Engine performance calibration processing method based on working condition portrait and related device

Abstract

The present application discloses an engine performance calibration processing method based on working condition portrait and related devices, the method comprising: obtaining the operating parameters of the engine, the operating parameters comprising at least multiple working condition parameters; obtaining the current working condition portrait of the engine according to the operating parameters; processing the current working condition portrait and the initial data version using a parameter calibration model to obtain a target data version corresponding to the current working condition portrait; the target data version comprising: supporting parameters corresponding to the control strategy of the engine; wherein the parameter calibration model is trained using training samples, the training samples are obtained using a typical working condition library, the typical working condition library comprising multiple typical working condition portraits; the target data version is sent to the engine controller, and the controller executes the control strategy according to the supporting parameters in the target data version, so that the performance indicators of the engine meet the optimization conditions.

Description

Technical Field

[0001] The present application relates to the field of engine technology, and in particular to an engine performance calibration processing method based on operating condition profiling and related devices. Background Art

[0002] At present, in order to ensure that the engine can achieve optimal performance indicators regardless of the operating conditions, engineers usually pre-calibrate the controller support parameters under various operating conditions.

[0003] However, in actual engine applications, operating conditions are ever-changing. Even if engineers have pre-calibrated multiple versions of support parameters, there may still be situations where the engine performance indicators cannot be optimized under certain operating conditions. Summary of the invention

[0004] In view of the above problems, the present application provides an engine performance calibration processing method and related device based on working condition portrait, so as to optimize the engine performance index. The specific scheme is as follows:

[0005] In a first aspect, the present application provides an engine performance calibration processing method based on a working condition profile, the method comprising:

[0006] Obtaining operating parameters of the engine, wherein the operating parameters include at least a plurality of operating condition parameters;

[0007] According to the operating parameters, a current operating condition profile of the engine is obtained;

[0008] The current operating condition portrait and the initial data version are processed by using a parameter calibration model to obtain a target data version corresponding to the current operating condition portrait; the target data version includes: support parameters corresponding to the control strategy of the engine;

[0009] The parameter calibration model is obtained by training using training samples, and the training samples are obtained using a typical working condition library, and the typical working condition library contains multiple typical working condition portraits;

[0010] The target data version is sent to the controller of the engine, and the controller executes the control strategy according to the supporting parameters in the target data version, so that the performance index of the engine meets the optimization condition.

[0011] In a possible implementation, the parameter calibration model is a machine learning model;

[0012] The current working condition portrait and the initial data version are processed by using a parameter calibration model to obtain a target data version corresponding to the current working condition portrait, including:

[0013] According to the current working condition portrait, a target working condition portrait and an initial data version are obtained in a data version library;

[0014] The data version library includes at least one typical working condition portrait, each of which corresponds to an optimized data version; the target working condition portrait and the current working condition portrait meet the matching condition; the initial data version is the optimized data version corresponding to the target working condition portrait;

[0015] The current operating condition portrait and the initial data version are input into the parameter calibration model to obtain a target data version corresponding to the current operating condition portrait output by the parameter calibration model.

[0016] In a possible implementation, the parameter calibration model includes a control simulation system corresponding to the controller of the engine;

[0017] The current working condition portrait and the initial data version are processed by using a parameter calibration model to obtain a target data version corresponding to the current working condition portrait, including:

[0018] According to the current working condition portrait, a target working condition portrait and an initial data version are obtained in a data version library;

[0019] The data version library includes at least one typical working condition portrait, each of which corresponds to an optimized data version; the target working condition portrait and the current working condition portrait meet the matching condition; the initial data version is the optimized data version corresponding to the target working condition portrait;

[0020] Inputting the current working condition portrait and the initial data version into the control simulation system, the control simulation system can output a control instruction according to the current working condition portrait with the initial data version as the control data version, the control instruction being used to control the engine to perform corresponding processing;

[0021] Adjusting the control data version used in the control simulation system so that the control instruction output by the control simulation system enables the performance index of the engine to meet the optimization condition;

[0022] The control data version used by the control simulation system when the control instruction makes the performance index of the engine meet the optimization condition is determined as the target data version corresponding to the current operating condition portrait.

[0023] In a possible implementation, the matching condition is: the portrait similarity between the current working condition portrait and the target working condition portrait is greater than or equal to a first threshold.

[0024] In a possible implementation, according to the current working condition portrait, obtaining a target working condition portrait and an initial data version in a data version library includes:

[0025] Using a clustering model, obtaining a current operating condition type corresponding to the current operating condition portrait;

[0026] Compare the current working condition type with the working condition type of each typical working condition portrait in the data version library;

[0027] If there is a typical operating condition type in the data version library that is consistent with the current operating condition type, the typical operating condition portrait in the data version library that is consistent with the current operating condition type is determined as the target operating condition portrait and the optimized data version corresponding to the target operating condition portrait is determined as the initial data version;

[0028] If there is no typical operating condition type in the data version library that is consistent with the current operating condition type, the typical operating condition portrait with the greatest portrait similarity with the current operating condition portrait in the data version library is used as the target operating condition portrait and the optimized data version corresponding to the target operating condition portrait is determined as the initial data version.

