Method and system for monitoring and analyzing real-time work of oscillator

Abstract

The invention relates to the technical field of oscillator work monitoring, in particular to a real-time work monitoring analysis method and system for an oscillator, and the method specifically comprises the steps: synchronously capturing the real-time road condition data of a bumpy road surface and the real-time driving data of a vehicle through a micro sensor; judging a vehicle avoidance mode; fitting and predicting actual amplitude data reaching the oscillator in different vehicle avoidance modes; predicting and calculating the real-time working frequency offset of the oscillator at different data acquisition time points according to the judgment result of the vehicle avoidance mode; and dynamically adjusting the real-time working frequency of the oscillator upwards according to the predicted offset of the real-time working frequency of the oscillator. According to the invention, the problem in the prior art that the working frequency of the vehicle-gauge-level active crystal oscillator is unstable due to the fact that the vehicle-gauge-level active crystal oscillator is easily influenced by the vibration of a vehicle body suspension in the vehicle driving process is solved.

Description

Technical Field

[0001] The invention relates to the technical field of oscillator operation monitoring, and relates to a real-time operation monitoring and analysis method and system for an oscillator. Background Art

[0002] With the development of the automobile industry, the application of in-vehicle electronic equipment is becoming more and more extensive. As a key component in the vehicle electronic system, automotive-grade active crystal oscillators are widely used in the vehicle's navigation, communication, entertainment systems and various sensors. These oscillators need to maintain high accuracy and stability in different working environments to ensure the normal operation of the vehicle's electronic system.

[0003] However, when the vehicle is driving, especially when passing through potholes, the body suspension system will vibrate, and these vibrations will be transmitted to the on-board oscillator. Due to the sensitivity of the oscillator to vibration, the suspension vibration will cause the oscillator's operating frequency to shift, thereby affecting its performance and accuracy. This frequency instability not only affects the normal operation of the oscillator, but may also cause the performance of the on-board electronic system to deteriorate, thereby affecting the safety and comfort of the entire vehicle.

[0004] Although there are some studies on the frequency stability of oscillators in the prior art, there are still some problems in practical applications. First, the existing technology usually fails to fully consider the actual impact of the complex vibration of the suspension system of the vehicle on the oscillator during driving. Secondly, most existing solutions can only take corrective measures after the oscillator has a frequency offset, and fail to achieve effective monitoring and pre-adjustment of the real-time working status of the oscillator. Therefore, how to monitor the working status of the oscillator in real time during vehicle driving, and dynamically adjust the working frequency of the oscillator according to the driving environment of the vehicle and the vibration of the suspension, is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The technical problem to be solved by the present invention is that in the prior art, the automotive-grade active crystal oscillator is easily affected by the vibration of the vehicle body suspension during vehicle driving, resulting in unstable operating frequency. A real-time working monitoring and analysis method and system for the oscillator are proposed.

[0006] In order to achieve the above object, the technical solution of a real-time operation monitoring and analysis method of an oscillator of the present invention comprises the following steps:

[0007] S1: Collect multiple sets of historical driving data when the vehicle passes through the pothole road and multiple sets of historical working adjustment data of the oscillator; synchronously capture the real-time road condition data of the pothole road and the real-time driving data of the vehicle through micro sensors;

[0008] S2: extract multiple sets of historical driving data, and determine the vehicle avoidance mode according to the real-time road condition data, the real-time vehicle driving data and the vehicle body structure data;

[0009] S3: fitting and predicting actual amplitude data reaching the oscillator in different vehicle avoidance modes based on multiple sets of historical driving data when the vehicle passes through potholes and multiple sets of historical working adjustment data of the oscillator;

[0010] S4: extract multiple groups of historical operating frequency offset data of the oscillator, and predict and calculate the real-time operating frequency offset of the oscillator at different data collection time points according to the vehicle avoidance mode judgment result;

[0011] S5: dynamically upwardly adjusting the real-time operating frequency of the oscillator according to the predicted real-time operating frequency offset of the oscillator.

[0012] Specifically, S2 includes the following specific steps:

[0013] S21: extract the historical driving data and construct a front wheel avoidance data driving prediction model, wherein the historical driving data includes: the maximum steering angle δ of the front wheels of the vehicle f , the actual avoidance distance from the pothole when the front wheel changes angle, the width KD and depth SD of the pothole, and the historical driving speed of the vehicle;

[0014] S22: performing data cleaning processing on the historical driving data, wherein the data cleaning processing includes: removing outliers, filling missing values ​​and data standardization;

[0015] S23: Importing the historical driving data after data cleaning into the feature interaction item calculation strategy, wherein the feature interaction items include: vehicle feature interaction items and pothole feature interaction items; the vehicle feature interaction item is the product of the maximum steering angle of the front wheels of the vehicle and the historical driving speed of the vehicle, and the pothole feature interaction item is the product of the actual avoidance distance from the pothole on the road surface when the angle of the front wheels is converted, and the width KD and depth SD of the pothole on the road surface;

[0016] S24: combining the vehicle feature interaction item and the pothole feature interaction item into a feature vector form, obtaining a historical front wheel avoidance data feature vector set, and normalizing the historical front wheel avoidance data feature vector set;

[0017] S25: dividing the historical front wheel avoidance data feature vector set into a training set and a test set in a ratio of 8:2, and training the front wheel avoidance data driven prediction model through the test set;

[0018] S26: Input the real-time road condition data of the vehicle's current driving road and the vehicle's real-time driving data into the trained front wheel avoidance data-driven prediction model, and output the avoidance results of the vehicle's front wheels on potholes in the road, wherein the avoidance results include: successful front wheel avoidance and failed front wheel avoidance.

