Maintenance reminding method and system based on time sequence data of motorcycle internet of vehicles

Abstract

The invention provides a maintenance reminding method and system based on motorcycle Internet of Vehicles time series data, and the method comprises the steps: obtaining a motorcycle identification code and multi-dimensional time series data according to a time series data package periodically transmitted by a motorcycle vehicle-mounted terminal; the multi-dimensional time sequence data and a plurality of previously obtained multi-dimensional time sequence data are constructed into a time sequence data sample, then the time sequence data sample is input into a maintenance diagnosis model corresponding to a motorcycle model determined according to a motorcycle identification code for maintenance diagnosis, a maintenance diagnosis result is obtained, and according to the maintenance diagnosis result, the motorcycle model is subjected to maintenance diagnosis. And whether a maintenance prompt is sent to the motorcycle-mounted terminal is determined. Therefore, maintenance diagnosis can be performed on the related time sequence data sent by the motorcycle vehicle-mounted terminal in a targeted manner by using the corresponding maintenance diagnosis model, so that the maintenance reminding is more accurate, and the maintenance requirement of the actual working condition of the motorcycle is better met.

Description

Technical Field

[0001] The present invention relates to the technical field of motorcycle data analysis, and in particular to a maintenance reminder method and system based on motorcycle vehicle networking time series data. Background Art

[0002] In order to stand out in the fierce market competition, motorcycle manufacturers and related enterprises have increased their R&D and investment in Internet of Vehicles technology. By launching motorcycle products with Internet of Vehicles functions, they can enhance the added value and competitiveness of products and attract more consumers to buy. At the same time, Internet of Vehicles also provides enterprises with more opportunities to interact and communicate with users, which helps enterprises better understand user needs and improve products and services.

[0003] The Internet of Vehicles system provides users with services such as navigation, vehicle condition monitoring, safety warning, and personalized customization by collecting and analyzing various data of motorcycles. In particular, the working intensity of motorcycle engines is relatively large, and the maintenance cycle of motorcycles is usually short; moreover, since the heat dissipation conditions and lubrication systems of motorcycles are relatively less perfect than those of cars, and considering the different driving environments and driving habits of motorcycles, even the maintenance opportunities of the same type of motorcycles will vary greatly. Therefore, the current motorcycle vehicle system uses mileage intervals and time cycles for maintenance reminders, which does not take into account the impact of the actual working conditions of the motorcycle on the maintenance timing. If maintenance is performed according to the maintenance reminders of the motorcycle vehicle system, it is easy to miss the best maintenance time, which will affect the service life and second-hand car value of the motorcycle, and even increase the probability of motorcycle failure; therefore, in order to facilitate motorcycle users to accurately grasp the maintenance time, improve the service life of the motorcycle and ensure driving safety, it is necessary to conduct big data analysis based on the motorcycle vehicle network and provide accurate maintenance reminders. Summary of the invention

[0004] In view of the deficiencies of the prior art mentioned above, the purpose of the present invention is to provide a maintenance reminder method based on motorcycle vehicle networking time series data, by configuring a pre-trained maintenance diagnosis model for each motorcycle model, and using the corresponding maintenance diagnosis model to perform maintenance diagnosis on the relevant time series data sent by the motorcycle on-board terminal in a targeted manner, so that the maintenance reminder is more accurate and more in line with the maintenance needs of the actual working conditions of the motorcycle.

[0005] In order to achieve the above-mentioned invention object, the present invention provides the following technical solutions:

[0006] The maintenance reminder method based on motorcycle vehicle networking time series data includes the following steps:

[0007] S1: Parse the time series data packets periodically sent by the motorcycle on-board terminal to obtain the motorcycle identification code and multi-dimensional time series data;

[0008] S2: constructing the currently obtained multidimensional time series data and the previously obtained multiple multidimensional time series data into a time series data sample;

[0009] S3: Determine the motorcycle model according to the motorcycle identification code, input the sequence data sample into the maintenance diagnosis model corresponding to the determined motorcycle model for maintenance diagnosis, and output the maintenance diagnosis result; wherein each maintenance diagnosis model is trained using the last sequence data sample constructed before each maintenance of a motorcycle model in the historical maintenance record;

[0010] S4: Determine whether to send a maintenance reminder to the motorcycle onboard terminal according to the maintenance diagnosis result.

