Methods, devices, and systems for determining brake malfunctions.

Abstract

This application provides a method, device, and system for determining braking anomalies. The method includes: receiving multiple real-time decelerations of a vehicle during a target braking action time, the multiple real-time decelerations being arranged in chronological order, with the first real-time deceleration being the initial deceleration and the last real-time deceleration being the target deceleration; determining the target braking distance corresponding to each real-time deceleration during the deceleration from the initial deceleration to the target deceleration based on the multiple real-time decelerations and the target braking action time, and constructing a target braking curve based on the multiple real-time decelerations and the multiple target braking distances; and determining whether the vehicle has a braking anomaly based on the target braking curve and a preset braking curve, thus solving the problem in the prior art of difficulty in timely detecting abnormal braking performance of a vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more specifically, to a method, apparatus, computer-readable storage medium, electronic device, and system for determining brake abnormalities. Background Technology

[0002] In the automotive field, as the brake pedal is used over time, its braking performance will gradually decrease or even malfunction. This gradual decline in braking performance is imperceptible to the user.

[0003] Furthermore, current methods for diagnosing brake malfunctions typically rely on visual inspection or warning lights on the vehicle. However, by the time users notice reduced braking performance or receive a warning light, the brake pedal is usually already unusable.

[0004] Therefore, there is an urgent need for a method that can detect brake abnormalities in vehicles in real time, so as to detect brake pedal abnormalities more promptly, thereby enabling timely inspection of related components and improving vehicle safety performance. Summary of the Invention

[0005] The main objective of this application is to provide a method, device, computer-readable storage medium, electronic device, and system for determining brake abnormalities, so as to at least solve the problem in the prior art that it is difficult to detect abnormal braking performance of a vehicle in a timely manner by judging braking performance through visual observation or warning light prompts.

[0006] To achieve the above objectives, according to one aspect of this application, a method for determining braking anomaly is provided, comprising: receiving multiple real-time decelerations of a vehicle during a target braking action time, wherein the multiple real-time decelerations are arranged in chronological order, and the first of the multiple real-time decelerations is an initial deceleration, and the last of the multiple real-time decelerations is a target deceleration; determining, based on the multiple real-time decelerations and the target braking action time, a target braking distance corresponding to each of the real-time decelerations during the deceleration from the initial deceleration to the target deceleration, and constructing a target braking curve based on the multiple real-time decelerations and the multiple target braking distances; and determining, based on the target braking curve and a preset braking curve, whether the vehicle has a braking anomaly, wherein the preset braking curve is a curve composed of multiple preset decelerations and multiple preset braking distances, and the preset braking curve is the braking curve of the vehicle when it is not in a braking anomaly state.

[0007] Optionally, determining whether the vehicle has a braking abnormality based on the target braking curve and the preset braking curve includes: calculating the similarity between the target braking curve and the preset braking curve to obtain a target similarity; determining that the vehicle has a braking abnormality if the target similarity is lower than a similarity threshold; and determining that the vehicle does not have a braking abnormality if the target similarity is higher than the similarity threshold.

[0008] Optionally, determining whether the vehicle has a braking abnormality based on the target braking curve and the preset braking curve includes: determining multiple target decelerations corresponding to multiple braking distance sampling points in the target braking curve, and determining multiple preset decelerations corresponding to multiple braking distance sampling points in the preset braking curve; comparing the target decelerations and preset decelerations one by one; determining that the vehicle has a braking abnormality if there are a predetermined number of consecutive target decelerations corresponding to the braking distance sampling points that are greater than the preset decelerations; and determining that the vehicle does not have a braking abnormality if there are no consecutive target decelerations corresponding to the predetermined number of braking distance sampling points that are greater than the preset decelerations.

[0009] Optionally, receiving multiple real-time decelerations of the vehicle during the target braking action time further includes: receiving current weather information, the vehicle's current position information, the current target brake pedal angle, and the current target braking action time; before determining whether the vehicle has a braking abnormality based on the target braking curve and a preset braking curve, the determination method further includes: determining, from multiple candidate braking curves, a preset braking curve that is identical to the current weather information, the vehicle's current position information, the current target brake pedal angle, and the current target braking action time, wherein the preset braking curve is one of the multiple candidate braking curves.

[0010] Optionally, the process of constructing the alternative braking curve includes: collecting multiple sets of preset data, each set of preset data including weather information, location information, brake pedal angle, braking action time, preset deceleration, and preset braking distance; classifying the multiple sets of preset data based on the weather information, location information, brake pedal angle, and braking action time to obtain multiple target categories; and using big data methods to fit the preset deceleration and preset braking distance in the multiple sets of preset data under the same target category to obtain the alternative braking curve.

[0011] Optionally, after determining that the vehicle has a braking malfunction, the determination method further includes: generating a prompt message and sending the prompt message to the vehicle, the prompt message being used to notify the user that the vehicle's braking performance is abnormal.

[0012] According to another aspect of this application, a braking anomaly determination device is provided, comprising: a receiving unit, configured to receive multiple real-time decelerations of a vehicle during a target braking action time, wherein the multiple real-time decelerations are arranged in chronological order, and the first of the multiple real-time decelerations is an initial deceleration, and the last of the multiple real-time decelerations is a target deceleration; a first determining unit, configured to determine, based on the multiple real-time decelerations and the target braking action time, a target braking distance corresponding to each of the real-time decelerations during the deceleration from the initial deceleration to the target deceleration, and to construct a target braking curve based on the multiple real-time decelerations and the multiple target braking distances; and a second determining unit, configured to determine whether the vehicle has a braking anomaly based on the target braking curve and a preset braking curve, wherein the preset braking curve is a curve composed of multiple preset decelerations and multiple preset braking distances, and the preset braking curve is the braking curve of the vehicle when it is not in a braking anomaly state.

[0013] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform any of the aforementioned brake anomaly determination methods.

[0014] According to another aspect of this application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute any of the methods for determining a braking anomaly through the computer program.

[0015] According to one aspect of this application, a braking anomaly determination system is provided, comprising: a cloud platform, the cloud platform including a braking anomaly determination device, the determination device being used to execute any of the braking anomaly determination methods described above; and a vehicle, the cloud platform communicating with the vehicle.

