Vehicle generator fault detection method, electronic equipment and storage medium

Abstract

The invention provides a vehicle generator fault detection method, electronic equipment and a storage medium. The method comprises the steps that multiple types of operation state signals of a vehicle are obtained, wherein the multiple types of operation state signals at least comprise a first type of operation state signals with data missing and a second type of operation state signals with complete data; based on correlation analysis on the multiple types of operation state signals, determining correlation types among the multiple types of operation state signals; based on the second type of operation state signals, performing interpolation processing on the first type of operation state signals by using a model corresponding to the correlation type to obtain update state signals corresponding to the multiple types of operation state signals; and determining a fault detection result of the generator according to the working state of the generator in the vehicle under the condition that the update state signal meets the fault judgment condition. By means of the method, missing data in the vehicle state signal can be accurately determined, and therefore fault detection can be accurately conducted on the vehicle generator.

Description

Technical Field

[0001] This application relates to the technical field of automobiles, and particularly to a method for detecting faults of a vehicle generator, an electronic device, and a storage medium. Background Art

[0002] With the development of new energy vehicle technology, the condition monitoring of generators has become an important link to ensure the stable operation of vehicles. To accurately evaluate whether a generator is in a healthy state, fault detection often relies on vehicle status signals.

[0003] However, vehicle status signals are easily affected by factors such as sensor failures, communication interruptions, and signal interference, resulting in problems such as data loss. In related technologies, it is often difficult to accurately determine the missing data, leading to inaccurate fault detection of generators. Summary of the Invention

[0004] In view of the above, it is necessary to provide a method for detecting faults of a vehicle generator, an electronic device, and a storage medium, which can solve the technical problem that inaccurate fault detection of the generator is caused by the difficulty in accurately determining the missing data in the vehicle status signal.

[0005] On the one hand, this application provides a method for detecting faults of a vehicle generator, the method includes: obtaining multiple types of operating status signals of the vehicle, where the multiple types of operating status signals at least include a first type of operating status signal with missing data and a second type of operating status signal with complete data; based on the correlation analysis of the multiple types of operating status signals, determining the correlation type between the multiple types of operating status signals; based on the second type of operating status signal, using the model corresponding to the correlation type to perform interpolation processing on the first type of operating status signal to obtain an updated status signal corresponding to the multiple types of operating status signals; in the case where the updated status signal meets the fault judgment condition, determining the fault detection result of the generator according to the working status of the generator in the vehicle.

[0006] In some embodiments of this application, the step of based on the second type of operating status signal, using the model corresponding to the correlation type to perform interpolation processing on the first type of operating status signal to obtain an updated status signal corresponding to the multiple types of operating status signals includes: if the correlation type is linearly correlated, based on the second type of operating status signal, using a first regression model to determine the missing target data in the first type of operating status signal; if the correlation type is non-linearly correlated, based on the second type of operating status signal, using a second regression model to determine the target data; and based on the target data, performing interpolation processing on the first type of operating status signal to obtain the updated status signal.

[0007] In some embodiments of the present application, the method further includes: obtaining multiple types of historical operating status signals of the vehicle, where the multiple types of historical operating status signals include the first type of historical operating status signal corresponding to the first type of operating status signal and the second type of historical operating status signal corresponding to the second type of operating status signal, and using a linear regression model to fit the mapping relationship between the first type of historical operating status signal and the second type of historical operating status signal to obtain the first regression model, or using a polynomial regression model to fit the mapping relationship between the first type of historical operating status signal and the second type of historical operating status signal to obtain the second regression model.

[0008] In some embodiments of the present application, the updated status signals include the operating duration of the vehicle, the rotational speed of the vehicle's engine, the driving speed of the vehicle, and the battery voltage of the vehicle. The updated status signals satisfying the fault judgment conditions include one or more of the following situations: the operating duration is greater than a first preset value, the rotational speeds are all greater than a second preset value, the driving speeds are all greater than a third preset value, and the battery voltage is all less than a fourth preset value.

[0009] In some embodiments of the present application, determining the fault detection result of the generator according to the working state of the generator in the vehicle includes: if the generator does not output electrical energy or does not output electrical energy according to the preset parameter specifications, determining that the fault detection result is that the generator is faulty; if the generator outputs electrical energy or outputs electrical energy according to the preset parameter specifications, determining that the fault detection result is that the generator is normal.

