Warning method for vehicle, vehicle and storage medium

Abstract

The application discloses a vehicle early warning method, a vehicle and a storage medium, and relates to the technical field of vehicle early warning methods. The vehicle early warning method comprises the following steps: obtaining target data of the vehicle, wherein the target data is data generated by a high-voltage loop device in the vehicle; dividing the target data into multiple data segments; determining an insulation value change trend of the vehicle according to the multiple data segments; and determining whether to perform a warning operation according to the insulation value change trend. The application solves the technical problem that the monitoring of the insulation state of a battery in the prior art has limitations, thereby causing safety hazards to easily occur.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving technology, and more specifically, to a vehicle early warning method, a vehicle, and a storage medium. Background Technology

[0002] With the rapid development of the electric vehicle industry, battery safety has become a major focus of industry attention. Insulation abnormalities in electric vehicles are generally caused by leakage of electrolyte inside the battery. When the leakage reaches a certain level, the insulation layer is damaged, creating a conductive circuit between the battery module and individual cells, thus posing a fire risk.

[0003] Existing battery management systems mainly rely on onboard hardware monitoring and diagnostics, but this diagnostic method has certain limitations. It cannot accurately detect the occurrence of abnormalities, thus failing to issue timely warnings to the driver.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a vehicle early warning method, a vehicle, and a storage medium to at least address the technical problem that the existing technology has limitations in monitoring the battery insulation status, which can easily lead to safety hazards.

[0006] According to one embodiment of the present invention, a vehicle early warning method is provided, comprising: acquiring target data of the vehicle, wherein the target data is data generated by high-voltage circuit devices in the vehicle; dividing the target data into multiple data segments; determining the insulation value change trend of the vehicle based on the multiple data segments; and determining whether to perform an early warning operation based on the insulation value change trend.

[0007] Optionally, the vehicle warning method may also include: acquiring initial cloud data of the vehicle; cleaning the initial cloud data to obtain target cloud data; and filtering target data from the target cloud data based on the vehicle's charging status.

[0008] Optionally, the vehicle warning method further includes: determining the status information of the high-voltage circuit device from each sub-data; determining whether a target sub-data set exists among multiple sub-data based on the status information of each sub-data, wherein the target sub-data set includes at least N sub-data, the N sub-data have the same status information, the N sub-data have adjacent time points, the N sub-data have time points within a preset time range, and N is a positive integer greater than 1; and generating a corresponding data segment based on each target sub-data set in response to the existence of a target sub-data set among multiple sub-data.

[0009] Optionally, the vehicle warning method further includes: dividing multiple insulation values ​​according to a preset interval threshold to obtain multiple insulation value sets; for each insulation value set, determining the minimum insulation value of the insulation value set; determining the time point corresponding to the minimum insulation value as the target time point of the insulation value set; and performing regression processing based on the target time points of multiple insulation value sets to obtain the insulation value change trend.

[0010] Optionally, the vehicle's warning method further includes: comparing the insulation value change trend with a first threshold and a second threshold; determining the insulation value change trend as a first change trend in response to the insulation value change trend being less than the first threshold; and controlling the vehicle to perform a first warning operation in response to the insulation value change trend being the first change trend, wherein the first warning operation includes a voice prompt operation.

[0011] Optionally, the vehicle's warning method further includes: determining the insulation value change trend as a second change trend in response to the insulation value change trend being greater than a first threshold and less than a second threshold; and controlling the vehicle to perform a second warning operation in response to the insulation value change trend being the second change trend, wherein the second warning operation includes a text prompt operation.

[0012] Optionally, the vehicle warning method may also include: in response to the vehicle performing a warning operation, analyzing the target data to obtain a warning report.

[0013] According to one embodiment of the present invention, a vehicle warning device is also provided, comprising: an acquisition module for acquiring target data of the vehicle, wherein the target data is data generated by a high-voltage circuit device inside the vehicle; a division module for dividing the target data into multiple data segments; a first determination module for determining the insulation value change trend of the vehicle based on the multiple data segments; and a second determination module for determining whether to perform a warning operation based on the insulation value change trend.

