Tail gas emission control method, device, equipment and vehicle

Abstract

The application relates to the field of intelligent vehicles and provides a tail gas emission control method, device, equipment and vehicle. The method comprises the following steps: obtaining tail gas change characteristics of a target vehicle and weather forecasts in a preset future period; inputting the tail gas change characteristics and the weather forecasts into a pre-trained tail gas emission prediction model for analysis to determine an emission prediction result in the preset future period; the emission prediction result is a tail gas emission range of the target vehicle in the future period; in the driving process of the preset future period, according to the tail gas emission range in the preset future period in the emission prediction result, a target parameter of tail gas emission corresponding to the tail gas emission range in the preset future period is called, and the tail gas emission of the target vehicle is controlled based on the target parameter of tail gas emission. The technical scheme can effectively improve the versatility of the prediction result generated by the model and the adaptability to weather, and improve the flexibility of tail gas emission management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent vehicles, and in particular to a tail gas emission control method, device, equipment and vehicle. BACKGROUND

[0002] With the continuous development and innovation of productivity and new technologies, vehicle manufacturing is constantly progressing towards intelligence. Today's vehicles have various intelligent functions that help vehicles with assisted driving, energy saving and emission reduction, etc.

[0003] For energy saving and emission reduction related functions, especially the tail gas purification system, the existing technology often performs tail gas treatment based on fixed parameters, which cannot adapt to complex and variable driving environments and vehicle states. Therefore, how to further improve the tail gas treatment capability of vehicles has become one of the focuses of researchers in the related field. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a tail gas emission control method, device, equipment and vehicle to solve the problem of single tail gas purification mode and poor flexibility in the prior art.

[0005] To achieve the above purpose, the present application provides a tail gas emission control method, which comprises:

[0006] obtaining tail gas change characteristics of a target vehicle and a weather forecast in a preset future period;

[0007] inputting the tail gas change characteristics and the weather forecast into a pre-trained tail gas emission prediction model for analysis to determine an emission prediction result in the preset future period; wherein the emission prediction result is a tail gas emission range of the target vehicle in the future period;

[0008] In the driving process of the preset future period, according to the tail gas emission range in the preset future period in the emission prediction result, the target parameters of tail gas emission corresponding to the tail gas emission range in the preset future period are retrieved, and the tail gas emission of the target vehicle is controlled based on the target parameters of tail gas emission.

[0009] Optionally, the tail gas emission prediction model comprises an input layer, a hidden layer and an output layer, and the inputting of the tail gas change characteristics and the weather forecast into the pre-trained tail gas emission prediction model for analysis to determine the emission prediction result in the preset future period comprises:

[0010] the tail gas change characteristics and the weather forecast are inputted into the hidden layer through the input layer to obtain a hidden layer result;

[0011] the hidden layer result is outputted through the output layer of the tail gas emission model to obtain the emission prediction result.

[0012] The above embodiment, through the prediction model, not only considers the characteristic factors of the exhaust change that the target vehicle may have in the driving process, but also considers the influence of weather change on the exhaust, and triggers the comprehensiveness and accuracy of the calculation of the emission prediction result from multiple angles.

[0013] Optionally, the exhaust emission prediction model is determined by the following method:

[0014] A preset initial model is constructed, and the preset initial model includes an input layer, a hidden layer, and an output layer;

[0015] Obtain the exhaust change characteristic data and historical weather data of the target vehicle in the historical period, and the emission result corresponding to the exhaust change characteristic data and the historical weather data;

[0016] The exhaust change characteristic data and the historical weather data are input to the hidden layer through the input layer for processing to obtain a processing result;

[0017] The processing result is output through the output layer of the preset initial model to obtain a training result;

[0018] Compare the training result with the emission result to determine the adjustment parameter of the preset initial model;

[0019] Adjust the preset initial model according to the adjustment parameter to obtain the exhaust emission prediction model.

[0020] In the above embodiment, different hidden layers in the pre-trained exhaust emission model process different types of data, analyze the exhaust change characteristics and weather data respectively, and obtain the prediction result. The prediction result can represent the possibility of future exhaust emission under the condition of exhaust change and weather influence, and can effectively and accurately analyze the exhaust emission condition that the vehicle may generate in the corresponding period from the exhaust change characteristics of the target vehicle and the corresponding weather forecast, as the exhaust emission prediction result. The emission prediction result also conforms to the exhaust emission rule of the target vehicle in the driving process and the exhaust emission change caused by the change of weather, and can effectively improve the versatility and weather adaptability of the prediction result generated by the model.

[0021] Optionally, the exhaust change characteristic of the target vehicle comprises:

[0022] Obtain the exhaust emission data and the current working condition information of the target vehicle;

[0023] According to the current working condition information, determine the driving habit characteristics of the target user using the target vehicle;

[0024] According to the driving habit characteristics and the exhaust emission data, determine the exhaust change characteristic of the target vehicle.

[0025] The embodiment provides a feasible solution for determining the tail gas change feature, and helps improve the accuracy of tail gas prediction.

[0026] Optionally, the current working condition information includes vehicle working condition and environmental working condition.

[0027] The driving habit feature of the target user of the target vehicle is determined according to the current working condition information, and the method comprises the following steps.

[0028] The vehicle working condition and the environmental working condition are analyzed based on a preset time domain analysis algorithm, and vehicle working condition change information and environmental working condition change information arranged according to a preset time sequence are obtained.

[0029] The first correlation between the vehicle working condition change information and the environmental working condition change information is determined according to the preset time sequence.

[0030] The first correlation is taken as the driving habit feature.

[0031] The vehicle working condition change information and the environmental working condition change information under the time domain analysis are associated via the time dimension as a medium, so as to explore how the change of the environmental working condition affects the control of the user driving the vehicle, thereby accurately obtaining the driving habit of the user, providing a basic condition for subsequently determining the tail gas change feature, and being beneficial to improving the accuracy and practicability of tail gas emission management.

[0032] Optionally, the tail gas change feature of the target vehicle is determined according to the driving habit feature and the tail gas emission data, and the method comprises the following steps.

[0033] The tail gas emission data is analyzed based on a preset time domain analysis algorithm, and tail gas emission change data arranged according to a preset time sequence is obtained.

[0034] The second correlation between the tail gas emission change data and the driving habit feature is determined according to the preset time sequence.

[0035] The second correlation is taken as the tail gas change feature.

