Spark plug service life prediction method and device, terminal equipment and medium

Abstract

The invention provides a spark plug service life prediction method and device, terminal equipment and a medium. The method comprises the steps that periodic driving parameters of a vehicle in a detection period are acquired; according to the periodic driving parameter, determining a periodic attenuation rate corresponding to a spark plug of the vehicle in the detection period; and determining the residual service life of the spark plug according to the initial attenuation rate and the periodic attenuation rate. According to the method, the residual service life of the spark plug can be periodically and accurately predicted, and whether the spark plug needs to be replaced or not is periodically judged, so that resource waste caused by premature replacement of the spark plug can be avoided, the spark plug can be timely replaced, and normal operation of a vehicle is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of vehicle detection technology, and in particular to a method, device, terminal equipment and medium for predicting the service life of a spark plug. Background Art

[0002] Spark plugs are one of the important components of the engine ignition system. Spark plugs introduce high voltage electricity into the combustion chamber and make it jump over the electrode gap to generate sparks, thereby igniting the combustible mixture in the cylinder and providing power for the vehicle to travel. As the spark plugs are used for a longer time, the anode will be burned, the cathode will be carbonized, and the electrode gap will increase, causing the spark plug ignition efficiency to continue to decline, resulting in vehicle starting difficulties, idle jitter, and even engine misfires.

[0003] In the current technical solution, the spark plugs are generally replaced according to a fixed cycle or a fixed mileage of the vehicle. However, due to the large differences in driving purposes, scenarios, and working conditions of different vehicles, if the spark plugs are replaced too early, that is, when the remaining service life of the spark plugs is long, it will cause a waste of resources; if the spark plugs are replaced too late, that is, when the remaining service life of the spark plugs is exceeded, it will affect the normal operation of the engine and the vehicle.

[0004] Therefore, how to accurately predict the remaining service life of the spark plug so that the spark plug can be replaced in time to ensure the normal operation of the vehicle is a technical problem that technical personnel in this field currently need to solve. Summary of the invention

[0005] The purpose of the present application is to provide a spark plug service life prediction method, device, terminal device and computer-readable storage medium, aiming to accurately predict the remaining service life of the spark plug, thereby avoiding premature replacement of the spark plug and causing waste of resources, and replacing the spark plug in time to ensure the normal operation of the vehicle.

[0006] In a first aspect, the present application provides a method for predicting the service life of a spark plug. The method comprises:

[0007] Obtaining periodic driving parameters of the vehicle within a detection period;

[0008] Determining a periodic attenuation rate of a spark plug of the vehicle corresponding to the detection period according to the periodic driving parameter;

[0009] The remaining service life of the spark plug is determined based on the initial decay rate and the periodic decay rate.

[0010] In one embodiment, determining the periodic attenuation rate of the spark plug of the vehicle corresponding to the detection period according to the periodic driving parameter includes:

[0011] Determining corresponding periodic driving characteristic data according to the periodic driving parameters;

[0012] Based on the decay rate function, the periodic driving characteristic data is used to determine the periodic decay rate of the spark plug of the vehicle corresponding to the detection period.

[0013] In one embodiment, determining the periodic attenuation rate of the spark plug of the vehicle corresponding to the detection period according to the periodic driving parameter includes:

[0014] The cyclic driving parameters are input into a pre-trained attenuation rate prediction model, the attenuation rate prediction model is used to extract cyclic driving characteristic data corresponding to the cyclic driving parameters, and the cyclic attenuation rate of the spark plug of the vehicle corresponding to the detection period is output according to the cyclic driving characteristic data.

[0015] In one embodiment, the process of determining the decay rate function includes:

[0016] Acquire a plurality of historical period driving parameters, and determine corresponding historical period driving characteristic data according to each of the historical period driving parameters;

[0017] The decay rate function is determined according to each of the historical period driving characteristic data and the corresponding historical period decay rate.

[0018] In one embodiment, the process of training the decay rate prediction model includes:

[0019] Acquire a training sample data set; each training sample data in the training sample data set includes a historical cycle driving parameter and a historical cycle attenuation rate corresponding to the historical cycle driving parameter;

[0020] The training sample data set is input into an initial neural network, corresponding historical period driving characteristic data is extracted according to the training sample data, and learning and training are performed based on the historical period driving characteristic data to obtain the attenuation rate prediction model.

