle and incorporating the anomaly impact coefficient aligns with the actual operation of the sensor. The dynamic relationship between operational status and maintenance needs has several advantages. First, in terms of resource utilization, it avoids over-maintenance or under-maintenance. If the sensor anomaly level is low and the anomaly impact coefficient is small, the actual maintenance cycle will be extended accordingly, reducing unnecessary maintenance work and resource waste. Conversely, when the sensor anomaly level is high and the anomaly impact coefficient is large, the actual maintenance cycle will be shortened, allowing for timely sensor maintenance and preventing greater losses due to malfunctions. Second, from the perspective of equipment reliability, it significantly improves the reliability and stability of gas sensors. Dynamically adjusting the maintenance cycle based on actual anomaly conditions ensures that the sensor is always in good operating condition, reducing the risk of safety accidents caused by sensor malfunctions. Finally, in terms of cost control, it effectively reduces maintenance costs. By precisely scheduling maintenance work, it avoids the high costs associated with blindly maintaining the system on a regular basis, while reducing production losses caused by downtime due to sensor malfunctions, achieving a win-win situation for both economic benefits and equipment performance. In this embodiment, the standard maintenance cycle is an attribute characteristic of the sensor after production. It is obtained by looking up a table. The method for obtaining the set gas sensor anomaly analysis threshold is as follows: collect a large amount of historical operating data of gas sensors, including normal operation data and fault data, perform statistical analysis on these data, find the maximum value of the sensor anomaly analysis result under normal operation, use it as a reference, and combine it with a certain safety factor (such as 1.2 times) to determine the gas sensor anomaly analysis threshold. S600. Perform gas sensor maintenance according to the actual gas sensor maintenance cycle reminder; specifically, when the gas sensor maintenance cycle time is reached, send maintenance information wirelessly to the corresponding user and / or maintenance personnel to perform scheduled maintenance of the gas sensor.

[0019] The advantages of this embodiment are as follows: By analyzing the damage to the sensor's operating process caused by the sensor's environment and the damage caused by changes in the detection data during the detection process, a comprehensive damage assessment of the gas sensor is conducted. Based on this comprehensive damage assessment, the maintenance cycle is ultimately determined, which significantly improves the reliability and stability of the gas sensor. Dynamically adjusting the maintenance cycle according to actual abnormal conditions ensures the sensor remains in good operating condition, reducing the risk of safety accidents caused by sensor failures. This effectively reduces maintenance costs. By precisely scheduling maintenance work, the high costs associated with blindly performing regular maintenance are avoided, while also reducing production losses caused by sensor failure downtime, achieving a win-win situation for both economic benefits and equipment performance.

[0020] Please see Figure 3 , Figure 3 This is a schematic diagram of a vehicle exhaust gas detection system based on a gas sensor array provided in an embodiment of the present invention, including: The data acquisition module acquires gas sensor detection data, exhaust gas condition data, and corresponding vehicle transportation condition data at the corresponding exhaust emission location. The damage anomaly analysis module analyzes the damage of gas sensors by analyzing exhaust gas data and corresponding vehicle transportation data. The detection process analysis module analyzes the damage to the gas sensor during the detection process by analyzing changes in the gas sensor detection data. The sensor anomaly analysis module performs gas sensor anomaly analysis based on the gas sensor damage anomaly analysis results and the gas sensor damage analysis results during the detection process. The maintenance cycle analysis module analyzes the actual gas sensor maintenance cycle based on the gas sensor anomaly analysis results and the standard maintenance cycle. The maintenance module reminds users to perform gas sensor maintenance according to the actual gas sensor maintenance cycle.

[0021] The parameters and steps for implementing the corresponding functions of each unit module in the above-described automobile exhaust gas detection system based on a gas sensor array of the present invention can be referred to the parameters and steps in the above-described embodiment of the automobile exhaust gas detection method based on a gas sensor array, and will not be repeated here.

[0022] Embodiments of the present invention also provide an electronic device, including a memory, a processor, and a communication bus; the memory and the processor are connected via the communication bus. The memory stores a method for detecting automobile exhaust gases based on a gas sensor array, which can be loaded and executed by the processor 320 as provided in the above embodiments.

[0023] The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory 310 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the vehicle exhaust gas detection method based on a gas sensor array provided in the above embodiments. The data storage area may store data involved in the vehicle exhaust gas detection method based on a gas sensor array provided in the above embodiments.

[0024] The processor may include one or more processing cores. The processor executes instructions, programs, code sets, or instruction sets stored in memory, and calls data stored in memory to perform various functions and process data according to the present invention. The processor may be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), controller, microcontroller, and microprocessor. It is understood that, for different devices, the electronic devices used to implement the functions of the processor 320 described above may also be other types, and the embodiments of the present invention do not specifically limit this.

[0025] A communication bus may include a pathway for transmitting information between the aforementioned components. The communication bus 330 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Communication buses can be categorized as address buses, data buses, control buses, etc.

[0026] This invention provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments, a method for detecting automobile exhaust gases based on a gas sensor array.

[0027] In this embodiment of the invention, the computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), spoofing random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0028] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0029] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to the technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this invention.

Claims

1. A method for detecting automobile exhaust gases based on a gas sensor array, characterized in that, Includes the following steps: Acquire gas sensor detection data, exhaust gas condition data, and corresponding vehicle transportation data at the corresponding exhaust emission location; Anomaly analysis of gas sensor damage was performed by analyzing exhaust gas data and corresponding vehicle transportation data. Damage analysis of the gas sensor during the detection process is performed by analyzing changes in the gas sensor detection data. Anomaly analysis of gas sensors is conducted based on the results of gas sensor damage anomaly analysis and the results of gas sensor damage analysis during the detection process. Analysis of actual gas sensor maintenance cycles based on gas sensor anomaly analysis results and standard maintenance cycles; Perform gas sensor maintenance according to the actual gas sensor maintenance cycle reminder.

