A method and apparatus for processing vehicle wheel speed

Abstract

This application discloses a vehicle wheel speed processing method and apparatus. The method includes: acquiring a first interrupt parameter and a second interrupt parameter of a target wheel within multiple task cycles; the first interrupt parameter characterizes the number of high and low level changes; the second interrupt parameter characterizes the duration of the high and low level changes; determining a first weighting factor and a second weighting factor corresponding to the task cycle; determining a first candidate wheel speed based on the first interrupt parameter of the wheel speed sensor within multiple task cycles and the first weighting factor corresponding to the task cycle; determining a second candidate wheel speed based on the second interrupt parameter of the wheel speed sensor within multiple task cycles and the second weighting factor corresponding to the task cycle; and selecting one from the first candidate wheel speed and the second candidate wheel speed as the target wheel speed.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation technology, and in particular to a method and apparatus for processing vehicle wheel speed. Background Technology

[0002] Accurate wheel speed information is crucial in advanced automotive safety and control systems such as vehicle dynamics control, anti-lock braking system (ABS), electronic stability program (ESP), and traction control system (TCS). To achieve this, many vehicles employ Hall effect wheel speed sensors. Given the tooth pitch of the Hall effect wheel speed sensor, the system can calculate the linear speed of the wheel by directly measuring the duration of the high-level segment in the PWM (pulse width modulation) waveform output by the sensor.

[0003] However, in practical applications, issues such as machining accuracy errors in the gear disc, tilt deviations during assembly, increased surface roughness due to long-term use, and potential uneven magnetic distribution between the sensor probe and the gear disc can all cause the high-low level change triggered when the tooth peak passes the probe to deviate from the standard 50:50 duty cycle PWM waveform. The presence of non-standard waveforms may result in longer or shorter high-level durations. When using these varying high-level durations to calculate wheel linear velocity, errors and noise will inevitably be introduced, affecting the accuracy of wheel linear velocity measurement. Summary of the Invention

[0004] In view of the above problems, this application provides a vehicle wheel speed processing method and apparatus to avoid sudden changes in wheel speed caused by single measurement errors or data anomalies, thereby achieving smooth wheel speed acquisition. Furthermore, it increases fault tolerance, ensuring continuous and accurate acquisition of the target wheel's speed information even if one candidate wheel speed fails. The specific solution is as follows:

[0005] The first aspect of this application provides a method for processing vehicle wheel speed, including:

[0006] The first interrupt parameter and the second interrupt parameter of the target wheel are obtained within multiple task cycles; the first interrupt parameter represents the number of high and low level changes collected by the wheel speed sensor corresponding to the target wheel during the movement of the target wheel; the second interrupt parameter represents the duration of the high and low level changes collected by the wheel speed sensor corresponding to the target wheel during the movement of the target wheel.

[0007] Determine a first weighting factor and a second weighting factor corresponding to the task cycle; the first weighting factor characterizes the reliability of a first interruption parameter within the task cycle; the second weighting factor characterizes the reliability of a second interruption parameter within the task cycle.

[0008] Based on the first interruption parameter of the wheel speed sensor in the multiple task cycles and the first weighting factor corresponding to the task cycle, the first candidate wheel speed is determined.

[0009] Based on the second interruption parameter of the wheel speed sensor in the multiple task cycles and the second weighting factor corresponding to the task cycle, the second candidate wheel speed is determined.

[0010] Select one of the first candidate wheel speed and the second candidate wheel speed to determine the target wheel speed of the target wheel.

[0011] In one possible implementation, determining the first weight factor corresponding to the task cycle includes:

[0012] Detect whether the first interrupt parameter within the task cycle is abnormal, and obtain the first detection result;

[0013] If the first detection result indicates that the first interruption parameter within the task cycle is abnormal, the first weighting factor corresponding to the task cycle is set to zero.

[0014] If the first detection result indicates that the first interruption parameter within the task cycle is normal, the first weight factor corresponding to the task cycle is determined according to the order of the task cycles; the later the order of the task cycles, the higher the corresponding first weight factor.

[0015] In one possible implementation, the first interrupt parameter includes: the number of rising edges and the number of falling edges; the number of rising edges represents the number of times a low level rises to a high level; the number of falling edges represents the number of times a high level falls to a low level.

[0016] The step of detecting whether the first interrupt parameter within the task cycle is abnormal, and obtaining the first detection result, includes:

[0017] Using a window of a set size, sliding it across the multiple task cycles with a set step size, the system detects whether the gradient of the number of rising edges within the window satisfies a first gradient threshold or whether the number of rising edges satisfies a first quantity threshold, and whether the gradient of the number of falling edges within the window satisfies a second gradient threshold or whether the number of falling edges satisfies a second quantity threshold, and whether the difference between the number of rising edges and the number of falling edges at the same time satisfies a quantity difference threshold, thereby obtaining a first detection result.

[0018] In one possible implementation, determining the second weighting factor corresponding to the task cycle includes:

[0019] Detect whether the second interrupt parameter within the task cycle is abnormal, and obtain the second detection result;

[0020] If the second detection result indicates that the second interruption parameter within the task cycle is abnormal, the second weighting factor corresponding to the task cycle is set to zero.

[0021] If the second detection result indicates that the second interruption parameter within the task cycle is normal, the second weighting factor corresponding to the task cycle is determined according to the order of the task cycles; the later the order of the task cycles, the higher the corresponding second weighting factor.