[0010] In some embodiments of the present application, the method further includes: aligning any two types of operating status signals with different sampling frequencies among the multiple types of operating status signals, including: selecting one of the two types of operating status signals as the target operating status signal, and determining the target time point from the time points corresponding to the target operating status signal. According to the arrangement order of the data corresponding to the target time point in the target operating status signal, determining the reference data corresponding to the target time point in the non-target operating status signal among the two types of operating status signals, determining the previous frame of data of the reference data in the non-target operating status signal. If the target time point is greater than or equal to the time point corresponding to the previous frame of data and less than the time point corresponding to the reference data, determining the data corresponding to the target time point in the non-target operating status signal according to the previous frame of data and / or the reference data.

[0011] In some embodiments of the present application, the method further includes: identifying abnormal data in the multiple types of operating status signals and deleting the abnormal data from the multiple types of operating status signals.

[0012] In some embodiments of the present application, the method further includes: selecting data from the first type of operating state signals according to the time points with missing data among the time points corresponding to the first type of operating state signals, where the selected data includes adjacent data of the data corresponding to the time points with missing data in the first type of operating state signals. If the selected data is valid and the time difference between the time points corresponding to the selected data is less than a preset threshold, the first type of operating state signals are interpolated using the model corresponding to the correlation type.

[0013] On the other hand, the present application provides an electronic device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the electronic device implements the vehicle generator fault detection method described above. On the other hand, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor in an electronic device, the vehicle generator fault detection method described above is implemented.

[0014] In the vehicle generator fault detection method provided by the embodiments of the present application, considering that the operating state signals of the vehicle affect and are related to each other, the correlation type between the multiple types of operating state signals is analyzed, so as to facilitate subsequent interpolation processing using the model corresponding to the correlation type. According to the correlation type of the multiple types of operating state signals, the association relationship between the multiple types of state signals can be accurately determined. By specifically selecting the model corresponding to the correlation type to perform interpolation processing on the first type of operating state signals, the accuracy of the updated state signals after interpolation can be ensured. By determining whether the updated state signals meet the fault judgment conditions, it can be determined whether the vehicle is in a working state. In the case where the vehicle is in a normal working state and the generator does not output electrical energy, it is determined that the generator has a fault, which can ensure the accuracy of the generator fault detection result. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flowchart of a vehicle generator fault detection method provided by an embodiment of the present application.

[0016] Figure 2 is a schematic diagram for determining target data provided by an embodiment of the present application.

[0017] Figure 3 is a schematic diagram for fault judgment of a generator provided by an embodiment of the present application.

[0018] Figure 4It is a flowchart of a method for training a first regression model and a second regression model provided by an embodiment of the present application.

[0019] Figure 5 It is a flowchart of a method for aligning operating state signals provided by an embodiment of the present application.

[0020] Figure 6 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0021] It should be noted that, in the present application, "at least one" means one or more, and "a plurality" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone, where A and B can be singular or plural. The terms "first", "second", "third", "fourth", etc. (if any) in the description, claims, and drawings of the present application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0022] In the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific manner. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0023] With the development of new energy vehicle technology, the state monitoring of generators has become an important link to ensure the stable operation of vehicles. To accurately evaluate whether a generator is in a healthy state, fault detection often relies on vehicle state signals.

[0024] However, vehicle state signals are easily affected by factors such as sensor failures, communication interruptions, and signal interferences, resulting in problems such as data loss. In related technologies, it is often difficult to accurately determine the missing data, resulting in inaccurate fault detection of generators.

[0025] To solve the above technical problems, the vehicle generator fault detection method provided by the embodiments of the present application can be applied to one or more electronic devices. The electronic device can be a server, an in-vehicle device, a computer, a mobile phone, a laptop, a tablet, a wearable device, etc. Among them, the server can be a cloud server, which can be a single server or a server cluster. The present application does not limit the type of the electronic device.

[0026] Such as Figure 1As shown in the figure, it is a flowchart of a vehicle generator fault detection method provided by an embodiment of the present application. According to different requirements, the order of each step in this flowchart can be adjusted according to actual requirements, and some steps can be omitted. The vehicle generator fault detection method is applied to an electronic device, such as Figure 6 the electronic device 10 shown in the figure.

[0027] S11, obtain multiple types of vehicle operating state signals, where the multiple types of vehicle operating state signals at least include a first type of vehicle operating state signal with missing data and a second type of vehicle operating state signal with complete data.

[0028] In some embodiments of the present application, the multiple types of vehicle operating state signals include data that can reflect the operating state of the vehicle. Among them, each type of vehicle operating state signal may include multiple data, and each data may have a corresponding time point. For example, the multiple types of vehicle operating state signals may include, but are not limited to: the running duration of the vehicle, the rotational speed of the vehicle's engine, the driving speed of the vehicle, and the voltage of the vehicle's battery.