2. The method for detecting automobile exhaust gases based on a gas sensor array according to claim 1, characterized in that, The gas sensor damage anomaly analysis includes the following specific steps: Acquire environmental data inside the exhaust pipe during historical vehicle operation and the corresponding average vibration caused by vehicle operation. Anomaly analysis of environmental impact is conducted by comparing environmental data from the exhaust pipe with the safe range of the corresponding environment that the gas sensor can withstand. The specific steps are as follows: obtain the average standard deviation of the environmental data and the safe range of the corresponding environmental type that the gas sensor can withstand within a set period, set it as the outlier value of the corresponding environmental type, and sum the outliers of all environmental types by weight to obtain the result of the environmental impact anomaly analysis. The average vibration caused by vehicle operation is obtained. The ratio of the average vibration amplitude to the maximum value of the safe vibration amplitude range of the gas sensor is set as the amplitude anomaly value, and the ratio of the average vibration frequency to the maximum value of the safe vibration frequency range of the gas sensor is set as the vibration frequency anomaly value. The vibration anomaly analysis result is obtained by multiplying the amplitude anomaly value and the vibration frequency anomaly value. The results of the environmental impact anomaly analysis and the vibration anomaly analysis are weighted and summed to obtain the gas sensor damage anomaly analysis results.

3. The method for detecting automobile exhaust gases based on a gas sensor array according to claim 1, characterized in that, The steps of acquiring gas sensor detection data, exhaust gas condition data, and corresponding vehicle transportation condition data at the corresponding exhaust emission location include the following specific contents: A gas sensor array is installed in the exhaust pipe of the corresponding vehicle to obtain the content of each component of the vehicle exhaust gas, and to obtain the detection status of each component of the exhaust gas during the operation of the gas sensor and the safe range of the corresponding gas sensor. At the same time, environmental data at the corresponding exhaust pipe location is acquired through environmental sensors during vehicle operation; The acquired data is stored in storage modules for later use.

4. The method for detecting automobile exhaust gases based on a gas sensor array according to claim 1, characterized in that, The gas sensor damage analysis during the detection process specifically includes the following: Acquire the changes in the corresponding gases emitted by the vehicle exhaust gas collected by the corresponding gas sensor, and at the same time acquire the safe range of the gas sensor. The changes in the corresponding gases emitted by the vehicle exhaust are obtained, and the abnormal changes in the concentration of the corresponding gases are obtained. The abnormal changes in the concentration of the corresponding gases are calculated by averaging the abnormal changes in the concentration of the corresponding gases at all times within the corresponding driving cycle. The concentration change anomaly at the corresponding moment within the corresponding vehicle driving cycle is obtained. At the same time, the ratio of the gas concentration value at the corresponding moment to the maximum value of the range is obtained to obtain the concentration magnitude anomaly at the corresponding moment. The concentration magnitude anomaly at the corresponding moment is multiplied by the concentration change anomaly to obtain the high concentration impact anomaly at the corresponding moment. The high concentration impact anomalies at all moments within the corresponding driving cycle duration are averaged to obtain the high concentration impact anomaly of the corresponding gas. The results of the gas sensor damage analysis during the detection process are obtained by weighted summation of the corresponding gas concentration change anomalies and the corresponding gas high concentration impact anomalies.

5. The method for detecting automobile exhaust gases based on a gas sensor array according to claim 4, characterized in that, The concentration change anomaly at the corresponding time is calculated as follows: the absolute value of the difference between the concentration value of the corresponding gas at the corresponding time within the corresponding vehicle driving cycle and the concentration of the corresponding gas at the previous time, divided by the safe range value of the gas sensor, yields the concentration change anomaly at the corresponding time.

6. The method for detecting automobile exhaust gases based on a gas sensor array according to claim 1, characterized in that, The gas sensor anomaly analysis includes the following specific aspects: The gas sensor damage anomaly analysis results and the gas sensor damage analysis results during the detection process are obtained, and the two are weighted and summed to obtain the gas sensor anomaly analysis result.

7. The method for detecting automobile exhaust gases based on a gas sensor array according to claim 1, characterized in that, The analysis of the maintenance cycle of the actual gas sensor includes the following specific contents: The gas sensor anomaly analysis result is obtained by dividing it by the set gas sensor anomaly analysis threshold to obtain the sensor anomaly impact coefficient. The standard maintenance cycle is then divided by the sensor anomaly impact coefficient to obtain the actual gas sensor maintenance cycle.

8. A vehicle exhaust gas detection system based on a gas sensor array, used to implement the vehicle exhaust gas detection method based on a gas sensor array as described in any one of claims 1-7, characterized in that, Specifically, it includes: The data acquisition module acquires gas sensor detection data, exhaust gas condition data, and corresponding vehicle transportation condition data at the corresponding exhaust emission location. The damage anomaly analysis module analyzes the damage of gas sensors by analyzing exhaust gas data and corresponding vehicle transportation data. The detection process analysis module analyzes the damage to the gas sensor during the detection process by analyzing changes in the gas sensor detection data. The sensor anomaly analysis module performs gas sensor anomaly analysis based on the gas sensor damage anomaly analysis results and the gas sensor damage analysis results during the detection process. The maintenance cycle analysis module analyzes the actual gas sensor maintenance cycle based on the gas sensor anomaly analysis results and the standard maintenance cycle. The maintenance module reminds users to perform gas sensor maintenance according to the actual gas sensor maintenance cycle.

9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes a vehicle exhaust gas detection method based on a gas sensor array as described in any one of claims 1-7 by calling the computer program stored in the memory.

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