Automobile exhaust analysis method through automobile exhaust component monitoring

Abstract

The invention discloses an automobile exhaust analysis method through automobile exhaust component monitoring, and relates to the technical field of data processing, and the method comprises the steps: constructing an exhaust component monitoring data set based on the operation state of an engine, carrying out the class uniformity analysis and data set expansion of the exhaust component monitoring data set, and obtaining a target data set; performing sample labeling on the target data set, and constructing a training data set; building a model framework, and performing model training convergence through the training data set to obtain an exhaust analysis model; on the basis of a preset treatment target, configuring tail gas detection equipment, and performing target detection; and inputting the engine monitoring parameters and the target detection result into the exhaust analysis model, and determining an exhaust recognition result. The technical problem that in the prior art, due to variability of automobile running states and complexity of emission data, the tail gas emission control effect is poor is solved, and the technical effects that through tail gas emission dynamic monitoring and engine control optimization, engine performance is improved, and tail gas emission is reduced are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to an automobile exhaust analysis method by monitoring automobile exhaust components. Background Art

[0002] Cars are the most common means of transportation in modern life. At the same time, the pollution of automobile exhaust to the environment has gradually become a negative factor affecting the quality of human life. Automobile exhaust analysis is an important means to evaluate engine performance and reduce exhaust emissions. Exhaust analysis can be used to determine the working status of the engine and whether the automobile's emissions meet the standards, thereby reducing automobile exhaust pollution to the atmosphere at the source. However, due to the variability of automobile operating conditions and the variety and complexity of emission data, the accuracy of emission analysis is low and the exhaust emission control effect is poor. Summary of the invention

[0003] The present application provides an automobile exhaust analysis method by monitoring automobile exhaust components, which is used to solve the technical problem in the prior art of poor exhaust emission control effect due to the variability of automobile operating conditions and the complexity of emission data.

[0004] The first aspect of the present application provides an automobile exhaust analysis method through automobile exhaust component monitoring, the method comprising: constructing an exhaust component monitoring data set based on the engine operating state, the exhaust component monitoring data set comprising exhaust components and engine state parameters; performing category uniformity analysis on the exhaust component monitoring data set, expanding the data set based on the category uniformity analysis result, and obtaining a target data set; labeling samples of the target data set according to a preset processing target, and constructing a training data set; building a model framework, performing model training convergence through the training data set, and obtaining an exhaust analysis model; configuring an exhaust detection device based on the preset processing target, and performing target detection on automobile exhaust through the exhaust detection device to obtain a target detection result; inputting the engine monitoring parameters and the target detection result into the exhaust analysis model to analyze the engine exhaust state, and determine the exhaust recognition result.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages: The automobile exhaust analysis method provided by the application through automobile exhaust component monitoring relates to the field of data processing technology. According to the engine operating state, an exhaust component monitoring data set is constructed. After the category uniformity analysis and data set expansion are performed on it, it is used as training data to train an exhaust analysis model. The engine monitoring parameters and exhaust gas detection results are input into the exhaust analysis model to determine the exhaust recognition result. The technical problem of poor exhaust emission control effect due to the variability of the automobile operating state and the complexity of the emission data in the prior art is solved, and the technical effect of improving engine performance and reducing exhaust emissions is achieved through real-time monitoring of automobile exhaust and dynamic control optimization of exhaust emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0007] Figure 1 A schematic flow chart of a method for analyzing automobile exhaust gas by monitoring automobile exhaust gas components provided in an embodiment of the present application; Figure 2 A schematic diagram of a flow chart of obtaining an engine operating state in a method for analyzing automobile exhaust gas by monitoring automobile exhaust components provided in an embodiment of the present application; Figure 3 A schematic flow chart of configuring an exhaust gas detection device in a method for analyzing automobile exhaust by monitoring automobile exhaust components provided in an embodiment of the present application. DETAILED DESCRIPTION

[0008] The present application provides an automobile exhaust analysis method by monitoring automobile exhaust components, which is used to solve the technical problem in the prior art of poor exhaust emission control effect due to the variability of automobile operating conditions and the complexity of emission data.