[0022] In one possible implementation, detecting the gradient and data range of the second interrupt parameter, determining whether the second interrupt parameter is abnormal within the task cycle, and obtaining a second detection result includes:

[0023] The second interrupt parameters within the multiple task cycles are stored using a circular queue of a set length. It is determined whether the gradient of the second interrupt parameters in the circular queue meets the second gradient threshold and whether the second interrupt parameters in the circular queue meet the set time range, thereby obtaining the second detection result.

[0024] In one possible implementation, the first interrupt parameter includes: the number of rising edges and the number of falling edges; the number of rising edges represents the number of times a low level rises to a high level; the number of falling edges represents the number of times a high level falls to a low level.

[0025] The step of determining the first candidate wheel speed based on the first interruption parameter of the wheel speed sensor in the multiple task cycles and the first weighting factor corresponding to the task cycle includes:

[0026] Based on the first weighting factor corresponding to each task cycle in the plurality of task cycles of the wheel speed sensor, the number of rising edges and the number of falling edges in each task cycle are weighted to obtain the weighted interruption number.

[0027] The tooth pitch of the wheel speed sensor is determined based on the rolling radius of the target wheel and the number of teeth of the wheel speed sensor.

[0028] The distance the target wheel moves is determined based on the tooth pitch of the wheel speed sensor and the weighted number of interruptions.

[0029] Based on the distance traveled by the target wheel and the mission cycle, a first candidate wheel speed is determined.

[0030] In one possible implementation, determining the second candidate wheel speed based on the second interruption parameter of the wheel speed sensor within the plurality of task cycles and the second weighting factor corresponding to the task cycle includes:

[0031] Based on the second weighting factor corresponding to each task cycle in the multiple task cycles of the wheel speed sensor, it is determined that the second interrupt parameter in the multiple task cycles meets the abnormal conditions, and the set time upper limit threshold is used as the weighted interrupt time.

[0032] Based on the second weighting factor corresponding to each task cycle in the multiple task cycles of the wheel speed sensor, it is determined that the second interrupt parameter in the multiple task cycles does not meet the abnormal condition. Based on the second weighting factor corresponding to each task cycle, the second interrupt parameter pair corresponding to each task cycle is weighted to obtain the weighted interrupt time. The second interrupt parameter pair corresponding to the task cycle includes the second interrupt parameter in the task cycle and the second interrupt parameter in the previous task cycle adjacent to the task cycle.

[0033] The tooth pitch of the wheel speed sensor is determined based on the rolling radius of the target wheel and the number of teeth of the wheel speed sensor.

[0034] The second candidate wheel speed is determined based on the tooth pitch of the wheel speed sensor and the weighted interruption time.

[0035] In one possible implementation, selecting one of the first candidate wheel speeds and the second candidate wheel speed as the target wheel speed includes:

[0036] If the difference between the first candidate wheel speed and the second candidate wheel speed meets the set difference threshold or the second interruption parameter within the multiple task cycles is abnormal as determined by the second weighting factor corresponding to each task cycle, the first candidate wheel speed is selected from the first candidate wheel speed and the second candidate wheel speed and determined as the target wheel speed of the target wheel.

[0037] If the difference between the first candidate wheel speed and the second candidate wheel speed does not meet the set difference threshold, or if the second interruption parameter abnormality is determined to have not occurred within the multiple task cycles based on the second weighting factor corresponding to each task cycle, the second candidate wheel speed is selected from the first candidate wheel speed and the second candidate wheel speed and determined as the target wheel speed of the target wheel.

[0038] In one possible implementation, the method further includes:

[0039] Based on the first weighting factor corresponding to each of the task cycles, the sensor abnormality flag bit of the target wheel is determined; the sensor abnormality flag bit is used to indicate whether the first interruption parameter has become abnormal within the multiple task cycles.

[0040] Output the sensor's abnormal flag bit.

[0041] Another aspect of this application provides a vehicle wheel speed control device, comprising:

[0042] The acquisition module is used to acquire a first interrupt parameter and a second interrupt parameter of the target wheel within multiple task cycles; the first interrupt parameter represents the number of high and low level changes collected by the wheel speed sensor corresponding to the target wheel during the movement of the target wheel; the second interrupt parameter represents the duration of the high and low level changes collected by the wheel speed sensor corresponding to the target wheel during the movement of the target wheel.

[0043] The first determining module is used to determine the first weighting factor corresponding to the task cycle; the first weighting factor characterizes the reliability of the first interruption parameter within the task cycle.

[0044] The second determining module is used to determine the second weighting factor corresponding to the task cycle; the second weighting factor characterizes the reliability of the second interruption parameter within the task cycle;

[0045] The third determining module is used to determine the first candidate wheel speed based on the first interruption parameter of the wheel speed sensor in the multiple task cycles and the first weighting factor corresponding to the task cycle;

[0046] The fourth determining module is used to determine the second candidate wheel speed based on the second interruption parameter of the wheel speed sensor in the multiple task cycles and the second weighting factor corresponding to the task cycle;

[0047] The fifth determining module is used to select one from the first candidate wheel speed and the second candidate wheel speed and determine it as the target wheel speed of the target wheel.

[0048] In this application, by obtaining the first and second interruption parameters of the target wheel over multiple task cycles, not only is the diversity and statistical reliability of the data increased, but a first and second weighting factor can also be introduced. The first and second weighting factors are used to quantitatively evaluate the reliability of the first and second interruption parameters over the task cycles, respectively, and can dynamically reflect the quality changes of the first and second interruption parameters of the target wheel over multiple task cycles.