[0016] The technical solution of this application first receives multiple real-time decelerations of the vehicle during the target braking action time; then, based on the received multiple real-time decelerations and the target braking action time, it determines the target braking distance corresponding to each real-time deceleration during the process of decelerating from the initial deceleration to the target deceleration, and constructs a target braking curve corresponding to the process of decelerating from the initial deceleration to the target deceleration based on the multiple real-time decelerations and the target braking distances corresponding to each real-time deceleration; finally, it determines whether the vehicle has a braking abnormality based on the target braking curve and a preset braking curve. This solution receives multiple real-time decelerations of the vehicle during the braking process, constructs a target braking curve based on the multiple real-time decelerations and the target braking distances corresponding to each real-time deceleration, and finally determines whether the vehicle has a braking abnormality based on the target braking curve and the preset braking curve. This achieves a relatively timely and simple determination of whether the vehicle has a braking abnormality, ensuring that the user can be promptly notified of the vehicle's current braking performance, ensuring high vehicle safety and a good user experience. This solves the problem in existing technologies where judging braking performance by visual observation or warning lights makes it difficult to detect abnormal braking performance in a timely manner. Attached Figure Description

[0017] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 A hardware structure block diagram of a mobile terminal for performing a method for determining braking anomalies, as provided in an embodiment of this application, is shown.

[0019] Figure 2 A flowchart illustrating a method for determining a braking anomaly according to an embodiment of this application is shown.

[0020] Figure 3 A flowchart illustrating another method for determining brake abnormalities provided by an embodiment of this application is shown;

[0021] Figure 4 A schematic diagram of a brake malfunction determination device provided in an embodiment of this application is shown.

[0022] The above figures include the following reference numerals:

[0023] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0024] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover 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.

[0027] As described in the background section, existing technologies rely on visual observation or warning lights to assess braking performance, which makes it difficult to detect abnormal braking performance in a timely manner. To address the aforementioned technical problems, embodiments of this application provide a method, apparatus, computer-readable storage medium, electronic device, and system for determining braking abnormalities.

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0029] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a method of determining braking anomalies according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0030] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the device information display method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 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 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0031] This embodiment provides a method for determining brake abnormalities running on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as 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.

[0032] Figure 2 This is a flowchart of a method for determining brake malfunction according to an embodiment of this application. Figure 2 As shown, the determination method includes the following steps:

[0033] Step S201: Receive multiple real-time decelerations of the vehicle during the target braking action time. The multiple real-time decelerations are arranged in chronological order, with the first real-time deceleration being the initial deceleration and the last real-time deceleration being the target deceleration.

[0034] Specifically, the target braking action time is the total time from when the driver begins to press the brake pedal (for the vehicle, the start time of receiving the braking command) to when the braking stops (for the vehicle, the start time of receiving the stop braking command).

[0035] In step S201 above, multiple real-time decelerations are arranged in chronological order. The first real-time deceleration among the multiple real-time decelerations is the initial deceleration, which is the current deceleration of the vehicle when the driver issues the braking command; the last real-time deceleration among the multiple real-time decelerations is the target deceleration, which is the current deceleration when the driver issues the stop braking command.

[0036] In one specific embodiment, the initial deceleration can be 80 km / h, and the target deceleration can be 60 km / h. In another specific embodiment, the initial deceleration can be 60 km / h, and the target deceleration can be 0 km / h.

[0037] Step S202: Based on the multiple real-time decelerations and the target braking action time, determine the target braking distance corresponding to each of the real-time decelerations during the process of decelerating from the initial deceleration to the target deceleration, and construct a target braking curve based on the multiple real-time decelerations and the multiple target braking distances;

[0038] In step S202 above, any feasible method in the prior art can be used to determine the target braking distance corresponding to each real-time deceleration during the process of decelerating from the initial deceleration to the target deceleration by using multiple real-time decelerations and the target braking action time.

[0039] In one specific embodiment, any feasible method in the prior art can be used to construct a target braking curve based on multiple real-time decelerations and multiple target braking distances. For example, curve fitting can be performed using curve fitting tools (such as MATLAB, Python, etc.) or by using the least squares method. This application does not limit the specific scheme for constructing the target braking curve based on multiple real-time decelerations and multiple target braking distances.

[0040] In addition, in step S202 above, the vertical axis of the constructed target braking curve can be the real-time deceleration (in km / h) and the horizontal axis can be the braking distance (in meters).

[0041] Step S203: Based on the target braking curve and the preset braking curve, determine whether the vehicle has a braking abnormality. The preset braking curve is a curve composed of multiple preset decelerations and multiple preset braking distances, and the preset braking curve is the braking curve of the vehicle when it is not in a braking abnormality state.

[0042] Specifically, the vertical axis of the aforementioned preset braking curve can be the real-time deceleration (in km / h) and the horizontal axis can be the braking distance (in meters).

[0043] This embodiment first receives multiple real-time decelerations of the vehicle during the target braking action time. Then, based on the received real-time decelerations and the target braking action time, it determines the target braking distance corresponding to each real-time deceleration during the vehicle's deceleration from the initial deceleration to the target deceleration. Based on the multiple real-time decelerations and their corresponding target braking distances, it constructs a target braking curve for the vehicle's deceleration from the initial deceleration to the target deceleration. Finally, based on the target braking curve and a preset braking curve, it determines whether the vehicle has a braking anomaly. This solution receives multiple real-time decelerations of the vehicle during braking, constructs a target braking curve based on these real-time decelerations and their corresponding target braking distances, and then determines whether the vehicle has a braking anomaly based on the target braking curve and the preset braking curve. This achieves a relatively timely and simple determination of whether the vehicle has a braking anomaly, ensuring timely notification to the user of the vehicle's current braking performance, guaranteeing high vehicle safety and a good user experience. This solves the problem in existing technologies where judging braking performance through visual observation or warning lights makes it difficult to detect abnormal braking performance in a timely manner.

[0044] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and 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.

[0045] In practical applications, in order to more accurately determine whether a vehicle has a braking malfunction, the above step S203 can be implemented through the following steps: Step S2031, calculate the similarity between the target braking curve and the preset braking curve to obtain the target similarity; Step S2032, if the target similarity is lower than the similarity threshold, determine that the vehicle has a braking malfunction; Step S2033, if the target similarity is higher than the similarity threshold, determine that the vehicle does not have a braking malfunction.

[0046] Specifically, any feasible method in the prior art can be used to calculate the similarity between the target braking curve and the preset braking curve. For example, calculating the Euclidean distance between the target braking curve and the preset braking curve, or calculating the longest common substring between the target braking curve and the preset braking curve, etc. This application does not limit the specific scheme for calculating the similarity between the target braking curve and the preset braking curve.