[0011] According to a specific implementation, the present invention provides a maintenance reminder method based on motorcycle vehicle networking time series data. In step S1, after obtaining the multidimensional time series data, the multidimensional time series data is placed in a FIFO cache queue established when registering the motorcycle identification code; wherein the queue length of the FIFO cache queue is adapted to the time window size of the time series data sample.

[0012] According to a specific implementation, in the maintenance reminder method based on motorcycle vehicle networking time series data provided by the present invention, in step S2, the method of constructing the currently obtained multidimensional time series data and the previously obtained multiple multidimensional time series data into a time series data sample is:

[0013] When new multidimensional time series data is added to the FIFO cache queue, all the multidimensional time series data cached in the FIFO cache queue are merged according to the same dimension to obtain a time series data set corresponding to different dimensions;

[0014] After processing the time series data sets corresponding to different dimensions into a data set with data features having a mean of 0 and a standard deviation of 1, principal component analysis is performed on each of the time series data sets to obtain the principal component time series data corresponding to different dimensions, and then the principal component time series data corresponding to all dimensions are used as the time series data samples.

[0015] According to a specific implementation, the maintenance reminder method based on motorcycle vehicle networking time series data provided by the present invention, in step S1, the time series data packet periodically sent by the motorcycle vehicle-mounted terminal is parsed to obtain the multi-dimensional time series data in the following manner:

[0016] Extracting the time series data of the power system state associated data, the brake system state associated data, the driving behavior state associated data and the driving environment associated data according to the data identifier of each time series data in the time series data packet;

[0017] The time series data of the power system state association data, the brake system state association data, the driving behavior state association data and the driving environment association data are respectively assigned different dimension labels to obtain the multidimensional time series data.

[0018] According to a specific embodiment, the present invention provides a maintenance reminder method based on motorcycle vehicle networking time series data, wherein the maintenance diagnosis model is constructed based on a long short-term memory network; moreover, the maintenance diagnosis model includes: an input layer with four neurons, a hidden layer with a plurality of LSTM units, and an output layer with a plurality of neurons; wherein the diagnosis result of the maintenance diagnosis model is the maintenance probability of each specific maintenance item of the power system and the maintenance probability of each specific maintenance item of the braking system.

[0019] According to a specific implementation, the maintenance reminder method based on motorcycle vehicle networking time series data provided by the present invention, in step S4, according to the maintenance diagnosis result, determines whether to send a maintenance reminder to the motorcycle vehicle-mounted terminal in the following manner:

[0020] If the maintenance probability of any specific maintenance item of the power system and the brake system reaches a set threshold, a maintenance reminder for the specific maintenance item is issued.

[0021] Based on the same inventive concept, the present invention also provides a maintenance reminder system based on motorcycle vehicle networking time series data, which includes:

[0022] The motorcycle onboard terminal is used to periodically send time-sequence data packets to the cloud server;

[0023] The cloud server interacts with the motorcycle onboard terminal to implement the maintenance reminder method based on motorcycle vehicle networking time series data provided by the present invention.

[0024] The present invention also provides a computer-readable storage medium on which one or more programs are stored. When the one or more programs are executed by one or more processors, the maintenance reminder method based on motorcycle vehicle networking time series data provided by the present invention is implemented.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. The maintenance reminder method based on motorcycle vehicle networking time series data provided by the present invention obtains the motorcycle vehicle identification code and multi-dimensional time series data according to the time series data packet periodically sent by the motorcycle vehicle terminal, and then constructs the multi-dimensional time series data and the multiple multi-dimensional time series data obtained previously into a time series data sample, and then inputs the time series data sample into the maintenance diagnosis model corresponding to the motorcycle model determined according to the motorcycle vehicle identification code for maintenance diagnosis, obtains the maintenance diagnosis result, and determines whether to send a maintenance reminder to the motorcycle vehicle terminal according to the maintenance diagnosis result. Therefore, the present invention can use the corresponding maintenance diagnosis model to perform maintenance diagnosis on the relevant time series data sent by the motorcycle vehicle terminal in a targeted manner, so that the maintenance reminder is more accurate and more in line with the maintenance needs of the actual working conditions of the motorcycle.