[0029] In a possible implementation, the training sample is obtained in the following manner:

[0030] The following processing is performed on each typical working condition image in the typical working condition library:

[0031] The typical operating condition portrait and the historical data version corresponding to the typical operating condition portrait are input into the control simulation system corresponding to the controller of the engine, and the control simulation system can output a control instruction according to the typical operating condition portrait using the historical data version corresponding to the typical operating condition portrait as the control data version; the control instruction is used to control the engine to perform corresponding processing;

[0032] Adjusting the control data version used in the control simulation system so that the control instruction output by the control simulation system enables the performance index of the engine to meet the optimization condition;

[0033] Among them, when the control instruction makes the performance index of the engine meet the optimization conditions, the control data version used by the control simulation system is the output sample in the training sample; the typical operating condition portrait and the historical data version corresponding to the typical operating condition portrait are the input samples in the training sample.

[0034] In a possible implementation, obtaining the operating parameters of the engine includes:

[0035] A plurality of sensors deployed on the engine are used to collect a plurality of operating parameters of the engine to obtain operating parameters of the engine.

[0036] In a possible implementation, after obtaining the target data version corresponding to the current operating condition portrait, the method further includes:

[0037] Generalizing at least one operating condition parameter in the current operating condition profile to obtain a new typical operating condition profile;

[0038] The new typical working condition portrait and its corresponding target data version are added to the data version library.

[0039] A second aspect of the present application provides an engine performance calibration processing device based on a working condition profile, the device comprising:

[0040] A parameter obtaining unit, used to obtain operating parameters of the engine, wherein the operating parameters at least include a plurality of operating condition parameters;

[0041] A portrait obtaining unit, used for obtaining a current operating condition portrait of the engine according to the operating parameters;

[0042] A data processing unit is used to process the current working condition portrait and the initial data version using a parameter calibration model to obtain a target data version corresponding to the current working condition portrait; the target data version includes: support parameters corresponding to the control strategy of the engine;

[0043] The parameter calibration model is obtained by training using training samples, and the training samples are obtained using a typical working condition library, and the typical working condition library contains multiple typical working condition portraits;

[0044] A data transmission unit is used to send the target data version to the controller of the engine, and the controller executes the control strategy according to the supporting parameters in the target data version so that the performance indicators of the engine meet the optimization conditions.

[0045] The third aspect of the present application provides a computer program product, including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements an engine performance calibration processing method based on operating condition profiling according to the above-mentioned first aspect or any implementation method of the first aspect.

[0046] A fourth aspect of the present application provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0047] The memory is used to store computer programs;

[0048] The processor is used to execute the computer program so that the electronic device can implement an engine performance calibration processing method based on operating condition profiling according to the above-mentioned first aspect or any implementation method of the first aspect.

[0049] A fifth aspect of the present application provides a computer storage medium, which carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement an engine performance calibration processing method based on operating condition profiling according to the above-mentioned first aspect or any implementation of the first aspect.

[0050] By means of the above technical scheme, in the engine performance calibration processing method and related device based on the working condition portrait provided by the present application, after obtaining the operating parameters of the engine including multiple working condition parameters, the current working condition portrait of the engine is first obtained according to the operating parameters, and then the parameter calibration model trained by the typical working condition portrait is used to process the current working condition portrait and the initial data version to obtain the target data version corresponding to the current working condition portrait, and the target data version includes: the support parameters corresponding to the control strategy of the engine, so that after sending the target data version to the controller of the engine, the controller executes the corresponding control strategy according to the support parameters in the target data version, thereby making the performance index of the engine meet the optimization conditions. It can be seen that in the present application, no matter what working condition state the current working condition portrait of the engine is, the parameter calibration model can be used to optimize the support parameters corresponding to the control strategy, so that when the controller of the engine executes the control strategy according to the optimized support parameters, the performance index of the engine can meet the optimization conditions, thereby achieving the purpose of calibrating the performance index of the engine to the optimal state. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 A flowchart of an engine performance calibration processing method based on a working condition profile provided in an embodiment of the present application;

[0053] Figure 2 A partial flow chart of an engine performance calibration processing method based on operating condition profiling provided in an embodiment of the present application;

[0054] Figure 3 Another partial flow chart of an engine performance calibration processing method based on operating condition profiling provided in an embodiment of the present application;

[0055] Figure 4 A flowchart of another part of an engine performance calibration processing method based on a working condition profile provided in an embodiment of the present application;

[0056] Figure 5 Another flow chart of an engine performance calibration processing method based on operating condition portrait provided in an embodiment of the present application;

[0057] Figure 6 Another flow chart of an engine performance calibration processing method based on a working condition profile provided in an embodiment of the present application;

[0058] Figure 7 A schematic diagram of the structure of an engine performance calibration processing device based on working condition profiling provided in an embodiment of the present application;

[0059] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0060] Fig. 9 It is the overall execution logic flow chart of the engine performance index calibration method based on working condition adaptive learning in this application;

[0061] Fig.10 This is a flow chart for obtaining training samples for the virtual calibration model in this application. DETAILED DESCRIPTION

[0062] The following describes the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. The terms used in the implementation method section of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application.

[0063] The embodiments of the present application are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0064] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, which is only to describe the distinction mode adopted by the objects of the same attributes when describing in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0065] The present application can be applied in the field of engines. Taking diesel engines as an example, multiple application scenarios of the products will be introduced below.

[0066] Reference Figure 1 , is a flow chart of an engine performance calibration processing method based on working condition profiling provided in an embodiment of the present application, the method can be applied to an electronic device capable of data processing, the electronic device can be a local device connected to the engine, or a cloud device connected to the engine. The technical solution in this embodiment is mainly used to optimize the performance indicators of the engine.

[0067] Specifically, the method in this embodiment may include the following steps:

[0068] Step 101: Obtain operating parameters of the engine.

[0069] Among them, the operating parameters include at least multiple operating parameters, such as rotation speed, torque, vehicle speed, altitude, temperature, load, acceleration, idle speed, etc.