[0047] Furthermore, the aforementioned similarity threshold is a flexible and calibrable threshold, and in this application, the size of the aforementioned similarity threshold is not limited.

[0048] The above-mentioned step S203 of this application can be implemented through the following steps: Step S2034, determining multiple target decelerations corresponding to multiple braking distance sampling points in the target braking curve, and determining multiple preset decelerations corresponding to multiple braking distance sampling points in the preset braking curve; Step S2035, comparing the target decelerations and preset decelerations corresponding to the multiple braking distance sampling points one by one; Step S2036, determining that the vehicle has a braking abnormality if there are a predetermined number of consecutive target decelerations corresponding to the braking distance sampling points that are greater than the preset decelerations; Step S2037, determining that the vehicle does not have a braking abnormality if there are no consecutive predetermined number of target decelerations corresponding to the braking distance sampling points that are greater than the preset decelerations. In this scheme, multiple braking distance sampling points are predetermined, and the target braking curve and the preset braking curve are sampled based on the multiple braking distance sampling points to obtain multiple target decelerations in the target braking curve and multiple preset decelerations in the preset braking curve. Then, multiple target decelerations are compared with multiple preset decelerations one by one to determine whether the vehicle has any braking abnormalities, thus ensuring a more accurate determination of vehicle braking abnormalities.

[0049] Specifically, among the aforementioned multiple braking distance sampling points, the points can be set at equal intervals, or they can be set at non-equal intervals. This application does not impose any restrictions on the setting of the multiple braking distance sampling points.

[0050] In practical applications, the aforementioned predetermined quantity can be flexibly determined according to the actual situation. In this application, there is no limitation on the size of the aforementioned predetermined quantity.

[0051] To further and more accurately determine whether a vehicle has a braking malfunction, in some embodiments, step S201 further includes: receiving current weather information, the vehicle's current position information, the current target brake pedal angle, and the current target braking action time. The determination method also includes step S204, whereby, before determining whether a vehicle has a braking malfunction based on the target braking curve and a preset braking curve, a preset braking curve is selected from multiple candidate braking curves. This preset braking curve is identical to the current weather information, the vehicle's current position information, the current target brake pedal angle, and the current target braking action time. In this scheme, using a candidate braking curve identical to the vehicle's current weather information, current position information, current target brake pedal angle, and current target braking action time as the preset braking curve ensures that the determined preset braking curve is reasonable and that the determination of whether a vehicle has a braking malfunction can be made more accurately.

[0052] In some implementations, the process of constructing the aforementioned alternative braking curves includes: collecting multiple sets of preset data, each set including weather information, location information, brake pedal angle, braking action time, preset deceleration, and preset braking distance; classifying the multiple sets of preset data based on the weather information, location information, brake pedal angle, and braking action time to obtain multiple target categories; and using big data methods to fit the preset deceleration and preset braking distance of the multiple sets of preset data within the same target category to obtain the aforementioned alternative braking curves. In this scheme, classifying multiple sets of preset data based on the weather information, location information, brake pedal angle, and braking action time corresponding to each set ensures that fitting the preset deceleration and preset braking distance of multiple sets of preset data within the same category yields relatively accurate alternative braking curves, further ensuring that subsequent determination of whether the vehicle has braking anomalies based on the target braking curve and alternative braking curves is relatively accurate.

[0053] Specifically, the weather information mentioned above can be sunny, rainy, or snowy, etc. The location information mentioned above refers to the vehicle's location, which can be obtained through a high-precision map. The brake pedal angle mentioned above refers to the opening and closing angle of the brake pedal. The braking action time mentioned above is the total time from when the driver begins to press the brake pedal (for the vehicle, this is the start time of receiving the braking command) to when the braking stops (for the vehicle, this is the start time of receiving the stop braking command).

[0054] Of course, in actual applications, each of the above preset data sets is not limited to weather information, location information, brake pedal angle, braking action time, preset deceleration, and preset braking distance, but may also include road slope information, etc.

[0055] In practical applications, in order to further remind users of vehicle braking abnormalities in a more timely manner and to further ensure vehicle safety, the above determination method also includes step S205, which involves generating a prompt message after determining that the vehicle has a braking abnormality and sending the prompt message to the vehicle. The prompt message is used to remind the user that the vehicle's braking performance is abnormal.

[0056] Of course, the system is not limited to generating a prompt message after determining that the vehicle has a braking malfunction. It can also generate a status message after determining that the vehicle does not have a braking malfunction, thus further ensuring a better user experience.

[0057] For users, after receiving the prompts from the cloud platform, they can promptly inspect and repair the relevant components.

[0058] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the method for determining brake abnormalities in this application will be described in detail below with reference to specific embodiments.

[0059] This embodiment relates to a specific method for determining braking anomalies, such as... Figure 3 As shown, it includes the following steps:

[0060] Step S1: Receive the vehicle's current weather information, current position information, current target brake pedal angle, current target braking action time, and multiple real-time decelerations during the target braking action time;

[0061] Step S2: Based on multiple real-time decelerations and target braking action times, determine the target braking distance corresponding to each real-time deceleration, i.e., one real-time deceleration corresponds to one target braking distance;

[0062] Step S3: Construct the target braking curve based on multiple real-time decelerations and the target braking distances corresponding to each real-time deceleration;

[0063] Step S4: Based on the current weather information, current location information, current target brake pedal angle, and current target braking action time, determine the preset brake curve that is the same as the current weather information, current location information, current target brake pedal angle, and current target braking action time from multiple alternative brake curves;

[0064] The process of constructing alternative braking curves includes:

[0065] Multiple sets of preset data are collected, each set including weather information, location information, brake pedal angle, braking action time, preset deceleration, and preset braking distance. Based on the weather information, location information, brake pedal angle, and braking action time, the multiple sets of preset data are classified to obtain multiple target categories. Using big data methods, the preset deceleration and preset braking distance in the multiple sets of preset data under the same target category are fitted to obtain alternative braking curves.

[0066] Step S5: Based on the target braking curve and the preset braking curve, determine whether the vehicle has any braking abnormalities;

[0067] Step S6: Based on the target braking curve and the preset braking curve, the specific process of determining whether the vehicle has braking abnormalities includes the following two methods:

[0068] The first approach is to calculate the similarity between the target braking curve and the preset braking curve to obtain the target similarity. If the target similarity is lower than the similarity threshold, it is determined that the vehicle has a braking anomaly. If the target similarity is higher than the similarity threshold, it is determined that the vehicle does not have a braking anomaly.