[0019] Specifically, S2 also includes the following specific steps:

[0020] S27: when the output of the data-driven prediction model is that the front wheel avoidance fails, it is determined that both the front wheel and the rear wheel of the vehicle cannot avoid the maneuver, and the vehicle avoidance mode is the first avoidance mode; when the output of the data-driven prediction model is that the front wheel avoidance succeeds, step S28 is executed;

[0021] S28: extracting real-time road condition data and vehicle body structure data to calculate the relative position of the rear wheels, and obtaining the relative position of the rear wheels. The specific calculation strategy of the relative position of the rear wheels is:

[0022]

[0023] Among them, X r D is the relative distance between the rear wheel and the edge of the pothole when the center of the front wheel leaves the edge of the pothole; f It indicates the minimum distance between the front tire and the edge of the pothole when the front wheel is avoiding the pothole. L is the wheel spacing between the center of the front wheel and the rear wheel. A is the axle distance between the front axle and the rear axle of the vehicle.

[0024] S29: According to S28, the width KD of the pothole is extracted, and the rear wheel avoidance mode is determined. Specifically, when X r >KD, the rear wheel avoidance mode of the vehicle is judged to be successful. r When ≤KD, the vehicle rear wheel avoidance mode is judged as avoidance failure;

[0025] S210: According to S27-S29, the vehicle avoidance mode is judged. When the front wheel avoids successfully and the rear wheel also avoids successfully, the vehicle avoidance mode is judged to be the second avoidance mode; when the front wheel avoids successfully but the rear wheel avoids unsuccessfully, the vehicle avoidance mode is judged to be the third avoidance mode.

[0026] Specifically, S3 includes the following specific steps:

[0027] S31: Based on multiple sets of historical driving data when the vehicle passes through a pothole, predict the front suspension amplitude and the rear suspension amplitude of the vehicle passing through the pothole, as follows:

[0028]

[0029] Among them, t is the data acquisition time point, and the total length of a real-time working monitoring period of the oscillator is T, 0 <t≤T;

[0030] zf fr (t) is the predicted front suspension amplitude when the vehicle passes through a pothole on the road, when the data is collected at time t;

[0031] is the mean value of the front suspension amplitude data in multiple groups of historical driving data when the data collection time point is t;

[0032] zf be (t) is the predicted rear suspension amplitude when the vehicle passes through a pothole on the road, when the data is collected at time t;

[0033] is the mean value of the rear suspension amplitude data in multiple groups of historical driving data when the data collection time point is t;

[0034] v t Indicates the real-time speed of the vehicle when the data collection time is t;

[0035] It represents the average speed of vehicles in multiple groups of historical driving data at the data collection time point t;

[0036] g t,fr It represents the load on the front suspension of the vehicle when the data is collected at time t;

[0037] It represents the mean value of the vehicle front suspension load in multiple groups of historical driving data when the data collection time point is t;

[0038] g t,be It represents the load on the rear suspension of the vehicle when the data is collected at time t;

[0039] It represents the mean value of the vehicle rear suspension load in multiple groups of historical driving data when the data collection time point is t;

[0040] ks represents the ratio of the width KD and depth SD of the pothole that the vehicle is currently passing;

[0041] It represents the average value of the ratio of the width KD and the depth SD of the potholes in multiple sets of historical driving data;

[0042] α1, α2, and α3 are the vehicle speed contribution factor, suspension load contribution factor, and pothole feature contribution factor, respectively;

[0043] S32: According to multiple groups of historical working adjustment data of the oscillator, actual amplitude data reaching the oscillator in different vehicle avoidance modes are fitted and predicted, as follows:

[0044]

[0045] Among them, q zdq,fr (t),q zdq,be (t) respectively represent the actual amplitude data transmitted to the oscillator by the front suspension and the rear suspension at the data acquisition time point t;

[0046] μ fr ,μ be are the transmission loss coefficient of the front suspension and the transmission loss coefficient of the rear suspension respectively;

[0047] is an average value of actual amplitude data transmitted from the front suspension to the oscillator among multiple groups of historical working adjustment data of the oscillator;

[0048] The actual amplitude data transmitted from the rear suspension to the oscillator are averaged among multiple groups of historical working adjustment data of the oscillator.