[0029] In some embodiments of the present application, the electronic device may obtain the multiple types of vehicle operating state data from the vehicle. Exemplarily, the electronic device is communicatively connected to the vehicle, and receives the signal data uploaded by the vehicle through a distributed stream processing platform (such as Kafka), so as to obtain the multiple types of vehicle operating state signals. Among them, the present application does not limit the communication connection method between the electronic device and the vehicle. For example, the electronic device may be communicatively connected to the vehicle through a mobile communication technology.

[0030] In other embodiments of the present application, in order to further ensure the quality of the vehicle operating state signals, the electronic device may identify the abnormal data in the multiple types of vehicle operating state signals, and delete the abnormal data from the multiple types of vehicle operating state signals.

[0031] Among them, the electronic device may determine the abnormal data in the multiple types of vehicle operating state signals through a preset reference value. The preset reference value corresponding to each type of vehicle operating state signal may be different, and the preset reference value may be set customarily, and the present application does not limit this. For example, if the multiple types of vehicle operating state signals include the rotational speed of the vehicle's engine, the driving speed of the vehicle, and the voltage of the vehicle's battery, the preset reference value corresponding to the rotational speed of the engine may be 12,000 revolutions per minute, the preset reference value corresponding to the driving speed of the vehicle may be 300 kilometers per hour, and the preset reference value corresponding to the battery voltage may be 16 volts.

[0032] Exemplarily, if any data in the operating state signal is greater than the corresponding preset reference value, it is determined that the data is abnormal, and the data is deleted from the corresponding operating state signal or the data is set to null (for example, the abnormal data is replaced with data such as NULL that is used to identify invalid or null values). For example, continuing from the above, if the rotational speed of the engine is greater than 12,000 revolutions per minute, it can be determined that the rotational speed of the engine is abnormal; if the driving speed of the vehicle is greater than 300 kilometers per hour, it can be determined that the driving speed of the vehicle is abnormal; if the battery voltage is greater than 16 volts, it can be determined that the battery voltage is abnormal.

[0033] In some embodiments of the present application, the situation of data loss in the operating state signal of the vehicle can stem from two cases: The first case is that abnormal data is deleted from the multiple types of operating state signals or the abnormal data is set to null, and the second case is that the multiple types of operating state signals themselves include data that is null.

[0034] For the convenience of clearly describing the processing of the operating state signal in the following text, in the embodiments of the present application, the operating state signal with data loss is determined as the first type of operating state signal, and the operating state signal including complete data is determined as the second type of operating state signal. The specific reasons for the data loss of the first type of operating state signal in the present application are not limited.

[0035] S12. Based on the correlation analysis of the multiple types of operating state signals, determine the correlation type between the multiple types of operating state signals.

[0036] In some embodiments of the present application, the correlation type includes linear correlation and non-linear correlation. The electronic device can perform correlation analysis on the multiple types of operating state signals through various methods, and the present application does not limit this.

[0037] Exemplarily, the electronic device can use correlation algorithms such as linear regression analysis, Pearson correlation coefficient, and Spearman rank correlation coefficient to determine the correlation type between the multiple types of operating state signals.

[0038] Considering that the operating state signals of the vehicle often influence and are related to each other. For example, the driving speed of the vehicle is positively correlated with the rotational speed of the engine, the battery voltage is positively correlated with the rotational speed of the engine, and the stability of the battery voltage affects the driving speed of the vehicle. Therefore, in this embodiment, correlation algorithms are used to analyze the correlation type between the multiple types of operating state signals, so as to facilitate subsequent interpolation processing using the model corresponding to the correlation type.

[0039] S13. Based on the second type of operating state signals, using the model corresponding to the correlation type, interpolate the first type of operating state signals to obtain the updated state signals corresponding to the multiple types of operating state signals.

[0040] In some embodiments of the present application, linear correlation and non - linear correlation in the correlation type can correspond to different regression models. The model can be a regression model. Hereinafter, taking the linear correlation corresponding to the first regression model and the non - linear correlation corresponding to the second regression model as examples, the determination process of the target data will be described in detail.

[0041] Exemplarily, the electronic device interpolates the first type of operating state signals based on the second type of operating state signals using the model corresponding to the correlation type to obtain the updated state signals corresponding to the multiple types of operating state signals, including: if the correlation type is linear correlation, based on the second type of operating state signals, use the first regression model to determine the missing target data in the first type of operating state signals; if the correlation type is non - linear correlation, based on the second type of operating state signals, use the second regression model to determine the target data, and according to the target data, interpolate the first type of operating state signals to obtain the updated state signals.

[0042] Among them, the first regression model can be trained through a linear regression model, and the second regression model can be trained through a polynomial regression model. When the second type of operating state signals corresponds to multiple categories, the first regression model can be trained through a multiple linear regression model, and the second regression model can be trained through a multiple polynomial regression model.