[0036] The change of the environmental working condition, the change of the vehicle working condition and the change of the tail gas emission are connected to form a causal relationship, so that the model precision of the tail gas emission prediction model can be effectively improved, and the prediction accuracy of the model for the prediction result of the emission is also improved.

[0037] Optionally, the method further comprises the following steps.

[0038] In the driving process of the preset future period, a current driving time point is determined, and an emission prediction result of the current driving time point is determined.

[0039] The preset exhaust emission standard is acquired, the exhaust emission standard is used to correct the emission prediction result, and an exhaust emission adjustment amount of the current driving time point is determined;

[0040] According to the exhaust emission adjustment amount, a working parameter of the exhaust purifier at the current driving time point is determined;

[0041] According to the working parameter, the exhaust purifier is controlled to perform exhaust treatment.

[0042] In the above embodiment, on the one hand, exhaust emission management according to the emission prediction result can effectively improve the flexibility of exhaust emission to adapt to changes in weather and driving operation habits of the user; on the other hand, correction according to the exhaust emission standard can ensure that the exhaust emission does not exceed the standard, thereby achieving the purpose of energy saving and emission reduction.

[0043] For the same purpose, the present application also provides an exhaust emission control device, comprising:

[0044] A data acquisition module is configured to acquire exhaust variation characteristics of a target vehicle and weather forecasts in a preset future period;

[0045] A result prediction module is configured to input the exhaust variation characteristics and the weather forecasts into a pre-trained exhaust emission prediction model for analysis to determine an emission prediction result in the preset future period; wherein the emission prediction result is an exhaust emission range of the target vehicle in the future period;

[0046] An emission control module is configured to, during driving in the preset future period, retrieve target parameters of exhaust emission corresponding to the exhaust emission range in the preset future period according to the exhaust emission range in the preset future period in the emission prediction result, and control exhaust emission of the target vehicle based on the target parameters of exhaust emission.

[0047] For the same purpose, the present application also provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the method provided by any embodiment of the present application.

[0048] For the same purpose, the present application also provides a vehicle comprising the above electronic device, so that the vehicle performs the method provided by any embodiment of the present application.

[0049] As can be seen from the above, in the technical solution of the embodiment of the present application, the exhaust gas change characteristics of the target vehicle and the obtained weather forecast for the preset future period are input into the exhaust gas emission prediction model to obtain the exhaust gas emission prediction result for the future period. When the target vehicle is driving within the period, the exhaust gas emission management is performed according to the emission prediction result. The exhaust gas emission prediction model is trained based on the exhaust gas change characteristics and weather data, which enables the model to obtain the recognition and analysis capabilities for the exhaust gas change characteristics and weather data. It is also possible to effectively and accurately analyze the exhaust gas change characteristics of the target vehicle and the corresponding weather forecast to obtain the exhaust gas emissions that may be generated when the vehicle is driving within the corresponding period as the exhaust gas emission prediction result. The emission prediction result also conforms to the exhaust gas emission law of the target vehicle during driving and the exhaust gas emission changes caused by weather changes. The operating parameters of the exhaust gas purifier are determined based on the emission prediction results, so that the exhaust gas purifier has a specific basis for the treatment of exhaust gas, and these bases are related to the exhaust gas changes and weather changes of the current vehicle driving, which improves the accuracy of exhaust gas treatment and can effectively improve the versatility and weather adaptability of the prediction results generated by the model. By controlling exhaust gas according to the emission prediction results, corresponding adjustments can be made according to different emission patterns and weather conditions, thereby improving the flexibility of exhaust emission management. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in this application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are merely embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0051] Figure 1 A schematic flow chart of an exhaust emission control method provided in an embodiment of the present application;

[0052] Figure 2 A schematic structural diagram of an exhaust emission control device provided in an embodiment of the present application;

[0053] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the objectives, technical solutions and advantages of this application more clear, this application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0055] It should be noted that, unless otherwise defined, technical terms or scientific terms used in the embodiments of the present application shall have the common meaning understood by one of ordinary skill in the art to which the present application pertains. The terms "first", "second", and similar terms used in the embodiments of the present application do not denote any order, quantity, or importance, but are merely used to distinguish different components. The terms "include", "contain", and similar terms mean that the elements or objects before the terms encompass the elements or objects listed after the terms and their equivalents, and do not exclude other elements or objects. The terms "connect" or "connected" and similar terms do not mean physical or mechanical connection, but can include electrical connection, whether direct or indirect. The terms "upper", "lower", "left", "right", and the like are merely used to indicate relative positional relationships, and when the absolute positions of the described objects change, the relative positional relationships can also change accordingly.

[0056] Before the embodiments of the present application are described, the prior art method for tail gas emission management is briefly described. At present, for traditional power vehicles, i.e. fuel vehicles, and new energy range extending vehicles with fuel capacity, relevant tail gas treatment devices, such as tail gas purifiers, are provided on the vehicles. However, the traditional tail gas purification process is often based on fixed parameters, which is difficult to cope with complex and changeable driving environments and vehicle states. Therefore, the embodiments of the present application and the various embodiments propose the following method to improve the flexibility and adaptability of tail gas treatment.

[0057] Figure 1 A tail gas emission control method is provided in the embodiments of the present application, which can be applied to the case of controlling the tail gas emission of a vehicle. The tail gas emission control method provided in the embodiments of the present application can be executed by a tail gas emission control device, which can be integrated in an electronic device in the form of hardware and / or software, such as in a vehicle controller. Referring to Figure 1 The method provided in the embodiments of the present application includes the following steps:

[0058] S110, obtaining tail gas change characteristics of a target vehicle and a weather forecast in a preset future period.

[0059] The target vehicle can be any vehicle that needs to control exhaust gas, for example, a traditional energy vehicle or a new energy extended range vehicle. The embodiments of the present application do not limit the type of the target vehicle. The exhaust gas change characteristic can be the characteristic information of the change of the exhaust gas of the target vehicle with time or driving mode. The exhaust gas change characteristic can record the change of the exhaust emission, such as the change of the amount of particulate matter and the concentration of harmful gas, during the driving process of the current vehicle. The change of these data with time or driving mode can be recorded. The preset future period can be a certain time period in which the target vehicle drives in the future. The weather forecast can be the weather information in the future period. The weather forecast can be directly obtained through weather software. The embodiments of the present application do not limit the way and path of obtaining the exhaust gas change characteristic and the weather forecast. It can be understood that the weather has an influence on the exhaust emission, for example, the change of temperature and humidity causes the change of the combustion process of the vehicle fuel, and the harmful gas and particulate matter generated accordingly also have certain differences. Therefore, considering the change of the weather helps to improve the accuracy of the exhaust emission management.