[0014] According to one embodiment of the present invention, a vehicle is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the vehicle warning method described in any of the above claims.

[0015] According to one embodiment of the present invention, a non-volatile storage medium is also provided, wherein a computer program is stored in the non-volatile storage medium, wherein the computer program is configured to execute the vehicle warning method described in any of the above embodiments when running.

[0016] In this embodiment of the invention, target data of the vehicle is acquired, wherein the target data is data generated by the high-voltage circuit device in the vehicle. The target data is divided into multiple data segments, and the trend of the insulation value change of the vehicle is determined based on the multiple data segments. This achieves the purpose of determining whether to perform a warning operation based on the trend of the insulation value change. Thus, the technical effect of dividing the target data into multiple data segments for regression analysis is realized, thereby solving the technical problem that the monitoring of battery insulation status in the prior art is limited, which leads to the technical problem that is prone to safety hazards. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart of a vehicle early warning method according to one embodiment of the present invention;

[0019] Figure 2 This is a schematic diagram of an insulation abnormality early warning system according to one embodiment of the present invention;

[0020] Figure 3 This is a flowchart of an insulation anomaly early warning method according to one embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of a data slice according to one embodiment of the present invention;

[0022] Figure 5 This is a structural block diagram of a vehicle warning device according to one embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] According to an embodiment of the present invention, an embodiment of a vehicle warning method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system containing at least a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0026] This method embodiment can also be executed in an electronic device, similar control device, or vehicle-mounted terminal that includes a memory and a processor. Taking a vehicle-mounted terminal as an example, the vehicle-mounted terminal may include one or more processors and a memory for storing data. Optionally, the vehicle-mounted terminal may also include a communication device for communication functions and a display device. Those skilled in the art will understand that the above structural description is merely illustrative and does not limit the structure of the vehicle-mounted terminal. For example, the vehicle-mounted terminal may include more or fewer components than those described above, or have a different configuration than those described above.

[0027] A processor may include one or more processing units. For example, a processor may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microcontroller unit (MCU), a field-programmable gate array (FPGA), a neural network processing unit (NPU), a tensor processing unit (TPU), or an artificial intelligence (AI) processor. Different processing units may be independent components or integrated into one or more processors. In some instances, electronic devices may also include one or more processors.

[0028] The memory can be used to store computer programs, such as the computer program corresponding to the control method of the target vehicle in the embodiments of the present invention. The processor implements the aforementioned control method of the target vehicle by running the computer program stored in the memory. The memory may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to electronic devices via a grid. Examples of such grids include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0029] The communication device is used to receive or transmit data via a grid. Specific examples of the aforementioned grid may include a wireless grid provided by the mobile terminal's communication provider. In one example, the communication device includes a network interface controller (NIC), which can connect to other grid devices via a base station to communicate with the Internet. In another example, the communication device may be a radio frequency (RF) module used for wireless communication with the Internet. In some embodiments of this solution, the communication device is used to connect to mobile devices such as mobile phones and tablets, enabling the mobile device to send commands to the vehicle-mounted terminal.

[0030] The display device can be a touchscreen liquid crystal display (LCD) or a touch display (also referred to as a "touchscreen" or "touch screen"). This LCD allows the user to interact with the user interface of the in-vehicle terminal. In some embodiments, the in-vehicle terminal has a graphical user interface (GUI), allowing the user to interact with the GUI through finger contact and / or gestures on a touch-sensitive surface. This human-machine interaction function may include a vehicle gear shifting function. Executable instructions for performing these human-machine interaction functions are configured / stored in one or more processor-executable computer program products or readable storage media.

[0031] Figure 1 This is a flowchart of a vehicle warning method according to one embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0032] Step S102: Obtain the target data of the vehicle, wherein the target data is the data generated by the high-voltage circuit device inside the vehicle.

[0033] Optionally, such as Figure 2 As shown, the execution subject in this embodiment is the vehicle warning system. It should be noted that other electronic devices and processors can also be used as execution subjects, and no further limitations are made here.

[0034] In the technical solution provided by step S102 of the present invention, the vehicle warning system can acquire data generated by the high-voltage circuit device inside the vehicle.

