A method and system for monitoring carbon emissions based on three-way catalysis

Abstract

The present invention discloses a carbon emission monitoring method and system based on three-way catalysis, which belongs to the technical field of carbon emission monitoring and analysis, including a data acquisition module, a carbon emission monitoring module, an abnormal alarm module, a short-term abnormal judgment module, and a long-term abnormal association module. The data acquisition module includes a GPS device and a gas sensor, which is used to collect the gas components and concentrations in the original exhaust gas, and the gas components and concentrations after the three-way catalytic converter is treated. The present invention discloses a carbon emission monitoring method and system based on three-way catalysis, which can realize exhaust data collection, carbon emission abnormality monitoring and judgment, distinction between occasional abnormalities and regular abnormalities, abnormal cause tracing and identification, carbon emission abnormality alarm, and realize comprehensive monitoring and management of carbon emission conditions. For the occasional and regular abnormal judgments in the carbon emission monitoring process, misjudgment is eliminated, and the practicality and functionality of the method and system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission monitoring and analysis, and specifically relates to a carbon emission monitoring method and system based on three-way catalysis. Background Art

[0002] The three-way catalytic converter is a device used for treating exhaust gas from internal combustion engines. It is located in the exhaust system of the car. Its main function is to convert harmful gases such as carbon monoxide, nitrogen oxides and hydrocarbons emitted by the engine into relatively harmless carbon dioxide, nitrogen and water vapor. By monitoring the carbon emissions of the converted exhaust gas, the effect of the three-way catalytic converter can be evaluated.

[0003] However, many bad behaviors of vehicles can easily lead to untimely treatment of the three-way catalytic converter, occasional abnormalities, and false alarms in carbon emission monitoring, which affects the normal judgment of drivers and has the problem of low practicality. In addition, the existing carbon emission monitoring system cannot record and analyze the process before the abnormality, making it inconvenient to trace the specific causes of the frequent abnormalities of the three-way catalytic converter, and has the problem of low functionality.

[0004] In view of the above, this case proposes a carbon emission monitoring method and system based on three-way catalysis to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a carbon emission monitoring method and system based on three-way catalysis, which solves the above technical problems by improving the detection method and the processing method.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A carbon emission monitoring system based on three-way catalysis includes a data acquisition module, a carbon emission monitoring module, an abnormal alarm module, a short-term abnormality determination module, and a long-term abnormality association module;

[0008] The data acquisition module includes a GPS device and a gas sensor, which is used to collect the gas components and concentrations in the original exhaust gas, the gas components and concentrations after the three-way catalytic converter treatment, the vehicle speed data, the vehicle travel distance, the travel time, the total mileage of the vehicle, and transmit the relevant data to the subsequent modules;

[0009] The carbon emission monitoring module determines the state of the three-way catalytic converter based on the gas components and concentrations in the received original exhaust gas and the gas components and concentrations in the exhaust gas treated by the three-way catalytic converter, and transmits the abnormal result to the short-term abnormality determination module based on the carbon emission monitoring result;

[0010] The short-term abnormality determination module determines the abnormal results of carbon emission monitoring based on the vehicle speed data collected by the data acquisition module and the driving status of the vehicle in the relevant time period, and transmits the relevant signal to the subsequent module based on the determination result;

[0011] The long-term abnormality association module identifies the vehicle behavior within the relevant time period based on the relevant data collected in the data collection module and in combination with the carbon emission monitoring abnormality, determines and records the abnormal cause, and transmits the abnormal signal to the carbon emission monitoring module;

[0012] The abnormal alarm module receives the abnormal signal transmitted by the carbon emission monitoring module and lights up different alarm lights based on different abnormal types.

[0013] Further, the data acquisition module includes a GPS device and a gas sensor, which are used to collect the gas components and concentrations in the original exhaust gas, the gas components and concentrations in the exhaust gas treated by the three-way catalytic converter, and the vehicle speed data, wherein the gases in the original exhaust gas include carbon monoxide, nitrogen oxides, and hydrocarbons, and the gases in the exhaust gas treated by the three-way catalytic converter include carbon dioxide, nitrogen, and water vapor. At the same time, based on the GPS device, the vehicle speed data, driving distance, driving time, and total vehicle mileage are collected to establish a vehicle driving database, and the vehicle speed is associated with the driving distance and driving time in the database. The specific steps are:

[0014] The collected gas data is synchronized with the GPS data by time stamp, the pre-processed data is stored in the vehicle driving database, and a data table is established in the vehicle driving database to store the original exhaust data collected each time, the processed exhaust data, and the corresponding vehicle speed, driving distance, and driving time information, and a unique timestamp is assigned to the stored information, and the three-way catalytic converter maintenance timestamp is recorded at the same time.

[0015] Furthermore, the carbon emission monitoring module determines the state of the three-way catalytic converter based on the gas components and concentrations in the received original exhaust gas and the gas components and concentrations in the exhaust gas after the three-way catalytic converter is treated, and transmits the abnormal result to the short-term abnormality determination module based on the carbon emission monitoring result. The specific steps are as follows:

[0016] Based on the relevant parameters of the three-way catalytic converter, the standard carbon emission monitoring value conversion rate α is obtained 1max , α 2max , α 1min , α 2min , where α 1max represents the maximum carbon monoxide to carbon dioxide conversion rate, α 2max represents the maximum nitrogen conversion rate of nitrogen oxides, represents the maximum carbon dioxide conversion rate of hydrocarbons, represents the maximum water vapor conversion rate of hydrocarbons, α 1min represents the minimum carbon monoxide to carbon dioxide conversion rate, α 2min represents the minimum nitrogen conversion rate of nitrogen oxides, represents the minimum carbon dioxide conversion rate of hydrocarbons, represents the minimum water vapor conversion rate of hydrocarbons;