[0021] In one embodiment, obtaining the historical period driving parameter and the historical period attenuation rate includes:

[0022] The historical cycle driving parameters and the historical cycle attenuation rate of the vehicle within the historical detection cycle are obtained based on the bench test and the vehicle test.

[0023] In a second aspect, the present application also provides a spark plug service life prediction device. The device comprises:

[0024] An acquisition module, used to acquire periodic driving parameters of the vehicle within a detection period;

[0025] A determination module, configured to determine a periodic attenuation rate of the spark plug of the vehicle corresponding to the detection period according to the periodic driving parameter;

[0026] The prediction module is used to determine the remaining service life of the spark plug according to the initial decay rate and the periodic decay rate.

[0027] In a third aspect, the present application further provides a terminal device, wherein the terminal device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0028] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0029] The present application provides a method for predicting the service life of a spark plug, which obtains the periodic driving parameters of a vehicle within a detection period; determines the periodic attenuation rate of the spark plug of the vehicle corresponding to the detection period according to the periodic driving parameters; and determines the remaining service life of the spark plug according to the initial attenuation rate and the periodic attenuation rate. Compared with the prior art method of replacing spark plugs according to a fixed period or a fixed mileage of the vehicle, in this method, the periodic attenuation rate is determined according to the periodic driving parameters of the vehicle within the detection period, and the remaining service life of the spark plug is determined based on the initial attenuation rate and the periodic attenuation rate. The remaining service life of the spark plug can be accurately predicted periodically, and it can be periodically determined whether the spark plug needs to be replaced. Therefore, it can avoid replacing the spark plug too early, which causes a waste of resources, and can replace the spark plug in time to ensure the normal operation of the vehicle.

[0030] It can be understood that the spark plug service life prediction device, terminal equipment and computer-readable storage medium provided in the embodiments of the present application have the same beneficial effects as the above-mentioned spark plug service life prediction method, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 A flowchart of a method for predicting the service life of a spark plug provided in an embodiment of the present application;

[0033] Figure 2 A schematic diagram of the structure of a spark plug provided in an embodiment of the present application;

[0034] Figure 3 A schematic diagram of a feature extraction process provided in an embodiment of the present application;

[0035] Figure 4 A schematic diagram of the structure of a spark plug service life prediction device provided in an embodiment of the present application;

[0036] Figure 5 A schematic diagram of the structure of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0038] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0039] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0040] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0041] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0042] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways. "Multiple" means "two or more".

[0043] A method for predicting the service life of a spark plug provided in an embodiment of the present application may be executed by a processor of a terminal device when running a corresponding computer program.

[0044] Figure 1 This is a flow chart of a method for predicting the service life of a spark plug provided in an embodiment of the present application. For ease of explanation, only the part related to the present embodiment is shown. The method provided in the present embodiment includes the following steps:

[0045] S100: Obtaining periodic driving parameters of the vehicle within a detection period.

[0046] The detection period refers to the time interval between two consecutive predictions of the service life of the spark plug. This embodiment does not limit the specific time interval of the detection period.

[0047] Among them, the periodic driving parameters refer to the corresponding driving parameters within the detection period. In practical applications, based on the factors that affect the service life of the spark plug: motor aging, carbon deposit pollution, and knock impact, the corresponding driving parameters such as speed, water temperature, load, knock, mileage, etc. are collected.

[0048] In this embodiment, the periodic driving parameters corresponding to the vehicle in the detection period can be obtained through the engine control unit (ECU) or the sensor. More specifically, the driving parameters and the time marks corresponding to the driving parameters can be collected, and the driving parameters in the detection period can be filtered according to the time marks to obtain the periodic driving parameters.

[0049] S200: Determine a periodic attenuation rate of a spark plug of the vehicle corresponding to a detection period according to periodic driving parameters.