[0035] Specifically, the data generated by the aforementioned high-voltage circuit devices includes motor controller signals, DC-DC signals, air conditioner start signals, and high-voltage circuit insulation value signals.

[0036] Optionally, the above data can be used as tags. The vehicle warning system can also obtain vehicle identification number (VIN) and signal upload date, etc. The above vehicle data and the data generated by the high-voltage circuit device are stored as tags in the vehicle's cloud system. It is worth noting that there are corresponding relationships between the above data. For example, the tag correspondence of a certain frame of data can be shown in Table 1.

[0037] Table 1. Schematic diagram of vehicle single-frame insulation signal related labels

[0038]

[0039] Step S104: Divide the target data into multiple data segments.

[0040] In the technical solution provided by step S104 of the present invention, the vehicle warning system can connect the same number of frames together to form a data segment according to the change state of the sub-data in the target data. At the same time, it can also obtain the time difference between two adjacent frame signals. If the time difference exceeds a preset value, the data is divided into two segments.

[0041] It is worth noting that the number of frames in a single data segment should be greater than a preset value. For example, if the preset value is 3, then a data segment should include 3 or more frames. Therefore, even if two consecutive frames have the same data state, they cannot be considered as a single data segment.

[0042] Step S106: Determine the trend of the vehicle's insulation value change based on multiple data segments.

[0043] In the technical solution provided by step S106 of the present invention, the vehicle warning system can divide the target data in the data segment obtained in step S104 into multiple sub-data sets, and then divide the insulating paper in the sub-data sets into different intervals according to the size of the values. The time point corresponding to the first insulation value of each interval is taken as the characterization value of the interval. Multiple characterization values ​​are input into a regression function for regression processing to obtain the trend of insulation value change of the target vehicle.

[0044] Optionally, the regression processing described above can be linear regression, random forest regression, or other regression methods. In other words, any method that can perform regression processing on the above representation values ​​is acceptable, and no specific limitation is made here.

[0045] Optionally, the number of the above-mentioned intervals can be set manually. In actual working conditions, it can generally be divided into 5 intervals, namely risk zone A1, early warning zone A2, observation zone A3, identification zone A4, and health zone A5.

[0046] Step S108: Determine whether to perform an early warning operation based on the trend of insulation value change.

[0047] In the technical solution provided by step S108 of the present invention, the vehicle warning system can determine whether to perform a warning operation based on the trend of insulation value change obtained in step S108.

[0048] Optionally, taking linear regression as an example, after inputting the characterization value in step S106 into the linear regression function for regression processing, a characterization value of the insulation value change trend can be obtained. This characterization value is compared with a preset threshold. If it is lower than the preset threshold, it indicates that the insulation value is decreasing rapidly, and the vehicle warning system determines that a vehicle warning operation should be performed. It is worth noting that in addition to comparing the characterization value of the insulation value change trend with the threshold, the range of change of the obtained insulation value should also be monitored. If the insulation value in the target data keeps fluctuating between the risk area A1 and the observation area A3, the vehicle warning system can also perform a warning operation.

[0049] From the above steps S102 to S108, it can be seen that in this invention, as... Figure 3 As shown, the method involves acquiring target data from the vehicle, specifically data generated by the high-voltage circuit devices within the vehicle. This target data is divided into multiple data segments, and the trend of insulation value changes is determined based on these segments. This achieves the goal of determining whether to execute a warning operation based on the trend of insulation value changes. Thus, the technical effect of dividing the target data into multiple data segments for regression analysis is realized, thereby solving the technical problem that the monitoring of battery insulation status in existing technologies is limited, which can easily lead to safety hazards.

[0050] The method described in this embodiment will now be described in further detail.

[0051] As an optional implementation, the initial cloud data of the vehicle is acquired, the initial cloud data is cleaned to obtain target cloud data, and the target data is filtered out from the target cloud data according to the charging status of the vehicle.

[0052] In this embodiment, before acquiring the target data, the vehicle warning system should first obtain the initial cloud data of the vehicle from the vehicle's cloud system, then clean the initial cloud data to remove some outliers and default values, and then filter out the cloud data of the vehicle when it is not charging from the remaining cloud data as the target data.