[0049] Specifically, S4 includes the following specific steps:

[0050] S41: extracting multiple groups of historical operating frequency offset data of the oscillator, and drawing a time series graph of the multiple groups of historical operating frequency offset data of the oscillator, wherein the historical operating frequency offset data of the oscillator is H groups in total, and the historical operating frequency offset data includes time period data of the front and rear wheels passing through the potholes on the road surface and the operating frequency offset value of the oscillator;

[0051] S42: Extract the starting time point of the historical operating frequency deviation of the oscillator caused by the front suspension in the hth group of time series graphs and end time Synchronous extraction of the starting time point when the suspension causes the historical operating frequency of the oscillator to shift and end time

[0052] S43: According to S42, determine the maximum start time of the historical operating frequency deviation of the oscillator in the hth group of time series graphs and minimum end time Among them, the maximum start time The minimum end time

[0053] S44: Set the maximum starting time of the historical operating frequency deviation of the oscillator in the hth group of time series graphs and minimum end time The coupling duration calculation strategy of the front and rear suspension coupling vibration period is introduced to calculate the coupling duration of the front and rear suspension coupling vibration period to which the oscillator in the hth group of time series graphs is subjected. The coupling duration calculation strategy of the front and rear suspension coupling vibration period is specifically as follows:

[0054]

[0055] S45: Calculate the average value R of the coupling duration of the front and rear suspension coupling vibration period to which the oscillator is subjected in the H group time series diagram H , the average value R H Set to standard coupling duration.

[0056] Specifically, in S4, the prediction calculation of the real-time operating frequency offset of the oscillator at different data collection time points includes: the real-time operating frequency offset of the oscillator caused by the front suspension and the real-time operating frequency offset of the oscillator caused by the rear suspension;

[0057] The real-time operating frequency offset of the oscillator caused by the front suspension is specifically:

[0058]

[0059] Among them, py new,fr (t) represents the predicted value of the real-time operating frequency offset of the oscillator caused by the front suspension when the data collection time point is t;

[0060] The average value of the historical working frequency deviation of the oscillator in multiple sets of historical working adjustment data of the oscillator

[0061] The real-time operating frequency offset of the oscillator caused by the rear suspension is specifically:

[0062]

[0063] Among them, py new,be (t) represents the predicted value of the real-time operating frequency offset of the oscillator caused by the rear suspension when the data collection time point is t.

[0064] Specifically, in S5, the real-time operating frequency of the oscillator is dynamically adjusted upward according to the predicted real-time operating frequency offset of the oscillator, as follows:

[0065] S51: When the vehicle avoidance mode is the first avoidance mode, predict the starting time point e1 of the working frequency deviation of the oscillator caused by the front suspension, and the starting time point e2 of the working frequency deviation of the oscillator caused by the rear suspension; within a real-time working monitoring period T of the oscillator, when t∈(0,e2), the pre-adjustment amount of the real-time working frequency of the oscillator is py new,fr (t); when t∈(e2,e2+R H ), the pre-adjustment amount of the real-time operating frequency of the oscillator is When t∈(e2+R H,T), the pre-adjustment amount of the real-time operating frequency of the oscillator is py new,be (t);

[0066] S52: When the vehicle avoidance mode is the second avoidance mode, there is no need to pre-adjust the real-time operating frequency of the oscillator;

[0067] S53: When the vehicle avoidance mode is the third avoidance mode, when t∈(e2,T), the pre-adjustment amount of the real-time operating frequency of the oscillator is py new,be (t).

[0068] In addition, the real-time operation monitoring and analysis system of an oscillator of the present invention includes the following modules:

[0069] Data acquisition module, avoidance mode judgment module, amplitude prediction module, oscillator real-time operation monitoring module and oscillator real-time operation adjustment module;

[0070] The data acquisition module is used to collect multiple sets of historical driving data when the vehicle passes through a pothole road and multiple sets of historical working adjustment data of the oscillator; and synchronously capture the real-time road condition data of the pothole road and the real-time driving data of the vehicle through the micro sensor;

[0071] The avoidance mode judgment module is used to extract multiple sets of historical driving data, and to judge the vehicle avoidance mode according to the real-time road condition data, the real-time vehicle driving data and the vehicle body structure data;

[0072] The amplitude prediction module fits and predicts the actual amplitude data reaching the oscillator under different vehicle avoidance modes based on multiple sets of historical driving data when the vehicle passes through potholes and multiple sets of historical working adjustment data of the oscillator;

[0073] The oscillator real-time operation monitoring module is used to extract multiple groups of historical operating frequency offset data of the oscillator, and predict and calculate the real-time operating frequency offset of the oscillator according to the vehicle avoidance mode judgment result;

[0074] The oscillator real-time operation adjustment module dynamically adjusts the real-time operation frequency of the oscillator upward according to the predicted real-time operation frequency offset of the oscillator.

[0075] A storage medium stores instructions, and when a computer reads the instructions, the computer executes the real-time operation monitoring and analysis method of an oscillator.

[0076] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned real-time operation monitoring and analysis method of an oscillator is implemented.

[0077] Compared with the prior art, the technical effects of the present invention are as follows:

[0078] 1. Through pre-calibration, the system of the present invention can automatically adjust before the front and rear suspensions are expected to cause vibration interference to the oscillator, thereby reducing the delay caused by real-time calibration.

[0079] 2. The present invention monitors the real-time working data of the oscillator and pre-calibrates the real-time working frequency of the oscillator, thereby reducing the frequency and phase fluctuations caused by vibration and impact and improving the stability of the overall system.