[0050] The spark plug in this embodiment is one of the important components of the engine ignition system. Figure 2 This is a schematic diagram of the structure of a spark plug provided in an embodiment of the present application. Figure 2The spark plug shown in the figure is 1 for the wiring nut, 2 for the insulator, 3 for the metal rod, 4 for the inner washer, 5 for the shell, 6 for the conductive glass, 7 for the sealing washer, 8 for the inner washer, 9 for the center electrode, 10 for the side electrode, and 11 for the insulator group. The spark plug introduces high voltage electricity into the combustion chamber and makes it jump over the electrode gap to generate sparks, thereby igniting the combustible mixture in the cylinder, thus providing power for the vehicle to travel. As the spark plug is used for a longer time, the spark plug will experience anode ablation, cathode carbon deposition, and increased electrode gap, which will cause the spark plug ignition efficiency to continue to decline, resulting in vehicle starting difficulties, idle jitter, and even engine misfires.

[0051] The periodic attenuation rate refers to the attenuation rate of the spark plug during the detection cycle; the attenuation rate indicates the degree of wear and damage of the spark plug. Generally, the attenuation rate of a newly replaced spark plug is 0. When the attenuation rate reaches a preset threshold, it indicates that the spark plug needs to be replaced.

[0052] In this embodiment, after the periodic driving parameters are determined, the periodic attenuation rate of the spark plug of the engine of the vehicle corresponding to the detection period is determined according to the periodic driving parameters.

[0053] S300: Determine the remaining service life of the spark plug according to the initial decay rate and the periodic decay rate.

[0054] The initial decay rate indicates the decay rate of the spark plug before the current detection cycle. After the cycle decay rate is determined, the sum of the initial decay rate and the cycle decay rate is calculated to obtain the current decay rate of the spark plug; the current decay rate indicates the degree of wear and damage of the spark plug after the current detection cycle.

[0055] Specifically, first obtain the correspondence between the preset attenuation rate and the remaining service life; if the attenuation rate is 0, the corresponding remaining service life is the maximum service life of the spark plug; when the attenuation rate reaches a preset threshold, the corresponding remaining service life is 0; after determining the current attenuation rate of the spark plug, based on the correspondence between the attenuation rate and the remaining service life, determine the remaining service life corresponding to the current attenuation rate.

[0056] The present application provides a method for predicting the service life of a spark plug, which obtains the periodic driving parameters of a vehicle within a detection period; determines the periodic attenuation rate of the vehicle's spark plug corresponding to the detection period based on the periodic driving parameters; and determines the remaining service life of the spark plug based on the initial attenuation rate and the periodic attenuation rate. Compared with the prior art method of replacing spark plugs according to a fixed period or a fixed mileage of the vehicle, in this method, the periodic attenuation rate is determined based on the periodic driving parameters of the vehicle within the detection period, and the remaining service life of the spark plug is determined based on the initial attenuation rate and the periodic attenuation rate. The remaining service life of the spark plug can be accurately predicted periodically, and it can be periodically determined whether the spark plug needs to be replaced. Therefore, it can avoid replacing the spark plug too early, resulting in a waste of resources, and can replace the spark plug in time to ensure the normal operation of the vehicle.

[0057] On the basis of the above-mentioned embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the periodic attenuation rate of the spark plug of the vehicle corresponding to the detection period is determined according to the periodic driving parameters, including:

[0058] Determining corresponding periodic driving characteristic data according to the periodic driving parameters;

[0059] Based on the decay rate function, the periodic decay rate of the spark plug of the vehicle corresponding to the detection period is determined using the periodic driving characteristic data.

[0060] The periodic driving characteristic data refers to the characteristic data corresponding to the periodic driving parameters. In practical applications, feature extraction is performed based on the originally collected periodic driving parameters to obtain the corresponding periodic driving characteristic data. Figure 3 A schematic diagram of a feature extraction process provided for an embodiment of the present application. Based on the three major factors that affect the service life of the spark plug, periodic driving parameters such as the number of ignitions, mileage, speed, cooling water temperature, load and knock are collected; according to the correlation between the periodic driving parameters, the corresponding periodic driving feature data is extracted. For example, the periodic driving feature data determined according to the number of ignitions and mileage include the total mileage of the engine operation; the corresponding periodic driving feature data determined according to the speed, cooling water temperature and load include the cold engine cycle count and the high load cycle count.

