Oil pressure control method and apparatus, electronic device, and storage medium

Abstract

The present application discloses an oil pressure control method and apparatus, an electronic device, and a storage medium. The oil pressure control method comprises: acquiring operating data of an engine under different operating conditions during actual driving to serve as sample data, and training a long short-term memory model to obtain an initial correction model; integrating the initial correction model into an oil pump control system of a target vehicle, acquiring target operating data of the engine during the actual driving by a target user, and optimizing the initial correction model to obtain a target correction model; and determining an oil pressure correction value for the target vehicle under the current operating condition on the basis of the target correction model, and correcting the oil pressure of the target vehicle on the basis of the oil pressure correction value.

Description

Engine oil pressure control method and device, electronic device and storage medium

[0001] The present application claims priority to the Chinese patent application No. 202410861777.0, filed on June 28, 2024, with the Chinese Patent Office, the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the technical field of vehicles, for example to an engine oil pressure control method and device, an electronic device and a storage medium. BACKGROUND

[0003] With the improvement of social residents' income, the private car ownership rate is also increasing, which leads to the diversification of vehicle operating conditions. With the difference in the personality, occupation and living habits of drivers, their driving habits are also different, such as violent driving, cold start, long time driving, etc. The above conditions may cause fluctuations in the engine oil pressure, and in turn cause different degrees of wear to the engine of the vehicle, and in severe cases, damage the engine components and cause vehicle failure. SUMMARY

[0004] The present application provides an engine oil pressure control method and device, an electronic device and a storage medium, which can correct the engine oil pressure under the operation of the vehicle that is prone to cause fluctuations in the engine oil pressure in the driving habits of the target user, to avoid fluctuations in the engine oil pressure causing wear to the engine.

[0005] The present application provides an engine oil pressure control method, which comprises: obtaining the operating data of an engine under different conditions in an actual driving process as sample data, training a long short-term memory model to obtain an initial correction model; integrating the initial correction model in an engine oil pump control system of a target vehicle, obtaining target operating data of the engine in an actual driving process of a target user, optimizing the initial correction model to obtain a target correction model; determining an engine oil pressure correction value of the target vehicle under a current condition according to the target correction model, and correcting the engine oil pressure of the target vehicle according to the engine oil pressure correction value.

[0006] The application provides a machine oil pressure control device, which comprises an initial model generation module, a target model generation module and a machine oil pressure correction module.

[0007] The application provides an electronic device, which comprises at least one processor and a memory connected with the at least one processor; wherein the memory stores a computer program which can be executed by the at least one processor; the computer program is executed by the at least one processor, so that the at least one processor can execute the machine oil pressure control method.

[0008] The application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used to make the processor execute the machine oil pressure control method. BRIEF DESCRIPTION OF DRAWINGS

[0009] Fig. 1 is a flow chart of a machine oil pressure control method according to an embodiment of the application;

[0010] Fig. 2 is a flow chart of a machine oil pressure control method according to another embodiment of the application;

[0011] Fig. 3 is a structural schematic diagram of a machine oil pressure control device according to an embodiment of the application;

[0012] Fig. 4 is a structural schematic diagram of an electronic device for implementing the machine oil pressure control method according to an embodiment of the application. DETAILED DESCRIPTION

[0013] The terms "first", "second", and the like, as used herein, are used as identifiers to distinguish between similar objects, and are not necessarily used to describe a particular sequential or chronological order. It will be understood that the use of such terms is appropriate in context where there can be interchanging use of these terms with each other, where appropriate, to refer to an embodiment of the application described herein in which an order is described other than the order described herein, without departing from the scope of the application. Furthermore, the terms "comprise" and "include", and variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, product, or apparatus that comprises a list of steps or units are not necessarily limited to those steps or units which are expressly listed, but can include other steps or units not expressly listed or inherent to such process, method, system, product, or apparatus.