[0060] In S120, the exhaust gas change characteristic and the weather forecast are input into a pre-trained exhaust emission prediction model for analysis to determine an emission prediction result in a preset future period. The emission prediction result is the exhaust emission range of the target vehicle in the future period.

[0061] The exhaust emission prediction model can be a model pre-trained for exhaust gas change characteristic data and weather data. The model inputs the exhaust gas change characteristic and the weather forecast, and outputs the emission prediction result of the exhaust emission of the target vehicle in the preset future period. The exhaust emission prediction model can be a pre-trained neural network model, and the embodiments of the present application do not limit the model structure of the exhaust emission model. The emission prediction result can be the prediction of the exhaust emission of the target vehicle driving in the preset future period. The emission prediction result is actually the prediction result of the range of the exhaust emission in the future period.

[0062] In S130, during the driving process in the preset future period, the target parameter of the exhaust emission corresponding to the exhaust emission range in the preset future period is retrieved according to the exhaust emission range in the preset future period in the emission prediction result, and the exhaust emission of the target vehicle is controlled based on the target parameter of the exhaust emission.

[0063] It can be understood that, since the emission prediction result can be a predicted condition of the exhaust emission of the target vehicle during driving in a preset future period, the vehicle can be controlled to perform exhaust emission control in the corresponding future period according to the emission prediction result, so that the exhaust emission condition in the period conforms to the emission prediction result. The target parameter of the exhaust emission can be a parameter basis for controlling the exhaust emission, for example, can be a working parameter of an exhaust purifier of the vehicle, and the embodiments of the present application are not limited thereto. The emission prediction result can be a range threshold of the exhaust emission in the future period. It can be understood that, the exhaust emission range and its corresponding working parameter for controlling the exhaust emission can have a pre-set corresponding relationship, which can be determined by a large number of tests by a person skilled in the art and pre-stored in a database or the like. Therefore, the control parameter of the exhaust emission in the future period corresponding to the exhaust emission range can be called to control the exhaust emission of the target vehicle, for example, the working parameter of the exhaust purifier is called to adjust the working mode of the exhaust purifier.

[0064] In the technical solution of the embodiments of the present application, the exhaust emission prediction model is input with the exhaust change characteristics of the target vehicle and the weather forecast of the preset future period, to obtain an emission prediction result of the exhaust emission in the future period. When the target vehicle drives in the period, the exhaust emission is managed according to the emission prediction result. The exhaust emission prediction model is trained for the exhaust change characteristics and the weather data, so that the model can obtain the recognition and analysis ability for the exhaust change characteristics and the weather data, and can effectively and accurately analyze the exhaust emission condition possibly generated by the vehicle driving in the corresponding period from the exhaust change characteristics of the target vehicle and the corresponding weather forecast, as the exhaust emission prediction result. The emission prediction result also conforms to the exhaust emission rule of the target vehicle driven and the exhaust emission change caused by the change of the weather, the working parameter of the exhaust purifier is determined according to the emission prediction result, so that the exhaust purifier has specific basis for processing the exhaust emission, and these basis are related to the exhaust change and the weather change of the current vehicle driving, which improves the accuracy of the exhaust processing, and can effectively improve the versatility of the prediction result generated by the model and the adaptability to the weather. According to the emission prediction result, the exhaust emission can also be controlled according to the different emission rules and weather conditions, so as to improve the flexibility of the exhaust emission management.

[0065] In the above embodiments, the tail gas emission prediction model plays a key role, and the following describes how to use the tail gas emission prediction model. Compared with other prediction models, the tail gas emission prediction model mentioned in the embodiments of the present application analyzes the tail gas change and weather change through multiple hidden layers in the model, respectively, to determine the law of tail gas change and the influence of weather change on tail gas emission, and then determine how to affect future tail gas emission. In an optional implementation, the tail gas emission prediction model includes an input layer, a hidden layer, and an output layer. The tail gas change characteristics and weather forecasts input into the pre-trained tail gas emission prediction model in S120 for analysis to determine the emission prediction result in the preset future period can include:

[0066] S121, input the tail gas change characteristics and weather forecasts into the hidden layer through the input layer to obtain a hidden layer result.

[0067] The tail gas change characteristics and weather forecasts are input into the hidden layer, and the calculation result of the hidden layer is obtained through the calculation of each algorithm in the hidden layer, that is, the hidden layer result.

[0068] For example, the tail gas change characteristics are input into the first hidden layer through the input layer to obtain a first prediction result. The tail gas emission prediction model can include multiple hidden layers, and different hidden layers can use different structures or algorithms, which are not limited in the embodiments of the present application. The first hidden layer is used to process the tail gas change characteristics, and performs feature extraction and transformation on the tail gas change characteristics, thereby providing high-level features corresponding to the tail gas emission change for the subsequent output layer, that is, the first prediction result.

[0069] The weather forecasts are input into the second hidden layer through the input layer to obtain a second prediction result. Similar to the foregoing steps, the second hidden layer is used to process the data of the weather forecasts, and performs feature extraction and transformation on the weather forecasts, thereby providing high-level features corresponding to the weather change for the subsequent output layer, that is, the second prediction result. The first prediction result and the second prediction result belong to the hidden layer result.

[0070] S122, the hidden layer result is output through the output layer of the tail gas emission model to obtain an emission prediction result. For example, the first prediction result and the second prediction result are output through the output layer of the tail gas emission model to obtain an emission prediction result.

[0071] The first prediction result and the second prediction result are combined at an output layer of the model, and a final emission prediction result is output. The first prediction result and the second prediction result can be weighted and output. Because each neuron in each hidden layer in the model has its own weight, the combination processing is performed through the weighted output. For example, a bias term can be set on the basis of the weighted sum for output. The first hidden layer and the second hidden layer have different bias terms because the types of data processed by the first hidden layer and the second hidden layer are different. The bias term can be used to enable the neural network to better fit the data for output. For another example, an activation function can be used for combination. The weighted sum result is nonlinearly transformed through the activation function. Different activation functions can capture different types of nonlinear relationships, thereby improving the fitting capability of the model for data. The structure of the output layer can be set according to the processing manner of the two prediction results, and the embodiments of the present application do not limit the structure of the output layer.