[0017] Based on the data acquisition module, the concentrations of carbon monoxide, nitrogen oxides, and hydrocarbons in the original exhaust gas are obtained, where A1 represents the concentration of carbon monoxide, A2 represents the concentration of nitrogen oxides, and A3 represents the concentration of hydrocarbons. Based on the standard carbon emission monitoring value conversion rate, the normal value range is obtained. The specific steps are:

[0018] The normal range of carbon dioxide values ​​is:

[0019]

[0020] The normal range of water vapor values ​​is:

[0021]

[0022] The normal value range of nitrogen is:

[0023] [A2·α 2min ,A2·α 2max ];

[0024] The values ​​of carbon dioxide, nitrogen and water vapor in the treated tail gas are obtained through the data acquisition module, wherein β1 represents the concentration of carbon dioxide in the treated tail gas, β2 represents the concentration of nitrogen in the treated tail gas, and β3 represents the concentration of water vapor in the treated tail gas. The concentration of each gas in the treated tail gas is determined based on the normal value range, including the following steps:

[0025] when It means the carbon dioxide concentration is normal;

[0026] when Indicates that the carbon dioxide concentration is abnormal, and transmits the abnormal signal and related data to the short-term abnormality judgment module;

[0027] When β2∈[A2·α 2min ,A2·α 2max ], indicating that the nitrogen concentration is normal;

[0028] When β2<A2·α 2min , representing abnormal nitrogen concentration, transmits abnormal signals and related data to the short-term abnormality judgment module;

[0029] when It means the water vapor concentration is normal;

[0030] when It represents abnormal water vapor concentration, and transmits abnormal signals and related data into the short-term abnormality judgment module.

[0031] Furthermore, the short-term abnormality determination module determines the abnormal results of carbon emission monitoring based on the vehicle speed data collected by the data acquisition module and the driving status of the vehicle in the relevant time period, and transmits the relevant signal to the subsequent module based on the determination result. The specific steps are as follows:

[0032] Based on the abnormal signal transmitted by the carbon emission monitoring module, the timestamp is recorded, and combined with the timestamp in the vehicle driving database in the data acquisition module, the relevant data before the abnormal signal is extracted and analyzed;

[0033] Based on the results of extracting and analyzing the relevant data before the abnormal signal, the vehicle-related abnormality is determined, and the relevant data is transmitted to the long-term abnormality association module based on the result of the determination.

[0034] Furthermore, the abnormal signal transmitted by the carbon emission monitoring module is recorded with a timestamp, and the relevant data before the abnormal signal is extracted and analyzed in combination with the timestamp in the vehicle driving database in the data acquisition module. The specific steps are:

[0035] Based on the timestamp of the abnormal signal transmitted by the carbon emission monitoring module, relevant data including vehicle speed data and vehicle travel distance data are extracted through the vehicle driving database;

[0036] Set the vehicle high speed threshold θ max and the vehicle low speed threshold θ min , get the vehicle speed v in the vehicle driving database at the time of abnormal signal timestamp t , based on the speed v t With high speed threshold θ max , low speed threshold θ min , to judge the vehicle status:

[0037] When θ min ≤v t ≤θ max When the vehicle is in normal driving state, it is marked in red;

[0038] When v t >θ max When the vehicle is in high-speed driving status, it is marked green;

[0039] When v t <θ minWhen the vehicle is in idling state, it is marked green;

[0040] Set the short-distance driving distance threshold χ of the vehicle, obtain the driving distance λ of the adjacent vehicles in the vehicle driving database before the abnormal signal timestamp, and judge the driving status of the vehicle:

[0041] When λ≥χ, the vehicle status is considered to be normal driving and marked in red;

[0042] When λ<χ, the vehicle status is considered to be short-distance driving and is marked green.

[0043] Based on the results of extracting and analyzing the relevant data before the abnormal signal, the vehicle-related abnormality is judged, and the relevant data is transmitted to the long-term abnormality association module based on the judgment result. The steps are as follows:

[0044] For the vehicle status marked as green, the short-term abnormality determination module ignores the related exhaust gas abnormality and does not transmit the abnormal signal to the long-term abnormality association module;

[0045] For the vehicle status marked in red, the short-term abnormality determination module integrates the data of related exhaust gas abnormalities and transmits the related data and abnormal signals to the long-term abnormality association module.

[0046] Furthermore, the long-term abnormality association module, based on the relevant data collected in the data collection module and in combination with the carbon emission monitoring abnormality, identifies the vehicle behavior within the relevant time period, determines and records the abnormal cause, and transmits the abnormal signal to the carbon emission monitoring module, including the following steps:

[0047] Receive the abnormal signal transmitted by the short-term abnormality determination module, and traverse the data in the vehicle driving database in the data acquisition module in combination with the abnormal signal timestamp. The relevant timestamp range includes the last maintenance timestamp to the abnormal signal timestamp;

[0048] Based on the relevant data in the vehicle driving database, the vehicle behavior within the relevant time period is identified and recorded, the carbon emission anomaly is determined, and the abnormal signal is transmitted to the carbon emission monitoring module based on the determination result.