[0009] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0010] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0011] Embodiment 1 like Figure 1 As shown, the present application provides a method for analyzing automobile exhaust by monitoring automobile exhaust components, the method comprising: P10: Based on the engine running state, construct an exhaust gas component monitoring data set, wherein the exhaust gas component monitoring data set includes exhaust gas components and engine state parameters; Specifically, according to the engine operating state, an exhaust gas component monitoring data set is constructed to collect and organize detailed information about the exhaust gas components and engine state parameters to provide a data basis for subsequent exhaust analysis, wherein the exhaust gas component monitoring data set includes exhaust gas components and engine state parameters. On the one hand, the exhaust gas components can be obtained by real-time measurement through professional exhaust gas detection equipment. The exhaust gas components refer to the chemical substances contained in the gas discharged by the automobile engine, such as carbon monoxide, carbon dioxide, nitrogen oxides, hydrocarbons, etc., which are important indicators for evaluating engine performance and whether the exhaust gas emission standards are met. On the other hand, the engine state parameters can be collected in real time through sensors on the engine, including various parameters describing the engine operating state, such as speed, load, temperature, fuel consumption rate, etc., which can reflect the operating conditions of the engine under different working conditions and the corresponding changes in exhaust gas components.

[0012] Exemplarily, a special exhaust gas monitoring device is used to collect exhaust gas under different engine operating conditions in real time. The exhaust gas monitoring device includes a gas analyzer, a sensor module, etc., which can be used to monitor the main components of CO (carbon oxides), NOx (nitrogen oxides), HC (hydrocarbons) and the like in the exhaust gas. Each sensor is connected to the exhaust pipe of the car through a pipeline, and can detect the emission components in real time when the engine is running. At the same time, the sensor module installed on the engine is used to collect the operating parameters of the engine. These parameters include engine speed, fuel consumption rate, load, exhaust temperature, etc., which are used to describe the performance of the engine under different operating conditions. These parameters are obtained in real time through a data collector to ensure the continuity and accuracy of the data. Finally, based on the engine operating state, the collected exhaust gas components are associated with the engine operating parameters to construct an exhaust gas component monitoring data set. The constructed data set covers exhaust characteristics and engine state parameters under different operating conditions such as idling, acceleration, deceleration, and uniform speed.

[0013] Further, such as Figure 2 As shown, before constructing the exhaust gas component monitoring data set, the embodiment of the present application further includes step P10a, and step P10a further includes: P11a: Configure the first engine operation state according to the instantaneous, intermittent and continuous emission states of the exhaust gas; P12a: Analyze the relationship between exhaust and power according to engine speed and torsional strength, and configure the second engine operating state; P13a: extracting operation state features from the first engine operation state and the second engine operation state, and performing random combination of features to construct a reconstructed state, performing engine operation evaluation on the reconstructed state, taking the reconstructed state that satisfies the operation evaluation as a newly added operation state, and taking the reconstructed state that does not satisfy the operation evaluation as a terminated operation state; P14a: searching and deriving according to a set step length based on the newly added operating state, and continuing to perform engine operation evaluation, and so on, until there is no newly added operating state; P15a: Summarize all newly added operating states, the first engine operating state, and the second engine operating state to obtain the engine operating state.

[0014] It should be understood that before constructing the exhaust gas composition monitoring data set, the engine operating state needs to be obtained. Specifically, the normal operating state of the engine is configured according to the instantaneous, intermittent, and continuous emission states of the exhaust gas emissions, and this is used as the first engine operating state, where instantaneous emissions usually occur when the engine is started or accelerated, intermittent emissions may occur under specific working conditions, and continuous emissions are the state when the engine is running stably. Different emission states will directly affect the exhaust gas composition and concentration, so it is necessary to configure the corresponding engine operating state for each state.

[0015] Furthermore, by deeply analyzing the engine speed and torsional strength, the torsional strength is the load capacity of the engine, and the relationship between exhaust and power is obtained, that is, the mapping relationship between the engine speed and load and exhaust emissions is quantitatively analyzed, and the exhaust composition and concentration under different speeds and loads are obtained, and the second engine operating state is obtained.

[0016] Furthermore, operating state features are extracted from the first engine operating state and the second engine operating state, and the operating state features may include engine speed, load, temperature, fuel consumption rate, etc., which can reflect the performance of the engine under different operating states. Furthermore, the extracted features are randomly grouped to generate a series of new reconstructed states with different characteristics, which can reflect various different working conditions and conditions that the engine may encounter in actual operation.