[0070] In one implementation, in step 101 , multiple sensors deployed on the engine may be used to collect multiple operating parameters of the engine to obtain the operating parameters of the engine.

[0071] For example, a data connection is established between the electronic device in this embodiment and the sensor deployed on the engine. After the sensor collects the operating parameters, the operating parameters are sent to the electronic device in this embodiment through the data connection, thereby obtaining operating parameters including these operating parameters on the electronic device.

[0072] It should be noted that the operating parameters may also include non-operating parameters, which refer to parameters that will not change after the engine leaves the factory.

[0073] It should be noted that this embodiment can be executed when the operating parameters of the engine change, that is, this embodiment continuously detects whether the operating parameters of the engine have changed. If it is monitored that the operating parameters of the engine have changed, then step 101 and subsequent steps are executed.

[0074] Step 102: Obtain a current operating condition profile of the engine according to the operating parameters.

[0075] Specifically, in step 102, the operating parameters can be first filtered for data standardization by using rules such as data correlation and range limits to remove abnormal data; then, the operating parameters are segmented according to the selected data processing method (such as driving cycle mode, mileage, operating time, etc.), so that corresponding parameter features are extracted from the segmented operating parameters, such as temperature, altitude, humidity, speed, torque, load, etc., and then the current working condition profile of the engine is constructed based on these parameter features. The current working condition profile of the engine can be represented by the extracted parameter features, as shown in Table 1.

[0076] Table 1 Current working condition portrait

[0077]

[0078] Step 103: Use the parameter calibration model to process the current operating condition profile and the initial data version to obtain the target data version corresponding to the current operating condition profile.

[0079] The target data version includes the supporting parameters corresponding to the control strategy of the engine. The supporting parameters refer to the parameters required by the engine controller to execute the control strategy. The control strategy refers to the strategy used by the controller to control the engine, for example, when the speed reaches a certain value, the strategy of the controller to control a certain operating parameter of the engine.

[0080] It should be noted that the parameter calibration model is trained using training samples, and the training samples are obtained using a typical working condition library. The typical working condition library may contain multiple typical working condition portraits.

[0081] In the initial state, the typical operating condition portraits in the typical operating condition library can be manually set by the user. As the engine runs, the engine may generate various different operating condition portraits. Therefore, these actual operating condition portraits can also be added to the typical operating condition library.

[0082] In one implementation, a parameter calibration model can be constructed based on a machine learning algorithm and trained using training samples so that the parameter calibration model can learn the relationship between input samples and output samples in the training samples. The input samples at least include an operating condition portrait and a data version (such as supporting parameters corresponding to the control strategy), and the output samples are optimized data versions corresponding to the operating condition portrait in the input samples. In this way, the parameter calibration model can process the current operating condition portrait and the initial data version, and then obtain the target data version corresponding to the current operating condition portrait.

[0083] Step 104: Send the target data version to the controller of the engine.

[0084] The controller executes the control strategy according to the supporting parameters in the target data version so that the performance indicators of the engine meet the optimization conditions.

[0085] Specifically, in step 104, the target data version can be sent to the engine controller via OTA (Over The Air), so that when the controller executes the control strategy according to the supporting parameters in the target data version, the performance indicators of the engine can meet the optimization conditions, specifically: the overall performance indicators of the engine are in a specific percentile interval such as the 95% percentile interval, the component performance indicators of the engine are each in a corresponding optimization value range, and the detailed indicators of the engine are each in a corresponding optimization value range.

[0086] By means of the above technical scheme, in an engine performance calibration processing method based on a working condition portrait provided by an embodiment of the present application, after obtaining the operating parameters of the engine including multiple working condition parameters, the current working condition portrait of the engine is first obtained according to the operating parameters, and then the parameter calibration model trained by the typical working condition portrait is used to process the current working condition portrait and the initial data version to obtain the target data version corresponding to the current working condition portrait, the target data version includes: the support parameters corresponding to the control strategy of the engine, so that after the target data version is sent to the controller of the engine, the controller executes the corresponding control strategy according to the support parameters in the target data version, thereby making the performance indicators of the engine meet the optimization conditions. It can be seen that in this application, no matter what working condition state the current working condition portrait of the engine is, the parameter calibration model can be used to optimize the support parameters corresponding to the control strategy, so that when the controller of the engine executes the control strategy according to the optimized support parameters, the performance indicators of the engine can meet the optimization conditions, thereby achieving the purpose of calibrating the performance indicators of the engine to the best.

[0087] In one implementation, the parameter calibration model is a machine learning model.

[0088] Based on this, step 103 can be implemented in the following ways: Figure 2 As shown in:

[0089] Step 201: According to the current working condition portrait, obtain the target working condition portrait and the initial data version in the data version library.

[0090] The data version library contains at least one typical working condition portrait, and each typical working condition portrait corresponds to an optimized data version. The optimized data version means that when the engine controller executes the control strategy according to the supporting parameters in the optimized data version, the engine performance index can meet the optimization conditions. For example, the optimized data version corresponding to the typical working condition portrait is shown in Table 2.

[0091] Table 2 Optimized data version

[0092]

[0093] The target working condition portrait and the current working condition portrait meet the matching conditions, and the initial data version is the optimized data version corresponding to the target working condition portrait.

[0094] Specifically, the matching condition is: the portrait similarity between the current working condition portrait and the target working condition portrait is greater than or equal to a first threshold.

[0095] In one case, the target working condition profile is a typical working condition profile in the data version library that is completely consistent with the current working condition profile;

[0096] In another case, the target operating condition portrait is not completely consistent with the current operating condition portrait, but the target operating condition portrait is a typical operating condition portrait in the data version library that is closest to the current operating condition portrait.