[0043] The second type of operating state signals corresponds to independent variables in the first regression model and the second regression model, and the target data corresponds to dependent variables in the first regression model and the second regression model.

[0044] For example, the first regression model can refer to the following formula (1), and the second regression model can refer to the following formula (2): ; (1) ; (2) Among them, represents the dependent variable, , … represent the independent variables, , , … represent the regression coefficients, represents the error term.

[0045] In this embodiment, according to the correlation type of the multiple types of operating state signals, the association relationship between the multiple types of state signals can be accurately determined, and by specifically selecting the regression model corresponding to the correlation type to determine the target data, the accuracy of the target data can be improved.

[0046] In other embodiments of the present application, the electronic device can determine whether the time points with data missing among the time points corresponding to the first type of operating state signal meet the interpolation condition. If there are time points with data missing that meet the preset interpolation condition, the corresponding regression model is used to determine the target data. If there are time points with data missing that do not meet the preset interpolation condition, the target data may not be determined. Among them, the preset interpolation condition can be set customarily, and the present application does not limit this.

[0047] Exemplarily, according to the time points with data missing, data is selected from the first type of operating state signals, and the selected data includes the adjacent data of the data corresponding to the time points with data missing in the first type of operating state signals. For example, the adjacent data includes the previous frame data and the subsequent frame data of the data corresponding to the time points with data missing in the first type of operating state signals. If the selected data is valid and the time difference between the time points corresponding to the selected data is less than the preset threshold, the model corresponding to the correlation type is used to determine the missing target data in the first type of operating state signals. If the selected data is invalid, or the time difference between the time points corresponding to the selected data is greater than or equal to the preset threshold, interpolation processing may not be performed on the first type of operating state signals.

[0048] Among them, there are various methods to determine whether the selected data is valid. For example, when the selected data are all non-abnormal data, it can be determined that the selected data is valid, and the method for judging abnormal data can refer to the description of step S11. The preset threshold can be set customarily, and the present application does not limit this. For example, the preset threshold can be 30 minutes.

[0049] In some other embodiments of the present application, if the selected data includes the previous frame data and the next frame data corresponding to the time points with missing data, and if both the previous frame data and the next frame data are invalid, or the time difference between the time points corresponding to the previous frame data and the next frame data is greater than 30 minutes, due to data errors or excessive time errors, the correlation between the time points with missing data and the data corresponding to the first type of operating state signal and the second type of operating state signal is relatively low. Therefore, if the target data is directly determined based on the second type of operating state signal, the accuracy of the obtained target data may be affected. Therefore, in this embodiment, it is possible to determine whether the time points with missing data are suitable for interpolation or frame filling through preset interpolation conditions. When the time points with missing data meet the preset interpolation conditions, the model corresponding to the correlation type is used to determine the target data, which can further improve the accuracy of the target data.

[0050] As Figure 2 shown, it is a schematic diagram for determining target data provided by an embodiment of the present application. In Figure 2 it, abnormal data in multiple types of operating state signals is identified, and the abnormal data is deleted from the multiple types of operating state signals to obtain updated multiple types of operating state signals. Based on the correlation analysis of the updated multiple types of operating state signals, the correlation type is determined, and the updated multiple types of operating state signals are classified to obtain the first type of operating state signal with missing data and the second type of operating state signal with complete data. It is determined whether the time points with missing data corresponding to the first type of operating state signal meet the interpolation conditions. When the time points with missing data meet the interpolation conditions, if the correlation type is linearly correlated, the second type of operating state signal is input into the linear regression model to obtain the target data corresponding to the time points with missing data. If the correlation type is non-linearly correlated, the second type of operating state signal is input into the polynomial regression model to obtain the target data corresponding to the time points with missing data.

[0051] In some embodiments of the present application, the electronic device performs interpolation processing on the first type of operating state signal according to the target data. The obtained updated state signal includes: determining the arrangement order of the target data in the first type of operating state signal according to the time points with missing data in the time points corresponding to the first type of operating state signal, and inserting the target data into the first type of operating state signal according to the determined arrangement order, so as to obtain the updated state signal.

[0052] In this embodiment, by performing interpolation processing on the multiple types of operating state signals using accurate target data, the accuracy of the updated state signal after interpolation can be ensured.

[0053] In other embodiments of the present application, the electronic device may complete the identification and deletion of abnormal data described in step S11, the correlation analysis of the multiple types of operating status signals in step S12, and the processes of determining the target data and performing interpolation processing based on the target data through a distributed processing engine (e.g., Spark).

[0054] S14. When the updated status signal meets the fault judgment condition, determine the fault detection result of the generator according to the operating status of the generator in the vehicle.