[0027] 2. The maintenance reminder method based on motorcycle Internet of Vehicles time series data provided by the present invention utilizes the multidimensional time series data obtained by caching the FIFO cache queue, and when new multidimensional time series data is added to the FIFO cache queue, all the multidimensional time series data in the FIFO cache queue are merged to obtain time series data sets of different dimensions, and finally principal component analysis is performed on the time series data sets of each dimension to obtain corresponding principal component time series data, and then obtain time series data samples; therefore, the present invention can reduce the complexity of the maintenance diagnosis model and improve the accuracy of the maintenance diagnosis results and the convergence speed of the maintenance diagnosis model during training by integrating the multidimensional time series data accumulated for a period of time and extracting valuable data.

[0028] 3. The maintenance reminder method based on motorcycle vehicle networking time series data provided by the present invention, the multi-dimensional time series data includes time series data of brake system status association data, driving behavior status association data and driving environment association data; in this way, by introducing the time series data of driving behavior status association data and driving environment association data, the actual working condition of the motorcycle is incorporated into the maintenance diagnosis, so that the maintenance diagnosis result is more in line with the actual working condition of the motorcycle, further ensuring the accuracy of the maintenance diagnosis and meeting the maintenance needs of the actual working condition of the motorcycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the process of the present invention;

[0030] Figure 2 It is a schematic diagram of the processing flow of multidimensional time series data of the method of the present invention;

[0031] Figure 3 It is a schematic diagram of the structure of the system of the present invention. DETAILED DESCRIPTION

[0032] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. However, this should not be understood as the scope of the above subject matter of the present invention being limited to the following embodiments, and all technologies realized based on the content of the present invention belong to the scope of the present invention.

[0033] like Figure 1 As shown, the present invention provides a maintenance reminder method based on motorcycle vehicle networking time series data, which includes the following steps:

[0034] S1: Parse the time series data packets periodically sent by the motorcycle on-board terminal to obtain the motorcycle identification code and multi-dimensional time series data;

[0035] S2: constructing the currently obtained multidimensional time series data and the previously obtained multiple multidimensional time series data into a time series data sample;

[0036] S3: Determine the motorcycle model according to the motorcycle identification code, input the sequence data sample into the maintenance diagnosis model corresponding to the determined motorcycle model for maintenance diagnosis, and output the maintenance diagnosis result; wherein each maintenance diagnosis model is trained using the last sequence data sample constructed before each maintenance of a motorcycle model in the historical maintenance record;

[0037] S4: Determine whether to send a maintenance reminder to the motorcycle onboard terminal according to the maintenance diagnosis result.

[0038] Specifically, the present invention provides a maintenance reminder method based on motorcycle vehicle networking time series data, which is mainly a value-added service provided by motorcycle manufacturers, which is convenient for motorcycle users to perform accurate maintenance on motorcycles, thereby further providing targeted maintenance services. In this way, every time a motorcycle manufacturer develops and produces a model of motorcycle, it will establish a maintenance diagnosis model corresponding to the motorcycle model, and use the test data and maintenance and repair data in the research and development stage for preliminary training to obtain a preliminary maintenance diagnosis model; of course, with the subsequent sale of this model of motorcycle, the historical maintenance records and the associated time series data samples increase, and the maintenance diagnosis model will be retrained to achieve optimization iteration. At the same time, after the motorcycle manufacturer produces the motorcycle for quality inspection and leaves the factory, it will store and record the motorcycle identification code. When the motorcycle is sold to the user, after the user registers his identity through the motorcycle identification code, the motorcycle manufacturer will activate a cloud storage space based on the motorcycle identification code. The cloud storage space uses a FIFO cache queue mechanism to store multi-dimensional time series data obtained based on the time series data packets periodically sent by the motorcycle on-board terminal.

[0039] Furthermore, in step S1, after obtaining the multidimensional time series data, the multidimensional time series data is placed in a FIFO cache queue established when registering the motorcycle identification code; wherein the queue length of the FIFO cache queue is adapted to the time window size of the time series data sample. In implementation, the queue length of the FIFO cache queue and the time window of the time series data sample can be adjusted according to the iterative optimization of the maintenance diagnosis model.

[0040] like Figure 2 As shown, in step S2, the method of constructing the currently obtained multidimensional time series data and the previously obtained multiple multidimensional time series data into a time series data sample is:

[0041] When new multidimensional time series data is added to the FIFO cache queue, all the multidimensional time series data cached in the FIFO cache queue are merged according to the same dimension to obtain a time series data set corresponding to different dimensions;

[0042] After processing the time series data sets corresponding to different dimensions into a data set with data features having a mean of 0 and a standard deviation of 1, principal component analysis is performed on each of the time series data sets to obtain the principal component time series data corresponding to different dimensions, and then the principal component time series data corresponding to all dimensions are used as the time series data samples.