[0072] In the above embodiment, the exhaust emission prediction model pre-trained is used to analyze the exhaust change characteristics and the weather forecast, so as to determine the emission prediction result corresponding to the preset future period. The exhaust emission prediction model has the recognition and analysis capability for the exhaust change characteristics and the weather data, and can effectively and accurately analyze the exhaust emission condition of the vehicle during driving in the corresponding period from the exhaust change characteristics of the target vehicle and the corresponding weather forecast, as the exhaust emission prediction result. Different hidden layers in the pre-trained exhaust emission prediction model process different types of data, analyze the exhaust change characteristics and the weather data, and obtain the prediction result. The prediction result can represent the possibility of future exhaust emission under the influence of the exhaust change and the weather. The exhaust emission prediction result is calculated from multiple angles, and the exhaust emission prediction result is comprehensive and accurate.

[0073] Before the exhaust emission prediction model mentioned in the above embodiment is used, the model is trained to have the exhaust emission prediction capability. In the training process, the exhaust change characteristic data and the weather data in the historical period are used to train different hidden layers of the model, so that different hidden layers generate prediction results for the exhaust change and the weather data. The correct results pre-labeled are used to compare the prediction results in the model training process, the parameters of the model are adjusted based on the deviation between the two, and the parameters of the model are continuously corrected in the subsequent training process in the same way, so that the prediction result is closer and closer to the correct result. In an optional embodiment, the exhaust emission prediction model is determined by the following method:

[0074] A1, an initial model is constructed, and the initial model includes an input layer, a hidden layer, and an output layer.

[0075] The preset initial model can be an initial model that has not been trained, for example, can be an initial neural network model that has not been trained, and the model includes an input layer, a hidden layer and an output layer. Of course, the model can include multiple hidden layers, and different hidden layers are used to train the model to obtain the processing capability of different data. It needs to be particularly pointed out that the input layer, the hidden layer and the output layer described in the embodiment are not trained, and are distinguished from the input layer, the hidden layer and the output layer in the exhaust emission prediction model which is completed training in the foregoing embodiment.

[0076] A2, obtaining exhaust change characteristic data and historical weather data of the target vehicle in a historical period, and emission results corresponding to the exhaust change characteristic data and the historical weather data.

[0077] The historical period can be any time period in which the target vehicle has driving records in the past. Because there are driving records in the historical period, the vehicle driving exists exhaust emission, and there is also exhaust change. Therefore, the exhaust change characteristic data in the historical period and the weather data in the historical period (i.e., the historical weather data) are obtained. At the same time, there are subsequent emission data of the target vehicle in the future corresponding to the historical period, and these subsequent emission data can be used as the emission results, which are used to mark the prediction results corresponding to the exhaust change characteristic data and the historical weather data in the historical period, that is, the emission results can be understood as the prediction results corresponding to the exhaust change characteristic data and the historical weather data in the historical period with correct labels. It is equivalent to providing samples for training and / or testing of the preset initial model and the labeled results corresponding to the samples.

[0078] A3, inputting the exhaust change characteristic data and the historical weather data to the hidden layer through the input layer for processing to obtain a processing result.

[0079] The model can include multiple hidden layers. For example, the exhaust change characteristic data is input to the first hidden layer through the input layer for processing to obtain a first processing result.

[0080] The first hidden layer is trained by using the exhaust change characteristic data. After analyzing the exhaust change characteristic data in the first hidden layer, a corresponding processing result, i.e., the first processing result, can be obtained.

[0081] The historical weather data is input to the second hidden layer through the input layer for processing to obtain a second processing result.

[0082] According to the same principle as A3, the second hidden layer is trained by using the historical weather data. After analyzing the historical weather data in the second hidden layer, a corresponding processing result, i.e., the second processing result, can be obtained.

[0083] A4, output the processing result through the output layer of the preset initial model to obtain a training result.

[0084] The first processing result and the second processing result are combined and output through the integration of the output layer to obtain a training result determined by the tail gas change characteristic data and the historical weather data.

[0085] A5, compare the training result with the emission result to determine an adjustment parameter of the preset initial model.

[0086] As introduced in A2, the emission result has a labeled label for marking the correct sample result. The training result obtained by the preset initial model analysis through the foregoing steps is compared with the emission result with the label. Exemplarily, the training result and the emission result can be calculated through a loss function. The loss function is a function for measuring the difference between the model prediction result and the true label. It takes the output of the model and the correct label as input and outputs a scalar value representing the size of the difference. The parameters, such as weights, biases, etc., of the preset initial model that need to be adjusted to obtain the correct result are determined. Exemplarily, the parameters of the model are adjusted through gradient descent. Gradient descent is an optimization algorithm based on the loss function to update the parameters of the model. The parameters (such as weights and biases) are updated in the direction of the fastest descent of the loss function (i.e., the opposite direction of the gradient) to reduce the loss value.

[0087] A6, adjust the preset initial model according to the adjustment parameter to obtain a tail gas emission prediction model.

[0088] It should be noted that the tail gas change characteristic data and the historical weather data in the historical period, and the emission result corresponding to the tail gas change characteristic data and the historical weather data have multiple sets of sample data. Each set of data can further adjust the parameters after the model training is completed, so that the prediction of the model on the tail gas emission is more and more accurate. After a certain condition is reached, the training is completed, and the preset initial model is trained into a tail gas emission prediction model. Of course, the arrival of the certain condition can be that all the data used for training participate in the training, or the effect of the model reaches a certain convergence degree, etc. The embodiments of the present application are not limited thereto.

[0089] In the above embodiments, the exhaust gas change characteristic data of the historical period and the weather data are used to train the preset initial model, so that the model can obtain the recognition and analysis capability for the exhaust gas change characteristic and the weather data, thereby training the exhaust gas emission prediction model, so that the exhaust gas emission prediction model can effectively and accurately analyze the exhaust gas emission condition possibly generated by the vehicle during driving in the corresponding period from the exhaust gas change characteristic of the target vehicle and the corresponding weather forecast, as the exhaust gas emission prediction result. The emission prediction result also conforms to the exhaust gas emission rule of the target vehicle during driving and the exhaust gas emission change caused by the change of the weather, which can effectively improve the versatility and weather adaptability of the prediction result generated by the model.