[0053] Optionally, the initial cloud data is the data uploaded to the cloud by the vehicle in real time, including key information such as high-voltage circuit insulation value, motor controller signal, DC-DC signal, and air conditioning start signal. Then, based on the actual operating conditions, the above cloud data is subjected to common data cleaning processes such as decoding, format conversion, and normalization.

[0054] Optionally, after data cleaning, interpolation, smoothing and other processing methods can be used to complete and optimize the cloud data in order to improve data quality and ensure the accuracy of the final target data.

[0055] Optionally, this application only collects cloud data when the vehicle is not charging. Since the insulation abnormality of the charging pile can also affect the monitoring results of the insulation abnormality of the vehicle battery when the vehicle is charging, in order to eliminate the influence of external components, this application only collects cloud data when the vehicle is not charging.

[0056] As an optional implementation, the target data includes multiple sub-data generated at different time points. Dividing the target data into multiple data segments includes: determining the state information of the high-voltage circuit device from each sub-data; determining whether a target sub-data set exists among the multiple sub-data based on the state information of each sub-data; wherein the target sub-data set includes at least N sub-data, the N sub-data have the same state information, the N sub-data have adjacent time points, the N sub-data have time points within a preset time range, and N is a positive integer greater than 1; and generating a corresponding data segment based on each target sub-data set in response to the existence of a target sub-data set among the multiple sub-data.

[0057] In this embodiment, the target data includes multiple sub-data at different time points. Each sub-data at any given time includes the status information of the high-voltage circuit device. Based on the status information, the vehicle warning system can determine the target sub-data set that exists among the multiple sub-data. The status information of the sub-data in the target sub-data set is the same, and the time points of all sub-data are consecutive and adjacent. At the same time, the time points of all sub-data in the target sub-data set are within a preset time range. If a target sub-data set exists among the multiple sub-data, then a target sub-data set can be determined as a data segment. According to the above method, the target data can be divided into multiple data segments.

[0058] Optionally, the aforementioned preset time range is an empirical value and can be set manually according to actual working conditions. For example, the preset time range in this application is 30 seconds. Figure 4 As shown, since the status information in the sub-data of the DC-DC signal, motor controller signal, and air conditioning signal is the same during the time period from 9:00:11 to 9:00:31, and the duration of this time period is less than 30 seconds, the above time period can be divided into a data segment, that is, the area within the dashed box can be considered as a data segment.

[0059] Optionally, if the state information of a single signal data point does not change within a preset time period, then that single signal data point within the preset time period can also be considered as a target subset of the dataset. Figure 4 As shown by the arrow in the image.

[0060] Optionally, the number of frames in a single data segment should be greater than a preset value. For example, Figure 4As shown, if the preset value is 3, that is, a data segment should include 3 or more frames. Therefore, even if the state information of the DC-DC signal and the motor controller signal are the same at consecutive time points of 9:01:01 and 9:01:11, and the time points are both within the preset time range, since a data segment in this application consists of at least 3, the sub-data at the above two consecutive time points constitute the target sub-data set.

[0061] As an optional implementation, the sub-data includes multiple insulation values. Determining the insulation value change trend of a vehicle based on the data in each data segment includes: dividing the multiple insulation values ​​according to a preset interval threshold to obtain multiple insulation value sets; for each insulation value set, determining the minimum insulation value of the insulation value set; determining the time point corresponding to the minimum insulation value as the target time point of the insulation value set; and performing regression processing based on the target time points of the multiple insulation value sets to obtain the insulation value change trend.

[0062] In this embodiment, the target data includes multiple sub-data, and the sub-data also includes multiple insulation value signal data. Determining the insulation value change trend of the vehicle based on multiple data segments includes the following steps: The vehicle warning system can set multiple insulation value sets. Based on the magnitude of the multiple insulation values ​​in the sub-data, the insulation values ​​are divided into various insulation value sets. Each insulation value interval contains multiple insulation values. For each insulation value set, the time point corresponding to the smallest insulation value (the first insulation value) in each insulation value set is taken as the target time point. Each target time point is used to represent the time point of the insulation value set. The multiple target time points are then input into a regression function for regression processing, and finally the insulation value change trend is output.