[0049] Furthermore, based on the relevant data in the vehicle driving database, the vehicle behavior in the relevant time period is identified and recorded, the carbon emission abnormality is determined, and the abnormal signal is transmitted to the carbon emission monitoring module based on the determination result. The specific steps are as follows:

[0050] Based on the data acquisition module, the vehicle data within the relevant time stamp range of the vehicle driving database is collected, including the vehicle speed v n , vehicle travel distance λ n, vehicle travel time T n ;

[0051] Set the driving speed high speed threshold v max , driving speed idling threshold v min , short-distance threshold λ min , Long-time threshold value of vehicle driving time T max , based on the vehicle data within the relevant timestamp range, the vehicle behavior is identified and recorded. The specific steps are:

[0052] Based on the short-distance threshold λ min , for short-distance driving of vehicles, whenever λ n ≤λ min , indicating that the relevant distance is a short-distance trip, the relevant data is marked with a symbol of C1, and the relevant timestamp is removed;

[0053] Based on the long-time threshold T of driving time max And the driving speed idle threshold v min , for vehicles running at low speed for a long time, whenever v n ≤v min And T n ≥T max When , it means that the relevant data is idling for a long time, the relevant data is marked with a symbol C1, and the relevant timestamp is removed;

[0054] Based on the long-time threshold T of driving time max And the high speed threshold v max , for vehicles running at high speed for a long time, whenever v n ≥v max And T n ≥T max When , it means that the relevant data is long-term high-speed driving, the relevant data is marked with a symbol of C2, and the relevant timestamp is removed;

[0055] Count the number of C1 and C2 to get the total number of C1 and the total number of C2 calculate and The specific algorithm formula is:

[0056]

[0057] Among them, H1 represents the proportion of C1 in the total number, and H2 represents the proportion of C2 in the total number. Based on the size of H1 and H2, the abnormal carbon emission is judged. The specific steps are as follows:

[0058] When H1>>H2, it is determined that the cause of abnormal carbon emission is carbon deposition in the three-way catalytic converter, and the abnormal signal K is transmitted to the carbon emission monitoring module;

[0059] When H1<<H2, it is determined that the cause of the abnormal carbon emission is thermal deactivation of the three-way catalytic converter, and the abnormal signal Y is transmitted to the carbon emission monitoring module;

[0060] When H1≈H2, the cause of the abnormal carbon emission is determined to be undetermined, and the abnormal signal G is transmitted to the carbon emission monitoring module.

[0061] Furthermore, the carbon emission monitoring module is provided with an advanced determination system, which receives abnormal signals based on the determination results of the long-term abnormal association module, obtains the total mileage of the vehicle based on the data acquisition module, and further determines the pending abnormal signals. The specific steps are as follows:

[0062] Based on the data acquisition module, the total mileage L of the vehicle is obtained and the mileage threshold L is set. n , an advanced determination is performed on the abnormal signal G, including the following steps:

[0063] When L ≥ L n When , it is determined that the abnormal cause of abnormal signal G is thermal deactivation of the three-way catalytic converter, and the abnormal signal G is changed to abnormal signal Y;

[0064] When L<L n When , it is determined that the abnormal cause of abnormal signal G is carbon deposition in the three-way catalytic converter, and abnormal signal G is changed to abnormal signal K;

[0065] Based on the received abnormal signal Y and abnormal signal K, the relevant abnormal signal is transmitted to the abnormal alarm module, and the abnormal alarm module lights up different alarm lights based on the received abnormal signal, wherein the abnormal signal Y is red and the abnormal signal K is blue.

[0066] A carbon emission monitoring method based on three-way catalysis comprises the following steps:

[0067] S1. Collect the gas components and concentrations in the original exhaust gas of relevant vehicles, the gas components and concentrations after treatment by the three-way catalytic converter, vehicle speed data, vehicle driving distance, driving time, and total vehicle mileage through GPS equipment and gas sensors, and establish a vehicle driving database;

[0068] S2. For each gas component and concentration in the received original exhaust gas, and each gas component and concentration in the exhaust gas treated by the three-way catalytic converter, determine the state of the three-way catalytic converter, and further analyze the determination result;

[0069] S3. Further analyze the abnormal results of carbon emission monitoring based on the collected vehicle speed data and the driving status of the vehicle in the relevant time period, and distinguish between occasional abnormalities and regular abnormalities;

[0070] S4. For frequent anomalies, extract data from the vehicle driving database in combination with the abnormal signal timestamp, identify vehicle behavior and analyze the cause of abnormal carbon emissions, make a determination and record the cause, and combine the vehicle mileage data in the vehicle driving database to make a final determination on the cause of abnormal carbon emissions. Different alarm lights will be turned on based on different causes of abnormal carbon emissions.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] 1. In the present invention, by setting a carbon emission monitoring module and a short-term abnormality determination module, the concentration of the treated gas in the three-way catalytic converter is identified, and the abnormality of the three-way catalytic converter is identified. At the same time, according to the timestamp of the abnormal information, the vehicle speed data and the vehicle driving status of the relevant timestamp are further identified for the abnormality, and the occasional abnormality and the frequent abnormality in the carbon emission monitoring process are identified. Different abnormalities can be processed in a targeted manner, thereby improving accuracy and practicality;

[0073] 2. In the present invention, by combining the driving state and speed of the vehicle before the abnormality, occasional abnormalities are filtered, which can avoid abnormal false alarms, eliminate the influence of occasional abnormalities on the driver's judgment, and enhance the practicality of the system;

[0074] 3. In the present invention, by setting up a long-term abnormality association module, based on the frequent abnormalities obtained by the carbon emission monitoring module and combined with the relevant vehicle driving information in the vehicle driving database, the abnormal state of the vehicle is traced back, and the causes of the frequent abnormalities are further confirmed, including carbon deposition and thermal deactivation of the three-way catalytic converter, so as to facilitate the adoption of targeted solutions and facilitate users to formulate more scientific maintenance plans to avoid invalid maintenance operations;