[0097] In one implementation, step 201 may be implemented in the following manner:

[0098] First, the portrait similarity between the current working condition portrait and each typical working condition portrait in the data version library is obtained respectively; then, the typical working condition portrait with the greatest portrait similarity is selected from the data version library as the target working condition portrait.

[0099] The similarity between the working condition portraits can be obtained by a similarity algorithm or a machine learning model that can obtain similarity. Alternatively, in this embodiment, the working condition portraits can be compared within the parameter range of each identical working condition parameter, and the portrait similarity can be obtained according to the difference between the parameter ranges.

[0100] In another implementation, step 202 may be implemented in the following manner:

[0101] First, the clustering model is used to obtain the current working condition type corresponding to the current working condition portrait.

[0102] Then, the current working condition type is compared with the working condition type of each typical working condition portrait in the data version library. If there is a typical working condition type consistent with the current working condition type in the data version library, then the target working condition portrait is determined by the typical working condition portrait consistent with the current working condition type in the data version library, and the optimized data version corresponding to the target working condition portrait is determined as the initial data version; if there is no typical working condition type consistent with the current working condition type in the data version, then the typical working condition portrait with the greatest portrait similarity with the current working condition portrait in the data version library is used as the target working condition portrait, and the optimized data version corresponding to the target working condition portrait is determined as the initial data version.

[0103] Among them, the clustering model can be trained through training samples, so that the clustering model can identify the working condition type corresponding to the input working condition portrait. Different working condition types have at least one working condition parameter that is different, specifically, at least one working condition parameter is in a different parameter range. The working condition types that the working condition clustering model has learned can be shown in Table 3:

[0104] Table 3 Working condition types

[0105]

[0106] Step 202: Input the current operating condition portrait and the initial data version into the parameter calibration model to obtain the target data version corresponding to the current operating condition portrait output by the parameter calibration model.

[0107] The parameter calibration model can be obtained by pre-training using training samples. The training samples of the data optimization model can be obtained by using typical working condition portraits in a typical working condition library.

[0108] Specifically, in this embodiment, each typical working condition image in the typical working condition library can be processed as follows: Figure 3 The corresponding training samples are obtained by the process shown in:

[0109] Step 301: Input the typical operating condition portrait and the historical data version corresponding to the typical operating condition portrait into the control simulation system corresponding to the engine controller.

[0110] The control simulation system can output control instructions according to the typical working condition portrait using the historical data version corresponding to the typical working condition portrait as the control data version. The control instructions are used to control the engine to perform corresponding processing, such as controlling the engine speed to increase or decrease.

[0111] Specifically, the control simulation system can also be called a virtual ECU (Electronic Control Unit) hardware system. The control simulation system can simulate the control function of the engine controller, that is, perform logical judgment according to the supporting parameters corresponding to the control strategy, specifically: execute the control strategy in the control data version according to the supporting parameters in the control data version.

[0112] Step 302: Adjust the control data version used in the control simulation system so that the control instructions output by the control simulation system make the performance indicators of the engine meet the optimization conditions.

[0113] Among them, the control instruction makes the performance index of the engine meet the optimization conditions, which can be understood as: after the engine executes the control instruction, the index parameter of the engine on at least one performance index changes, and the performance index after the change meets the optimization conditions, that is, the overall performance index of the engine is in a specific percentile interval, the component performance indicators of the engine are each in the corresponding optimization value range, and the detailed indicators of the engine are each in the corresponding optimization value range.

[0114] It should be noted that the overall performance indicators of the engine can be divided into multiple component performance indicators, such as economic indicators, power indicators, environmental indicators, reliability indicators, and durability indicators; each component performance indicator can be divided into multiple detail indicators.

[0115] Specifically, in this embodiment, according to the changes in the engine performance indicators caused by the control instructions output by the control simulation system, at least one supporting parameter corresponding to the control strategy in the control data version can be adjusted in sequence until the engine performance indicators meet the optimization conditions.

[0116] Based on this, the control instruction makes the control data version used by the control simulation system when the performance index of the engine meets the optimization condition be the output sample in the training sample; the typical working condition portrait and the historical data version corresponding to the typical working condition portrait are the input samples in the training sample. In this way, the input sample is input into the parameter calibration model, thereby obtaining the predicted data version output by the parameter calibration model, and then the parameter calibration model is optimized according to the loss function value between the predicted data version and the data version in the sample. After optimizing multiple training samples, the parameter calibration model can output the target data version corresponding to the target working condition portrait for the input data.

[0117] In one implementation, the parameter calibration model includes a control simulation system corresponding to a controller of the engine.

[0118] Based on this, step 103 can be implemented in the following ways: Figure 4 As shown in:

[0119] Step 401: According to the current working condition portrait, obtain the target working condition portrait and the initial data version in the data version library.

[0120] Among them, the data version library contains at least one typical working condition portrait, each typical working condition portrait corresponds to an optimized data version; the target working condition portrait and the current working condition portrait meet the matching conditions; the initial data version is the optimized data version corresponding to the target working condition portrait.

[0121] Step 402: Input the current working condition portrait and the initial data version into the control simulation system, and the control simulation system can output control instructions according to the current working condition portrait with the initial data version as the control data version.

[0122] Among them, the control instruction is used to control the engine to perform corresponding processing.

[0123] Step 403: Adjust the control data version used in the control simulation system so that the control instructions output by the control simulation system make the performance indicators of the engine meet the optimization conditions.