[0063] Optionally, in actual working conditions, the vehicle warning system generally sets 5 insulation value sets, namely risk zone A1, warning zone A2, observation zone A3, identification zone A4, and healthy zone A5. In this application, the range of A1 is 0-272KΩ, the range of A2 is 272KΩ-1000KΩ, the range of A3 is 1000KΩ-3000KΩ, the range of A4 is 3000KΩ-5000KΩ, and the range of A5 is above 5000KΩ.

[0064] Optionally, this application takes linear regression as an example, setting the regression function as Y = f(x1,x2,...,xn; θ1,θ2,...,θm); Y = kx + b.

[0065] Where Y is the insulation value, x1, x2, ..., xn are the relevant signals affecting the insulation value, θ1, θ2, ..., θm are the parameters of the regression function, and k is the trend of the insulation value.

[0066] As an optional implementation, the trend of insulation value change is compared with a first threshold and a second threshold. In response to the insulation value change trend being less than the first threshold, the insulation value change trend is determined to be a first change trend. In response to the insulation value change trend being the first change trend, the vehicle is controlled to perform a first warning operation, wherein the first warning operation includes a voice prompt operation.

[0067] In this embodiment, after obtaining the above-mentioned insulation value change trend, in order to further determine whether to perform the warning operation, the characteristic value of the obtained insulation value change trend should be compared with the first threshold and the second threshold. When the characteristic value of the insulation value change trend is less than the first threshold, it indicates that the insulation value of the target vehicle is decreasing rapidly. That is, the vehicle is in the first-level insulation abnormality risk zone. The vehicle should be controlled to immediately perform the first warning operation. The first warning operation is a buzzer alarm to prompt the driver as soon as possible that the vehicle has an insulation abnormality fault.

[0068] Optionally, the above-mentioned first threshold and second threshold are set as empirical values ​​and can be manually set according to real-time operating conditions.

[0069] As an optional implementation, in response to the insulation value change trend being greater than a first threshold and less than a second threshold, the insulation value change trend is determined to be a second change trend, and in response to the insulation value change trend being the second change trend, the vehicle is controlled to perform a second warning operation, wherein the second warning operation includes a text prompt operation.

[0070] In this embodiment, after obtaining the insulation value change trend, in order to further determine whether to execute the warning operation, the characteristic value of the obtained insulation value change trend should be compared with the first threshold and the second threshold. When the characteristic value of the insulation value change trend is greater than the first threshold and less than the second threshold, it indicates that the insulation value of the target vehicle is decreasing slowly, but is still showing a downward trend. Therefore, it is determined that the vehicle is in the secondary insulation abnormality risk zone and the vehicle should be controlled to immediately execute the second warning operation. The second warning operation is to issue a text prompt message to warn the driver that the vehicle has an insulation abnormality fault.

[0071] It is worth noting that when determining whether to execute a warning operation, in addition to comparing the trend of insulation value change with the first and second thresholds, the range of insulation value change should also be monitored. If the trend of insulation value change is not downward, but the range of insulation value fluctuation is always between the risk zone and the observation zone, the vehicle warning system should also execute a warning operation.

[0072] As an optional implementation, in response to a vehicle performing a warning operation, target data is analyzed to obtain a warning report.

[0073] In this embodiment, after determining that the vehicle has an insulation abnormality, the vehicle warning system, in addition to performing a warning operation, should also integrate the status information of the target data when the insulation abnormality occurs to generate a corresponding warning report. Furthermore, the vehicle warning system can also perform statistical analysis on the occurrence rate, time, and location of vehicle insulation abnormalities based on the warning report, providing data support for further optimization of the warning system.

[0074] In addition, the vehicle warning system can also input the insulation value from the target data into a scoring function to score the degree of insulation abnormality of the vehicle. The scoring function is as follows:

[0075] score=g(y,x1,x2,...,xn;ψ1,ψ2,...,ψp)

[0076] Where score is the early warning score, y is the insulation value, x1,x2,...,xn are the relevant signals affecting the insulation value, and ψ1,ψ2,...,ψp are the parameters of the scoring function.