[0014] Embodiment One

[0015] FIG. 1 is a flowchart of a method for controlling engine oil pressure according to an embodiment of the present application. The embodiment can be applied to the case of controlling engine oil pressure of a vehicle, typically, the case of controlling engine oil pressure of a vehicle under different target user driving habits. The method for controlling engine oil pressure can be executed by an engine oil pressure control device, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device. As shown in FIG. 1, the method comprises:

[0016] S110, obtaining running data of the engine under different working conditions in an actual driving process as sample data, training a long short-term memory model to obtain an initial correction model.

[0017] The long short-term memory model (LSTM) is a special recurrent neural network architecture, which includes a cell state, a forget gate, an input gate, and an output gate. The cell state is a band of information transmission in the LSTM, information can be stored in the cell state, the forget gate decides what information to discard from the cell state, the input gate decides what new information will be stored in the cell state, and the output gate decides what value to output based on the cell state. Due to the control of the forget gate, the cell state can save information a long time ago, and due to the control of the input gate, it can avoid storing irrelevant information into the cell state.

[0018] The running data of the engine under different working conditions in the actual driving process includes key parameters such as engine speed, load, oil temperature, oil pressure and the like, which can be obtained through the electronic control unit (ECU) of the vehicle. In the embodiment of the present application, after obtaining the running data of the engine under different working conditions in the actual driving process, the running data can be processed, such as data cleaning, data standardization or normalization, etc., so as to be used as sample data for training the LSMT model subsequently.

[0019] In the embodiment of the present application, the initial correction model is used to determine the correction value of the oil pressure of the engine according to the running data of the engine, so that the oil pressure in the running data can be used as a target variable, and other parameters can be used as input features to construct a sample data set. Then, the LSTM model is trained using the sample data set to generate the initial correction model. After the LSTM model is trained to obtain the initial correction model, the performance of the initial correction model can be evaluated by using a pre-constructed test data set to ensure the reliability of the results output by the model.

[0020] S120, integrating the initial correction model into the oil pump control system of the target vehicle, obtaining target running data of the engine in the actual driving process of the target user, optimizing the initial correction model to obtain a target correction model.

[0021] The oil pump control system is used to control the valve opening degree and oil pressure of the oil pump according to the engine running data obtained from the vehicle ECU. The target running data refers to the engine running data generated by the target user when driving the target vehicle, which is used to reflect the driving habits of the target user in the actual driving process.

[0022] In the embodiment of the present application, the initial correction model is integrated into the oil pump control system of the target vehicle, and the target running data of the engine in the actual driving process of the target user can be obtained from the vehicle ECU through the oil pump control system. In an online learning manner, the initial correction model is optimized in real time through the target running data to obtain a target correction model that adapts to the driving habits of the target user.

[0023] The initial correction model is integrated into an oil pump control system of a target vehicle, target operation data of the engine in an actual driving process of a target user is acquired, the initial correction model is optimized to obtain a target correction model, including: acquiring target operation data of the engine in an actual driving process under driving habits of the target user, determining abnormal operation data of the engine under oil pressure fluctuation in the target operation data; adjusting memory weights of a plurality of parameters in the initial correction model according to frequencies of mutations of the plurality of parameters in the abnormal operation data to obtain the target correction model.

[0024] The oil pressure fluctuation refers to unstable changes of oil pressure generated in the engine during operation, at this time, the oil pressure value displayed by the oil pressure gauge fluctuates up and down within a certain range, and the oil pressure fluctuation will cause different degrees of wear of the engine of the vehicle, and will seriously damage the engine parts and cause vehicle failure. The abnormal operation data refers to operation data of the engine when the oil pressure fluctuates, and the driving habits of the target user in the driving process will cause mutations of some parameters of the engine, and these mutations may cause the oil pressure to fluctuate, and the causes of the oil pressure fluctuation can be found by analyzing the abnormal operation data.

[0025] In the embodiments of the present application, the target operation data of the engine in an actual driving process under driving habits of the target user can be acquired first, and then the abnormal operation data of the engine under oil pressure fluctuation can be determined from the target operation data, and the frequencies of mutations of a plurality of parameters in the abnormal operation data are counted. The more the frequency of mutations of a parameter, the greater the possibility of causing the oil pressure to fluctuate. Then, the memory weights of a plurality of parameters in the initial correction model can be adjusted according to the frequencies of mutations of the plurality of parameters, and the memory weight of a parameter is high when the number of mutations of the parameter is large. By updating the memory weights of the parameters, the initial correction model is continuously corrected, and a target correction model special for the target user is gradually formed to correct the operation of the target user that is most likely to cause the oil pressure to fluctuate.