[0017] Furthermore, an engine operation evaluation is performed for each reconstructed state, including a comprehensive evaluation of engine performance, emission level, fuel economy, and other aspects, and a reconstructed state that meets the operation evaluation is used as a newly added operation state, and a reconstructed state that does not meet the operation evaluation is used as a terminated operation state. Furthermore, for the reconstructed state that meets the operation evaluation, a search is performed according to a set step size to further expand the range of the engine operation state, and the engine operation evaluation is continued for the derived new state, and so on and so forth, and the process is iterated continuously until there is no new operation state that meets the evaluation criteria.

[0018] Finally, all newly added operating states, the first engine operating state and the second engine operating state are summarized to obtain a comprehensive engine operating state set, which serves as the basis for the subsequent construction of the exhaust composition monitoring data set to ensure that the exhaust composition monitoring data set can cover the exhaust emissions of the engine under various operating states.

[0019] P20: performing a category uniformity analysis on the exhaust gas component monitoring data set, and expanding the data set based on the category uniformity analysis result to obtain a target data set; Furthermore, step P20 of the embodiment of the present application further includes: P21: Based on the engine operating state, classify the exhaust gas component monitoring data set to obtain clusters of each state category; P22: Decentralize the number of each state category cluster to determine the sample deviation; P23: For those that do not meet the preset uniformity requirements, the state category clusters are supplemented to obtain the target data set. The state category cluster supplementation includes: performing genetic fission based on the original sample data and obtaining a similar sample set of the same family for correction and adjustment.

[0020] Optionally, the exhaust gas component monitoring data set is subjected to a process of class uniformity analysis and data set expansion to ensure that the number of samples of each category in the data set is relatively balanced, thereby improving the accuracy and generalization ability of subsequent model training. Specifically, based on the engine operating state, the exhaust gas component monitoring data set is classified according to different engine operating states to obtain each state category cluster, wherein each state category cluster contains multiple exhaust gas component data with similar engine operating states.

[0021] Furthermore, the number of each state category cluster is decentralized to evaluate the distribution of the number of samples in each cluster and determine the sample deviation. The decentralized processing is a data preprocessing technology used to eliminate the offset or trend in the data set so that the data is more in line with the normal distribution. The sample deviation can reflect the imbalance of the number of samples in each state category cluster. If the number of samples in some state categories is too small, it is supplemented by the data set expansion technology to ensure that the data set has enough samples to support model training.

[0022] Further, the uniformity of each state category cluster is determined by referring to the sample deviation. If the number of samples of any state category cluster does not meet the preset uniformity requirements, for example, the number of samples of certain categories is too small, the state category cluster needs to be supplemented. The supplementation method may include genetic fission based on the original sample data, that is, generating new sample data through operations such as replication and mutation. For example, for the category cluster that does not meet the uniformity requirements, the genetic fission algorithm is applied to expand. Select some samples in the cluster. Use crossover and mutation operations to generate new samples. Crossover is to take some features from each of the two samples to combine into a new sample; mutation is to apply random perturbations to the sample features to generate mutant samples. Find samples similar to the original samples through the K nearest neighbor (KNN) method, perform correction adjustments, and ensure that the generated samples are similar to the original sample features. Perform category uniformity analysis on the expanded data set again to verify whether the number of samples meets the balance requirements and ensure that the data set has enough samples under various operating conditions. Further, obtain a similar sample set of the same family for correction adjustments, that is, obtain more samples from similar data sets to supplement the insufficient categories. By analyzing the similarity between clusters of each state category, samples are extracted from similar clusters to perform data correction and expansion. For example, in view of the insufficient exhaust data under high load state, samples can be extracted from the similar medium load state cluster, and interpolated and adjusted using machine learning methods to meet the data expansion requirements. This effectively increases the number of samples in the insufficient category, making the number of samples in each category in the data set more balanced, and obtaining the target data set.

[0023] P30: labeling samples of the target data set according to the preset processing target to construct a training data set; Furthermore, step P30 of the embodiment of the present application further includes: P31: The preset processing target includes exhaust target components and engine target state parameters. Samples are screened in the target data set according to the preset processing target, and the preset processing target is used as an identification target for sample labeling; P32: According to the preset processing target, the categories of positive samples and negative samples are set, and the positive samples and negative samples of the target data set are screened according to the category setting requirements. The positive samples have a direct recognition relationship with the preset processing target, and the negative samples have an inverse recognition relationship with the positive samples.