[0077] Optionally, the scoring function can be a linear function or a nonlinear function, which can be adaptively selected according to the actual application scenario and needs.

[0078] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or grid device, etc.) to execute the methods of the various embodiments of the present invention.

[0079] This embodiment also provides a vehicle warning device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0080] Figure 5 This is a structural block diagram of a vehicle warning device 500 according to one embodiment of the present invention, such as... Figure 5 As shown, the device includes: an acquisition module 501, a division module 502, a first determination module 503, and a second determination module 504.

[0081] The acquisition module 501 is used to acquire target data of the vehicle, wherein the target data is data generated by the high-voltage circuit device inside the vehicle;

[0082] The partitioning module 502 is used to divide the target data into multiple data segments;

[0083] The first determining module 503 is used to determine the trend of the insulation value change of the vehicle based on multiple data segments;

[0084] The second determining module 504 is used to determine whether to perform an early warning operation based on the trend of insulation value change.

[0085] Optionally, the acquisition module 501 includes: an acquisition unit for acquiring initial cloud data of the vehicle; a data cleaning unit for cleaning the initial cloud data to obtain target cloud data; and a filtering unit for filtering target data from the target cloud data based on the vehicle's charging status.

[0086] Optionally, the partitioning module 502 includes: a first determining unit, configured to determine the state information of the high-voltage circuit device from each sub-data; a second determining unit, configured to determine whether a target sub-data set exists among the multiple sub-data based on the state information of each sub-data, wherein the target sub-data set includes at least N sub-data, the N sub-data have the same state information, the N sub-data have adjacent time points, the N sub-data have time points within a preset time range, and N is a positive integer greater than 1; and a generating unit, configured to generate a corresponding data segment based on each target sub-data set in response to the existence of a target sub-data set among the multiple sub-data.

[0087] Optionally, the first determining module 503 includes: a dividing unit, used to divide multiple insulation values ​​according to a preset interval threshold to obtain multiple insulation value sets; a third determining unit, used to determine the minimum insulation value of each insulation value set; a fourth determining unit, used to determine the time point corresponding to the minimum insulation value as the target time point of the insulation value set; and a processing unit, used to perform regression processing based on the target time points of the multiple insulation value sets to obtain the insulation value change trend.

[0088] Optionally, the second determining module 504 includes: a comparison unit for comparing the insulation value change trend with a first threshold and a second threshold; a fifth determining unit for determining the insulation value change trend as a first change trend in response to the insulation value change trend being less than the first threshold; and a first control unit for controlling the vehicle to perform a first warning operation in response to the insulation value change trend being the first change trend, wherein the first warning operation includes a voice prompt operation.

[0089] Optionally, the second determining module 504 further includes: a sixth determining unit, configured to determine the insulation value change trend as a second change trend in response to the insulation value change trend being greater than a first threshold and less than a second threshold; and a second control unit, configured to control the vehicle to perform a second warning operation in response to the insulation value change trend being the second change trend, wherein the second warning operation includes a text prompt operation.

[0090] Optionally, the vehicle's warning device 500 further includes an analysis module for analyzing target data and obtaining a warning report in response to the vehicle performing a warning operation.

[0091] Embodiments of the present invention also provide a vehicle, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the above-described control method for the target vehicle.

[0092] Optionally, in this embodiment, the vehicle may be configured to store a computer program for performing the following steps:

[0093] Step S102: Obtain the target data of the vehicle, wherein the target data is the data generated by the high-voltage circuit device inside the vehicle;

[0094] Step S104: Divide the target data into multiple data segments;

[0095] Step S106: Determine the trend of the vehicle's insulation value change based on multiple data segments;

[0096] Step S108: Determine whether to perform an early warning operation based on the trend of insulation value change.