[0026] Exemplarily, when the target user has the habit of violent driving, the engine speed often fluctuates, and the LSTM model increases the memory weight of the engine speed, so that the current speed is fitted with the speed at the last time, the speed is controlled to ensure a stable rising trend instead of steep rising and steep falling, so that the oil pressure also rises stably according to the current speed, and the oil pressure fluctuation is avoided. In addition to this, the oil temperature, the throttle opening degree, the vehicle speed, the speed change rate and other parameters are also within the adjustment range.

[0027] S130, determine an oil pressure correction value of the target vehicle in the current working condition according to the target correction model, and correct the oil pressure of the target vehicle according to the oil pressure correction value.

[0028] In the embodiment of the application, after the initial correction model is optimized to obtain the target correction model, the oil pressure correction value of the target vehicle in the current working condition can be determined according to the target correction model, and the oil pressure of the target vehicle is corrected according to the oil pressure correction value, thereby avoiding the loss of the engine caused by the fluctuation of the oil pressure.

[0029] According to the target correction model, the oil pressure correction value of the target vehicle in the current working condition is determined, and the oil pressure of the target vehicle is corrected according to the oil pressure correction value, including: obtaining the current running data of the engine of the target vehicle in the current working condition through the oil pump control system, inputting the current running data into the target correction model; obtaining the oil pressure correction value output by the target correction model, and correcting the oil pressure of the target vehicle according to the oil pressure correction value through the oil pump control system.

[0030] In the embodiment of the application, the current running data of the engine of the target vehicle in the current working condition can be obtained from the vehicle ECU through the oil pump control system, and the current running data is input into the target correction model. The target correction model outputs the oil pressure correction value suitable for the current working condition according to the input current running data, so that the oil pump control system corrects the oil pressure of the target vehicle according to the oil pressure correction value, thereby avoiding the loss of the engine caused by the fluctuation of the oil pressure.

[0031] The technical scheme of the embodiment of the application obtains the running data of the engine in different working conditions in the actual driving process as sample data, trains the long short-term memory model to obtain the initial correction model, integrates the initial correction model in the oil pump control system of the target vehicle, obtains the target running data of the engine in the actual driving process of the target user, optimizes the initial correction model to obtain the target correction model, determines the oil pressure correction value of the target vehicle in the current working condition according to the target correction model, and corrects the oil pressure of the target vehicle according to the oil pressure correction value. The technical scheme of the embodiment of the application integrates the initial correction model in the oil pump control system, optimizes the initial correction model by using the target running data reflecting the driving habits of the target user, can correct the oil pressure of the vehicle under the operation that is easy to cause the fluctuation of the oil pressure in the driving habits of the target user, and avoids the loss of the engine caused by the fluctuation of the oil pressure.

[0032] Embodiment two

[0033] FIG. 2 is a flowchart of an engine oil pressure control method according to an embodiment of the present application. The embodiment of the present application is based on the above-described embodiments, and the solutions not described in detail in the embodiment of the present application are described in the above-described embodiments. As shown in FIG. 2, the method comprises the following steps.

[0034] In S210, running data of the engine under normal oil pressure in actual driving is obtained as first sample data, and running data of the engine under fluctuating oil pressure in actual driving is obtained as second sample data.

[0035] In the embodiment of the present application, after obtaining the running data of the engine under different working conditions in actual driving, the running data can be divided into running data under normal oil pressure and running data under fluctuating oil pressure according to whether the oil pressure fluctuates in the running period corresponding to the running data, and the running data under normal oil pressure is taken as the first sample data, and the running data under fluctuating oil pressure is taken as the second sample data. In order to ensure the accuracy of the trained model, the number of samples in the first sample data and the second sample data should not be less than a preset value, such as 100. Considering that in most cases the oil pressure of the engine in actual driving is in a normal state, the running data under fluctuating oil pressure is less, which leads to insufficient number of samples in the second sample data. Therefore, after obtaining the running data of the engine under fluctuating oil pressure in actual driving, the running data under fluctuating oil pressure can be subjected to data enhancement processing, so that the number of samples in the second sample data meets the accuracy requirement of model training.