[0024] Specifically, the target data set is sample-labeled according to a preset processing target, which is usually set according to task requirements and includes exhaust target components, such as carbon monoxide, nitrogen oxides, etc., and engine target state parameters, such as speed, load, etc. Sample screening is performed in the target data set according to the preset processing target, and qualified samples are screened out by judging whether the content of exhaust components in the sample reaches a threshold, or whether the engine state parameter meets a specific condition, and the corresponding preset processing target is used as an identification target to label the screened samples, so as to obtain a screening target data set, and the labeling can be binary, for example, whether the target component exists or not, or continuous, for example, the specific content of the target component or the specific value of the engine state parameter.

[0025] Furthermore, the categories of positive samples and negative samples are set according to the preset processing target, wherein the positive sample generally refers to a sample that meets certain conditions or has certain characteristics, and the positive sample has a direct identification relationship with the preset processing target, while the negative sample is a sample opposite to the positive sample. Exemplarily, if the preset processing target is to detect whether the carbon monoxide content in the exhaust gas exceeds the standard, then the sample with a carbon monoxide content exceeding the threshold can be set as a positive sample, and the sample with a content below the threshold can be set as a negative sample. Furthermore, the samples in the target data set are filtered and classified according to the category setting requirements, positive samples and negative samples are constructed, and a training data set is obtained for training the model to distinguish different sample categories.

[0026] P40: Build a model framework, perform model training convergence through the training data set, and obtain an exhaust analysis model; Furthermore, step P40 of the embodiment of the present application also includes: P41: Building the model framework includes identification and screening layer, input layer, hidden layer, output layer, and fully connected layer; P42: Setting identification and screening targets based on the target data set, obtaining samples whose labels match the target data set through the input layer; P43: The hidden layer is trained using the training data entering the input layer, and the analysis results are obtained and output through the output layer. The fully connected layer connects all neurons to obtain exhaust recognition results, which are used to describe the exhaust results caused by the engine operating parameters. A convergence target is set based on the training data set, and a target test is performed based on the output results. The training is completed when the convergence target is met.

[0027] It should be understood that based on the principle of machine learning, a model framework is built, and the model framework includes an identification and screening layer, an input layer, a hidden layer, an output layer, and a fully connected layer. Among them, the identification and screening layer is responsible for screening samples that match the preset processing target from the target data set to ensure that only relevant samples enter the model for training. The input layer is used to receive the screened samples as input data. The hidden layer is the core part of the model and is responsible for learning and extracting the features of the data. The output layer outputs the learning results of the hidden layer in a specific form. The fully connected layer is responsible for connecting all neurons to obtain the final exhaust recognition result.

[0028] Furthermore, according to the characteristics of the target data set and the preset processing goals, identification and screening targets are set, such as specific exhaust components, engine state parameters, etc., and then samples matching the identification and screening targets are screened out from the target data set and enter the model through the input layer for training.

[0029] Furthermore, the hidden layer is trained using the training data entering the input layer, and a convergence target is set based on the characteristics of the training data set and the model performance requirements. The convergence target may include the accuracy of the model output data and the number of iterations. Specifically, the training data is analyzed through the hidden layer, the analysis results are output through the output layer, and the network parameters of the model are adjusted by comparing the difference between the output data and the expected data. Multiple iterations of training and network parameter adjustment are performed in this way until the output result reaches the convergence target, the training of the hidden layer is completed, all neurons are connected through the fully connected layer, and the exhaust analysis model is obtained. The exhaust recognition result can be obtained through the exhaust analysis model, and the exhaust recognition result is used to describe the exhaust result caused by the engine operating parameters.