[0049] Based on this, a first candidate wheel speed is determined using the first interruption parameter of the wheel speed sensor within the multiple task cycles and the first weighting factor corresponding to the task cycle. A second candidate wheel speed is determined using the second interruption parameter of the wheel speed sensor within the multiple task cycles and the second weighting factor corresponding to the task cycle. This effectively reduces the adverse effects of abnormal or low-quality data on wheel speed calculation results, improving the accuracy of both the first and second candidate wheel speeds. Then, by selecting a more reliable candidate wheel speed as the target wheel speed, sudden changes in wheel speed caused by single measurement errors or data anomalies can be avoided, achieving smooth wheel speed acquisition. Furthermore, it increases fault tolerance, ensuring continuous and accurate acquisition of the target wheel speed information even if one candidate wheel speed has a problem. Attached Figure Description

[0050] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0051] Figure 1 A schematic flowchart of a vehicle wheel speed processing method provided in this application;

[0052] Figure 2 Another flowchart illustrating a vehicle wheel speed processing method provided in this application;

[0053] Figure 3 Another flowchart illustrating a vehicle wheel speed processing method provided in this application;

[0054] Figure 4 Another flowchart illustrating a vehicle wheel speed processing method provided in this application;

[0055] Figure 5 This application provides a schematic diagram of an implementation scenario for a vehicle wheel speed processing method.

[0056] Figure 6 This is a schematic diagram of the structure of a vehicle wheel speed device provided in this application. Detailed Implementation

[0057] The embodiments of this application are described below with reference to the accompanying drawings. The terminology used in the implementation section of this application is for explaining specific embodiments only and is not intended to limit the scope of this application.

[0058] The embodiments of this application will now be described with reference to the accompanying drawings. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided in the embodiments of this application are equally applicable to similar technical problems.

[0059] The terms "first," "second," etc., used 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 terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not explicitly listed or inherent to those processes, methods, products, or apparatuses.

[0060] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0061] Reference Figure 1 This is a flowchart illustrating a vehicle wheel speed processing method provided in Embodiment 1 of this application. Figure 1 As shown, the method may include, but is not limited to, the following steps:

[0062] Step S101: Obtain the first interrupt parameter and the second interrupt parameter of the target wheel in multiple task cycles; the first interrupt parameter represents the number of high and low level changes collected by the wheel speed sensor corresponding to the target wheel during the movement of the target wheel; the second interrupt parameter represents the duration of the high and low level collected by the wheel speed sensor corresponding to the target wheel during the movement of the target wheel.

[0063] In this embodiment, a Hall effect wheel speed sensor can be used to sample the rotation state of the target wheel every preset task cycle. The Hall effect wheel speed sensor can sense the change in magnetic flux when the target wheel rotates and convert this change into a change in potential difference. Subsequently, the change in potential difference can be modulated into a PWM waveform.

[0064] In this embodiment, the number of high and low level changes in each task cycle can be recorded by edge detection (such as rising or falling edge) of the PWM waveform as the first interrupt parameter. At the same time, the duration of high and low levels in the PWM waveform can be measured as the second interrupt parameter.

[0065] Step S102: Determine the first weight factor and the second weight factor corresponding to the task cycle.

[0066] The first weighting factor can characterize the reliability of the first interruption parameter within the task cycle.

[0067] The second weighting factor can characterize the reliability of the second interruption parameter within the task cycle.

[0068] Step S103: Determine the first candidate wheel speed based on the first interruption parameter of the wheel speed sensor in the multiple task cycles and the first weighting factor corresponding to the task cycle.

[0069] In this embodiment, in reality, due to the presence of various interference factors (such as electromagnetic noise, sensor aging, etc.), the first interrupt parameter may have a certain error. In order to reduce the impact of this error on the wheel speed calculation, the first candidate wheel speed can be determined by the first interrupt parameter in multiple task cycles and the first weighting factor corresponding to the task cycle.

[0070] Among them, the higher the credibility of the first interrupt parameter, the greater its contribution to determining the first candidate wheel speed.

[0071] Step S104: Determine the second candidate wheel speed based on the second interruption parameter of the wheel speed sensor in the multiple task cycles and the second weighting factor corresponding to the task cycle.

[0072] In this embodiment, in reality, due to the presence of various interference factors (such as electromagnetic noise, sensor aging, etc.), the second interrupt parameter may have a certain error. In order to reduce the impact of this error on the wheel speed calculation, the second candidate wheel speed can be determined by the second interrupt parameter in multiple task cycles and the second weighting factor corresponding to the task cycle.

[0073] Among them, the higher the credibility of the second interruption parameter, the greater its contribution to determining the second candidate wheel speed.

[0074] Step S105: Select one from the first candidate wheel speed and the second candidate wheel speed to determine it as the target wheel speed of the target wheel.

[0075] In this embodiment, by acquiring the first and second interruption parameters of the target wheel over multiple task cycles, not only is the diversity and statistical reliability of the data increased, but a first and second weighting factor can also be introduced. The first and second weighting factors are used to quantitatively evaluate the reliability of the first and second interruption parameters within the task cycles, respectively, and can dynamically reflect the quality changes of the first and second interruption parameters of the target wheel over multiple task cycles.