[0036] In S220, a first training set and a first test set are determined according to the first sample data, a first correction model is obtained by training the LSTM model according to the first training set, and the first correction model is tested according to the first test set.

[0037] In the embodiment of the present application, samples can be randomly selected from the first sample data to integrate the first training set and the first test set. There can be multiple first training sets, such as three first training sets and one first test set determined according to the first sample data. Then, the LSTM model is trained according to the first training set to generate a first correction model, and the first correction model is tested according to the first test set to determine whether the generated first correction model meets the expectation. Since the first sample data is composed of running data of the engine under normal oil pressure in actual driving, when the generated first correction model meets the expectation, the output of the first correction model should meet the oil pressure threshold interval under normal conditions.

[0038] In the embodiment of the present application, after the first correction model is tested according to the first test set, it is also necessary to judge the test result, that is, to judge whether the result output by the first correction model conforms to the engine oil pressure threshold interval in the normal situation. If the result output by the first correction model does not conform to the engine oil pressure threshold interval in the normal situation, step S230 is performed, that is, the running data of the engine in different working conditions in the actual driving process is reacquired as sample data, and the long short-term memory model is trained. If the result output by the first correction model conforms to the engine oil pressure threshold interval in the normal situation, steps S240-S250 are performed, that is, the first correction model is trained according to the second sample data.

[0039] S230, reacquire the running data of the engine in different working conditions in the actual driving process as sample data, and train the long short-term memory model.

[0040] In the embodiment of the present application, when the result output by the first correction model does not conform to the engine oil pressure threshold interval in the normal situation, it indicates that the first correction model obtained in step S220 does not conform to the expectation, at this time, it is necessary to reacquire the running data of the engine in different working conditions in the actual driving process as sample data, train the long short-term memory model, and return to perform step S210.

[0041] S240, determine a second training set and a second test set according to the second sample data, label the sample data in the second training set, and add the correction value corresponding to the abnormal engine oil pressure in the sample data in the second training set.

[0042] In the embodiment of the present application, when the result output by the first correction model conforms to the engine oil pressure threshold interval in the normal situation, it indicates that the first correction model obtained in step S220 conforms to the expectation, at this time, the first correction model can be trained according to the second sample data. The samples can be randomly selected in the second sample data, and the second training set and the second test set are integrated, wherein the second training set can be multiple, for example, three second training sets and one second test set are determined according to the second sample data. After that, different from the first training set, after the second training set is determined, it is necessary to label the sample data in the second training set, add the related description that the data with the label is abnormal in the code, and at the same time, it is also necessary to correct the abnormal engine oil pressure, that is, add the correction value corresponding to the abnormal engine oil pressure in the sample data in the second training set.

[0043] S250, train the first correction model through the second training set to obtain a second correction model, and test the second correction model according to the second test set.

[0044] In the embodiment of the present application, the first correction model is trained according to the second training set to generate a second correction model, and the second correction model is tested according to the second test set to determine whether the generated second correction model meets the expectation. Since the second sample data includes the running data under the fluctuation of the engine oil pressure and the correction value corresponding to the abnormal engine oil pressure, when the generated second correction model meets the expectation, the second correction model can identify the running data of the engine under the fluctuation of the engine oil pressure and correct the abnormal engine oil pressure.

[0045] In the embodiment of the present application, after the second correction model is tested according to the second test set, the test result needs to be determined, that is, whether the second correction model can identify the running data of the engine under the fluctuation of the engine oil pressure and correct the abnormal engine oil pressure. If the second correction model cannot identify the running data of the engine under the fluctuation of the engine oil pressure or make an error correction to the abnormal engine oil pressure, step S260 is performed to reacquire the running data of the engine under different working conditions in the actual driving process as sample data and train the long short-term memory model. If the second correction model can identify the running data of the engine under the fluctuation of the engine oil pressure and correct the abnormal engine oil pressure, step S270 is performed to take the second correction model as an initial correction model.