[0069] The second approach involves determining multiple target decelerations corresponding to multiple braking distance sampling points in the target braking curve, and determining multiple preset decelerations corresponding to multiple braking distance sampling points in the preset braking curve. The target decelerations and preset decelerations corresponding to the multiple braking distance sampling points are compared one by one. If there are a predetermined number of consecutive braking distance sampling points where the target deceleration is greater than the preset deceleration, the vehicle is determined to have a braking anomaly. If there are no consecutive predetermined number of braking distance sampling points where the target deceleration is greater than the preset deceleration, the vehicle is determined not to have a braking anomaly.

[0070] This application also provides a device for determining brake malfunction. It should be noted that this device can be used to execute the method for determining brake malfunction provided in this application. This device is used to implement 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.

[0071] The following describes the brake malfunction determination device provided in the embodiments of this application.

[0072] Figure 4 This is a schematic diagram of the brake malfunction determination device according to an embodiment of this application. Figure 4 As shown, the determining device includes:

[0073] The receiving unit 10 is used to receive multiple real-time decelerations of the vehicle during the target braking action time. The multiple real-time decelerations are arranged in chronological order, and the first of the multiple real-time decelerations is the initial deceleration, and the last of the multiple real-time decelerations is the target deceleration.

[0074] Specifically, the target braking action time is the total time from when the driver begins to press the brake pedal (for the vehicle, the start time of receiving the braking command) to when the braking stops (for the vehicle, the start time of receiving the stop braking command).

[0075] In the aforementioned receiving unit, multiple real-time decelerations are arranged in chronological order. The first real-time deceleration among the multiple real-time decelerations is the initial deceleration, which is the current deceleration of the vehicle when the driver issues the braking command; the last real-time deceleration among the multiple real-time decelerations is the target deceleration, which is the current deceleration when the driver issues the stop braking command.

[0076] In one specific embodiment, the initial deceleration can be 80 km / h, and the target deceleration can be 60 km / h. In another specific embodiment, the initial deceleration can be 60 km / h, and the target deceleration can be 0 km / h.

[0077] The first determining unit 20 is used to determine the target braking distance corresponding to each of the above-mentioned real-time decelerations during the process of decelerating from the initial deceleration to the target deceleration based on the multiple real-time decelerations and the target braking action time, and to construct a target braking curve based on the multiple real-time decelerations and the multiple target braking distances.

[0078] In the aforementioned first determining unit, any feasible method in the prior art can be used to determine the target braking distance corresponding to each real-time deceleration during the process of decelerating from the initial deceleration to the target deceleration by using multiple real-time decelerations and the target braking action time.

[0079] In one specific embodiment, any feasible method in the prior art can be used to construct a target braking curve based on multiple real-time decelerations and multiple target braking distances. For example, curve fitting can be performed using curve fitting tools (such as MATLAB, Python, etc.) or by using the least squares method. This application does not limit the specific scheme for constructing the target braking curve based on multiple real-time decelerations and multiple target braking distances.

[0080] In addition, in the first determining unit mentioned above, the vertical axis of the constructed target braking curve can be the real-time deceleration (in km / h) and the horizontal axis can be the braking distance (in meters).

[0081] The second determining unit 30 is used to determine whether the vehicle has a braking abnormality based on the target braking curve and the preset braking curve. The preset braking curve is a curve composed of multiple preset decelerations and multiple preset braking distances, and the preset braking curve is the braking curve of the vehicle when it is not in a braking abnormality state.

[0082] Specifically, the vertical axis of the aforementioned preset braking curve can be the real-time deceleration (in km / h) and the horizontal axis can be the braking distance (in meters).

[0083] In this embodiment, the receiving unit receives multiple real-time decelerations of the vehicle during the target braking action time; the first determining unit determines the target braking distance corresponding to each real-time deceleration during the process of the vehicle decelerating from the initial deceleration to the target deceleration based on the received multiple real-time decelerations and the target braking action time, and constructs a target braking curve corresponding to the process of the vehicle decelerating from the initial deceleration to the target deceleration based on the multiple real-time decelerations and the target braking distances corresponding to each real-time deceleration; the second determining unit determines whether the vehicle has a braking abnormality based on the target braking curve and a preset braking curve. The determining device of this application receives multiple real-time decelerations of the vehicle during the braking process, constructs a target braking curve based on the multiple real-time decelerations and the target braking distances corresponding to each real-time deceleration, and finally determines whether the vehicle has a braking abnormality based on the target braking curve and the preset braking curve. This achieves a relatively timely and simple determination of whether the vehicle has a braking abnormality, ensuring that the user can be promptly notified of the vehicle's current braking performance, ensuring high vehicle safety and a good user experience, thereby solving the problem in the prior art where judging braking performance by visual observation or warning lights is difficult to detect abnormalities in a timely manner.

[0084] In practical applications, to accurately determine whether a vehicle has a braking malfunction, the second determining unit includes a calculation module, a first determining module, and a second determining module. The calculation module calculates the similarity between the target braking curve and the preset braking curve to obtain a target similarity. The second determining module determines that the vehicle has a braking malfunction when the target similarity is lower than a similarity threshold, and determines that the vehicle does not have a braking malfunction when the target similarity is higher than the similarity threshold.

[0085] Specifically, any feasible method in the prior art can be used to calculate the similarity between the target braking curve and the preset braking curve. For example, calculating the Euclidean distance between the target braking curve and the preset braking curve, or calculating the longest common substring between the target braking curve and the preset braking curve, etc. This application does not limit the specific scheme for calculating the similarity between the target braking curve and the preset braking curve.

[0086] Furthermore, the aforementioned similarity threshold is a flexible and calibrable threshold, and in this application, the size of the aforementioned similarity threshold is not limited.

[0087] The second determining unit of this application further includes a third determining module, a comparison module, a fourth determining module, and a fifth determining module. The third determining module is used to determine multiple target decelerations corresponding to multiple braking distance sampling points in the target braking curve, and to determine multiple preset decelerations corresponding to multiple braking distance sampling points in the preset braking curve. The comparison module is used to compare the target decelerations and preset decelerations corresponding to the multiple braking distance sampling points one by one. The fourth determining module is used to determine that the vehicle has a braking abnormality if there are a predetermined number of consecutive target decelerations corresponding to the braking distance sampling points that are greater than the preset decelerations. The fifth determining module is used to determine that the vehicle does not have a braking abnormality if there are no consecutive predetermined number of target decelerations corresponding to the braking distance sampling points that are greater than the preset decelerations. In this scheme, multiple braking distance sampling points are predetermined. Based on these multiple braking distance sampling points, the target braking curve and the preset braking curve are sampled respectively to obtain multiple target decelerations in the target braking curve and multiple preset decelerations in the preset braking curve. Then, multiple target decelerations are compared with multiple preset decelerations one by one to determine whether the vehicle has any braking abnormalities, thus ensuring a more accurate determination of vehicle braking abnormalities.