[0055] In some embodiments of the present application, the updated status signal meeting the fault judgment condition includes one or more of the following situations: the running duration of the vehicle is greater than a first preset value, the rotational speeds of the engines in the vehicle are all greater than a second preset value, the driving speeds of the vehicle are all greater than a third preset value and the battery voltages in the vehicle are all less than a fourth preset value. Among them, the first preset value, the second preset value, the third preset value and the fourth preset value can be set customarily, and the present application does not limit this. For example, the first preset value can be 5 minutes, the second preset value can be 500 revolutions per minute, the third preset value can be zero, and the fourth preset value can be 13 volts.

[0056] In other embodiments of the present application, when the updated status signal does not meet the fault judgment condition, the electronic device may output a prompt to remind that the signal does not meet the fault judgment condition and it is impossible to determine whether the generator has a fault.

[0057] In some embodiments of the present application, the electronic device determines the fault detection result of the generator according to the operating status of the generator in the vehicle, including: if the generator does not output electrical energy or does not output electrical energy according to the preset parameter specifications, determine that the fault detection result is that the generator has a fault; if the generator outputs electrical energy or outputs electrical energy according to the preset parameter specifications, determine that the fault detection result is that the generator is normal. Among them, the preset parameter specifications can be set customarily. For example, the preset parameter specifications can be the specifications for parameters such as voltage and current. Exemplarily, if the generator outputs electrical energy at a preset voltage, it can be determined that the fault detection result is that the generator is normal; or, if the generator does not output electrical energy at the preset voltage, it can be determined that the fault detection result is that the generator has a fault.

[0058] In other embodiments of the present application, the electronic device may send the fault detection result of the generator to at least one external device, so as to realize the early warning of the generator fault. For example, the electronic device may send the fault detection result of the generator to the terminal device of the vehicle owner and the server of the vehicle operation and maintenance service point, etc.

[0059] In some embodiments, the electronic device may store the updated status signal after interpolation processing through a distributed file management system (Hadoop Distributed File System, Hdfs), so as to facilitate the processor in the electronic device to quickly and batch-read the updated status signal.

[0060] In the related art, it is often only possible to detect the situation where the control unit of the generator fails, but it is impossible to accurately identify mechanical failures of the generator, such as belt breakage, slipping, etc., resulting in inaccurate detection of generator failures. Considering that during the normal driving of the vehicle, the vehicle has a certain driving speed, the engine in the vehicle is in the starting state, and the battery voltage in the vehicle is in the discharging state. Therefore, in this embodiment, by determining whether the updated status signal meets the fault judgment condition, it is possible to determine whether the vehicle is in the working state. For example, by judging whether the running duration of the vehicle exceeds 5 minutes, the possibility of misdiagnosis caused by the sudden voltage drop at the moment of vehicle startup can be avoided; by judging that the rotational speed of the engine in the vehicle is greater than 500 revolutions per minute, it is possible to identify whether the engine has started; by judging that the driving speed of the vehicle is greater than zero, it is possible to determine whether the vehicle is in the driving state; by judging whether the battery voltage of the vehicle is less than 13 volts, it is possible to determine whether the battery is in the discharging state. When the vehicle is in the normal working state and the generator does not output electrical energy or does not output electrical energy at a preset voltage, it is determined that the generator has failed, which can ensure the accuracy of the generator fault detection result. Thus, whether it is a mechanical failure of the generator or a failure of the control unit of the generator, the embodiments of the present application can accurately determine the generator fault detection result.

[0061] As Figure 3 shown, it is a schematic diagram of generator fault judgment provided by an embodiment of the present application. In Figure 3 , judge whether the continuous running duration of the vehicle exceeds 5 minutes. When the continuous running duration of the vehicle is less than 5 minutes, it is determined that the continuous running duration does not meet the fault judgment condition. When the continuous running duration of the vehicle exceeds 5 minutes, judge whether the rotational speed of the engine is greater than 500. When the rotational speed of the engine is less than or equal to 500, it is determined that the rotational speed of the engine does not meet the fault judgment condition. When the rotational speed of the engine is greater than 500, judge whether the driving speed of the vehicle is greater than 0. When the driving speed of the vehicle is equal to 0, it is determined that the driving speed of the vehicle does not meet the fault judgment condition. When the driving speed of the vehicle is greater than 0, judge whether the battery voltage of the vehicle is less than 13 volts. When the battery voltage is greater than or equal to 13 volts, it is determined that the battery voltage does not meet the fault judgment condition. When the battery voltage is less than 13 volts, judge whether the generator of the vehicle outputs electrical energy. When the generator of the vehicle outputs electrical energy, it is determined that the generator is normal. When the generator of the vehicle does not output electrical energy, it is determined that the generator has failed.