[0080] 3. The present invention takes into account the coupled vibration of the front and rear suspensions and improves the reliability of the oscillator in various dynamic environments through a precise pre-calibration strategy, thereby ensuring the normal operation of the electronic system. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A schematic diagram of a flow chart of a real-time operation monitoring and analysis method of an oscillator according to the present invention;

[0082] Figure 2 The present invention is a schematic structural diagram of a real-time operation monitoring and analysis system for an oscillator. DETAILED DESCRIPTION

[0083] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0084] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0085] Embodiment 1:

[0086] like Figure 1 As shown, a real-time operation monitoring and analysis method of an oscillator according to an embodiment of the present invention is as follows: Figure 1 As shown, the specific steps are as follows:

[0087] S1: Collect multiple sets of historical driving data when the vehicle passes through the pothole road and multiple sets of historical working adjustment data of the oscillator; synchronously capture the real-time road condition data of the pothole road and the real-time driving data of the vehicle through micro sensors;

[0088] S2: extract multiple sets of historical driving data, and determine the vehicle avoidance mode according to the real-time road condition data, the real-time vehicle driving data and the vehicle body structure data;

[0089] S2 includes the following specific steps:

[0090] S21: extract the historical driving data and construct a front wheel avoidance data driving prediction model, wherein the historical driving data includes: the maximum steering angle δ of the front wheels of the vehicle f , the actual avoidance distance from the pothole when the front wheel changes angle, the width KD and depth SD of the pothole, and the historical driving speed of the vehicle;

[0091] S22: performing data cleaning processing on the historical driving data, wherein the data cleaning processing includes: removing outliers, filling missing values ​​and data standardization;

[0092] S23: Importing the historical driving data after data cleaning into the feature interaction item calculation strategy, wherein the feature interaction items include: vehicle feature interaction items and pothole feature interaction items; the vehicle feature interaction item is the product of the maximum steering angle of the front wheels of the vehicle and the historical driving speed of the vehicle, and the pothole feature interaction item is the product of the actual avoidance distance from the pothole on the road surface when the angle of the front wheels is converted, and the width KD and depth SD of the pothole on the road surface;

[0093] S24: combining the vehicle feature interaction item and the pothole feature interaction item into a feature vector form, obtaining a historical front wheel avoidance data feature vector set, and normalizing the historical front wheel avoidance data feature vector set;

[0094] S25: dividing the historical front wheel avoidance data feature vector set into a training set and a test set in a ratio of 8:2, and training the front wheel avoidance data driven prediction model through the test set;

[0095] S26: Input the real-time road condition data of the vehicle's current driving road and the vehicle's real-time driving data into the trained front wheel avoidance data-driven prediction model, and output the avoidance results of the vehicle's front wheels on potholes in the road, wherein the avoidance results include: successful front wheel avoidance and failed front wheel avoidance.

[0096] S2 also includes the following specific steps:

[0097] S27: when the output of the data-driven prediction model is that the front wheel avoidance fails, it is determined that both the front wheel and the rear wheel of the vehicle cannot avoid the maneuver, and the vehicle avoidance mode is the first avoidance mode; when the output of the data-driven prediction model is that the front wheel avoidance succeeds, step S28 is executed;

[0098] S28: extracting real-time road condition data and vehicle body structure data to calculate the relative position of the rear wheels, and obtaining the relative position of the rear wheels. The specific calculation strategy of the relative position of the rear wheels is:

[0099]

[0100] Among them, Xr D is the relative distance between the rear wheel and the edge of the pothole when the center of the front wheel leaves the edge of the pothole; f It indicates the minimum distance between the front tire and the edge of the pothole when the front wheel is avoiding the pothole. L is the wheel spacing between the center of the front wheel and the rear wheel. A is the axle distance between the front axle and the rear axle of the vehicle.

[0101] S29: According to S28, the width KD of the pothole is extracted, and the rear wheel avoidance mode is determined. Specifically, when X r >KD, the rear wheel avoidance mode of the vehicle is judged to be successful. r When ≤KD, the vehicle rear wheel avoidance mode is judged as avoidance failure;

[0102] S210: According to S27-S29, the vehicle avoidance mode is judged. When the front wheel avoids successfully and the rear wheel also avoids successfully, the vehicle avoidance mode is judged to be the second avoidance mode; when the front wheel avoids successfully but the rear wheel avoids unsuccessfully, the vehicle avoidance mode is judged to be the third avoidance mode.

[0103] S3: fitting and predicting actual amplitude data reaching the oscillator in different vehicle avoidance modes based on multiple sets of historical driving data when the vehicle passes through potholes and multiple sets of historical working adjustment data of the oscillator;

[0104] S3 includes the following specific steps:

[0105] S31: Based on multiple sets of historical driving data when the vehicle passes through a pothole, predict the front suspension amplitude and the rear suspension amplitude of the vehicle passing through the pothole, as follows:

[0106]

[0107] Among them, t is the data acquisition time point, and the total length of a real-time working monitoring period of the oscillator is T, 0 <t≤T;

[0108] Exemplarily, in this embodiment, the starting data collection point of a real-time working monitoring period of the oscillator is the time point when the front wheel first contacts the edge of the pothole on the road surface; the ending data collection point of a real-time working monitoring period of the oscillator is the time point when the rear wheel finally leaves the edge of the pothole on the road surface;