[0030] Exemplarily, the specific process of model training may be, first, from the expanded target data set, select specific exhaust components (such as carbon monoxide, carbon dioxide) and engine state parameters (such as speed, load) as preset processing targets, annotate positive samples (samples that meet the target) and negative samples (samples that do not meet the target), and construct a training data set. Specifically, positive samples are samples that meet the emission standards or optimization targets, and negative samples are samples that do not meet the standards. The annotation process is based on whether the samples meet these preset conditions. Next, a neural network framework for the exhaust analysis model is built, and the model includes an input layer, a hidden layer, an output layer, and a fully connected layer. The annotated training data is input into the model through the input layer, and the data includes exhaust components and engine state parameters. The data features are extracted and learned by using the convolutional neural network (CNN) in deep learning through the hidden layer, and the implicit association between each state and the exhaust is extracted through inter-layer operations. The exhaust analysis results are output according to the input data through the output layer, and the output of the model is the identification result of the exhaust components, including the concentration value of each component and whether the emission state meets the standard.

[0031] Furthermore, a convergence target for model training is set, such as a classification accuracy of 90% or a loss function value of less than 0.01. The training data set is input into the model for training, and the model parameters are gradually adjusted. During the training process, the model is iterated multiple times according to the loss function and the set convergence target. After each iteration, the model is evaluated through the validation set to calculate the model's accuracy and loss function value. When the model reaches the convergence target, the training ends and the final model is saved.

[0032] P50: Based on the preset processing target, configure an exhaust gas detection device, perform target detection on automobile exhaust through the exhaust gas detection device, and obtain a target detection result; Further, such as Figure 3 As shown, step P50 of the embodiment of the present application also includes: P51: Perform target recognition and extraction according to the preset processing target to obtain target features, wherein the target features include exhaust gas composition features and engine status features; P52: When the target feature is the exhaust gas component feature, configuring the detection component parameters of the exhaust gas detection device based on the exhaust gas component; P53: When the target characteristic is the engine state characteristic, an activation instruction for the exhaust gas detection device is configured, and the activation instruction is used to start the exhaust gas detection device when the engine operating state parameters meet the activation target characteristic.

[0033] Exemplarily, the exhaust gas detection device is configured according to the preset processing target, and the exhaust gas detection device is used to perform target detection on the automobile exhaust gas to obtain the target detection result. Specifically, target recognition and extraction are performed according to the preset processing target to obtain target features related to exhaust gas detection and engine status, and the target features include exhaust gas composition features, such as the content of harmful substances such as carbon monoxide and nitrogen oxides, and engine status features, such as speed, load, temperature, etc.

[0034] Furthermore, when the target characteristic is the exhaust gas component characteristic, the detection component parameters of the exhaust gas detection equipment are configured according to the exhaust gas components, that is, the detection range and sensitivity of the exhaust gas detection equipment are adjusted according to the exhaust gas components specified in the preset processing target to ensure that the content of the specified exhaust gas components can be accurately measured and the actual emission data of specific components in the automobile exhaust can be obtained in real time.

[0035] Furthermore, when the target feature is the engine state feature, an activation instruction for the exhaust gas detection device is configured, and the activation instruction is used to automatically start the exhaust gas detection device when the engine operating state parameter meets the activation target feature. For example, it can be set that when the engine speed reaches a speed threshold or the load exceeds a load range, the exhaust gas detection device automatically starts working to ensure that the exhaust gas detection device can capture exhaust gas data in time when the engine is in a critical operating state, so as to more comprehensively understand the impact of engine performance on exhaust emissions.

[0036] P60: Input the engine monitoring parameters and the target detection results into the exhaust analysis model to analyze the engine exhaust state and determine the exhaust recognition result.

[0037] Optionally, engine monitoring parameters, such as engine speed, load, fuel consumption rate, intake and exhaust temperature, etc., and the target detection result are input into the exhaust analysis model, and the engine exhaust state is analyzed by the exhaust analysis model to identify key information related to the exhaust state in the input data, and the exhaust identification result is obtained accordingly. The exhaust identification result can reflect the engine exhaust state, and may include information such as the specific content of exhaust gas components, whether the emission level is up to standard, whether there are potential faults or abnormalities, etc., which has important guiding significance for engine optimization, formulation of exhaust gas treatment strategies and fault prevention.

[0038] Furthermore, the embodiment of the present application further includes step P70, and step P70 further includes: P71: Determine the exhaust relationship between the engine monitoring parameters and the exhaust gas components according to the exhaust gas identification result; P72: collecting the running speed and road condition of the vehicle, performing matching relationship analysis according to the engine monitoring parameters, the running speed of the vehicle and the road condition, and determining useless performance loss; P73: performing engine tracing according to the useless performance loss to determine the power control variable; performing exhaust control variable analysis according to the exhaust relationship between the engine monitoring parameters and exhaust gas components to determine the exhaust control variable; P74: Optimize power loss and exhaust gas composition according to the power control variable and exhaust gas control variable, obtain engine optimization control strategy for feedback, and guide the driver to perform engine operation and refueling adjustment control.