[0075] 4. In the present invention, by combining the vehicle mileage data with the carbon emission abnormality results, further determination is made for the abnormalities that are not clearly determined, and the abnormalities are prompted by warning lights of different colors. The user can quickly understand the current abnormal exhaust emission of the vehicle according to the color of the warning light, so as to take corresponding measures;

[0076] The carbon emission monitoring method and system based on three-way catalysis can realize exhaust data collection, carbon emission abnormality judgment, distinction between occasional and regular abnormalities, abnormal cause tracing and identification, and carbon emission abnormality alarm, so as to realize comprehensive monitoring and management of carbon emissions. By judging occasional and regular abnormalities in the carbon emission monitoring process, misjudgment can be eliminated, thereby improving the practicality and functionality of the method and system. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 The block diagram of a carbon emission monitoring system based on three-way catalysis of the present invention. DETAILED DESCRIPTION

[0078] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] like Figure 1 As shown, a carbon emission monitoring system based on three-way catalysis includes a data acquisition module, a carbon emission monitoring module, an abnormal alarm module, a short-term abnormality determination module, and a long-term abnormality association module;

[0080] The data acquisition module includes a GPS device and a gas sensor, which are used to collect the gas components and concentrations in the original exhaust gas, the gas components and concentrations in the exhaust gas after the three-way catalytic converter treatment, and the vehicle speed data, wherein the gases in the original exhaust gas include carbon monoxide, nitrogen oxides, and hydrocarbons, and the gases in the exhaust gas after the three-way catalytic converter treatment include carbon dioxide, nitrogen, and water vapor. At the same time, based on the GPS device, the vehicle speed data, driving distance, driving time, and total mileage of the vehicle are collected, and a vehicle driving database is established, and the vehicle speed is associated with the driving distance and driving time in the database. The specific steps are as follows:

[0081] The collected gas data is synchronized with the GPS data by time stamp, the pre-processed data is stored in the vehicle driving database, and a data table is established in the vehicle driving database to store the original exhaust data collected each time, the processed exhaust data, and the corresponding vehicle speed, driving distance, and driving time information, and a unique timestamp is assigned to the stored information, and the three-way catalytic converter maintenance timestamp is recorded at the same time.

[0082] It should be noted that by assigning a unique timestamp to each record, it is convenient for subsequent modules to extract and retrieve data from the vehicle driving database. The data acquisition module can continuously and accurately collect and store key data such as vehicle exhaust composition, vehicle speed and driving distance, providing a solid data foundation for subsequent carbon emission monitoring, anomaly judgment and correlation analysis.

[0083] The carbon emission monitoring module determines the status of the three-way catalytic converter based on the gas components and concentrations in the received original exhaust gas and the gas components and concentrations in the exhaust gas after the three-way catalytic converter is treated, and transmits the abnormal results to the short-term abnormality judgment module based on the carbon emission monitoring results. The specific steps are as follows:

[0084] Based on the relevant parameters of the three-way catalytic converter, the standard carbon emission monitoring value conversion rate α is obtained 1max , α 2max , α 1min , α 2min , where α 1max represents the maximum carbon monoxide to carbon dioxide conversion rate, α 2max represents the maximum nitrogen conversion rate of nitrogen oxides, represents the maximum carbon dioxide conversion rate of hydrocarbons, represents the maximum water vapor conversion rate of hydrocarbons, α 1min represents the minimum carbon monoxide to carbon dioxide conversion rate, α 2min represents the minimum nitrogen conversion rate of nitrogen oxides, represents the minimum carbon dioxide conversion rate of hydrocarbons, represents the minimum water vapor conversion rate of hydrocarbons;

[0085] Based on the data acquisition module, the concentrations of carbon monoxide, nitrogen oxides, and hydrocarbons in the original exhaust gas are obtained, where A1 represents the concentration of carbon monoxide, A2 represents the concentration of nitrogen oxides, and A3 represents the concentration of hydrocarbons. Based on the standard carbon emission monitoring value conversion rate, the normal value range is obtained. The specific steps are:

[0086] The normal range of carbon dioxide values ​​is:

[0087]

[0088] The normal range of water vapor values ​​is:

[0089]

[0090] The normal value range of nitrogen is:

[0091] [A2·α 2min ,A2·α2max ];

[0092] The values ​​of carbon dioxide, nitrogen and water vapor in the treated tail gas are obtained through the data acquisition module, where β1 represents the carbon dioxide concentration in the treated tail gas, β2 represents the nitrogen concentration in the treated tail gas, and β3 represents the water vapor concentration in the treated tail gas. The concentration of each gas in the treated tail gas is determined based on the normal value range. The specific steps are:

[0093] when It means the carbon dioxide concentration is normal;

[0094] when Indicates that the carbon dioxide concentration is abnormal, and transmits the abnormal signal and related data to the short-term abnormality judgment module;

[0095] When β2∈[A2·α 2min ,A2·α 2max ], indicating that the nitrogen concentration is normal;

[0096] When β2<A2·α 2min , representing abnormal nitrogen concentration, transmits abnormal signals and related data to the short-term abnormality judgment module;

[0097] when It means the water vapor concentration is normal;

[0098] when It represents abnormal water vapor concentration, and transmits abnormal signals and related data into the short-term abnormality judgment module.