[0109] zf fr (t) is the predicted front suspension amplitude when the vehicle passes through a pothole on the road, when the data is collected at time t;

[0110] is the mean value of the front suspension amplitude data in multiple groups of historical driving data when the data collection time point is t;

[0111] zfbe (t) is the predicted rear suspension amplitude when the vehicle passes through a pothole on the road, when the data is collected at time t;

[0112] is the mean value of the rear suspension amplitude data in multiple groups of historical driving data when the data collection time point is t;

[0113] v t Indicates the real-time speed of the vehicle when the data collection time is t;

[0114] It represents the average speed of vehicles in multiple groups of historical driving data at the data collection time point t;

[0115] g t,fr It represents the load on the front suspension of the vehicle when the data is collected at time t;

[0116] It represents the mean value of the vehicle front suspension load in multiple groups of historical driving data when the data collection time point is t;

[0117] g t,be It represents the load on the rear suspension of the vehicle when the data is collected at time t;

[0118] It represents the mean value of the vehicle rear suspension load in multiple groups of historical driving data when the data collection time point is t;

[0119] ks represents the ratio of the width KD and depth SD of the pothole that the vehicle is currently passing;

[0120] It represents the average value of the ratio of the width KD and the depth SD of the potholes in multiple sets of historical driving data;

[0121] α1, α2, and α3 are the vehicle speed contribution factor, suspension load contribution factor, and pothole feature contribution factor, respectively;

[0122] S32: According to multiple groups of historical working adjustment data of the oscillator, actual amplitude data reaching the oscillator in different vehicle avoidance modes are fitted and predicted, as follows:

[0123]

[0124] Among them, q zdq,fr (t),q zdq,be (t) respectively represent the actual amplitude data transmitted to the oscillator by the front suspension and the rear suspension at the data acquisition time point t;

[0125] μ fr ,μ beare the transmission loss coefficient of the front suspension and the transmission loss coefficient of the rear suspension respectively;

[0126] is an average value of actual amplitude data transmitted from the front suspension to the oscillator among multiple groups of historical working adjustment data of the oscillator;

[0127] The actual amplitude data transmitted from the rear suspension to the oscillator are averaged among multiple groups of historical working adjustment data of the oscillator.

[0128] S4: extract multiple groups of historical operating frequency offset data of the oscillator, and predict and calculate the real-time operating frequency offset of the oscillator at different data collection time points according to the vehicle avoidance mode judgment result;

[0129] S4 includes the following specific steps:

[0130] S41: extracting multiple groups of historical operating frequency offset data of the oscillator, and drawing a time series graph of the multiple groups of historical operating frequency offset data of the oscillator, wherein the historical operating frequency offset data of the oscillator is H groups in total, and the historical operating frequency offset data includes time period data of the front and rear wheels passing through the potholes on the road surface and the operating frequency offset value of the oscillator;

[0131] S42: Extract the starting time point of the historical operating frequency deviation of the oscillator caused by the front suspension in the hth group of time series graphs and end time Synchronous extraction of the starting time point when the suspension causes the historical operating frequency of the oscillator to shift and end time

[0132] S43: According to S42, determine the maximum start time of the historical operating frequency deviation of the oscillator in the hth group of time series graphs and minimum end time Among them, the maximum start time The minimum end time

[0133] S44: Set the maximum starting time of the historical operating frequency deviation of the oscillator in the hth group of time series graphs and minimum end time The coupling duration calculation strategy of the front and rear suspension coupling vibration period is introduced to calculate the coupling duration of the front and rear suspension coupling vibration period to which the oscillator in the hth group of time series graphs is subjected. The coupling duration calculation strategy of the front and rear suspension coupling vibration period is specifically as follows:

[0134]

[0135] S45: Calculate the average value R of the coupling duration of the front and rear suspension coupling vibration period to which the oscillator is subjected in the H group time series diagram H , the average value R H Set to standard coupling duration.

[0136] In S4, the prediction calculation of the real-time operating frequency offset of the oscillator at different data collection time points includes: the real-time operating frequency offset of the oscillator caused by the front suspension and the real-time operating frequency offset of the oscillator caused by the rear suspension;

[0137] The real-time operating frequency offset of the oscillator caused by the front suspension is specifically:

[0138]

[0139] Among them, py new,fr (t) represents the predicted value of the real-time operating frequency offset of the oscillator caused by the front suspension when the data collection time point is t;

[0140] The average value of the historical working frequency deviation of the oscillator in multiple sets of historical working adjustment data of the oscillator

[0141] The real-time operating frequency offset of the oscillator caused by the rear suspension is specifically:

[0142]

[0143] Among them, py new,be (t) represents the predicted value of the real-time operating frequency offset of the oscillator caused by the rear suspension when the data collection time point is t.

[0144] S5: dynamically upwardly adjusting the real-time operating frequency of the oscillator according to the predicted real-time operating frequency offset of the oscillator.