[0039] Specifically, based on the exhaust identification results, the exhaust relationship between engine monitoring parameters and exhaust components is deeply analyzed, including the analysis of the specific impact of different engine monitoring parameters on exhaust components, as well as the interaction and correlation between different engine monitoring parameters and exhaust components, to accurately understand how the engine's operating status affects exhaust emissions.

[0040] Furthermore, information such as the vehicle's running speed and road conditions is collected and matched with the engine monitoring parameters for analysis. By comparing the actual operating parameters with the parameters under ideal conditions, useless performance losses caused by poor engine operation or poor road conditions are identified. The useless performance losses may include power waste, increased fuel consumption, etc., which have a negative impact on engine performance and exhaust emissions.

[0041] Furthermore, the engine is traced back according to the useless performance loss, the specific reasons for the loss are analyzed, and the power control variables are determined. The power control variables may include adjusting the engine intake and exhaust system, optimizing the fuel injection strategy, etc., in order to improve the efficiency and performance of the engine. At the same time, the exhaust control variable analysis is performed according to the exhaust relationship between the engine monitoring parameters and the exhaust components to determine the exhaust control variables, that is, to determine the adjustment amount of the engine operating parameters to reduce harmful exhaust emissions.

[0042] Furthermore, according to the power control variable and the exhaust gas control variable, the power loss and the exhaust gas composition are optimized, and the corresponding engine optimization control strategy is formulated. For example, the linear regression method is used to fit the relationship between the engine power loss and the control variable, and the fitness function is set. With the minimum power loss as the goal, the control variable is adjusted to achieve the optimal power output. Through the fitting relationship between the exhaust gas identification result and the exhaust gas composition optimization, the operating parameters of the exhaust gas treatment equipment (such as EGR opening) are adjusted to ensure that the exhaust gas composition meets the standard. For example, the engine speed, load, fuel injection amount, etc. are adjusted to minimize the power loss and optimize the exhaust gas composition, and the engine optimization control strategy is fed back to guide the driver to perform engine operation and refueling adjustment control, so as to achieve the improvement of engine performance and the reduction of exhaust emissions in actual operation.

[0043] Furthermore, step P74 of the embodiment of the present application also includes: P74-1: Based on the sample set collected from the running speed and road condition, a relationship fitting is performed with the power control variable as an independent variable and the power loss as a dependent variable to determine a first fitting relationship; P74-2: Based on the exhaust gas analysis model, extract model parameters and determine a second fitting relationship between the exhaust gas control variable and the exhaust gas component; P74-3: Set the optimization coefficients for power loss and exhaust gas composition optimization, integrate the first fitting relationship and the second fitting relationship, construct a fitness function, and establish an optimization space with the maximum target sum as the optimization target; P74-4: Setting the exhaust gas composition optimization target as a constraint condition and adding it to the optimization space, randomly selecting a combination of power control variables and exhaust gas control variables, wherein the power control variables and exhaust gas control variables have a unified value of overlapping variables, and evaluating the random combination based on the fitness function as the current optimization strategy; P74-5: Randomly select a second combination of power control variables and exhaust control variables, perform fitness evaluation, and compare it with the evaluation result of the current optimization strategy. Select the combination with the larger evaluation result as the current optimization strategy, repeat the iteration until the optimization goal is met or the number of iterations is reached, and obtain the optimal optimization strategy for feedback.

[0044] Optionally, based on the sample set collected from the operating speed and road condition, a relationship fitting is performed with the power control variable as the independent variable and the power loss as the dependent variable, and a fitting relationship between the power control variable and the power loss, i.e., a first fitting relationship, is determined by a statistical or machine learning method. Furthermore, based on the exhaust analysis model, model parameters are extracted, the influence of the exhaust control variable on exhaust emissions is quantified, and a second fitting relationship between the exhaust control variable and exhaust components is determined.