[0099] It should be noted that the working principle of the three-way catalytic converter is to accelerate the conversion of carbon monoxide, hydrocarbons, and nitrogen oxides in the exhaust gas through the precious metals inside. Platinum is mainly used to catalyze the conversion of carbon monoxide and nitrogen oxides, palladium is mainly used to catalyze the conversion of hydrocarbons and carbon monoxide, and rhodium is mainly used to catalyze the reduction reaction of nitrogen oxides. The reaction formula is:

[0100] HC+O2→CO2+H2O;

[0101]

[0102] 2NO x →xO2+N2;

[0103] By obtaining the standard conversion rate, the conversion efficiency of the three-way catalytic converter can be obtained, thereby obtaining the normal value range of carbon dioxide:

[0104]

[0105] Normal range of water vapor:

[0106]

[0107] Normal nitrogen value range:

[0108] [A2·α 2min ,A2·α 2max ];

[0109] By obtaining the carbon dioxide, water vapor and nitrogen values ​​in the processed exhaust gas, the carbon emissions in the exhaust gas can be monitored to determine whether the three-way catalytic converter is working normally.

[0110] The short-term abnormality determination module determines the abnormal results of carbon emission monitoring based on the vehicle speed data collected by the data acquisition module and the driving status of the vehicle in the relevant time period, and transmits the relevant signals to the subsequent modules based on the determination results. The specific steps are as follows:

[0111] Based on the abnormal signal transmitted by the carbon emission monitoring module, the timestamp is recorded, and combined with the timestamp in the vehicle driving database in the data acquisition module, the relevant data before the abnormal signal is extracted and analyzed. The specific steps are as follows:

[0112] Based on the timestamp of the abnormal signal transmitted by the carbon emission monitoring module, relevant data including vehicle speed data and vehicle travel distance data are extracted through the vehicle driving database;

[0113] Set the vehicle high speed threshold θ max and the vehicle low speed threshold θ min , get the vehicle speed v in the vehicle driving database at the time of abnormal signal timestamp t , based on the speed v t With high speed threshold θ max , low speed threshold θ min , to judge the vehicle status:

[0114] When θ min ≤v t ≤θ max When the vehicle is in normal driving state, it is marked in red;

[0115] When v t >θ max When the vehicle is in high-speed driving status, it is marked green;

[0116] When v t <θ min When the vehicle is in idling state, it is marked green;

[0117] Set the short-distance driving distance threshold χ of the vehicle, obtain the driving distance λ of the adjacent vehicles in the vehicle driving database before the abnormal signal timestamp, and judge the driving status of the vehicle:

[0118] When λ≥χ, the vehicle status is considered to be normal driving and marked in red;

[0119] When λ<χ, the vehicle status is considered to be short-distance driving and marked as green;

[0120] It should be noted that, by considering the vehicle speed v t To obtain, and through the vehicle high speed threshold θ max and the vehicle low speed threshold θ min By comparison, we can judge the vehicle conditions under abnormal carbon emission conditions. When driving at high speed, the operating temperature of the three-way catalytic converter rises sharply, making it temporarily unable to effectively treat the exhaust gas, thus causing abnormal emission data. Idling and short-distance driving will prevent the engine and three-way catalytic converter from reaching normal operating temperature, resulting in reduced catalytic efficiency, which in turn affects the emission data. These are all occasional situations.

[0121] Based on the results of extracting and analyzing the relevant data before the abnormal signal, the vehicle-related abnormality is judged, and the relevant data is transmitted to the long-term abnormality association module based on the judgment result. The steps are as follows:

[0122] For the vehicle status marked as green, the short-term abnormality determination module ignores the related exhaust gas abnormality and does not transmit the abnormal signal to the long-term abnormality association module;

[0123] For the vehicle status marked in red, the short-term abnormality determination module integrates the data of the relevant exhaust gas abnormalities and transmits the relevant data and abnormal signals to the long-term abnormality association module;

[0124] Based on the results of extracting and analyzing the relevant data before the abnormal signal, the vehicle-related abnormality is determined, and the relevant data is transmitted to the long-term abnormality association module based on the result of the determination.

[0125] It should be noted that, by for the vehicle speed v t To obtain, and through the vehicle high speed threshold θ max and the vehicle low speed threshold θ min By comparison and setting the short-distance driving distance threshold χ of the vehicle, the driving distance λ of the adjacent vehicles in the vehicle driving database before the abnormal signal timestamp is obtained, and the driving status of the vehicle is judged. The carbon emission anomalies can be classified, the occasional anomalies can be ignored, and the frequent anomalies can be traced and judged.

[0126] The long-term abnormality association module, based on the relevant data collected in the data acquisition module and combined with the carbon emission monitoring abnormalities, identifies the vehicle behavior within the relevant time period, determines and records the abnormal causes, and transmits the abnormal signal to the carbon emission monitoring module. The specific steps are as follows:

[0127] Receive the abnormal signal transmitted by the short-term abnormality determination module, and traverse the data in the vehicle driving database in the data acquisition module in combination with the abnormal signal timestamp. The relevant timestamp range includes the last maintenance timestamp to the abnormal signal timestamp;

[0128] Based on the relevant data in the vehicle driving database, the vehicle behavior in the relevant time period is identified and recorded, the carbon emission abnormality is determined, and the abnormal signal is transmitted to the carbon emission monitoring module based on the determination result. The specific steps are as follows:

[0129] Based on the data acquisition module, the vehicle data within the relevant time stamp range of the vehicle driving database is collected, including the vehicle speed v n , vehicle travel distance λ n , vehicle travel time T n ;

[0130] Set the driving speed high speed threshold v max , driving speed idling threshold v min , short-distance threshold λ min , Long-time threshold value of vehicle driving time T max , based on the vehicle data within the relevant timestamp range, the vehicle behavior is identified and recorded. The specific steps are:

[0131] Based on the short-distance threshold λ min , for short-distance driving of vehicles, whenever λ n ≤λ min , indicating that the relevant distance is a short-distance trip, the relevant data is marked with a symbol of C1, and the relevant timestamp is removed;