[0061] The decay rate function refers to a function that characterizes the changing relationship between the periodic driving characteristic data and the decay rate. In this embodiment, the decay rate function is pre-set, and after the periodic driving parameters corresponding to the current detection period are obtained and the corresponding periodic driving characteristic data are determined according to the periodic driving parameters, the periodic driving characteristic data are input into the decay rate function, and the periodic decay rate corresponding to the periodic driving characteristic data is outputted using the decay rate function, that is, the periodic decay rate corresponding to the spark plug of the vehicle in the detection period is obtained.

[0062] In this embodiment, the decay rate function is used to determine the periodic decay rate of the spark plug of the vehicle corresponding to the detection period, which can determine the periodic decay rate conveniently and accurately.

[0063] In one embodiment, the process of determining the decay rate function includes:

[0064] Acquire multiple historical cycle driving parameters, and determine corresponding historical cycle driving characteristic data according to each historical cycle driving parameter;

[0065] The decay rate function is determined according to the driving characteristic data of each historical period and the corresponding decay rate of the historical period.

[0066] Among them, historical cycle driving parameters refer to the cycle driving parameters collected during the historical detection cycle; historical cycle driving feature data refers to the driving feature data extracted based on the historical cycle driving parameters; historical cycle attenuation rate refers to the attenuation rate of the spark plug corresponding to the historical detection cycle and historical cycle driving parameters.

[0067] In this embodiment, a plurality of historical cycle driving parameters and the historical cycle attenuation rates corresponding to the respective historical cycle driving parameters are first obtained; then the corresponding historical cycle driving characteristic data are determined according to the respective historical cycle driving parameters; and according to the plurality of historical cycle driving characteristic data and the historical cycle attenuation rates corresponding to the respective historical cycle driving parameters, a linear or nonlinear functional relationship between the periodic driving characteristics and the periodic attenuation rate is determined by functional relationship fitting, thereby obtaining the attenuation rate function.

[0068] In a specific embodiment, the decay rate function can be expressed as:

[0069] L = a*P+b*Q+c*R+d*S…;

[0070] Among them, P, Q, R, and S represent different periodic driving characteristic data respectively, and a, b, c, and d represent coefficients corresponding to each periodic driving characteristic respectively, and the coefficients can be determined according to the functional relationship fitting.

[0071] According to the method of this embodiment, the attenuation rate function can be determined efficiently and conveniently, and then the periodic attenuation rate corresponding to the periodic driving parameter can be determined efficiently and conveniently based on the attenuation rate function.

[0072] On the basis of the above-mentioned embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the periodic attenuation rate of the spark plug of the vehicle corresponding to the detection period is determined according to the periodic driving parameters, including:

[0073] The periodic driving parameters are input into a pre-trained attenuation rate prediction model, and the attenuation rate prediction model is used to extract periodic driving characteristic data corresponding to the periodic driving parameters. The periodic attenuation rate of the vehicle's spark plug corresponding to the detection period is output based on the periodic driving characteristic data.

[0074] Among them, the decay rate prediction model refers to a pre-trained machine learning model for predicting the periodic decay rate; the decay rate prediction model can extract the periodic driving characteristic data corresponding to the periodic driving parameters, and determine the corresponding periodic decay rate according to the periodic driving characteristic parameters.

[0075] In this embodiment, a decay rate prediction model is pre-trained. After the periodic driving parameters corresponding to the current detection cycle are obtained, the periodic driving parameters are input into the decay rate prediction model, the periodic driving characteristic data corresponding to the periodic driving parameters are extracted, and the periodic decay rate corresponding to the detection cycle is output based on the periodic driving characteristic data, that is, the decay rate of the vehicle's spark plug corresponding to the detection cycle is obtained.

[0076] In this embodiment, the decay rate prediction model is used to determine the periodic decay rate of the vehicle's spark plug corresponding to the detection period, which can determine the periodic decay rate conveniently and accurately.

[0077] Based on the above embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the process of training the attenuation rate prediction model includes:

[0078] Acquire a training sample data set; each training sample data in the training sample data set includes a historical cycle driving parameter and a historical cycle attenuation rate corresponding to the historical cycle driving parameter;

[0079] The training sample data set is input into the initial neural network, the corresponding historical period driving characteristic data is extracted according to the training sample data, and learning and training are performed based on the historical period driving characteristic data to obtain the attenuation rate prediction model.