[0124] Specifically, in this embodiment, according to the changes in the engine performance indicators caused by the control instructions output by the control simulation system, at least one supporting parameter corresponding to the control strategy in the control data version can be adjusted in sequence until the engine performance indicators meet the optimization conditions.

[0125] Step 404: Determine the control data version used by the control simulation system when the control instruction makes the engine performance index meet the optimization condition as the target data version corresponding to the current operating condition profile.

[0126] It should be noted that Figure 4 In the process shown, multiple adjustments are required in step 403 to make the engine performance index meet the optimization conditions and obtain the target data version. Figure 2 In the process shown, in step 202, the parameter calibration model only needs to be run once to obtain the target data version that makes the performance index of the engine meet the optimization conditions, which can improve the efficiency of obtaining the optimized data version.

[0127] Based on the above implementation scheme, after step 103, the method in this embodiment may further include the following processing, such as Figure 5 As shown in:

[0128] Step 105: Generalize at least one operating condition parameter in the current operating condition profile to obtain a new typical operating condition profile.

[0129] The generalization processing in this embodiment refers to generalizing the current working condition portrait into a new working condition portrait that can represent multiple similar working condition states, that is, a typical working condition portrait.

[0130] Specifically, in this embodiment, at least one operating condition parameter in the current operating condition portrait can be adjusted to a parameter range including multiple parameter values ​​to obtain a new typical operating condition portrait.

[0131] Step 106: Add the new typical working condition portrait and its corresponding target data version to the data version library.

[0132] Therefore, each time the target data version is obtained, it is added to the data version library. In subsequent processing, when the operating parameters of the engine change again, after obtaining the new operating parameters and the corresponding new operating condition portrait, the target operating condition portrait that meets the matching conditions with the new operating condition portrait is first obtained in the data version library, and the corresponding target data version is obtained according to the implementation scheme in the previous article. After the new target data version is transmitted to the engine controller, the controller executes the control strategy according to the supporting parameters in the new target data version, so that the performance indicators of the engine under the new operating parameters can also meet the optimization conditions, so that the engine performance is optimized.

[0133] In another implementation, the optimized data version in the data version library can also be preset by the user. For example, the support data manually calibrated by the engineer before the engine leaves the factory is added to the data version library as the optimized data version.

[0134] In one implementation, before step 104, the method in this embodiment may further include the following processing: Figure 6 As shown in:

[0135] Step 107: Determine whether the target data version is consistent with the current data version used by the engine controller. If the target data version is inconsistent with the current data version, execute step 104. At this time, the engine controller uses the support parameters in the received target data version to execute the control strategy. If the target data version is consistent with the current data version, end the current process, that is, do not execute step 104. At this time, the engine controller continues to use the support parameters in the original data version to execute the control strategy, so that the engine performance indicators can continue to meet the optimization conditions while reducing the amount of data transmission.

[0136] The above introduces an engine performance calibration processing method based on operating condition portrait provided in an embodiment of the present application. The following will introduce a device for executing the above-mentioned engine performance calibration processing method based on operating condition portrait.

[0137] See also Figure 7 , is a schematic diagram of the structure of an engine performance calibration processing device based on working condition profiling provided in an embodiment of the present application. The device can be deployed in an electronic device capable of data processing, and the electronic device can be a local device connected to the engine, or a cloud device connected to the engine. The technical solution in this embodiment is mainly used to optimize the performance indicators of the engine.

[0138] Specifically, the device in this embodiment may include the following units:

[0139] The parameter obtaining unit 701 is used to obtain the operating parameters of the engine, wherein the operating parameters at least include a plurality of operating condition parameters;

[0140] A portrait obtaining unit 702, configured to obtain a current operating condition portrait of the engine according to the operating parameters;

[0141] The data processing unit 703 is used to process the current working condition portrait and the initial data version using a parameter calibration model to obtain a target data version corresponding to the current working condition portrait; the target data version includes: support parameters corresponding to the control strategy of the engine;

[0142] The parameter calibration model is obtained by training using training samples, and the training samples are obtained using a typical working condition library, and the typical working condition library contains multiple typical working condition portraits;

[0143] The data transmission unit 704 is used to send the target data version to the controller of the engine, and the controller executes the control strategy according to the supporting parameters in the target data version so that the performance index of the engine meets the optimization condition.

[0144] By means of the above technical scheme, in an engine performance calibration processing device based on a working condition portrait provided by an embodiment of the present application, after obtaining the operating parameters of the engine including multiple working condition parameters, the current working condition portrait of the engine is first obtained according to the operating parameters, and then the parameter calibration model trained by the typical working condition portrait is used to process the current working condition portrait and the initial data version to obtain the target data version corresponding to the current working condition portrait, and the target data version includes: the support parameters corresponding to the control strategy of the engine, so that after the target data version is sent to the controller of the engine, the controller executes the corresponding control strategy according to the support parameters in the target data version, thereby making the performance index of the engine meet the optimization conditions. It can be seen that in this application, no matter what working condition state the current working condition portrait of the engine is, the parameter calibration model can be used to optimize the support parameters corresponding to the control strategy, so that when the controller of the engine executes the control strategy according to the optimized support parameters, the performance index of the engine can meet the optimization conditions, thereby achieving the purpose of calibrating the performance index of the engine to the optimal state.

[0145] The present application also provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0146] The memory is used to store computer programs;

[0147] The processor is used to execute the computer program so that the electronic device can implement an engine performance calibration processing method based on operating condition profiling according to the above-mentioned first aspect or any implementation method of the first aspect.