[0132] Based on the long-time threshold T of driving time max And the driving speed idle threshold v min , for vehicles running at low speed for a long time, whenever v n ≤v min And T n ≥T max When , it means that the relevant data is idling for a long time, the relevant data is marked with a symbol C1, and the relevant timestamp is removed;

[0133] Based on the long-time threshold T of driving time max And the high speed threshold v max, for vehicles running at high speed for a long time, whenever v n ≥v max And T n ≥T max When , it means that the relevant data is long-term high-speed driving, the relevant data is marked with a symbol of C2, and the relevant timestamp is removed;

[0134] Count the number of C1 and C2 to get the total number of C1 and the total number of C2 calculate and The algorithm formula is:

[0135]

[0136] Among them, H1 represents the proportion of C1 in the total number, and H2 represents the proportion of C2 in the total number. Based on the size of H1 and H2, the abnormal carbon emission is judged. The specific steps are as follows:

[0137] When H1>>H2, it is determined that the cause of abnormal carbon emission is carbon deposition in the three-way catalytic converter, and the abnormal signal K is transmitted to the carbon emission monitoring module;

[0138] When H1<<H2, it is determined that the cause of the abnormal carbon emission is thermal deactivation of the three-way catalytic converter, and the abnormal signal Y is transmitted to the carbon emission monitoring module;

[0139] When H1≈H2, the cause of the abnormal carbon emission is determined to be undetermined, and the abnormal signal G is transmitted to the carbon emission monitoring module.

[0140] It should be noted that frequent short-distance driving and long-term low-speed driving will make it impossible for the three-way catalytic converter to reach the required operating temperature, making it difficult to effectively catalyze harmful substances in the exhaust gas and convert them into relatively harmless substances, thereby causing the harmful substances to accumulate inside the catalyst and form carbon deposits. The carbon deposits will cover the surface of the catalyst, hindering the catalytic reaction, resulting in a reduction in the contact area between the catalyst and the harmful substances in the exhaust gas, further reducing the catalytic efficiency and causing carbon emissions to fail to meet the standards;

[0141] Long-term high-speed driving will cause the engine and exhaust system to continuously operate at a high temperature, thus exceeding the optimal operating temperature range of the three-way catalytic converter. The catalyst surface is prone to structural or chemical changes, the catalyst surface area is reduced or the active center is deactivated. By counting the number of C1 and C2, the total number of C1 is obtained. and the total number of C2 calculate and By analyzing the proportion of common abnormalities caused by different behaviors, different strategies can be adopted to maintain the three-way catalytic converter.

[0142] In the carbon emission monitoring module, an advanced judgment system is set up. Based on the judgment result of the long-term abnormal association module, the abnormal signal is received, and the total mileage of the vehicle is obtained based on the data acquisition module. Further judgment is made on the pending abnormal signal. The specific steps are as follows:

[0143] Based on the data acquisition module, the total mileage L of the vehicle is obtained and the mileage threshold L is set. n , perform advanced judgment on abnormal signal G, the specific steps are:

[0144] When L ≥ L n When , it is determined that the abnormal cause of abnormal signal G is thermal deactivation of the three-way catalytic converter, and the abnormal signal G is changed to abnormal signal Y;

[0145] When L<L n When , it is determined that the abnormal cause of abnormal signal G is carbon deposition in the three-way catalytic converter, and abnormal signal G is changed to abnormal signal K;

[0146] Based on the received abnormal signal Y and abnormal signal K, the relevant abnormal signal is transmitted to the abnormal alarm module, and the abnormal alarm module lights up different alarm lights based on the received abnormal signal, wherein the abnormal signal Y is red and the abnormal signal K is blue.

[0147] It should be noted that when H1≈H2 is similar, it is difficult for the long-term abnormality association module to determine the specific cause of the carbon emission abnormality. At this time, the total mileage of the associated vehicle, that is, the mileage from the last repair of the three-way catalytic converter to the current fault, is used to further analyze the cause of the carbon emission abnormality. Usually, the threshold L n The value is around 35,000 kilometers, that is, within 35,000 kilometers, it is often manifested as abnormal carbon emissions caused by carbon deposits, which can be eliminated through maintenance and cleaning. Abnormal carbon emissions exceeding 35,000 kilometers are often manifested as thermal deactivation of the internal catalyst, and the three-way catalytic converter needs to be replaced.

[0148] The abnormal alarm module receives the abnormal signal transmitted by the carbon emission monitoring module and lights up different alarm lights based on different abnormal types.

[0149] A carbon emission monitoring method based on three-way catalysis comprises the following steps:

[0150] S1. Collect the gas components and concentrations in the original exhaust gas of relevant vehicles, the gas components and concentrations after treatment by the three-way catalytic converter, vehicle speed data, vehicle driving distance, driving time, and total vehicle mileage through GPS equipment and gas sensors, and establish a vehicle driving database;

[0151] S2. For each gas component and concentration in the received original exhaust gas, and each gas component and concentration in the exhaust gas treated by the three-way catalytic converter, determine the state of the three-way catalytic converter, and further analyze the determination result;

[0152] S3. Further analyze the abnormal results of carbon emission monitoring based on the collected vehicle speed data and the driving status of the vehicle in the relevant time period, and distinguish between occasional abnormalities and regular abnormalities;

[0153] S4. For frequent anomalies, extract data from the vehicle driving database in combination with the abnormal signal timestamp, identify vehicle behavior and analyze the cause of abnormal carbon emissions, make a determination and record the cause, and combine the vehicle mileage data in the vehicle driving database to make a final determination on the cause of abnormal carbon emissions. Different alarm lights will be turned on based on different causes of abnormal carbon emissions.

[0154] In the embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation; the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of this embodiment.