[0080] The historical cycle driving parameters refer to the cycle driving parameters collected during the historical detection cycle; the historical cycle attenuation rate refers to the attenuation rate of the spark plug corresponding to the historical detection cycle and the historical cycle driving parameters.

[0081] In a specific embodiment, the specific process of training the decay rate prediction model is as follows:

[0082] Acquire a training sample data set; the training sample data set includes a large number of training sample data; each training sample data includes a historical cycle driving parameter and a historical cycle attenuation rate corresponding to the historical cycle driving parameter;

[0083] Inputting the training sample data set into the initial neural network, using the initial neural network to analyze each training sample data in the training sample data set, and extracting the historical cycle driving feature data corresponding to the historical cycle driving parameters; wherein the initial neural network can be a deep neural network (Deep Neural Networks, DNN), a convolutional neural network (Convolutional Neural Networks, CNN), a recurrent neural network (Recurrent Neural Networks, RNN), a long short-term memory neural network (Long Short-term Memory Networks, LSTM) and a feed-forward neural network (Feed-forward Neural Networks, FNNs), etc., and this embodiment does not limit the specific type of the initial neural network;

[0084] Based on the extracted historical cycle driving feature data, a machine learning algorithm is used to perform model learning and training. During the learning and training process, the parameters and structure of the initial neural network are adjusted to improve the prediction accuracy of the model. When the model meets the preset accuracy conditions, the model is determined as a decay rate prediction model.

[0085] According to the method of this embodiment, the attenuation rate function can be determined efficiently and conveniently, and then the periodic attenuation rate corresponding to the periodic driving parameter can be determined efficiently and conveniently based on the attenuation rate function.

[0086] Based on the above embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, obtaining the historical cycle driving parameters and the historical cycle attenuation rate includes:

[0087] The historical cycle driving parameters and historical cycle attenuation rate of the vehicle within the historical inspection cycle are obtained based on bench tests and vehicle tests.

[0088] The historical cycle decay rate refers to the decay rate corresponding to the historical cycle driving parameters of the historical detection cycle. In practical applications, the historical cycle decay rate can be determined based on the spark plug status; the spark plug status includes the gap and misfire frequency of the spark plug and the energy value or voltage value when the spark plug is ignited.

[0089] Among them, bench testing refers to various performance tests on vehicles and their components in a laboratory environment using specially designed test benches to obtain the historical cycle driving parameters of the vehicle within the historical detection cycle and the corresponding historical cycle attenuation rate. Bench tests include endurance tests, durability tests, and spark plug status tests. Among them, the endurance test standards for different models are different, including the European Endurance Test Evaluation Standard (NEDC, New European Driving Cycle), the World Light Vehicle Test Cycle (World Light Vehicle Test Cycle, WLTC) and the China Light Vehicle Test Cycle (CLTC, China Light-Duty Vehicle Test Cycle), etc., which can be selected according to actual needs.

[0090] For bench durability testing, different operating parameters are set according to the test requirements, including throttle, gear position, and brake status. The actual operating status of the engine is simulated on the bench based on the operating parameters, and the historical cycle driving parameters and the corresponding spark plug status are recorded. Subsequently, the historical cycle driving parameters are analyzed, the historical cycle driving feature data is extracted, and the historical cycle attenuation rate is determined according to the spark plug status to evaluate the durability and reliability of the engine.

[0091] Among them, the whole vehicle test is also called road test, which refers to the collection of the vehicle's historical cycle driving parameters and corresponding spark plug status during the inspection cycle under normal driving conditions; the whole vehicle test also includes endurance test, durability test and spark plug status test.

[0092] In this embodiment, the historical cycle driving parameters of the vehicle within the historical detection cycle are obtained by combining the bench test and the whole vehicle test, so that the cycle driving parameters of the vehicle can be comprehensively and accurately obtained to more accurately predict the remaining service life of the spark plug.

[0093] On the basis of the above embodiments, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the method further includes:

[0094] When spark plug replacement information is detected, the remaining service life is updated.

[0095] In this embodiment, a body controller or a sensor is used to detect whether the vehicle has replaced the spark plug; when the spark plug replacement information is detected, it indicates that the vehicle has replaced the spark plug, and therefore the remaining service life of the spark plug needs to be updated.