[0046] S260, reacquire the running data of the engine under different working conditions in the actual driving process as sample data and train the long short-term memory model.

[0047] In the embodiment of the present application, when the second correction model cannot identify the running data of the engine under the fluctuation of the engine oil pressure or make an error correction to the abnormal engine oil pressure, it indicates that the second correction model obtained in step S250 does not meet the expectation. At this time, the running data of the engine under different working conditions in the actual driving process needs to be reacquired as sample data to train the long short-term memory model, that is, to return to step S210.

[0048] S270, take the second correction model as an initial correction model.

[0049] In the embodiment of the present application, when the second correction model can identify the running data of the engine under the fluctuation of the engine oil pressure and correct the abnormal engine oil pressure, it indicates that the second correction model obtained in step S250 meets the expectation. At this time, the second correction model is taken as an initial correction model.

[0050] S280, integrate the initial correction model into the engine oil pump control system of the target vehicle, acquire target running data of the engine in the actual driving process of the target user, optimize the initial correction model to obtain a target correction model.

[0051] S290, determine an oil pressure correction value of the target vehicle in the current working condition according to the target correction model, and correct the oil pressure of the target vehicle according to the oil pressure correction value.

[0052] The technical scheme of the embodiment of the application obtains the running data of the engine under the normal oil pressure in the actual driving process as the first sample data, and obtains the running data of the engine under the fluctuation of the oil pressure in the actual driving process as the second sample data; the first training set and the first test set are determined according to the first sample data, the long short-term memory model is trained according to the first training set to obtain the first correction model, and the first correction model is tested according to the first test set; if the result output by the first correction model does not conform to the oil pressure threshold interval under the normal condition, the running data of the engine under different working conditions in the actual driving process is reacquired as sample data, and the long short-term memory model is trained; if the result output by the first correction model conforms to the oil pressure threshold interval under the normal condition, the second training set and the second test set are determined according to the second sample data, the sample data in the second training set is labeled, and the correction value corresponding to the abnormal oil pressure is added to the sample data in the second training set; the first correction model is trained through the second training set to obtain the second correction model, and the second correction model is tested according to the second test set; if the second correction model cannot identify the running data of the engine under the fluctuation of the oil pressure, or makes an error correction to the abnormal oil pressure, the running data of the engine under different working conditions in the actual driving process is reacquired as sample data, and the long short-term memory model is trained; if the second correction model can identify the running data of the engine under the fluctuation of the oil pressure and correct the abnormal oil pressure, the second correction model is taken as the initial correction model; the initial correction model is integrated into the oil pump control system of the target vehicle, the target running data of the engine in the actual driving process of the target user is obtained, the initial correction model is optimized to obtain the target correction model; the oil pressure correction value of the target vehicle in the current working condition is determined according to the target correction model, and the oil pressure of the target vehicle is corrected according to the oil pressure correction value. The technical scheme of the embodiment of the application integrates the initial correction model into the oil pump control system, optimizes the initial correction model by using the target running data reflecting the driving habits of the target user, can correct the oil pressure under the operation that is easy to cause the fluctuation of the oil pressure in the driving habits of the target user, and avoids the loss of the engine caused by the fluctuation of the oil pressure.

[0053] Embodiment three

[0054] FIG. 3 is a structural schematic diagram of an oil pressure control device according to an embodiment of the present application. As shown in FIG. 3, the device comprises: an initial model generation module 310 configured to obtain running data of an engine under different working conditions in actual driving as sample data, train a long short-term memory model to obtain an initial correction model; a target model generation module 320 configured to integrate the initial correction model into an oil pump control system of a target vehicle, obtain target running data of the engine in actual driving of a target user, optimize the initial correction model to obtain a target correction model; and an oil pressure correction module 330 configured to determine an oil pressure correction value of the target vehicle under a current working condition according to the target correction model, and correct oil pressure of the target vehicle according to the oil pressure correction value.