[0155] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A carbon emission monitoring system based on three-way catalysis, characterized in that: It includes data acquisition module, carbon emission monitoring module, abnormal alarm module, short-term abnormality determination module and long-term abnormality association module; The data acquisition module includes a GPS device and a gas sensor, which is used to collect the gas components and concentrations in the original exhaust gas, the gas components and concentrations after the three-way catalytic converter treatment, the vehicle speed data, the vehicle travel distance, the travel time, the total mileage of the vehicle, and transmit the relevant data to the subsequent modules; The carbon emission monitoring module determines the state of the three-way catalytic converter based on the gas components and concentrations in the received original exhaust gas and the gas components and concentrations in the exhaust gas after the three-way catalytic converter is treated, and transmits the abnormal result to the short-term abnormality determination module based on the carbon emission monitoring result. The specific steps are as follows: Based on the relevant parameters of the three-way catalytic converter, the standard carbon emission monitoring value conversion rate α is obtained 1max , α 2max , α 1min , α 2min , where α 1max represents the maximum carbon monoxide to carbon dioxide conversion rate, α 2max represents the maximum nitrogen conversion rate of nitrogen oxides, represents the maximum carbon dioxide conversion rate of hydrocarbons, represents the maximum water vapor conversion rate of hydrocarbons, α 1min represents the minimum carbon monoxide to carbon dioxide conversion rate, α 2min represents the minimum nitrogen conversion rate of nitrogen oxides, represents the minimum carbon dioxide conversion rate of hydrocarbons, represents the minimum water vapor conversion rate of hydrocarbons; Based on the data acquisition module, the concentrations of carbon monoxide, nitrogen oxides, and hydrocarbons in the original exhaust gas are obtained, where A1 represents the concentration of carbon monoxide, A2 represents the concentration of nitrogen oxides, and A3 represents the concentration of hydrocarbons. Based on the standard carbon emission monitoring value conversion rate, the normal value range is obtained. The specific steps are: The normal range of carbon dioxide values ​​is: The normal range of water vapor values ​​is: The normal value range of nitrogen is: [A2·α 2min ,A2·α 2max ]; The values ​​of carbon dioxide, nitrogen and water vapor in the treated tail gas are obtained through the data acquisition module, where β1 represents the carbon dioxide concentration in the treated tail gas, β2 represents the nitrogen concentration in the treated tail gas, and β3 represents the water vapor concentration in the treated tail gas. The concentration of each gas in the treated tail gas is determined based on the normal value range. The specific steps are: when It means the carbon dioxide concentration is normal; when Indicates that the carbon dioxide concentration is abnormal, and transmits the abnormal signal and related data to the short-term abnormality judgment module; When β2∈[A2·α 2min ,A2·α 2max ], indicating that the nitrogen concentration is normal; When β2<A2·α 2min , representing abnormal nitrogen concentration, transmits abnormal signals and related data to the short-term abnormality judgment module; when It means the water vapor concentration is normal; when Represents abnormal water vapor concentration, transmits abnormal signals and related data to the short-term abnormality judgment module; The short-term abnormality determination module determines the abnormal results of carbon emission monitoring based on the vehicle speed data collected by the data acquisition module and the driving status of the vehicle in the relevant time period, and transmits the relevant signal to the subsequent module based on the determination result. The specific steps are as follows: Based on the abnormal signal transmitted by the carbon emission monitoring module, the timestamp is recorded, and combined with the timestamp in the vehicle driving database in the data acquisition module, the relevant data before the abnormal signal is extracted and analyzed. The specific steps are as follows: Based on the timestamp of the abnormal signal transmitted by the carbon emission monitoring module, relevant data including vehicle speed data and vehicle travel distance data are extracted through the vehicle driving database; Set the vehicle high speed threshold θ max and the vehicle low speed threshold θ min , get the vehicle speed v in the vehicle driving database at the time of the abnormal signal timestamp t , based on the speed v t With high speed threshold θ max , low speed threshold θ min , to judge the vehicle status: When θ min ≤v t ≤θ max When the vehicle is in normal driving state, it is marked in red; When v t >θ max When the vehicle is in high-speed driving status, it is marked green; When v t <θ min When the vehicle is in idling state, it is marked green; Set the short-distance driving distance threshold χ of the vehicle, obtain the driving distance λ of the adjacent vehicles in the vehicle driving database before the abnormal signal timestamp, and judge the driving status of the vehicle: When λ≥χ, the vehicle status is considered to be normal driving and marked in red; When λ<χ, the vehicle status is considered to be short-distance driving and marked as green; Based on the results of extracting and analyzing the relevant data before the abnormal signal, the vehicle-related abnormality is judged, and the relevant data is transmitted to the long-term abnormality association module based on the judgment result. The steps are as follows: For the vehicle status marked as green, the short-term abnormality determination module ignores the related exhaust gas abnormality and does not transmit the abnormal signal to the long-term abnormality association module; For the vehicle status marked in red, the short-term abnormality determination module integrates the data of the relevant exhaust gas abnormalities and transmits the relevant data and abnormal signals to the long-term abnormality association module; The long-term abnormality association module, based on the relevant data collected in the data acquisition module and in combination with the carbon emission monitoring abnormality, identifies the vehicle behavior within the relevant time period, determines and records the abnormal cause, and transmits the abnormal signal to the carbon emission monitoring module. The specific steps are as follows: Receive the abnormal signal transmitted by the short-term abnormality determination module, and traverse the data in the vehicle driving database in the data acquisition module in combination with the abnormal signal timestamp. The relevant timestamp range includes the last maintenance timestamp to the abnormal signal timestamp; Based on the relevant data in the vehicle driving database, the vehicle behavior in the relevant time period is identified and recorded, the carbon emission abnormality is determined, and the abnormal signal is transmitted to the carbon emission monitoring module based on the determination result. The specific steps are as follows: Based on the data acquisition module, the vehicle data within the relevant time stamp range of the vehicle driving database is collected, including the vehicle speed v n , vehicle travel distance λ n , vehicle travel time T n ; Set the driving speed high speed threshold v max , driving speed idling threshold v min , short-distance threshold λ min , Long-time threshold value of vehicle driving time T max , based on the vehicle data within the relevant timestamp range, the vehicle behavior is identified and recorded. The specific steps are: Based on the short-distance threshold λ min , for short-distance driving of vehicles, whenever λ n ≤λ min , indicating that the relevant distance is a short-distance trip, the relevant data is marked with a symbol of C1, and the relevant timestamp is removed; Based on the long-time threshold T of driving time max And the driving speed idle threshold v min , for vehicles running at low speed for a long time, whenever v n ≤v min And T n ≥T max When , it means that the relevant data is idling for a long time, the relevant data is marked with a symbol C1, and the relevant timestamp is removed; Based on the long-time threshold T of driving time max And the high speed threshold v max , for vehicles running at high speed for a long time, whenever v n ≥v max And T n ≥T max When , it means that the relevant data is long-term high-speed driving, the relevant data is marked with a symbol of C2, and the relevant timestamp is removed; Count the number of C1 and C2 to get the total number of C1 and the total number of C2 calculate and The algorithm formula is: Among them, H1 represents the proportion of C1 in the total number, and H2 represents the proportion of C2 in the total number. Based on the size of H1 and H2, the abnormal carbon emission is judged. The specific steps are as follows: When H1>>H2, it is determined that the cause of abnormal carbon emission is carbon deposition in the three-way catalytic converter, and the abnormal signal K is transmitted to the carbon emission monitoring module; When H1<<H2, it is determined that the cause of the abnormal carbon emission is thermal deactivation of the three-way catalytic converter, and the abnormal signal Y is transmitted to the carbon emission monitoring module; When H1≈H2, the cause of abnormal carbon emission is determined to be undetermined, and the abnormal signal G is transmitted to the carbon emission monitoring module; The carbon emission monitoring module is provided with an advanced determination system, which receives abnormal signals based on the determination results of the long-term abnormal association module, obtains the total mileage of the vehicle based on the data acquisition module, and further determines the pending abnormal signals. The specific steps are as follows: Based on the data acquisition module, the total mileage L of the vehicle is obtained and the mileage threshold L is set. n , perform advanced judgment on abnormal signal G, the specific steps are: When L ≥ L n When , it is determined that the abnormal cause of abnormal signal G is thermal deactivation of the three-way catalytic converter, and the abnormal signal G is changed to abnormal signal Y; When L<L n When , it is determined that the abnormal cause of abnormal signal G is carbon deposition in the three-way catalytic converter, and abnormal signal G is changed to abnormal signal K; Based on the received abnormal signal Y and abnormal signal K, the relevant abnormal signal is transmitted to the abnormal alarm module, and the abnormal alarm module lights up different alarm lights based on the received abnormal signal, wherein the abnormal signal Y is red and the abnormal signal K is blue; The abnormal alarm module receives the abnormal signal transmitted by the carbon emission monitoring module and lights up different alarm lights based on different abnormal types.