[0088] Specifically, among the aforementioned multiple braking distance sampling points, the points can be set at equal intervals, or they can be set at non-equal intervals. This application does not impose any restrictions on the setting of the multiple braking distance sampling points.

[0089] In practical applications, the aforementioned predetermined quantity can be flexibly determined according to the actual situation. In this application, there is no limitation on the size of the aforementioned predetermined quantity.

[0090] To further and more accurately determine whether a vehicle has a braking malfunction, in some embodiments, the receiving unit further includes: receiving current weather information, the vehicle's current position information, the current target brake pedal angle, and the current target braking action time; the determining device further includes a third determining unit, used to determine, from multiple candidate brake curves, a preset brake curve that is identical to the current weather information, the vehicle's current position information, the current target brake pedal angle, and the current target braking action time, before determining whether the vehicle has a braking malfunction based on the target braking curve and a preset brake curve. The preset brake curve is one of multiple candidate brake curves. In this solution, using a candidate brake curve that is identical to the vehicle's current weather information, current position information, current target brake pedal angle, and current target braking action time as the preset brake curve ensures that the determined preset brake curve is reasonable and that the determination of whether the vehicle has a braking malfunction can be made more accurately.

[0091] In some implementations, the aforementioned third determining unit includes a data acquisition module, a classification module, and a fitting module. The data acquisition module collects multiple sets of preset data, each set including weather information, location information, brake pedal angle, braking action time, preset deceleration, and preset braking distance. The classification module classifies the multiple sets of preset data based on the weather information, location information, brake pedal angle, and braking action time to obtain multiple target categories. The fitting module uses big data methods to fit the preset deceleration and preset braking distance of the multiple sets of preset data within the same target category to obtain the candidate braking curves. In this scheme, classifying multiple sets of preset data based on the weather information, location information, brake pedal angle, and braking action time corresponding to each set ensures that fitting the preset deceleration and preset braking distance of multiple sets of preset data within the same category yields relatively accurate candidate braking curves. This further ensures that subsequent determination of whether the vehicle has braking anomalies based on the target braking curve and candidate braking curves is relatively accurate.

[0092] Specifically, the weather information mentioned above can be sunny, rainy, or snowy, etc. The location information mentioned above refers to the vehicle's location, which can be obtained through a high-precision map. The brake pedal angle mentioned above refers to the opening and closing angle of the brake pedal. The braking action time mentioned above is the total time from when the driver begins to press the brake pedal (for the vehicle, this is the start time of receiving the braking command) to when the braking stops (for the vehicle, this is the start time of receiving the stop braking command).

[0093] Of course, in actual applications, each of the above preset data sets is not limited to weather information, location information, brake pedal angle, braking action time, preset deceleration, and preset braking distance, but may also include road slope information, etc.

[0094] In practical applications, in order to promptly alert users to any brake malfunctions in the vehicle and further ensure vehicle safety, the aforementioned determining device also includes a step sending unit. This unit generates a prompt message after determining that the vehicle has brake malfunctions and sends the prompt message to the vehicle. The prompt message is used to alert the user that the vehicle's braking performance is abnormal.

[0095] Of course, the system is not limited to generating a prompt message after determining that the vehicle has a braking malfunction. It can also generate a status message after determining that the vehicle does not have a braking malfunction, thus further ensuring a better user experience.

[0096] For users, after receiving the prompts from the cloud platform, they can promptly inspect and repair the relevant components.

[0097] The aforementioned brake malfunction detection device includes a processor and a memory. The receiving unit, the first determining unit, and the second determining unit, etc., are all stored as program units in the memory. The processor executes these program units stored in the memory to achieve the corresponding functions. All of the above modules are located in the same processor; alternatively, the modules may be located in different processors in any combination.

[0098] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can address the problem in existing technologies where judging braking performance relies on visual observation or warning lights, making it difficult to detect abnormal braking performance in a timely manner.

[0099] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0100] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the method for determining the braking anomaly.

[0101] Specifically, methods for determining brake malfunctions include:

[0102] Step S201: Receive multiple real-time decelerations of the vehicle during the target braking action time. The multiple real-time decelerations are arranged in chronological order, with the first real-time deceleration being the initial deceleration and the last real-time deceleration being the target deceleration.

[0103] Specifically, the target braking action time is the total time from when the driver begins to press the brake pedal (for the vehicle, the start time of receiving the braking command) to when the braking stops (for the vehicle, the start time of receiving the stop braking command).

[0104] In step S201 above, multiple real-time decelerations are arranged in chronological order. The first real-time deceleration among the multiple real-time decelerations is the initial deceleration, which is the current deceleration of the vehicle when the driver issues the braking command; the last real-time deceleration among the multiple real-time decelerations is the target deceleration, which is the current deceleration when the driver issues the stop braking command.

[0105] In one specific embodiment, the initial deceleration can be 80 km / h, and the target deceleration can be 60 km / h. In another specific embodiment, the initial deceleration can be 60 km / h, and the target deceleration can be 0 km / h.

[0106] Step S202: Based on the multiple real-time decelerations and the target braking action time, determine the target braking distance corresponding to each of the real-time decelerations during the process of decelerating from the initial deceleration to the target deceleration, and construct a target braking curve based on the multiple real-time decelerations and the multiple target braking distances;

[0107] In step S202 above, any feasible method in the prior art can be used to determine the target braking distance corresponding to each real-time deceleration during the process of decelerating from the initial deceleration to the target deceleration by using multiple real-time decelerations and the target braking action time.

[0108] In one specific embodiment, any feasible method in the prior art can be used to construct a target braking curve based on multiple real-time decelerations and multiple target braking distances. For example, curve fitting can be performed using curve fitting tools (such as MATLAB, Python, etc.) or by using the least squares method. This application does not limit the specific scheme for constructing the target braking curve based on multiple real-time decelerations and multiple target braking distances.