[0148] refer to Figure 8 As shown, it shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiment of the present application. The electronic device in the embodiment of the present application may include but is not limited to fixed terminals such as mobile phones, laptops, PDAs (personal digital assistants), PADs (tablet computers), desktop computers, etc. Figure 8 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0149] like Figure 8 As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage device 808 to a random access memory (RAM) 803. When the electronic device is powered on, various programs and data required for the operation of the electronic device are also stored in the RAM 803. The processing device 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0150] Typically, the following devices may be connected to the I / O interface 805: input devices 806 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 807 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 808 including, for example, a memory card, a hard disk, etc.; and communication devices 809. The communication device 809 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 8 An electronic device having various devices is shown, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0151] Also provided in an embodiment of the present application is a computer program product including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements any one of the engine performance calibration processing methods based on operating condition profiling provided in an embodiment of the present application.

[0152] A computer storage medium is also provided in an embodiment of the present application. The storage medium carries one or more computer programs. When one or more computer programs are executed by an electronic device, the electronic device can implement any engine performance calibration processing method based on operating condition profiling provided in an embodiment of the present application.

[0153] The following takes a diesel engine as an example to explain the technical solution of the present application in detail:

[0154] At present, the input operating conditions for the performance parameter calibration of each system department of the engine are mainly based on the accumulation of R&D experience (existing map). Test calibration is carried out on the test bench to meet the index requirements of the development task number design. However, the laboratory operating conditions can never replace the actual operating conditions of the user's vehicle, which makes the early failure rate of new products very high. However, in the actual repair and maintenance process, it is found that most of the faults are false alarms.

[0155] In the production and calibration of engines, existing road spectrum data (i.e. operating parameters) are generally used to determine the current operating conditions of the engine and adapt the corresponding control strategy. However, due to the limited road spectrum data and the resource limitations of the ECU controller, it is impossible to finely calibrate and switch strategies for all road conditions and control situations. Therefore, the current application strategy is a limited control strategy based on limited data, and various performance indicators of the engine cannot be brought into optimal condition.

[0156] With the development of connected technology, the frequency and number of engine configuration parameters can be collected and configured, which has become feasible. However, how to extract typical operating conditions based on massive data and store them in the product life cycle operating condition library, so that the engine system components can adaptively optimize and make judgment strategies under dynamic operating conditions, is a technical problem that R&D engineers urgently need to overcome.

[0157] It can be seen that the current engine performance calibration has the following defects:

[0158] (1) The road database collected by R&D engineers is limited in types and cannot represent the actual operating conditions of user vehicles;

[0159] (2) The laboratory bench conducts engine performance index calibration tests based on existing road spectra, which consumes a lot of fuel and has an incomplete range of road spectra;

[0160] (3) The research and development of road spectrum extraction technology has not yet been closed-loop with the product performance indicator calibration work, and adaptive learning verification has not been achieved.

[0161] In view of this, in order to overcome the above-mentioned defects, the present application provides a vehicle self-learning optimization method and application method based on an automatic virtual calibration model (i.e., the parameter calibration model mentioned above), a clustering model for vehicle portrait extraction, and a controller generalization database based on portrait classification.

[0162] like Fig. 9 As shown in the figure, it is the overall execution logic flow chart of the engine performance index calibration method based on working condition adaptive learning in this application. Among them, the engine performance index calibration method based on working condition adaptive learning can be divided into two stages: the first stage is the offline manual optimization stage, Fig. 9 As shown in the left part of the figure, the engine performance parameters are calibrated by the manual test bench to form the controller data version V1 and update the controller data accordingly; the second stage is the cloud self-learning optimization stage, such as Fig. 9 The right part shows:

[0163] First, in the cloud, the predefined engine operating condition library is imported into the typical engine operating condition library (i.e., the typical operating condition library mentioned above), and the engine operating conditions manually updated and extracted in the offline manual optimization stage on the left are also imported into the typical engine operating condition library. Based on this, the initial data version is optimized using the brake calibration model (library) for the typical operating condition portrait in the typical operating condition library to obtain the controller data (i.e., the target data version) based on the local (engine) optimization, and then the controller data based on the local optimization is transmitted to the engine controller via OTA to update the controller data; and as the engine runs out of the factory, the engine operating condition parameters can be detected by sensors to realize data collection, and the data features can be extracted in the cloud to construct the vehicle engine operating condition portrait (i.e., the current operating condition portrait), and then, the clustering model in the cloud is used for automatic clustering.

[0164] If the operating condition type obtained by clustering is an existing classification, that is, in the portrait-based data version library, there is a target operating condition portrait whose operating condition type matches the current operating condition portrait, the controller data version (that is, the corresponding optimized data version) in the clustering result is extracted as the initial data version, and then the target operating condition portrait and the initial data version are processed using the automatic virtual calibration model to obtain the target data version (that is, the controller data based on the local optimization); and, the typical engine operating condition library is updated. For example, if the current operating condition portrait does not exist in the typical engine operating condition library, the current operating condition portrait is added to the typical engine operating condition library.

[0165] If the operating condition type obtained by clustering is a new classification, then in addition to manually extracting the new operating condition portrait of the vehicle and then using it to train the clustering model so that the clustering model is added with the new portrait classification, a new operating condition portrait is generated as the data of the typical engine operating condition library and updated to the typical engine operating condition library. In addition, after extracting the new vehicle portrait, the data version of the similar clustering result can be extracted from the portrait-based data version library, and then the target operating condition portrait and the initial data version are processed using the automatic virtual calibration model to obtain the target data version.