2. The carbon emission monitoring system based on three-way catalysis according to claim 1 is characterized in that: The data acquisition module includes a GPS device and a gas sensor, which are used to collect the gas components and concentrations in the original exhaust gas, the gas components and concentrations in the exhaust gas treated by the three-way catalytic converter, and the vehicle speed data, wherein the gases in the original exhaust gas include carbon monoxide, nitrogen oxides, and hydrocarbons, and the gases in the exhaust gas treated by the three-way catalytic converter include carbon dioxide, nitrogen, and water vapor. At the same time, based on the GPS device, the vehicle speed data, driving distance, driving time, and total vehicle mileage are collected to establish a vehicle driving database, and the vehicle speed is associated with the driving distance and driving time in the database. The specific steps are as follows: The collected gas data is synchronized with the GPS data by time stamp, the pre-processed data is stored in the vehicle driving database, and a data table is established in the vehicle driving database to store the original exhaust data collected each time, the processed exhaust data, and the corresponding vehicle speed, driving distance, and driving time information, and a unique timestamp is assigned to the stored information, and the three-way catalytic converter maintenance timestamp is recorded at the same time.

3. A carbon emission monitoring method based on three-way catalysis, characterized in that: The method adopts the carbon emission monitoring system based on three-way catalysis as described in any one of claims 1-2, comprising the following steps: S1. Collect the gas components and concentrations in the original exhaust gas of relevant vehicles, the gas components and concentrations after three-way catalytic converter treatment, vehicle speed data, vehicle driving distance, driving time, and total vehicle mileage through GPS equipment and gas sensors, and establish a vehicle driving database; S2. For each gas component and concentration in the received original exhaust gas, and each gas component and concentration in the exhaust gas treated by the three-way catalytic converter, determine the state of the three-way catalytic converter, and further analyze the determination result; S3. Further analyze the abnormal results of carbon emission monitoring based on the collected vehicle speed data and the driving status of the vehicle in the relevant time period, and distinguish between occasional abnormalities and regular abnormalities; S4. For frequent anomalies, extract data from the vehicle driving database in combination with the abnormal signal timestamp, identify vehicle behavior and analyze the cause of abnormal carbon emissions, make a determination and record the cause, and combine the vehicle mileage data in the vehicle driving database to make a final determination on the cause of abnormal carbon emissions. Different alarm lights will be turned on based on different causes of abnormal carbon emissions.

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