[0096] Specifically, when the vehicle has replaced the spark plug, the remaining service life of the spark plug is updated to the maximum service life of the spark plug, and the attenuation rate of the spark plug is updated to 0.

[0097] According to the method of this embodiment, the remaining service life of the spark plug can be updated when the spark plug is updated, thereby ensuring the accuracy of subsequent prediction of the remaining service life of the spark plug.

[0098] Based on the above embodiments, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment,

[0099] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0100] Figure 4 FIG. 1 is a schematic diagram of the structure of a spark plug service life prediction device provided in an embodiment of the present application. Figure 4 As shown, a spark plug service life prediction device of this embodiment includes an acquisition module 410, a determination module 420 and a prediction module 430; wherein,

[0101] An acquisition module 410 is used to acquire periodic driving parameters of the vehicle within a detection period;

[0102] A determination module 420, for determining a periodic attenuation rate of a spark plug of a vehicle corresponding to a detection period according to periodic driving parameters;

[0103] The prediction module 430 is used to determine the remaining service life of the spark plug according to the initial decay rate and the periodic decay rate.

[0104] A spark plug service life prediction device provided in an embodiment of the present application has the same beneficial effects as the above-mentioned spark plug service life prediction method.

[0105] In one embodiment, the determination module 420 includes:

[0106] A periodic driving characteristic data determination submodule, used to determine corresponding periodic driving characteristic data according to periodic driving parameters;

[0107] The first determination submodule is used to determine the periodic attenuation rate of the spark plug of the vehicle corresponding to the detection period based on the attenuation rate function and using the periodic driving characteristic data.

[0108] In one embodiment, the determination module 420 includes:

[0109] The second determination submodule is used to input the periodic driving parameters into a pre-trained attenuation rate prediction model, use the attenuation rate prediction model to extract periodic driving characteristic data corresponding to the periodic driving parameters, and output the periodic attenuation rate of the vehicle's spark plug corresponding to the detection period based on the periodic driving characteristic data.

[0110] In one embodiment, the first determining submodule includes:

[0111] A parameter acquisition unit, used to acquire a plurality of historical period driving parameters, and determine corresponding historical period driving characteristic data according to each historical period driving parameter;

[0112] The function determination unit is used to determine the decay rate function according to the driving characteristic data of each historical period and the corresponding decay rate of the historical period.

[0113] In one embodiment, the second determining submodule includes:

[0114] A sample acquisition unit, used to acquire a training sample data set; each training sample data in the training sample data set includes a historical cycle driving parameter and a historical cycle attenuation rate corresponding to the historical cycle driving parameter;

[0115] The model training unit is used to input the training sample data set into the initial neural network, extract the corresponding historical period driving characteristic data according to the training sample data, and perform learning and training based on the historical period driving characteristic data to obtain the attenuation rate prediction model.

[0116] In one embodiment, the determination module 420 includes:

[0117] The acquisition submodule is used to obtain the historical cycle driving parameters and historical cycle attenuation rate of the vehicle within the historical detection cycle based on bench tests and vehicle tests.

[0118] In one embodiment, a spark plug service life prediction device further includes:

[0119] The update module is used to update the remaining service life when the spark plug replacement information is detected.

[0120] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0121] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0122] Figure 5 A schematic diagram of the structure of a terminal device provided in an embodiment of the present application. Figure 5 As shown, the terminal device 500 of this embodiment includes a memory 501, a processor 502, and a computer program 503 stored in the memory 501 and executable on the processor 502; when the processor 502 executes the computer program 503, the steps in the above-mentioned spark plug service life prediction method embodiments are implemented; or when the processor 502 executes the computer program 503, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0123] Exemplarily, the computer program 503 may be divided into one or more modules / units, one or more modules / units are stored in the memory 501, and are executed by the processor 502 to implement the method of the embodiment of the present application. One or more modules / units may be a series of computer program instruction segments that can perform specific functions, and the instruction segments are used to describe the execution process of the computer program 503 in the terminal device 500. For example, the computer program 503 may be divided into an acquisition module, a determination module, and a prediction module, and the specific functions of each module are as follows:

[0124] An acquisition module, used to acquire periodic driving parameters of the vehicle within a detection period;