[0109] In addition, in step S202 above, the vertical axis of the constructed target braking curve can be the real-time deceleration (in km / h) and the horizontal axis can be the braking distance (in meters).

[0110] Step S203: Based on the target braking curve and the preset braking curve, determine whether the vehicle has a braking abnormality. The preset braking curve is a curve composed of multiple preset decelerations and multiple preset braking distances, and the preset braking curve is the braking curve of the vehicle when it is not in a braking abnormality state.

[0111] Specifically, the vertical axis of the aforementioned preset braking curve can be the real-time deceleration (in km / h) and the horizontal axis can be the braking distance (in meters).

[0112] Optionally, determining whether the vehicle has a braking abnormality based on the target braking curve and the preset braking curve includes: calculating the similarity between the target braking curve and the preset braking curve to obtain a target similarity; determining that the vehicle has a braking abnormality if the target similarity is lower than a similarity threshold; and determining that the vehicle does not have a braking abnormality if the target similarity is higher than the similarity threshold.

[0113] Optionally, determining whether the vehicle has a braking abnormality based on the target braking curve and the preset braking curve includes: determining multiple target decelerations corresponding to multiple braking distance sampling points in the target braking curve, and determining multiple preset decelerations corresponding to multiple braking distance sampling points in the preset braking curve; comparing the target decelerations and preset decelerations corresponding to the multiple braking distance sampling points one by one; determining that the vehicle has a braking abnormality if there are a predetermined number of consecutive target decelerations corresponding to the braking distance sampling points that are greater than the preset decelerations; and determining that the vehicle does not have a braking abnormality if there are no consecutive predetermined number of target decelerations corresponding to the braking distance sampling points that are greater than the preset decelerations.

[0114] Optionally, receiving multiple real-time decelerations of the vehicle during the target braking action time further includes: receiving current weather information, the current position information of the vehicle, the current target brake pedal angle, and the current target braking action time; before determining whether the vehicle has a braking abnormality based on the target braking curve and the preset braking curve, the determination method further includes: determining, from multiple candidate braking curves, the preset braking curve that is identical to the current weather information, the current position information of the vehicle, the current target brake pedal angle, and the current target braking action time, wherein the preset braking curve is one of the multiple candidate braking curves.

[0115] Optionally, the process of constructing the above-mentioned alternative braking curves includes: collecting multiple sets of preset data, each set of preset data including weather information, location information, brake pedal angle, braking action time, preset deceleration, and preset braking distance; classifying the multiple sets of preset data based on the weather information, location information, brake pedal angle, and braking action time to obtain multiple target categories; and using big data methods to fit the preset deceleration and preset braking distance in the multiple sets of preset data under the same target category to obtain the above-mentioned alternative braking curves.

[0116] Optionally, after determining that the vehicle has a braking malfunction, the determination method further includes: generating a prompt message and sending the prompt message to the vehicle, wherein the prompt message is used to notify the user that the braking performance of the vehicle is abnormal.

[0117] This invention provides a processor for running a program, wherein the program executes the method for determining the brake abnormality.

[0118] Specifically, methods for determining brake malfunctions include:

[0119] Step S201: Receive multiple real-time decelerations of the vehicle during the target braking action time. The multiple real-time decelerations are arranged in chronological order, with the first real-time deceleration being the initial deceleration and the last real-time deceleration being the target deceleration.

[0120] Specifically, the target braking action time is the total time from when the driver begins to press the brake pedal (for the vehicle, the start time of receiving the braking command) to when the braking stops (for the vehicle, the start time of receiving the stop braking command).

[0121] In step S201 above, multiple real-time decelerations are arranged in chronological order. The first real-time deceleration among the multiple real-time decelerations is the initial deceleration, which is the current deceleration of the vehicle when the driver issues the braking command; the last real-time deceleration among the multiple real-time decelerations is the target deceleration, which is the current deceleration when the driver issues the stop braking command.

[0122] In one specific embodiment, the initial deceleration can be 80 km / h, and the target deceleration can be 60 km / h. In another specific embodiment, the initial deceleration can be 60 km / h, and the target deceleration can be 0 km / h.

[0123] Step S202: Based on the multiple real-time decelerations and the target braking action time, determine the target braking distance corresponding to each of the real-time decelerations during the process of decelerating from the initial deceleration to the target deceleration, and construct a target braking curve based on the multiple real-time decelerations and the multiple target braking distances;

[0124] In step S202 above, any feasible method in the prior art can be used to determine the target braking distance corresponding to each real-time deceleration during the process of decelerating from the initial deceleration to the target deceleration by using multiple real-time decelerations and the target braking action time.

[0125] In one specific embodiment, any feasible method in the prior art can be used to construct a target braking curve based on multiple real-time decelerations and multiple target braking distances. For example, curve fitting can be performed using curve fitting tools (such as MATLAB, Python, etc.) or by using the least squares method. This application does not limit the specific scheme for constructing the target braking curve based on multiple real-time decelerations and multiple target braking distances.

[0126] In addition, in step S202 above, the vertical axis of the constructed target braking curve can be the real-time deceleration (in km / h) and the horizontal axis can be the braking distance (in meters).

[0127] Step S203: Based on the target braking curve and the preset braking curve, determine whether the vehicle has a braking abnormality. The preset braking curve is a curve composed of multiple preset decelerations and multiple preset braking distances, and the preset braking curve is the braking curve of the vehicle when it is not in a braking abnormality state.

[0128] Specifically, the vertical axis of the aforementioned preset braking curve can be the real-time deceleration (in km / h) and the horizontal axis can be the braking distance (in meters).

[0129] Optionally, determining whether the vehicle has a braking abnormality based on the target braking curve and the preset braking curve includes: calculating the similarity between the target braking curve and the preset braking curve to obtain a target similarity; determining that the vehicle has a braking abnormality if the target similarity is lower than a similarity threshold; and determining that the vehicle does not have a braking abnormality if the target similarity is higher than the similarity threshold.

[0130] Optionally, determining whether the vehicle has a braking abnormality based on the target braking curve and the preset braking curve includes: determining multiple target decelerations corresponding to multiple braking distance sampling points in the target braking curve, and determining multiple preset decelerations corresponding to multiple braking distance sampling points in the preset braking curve; comparing the target decelerations and preset decelerations corresponding to the multiple braking distance sampling points one by one; determining that the vehicle has a braking abnormality if there are a predetermined number of consecutive target decelerations corresponding to the braking distance sampling points that are greater than the preset decelerations; and determining that the vehicle does not have a braking abnormality if there are no consecutive predetermined number of target decelerations corresponding to the braking distance sampling points that are greater than the preset decelerations.