[0090] The exhaust gas change characteristic is one of the input quantities of the exhaust gas emission prediction model, and has great significance for exhaust gas emission management. In an optional embodiment, the exhaust gas change characteristic of the target vehicle in S110 can include:

[0091] S111, acquiring exhaust gas emission data and current working condition information of the target vehicle.

[0092] The exhaust gas emission data can be specific quantitative data of the exhaust gas emitted by the target vehicle, for example, can include but is not limited to harmful gas concentration, temperature and flow, and emission amount of pollutants and particulate matter, etc. The current working condition information can be various working condition information of the target vehicle currently driving on the road, for example, can include but is not limited to vehicle working condition and environmental working condition, etc. The vehicle working condition can be the working condition data of the target vehicle itself, for example, can include but is not limited to vehicle speed, steering, power output, etc. The environmental working condition can be the environmental condition data of the target vehicle during driving, for example, can include but is not limited to current weather condition, road condition, traffic flow and road network data, etc. The exhaust gas emission data can be directly acquired by various exhaust gas emission detection sensors arranged in the target vehicle, and the current working condition information can be directly or indirectly acquired by the target vehicle, which is not limited herein.

[0093] S112, determining the driving habit characteristic of the target user using the target vehicle according to the current working condition information.

[0094] The target user can be a person using the target vehicle, and the driving habit feature can be operation preference information of the target user when driving the target vehicle, such as when to prefer to accelerate, when to prefer to brake, how to control the target vehicle when facing changes in road conditions or environment, and the like. By analyzing the working condition information, it is determined that the target user faces changes in road conditions and weather, and the vehicle has changed in what working condition, so as to know what operation orientation the target user takes when facing changes in road conditions and weather, that is, the driving habit of the user. For example, the vehicle working condition and the environment working condition of the current working condition information are corresponded according to the time sequence, that is, as time changes, what changes have occurred in the environment (road conditions and weather) around the vehicle, and what changes have occurred in the driving of the vehicle (such as acceleration, deceleration, and the duration of maintaining a certain speed) at the same time of the change in time and the change in environment. It can be correspondingly determined that what driving operation has occurred in the vehicle when facing different road conditions and weather, and the driving operation of the user when facing these changes in road conditions and weather is recorded, that is, the driving habit of the user is obtained.

[0095] S113, determining the tail gas change feature of the target vehicle according to the driving habit feature and the tail gas emission data.

[0096] It can be understood that it is the operation of the target user on the target vehicle that causes the change in tail gas emission, and therefore the driving habit feature of the user and the tail gas emission data are compared to determine the rule or corresponding relationship of the change in tail gas emission of the target vehicle with the change in driving operation as the tail gas change feature.

[0097] In the above embodiments, the driving habit feature of the target user using the target vehicle is determined based on the current working condition information, and further the tail gas change feature is determined according to the driving habit feature and the tail gas emission data. The determination of the driving habit of the user can help the vehicle to identify the operation habit of the user when driving the vehicle in different working conditions, and the change in operation of the vehicle is closely related to the change in generation and emission of tail gas, so as to obtain the change feature of the tail gas, which provides a feasible scheme for determining the tail gas change feature and helps to improve the accuracy of tail gas prediction.

[0098] Based on the foregoing embodiments, how to determine the driving habit feature is explained. In an optional embodiment, the current working condition information includes vehicle working condition and environment working condition.

[0099] S112, determining the driving habit feature of the target user using the target vehicle according to the current working condition information, can include:

[0100] B1, based on a preset time domain analysis algorithm, respectively analyze the vehicle working condition and the environmental working condition, and respectively obtain vehicle working condition change information and environmental working condition change information arranged according to a preset time sequence.

[0101] It can be understood that as long as the target vehicle is driving on the road, the vehicle working condition, the environmental working condition and the emission condition of the vehicle are changing in real time, and on the time scale, various working conditions and emission conditions can be found to be time-matched. Therefore, by using a preset time domain analysis algorithm, the vehicle working condition is analyzed, for example, the data of the vehicle working condition is arranged in time sequence, a time period for calculation is determined, that is, an observation time window, and the key feature information of the vehicle working condition data in the time period is extracted, such as mean, variance, peak value, wave crest, wave trough and cross rate, etc. Through the analysis of these data, the characteristics of the vehicle working condition changing with time are obtained, that is, the trend of the vehicle working condition changing with time; similarly, by using a preset time domain analysis algorithm, the environmental working condition is analyzed, and the environmental working condition change information arranged according to the preset time sequence is obtained, that is, the trend of the environmental working condition changing with time.

[0102] B2, according to a preset time sequence, determine the first correlation between the vehicle working condition change information and the environmental working condition change information; take the first correlation as a driving habit feature.

[0103] It can be understood that the change of the vehicle working condition will be affected by the change of the environmental working condition. The changes of the vehicle working condition and the environmental working condition occurring in the same time range can make them correspond according to the time sequence. Therefore, according to the preset time sequence, the vehicle working condition change information and the environmental working condition change information are associated to obtain the first correlation which can reflect how the target user controls the target vehicle when facing the change of the environmental working condition, so that the vehicle working condition of the target vehicle changes. And take the first correlation as a driving habit feature.

[0104] Specifically, the current working condition information includes vehicle speed, acceleration, power output and other data, so that the changes of these data in a certain time range can be determined, and the road condition information and the weather information in the corresponding time range in the current working condition information can determine that at the same time when the road condition information and the weather information change in the time range, the vehicle speed, acceleration and power output and other data have changed. And the speed, acceleration and power output and other data of the vehicle are all caused by the operation habit of the target user who controls the target vehicle, so the driving habit feature of the target user is obtained.

[0105] In the above-mentioned embodiments, by performing time domain analysis on the vehicle working condition and the environmental working condition, the change of the vehicle working condition in the time dimension and the change of the environmental working condition in the time dimension are obtained respectively. Since the time dimensions are consistent, the correlation between the change information of the vehicle working condition and the change information of the environmental working condition can be obtained. The change information of the vehicle working condition and the change information of the environmental working condition are correlated via the time dimension as a medium, so as to explore how the change of the environmental working condition affects the user's operation of driving the vehicle, thereby accurately obtaining the driving habit of the user, providing a basic condition for subsequently determining the tail gas change feature, and being beneficial to improving the accuracy and practicability of tail gas emission management.