[0166] Furthermore, after obtaining the target data version (i.e., controller data based on local optimization), the new portrait data can be added to the data version library after data generalization processing.

[0167] The detailed scheme is described as follows:

[0168] 1. Offline manual optimization part:

[0169] This part is used when the engine or vehicle of this model is developed for the first time or after a major update. The execution process is as follows:

[0170] 1. Extract operating condition information from the typical engine operating condition library;

[0171] 2. Modify the original initial controller data through manual bench calibration;

[0172] 3. Cycle adjustment to obtain the best performance parameter, and use this parameter as the target value for subsequent calibration;

[0173] 4. Based on the above adjustments, generate a controller data version applicable to this type of information;

[0174] 5. Add the above empty machine data version and predefined working condition information to the cloud-based portrait-based data version library as the initial data version;

[0175] 6. Generate the final production version data based on the above data, and flash the data on multiple controllers during the production process;

[0176] 7. After the vehicle or engine is delivered to the user, the operating conditions, road spectrum information and various performance parameters of various engines are continuously collected;

[0177] 8. Manually analyze the data to extract the working conditions, and manually update them to the typical engine working condition library in the cloud; 2. Cloud self-learning optimization model part:

[0178] After the engine or vehicle is delivered to the user, the continuous optimization work is carried out, combined with the Internet of Vehicles function, to continuously optimize the vehicle controller data. The execution process is as follows:

[0179] 1. Data extraction and vehicle profiling are performed through the data collected by the vehicle end to generate working condition characteristic data based on this specific vehicle;

[0180] 2. Use the clustering algorithm model to classify the current features;

[0181] If the classification is normal:

[0182] 2.1. Use the operating condition information of this portrait to update the data in the telecom engine operating condition library to make it more complete, and simultaneously extract controller data suitable for the portrait from the portrait-based data version library based on the clustering results;

[0183] If the classification is abnormal:

[0184] 2.2. Push data to the manual portrait extraction function; through manual participation, the clustering model can be retrained to achieve normal classification; at the same time, manual participation can also redefine this portrait as a new vehicle portrait, and then add a new classification identifier to the data version library based on the portrait, and simultaneously extract a controller data closest to this portrait from the data version library;

[0185] 3. The extracted controller data is sent to the automatic virtual calibration model library, and virtual calibration is performed in conjunction with the data in the electronic control engine operating condition library (the virtual calibration process can be performed simultaneously using the offline calibration method, providing more accurate calibration results for the data determination of the new portrait), and finally the optimal controller data suitable for the machine is obtained, and then updated to the engine controller through OTA;

[0186] 4. If the new data updated here is a newly added portrait data, the data will be generalized and added to the portrait-based data version library for subsequent use;

[0187] The profiling function is to extract data features from data, and the clustering function is to classify data through the previous feature values. Both functions are implemented through machine learning classification models, which are implemented through a large amount of historical data annotation learning. For data that cannot be accurately classified or critical point classification data, manual classification is used to achieve it. When a sufficient amount of new classification data is accumulated, the model is retrained.

[0188] The training samples of the automatic virtual calibration model can be obtained by Fig.10 The process shown obtains:

[0189] First of all, the ECU control logic library is a Simulink-based control logic model of all control logic in the ECU during the electronic control development process. It is a functional module that realizes the entire control logic of the engine.

[0190] The control model is a large logic-corrected model obtained through a large number of engine calibrations, based on the need to adjust the model parameters in various situations, and is generated through data training.

[0191] The working condition scanning input function is a working condition operation logic library generated based on a large amount of calibration experience. This logic library is obtained by generalizing all the data of the classification of the above working condition portraits. The various types of data here are large amounts of automatically generated multidimensional data arrays.

[0192] The virtual ECU hardware system is responsible for performing virtual execution (i.e., simulation) according to the extracted ECU control logic (i.e., data version), and performing individual and multi-dimensional scans according to each parameter of each of the above-mentioned data arrays, and constantly monitoring the output parameters or intermediate parameters to ensure that they meet the predefined standards (i.e., the optimization conditions mentioned above).

[0193] The process stops when the data meets expectations (i.e. the engine performance indicators meet the optimization conditions) or converges to a certain degree.

[0194] For parameters with divergent results, manual confirmation is required later to obtain the final result data, that is, the output samples in the training samples.

[0195] It should also be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines.

[0196] Through the description of the above implementation mode, the technicians in the field can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course, it can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. In general, all functions completed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present application, software program implementation is a better implementation mode in more cases. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, a U disk, a mobile hard disk, a ROM, a RAM, a disk or an optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, a training device, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0197] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0198] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a training device, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, training device, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device, a data center, etc. that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

Claims

1. An engine performance calibration processing method based on working condition portrait, characterized in that: The method comprises: Obtaining operating parameters of the engine, wherein the operating parameters include at least a plurality of operating condition parameters; According to the operating parameters, a current operating condition profile of the engine is obtained; The current operating condition portrait and the initial data version are processed by using a parameter calibration model to obtain a target data version corresponding to the current operating condition portrait; the target data version includes: support parameters corresponding to the control strategy of the engine; The parameter calibration model is obtained by training using training samples, and the training samples are obtained using a typical working condition library, and the typical working condition library contains multiple typical working condition portraits; Sending the target data version to the controller of the engine, and having the controller execute the control strategy according to the supporting parameters in the target data version, so that the performance index of the engine meets the optimization condition; The initial data version is an optimized data version corresponding to the target working condition portrait; the target working condition portrait and the current working condition portrait meet a matching condition; the target working condition portrait and the initial data version are obtained from a data version library based on the current working condition portrait, and the data version library contains at least one typical working condition portrait, and each typical working condition portrait corresponds to an optimized data version; Wherein, according to the current working condition portrait, obtaining the target working condition portrait and the initial data version in the data version library includes: Using a clustering model, obtaining a current operating condition type corresponding to the current operating condition portrait; Compare the current working condition type with the working condition type of each typical working condition portrait in the data version library; If there is a typical operating condition type in the data version library that is consistent with the current operating condition type, the typical operating condition portrait in the data version library that is consistent with the current operating condition type is determined as the target operating condition portrait and the optimized data version corresponding to the target operating condition portrait is determined as the initial data version; If there is no typical operating condition type in the data version library that is consistent with the current operating condition type, the typical operating condition portrait with the greatest portrait similarity with the current operating condition portrait in the data version library is used as the target operating condition portrait and the optimized data version corresponding to the target operating condition portrait is determined as the initial data version.