[0145] In S5, the real-time operating frequency of the oscillator is dynamically adjusted upward according to the predicted real-time operating frequency offset of the oscillator, as follows:

[0146] S51: When the vehicle avoidance mode is the first avoidance mode, predict the starting time point e1 of the working frequency deviation of the oscillator caused by the front suspension, and the starting time point e2 of the working frequency deviation of the oscillator caused by the rear suspension; within a real-time working monitoring period T of the oscillator, when t∈(0,e2), the pre-adjustment amount of the real-time working frequency of the oscillator is py new,fr (t); when t∈(e2,e2+R H ), the pre-adjustment amount of the real-time operating frequency of the oscillator is When t∈(e2+R H,T), the pre-adjustment amount of the real-time operating frequency of the oscillator is py new,be (t);

[0147] S52: When the vehicle avoidance mode is the second avoidance mode, there is no need to pre-adjust the real-time operating frequency of the oscillator;

[0148] S53: When the vehicle avoidance mode is the third avoidance mode, when t∈(e2,T), the pre-adjustment amount of the real-time operating frequency of the oscillator is py new,be (t).

[0149] Embodiment 2:

[0150] like Figure 2 As shown, a real-time operation monitoring and analysis system of an oscillator according to an embodiment of the present invention is as follows: Figure 2 As shown, it includes the following modules:

[0151] Data acquisition module, avoidance mode judgment module, amplitude prediction module, oscillator real-time operation monitoring module and oscillator real-time operation adjustment module;

[0152] The data acquisition module is used to collect multiple sets of historical driving data when the vehicle passes through a pothole road and multiple sets of historical working adjustment data of the oscillator; and synchronously capture the real-time road condition data of the pothole road and the real-time driving data of the vehicle through the micro sensor;

[0153] The avoidance mode judgment module is used to extract multiple sets of historical driving data, and to judge the vehicle avoidance mode according to the real-time road condition data, the real-time vehicle driving data and the vehicle body structure data;

[0154] The amplitude prediction module fits and predicts the actual amplitude data reaching the oscillator under different vehicle avoidance modes based on multiple sets of historical driving data when the vehicle passes through potholes and multiple sets of historical working adjustment data of the oscillator;

[0155] The oscillator real-time operation monitoring module is used to extract multiple groups of historical operating frequency offset data of the oscillator, and predict and calculate the real-time operating frequency offset of the oscillator according to the vehicle avoidance mode judgment result;

[0156] The oscillator real-time operation adjustment module dynamically adjusts the real-time operation frequency of the oscillator upward according to the predicted real-time operation frequency offset of the oscillator.

[0157] Embodiment three:

[0158] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0159] The processor executes the above-mentioned real-time operation monitoring and analysis method of an oscillator by calling the computer program stored in the memory.

[0160] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein at least one computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement a real-time operation monitoring and analysis method of an oscillator provided in the above method embodiment. The electronic device may also include other components for implementing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface, so as to input and output data. This embodiment will not be described in detail here.

[0161] Embodiment 4:

[0162] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0163] When the computer program runs on a computer device, the computer device executes the above-mentioned real-time operation monitoring and analysis method of an oscillator.

[0164] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0165] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes 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.

[0166] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0167] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0168] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention 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 the present invention.

[0169] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0170] In the several embodiments provided by the present invention, 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 only schematic, for example, the division of units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0171] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0172] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0173] In summary, compared with the prior art, the technical effects of the present invention are as follows:

[0174] 1. Through pre-calibration, the system of the present invention can automatically adjust before the front and rear suspensions are expected to cause vibration interference to the oscillator, thereby reducing the delay caused by real-time calibration.

[0175] 2. The present invention monitors the real-time working data of the oscillator and pre-calibrates the real-time working frequency of the oscillator, thereby reducing the frequency and phase fluctuations caused by vibration and impact and improving the stability of the overall system.

[0176] 3. The present invention takes into account the coupled vibration of the front and rear suspensions and improves the reliability of the oscillator in various dynamic environments through a precise pre-calibration strategy, thereby ensuring the normal operation of the electronic system.

[0177] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A real-time operation monitoring and analysis method for an oscillator, characterized in that: The method comprises the following specific steps: S1: Collect multiple sets of historical driving data when the vehicle passes through the pothole road and multiple sets of historical working adjustment data of the oscillator; synchronously capture the real-time road condition data of the pothole road and the real-time driving data of the vehicle through micro sensors; S2: extract multiple sets of historical driving data, and determine the vehicle avoidance mode according to the real-time road condition data, the real-time vehicle driving data and the vehicle body structure data; S3: fitting and predicting actual amplitude data reaching the oscillator in different vehicle avoidance modes based on multiple sets of historical driving data when the vehicle passes through potholes and multiple sets of historical working adjustment data of the oscillator; S4: extract multiple groups of historical operating frequency offset data of the oscillator, and predict and calculate the real-time operating frequency offset of the oscillator at different data collection time points according to the vehicle avoidance mode judgment result; S5: dynamically upwardly adjusting the real-time operating frequency of the oscillator according to the predicted real-time operating frequency offset of the oscillator.