[0045] Furthermore, the optimization coefficients for power loss and exhaust gas composition optimization are set, and the optimization coefficients represent the degree of importance attached to different optimization targets. Furthermore, the first fitting relationship and the second fitting relationship are integrated to construct a fitness function, which is used to evaluate the exhaust emission optimization effect under different control variable combinations. Finally, the optimization space is established with the maximum target sum as the optimization target, that is, the parameter optimization range is established with the optimization target of achieving the optimal power loss and exhaust gas composition at the same time.

[0046] Furthermore, the exhaust gas composition optimization target is set as a constraint condition and added to the optimization space to ensure that when searching for the optimal control strategy, the selected strategy can meet the exhaust gas emission requirements. A combination of power control variables and exhaust gas control variables is randomly selected as the initial optimization strategy. If there are overlapping variables in the power control variables and the exhaust gas control variables, it is necessary to ensure that these variables remain consistent when randomly taking values ​​to ensure the consistency of the strategy. Finally, the random combination is evaluated according to the fitness function constructed previously, and the evaluation result is used as the current optimization strategy.

[0047] Furthermore, a second combination of power control variables and exhaust gas control variables is randomly selected, fitness evaluation is performed, and the evaluation result is compared with the evaluation result of the current optimization strategy. If the evaluation result of the new strategy is better than the current optimization strategy, the current optimization strategy is replaced by the new strategy, otherwise, the current optimization strategy is retained and the next iteration is continued. The optimization iteration is performed continuously in this way until the optimization target is met, that is, the power loss is minimized and the exhaust gas composition meets the optimization requirements, or the preset number of iterations is reached. An optimal optimization strategy that meets all constraints and can minimize power loss is obtained. The optimization strategy is fed back to the driver or the engine control system to improve engine performance and reduce exhaust emissions.

[0048] In summary, the embodiments of the present application have at least the following technical effects: This application constructs an exhaust composition monitoring dataset through the engine operating status, and after performing category uniformity analysis and dataset expansion, uses it as a training dataset for model training convergence to obtain an exhaust analysis model. Based on preset processing goals, the exhaust gas detection equipment is configured to perform target detection, and the engine monitoring parameters and target detection results are input into the exhaust analysis model to determine the exhaust recognition results.

[0049] The technical effect of improving engine performance and reducing exhaust emissions has been achieved through dynamic monitoring of exhaust emissions and optimization of engine control.

[0050] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0051] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0052] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for analyzing automobile exhaust by monitoring the composition of automobile exhaust, characterized in that: include: Based on the engine running state, constructing an exhaust gas component monitoring data set, wherein the exhaust gas component monitoring data set includes exhaust gas components and engine state parameters; Performing a category uniformity analysis on the exhaust gas component monitoring data set, and expanding the data set based on the category uniformity analysis result to obtain a target data set; Annotate samples of the target data set according to preset processing objectives to construct a training data set; Building a model framework, performing model training convergence through the training data set, and obtaining an exhaust analysis model; Based on the preset processing target, an exhaust gas detection device is configured, and target detection of automobile exhaust is performed by the exhaust gas detection device to obtain a target detection result; The engine monitoring parameters and the target detection results are input into the exhaust analysis model to analyze the engine exhaust state and determine the exhaust recognition result.

2. The method according to claim 1, characterized in that Before constructing the exhaust gas component monitoring data set based on the engine running state, the following steps are included: According to the instantaneous, intermittent and continuous emission states of the exhaust gas, the first engine operation state is configured; According to the engine speed and torsional strength, the relationship between exhaust and power is analyzed, and the second engine operation state is configured; Extracting operation state features from the first engine operation state and the second engine operation state, and performing random combination of features to construct a reconstructed state, performing engine operation evaluation on the reconstructed state, taking the reconstructed state that satisfies the operation evaluation as a newly added operation state, and taking the reconstructed state that does not satisfy the operation evaluation as a terminated operation state; Searching and deriving according to a set step length based on the newly added operating state, continuing to evaluate the engine operation, and so on, until there is no newly added operating state; All newly added operating states, the first engine operating state, and the second engine operating state are aggregated to obtain the engine operating state.