2. The method according to claim 1, characterized in that: The parameter calibration model is a machine learning model; The current working condition portrait and the initial data version are processed by using a parameter calibration model to obtain a target data version corresponding to the current working condition portrait, including: The current operating condition portrait and the initial data version are input into the parameter calibration model to obtain a target data version corresponding to the current operating condition portrait output by the parameter calibration model.

3. The method according to claim 1, characterized in that The parameter calibration model includes a control simulation system corresponding to the controller of the engine; The current working condition portrait and the initial data version are processed by using a parameter calibration model to obtain a target data version corresponding to the current working condition portrait, including: Inputting the current working condition portrait and the initial data version into the control simulation system, the control simulation system can output a control instruction according to the current working condition portrait with the initial data version as the control data version, the control instruction being used to control the engine to perform corresponding processing; Adjusting the control data version used in the control simulation system so that the control instruction output by the control simulation system enables the performance index of the engine to meet the optimization condition; The control data version used by the control simulation system when the control instruction makes the performance index of the engine meet the optimization condition is determined as the target data version corresponding to the current operating condition portrait.

4. The method according to claim 2 or 3, characterized in that: The matching condition is: the portrait similarity between the current working condition portrait and the target working condition portrait is greater than or equal to a first threshold.

5. The method according to claim 2, characterized in that: The training samples are obtained in the following way: The following processing is performed on each typical working condition image in the typical working condition library: The typical operating condition portrait and the historical data version corresponding to the typical operating condition portrait are input into the control simulation system corresponding to the controller of the engine, and the control simulation system can output a control instruction according to the typical operating condition portrait using the historical data version corresponding to the typical operating condition portrait as the control data version; the control instruction is used to control the engine to perform corresponding processing; Adjusting the control data version used in the control simulation system so that the control instruction output by the control simulation system enables the performance index of the engine to meet the optimization condition; Among them, when the control instruction makes the performance index of the engine meet the optimization conditions, the control data version used by the control simulation system is the output sample in the training sample; the typical operating condition portrait and the historical data version corresponding to the typical operating condition portrait are the input samples in the training sample.

6. The method according to claim 1, 2 or 3, characterized in that: Obtain engine operating parameters, including: A plurality of sensors deployed on the engine are used to collect a plurality of operating parameters of the engine to obtain operating parameters of the engine.

7. The method according to claim 2 or 3, characterized in that: After obtaining the target data version corresponding to the current working condition portrait, the method further includes: Generalizing at least one operating condition parameter in the current operating condition profile to obtain a new typical operating condition profile; The new typical working condition portrait and its corresponding target data version are added to the data version library.

8. An engine performance calibration processing device based on working condition portrait, characterized in that: The device comprises: A parameter obtaining unit, used to obtain operating parameters of the engine, wherein the operating parameters at least include a plurality of operating condition parameters; A portrait obtaining unit, used for obtaining a current operating condition portrait of the engine according to the operating parameters; A data processing unit is used to process the current working condition portrait and the initial data version using a parameter calibration model to obtain a target data version corresponding to the current working condition portrait; the target data version includes: support parameters corresponding to the control strategy of the engine; The parameter calibration model is obtained by training using training samples, and the training samples are obtained using a typical working condition library, and the typical working condition library contains multiple typical working condition portraits; A data transmission unit, used for sending the target data version to the controller of the engine, and the controller executes the control strategy according to the supporting parameters in the target data version, so that the performance index of the engine meets the optimization condition; The initial data version is an optimized data version corresponding to the target working condition portrait; the target working condition portrait and the current working condition portrait meet a matching condition; the target working condition portrait and the initial data version are obtained from a data version library based on the current working condition portrait, and the data version library contains at least one typical working condition portrait, and each typical working condition portrait corresponds to an optimized data version; Wherein, according to the current working condition portrait, obtaining the target working condition portrait and the initial data version in the data version library includes: Using a clustering model, obtaining a current operating condition type corresponding to the current operating condition portrait; Compare the current working condition type with the working condition type of each typical working condition portrait in the data version library; If there is a typical operating condition type in the data version library that is consistent with the current operating condition type, the typical operating condition portrait in the data version library that is consistent with the current operating condition type is determined as the target operating condition portrait and the optimized data version corresponding to the target operating condition portrait is determined as the initial data version; If there is no typical operating condition type in the data version library that is consistent with the current operating condition type, the typical operating condition portrait with the greatest portrait similarity with the current operating condition portrait in the data version library is used as the target operating condition portrait and the optimized data version corresponding to the target operating condition portrait is determined as the initial data version.

9. An electronic device comprising at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program so that the electronic device can implement an engine performance calibration processing method based on operating condition profiling as described in any one of claims 1 to 7.

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