[0062] In the vehicle generator fault detection method provided in the embodiments of the present application, considering that the operating state signals of the vehicle affect and are correlated with each other, the correlation types between the multiple types of operating state signals are analyzed, so as to facilitate subsequent interpolation processing using the model corresponding to the correlation type. According to the correlation types of the multiple types of operating state signals, the association relationship between the multiple types of state signals can be accurately determined. By specifically selecting the regression model corresponding to the correlation type to determine the target data, the accuracy of the target data can be improved. By performing interpolation processing on the multiple types of operating state signals using the accurate target data, the accuracy of the updated state signal after interpolation can be ensured. By determining whether the updated state signal meets the fault judgment condition, it can be determined whether the vehicle is in an operating state. In the case where the vehicle is in a normal operating state and the generator does not output electrical energy, it is determined that the generator has a fault, which can ensure the accuracy of the generator fault detection result.

[0063] For example, as Figure 4 shown, is a flowchart of a training method for a first regression model and a second regression model provided in an embodiment of the present application, including the following steps: S41, obtain multiple types of historical operating state signals of the vehicle, where the multiple types of historical operating state signals include the first type of historical operating state signal corresponding to the first type of operating state signal and the second type of historical operating state signal corresponding to the second type of operating state signal.

[0064] In some embodiments of the present application, the method for obtaining the multiple types of historical operating state signals may refer to the process of obtaining the multiple types of operating state signals in step S11.

[0065] S42, use a linear regression model to fit the mapping relationship between the first type of historical operating state signal and the second type of historical operating state signal to obtain a first regression model, or use a polynomial regression model to fit the mapping relationship between the first type of historical operating state signal and the second type of historical operating state signal to obtain a second regression model.

[0066] Among them, in the process of fitting the first regression model and the second regression model, the electronic device may use the second type of historical operating state signal as the independent variable and the first type of historical operating state signal as the dependent variable for fitting. Considering that when the sampling frequencies of two types of operating state signals are different, it will be difficult to perform correlation analysis on these two types of operating state signals at the same time point, resulting in inaccurate correlation types obtained by analysis. To solve the above problem, the electronic device can align any two types of operating state signals with different sampling frequencies in the multiple types of operating state signals, such as Figure 5As shown, it is a flowchart of a method for aligning operating status signals provided by an embodiment of the present application, including the following steps: S51, select one type of operating status signal from any two types of operating status signals with different sampling frequencies as the target operating status signal, and determine the target time point from the time points corresponding to the target operating status signal.

[0067] In some embodiments of the present application, the electronic device can determine the target time point through various methods.

[0068] Exemplarily, the electronic device can randomly select a time point as the target time point from the time points corresponding to the target operating status signal, or can respond to user selection and select the target time point from the time points corresponding to the target operating status signal. The above examples of the selection method of the target time point are only examples, and are not limited to this in actual applications.

[0069] S52, determine the reference data corresponding to the non-target operating status signal of the target time point in the any two types of operating status signals according to the arrangement order of the data corresponding to the target time point in the target operating status signal.

[0070] In some embodiments of the present application, each data in the target operating status signal can be arranged according to the corresponding time point. The electronic device can select the data located in the arrangement order of the data corresponding to the target time point from the non-target operating status signal as the reference data.

[0071] S53, determine the previous frame data of the reference data in the non-target operating status signal.

[0072] For example, if the reference data is the data ranked 8th in the non-target operating status signal, the previous frame data of the reference data can be the data ranked 7th in the non-target operating status signal.

[0073] S54, if the target time point is greater than or equal to the time point corresponding to the previous frame data and less than the time point corresponding to the reference data, determine the data corresponding to the non-target operating status signal at the target time point according to the previous frame data and / or the reference data.

[0074] In some embodiments of the present application, based on the reference data and / or the previous frame data of the reference data, and / or the next frame data of the reference data, the electronic device can determine the data corresponding to the non-target operating status signal at the target time point through various methods. For example, the electronic device can use the average value between the reference data and the previous frame data of the reference data as the data corresponding to the non-target operating status signal at the target time point.