[0106] On the basis of determining the driving habit feature in the foregoing embodiments, the tail gas change feature needs to be further calculated. In an optional embodiment, the determining, in S113, of the tail gas change feature of the target vehicle according to the driving habit feature and the tail gas emission data can include:

[0107] C1, performing analysis on the tail gas emission data based on a preset time domain analysis algorithm to obtain tail gas emission change data arranged in a preset time sequence.

[0108] The tail gas emission data also changes over time, and by time domain analysis, the change of the tail gas emission in the time dimension, i.e., the tail gas emission change data, is obtained. It should be noted that the time domain analysis algorithm in the embodiments of the present application can adopt any one of the related technologies, which is not limited in the present application.

[0109] C2, determining a second correlation between the tail gas emission change data and the driving habit feature according to the preset time sequence; and taking the second correlation as the tail gas change feature.

[0110] It can be understood that, similar to the foregoing embodiments, the driving habit of the target user affects his operation when driving the target vehicle, and these driving operations affect the change of the tail gas emission. Because the time dimensions are consistent, the second correlation between the tail gas emission change data and the driving habit feature can be determined as the tail gas change feature, i.e., how the driving habit or the driving operation affects the change of the tail gas emission in the time dimension. At this point, the change of the environmental working condition causes the user to operate the vehicle to cause the change of the vehicle working condition, which to some extent reflects the driving habit of the user, and the change of the user's operation of the vehicle causes the change of the tail gas emission, thereby determining the tail gas change feature.

[0111] In the above embodiments, time domain analysis is performed on the tail gas emission data to obtain the change of the tail gas emission over time, and the driving habit features are associated in the time dimension to obtain the influence of the change of the tail gas emission on the driving habit or user operation, thereby providing a feasible scheme for determining the tail gas change feature. The change of the environmental condition, the change of the vehicle condition, and the change of the tail gas emission are linked to form a causal relationship, thereby effectively helping the tail gas emission prediction model to improve the model precision, and thus helping to improve the prediction accuracy of the prediction result of the model.

[0112] On the basis of the above embodiments, the tail gas emission is obtained based on the emission prediction result obtained by the prediction model, and the tail gas emission management has practical significance. In an optional embodiment, the method can further include:

[0113] S141, determining a current driving time point and determining an emission prediction result of the current driving time point in a preset future period.

[0114] The current driving time point can be a time point at which the emission prediction and tail gas treatment need to be continued, or can be any time point in the preset future period. In the future period, the emission prediction result corresponding to the driving time point is determined according to the tail gas emission prediction model introduced in the above embodiments and embodiments.

[0115] S142, obtaining a preset tail gas emission standard, correcting the emission prediction result by using the tail gas emission standard, and determining a tail gas emission adjustment amount of the current driving time point.

[0116] The tail gas emission standard can be a limitation requirement for the vehicle tail gas emission (for example, it can be a national standard). After obtaining the emission prediction result according to the tail gas emission prediction model, the tail gas emission management can be performed according to the emission prediction result. However, in order to prevent the tail gas emission standard from being exceeded, the tail gas emission standard is used to limit the correction of the emission prediction result that may exceed the standard, thereby determining how much the tail gas emission adjustment amount is reduced.

[0117] S143, determining a working parameter of a tail gas purifier of the current driving time point according to the tail gas emission adjustment amount.

[0118] On the basis of determining the tail gas emission adjustment amount in the foregoing step, it is determined that the tail gas purifier of the target vehicle should work in which working model at the driving time point, so as to make the tail gas emission meet the requirements of the emission prediction result and the tail gas emission standard. The working parameters of the tail gas purifier can include but are not limited to the purifier working temperature, the catalyst activity index, and the filtering standard of the filtering material, and the like, which are not exhaustively listed herein. Exemplarily, the tail gas emission adjustment amount can include the adjustment amount of particulate emission and the adjustment amount of harmful gas emission. It can be understood that, in order to reduce the particulate emission and the harmful gas emission, the working temperature of the tail gas purifier, the catalyst, and the working intensity index of the tail gas purifier can be adjusted in response, so that the reduced particulate emission amount and the harmful gas emission amount can certainly meet the tail gas emission adjustment amount.

[0119] S144, controlling the tail gas purifier to perform tail gas treatment according to the working parameters.

[0120] The working mode of the tail gas purifier is adjusted according to the working parameters determined in the foregoing step to perform tail gas treatment, so as to control the particulate emission and the toxic gas emission, so as to realize comprehensive management of the tail gas emission.

[0121] In the foregoing embodiment, the emission prediction result and the tail gas emission standard are double limitations or double guarantees for tail gas emission management. On the one hand, tail gas emission management according to the emission prediction result can effectively improve the flexibility of tail gas emission, so as to adapt to the change of weather and the driving operation habit of the user; on the other hand, correction according to the tail gas emission standard can ensure that the tail gas emission does not exceed the standard, so as to achieve the purpose of energy saving and emission reduction.

[0122] It should be noted that the method of the embodiments of the present application can be executed by a single device, such as a computer or a server, and the like. The method of the embodiments can also be applied to a distributed scenario, and be completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only execute one or more steps in the method of the embodiments of the present application, and the multiple devices can interact with each other to complete the method.

[0123] It should be noted that some embodiments of the present application are described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than the order described above and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0124] Corresponding to the method of any of the above embodiments, the application also provides a tail gas emission control device based on the same inventive concept.

[0125] Figure 2 A structural schematic diagram of a tail gas emission control device provided by the embodiment of the application is shown in Figure 2 The tail gas emission control device 200 comprises a data acquisition module 210, a result prediction module 220 and an emission control module 230, wherein:

[0126] The data acquisition module 210 is configured to acquire tail gas variation characteristics of a target vehicle and weather forecasts in a preset future period.

[0127] The result prediction module 220 is configured to input the tail gas variation characteristics and the weather forecasts into a pre-trained tail gas emission prediction model for analysis to determine an emission prediction result in the preset future period, wherein the emission prediction result is a tail gas emission range of the target vehicle in the future period.

[0128] The emission control module 230 is configured to, during driving in the preset future period, retrieve target parameters of tail gas emission corresponding to the tail gas emission range in the preset future period according to the tail gas emission range in the preset future period in the emission prediction result, and control tail gas emission of the target vehicle based on the target parameters of tail gas emission.