[0043] During implementation, the time series data sets corresponding to different dimensions are respectively processed into a data set with data features having a mean of 0 and a standard deviation of 1, and the Z-score normalization method is used to convert the data points contained in the time series data set into a distribution with a mean of 0 and a standard deviation of 1.

[0044] Furthermore, in step S1, the time series data packets periodically sent by the motorcycle onboard terminal are parsed to obtain the multi-dimensional time series data in the following manner:

[0045] Extracting the time series data of the power system state associated data, the brake system state associated data, the driving behavior state associated data and the driving environment associated data according to the data identifier of each time series data in the time series data packet;

[0046] The time series data of the power system state association data, the brake system state association data, the driving behavior state association data and the driving environment association data are respectively assigned different dimension labels to obtain the multidimensional time series data.

[0047] During implementation, the power system status-related data of a motorcycle include: engine data (speed: reflects the number of engine rotations per minute, and is a key indicator for measuring the working intensity and operating status of the engine; temperature: includes coolant temperature or oil temperature; oil pressure: appropriate oil pressure can ensure good circulation of oil in various parts of the engine, and insufficient oil pressure may cause increased wear of parts; intake volume: intake volume directly affects the combustion efficiency and power output of the engine, and it is related to factors such as the throttle opening and the smoothness of the air filter; exhaust temperature: reflects the temperature of exhaust gas after engine combustion, and excessively high exhaust temperature may indicate incomplete combustion or excessive engine load), fuel system data (fuel pressure: ensures that fuel can be stably supplied to the engine for combustion; fuel injection volume of the fuel injector: matches the working conditions of the engine (such as speed and load)), and transmission system data (clutch status: includes the degree of engagement and wear of the clutch; transmission oil temperature: transmission oil temperature affects the smoothness of gear shifting and transmission efficiency).

[0048] The motorcycle's brake system status-related data include: brake pressure data (front wheel brake pressure and rear wheel brake pressure), brake pad wear data (front wheel brake pad thickness and rear wheel brake pad thickness), brake disc status data, brake fluid data (brake fluid level: too low brake fluid level will cause air to enter the brake system, affecting the braking effect; brake fluid water content: brake fluid is hygroscopic, and will absorb water as time goes by and the usage environment changes).

[0049] The motorcycle driving behavior status-related data include: operation data (throttle opening: reflects the driver's control over power output; brake operation force and frequency: records the force and number of times the driver steps on the brake pedal or pulls the brake handle; clutch operation: includes the frequency of clutch disengagement and engagement, and the duration of each operation; gear shift operation data: records the timing of gear shifting, gear change sequence, and other information), driving style data (acceleration mode: determined by speed change rate and throttle opening change; deceleration mode: determined by speed change rate and brake operation force change; vehicle speed stability: observes the fluctuation of vehicle speed over a period of time)

[0050] The driving environment-related data of motorcycles include: road data (road type: different road types have different road conditions, including highways, urban roads, rural roads, mountain roads, etc.; road conditions: divided into dry, wet, waterlogged, icy, muddy, etc.; slope information: record the slope size and direction of the road), meteorological data (weather conditions: such as sunny, cloudy, rainy, snowy, foggy, etc. Different weather has an impact on driving safety and vehicle performance; temperature: low temperature may affect battery performance, tire pressure and rubber elasticity; high temperature may cause problems such as engine heat dissipation difficulties and fuel evaporation; longitude and latitude coordinates and altitude: different altitudes will affect engine power, torque output, oil pressure and tire performance, etc.)

[0051] Considering that the time series data set of each dimension also contains a variety of different types of data, it cannot be directly input into the maintenance diagnosis model. Therefore, it is necessary to further process the time series data set of each dimension. In view of the dimensionality reduction of the principal component analysis method (PCA), it can not only retain most of the information of the original data, but also reduce the amount and complexity of subsequent data analysis and modeling, and improve the calculation efficiency. Therefore, after completing the standardization of the time series data set of each dimension using the Z-score standardization method, the principal component analysis of the time series data set of each dimension is further performed. The analysis process is as follows:

[0052] 1. The calculation of the covariance matrix is ​​the key step: Let the multi-dimensional time series data matrix be X n×p (t), where n is the length of the time series, p is the dimension of the data, and t represents time; the covariance matrix is:

[0053]

[0054] Among them, x ki (t) is the data of the i-th dimension at the k-th time point, is the data of the i-th dimension. By calculating the covariance matrix, we can obtain the correlation information between the dimensions, laying the foundation for the subsequent extraction of the principal components.