[0075] To more clearly illustrate the alignment method provided by the embodiments of the present application, the following will be described by way of example. For example, if the acquisition starts at 10:00, the sampling frequency of the target operating state signal is once every 5 s, and the sampling frequency of the non-target operating state signal is once every 8 s. The data in the target operating state signal includes 0.2, 0.1... The data of the non-target operating state signal is 0.5, 0.4... Then 0.2 in the target operating state signal corresponds to the time point 10:05, 0.1 in the target operating state signal corresponds to the time point 10:10... and so on. 0.5 in the non-target operating state signal corresponds to the time point 10:08, 0.4 in the non-target operating state signal corresponds to the time point 10:16... and so on. At the target time point of 10:10, since the data 0.1 corresponding to the target time point 10:10 in the target operating state signal is ranked second, the reference data is 0.4 ranked second in the non-target operating state signal. The previous frame data of the reference data 0.4 is 0.5. Since the target time point 10:10 is greater than the time point 10:05 corresponding to the previous frame data 0.5 and less than the time point 10:16 corresponding to the reference data 0.4, the data corresponding to the non-target operating state signal at the target time point 10:10 can be determined according to the reference data 0.4 and / or the previous frame data 0.5 of the reference data.

[0076] In this embodiment, by aligning the target operating state signal and the non-target operating state signal, the data of the target operating state signal corresponding to the time point of the non-target operating state signal can be determined, and the data of the non-target operating state signal corresponding to the time point of the target operating state signal can be determined, so that the correlation analysis can be performed on any two types of operating state signals with different sampling frequencies at the same time point, and further the accuracy of the correlation type obtained by the analysis can be improved.

[0077] As Figure 6 shown, it is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device 10 can be a device such as a server, a vehicle-mounted device, a computer, a mobile phone, a tablet computer, and a notebook computer. The server can be a cloud server, which can be a single server or a server cluster. The embodiments of the present application do not impose any restrictions on the specific type of the electronic device 10.

[0078] In Figure 6 it, the electronic device 10 may include a communication module 101, a memory 102, a processor 103, an input / output (I / O) interface 104, and a bus 105. The processor 103 is respectively coupled to the communication module 101, the memory 102, and the input / output interface 104 through the bus 105.

[0079] The communication module 101 may include a wired communication module and / or a wireless communication module. The wired communication module may provide one or more of the solutions for wired communication such as the universal serial bus (USB), the Controller Area Network (CAN), etc. The wireless communication module may provide one or more of the solutions for wireless communication such as wireless fidelity (Wi-Fi), Bluetooth (BT), a mobile communication network, frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc.

[0080] The memory 102 may include one or more random access memories (RAM) and one or more non-volatile memories (NVM). The random access memory can be directly read and written by the processor 103, can be used to store the executable programs (such as machine instructions) of other running programs, and can also be used to store the data of users and applications, etc. The random access memory may include a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDR SDRAM), etc.

[0081] The non-volatile memory can also store executable programs and store the data of users and applications, etc., and can be pre-loaded into the random access memory for direct reading and writing by the processor 103. The non-volatile memory may include a disk storage device, a flash memory.

[0082] The memory 102 is used to store one or more computer programs. The one or more computer programs are configured to be executed by the processor 103. The one or more computer programs include a plurality of instructions. When the plurality of instructions are executed by the processor 103, a vehicle generator fault detection method executable on the electronic device 10 can be implemented.

[0083] In other embodiments, such as Figure 6 the electronic device 10 shown in FIG. also includes an external memory interface for connecting to an external memory to expand the storage capacity of the electronic device 10.

[0084] The processor 103 may include one or more processing units. For example, the processor 103 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated in one or more processors.

[0085] The processor 103 provides computing and control capabilities. For example, the processor 103 is used to execute the computer program stored in the memory 102 to implement the vehicle generator fault detection method described above.

[0086] The input / output interface 104 is used to provide a channel for user input or output. For example, the input / output interface 104 can be used to connect various input / output devices, such as a mouse, a keyboard, a touch device, a display screen, etc., so that the user can input information or visualize the information.

[0087] The bus 105 is at least used to provide a communication channel between the communication module 101, the memory 102, the processor 103, and the input / output interface 104 in the electronic device 10.

[0088] It can be understood that the structure shown in the embodiments of the present application does not constitute a specific limitation on the electronic device 10. In other embodiments of the present application, the electronic device 10 may include more or fewer components than shown, or combine certain components, or split certain components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0089] The embodiments of the present application also provide a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. The computer program includes program instructions. The method implemented when the program instructions are executed may refer to the methods in the above various embodiments of the present application.

[0090] Among them, the computer-readable storage medium can be the internal memory of the electronic device described in the above embodiments, such as the hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk equipped on the electronic device, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0091] In some embodiments, the computer-readable storage medium may include a storage program area and a storage data area. Among them, the storage program area can store an operating system, application programs required for at least one function, etc.; the storage data area can store data created according to the use of the electronic device, etc.

[0092] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0093] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] In addition, in each embodiment of the present application, the various functional modules can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware, or in the form of a hardware plus a software functional module.

[0095] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present application is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. Any associated drawing marks in the claims should not be regarded as limiting the claimed rights.