[0129] In the technical scheme of the embodiment of the application, the tail gas variation characteristics of the target vehicle and the acquired weather forecasts in the preset future period are input into the tail gas emission prediction model to obtain an emission prediction result of the future period, and when the target vehicle drives in the period, tail gas emission is managed according to the emission prediction result. The tail gas emission prediction model is trained for tail gas variation characteristics and weather data, which enables the model to obtain recognition and analysis capabilities for tail gas variation characteristics and weather data, so as to effectively and accurately analyze the possible tail gas emission during vehicle driving in the corresponding period from the tail gas variation characteristics of the target vehicle and the corresponding weather forecasts, as the emission prediction result. The emission prediction result also conforms to the tail gas emission rules of the target vehicle during driving and the tail gas emission changes caused by weather changes. According to the emission prediction result, the working parameters of the tail gas purifier are determined, so that the tail gas purifier has specific basis for tail gas treatment, and these basis are related to the current tail gas variation and weather changes during vehicle driving, which improves the accuracy of tail gas treatment and effectively improves the versatility and weather adaptability of the prediction result generated by the model. According to the emission prediction result, tail gas control can also be adjusted according to different emission rules and weather conditions, thereby improving the flexibility of tail gas emission management.

[0130] In an optional implementation, the tail gas emission prediction model comprises an input layer, a hidden layer and an output layer, and the result prediction module 220 can comprise:

[0131] a hidden layer result determination unit configured to input the tail gas change feature and the weather forecast into the hidden layer through the input layer to obtain a hidden layer result;

[0132] an emission prediction result determination unit configured to output the hidden layer result through the output layer of the tail gas emission prediction model to obtain an emission prediction result.

[0133] In an optional implementation, the tail gas emission control device 200 can comprise a model training module, and the model training module can comprise:

[0134] a model construction unit configured to construct a preset initial model, the preset initial model comprising an input layer, a hidden layer and an output layer;

[0135] a sample acquisition unit configured to acquire tail gas change feature data and historical weather data of a target vehicle in a historical period, and an emission result corresponding to the tail gas change feature data and the historical weather data;

[0136] a processing result determination unit configured to input the tail gas change feature data and the historical weather data into the hidden layer through the input layer for processing to obtain a processing result;

[0137] a training result determination unit configured to output the processing result through the output layer of the preset initial model to obtain a training result;

[0138] an adjustment parameter determination unit configured to compare the training result with the emission result to determine an adjustment parameter of the preset initial model;

[0139] a model adjustment unit configured to adjust the preset initial model according to the adjustment parameter to obtain a tail gas emission prediction model.

[0140] In an optional implementation, the data acquisition module 210 can comprise:

[0141] a tail gas working condition acquisition unit configured to acquire tail gas emission data and current working condition information of a target vehicle;

[0142] a driving habit determination unit configured to determine a driving habit feature of a target user using the target vehicle according to the current working condition information;

[0143] a change feature determination unit configured to determine a tail gas change feature of the target vehicle according to the driving habit feature and the tail gas emission data.

[0144] In an optional implementation, the current working condition information comprises vehicle working condition and environmental working condition.

[0145] The driving habit determining unit can comprise:

[0146] A working condition analysis subunit is configured to analyze the vehicle working condition and the environmental working condition based on a preset time domain analysis algorithm, and obtain vehicle working condition change information and environmental working condition change information arranged in a preset time sequence, respectively.

[0147] A first correlation relationship determining subunit is configured to determine a first correlation relationship between the vehicle working condition change information and the environmental working condition change information in a preset time sequence, and take the first correlation relationship as the driving habit feature.

[0148] In an optional embodiment, the change feature determining unit can comprise:

[0149] An emission change determining subunit is configured to analyze the exhaust emission data based on a preset time domain analysis algorithm, and obtain exhaust emission change data arranged in a preset time sequence.

[0150] A second correlation relationship determining subunit is configured to determine a second correlation relationship between the exhaust emission change data and the driving habit feature in a preset time sequence, and take the second correlation relationship as the exhaust change feature.

[0151] In an optional embodiment, the device 200 can further comprise:

[0152] A prediction result determining unit is configured to determine a current driving time point in a preset future driving process, and determine an emission prediction result of the current driving time point.

[0153] An emission adjustment amount determining unit is configured to obtain a preset exhaust emission standard, correct the emission prediction result by using the exhaust emission standard, and determine an exhaust emission adjustment amount of the current driving time point.

[0154] A working parameter determining unit is configured to determine a working parameter of an exhaust purifier of the current driving time point according to the exhaust emission adjustment amount.

[0155] An exhaust treatment unit is configured to control the exhaust purifier to perform exhaust treatment according to the working parameter.

[0156] For the convenience of description, the above device is described in various modules according to functions. Of course, the functions of the modules can be implemented in one or more software and / or hardware in the implementation of the present application.

[0157] The device of the above embodiment is used to implement the corresponding exhaust emission control method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein again.

[0158] Based on the same inventive concept, the application also provides an electronic device corresponding to the exhaust emission control method of any of the above embodiments, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the exhaust emission control method of any of the above embodiments.

[0159] Figure 3 A structural schematic diagram of an electronic device provided by the embodiments of the application is shown in FIG. 1. Figure 3 A specific hardware structure of an electronic device is shown in FIG. 1, which can include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040 and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030 and the communication interface 1040 are connected to each other through the bus 1050 for internal communication.

[0160] The processor 1010 can be implemented by a general CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit) or one or more integrated circuits, etc., for executing related programs to implement the technical solutions provided by the embodiments of the present application.

[0161] The memory 1020 can be implemented by a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 can store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 1020 and executed by the processor 1010.

[0162] The input / output interface 1030 is used to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. The input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0163] The communication interface 1040 is used to connect a communication module (not shown in the figure) to realize the communication interaction between the device and other devices. The communication module can realize communication through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.).

[0164] Bus 1050 includes a path that transfers information between the various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0165] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device can also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device can also only contain the components necessary to implement the embodiments of the present application, and does not have to contain all the components shown in the figure.

[0166] The electronic device of the above embodiment is used to implement the corresponding tail gas emission control method in any of the preceding embodiments, and has the beneficial effects of the corresponding method embodiments, which are not repeated here.

[0167] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application also provides a vehicle, which comprises:

[0168] A memory for storing executable program code;

[0169] A processor for calling and running the executable program code from the memory, so that the vehicle executes the method provided by any embodiment of the present application.