[0055] 2. Solving the eigenvalues ​​and eigenvectors of the covariance matrix is ​​the core step of PCA: solving the characteristic equation ∑-λI | =0, we can get p eigenvalues ​​λ1,λ2,λ3···λ p , arranged in descending order, these eigenvalues ​​represent the variance explained by each principal component. For each eigenvalue λ i , by solving the linear equation system (∑-λ i I)v i = 0, we can get the corresponding eigenvector vi , the eigenvector represents the direction of the principal component, that is, the projection direction of the original data on the new coordinate axis. In multi-dimensional time series data, these eigenvectors convert the original time series data into a set of new principal component time series, thereby obtaining principal component time series data corresponding to different dimensions.

[0056] Specifically, the present invention provides a maintenance reminder method based on motorcycle vehicle networking time series data, wherein the maintenance diagnosis model is constructed based on a long short-term memory network; moreover, the maintenance diagnosis model includes: an input layer with four neurons, a hidden layer with a plurality of LSTM units, and an output layer with a plurality of neurons; wherein the diagnosis result of the maintenance diagnosis model is the maintenance probability of each specific maintenance item of the power system and the maintenance probability of each specific maintenance item of the braking system.

[0057] During implementation, the maintenance diagnosis model was trained by first determining the architecture of the long short-term memory network, which is an input layer with 4 neurons, a hidden layer with about 500 LSTM units, and an output layer with 5 neurons (considering only core maintenance items, such as changing the oil and oil filter, cleaning or replacing the air filter, checking and adjusting the chain, checking the brake system, and checking the electrical system).

[0058] Next, we collect training samples. As the number of users increases, we can collect more historical maintenance records of users. Of course, we can also collect the last time series data sample constructed by the user before a specific maintenance project. In this way, as time goes by, the collected training samples gradually approach the needs of maintenance projects under the user's actual working conditions.

[0059] After collecting the training samples, training begins. The LSTM unit in the hidden layer processes the four input features and controls the flow and memory of information through its internal gating mechanism (input gate, forget gate, and output gate). The LSTM unit in the hidden layer can learn the long-term dependencies of these features in specific maintenance items. In order to convert the output into a probabilistic form, the Softmax activation function is usually used in the output layer. The Softmax function can convert the output value of the output layer neuron into a probability distribution so that the sum of the two outputs is 1.

[0060] During the training process, the LSTM model is trained by defining a suitable loss function (such as the cross entropy loss function, which is used to measure the difference between the predicted probability and the true probability) and selecting an appropriate optimization algorithm (such as Adam, RMSProp, etc.). During the training process, the model will continuously adjust its weights to minimize the loss function. At the same time, validation data can be used to prevent overfitting, and the performance of the model can be optimized by adjusting the hyperparameters of the model (such as the number of hidden layers, the number of LSTM units, the learning rate, etc.). It should be pointed out that the present invention does not make a technical contribution to the long short-term memory network (LSTM), but uses the long short-term memory network (LSTM) to realize the maintenance diagnosis of motorcycles and output the probability distribution of specific maintenance items.

[0061] Specifically, in the maintenance reminder method based on motorcycle vehicle networking time series data provided by the present invention, in step S4, according to the maintenance diagnosis result, it is determined whether to send a maintenance reminder to the motorcycle vehicle-mounted terminal in the following manner:

[0062] If the maintenance probability of any specific maintenance item of the power system and the brake system reaches a set threshold, a maintenance reminder for the specific maintenance item is issued.

[0063] like Figure 3 As shown, the present invention provides a maintenance reminder system based on motorcycle vehicle networking time series data, which includes:

[0064] The motorcycle onboard terminal is used to periodically send time-sequence data packets to the cloud server;

[0065] The cloud server interacts with the motorcycle onboard terminal to implement the maintenance reminder method based on motorcycle vehicle networking time series data provided by the present invention.

[0066] During implementation, the cloud server builds several maintenance diagnosis models corresponding to motorcycle models. At the same time, the cloud server also activates a cloud storage space for each motorcycle identification code. The cloud storage space uses a FIFO cache queue mechanism to store multi-dimensional time series data obtained based on the time series data packets periodically sent by the motorcycle on-board terminal. In addition, the cloud server also performs standardization processing, data set merging, principal component analysis and other operations on the multi-dimensional time series data.