[0096] In addition, obviously, the term "including" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units or devices described in the present application can also be implemented by one unit or device through software or hardware. The terms such as first and second are used to represent names and do not indicate any specific order.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit them. Although the present application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for detecting vehicle generator faults, characterized in that, The method includes: Obtaining multiple types of vehicle operating status signals, where the multiple types of vehicle operating status signals at least include a first type of vehicle operating status signal with missing data and a second type of vehicle operating status signal with complete data; Based on the correlation analysis of the multiple types of vehicle operating status signals, determining the correlation type between the multiple types of vehicle operating status signals; Based on the second type of vehicle operating status signal, using the model corresponding to the correlation type, performing interpolation processing on the first type of vehicle operating status signal to obtain an updated status signal corresponding to the multiple types of vehicle operating status signals; In the case where the updated status signal meets the fault judgment condition, determining the fault detection result of the generator according to the operating status of the generator in the vehicle.

2. The vehicle generator fault detection method according to claim 1, wherein, The step of based on the second type of vehicle operating status signal, using the model corresponding to the correlation type, performing interpolation processing on the first type of vehicle operating status signal to obtain an updated status signal corresponding to the multiple types of vehicle operating status signals includes: If the correlation type is linear correlation, based on the second type of vehicle operating status signal, using a first regression model to determine the missing target data in the first type of vehicle operating status signal; If the correlation type is non - linear correlation, based on the second type of vehicle operating status signal, using a second regression model to determine the target data; According to the target data, performing interpolation processing on the first type of vehicle operating status signal to obtain the updated status signal.

3. The vehicle generator fault detection method according to claim 2, wherein The method further includes: Obtaining multiple types of historical vehicle operating status signals, where the multiple types of historical vehicle operating status signals include a first type of historical vehicle operating status signal corresponding to the first type of vehicle operating status signal and a second type of historical vehicle operating status signal corresponding to the second type of vehicle operating status signal; Using a linear regression model to fit the mapping relationship between the first type of historical vehicle operating status signal and the second type of historical vehicle operating status signal to obtain the first regression model; or Using a polynomial regression model to fit the mapping relationship between the first type of historical vehicle operating status signal and the second type of historical vehicle operating status signal to obtain the second regression model.

4. The vehicle generator fault detection method according to claim 1, characterized in that, The updated status signal includes the running duration of the vehicle, the rotational speed of the engine of the vehicle, the driving speed of the vehicle, and the battery voltage of the vehicle. The updated status signal meeting the fault judgment condition includes one or more of the following situations: The running duration is greater than a first preset value, the rotational speeds are all greater than a second preset value, the driving speeds are all greater than a third preset value, and the battery voltage is all less than a fourth preset value.

5. The vehicle generator fault detection method according to claim 1, wherein, The step of determining the fault detection result of the generator according to the operating status of the generator in the vehicle includes: If the generator does not output electrical energy or does not output electrical energy according to the preset parameter specifications, determining that the fault detection result is that the generator is faulty; If the generator outputs electrical energy or outputs electrical energy according to the preset parameter specifications, determining that the fault detection result is that the generator is normal.

6. The vehicle generator fault detection method according to claim 1, wherein, The method further includes: aligning any two types of vehicle operating status signals with different sampling frequencies among the multiple types of vehicle operating status signals, including: Select one type of operating state signal from any two types of operating state signals as the target operating state signal, and determine the target time point from the time points corresponding to the target operating state signal; Determine the reference data corresponding to the non-target operating state signal in any two types of operating state signals for the target time point according to the arrangement order of the data corresponding to the target time point in the target operating state signal; Determine the previous frame of data of the reference data in the non-target operating state signal; If the target time point is greater than or equal to the time point corresponding to the previous frame of data and less than the time point corresponding to the reference data, determine the data corresponding to the non-target operating state signal at the target time point according to the previous frame of data and / or the reference data.

7. The vehicle generator fault detection method according to claim 1, characterized in that, The method further includes: Identify abnormal data in the multiple types of operating state signals; Delete the abnormal data from the multiple types of operating state signals.

8. The vehicle generator fault detection method according to claim 1, characterized in that, The method further includes: Select data from the first type of operating state signal according to the time points with missing data among the time points corresponding to the first type of operating state signal, where the selected data includes the adjacent data of the data corresponding to the time points with missing data in the first type of operating state signal; If the selected data is valid and the time difference between the time points corresponding to the selected data is less than a preset threshold, perform interpolation processing on the first type of operating state signal by using the model corresponding to the correlation type.

9. An electronic device, characterized in that, The electronic device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the electronic device implements the vehicle generator fault detection method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor in an electronic device, the vehicle generator fault detection method according to any one of claims 1 to 8 is implemented.

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