2. The real-time operation monitoring and analysis method of an oscillator according to claim 1, characterized in that: S2 includes the following specific steps: S21: extract the historical driving data and construct a front wheel avoidance data driving prediction model, wherein the historical driving data includes: the maximum steering angle δ of the front wheels of the vehicle f , the actual avoidance distance from the pothole when the front wheel changes angle, the width KD and depth SD of the pothole, and the historical driving speed of the vehicle; S22: performing data cleaning processing on the historical driving data, wherein the data cleaning processing includes: removing outliers, filling missing values ​​and data standardization; S23: Importing the historical driving data after data cleaning into the feature interaction item calculation strategy, wherein the feature interaction items include: vehicle feature interaction items and pothole feature interaction items; the vehicle feature interaction item is the product of the maximum steering angle of the front wheels of the vehicle and the historical driving speed of the vehicle, and the pothole feature interaction item is the product of the actual avoidance distance from the pothole on the road surface when the angle of the front wheels is converted, and the width KD and depth SD of the pothole on the road surface; S24: combining the vehicle feature interaction item and the pothole feature interaction item into a feature vector form, obtaining a historical front wheel avoidance data feature vector set, and normalizing the historical front wheel avoidance data feature vector set; S25: dividing the historical front wheel avoidance data feature vector set into a training set and a test set in a ratio of 8:2, and training the front wheel avoidance data driven prediction model through the test set; S26: Input the real-time road condition data of the vehicle's current driving road and the vehicle's real-time driving data into the trained front wheel avoidance data-driven prediction model, and output the avoidance results of the vehicle's front wheels on potholes in the road, wherein the avoidance results include: successful front wheel avoidance and failed front wheel avoidance.

3. The real-time operation monitoring and analysis method of an oscillator according to claim 2, characterized in that: S2 also includes the following specific steps: S27: when the output of the data-driven prediction model is that the front wheel avoidance fails, it is determined that both the front wheel and the rear wheel of the vehicle cannot avoid the maneuver, and the vehicle avoidance mode is the first avoidance mode; when the output of the data-driven prediction model is that the front wheel avoidance succeeds, step S28 is executed; S28: extracting real-time road condition data and vehicle body structure data to calculate the relative position of the rear wheels, and obtaining the relative position of the rear wheels. The specific calculation strategy of the relative position of the rear wheels is: Among them, X r D is the relative distance between the rear wheel and the edge of the pothole when the center of the front wheel leaves the edge of the pothole; f It indicates the minimum distance between the front tire and the edge of the pothole when the front wheel is avoiding the pothole. L is the wheel spacing between the center of the front wheel and the rear wheel. A is the axle distance between the front axle and the rear axle of the vehicle. S29: According to S28, the width KD of the pothole is extracted, and the rear wheel avoidance mode is determined. Specifically, when X r >KD, the rear wheel avoidance mode of the vehicle is judged to be successful. r When ≤KD, the vehicle rear wheel avoidance mode is judged as avoidance failure; S210: According to S27-S29, the vehicle avoidance mode is judged. When the front wheel avoids successfully and the rear wheel also avoids successfully, the vehicle avoidance mode is judged to be the second avoidance mode; when the front wheel avoids successfully but the rear wheel avoids unsuccessfully, the vehicle avoidance mode is judged to be the third avoidance mode.

4. The real-time operation monitoring and analysis method of an oscillator according to claim 3, characterized in that: S3 includes the following specific steps: S31: Based on multiple sets of historical driving data when the vehicle passes through a pothole, predict the front suspension amplitude and the rear suspension amplitude of the vehicle passing through the pothole, as follows: Among them, t is the data acquisition time point, and the total length of a real-time working monitoring period of the oscillator is T, 0 <t≤T; zf fr (t) is the predicted front suspension amplitude when the vehicle passes through a pothole on the road, when the data is collected at time t; is the mean value of the front suspension amplitude data in multiple groups of historical driving data when the data collection time point is t; zf be (t) is the predicted rear suspension amplitude when the vehicle passes through a pothole on the road, when the data is collected at time t; is the mean value of the rear suspension amplitude data in multiple groups of historical driving data when the data collection time point is t; v t Indicates the real-time speed of the vehicle when the data collection time is t; It represents the average speed of vehicles in multiple groups of historical driving data at the data collection time point t; g t,fr It represents the load on the front suspension of the vehicle when the data is collected at time t; It represents the mean value of the vehicle front suspension load in multiple groups of historical driving data when the data collection time point is t; g t,be It represents the load on the rear suspension of the vehicle when the data is collected at time t; It represents the mean value of the vehicle rear suspension load in multiple groups of historical driving data when the data collection time point is t; ks represents the ratio of the width KD and depth SD of the pothole that the vehicle is currently passing; It represents the average value of the ratio of the width KD and the depth SD of the potholes in multiple sets of historical driving data; α1, α2, and α3 are the vehicle speed contribution factor, suspension load contribution factor, and pothole feature contribution factor, respectively; S32: According to multiple groups of historical working adjustment data of the oscillator, actual amplitude data reaching the oscillator in different vehicle avoidance modes are fitted and predicted, as follows: Among them, q zdq,fr (t),q zdq,be (t) respectively represent the actual amplitude data transmitted to the oscillator by the front suspension and the rear suspension at the data acquisition time point t; μ fr ,μ be are the transmission loss coefficient of the front suspension and the transmission loss coefficient of the rear suspension respectively; is an average value of actual amplitude data transmitted from the front suspension to the oscillator among multiple groups of historical working adjustment data of the oscillator; The actual amplitude data transmitted from the rear suspension to the oscillator are averaged among multiple groups of historical working adjustment data of the oscillator.