[0076] Based on this, a first candidate wheel speed is determined using the first interruption parameter of the wheel speed sensor within the multiple task cycles and the first weighting factor corresponding to the task cycle. A second candidate wheel speed is determined using the second interruption parameter of the wheel speed sensor within the multiple task cycles and the second weighting factor corresponding to the task cycle. This effectively reduces the adverse effects of abnormal or low-quality data on wheel speed calculation results, improving the accuracy of both the first and second candidate wheel speeds. Then, by selecting a more reliable candidate wheel speed as the target wheel speed, sudden changes in wheel speed caused by single measurement errors or data anomalies can be avoided, achieving smooth wheel speed acquisition. Furthermore, it increases fault tolerance, ensuring continuous and accurate acquisition of the target wheel speed information even if one candidate wheel speed has a problem.

[0077] As another optional embodiment of this application, a vehicle wheel speed processing method provided in Embodiment 2 of this application is mainly an implementation of determining the first weight factor corresponding to the task cycle in Embodiment 1. Determining the first weight factor corresponding to the task cycle may include, but is not limited to, the following steps:

[0078] Step S11: Detect whether the first interrupt parameter within the task cycle is abnormal, and obtain the first detection result.

[0079] Step S12: If the first detection result indicates that the first interruption parameter within the task cycle is abnormal, the first weight factor corresponding to the task cycle is set to zero.

[0080] Step S13: If the first detection result indicates that the first interruption parameter within the task cycle is normal, determine the first weight factor corresponding to the task cycle according to the order of the task cycles; the later the order of the task cycles, the higher the corresponding first weight factor.

[0081] In this embodiment, the index value of the task cycle can be used as the first weighting factor corresponding to the task cycle. The index value of the task cycle can represent the order of the task cycles. As the index value increases, that is, the later the order of the task cycle, the newer the first interrupt parameter within the task cycle, and therefore a higher weight can be assigned.

[0082] In this embodiment, when an anomaly is detected in the first interruption parameter, the first weighting factor corresponding to the task cycle is set to zero, which can eliminate the influence of abnormal data on wheel speed calculation. Since abnormal data often cannot accurately reflect the rotational state of the target wheel, setting its weight to zero can prevent it from misleading the wheel speed calculation results.

[0083] When the first interrupt parameter is detected to be normal, the corresponding first weight factor is determined according to the order of the task cycles. The later the task cycle is in the order, that is, the newer the first interrupt parameter, the higher the corresponding first weight factor. This method fully considers the impact of the newness of the first interrupt parameter on the wheel speed calculation, making the wheel speed calculation more accurate and reasonable.

[0084] As another optional embodiment of this application, this embodiment provides a vehicle wheel speed processing method according to embodiment 3. This embodiment is mainly an implementation of step S11 in embodiment 2. The first interrupt parameter may include: the number of rising edges and the number of falling edges; the number of rising edges can represent the number of times a low level rises to a high level; the number of falling edges can represent the number of times a high level falls to a low level; step S11 may include, but is not limited to, the following steps:

[0085] Step S111: Using a window of a set size, slide it in the multiple task cycles with a set step size, detect whether the gradient of the number of rising edges in the window meets the first gradient threshold or whether the number of rising edges meets the first quantity threshold, and whether the gradient of the number of falling edges in the window meets the second gradient threshold or whether the number of falling edges meets the second quantity threshold, and whether the difference between the number of rising edges and the number of falling edges at the same time meets the quantity difference threshold, to obtain the first detection result.

[0086] The size and step size can be set as needed and are not limited in this application. To ensure that the wheel speed jump between adjacent task cycles does not exceed 0.2m / s and the wheel speed delay does not exceed 150ms during the vehicle's uniform speed driving process, the size can be set to 30, that is, the length of the window is 30.

[0087] In this embodiment, the gradient of the number of rising edges can be understood as the change in the number of rising edges within adjacent task cycles.

[0088] The gradient of the number of falling edges can be understood as the change in the number of falling edges within adjacent task cycles.

[0089] The first gradient threshold, the first quantity threshold, the second gradient threshold, and the second quantity threshold can be set as needed, and are not restricted in this application.

[0090] For example, within the window, if the gradient of the number of rising edges satisfies the condition... Or the number of rising edges within the task cycle meets the condition. If so, it is considered an abnormal number of rising edges. This represents the number of rising edges within the i-th task cycle. This represents the number of rising edges during the (i-1)th task cycle. This represents the first gradient threshold. This represents the first quantity threshold.

[0091] Among them, in settings When calculating the wheel speed gradient caused by the vehicle's maximum acceleration and braking capabilities, a safety factor (e.g., 200%) can be considered to ensure accurate identification even in extreme conditions. Simultaneously, the error range of the number of rising and falling edges in adjacent task cycles that may be caused by periodic data acquisition can be considered to ensure that the first gradient threshold is neither too sensitive nor too insensitive.

[0092] In settings When setting the equivalent number of rising edges at the vehicle's maximum operating speed, you can consider multiplying it by a safety factor (e.g., 150%).

[0093] Within the window, if the gradient of the number of falling edges satisfies the condition... Or the number of falling edges within the task cycle meets the condition. If so, it is considered an abnormal number of falling edges. This represents the number of falling edges within the i-th task cycle. This represents the number of falling edges during the (i-1)th task cycle. This represents the second gradient threshold. This represents the second quantity threshold.