[0055] In an embodiment, the initial model generation module 310 comprises: a sample data acquisition unit configured to obtain running data of an engine under normal oil pressure in actual driving as first sample data, and obtain running data of the engine under fluctuating oil pressure in actual driving as second sample data; a first correction model generation unit configured to determine a first training set and a first test set according to the first sample data, train a long short-term memory model according to the first training set to obtain a first correction model, and test the first correction model according to the first test set; and a second correction model training unit configured to train the first correction model according to the second sample data if a result output by the first correction model conforms to an oil pressure threshold interval under normal conditions.

[0056] In an embodiment, the initial model generation module 310 further comprises: a sample data updating unit configured to reacquire running data of an engine under different working conditions in actual driving as sample data to train a long short-term memory model if a result output by the first correction model does not conform to an oil pressure threshold interval under normal conditions.

[0057] In an embodiment, the second correction model training unit comprises: a sample data labeling subunit configured to determine a second training set and a second test set according to the second sample data, label sample data in the second training set, and add a correction value corresponding to abnormal oil pressure in the sample data in the second training set; a second correction model generation subunit configured to train the first correction model according to the second training set to obtain a second correction model, and test the second correction model according to the second test set; and an initial correction model generation subunit configured to take the second correction model as an initial correction model if the second correction model can identify running data of an engine under fluctuating oil pressure and correct abnormal oil pressure.

[0058] In an embodiment, the second correction model training unit further comprises a sample data updating subunit configured to, if the second correction model fails to identify the running data of the engine under oil fluctuation or makes an error correction to the abnormal oil pressure, reacquire running data of the engine under different working conditions in an actual driving process as sample data, and train the long short-term memory model.

[0059] In an embodiment, the target model generation module 320 comprises an abnormal running data determination unit configured to acquire target running data of the engine in an actual driving process under driving habits of a target user, and determine abnormal running data under oil pressure fluctuation in the target running data; and a target correction model generation unit configured to adjust memory weights corresponding to multiple parameters in the initial correction model according to a frequency of mutation of the multiple parameters in the abnormal running data, to obtain the target correction model.

[0060] In an embodiment, the oil pressure correction module 330 comprises a current running data acquisition unit configured to acquire current running data of the engine of the target vehicle under a current working condition through the oil pump control system, and input the current running data into the target correction model; and an oil pressure correction unit configured to acquire an oil pressure correction value output by the target correction model, and correct the oil pressure of the target vehicle according to the oil pressure correction value through the oil pump control system.

[0061] The oil pressure control device provided in the embodiments of the present application can perform the oil pressure control method provided in any of the embodiments of the present application, and has the corresponding function modules and effects of the execution method.

[0062] Embodiment Four

[0063] FIG. 4 shows a structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application. The electronic device is intended to represent a variety of forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent a variety of forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0064] As shown in FIG. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read-Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, where the memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer programs stored in the ROM 12 or loaded into the RAM 13 from the storage unit 18. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An Input / Output (I / O) interface 15 is also connected to the bus 14.

[0065] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, a speaker, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0066] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various special-purpose Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the engine oil pressure control method.

[0067] In some embodiments, the engine oil pressure control method can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 18. In some embodiments, parts or all of the computer program can be loaded and / or installed onto electronic device 10 via, e.g., ROM 12 and / or communication unit 19. When the computer program is loaded onto RAM 13 and executed by processor 11, one or more steps of the engine oil pressure control method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the engine oil pressure control method by way of other means, e.g., by way of firmware.

[0068] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a Field-Programmable Gate Array (FPGA), an Application-Specific Integrated Circuit (ASIC), a System on Chip (SOC), a Complex Programmable Logic Device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0069] Computer programs used to implement the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0070] In the context of this application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. Examples of a machine-readable storage medium can include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, RAM, ROM, erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0071] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a Cathode Ray Tube (CRT) or a Liquid Crystal Display (LCD) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0072] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0073] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and virtual private server (VPS) services.