5. The real-time operation monitoring and analysis method of an oscillator according to claim 4, characterized in that: S4 includes the following specific steps: S41: extracting multiple groups of historical operating frequency offset data of the oscillator, and drawing a time series graph of the multiple groups of historical operating frequency offset data of the oscillator, wherein the historical operating frequency offset data of the oscillator is H groups in total, and the historical operating frequency offset data includes time period data of the front and rear wheels passing through the potholes on the road surface and the operating frequency offset value of the oscillator; S42: Extract the starting time point of the historical operating frequency deviation of the oscillator caused by the front suspension in the hth group of time series graphs and end time Synchronous extraction of the starting time point when the suspension causes the historical operating frequency of the oscillator to shift and end time S43: According to S42, determine the maximum start time of the historical operating frequency deviation of the oscillator in the hth group of time series graphs and minimum end time Among them, the maximum start time The minimum end time S44: Set the maximum starting time of the historical operating frequency deviation of the oscillator in the hth group of time series graphs and minimum end time The coupling duration calculation strategy of the front and rear suspension coupling vibration period is introduced to calculate the coupling duration of the front and rear suspension coupling vibration period to which the oscillator in the hth group of time series graphs is subjected. The coupling duration calculation strategy of the front and rear suspension coupling vibration period is specifically as follows: S45: Calculate the average value R of the coupling duration of the front and rear suspension coupling vibration period to which the oscillator is subjected in the H group time series diagram H , the average value R H Set to standard coupling duration.

6. The real-time operation monitoring and analysis method of an oscillator according to claim 5, characterized in that: In S4, the prediction calculation of the real-time operating frequency offset of the oscillator at different data collection time points includes: the real-time operating frequency offset of the oscillator caused by the front suspension and the real-time operating frequency offset of the oscillator caused by the rear suspension; The real-time operating frequency offset of the oscillator caused by the front suspension is specifically: Among them, py new,fr (t) represents the predicted value of the real-time operating frequency offset of the oscillator caused by the front suspension when the data collection time point is t; The average value of the historical working frequency deviation of the oscillator in multiple sets of historical working adjustment data of the oscillator The real-time operating frequency offset of the oscillator caused by the rear suspension is specifically: Among them, py new,be (t) represents the predicted value of the real-time operating frequency offset of the oscillator caused by the rear suspension when the data collection time point is t.

7. The real-time operation monitoring and analysis method of an oscillator according to claim 6, characterized in that: In S5, the real-time operating frequency of the oscillator is dynamically adjusted upward according to the predicted real-time operating frequency offset of the oscillator, as follows: S51: When the vehicle avoidance mode is the first avoidance mode, predict the starting time point e1 of the working frequency deviation of the oscillator caused by the front suspension, and the starting time point e2 of the working frequency deviation of the oscillator caused by the rear suspension; within a real-time working monitoring period T of the oscillator, when t∈(0,e2), the pre-adjustment amount of the real-time working frequency of the oscillator is py new,fr (t); when t∈(e2,e2+R H ), the pre-adjustment amount of the real-time operating frequency of the oscillator is When t∈(e2+R H ,T), the pre-adjustment amount of the real-time operating frequency of the oscillator is py new,be (t); S52: When the vehicle avoidance mode is the second avoidance mode, there is no need to pre-adjust the real-time operating frequency of the oscillator; S53: When the vehicle avoidance mode is the third avoidance mode, when t∈(e2,T), the pre-adjustment amount of the real-time operating frequency of the oscillator is py new,be (t).

8. A real-time operation monitoring and analysis system for an oscillator, which is implemented based on a real-time operation monitoring and analysis method for an oscillator as claimed in any one of claims 1 to 7, characterized in that: The system includes the following modules: Data acquisition module, avoidance mode judgment module, amplitude prediction module, oscillator real-time operation monitoring module and oscillator real-time operation adjustment module; The data acquisition module is used to collect multiple sets of historical driving data when the vehicle passes through a pothole road and multiple sets of historical working adjustment data of the oscillator; Use micro sensors to simultaneously capture real-time road condition data of potholes and real-time vehicle driving data; The avoidance mode judgment module is used to extract multiple sets of historical driving data, and to judge the vehicle avoidance mode according to the real-time road condition data, the real-time vehicle driving data and the vehicle body structure data; The amplitude prediction module fits and predicts the actual amplitude data reaching the oscillator under different vehicle avoidance modes based on multiple sets of historical driving data when the vehicle passes through potholes and multiple sets of historical working adjustment data of the oscillator; The oscillator real-time operation monitoring module is used to extract multiple groups of historical operating frequency offset data of the oscillator, and predict and calculate the real-time operating frequency offset of the oscillator according to the vehicle avoidance mode judgment result; The oscillator real-time operation adjustment module dynamically adjusts the real-time operation frequency of the oscillator upward according to the predicted real-time operation frequency offset of the oscillator.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a real-time operation monitoring and analysis method of an oscillator as claimed in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: A memory for storing instructions; The processor is used to execute the instruction so that the device performs the operation of implementing the real-time operation monitoring and analysis method of an oscillator as described in any one of claims 1-7.

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