[0097] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0098] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0099] In the embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0100] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0102] If the integrated unit is implemented as 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 this invention, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or grid device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0103] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A vehicle early warning method, characterized in that, include: Acquire target data of the vehicle, wherein the target data is data generated by high-voltage circuit devices within the vehicle; The target data is divided into multiple data segments, wherein the target data includes multiple sub-data generated at different time points, and the sub-data includes multiple insulation values; The trend of insulation value change of the vehicle is determined based on the multiple data segments; Determine whether to execute an early warning operation based on the trend of the insulation value change; The process of dividing the target data into the multiple data segments includes: The status information of the high-voltage circuit device is determined from each sub-data; Based on the state information of each sub-data, determine whether there is a target sub-data set among the multiple sub-data, wherein the target sub-data set includes at least N sub-data, the state information corresponding to the N sub-data is the same, the time points corresponding to the N sub-data are adjacent, the time points corresponding to the N sub-data are within a preset time range, and N is a positive integer greater than 1; In response to the existence of the target sub-data set among the plurality of sub-data sets, a corresponding data fragment is generated based on each target sub-data set; Determining the trend of insulation value change of the vehicle based on the multiple data segments includes: The multiple insulation values ​​are divided according to a preset interval threshold to obtain multiple sets of insulation values; For each set of insulation values, determine the minimum insulation value of the set of insulation values; The time point corresponding to the minimum insulation value is determined as the target time point of the insulation value set; Regression processing is performed on the target time points of the multiple insulation value sets to obtain the trend of insulation value changes.

2. The vehicle early warning method according to claim 1, characterized in that, Obtaining the target data of the vehicle includes: Obtain the initial cloud data of the vehicle; The initial cloud data is cleaned to obtain the target cloud data; The target data is filtered from the target cloud data based on the vehicle's charging status.

3. The vehicle early warning method according to claim 1, characterized in that, Determining whether to execute an early warning operation based on the insulation value change trend includes: The trend of the insulation value change is compared with the first threshold and the second threshold; In response to the insulation value change trend being less than a first threshold, the insulation value change trend is determined to be a first change trend; In response to the insulation value change trend being the first change trend, the vehicle is controlled to perform a first warning operation, wherein the first warning operation includes a voice prompt operation.

4. The vehicle early warning method according to claim 3, characterized in that, The method further includes: In response to the insulation value change trend being greater than the first threshold and less than the second threshold, the insulation value change trend is determined to be the second change trend; In response to the insulation value change trend being the second change trend, the vehicle is controlled to perform a second warning operation, wherein the second warning operation includes a text prompt operation.

5. The vehicle early warning method according to claim 1, characterized in that, The method further includes: In response to the vehicle performing the warning operation, the target data is analyzed to obtain a warning report.

6. A vehicle warning device, characterized in that, include: An acquisition module is used to acquire target data of the vehicle, wherein the target data is data generated by high-voltage circuit devices inside the vehicle; A segmentation module is used to divide the target data into multiple data segments, wherein the target data includes multiple sub-data generated at different time points, and the sub-data includes multiple insulation values; The first determining module is used to determine the trend of the insulation value change of the vehicle based on the multiple data segments; The second determining module is used to determine whether to perform a warning operation based on the trend of the insulation value change. The vehicle's warning device is also used for: The status information of the high-voltage circuit device is determined from each sub-data; Based on the state information of each sub-data, determine whether there is a target sub-data set among the multiple sub-data, wherein the target sub-data set includes at least N sub-data, the state information corresponding to the N sub-data is the same, the time points corresponding to the N sub-data are adjacent, the time points corresponding to the N sub-data are within a preset time range, and N is a positive integer greater than 1; In response to the existence of the target sub-data set among the plurality of sub-data sets, a corresponding data fragment is generated based on each target sub-data set; The multiple insulation values ​​are divided according to a preset interval threshold to obtain multiple sets of insulation values; For each set of insulation values, determine the minimum insulation value of the set of insulation values; The time point corresponding to the minimum insulation value is determined as the target time point of the insulation value set; Regression processing is performed on the target time points of the multiple insulation value sets to obtain the trend of insulation value changes.

7. A vehicle comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the vehicle warning method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the vehicle warning method as described in any one of claims 1 to 5 when run on a computer or processor.

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