[0094] If the difference in the number of rising and falling edges at the same time satisfies the condition If so, it is considered that the number of rising edges and falling edges is abnormal.

[0095] In this embodiment, by using a dual detection mechanism that checks whether the gradient of the number of rising and falling edges within the detection window meets a preset gradient threshold, and whether the number of edges meets a preset quantity threshold, the probability of false alarms and false negatives can be effectively reduced, and the accuracy of anomaly detection can be improved.

[0096] Furthermore, the system also detects differences in the number of rising and falling edges at the same time to ensure consistency. This consistency check helps eliminate abnormal data caused by sensor malfunctions or signal processing errors, thereby improving the reliability of wheel speed calculation.

[0097] Furthermore, the window length and step size can be set as needed to balance the smoothness and latency of wheel speed acquisition.

[0098] As another optional embodiment of this application, a vehicle wheel speed processing method provided in Embodiment 4 of this application is mainly an implementation of determining the second weighting factor corresponding to the task cycle in Embodiment 1. Determining the second weighting factor corresponding to the task cycle may include, but is not limited to, the following steps:

[0099] Step S21: Detect whether the second interrupt parameter within the task cycle is abnormal, and obtain the second detection result.

[0100] Step S22: If the second detection result indicates that the second interruption parameter within the task cycle is abnormal, the second weighting factor corresponding to the task cycle is set to zero.

[0101] Step S23: If the second detection result indicates that the second interruption parameter within the task cycle is normal, determine the second weighting factor corresponding to the task cycle according to the order of the task cycles; the later the order of the task cycles, the higher the corresponding second weighting factor.

[0102] In this embodiment, the index value of the task cycle can be used as the first weighting factor corresponding to the task cycle. The index value of the task cycle can represent the order of the task cycles. As the index value increases, that is, the later the order of the task cycle, the newer the first interrupt parameter within the task cycle, and therefore a higher weight can be assigned.

[0103] In this embodiment, when an anomaly is detected in the second interrupt parameter, the second weighting factor corresponding to the task cycle is set to zero, which can eliminate the influence of abnormal data on wheel speed calculation. Since abnormal data often cannot accurately reflect the rotational state of the target wheel, setting its weight to zero can prevent it from misleading the wheel speed calculation results.

[0104] When the second interrupt parameter is detected to be normal, the corresponding second weight factor is determined according to the order of the task cycles. The later the task cycle is in the order, that is, the newer the second interrupt parameter, the higher the corresponding second weight factor. This method fully considers the impact of the newness of the second interrupt parameter on the wheel speed calculation, making the wheel speed calculation more accurate and reasonable.

[0105] As another optional embodiment of this application, this embodiment provides a vehicle wheel speed processing method in embodiment 5. This embodiment is mainly an implementation of step S21 in embodiment 4. Step S21 may include, but is not limited to, the following steps:

[0106] Step S211: Use a circular queue of a set length to store the second interrupt parameters within the multiple task cycles, determine whether the gradient of the second interrupt parameters in the circular queue meets the second gradient threshold, and whether the second interrupt parameters in the circular queue meet the set time range, to obtain the second detection result.

[0107] In this embodiment, the set length can be set as needed and is not limited in this application. For example, to ensure that the wheel speed jump between adjacent cycles does not exceed 0.2 m / s and the wheel speed delay does not exceed 150 ms during the vehicle's uniform speed driving process, the set length is defined as 10 in this embodiment.

[0108] The gradient of the second interrupt parameter in the circular queue can be understood as the change between the second interrupt parameter in the task cycle and the second interrupt parameter in the task cycle adjacent to the task cycle.

[0109] The second gradient threshold and the set time range can be set as needed, and are not limited in this application.

[0110] For example, if the gradient of the second interrupt parameter satisfies the condition Or the second interrupt parameter does not meet the condition. If this occurs, it is considered an abnormal sensor acquisition interruption time. This represents the second interrupt parameter within the i-th task cycle. This represents the second interrupt parameter within the (i-1)th task cycle. This represents the second gradient threshold. This indicates the setting of a lower time limit. This indicates the upper limit of the set time. The time range between the lower limit of the set time and the upper limit of the set time is the set time range.

[0111] Among them, in settings In this case, the gradient of high and low level duration changes caused by the vehicle's maximum acceleration and braking capabilities can be considered and multiplied by a safety factor (e.g., 200%) to ensure accurate identification even in extreme cases. Simultaneously, the error range of high and low level durations between adjacent task cycles that may be caused by periodic sampling can be considered to ensure that the second gradient threshold is neither too sensitive nor too insensitive.

[0112] You can refer to the high and low level durations corresponding to 150% of the vehicle's maximum operating speed for setting. The high and low level durations can be set by referring to the lowest speed (e.g., 0.01m / s) that the wheel speed signal data type can cover.

[0113] In this embodiment, by calculating the gradient of the second interrupt parameter of adjacent task cycles in the circular queue and comparing it with a preset second gradient threshold, abnormal second interrupt parameters can be accurately detected.

[0114] At the same time, by checking whether the second interrupt parameter falls within the set time range, unreasonable second interrupt parameters can be further eliminated.

[0115] Furthermore, using a circular queue can avoid repeated read and write operations caused by frequent insertion of elements at the end of the array, thus reducing system overhead caused by data updates while ensuring data real-time performance.

[0116] Furthermore, the length of the circular queue can be set according to actual needs to balance data smoothness and real-time performance.