[0067] In addition, the present invention also provides a computer-readable storage medium on which one or more programs are stored. When the one or more programs are executed by one or more processors, the maintenance reminder method based on motorcycle vehicle networking time series data provided by the present invention is implemented.

[0068] It should be understood that the system disclosed in the present invention can be implemented in other ways. For example, the division of the modules is only a logical function division, 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 communication connection between modules can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical or other forms.

[0069] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each module may exist physically separately, or two or more modules may be integrated into one processing unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional unit.

[0070] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0071] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A maintenance reminder method based on motorcycle vehicle networking time series data, characterized in that: The following steps are involved: S1: Parse the time series data packets periodically sent by the motorcycle on-board terminal to obtain the motorcycle identification code and multi-dimensional time series data; S2: constructing the currently obtained multidimensional time series data and the previously obtained multiple multidimensional time series data into a time series data sample; S3: Determine the motorcycle model according to the motorcycle identification code, input the sequence data sample into the maintenance diagnosis model corresponding to the determined motorcycle model for maintenance diagnosis, and output the maintenance diagnosis result; wherein each maintenance diagnosis model is trained using the last sequence data sample constructed before each maintenance of a motorcycle model in the historical maintenance record; S4: Determine whether to send a maintenance reminder to the motorcycle onboard terminal according to the maintenance diagnosis result.

2. The maintenance reminder method based on motorcycle vehicle networking time series data as claimed in claim 1, characterized in that: In step S1, after obtaining the multidimensional time series data, the multidimensional time series data is placed in a FIFO cache queue established when registering the motorcycle identification code; wherein the queue length of the FIFO cache queue is adapted to the time window size of the time series data sample.

3. The maintenance reminder method based on motorcycle vehicle networking time series data as claimed in claim 2, characterized in that: In step S2, the method of constructing the currently obtained multidimensional time series data and the previously obtained multiple multidimensional time series data into a time series data sample is: When new multidimensional time series data is added to the FIFO cache queue, all the multidimensional time series data cached in the FIFO cache queue are merged according to the same dimension to obtain a time series data set corresponding to different dimensions; After processing the time series data sets corresponding to different dimensions into a data set with data features having a mean of 0 and a standard deviation of 1, principal component analysis is performed on each of the time series data sets to obtain the principal component time series data corresponding to different dimensions, and then the principal component time series data corresponding to all dimensions are used as the time series data samples.

4. The maintenance reminder method based on motorcycle vehicle networking time series data as claimed in claim 3, characterized in that: In step S1, the time series data packets periodically sent by the motorcycle onboard terminal are parsed to obtain the multi-dimensional time series data in the following manner: Extracting the time series data of the power system state associated data, the brake system state associated data, the driving behavior state associated data and the driving environment associated data according to the data identifier of each time series data in the time series data packet; The time series data of the power system state association data, the brake system state association data, the driving behavior state association data and the driving environment association data are respectively assigned different dimension labels to obtain the multidimensional time series data.

5. The maintenance reminder method based on motorcycle vehicle networking time series data as claimed in claim 4, characterized in that: The maintenance diagnosis model is constructed based on a long short-term memory network; and the maintenance diagnosis model includes: an input layer with four neurons, a hidden layer with a plurality of LSTM units, and an output layer with a plurality of neurons; wherein the diagnosis result of the maintenance diagnosis model is the maintenance probability of each specific maintenance item of the power system and the maintenance probability of each specific maintenance item of the braking system.

6. The maintenance reminder method based on motorcycle vehicle networking time series data as claimed in claim 5, characterized in that: In step S4, according to the maintenance diagnosis result, it is determined whether to send a maintenance reminder to the motorcycle onboard terminal: If the maintenance probability of any specific maintenance item of the power system and the brake system reaches a set threshold, a maintenance reminder for the specific maintenance item is issued.

7. A maintenance reminder system based on motorcycle vehicle networking time series data, characterized in that: include: The motorcycle onboard terminal is used to periodically send time-sequence data packets to the cloud server; The cloud server interacts with the motorcycle on-board terminal to implement the maintenance reminder method based on motorcycle vehicle networking time series data as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: One or more programs are stored thereon, characterized in that when the one or more programs are executed by one or more processors, the maintenance reminder method based on motorcycle vehicle networking time series data as described in any one of claims 1 to 6 is implemented.

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