[0131] Optionally, receiving multiple real-time decelerations of the vehicle during the target braking action time further includes: receiving current weather information, the current position information of the vehicle, the current target brake pedal angle, and the current target braking action time; before determining whether the vehicle has a braking abnormality based on the target braking curve and the preset braking curve, the determination method further includes: determining, from multiple candidate braking curves, the preset braking curve that is identical to the current weather information, the current position information of the vehicle, the current target brake pedal angle, and the current target braking action time, wherein the preset braking curve is one of the multiple candidate braking curves.

[0132] Optionally, the process of constructing the above-mentioned alternative braking curves includes: collecting multiple sets of preset data, each set of preset data including weather information, location information, brake pedal angle, braking action time, preset deceleration, and preset braking distance; classifying the multiple sets of preset data based on the weather information, location information, brake pedal angle, and braking action time to obtain multiple target categories; and using big data methods to fit the preset deceleration and preset braking distance in the multiple sets of preset data under the same target category to obtain the above-mentioned alternative braking curves.

[0133] Optionally, after determining that the vehicle has a braking malfunction, the determination method further includes: generating a prompt message and sending the prompt message to the vehicle, wherein the prompt message is used to notify the user that the braking performance of the vehicle is abnormal.

[0134] In a typical embodiment of this application, a braking anomaly determination system is also provided. This braking anomaly determination system includes a cloud platform and a vehicle. The cloud platform includes a braking anomaly determination device, which is used to execute any of the above-described braking anomaly determination methods; the cloud platform communicates with the vehicle.

[0135] The aforementioned determination system includes a cloud platform and a vehicle. The cloud platform includes a braking anomaly determination device, which executes any of the aforementioned braking anomaly determination methods. The cloud platform communicates with the vehicle. In the aforementioned determination method, multiple real-time decelerations of the vehicle during braking are received. Based on these multiple real-time decelerations and the target braking distances corresponding to each deceleration, a target braking curve is constructed. Finally, based on the target braking curve and a preset braking curve, it is determined whether the vehicle has a braking anomaly. This achieves a relatively timely and simple determination of whether the vehicle has a braking anomaly, ensuring timely notification of the vehicle's current braking performance to the user, guaranteeing high vehicle safety and a good user experience. This solves the problem in existing technologies where judging braking performance through visual observation or warning lights makes it difficult to detect braking performance anomalies in a timely manner.

[0136] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0137] Step S201: Receive multiple real-time decelerations of the vehicle during the target braking action time. The multiple real-time decelerations are arranged in chronological order, with the first real-time deceleration being the initial deceleration and the last real-time deceleration being the target deceleration.

[0138] Step S202: Based on the multiple real-time decelerations and the target braking action time, determine the target braking distance corresponding to each of the real-time decelerations during the process of decelerating from the initial deceleration to the target deceleration, and construct a target braking curve based on the multiple real-time decelerations and the multiple target braking distances;

[0139] Step S203: Based on the target braking curve and the preset braking curve, determine whether the vehicle has a braking abnormality. The preset braking curve is a curve composed of multiple preset decelerations and multiple preset braking distances, and the preset braking curve is the braking curve of the vehicle when it is not in a braking abnormality state.

[0140] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0141] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0142] Step S201: Receive multiple real-time decelerations of the vehicle during the target braking action time. The multiple real-time decelerations are arranged in chronological order, with the first real-time deceleration being the initial deceleration and the last real-time deceleration being the target deceleration.

[0143] Step S202: Based on the multiple real-time decelerations and the target braking action time, determine the target braking distance corresponding to each of the real-time decelerations during the process of decelerating from the initial deceleration to the target deceleration, and construct a target braking curve based on the multiple real-time decelerations and the multiple target braking distances;

[0144] Step S203: Based on the target braking curve and the preset braking curve, determine whether the vehicle has a braking abnormality. The preset braking curve is a curve composed of multiple preset decelerations and multiple preset braking distances, and the preset braking curve is the braking curve of the vehicle when it is not in a braking abnormality state.

[0145] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0146] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0147] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0148] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0149] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0150] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0151] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0152] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0153] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0154] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0155] 1) In the braking anomaly determination method of this application, firstly, multiple real-time decelerations of the vehicle during the target braking action time are received; then, based on the received multiple real-time decelerations and the target braking action time, the target braking distance corresponding to each real-time deceleration during the process of the vehicle decelerating from the initial deceleration to the target deceleration is determined, and based on the multiple real-time decelerations and the target braking distances corresponding to each real-time deceleration, a target braking curve corresponding to the process of the vehicle decelerating from the initial deceleration to the target deceleration is constructed; finally, based on the target braking curve and a preset braking curve, it is determined whether the vehicle has a braking anomaly. This solution receives multiple real-time decelerations of the vehicle during the braking process, constructs a target braking curve based on the multiple real-time decelerations and the target braking distances corresponding to each real-time deceleration, and finally determines whether the vehicle has a braking anomaly based on the target braking curve and the preset braking curve. This achieves a relatively timely and simple determination of whether the vehicle has a braking anomaly, ensuring that the user is promptly alerted to the vehicle's current braking performance, ensuring high vehicle safety and a good user experience. This solves the problem in the prior art where judging braking performance by visual observation or warning lights makes it difficult to detect abnormal braking performance in a timely manner.

[0156] 2) In the braking anomaly determination device of this application, the receiving unit is used to receive multiple real-time decelerations of the vehicle during the target braking action time; the first determining unit is used to determine the target braking distance corresponding to each real-time deceleration during the process of the vehicle decelerating from the initial deceleration to the target deceleration based on the received multiple real-time decelerations and the target braking action time, and to construct the target braking curve corresponding to the process of the vehicle decelerating from the initial deceleration to the target deceleration based on the multiple real-time decelerations and the target braking distances corresponding to each real-time deceleration; the second determining unit determines whether the vehicle has a braking anomaly based on the target braking curve and the preset braking curve. The determining device of this application receives multiple real-time decelerations of the vehicle during braking, and constructs a target braking curve based on the multiple real-time decelerations and the target braking distances corresponding to each real-time deceleration. Finally, based on the target braking curve and a preset braking curve, it determines whether the vehicle has a braking abnormality. This achieves a relatively timely and simple determination of whether the vehicle has a braking abnormality, ensuring that the user can be promptly notified of the vehicle's current braking performance, thus ensuring high vehicle safety and a good user experience. This solves the problem in the prior art where judging braking performance by visual observation or warning lights makes it difficult to detect abnormal braking performance in a timely manner.