[0074] The steps described above can be reordered, added to, or deleted from using the various forms of flow shown above. For example, the steps described in this application can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of this application can be achieved, and this application does not limit here.

Claims

1. A method for controlling engine oil pressure, comprising: The engine's operating data under different conditions during actual driving is obtained as sample data, and the long short-term memory model is trained to obtain the initial corrected model. The initial correction model is integrated into the oil pump control system of the target vehicle, the target operating data of the engine during the actual driving process of the target user is obtained, and the initial correction model is optimized to obtain the target correction model. The target correction model determines the oil pressure correction value of the target vehicle under the current operating conditions, and the oil pressure of the target vehicle is corrected according to the oil pressure correction value.

2. The method according to claim 1, wherein, The initial modified model is obtained by acquiring the engine's operating data under different operating conditions during actual driving as the sample data and training the long short-term memory model, including: The engine's operating data under normal oil pressure during actual driving is used as the first sample data, and the engine's operating data under fluctuating oil pressure during actual driving is used as the second sample data. A first training set and a first test set are determined based on the first sample data. The long short-term memory model is trained based on the first training set to obtain a first modified model. The first modified model is then tested based on the first test set. In response to the fact that the output of the first correction model conforms to the oil pressure threshold range under normal conditions, the first correction model is trained based on the second sample data.

3. The method according to claim 2, further comprising: In response to the fact that the output of the first correction model does not conform to the oil pressure threshold range under normal conditions, the operating data of the engine under different working conditions during actual driving is re-acquired as the sample data to train the long short-term memory model.

4. The method according to claim 2, wherein, Training the first modified model based on the second sample data includes: The second training set and the second test set are determined based on the second sample data. The sample data in the second training set are labeled, and the correction value corresponding to the abnormal oil pressure is added to the sample data in the second training set. The first modified model is trained using the second training set to obtain the second modified model, and the second modified model is tested using the second test set. In response to the second correction model identifying the engine's operating data under oil fluctuations and correcting abnormal oil pressure, the second correction model is used as the initial correction model.

5. The method according to claim 4, further comprising: In response to the second correction model's inability to recognize the engine's operating data under oil fluctuations, or its incorrect correction of abnormal oil pressure, the engine's operating data under different operating conditions during actual driving is reacquired as the sample data to train the long short-term memory model.

6. The method according to claim 1, wherein, The initial correction model is integrated into the oil pump control system of the target vehicle. The target operating data of the engine during the actual driving process of the target user is acquired. The initial correction model is then optimized to obtain the target correction model, which includes: Acquire the target operating data of the engine during actual driving in accordance with the driving habits of the target user, and identify abnormal operating data under oil pressure fluctuations in the target operating data; Based on the frequency of abrupt changes in multiple parameters in the abnormal operation data, the memory weights corresponding to multiple parameters in the initial correction model are adjusted to obtain the target correction model.

7. The method according to claim 1, wherein, The target correction model determines the oil pressure correction value for the target vehicle under the current operating conditions, and the oil pressure of the target vehicle is corrected based on the oil pressure correction value, including: The oil pump control system acquires the current operating data of the engine of the target vehicle under the current operating conditions, and inputs the current operating data into the target correction model. The oil pressure correction value output by the target correction model is obtained, and the oil pump control system corrects the oil pressure of the target vehicle according to the oil pressure correction value.

8. An oil pressure control device, comprising: The initial model generation module is set to obtain the engine's operating data under different working conditions during actual driving as sample data, and train the long short-term memory model to obtain the initial corrected model. The target model generation module is configured to integrate the initial correction model into the oil pump control system of the target vehicle, acquire the target operating data of the engine during the actual driving process of the target user, optimize the initial correction model, and obtain the target correction model. The oil pressure correction module is configured to determine the oil pressure correction value of the target vehicle under the current operating conditions based on the target correction model, and to correct the oil pressure of the target vehicle based on the oil pressure correction value.

9. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the oil pressure control method according to any one of claims 1-7.

10. A computer-readable storage medium storing computer instructions for causing a processor to execute the oil pressure control method of any one of claims 1-7.

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