[0117] As another optional embodiment of this application, refer to Figure 2 This is a flowchart illustrating a vehicle wheel speed processing method provided in Embodiment 6 of this application. This embodiment is mainly an implementation of step S103 in Embodiment 1. In this embodiment, the first interrupt parameter includes: the number of rising edges and the number of falling edges; the number of rising edges represents the number of times a low level rises to a high level; the number of falling edges represents the number of times a high level falls to a low level; as shown below. Figure 2 As shown, step S103 may include, but is not limited to, the following steps:

[0118] Step S1031: Based on the first weighting factor corresponding to each task cycle in the plurality of task cycles of the wheel speed sensor, the number of rising edges and the number of falling edges in each task cycle are weighted to obtain the weighted interruption number.

[0119] In this embodiment, corresponding to an implementation with a window length of 30, the weighted interruption count can be calculated using the following formula:

[0120]

[0121] This represents the number of rising edges within the i-th task cycle. This represents the number of falling edges within the i-th task cycle. This represents the first weight factor corresponding to the i-th task cycle. This represents the weighted number of interruptions.

[0122] Step S1032: Determine the tooth pitch of the wheel speed sensor based on the rolling radius of the target wheel and the number of teeth of the wheel speed sensor.

[0123] In this embodiment, the tooth pitch of the wheel speed sensor can be determined in the following way:

[0124] Tooth pitch =

[0125] Where R represents the rolling radius of the target wheel, and N represents the number of teeth of the wheel speed sensor.

[0126] Step S1033: Determine the movement distance of the target wheel based on the tooth pitch of the wheel speed sensor and the weighted number of interruptions.

[0127] In this embodiment, the distance the target wheel travels can be determined using the following formula:

[0128] Distance traveled =

[0129] in, The pitch of the wheel speed sensor is represented by n, and the weighted interrupt number is represented by n.

[0130] Step S1034: Determine the first candidate wheel speed based on the travel distance of the target wheel and the task cycle.

[0131] In this embodiment, the first candidate wheel speed can be determined using the following relationship:

[0132]

[0133] in, T represents the distance the target wheel has traveled, and T represents the mission cycle.

[0134] In this embodiment, by weighting the number of rising edges and falling edges in each task cycle, the interference of abnormal or low-quality data (such as single measurement error) on the calculation process is reduced. Based on this, the target wheel's movement distance in each task cycle can be calculated based on the weighted interruption number and tooth pitch, making the movement distance more accurate and reliable, and thus making the first candidate wheel speed more accurate.

[0135] As another optional embodiment of this application, refer to Figure 3 This is a flowchart illustrating a vehicle wheel speed processing method provided in Embodiment 7 of this application. This embodiment is mainly an implementation of step S104 in Embodiment 1, such as... Figure 3 As shown, step S104 may include, but is not limited to, the following steps:

[0136] Step S1041: Based on the second weighting factor corresponding to each task cycle in the multiple task cycles of the wheel speed sensor, determine that the second interrupt parameter in the multiple task cycles meets the abnormal conditions, and set the upper limit threshold of the time as the weighted interrupt time.

[0137] In this embodiment, based on the second weighting factor corresponding to each task cycle in the multiple task cycles of the wheel speed sensor, it is determined that the second interruption parameter in the multiple task cycles meets the abnormal conditions, which may include, but is not limited to: .

[0138] This means that the second interrupt parameter is abnormal (i.e., the second weighting factor is set to 0) in most or all of the multiple task cycles, and therefore there is not enough reliable data to calculate an accurate weighted interrupt time. In this case, It can be used as a conservative and safe estimate.

[0139] Step S1042: Based on the second weighting factor corresponding to each task cycle in the multiple task cycles of the wheel speed sensor, determine that the second interrupt parameter in the multiple task cycles does not meet the abnormal condition. Based on the second weighting factor corresponding to each task cycle, perform weighted processing on the second interrupt parameter pair corresponding to each task cycle to obtain the weighted interrupt time.

[0140] Based on the second weighting factor corresponding to each task cycle in the multiple task cycles of the wheel speed sensor, it is determined that the second interrupt parameter in the multiple task cycles does not meet the abnormal condition, which may include, but is not limited to: .

[0141] This means that the second interrupt parameter is not abnormal in most or all of the multiple task cycles (i.e., the second weighting factor is set to 1), so there is enough reliable data to calculate an accurate weighted interrupt time.

[0142] The second interrupt parameter pair corresponding to the task cycle includes the second interrupt parameter within the task cycle and the second interrupt parameter within the previous task cycle adjacent to the task cycle.

[0143] For example, the weighted interruption time can be obtained according to the following relationship:

[0144]

[0145] This represents the second interrupt parameter within the i-th task cycle. This represents the second interrupt parameter within the (i-1)th task cycle. This represents the second weight factor corresponding to the i-th task cycle. Indicates the weighted interruption time.

[0146] Step S1043: Determine the tooth pitch of the wheel speed sensor based on the rolling radius of the target wheel and the number of teeth of the wheel speed sensor.

[0147] In this embodiment, the tooth pitch of the wheel speed sensor can be determined using the following relationship:

[0148] Tooth pitch

[0149] Where R represents the rolling radius of the target wheel, and N represents the number of teeth of the wheel speed sensor.

[0150] Step S1044: Determine the second candidate wheel speed based on the tooth pitch of the wheel speed sensor and the weighted interruption time.