[0125] A determination module, used to determine a periodic attenuation rate of a spark plug of a vehicle corresponding to a detection period according to periodic driving parameters;

[0126] The prediction module is used to determine the remaining service life of the spark plug according to the initial decay rate and the periodic decay rate. In the application, the terminal device 500 can be a computing device such as a desktop computer, a notebook, a handheld computer, and a cloud server. The terminal device 500 may include but is not limited to a memory 501 and a processor 502. Those skilled in the art will understand that Figure 5 It is only an example of a terminal device and does not constitute a limitation of the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.; among which, the input and output devices may include cameras, audio acquisition / playback devices, display screens, etc.; the network access device may include a communication module for wireless communication with external devices.

[0127] In applications, the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0128] In applications, the memory can be an internal storage unit of a terminal device, such as a hard disk or memory of the terminal device; it can also be an external storage device of the terminal device, such as a plug-in hard disk equipped on the terminal device, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.; it can also include both the internal storage unit and the external storage device of the terminal device. The memory is used to store operating systems, applications, boot loaders, data, and other programs, such as program codes of computer programs. The memory can also be used to temporarily store data that has been output or is about to be output.

[0129] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0130] The present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to a terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0131] A computer-readable storage medium provided in an embodiment of the present application has the same beneficial effects as the above-mentioned method for predicting the service life of a spark plug.

[0132] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0133] Those of ordinary skill in the art will appreciate that the devices and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0134] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices, which can be electrical, mechanical or other forms.

[0135] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for predicting the service life of a spark plug, characterized in that: The method comprises: Obtaining periodic driving parameters of the vehicle within a detection period; Determining a periodic attenuation rate of a spark plug of the vehicle corresponding to the detection period according to the periodic driving parameter; The remaining service life of the spark plug is determined based on the initial decay rate and the periodic decay rate.

2. The method according to claim 1, characterized in that: The step of determining the periodic attenuation rate of the spark plug of the vehicle corresponding to the detection period according to the periodic driving parameter includes: Determining corresponding periodic driving characteristic data according to the periodic driving parameters; Based on the decay rate function, the periodic driving characteristic data is used to determine the periodic decay rate of the spark plug of the vehicle corresponding to the detection period.

3. The method according to claim 1, characterized in that The step of determining the periodic attenuation rate of the spark plug of the vehicle corresponding to the detection period according to the periodic driving parameter includes: The cyclic driving parameters are input into a pre-trained attenuation rate prediction model, the attenuation rate prediction model is used to extract cyclic driving characteristic data corresponding to the cyclic driving parameters, and the cyclic attenuation rate of the spark plug of the vehicle corresponding to the detection period is output according to the cyclic driving characteristic data.

4. The method according to claim 2, characterized in that: The process of determining the decay rate function comprises: Acquire a plurality of historical period driving parameters, and determine corresponding historical period driving characteristic data according to each of the historical period driving parameters; The decay rate function is determined according to each of the historical period driving characteristic data and the corresponding historical period decay rate.

5. The method according to claim 3, characterized in that: The process of training the decay rate prediction model includes: Acquire a training sample data set; each training sample data in the training sample data set includes a historical cycle driving parameter and a historical cycle attenuation rate corresponding to the historical cycle driving parameter; The training sample data set is input into an initial neural network, corresponding historical period driving characteristic data is extracted according to the training sample data, and learning and training are performed based on the historical period driving characteristic data to obtain the attenuation rate prediction model.

6. The method according to claim 4 or 5, characterized in that: Acquiring the historical period driving parameter and the historical period attenuation rate includes: The historical cycle driving parameters and the historical cycle attenuation rate of the vehicle within the historical detection cycle are obtained based on the bench test and the vehicle test.

7. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: When spark plug replacement information is detected, the remaining service life is updated.

8. A spark plug service life prediction device, characterized in that: The device comprises: An acquisition module, used to acquire periodic driving parameters of the vehicle within a detection period; A determination module, configured to determine a periodic attenuation rate of the spark plug of the vehicle corresponding to the detection period according to the periodic driving parameter; The prediction module is used to determine the remaining service life of the spark plug according to the initial decay rate and the periodic decay rate.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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