3. The method according to claim 2, characterized in that Performing a category uniformity analysis on the exhaust gas component monitoring data set, and expanding the data set based on the category uniformity analysis result to obtain a target data set, including: Based on the engine running state, classifying the exhaust gas component monitoring data set to obtain clusters of each state category; Decentralizing the number of each state category cluster to determine the sample deviation; For those that do not meet the preset uniformity requirements, state category cluster supplementation is performed to obtain the target data set, and the state category cluster supplementation includes: performing genetic fission based on the original sample data and obtaining a similar sample set of the same family for correction adjustment.

4. The method according to claim 2, characterized in that The target data set is labeled with samples according to the preset processing objectives to construct a training data set, including: The preset processing target includes exhaust target components and engine target state parameters, and samples are screened in the target data set according to the preset processing target, and the preset processing target is used as an identification target for sample labeling; The positive sample and the negative sample categories are set according to the preset processing target, and the positive sample and the negative sample of the target data set are screened according to the category setting requirements. The positive sample and the preset processing target have a direct identification relationship, and the negative sample and the positive sample have an inverse identification relationship.

5. The method according to claim 4, characterized in that The building of the model framework, performing model training convergence through the training data set, and obtaining the exhaust analysis model includes: Building the model framework includes identifying and filtering layers, input layers, hidden layers, output layers, and fully connected layers; Setting a recognition and screening target based on a target data set, obtaining samples whose annotations match the target data set through the input layer; The hidden layer is trained using the training data entering the input layer, and the analysis results are obtained and output through the output layer. The fully connected layer connects all neurons to obtain exhaust recognition results, and the exhaust recognition results are used to describe the exhaust results caused by the engine operating parameters. A convergence target is set based on the training data set, and a target test is performed according to the output result. When the convergence target is met, the training is completed.

6. The method according to claim 4, characterized in that Based on the preset processing target, the exhaust gas detection equipment is configured, including: Performing target recognition and extraction according to the preset processing target to obtain target features, wherein the target features include exhaust gas composition features and engine state features; When the target feature is the exhaust gas component feature, configuring the detection component parameters of the exhaust gas detection device based on the exhaust gas component; When the target feature is the engine state feature, an activation instruction for the exhaust gas detection device is configured, and the activation instruction is used to start the exhaust gas detection device when the engine operating state parameter meets the activation target feature.

7. The method according to claim 1, characterized in that After inputting the engine monitoring parameters and the target detection results into the exhaust analysis model to analyze the engine exhaust state and determine the exhaust recognition result, the method further includes: Determining an exhaust relationship between engine monitoring parameters and exhaust gas components according to the exhaust gas identification result; The running speed and road condition of the vehicle are collected, and matching relationship analysis is performed according to the engine monitoring parameters, the running speed of the vehicle, and the road condition to determine useless performance loss; Perform engine tracing according to the useless performance loss to determine the power control variable; perform exhaust control variable analysis according to the exhaust relationship between the engine monitoring parameters and exhaust components to determine the exhaust control variable; The power loss and exhaust gas composition are optimized according to the power control variable and the exhaust gas control variable, and the engine optimization control strategy is obtained for feedback to guide the driver to perform engine operation and refueling adjustment control.

8. The method according to claim 7, characterized in that According to the power control variable and the exhaust gas control variable, the power loss and the exhaust gas composition are optimized, and the engine optimization control strategy is obtained for feedback, including: Based on the sample set collected from the running speed and road condition, a relationship fitting is performed with the power control variable as an independent variable and the power loss as a dependent variable to determine a first fitting relationship; Based on the exhaust gas analysis model, extracting model parameters, and determining a second fitting relationship between the exhaust gas control variable and the exhaust gas component; Setting optimization coefficients for power loss and exhaust gas composition optimization, integrating the first fitting relationship and the second fitting relationship, constructing a fitness function, and establishing an optimization space with the maximum target sum as the optimization target; Setting the exhaust gas composition optimization target as a constraint condition and adding it to the optimization space, randomly selecting a combination of power control variables and exhaust gas control variables, wherein the power control variables and exhaust gas control variables have a unified value of overlapping variables, and evaluating the random combination based on the fitness function as the current optimization strategy; A second combination of power control variables and exhaust gas control variables is randomly selected for fitness evaluation, and compared with the evaluation result of the current optimization strategy. The combination with the larger evaluation result is selected as the current optimization strategy, and it is iterated repeatedly until the optimization goal is met or the number of iterations is reached, and the optimal optimization strategy is obtained for feedback.

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