[0151] In this embodiment, the second candidate wheel speed can be determined using the following relationship:

[0152]

[0153] Indicates the second candidate wheel speed. Indicates the weighted interruption time. Indicates tooth pitch.

[0154] In this embodiment, when it is determined that most or all of the second interrupt parameters in multiple task cycles are abnormal, a preset, conservative estimate (i.e., a set time upper limit threshold t_ThrsH) can be used as the weighted interrupt time. This approach avoids inaccurate calculations when data is unreliable, thereby ensuring the accuracy of the weighted interrupt time.

[0155] When it is determined that most or all of the second interrupt parameters are normal across multiple task cycles, weighted interrupt times can be calculated through weighted processing. This approach fully considers the reliability and age of the data, resulting in more accurate and reasonable calculations.

[0156] On the premise that the accuracy and rationality of the weighted interruption time can be guaranteed, the accuracy and reliability of the second candidate wheel speed can be guaranteed by calculating the weighted interruption time and tooth pitch.

[0157] As another optional embodiment of this application, this embodiment provides a vehicle wheel speed processing method in embodiment 8 of this application. This embodiment is mainly an implementation of step S105 in embodiment 1. Step S105 may include, but is not limited to, the following steps:

[0158] Step S1051: If the difference between the first candidate wheel speed and the second candidate wheel speed meets the set difference threshold or the second interruption parameter within the plurality of task cycles is determined to be abnormal based on the second weighting factor corresponding to each task cycle, the first candidate wheel speed is selected from the first candidate wheel speed and the second candidate wheel speed and determined as the target wheel speed of the target wheel.

[0159] The difference between the first candidate wheel speed and the second candidate wheel speed satisfying a set difference threshold may include, but is not limited to:

[0160]

[0161] This represents the absolute value of the difference between the second candidate wheel speed and the first candidate wheel speed. This indicates that a difference threshold is set.

[0162] Determining the second interrupt parameter anomaly within the plurality of task cycles based on the second weighting factor corresponding to each of the aforementioned task cycles may include, but is not limited to:

[0163]

[0164] Indicates the weighted interruption time. This indicates that a time limit threshold is set.

[0165] In this embodiment, the first interruption parameter is less affected by the machining dimensional error of the gear disc and the magnetic non-uniformity of the probe. Therefore, the second candidate wheel speed can be verified by using Vn as a benchmark. If the difference between the first candidate wheel speed and the second candidate wheel speed meets the set difference threshold or the second interruption parameter in the multiple task cycles is determined to be abnormal based on the second weighting factor corresponding to each task cycle, it indicates that the credibility of the second candidate wheel speed is low, and the first candidate wheel speed can be selected as the target wheel speed of the target wheel.

[0166] Step S1052: If the difference between the first candidate wheel speed and the second candidate wheel speed does not meet the set difference threshold, or if the second interruption parameter abnormality is determined to have not occurred within the multiple task cycles based on the second weighting factor corresponding to each task cycle, the second candidate wheel speed is selected from the first candidate wheel speed and the second candidate wheel speed and determined as the target wheel speed of the target wheel.

[0167] In this embodiment, when there is a significant difference between two candidate wheel speeds, it is likely due to some kind of anomaly (such as sensor failure, signal interference, etc.). In this case, selecting the first candidate wheel speed, which is less affected by the machining dimensional error of the gear disc and the magnetic inhomogeneity of the probe, can more effectively eliminate abnormal data and improve the accuracy of the target wheel speed.

[0168] When the difference between two candidate wheel speeds does not exceed the set difference threshold, and the second interrupt parameter within multiple task cycles is determined based on the second weighting factor corresponding to the task cycle without any abnormality, the second candidate wheel speed has high reliability because the weighted interrupt time can usually reflect the wheel rotation speed more directly and is more sensitive to transient anomalies. Therefore, selecting the second candidate wheel speed as the target wheel speed can ensure the real-time performance and accuracy of the target wheel speed.

[0169] As another optional embodiment of this application, refer to Figure 4 This is a flowchart illustrating a vehicle wheel speed processing method provided in Embodiment 9 of this application. Figure 4 As shown, the method may include, but is not limited to, the following steps:

[0170] Step S201: Obtain the first interrupt parameter and the second interrupt parameter of the target wheel in multiple task cycles; the first interrupt parameter represents the number of high and low level changes collected by the wheel speed sensor corresponding to the target wheel during the movement of the target wheel; the second interrupt parameter represents the duration of the high and low level collected by the wheel speed sensor corresponding to the target wheel during the movement of the target wheel.

[0171] Step S202: Detect whether the first interrupt parameter within the task cycle is abnormal, and obtain the first detection result.

[0172] Step S203: If the first detection result indicates that the first interruption parameter within the task cycle is abnormal, the first weight factor corresponding to the task cycle is set to zero.

[0173] Step S204: If the first detection result indicates that the first interruption parameter within the task cycle is normal, determine the first weight factor corresponding to the task cycle according to the order of the task cycles; the later the order of the task cycles, the higher the corresponding first weight factor.

[0174] For a detailed description of steps S202-S204, please refer to the relevant description of steps S11-S13 in Example 2, which will not be repeated here.

[0175] Step S205: Based on the first weighting factor corresponding to each of the task cycles, determine the sensor abnormality flag bit of the target wheel; the sensor abnormality flag bit is used to indicate whether the first interruption parameter in the multiple task cycles is abnormal.