[0157] 3) The determination system of this application includes a cloud platform and a vehicle. The cloud platform includes a braking anomaly determination device, which is used to execute any of the above-described braking anomaly determination methods; the cloud platform communicates with the vehicle. In the above determination method, multiple real-time decelerations of the vehicle during braking are received, and a target braking curve is constructed based on the multiple real-time decelerations and the target braking distances corresponding to each real-time deceleration. Finally, based on the target braking curve and a preset braking curve, it is determined whether the vehicle has a braking anomaly. This achieves a relatively timely and simple determination of whether the vehicle has a braking anomaly, ensuring that the user is promptly alerted to the vehicle's current braking performance, guaranteeing high vehicle safety and a good user experience. This solves the problem in the prior art where judging braking performance through visual observation or warning lights makes it difficult to detect abnormal braking performance in a timely manner.

[0158] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining brake malfunction, characterized in that, include: Receive multiple real-time decelerations of the vehicle during the target braking action time, the multiple real-time decelerations are arranged in chronological order, the first of the multiple real-time decelerations is the initial deceleration, and the last of the multiple real-time decelerations is the target deceleration; Based on multiple real-time decelerations and the target braking action time, the target braking distance corresponding to each real-time deceleration is determined during the process of decelerating from the initial deceleration to the target deceleration, and a target braking curve is constructed based on multiple real-time decelerations and multiple target braking distances; Based on the target braking curve and the preset braking curve, it is determined whether the vehicle has a braking abnormality. The preset braking curve is a curve composed of multiple preset decelerations and multiple preset braking distances, and the preset braking curve is the braking curve of the vehicle when it is not in a braking abnormality state. The method of receiving multiple real-time decelerations of the vehicle during the target braking action time also includes: receiving current weather information, the current position information of the vehicle, the current target brake pedal angle, and the current target braking action time. Before determining whether the vehicle has a braking abnormality based on the target braking curve and the preset braking curve, the determination method further includes: determining, from a plurality of candidate braking curves, a preset braking curve that is identical to the current weather information, the current position information of the vehicle, the current target brake pedal angle, and the current target braking action time, wherein the preset braking curve is one of the plurality of candidate braking curves; The process of constructing the alternative braking curve includes: Collect multiple sets of preset data, each set of preset data including weather information, location information, brake pedal angle, braking action time, preset deceleration, and preset braking distance; Based on the weather information, the location information, the brake pedal angle, and the braking action time, multiple sets of preset data are classified to obtain multiple target categories; Using big data methods, the preset deceleration and preset braking distance in multiple sets of preset data under the same target category are fitted to obtain the alternative braking curves.

2. The determination method according to claim 1, characterized in that, Based on the target braking curve and the preset braking curve, determine whether the vehicle has a braking abnormality, including: Calculate the similarity between the target braking curve and the preset braking curve to obtain the target similarity. If the target similarity is lower than the similarity threshold, it is determined that the vehicle has a braking abnormality; If the target similarity is higher than the similarity threshold, it is determined that the vehicle does not have braking abnormalities.

3. The determination method according to claim 1, characterized in that, Based on the target braking curve and the preset braking curve, determine whether the vehicle has a braking abnormality, including: Determine multiple target decelerations corresponding to multiple braking distance sampling points in the target braking curve, and determine multiple preset decelerations corresponding to multiple braking distance sampling points in the preset braking curve; The target deceleration and the preset deceleration corresponding to multiple braking distance sampling points are compared one by one; If there are a predetermined number of consecutive braking distance sampling points where the target deceleration is greater than the preset deceleration, it is determined that the vehicle has a braking abnormality. If there are no consecutive predetermined number of braking distance sampling points where the target deceleration is greater than the preset deceleration, it is determined that the vehicle does not have a braking abnormality.

4. The determining method according to claim 2 or 3, characterized in that, After determining that the vehicle has a braking malfunction, the determination method further includes: A prompt message is generated and sent to the vehicle. The prompt message is used to alert the user that the vehicle's braking performance is abnormal.

5. A device for determining brake malfunction, characterized in that, include: The receiving unit is used to receive multiple real-time decelerations of the vehicle during the target braking action time. The multiple real-time decelerations are arranged in chronological order, and the first real-time deceleration among the multiple real-time decelerations is the initial deceleration, and the last real-time deceleration among the multiple real-time decelerations is the target deceleration. The first determining unit is configured to determine, based on the multiple real-time decelerations and the target braking action time, the target braking distance corresponding to each of the real-time decelerations during the process of decelerating from the initial deceleration to the target deceleration, and to construct a target braking curve based on the multiple real-time decelerations and the multiple target braking distances; The second determining unit is used to determine whether the vehicle has a braking abnormality based on the target braking curve and the preset braking curve. The preset braking curve is a curve composed of multiple preset decelerations and multiple preset braking distances, and the preset braking curve is the braking curve of the vehicle when it is not in a braking abnormality state. The receiving unit further includes: receiving current weather information, the current location information of the vehicle, the current target brake pedal angle, and the current target braking action time; The device further includes: a third determining unit, configured to determine, from a plurality of alternative braking curves, a preset braking curve that is identical to the current weather information, the current position information of the vehicle, the current target brake pedal angle, and the current target brake action time, before determining whether the vehicle has a braking abnormality based on the target braking curve and the preset braking curve; the preset braking curve is one of the plurality of alternative braking curves. The third determining unit includes: The data acquisition module is used to collect multiple sets of preset data. Each set of preset data includes weather information, location information, brake pedal angle, braking action time, preset deceleration, and preset braking distance. The classification module is used to classify multiple sets of preset data based on the weather information, the location information, the brake pedal angle, and the braking action time to obtain multiple target categories; The fitting module is used to fit the preset deceleration and the preset braking distance in multiple sets of preset data under the same target category using big data methods, so as to obtain the alternative braking curve.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the braking anomaly determination method according to any one of claims 1 to 4.

7. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method for determining the brake abnormality according to any one of claims 1 to 4 through the computer program.

8. A system for determining brake malfunction, characterized in that, include: A cloud platform, the cloud platform including a brake anomaly determination device, the determination device being used to execute the brake anomaly determination method according to any one of claims 1 to 4; The cloud platform communicates with the vehicle.

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