[0170] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application also provides a non-transitory computer readable storage medium, which stores computer instructions for causing the computer to execute the tail gas emission control method as described in any of the above embodiments.

[0171] The computer readable medium of the present embodiment includes permanent and non-permanent, removable and non-removable media, which can be implemented by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0172] The storage medium of the above-mentioned embodiments stores computer instructions for causing the computer to execute the exhaust emission control method according to any one of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiments, which are not repeated here.

[0173] It should be understood by those of ordinary skill in the art that the above discussion of any of the embodiments is merely exemplary and is not intended to suggest the scope of the application (including the claims) is limited to these examples; the embodiments above or technical features among different embodiments can also be combined, steps can be implemented in any order, and there are many other changes to the aspects of the embodiments of the application as described above, which are not provided in detail for the sake of brevity. In addition, the technical features of the embodiments of the application can be combined with each other in any manner.

[0174] In addition, in order to simplify the description and discussion, and so as not to make the embodiments of the application difficult to understand, the well-known power / ground connections of integrated circuit (IC) chips and other components can or can not be shown in the provided drawings. In addition, the devices can be shown in the form of block diagrams in order to avoid making the embodiments of the application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the application (i.e., these details should be fully within the understanding of those skilled in the art). Where specific details (e.g., circuitry) are set forth in order to describe an illustrative embodiment of the application, it will be apparent to those skilled in the art that the embodiments of the application can be practiced without these specific details or with variations on these specific details. Therefore, these descriptions should be considered as illustrative rather than limiting.

[0175] Although the application has been described in conjunction with specific embodiments thereof, many alternatives, modifications and variations will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) can use the embodiments discussed.

[0176] The embodiments of the application are intended to cover all such alternatives, modifications and variations as falling within the broad scope of the appended claims. Accordingly, any one of the abovementioned embodiments of the application is intended to encompass all such alternatives, modifications and variations as falling within the scope of the appended claims.

Claims

1. A tail gas emission control method characterized by, The method comprises: obtaining the exhaust change characteristics of the target vehicle and weather forecasts in a preset future period; inputting the exhaust change characteristics and the weather forecasts into a pre-trained exhaust emission prediction model for analysis to determine an emission prediction result in the preset future period; wherein the emission prediction result is an exhaust emission range of the target vehicle in the future period; during driving in the preset future period, according to the exhaust emission range in the preset future period in the emission prediction result, the target parameters of exhaust emission corresponding to the exhaust emission range in the preset future period are retrieved, and the exhaust emission of the target vehicle is controlled based on the target parameters of exhaust emission.

2. The method of claim 1, wherein, The exhaust emission prediction model comprises an input layer, a hidden layer and an output layer, and the exhaust change characteristics and the weather forecasts are inputted into the pre-trained exhaust emission prediction model for analysis to determine the emission prediction result in the preset future period, which comprises: the exhaust change characteristics and the weather forecasts are inputted into the hidden layer through the input layer to obtain a hidden layer result; the hidden layer result is outputted through the output layer of the exhaust emission prediction model to obtain the emission prediction result.

3. The method of claim 1, wherein, The exhaust emission prediction model is determined by the following method: constructing a preset initial model, the preset initial model comprising an input layer, a hidden layer and an output layer; obtaining the exhaust change characteristic data and historical weather data of the target vehicle in a historical period, and the emission results corresponding to the exhaust change characteristic data and the historical weather data; the exhaust change characteristic data and the historical weather data are inputted into the hidden layer through the input layer for processing to obtain a processing result; the processing result is outputted through the output layer of the preset initial model to obtain a training result; the training result is compared with the emission result to determine the adjustment parameters of the preset initial model; the preset initial model is adjusted according to the adjustment parameters to obtain the exhaust emission prediction model.

4. The method of claim 1, wherein, The exhaust change characteristics of the target vehicle are obtained, which comprises: obtaining the exhaust emission data and current working condition information of the target vehicle; determining the driving habit characteristics of the target user using the target vehicle according to the current working condition information; determining the exhaust change characteristics of the target vehicle according to the driving habit characteristics and the exhaust emission data.

5. The method of claim 4, wherein, The current working condition information comprises vehicle working condition and environmental working condition; determining the driving habit characteristics of the target user using the target vehicle according to the current working condition information, which comprises: based on a preset time domain analysis algorithm, the vehicle working condition and the environmental working condition are analyzed respectively to obtain vehicle working condition change information and environmental working condition change information arranged in a preset time sequence respectively; determining a first correlation between the vehicle working condition change information and the environmental working condition change information according to the preset time sequence; the first correlation is taken as the driving habit characteristics.

6. The method of claim 4, wherein, determining the exhaust change characteristics of the target vehicle according to the driving habit characteristics and the exhaust emission data, which comprises: The tail gas emission data is analyzed based on a preset time domain analysis algorithm to obtain tail gas emission change data arranged according to a preset time sequence; A second correlation between the tail gas emission change data and the driving habit features is determined according to the preset time sequence; The second correlation is taken as the tail gas change feature.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: During driving in a preset future period, a current driving time point is determined, and the emission prediction result at the current driving time point is determined; A preset tail gas emission standard is obtained, the emission prediction result is corrected using the tail gas emission standard, and a tail gas emission adjustment amount at the current driving time point is determined; According to the tail gas emission adjustment amount, a working parameter of a tail gas purifier at the current driving time point is determined; According to the working parameter, the tail gas purifier is controlled to perform tail gas treatment.

8. An exhaust emission control device characterized by comprising: The device includes: A data acquisition module is configured to acquire tail gas change features of a target vehicle and weather forecasts in a preset future period; An result prediction module is configured to input the tail gas change features and the weather forecasts into a pre-trained tail gas emission prediction model to analyze and determine an emission prediction result in the preset future period; wherein the emission prediction result is a tail gas emission range of the target vehicle in the future period; An emission control module is configured to, during driving in a preset future period, retrieve target parameters of tail gas emission corresponding to the tail gas emission range in the preset future period according to the tail gas emission range in the preset future period in the emission prediction result, and control tail gas emission of the target vehicle based on the target parameters of tail gas emission.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the method of any one of claims 1-7.

10. A vehicle characterized by comprising: The vehicle includes an electronic device as claimed in claim 9, So that the vehicle performs the method of any one of claims 1-7.

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