[0176] In this embodiment, if the sum of the first weighting factors corresponding to each task is 0, the sensor anomaly flag E of the target wheel can be determined to be 1. If the sum of the first weighting factors corresponding to each task is not 0, the sensor anomaly flag E of the target wheel can be determined to be 0.

[0177] Step S206: Output the sensor abnormality flag bit.

[0178] Step S207: Detect whether the second interrupt parameter within the task cycle is abnormal, and obtain the second detection result.

[0179] Step S208: If the second detection result indicates that the second interruption parameter within the task cycle is abnormal, the second weighting factor corresponding to the task cycle is set to zero.

[0180] Step S209: If the second detection result indicates that the second interruption parameter within the task cycle is normal, determine the second weighting factor corresponding to the task cycle according to the order of the task cycles; the later the order of the task cycles, the higher the corresponding second weighting factor.

[0181] For a detailed description of steps S207-S209, please refer to the relevant description of steps S21-S23 in Example 4, which will not be repeated here.

[0182] Step S210: Determine the first candidate wheel speed based on the first interruption parameter of the wheel speed sensor in the multiple task cycles and the first weighting factor corresponding to the task cycle.

[0183] Step S211: Determine the second candidate wheel speed based on the second interruption parameter of the wheel speed sensor in the multiple task cycles and the second weighting factor corresponding to the task cycle.

[0184] Step S212: Select one from the first candidate wheel speed and the second candidate wheel speed to determine it as the target wheel speed of the target wheel.

[0185] For a detailed description of steps S210-S212, please refer to the relevant description of steps S13-S15 in Example 1, which will not be repeated here.

[0186] In this embodiment, by accumulating the first weighting factor across multiple task cycles, the operating status of the wheel speed sensor can be reflected more comprehensively. Anomalies within a single task cycle may be caused by noise or transient interference, while the accumulation across multiple task cycles reduces this randomness and improves diagnostic accuracy. Based on this, by outputting the sensor anomaly flag, the problem can be quickly located, allowing for timely and targeted maintenance.

[0187] In this embodiment, the target wheel can be any one of the four wheels. For example, such as Figure 5 As shown, the processor's inputs may include: the wheel speed sensors of the left front wheel, left rear wheel, right front wheel, and right rear wheel. The number of rising edges collected by these sensors can be respectively... The number of falling edges can be respectively The duration of a high level or the duration of a low level can be expressed as follows: The rolling radius of each wheel can be represented by R, the number of teeth of the wheel speed sensor can be represented by N, and the task cycle can be represented by T. The processor, by executing the vehicle wheel speed processing method, can obtain the wheel speed of each of the four wheels based on the input. and wheel speed abnormality flag .

[0188] The vehicle wheel speed processing device provided in this application will be described below. The vehicle wheel speed processing device described below can be referred to in correspondence with the vehicle wheel speed processing method described above.

[0189] Reference Figure 6 The vehicle wheel speed processing device includes: an acquisition module 100, a first determination module 200, a second determination module 300, a third determination module 400, a fourth determination module 500, and a sixth determination module 600.

[0190] The acquisition module 100 is used to acquire a first interruption parameter and a second interruption parameter of the target wheel within multiple task cycles; the first interruption parameter represents the number of high and low level changes collected by the wheel speed sensor corresponding to the target wheel during the movement of the target wheel; the second interruption parameter represents the duration of the high and low level changes collected by the wheel speed sensor corresponding to the target wheel during the movement of the target wheel.

[0191] The first determining module 200 is used to determine the first weighting factor corresponding to the task cycle; the first weighting factor characterizes the reliability of the first interruption parameter within the task cycle.

[0192] The second determining module 300 is used to determine the second weighting factor corresponding to the task cycle; the second weighting factor characterizes the reliability of the second interruption parameter within the task cycle.

[0193] The third determining module 400 is used to determine the first candidate wheel speed based on the first interruption parameter of the wheel speed sensor in the multiple task cycles and the first weighting factor corresponding to the task cycle.

[0194] The fourth determining module 500 is used to determine the second candidate wheel speed based on the second interruption parameter of the wheel speed sensor in the multiple task cycles and the second weighting factor corresponding to the task cycle.

[0195] The fifth determining module 600 is used to select one from the first candidate wheel speed and the second candidate wheel speed and determine it as the target wheel speed of the target wheel.

[0196] The first determining module 200 can be specifically used for:

[0197] Detect whether the first interrupt parameter within the task cycle is abnormal, and obtain the first detection result;

[0198] If the first detection result indicates that the first interruption parameter within the task cycle is abnormal, the first weighting factor corresponding to the task cycle is set to zero.

[0199] If the first detection result indicates that the first interruption parameter within the task cycle is normal, the first weight factor corresponding to the task cycle is determined according to the order of the task cycles; the later the order of the task cycles, the higher the corresponding first weight factor.

[0200] In this embodiment, the first interrupt parameter may include: the number of rising edges and the number of falling edges; the number of rising edges represents the number of times a low level rises to a high level; the number of falling edges represents the number of times a high level falls to a low level;

[0201] The first determining module 200 detects whether the first interrupt parameter within the task cycle is abnormal and obtains a first detection result, which may include:

[0202] Using a window of a set size, sliding it across the multiple task cycles with a set step size, the system detects whether the gradient of the number of rising edges within the window satisfies a first gradient threshold or whether the number of rising edges satisfies a first quantity threshold, and whether the gradient of the number of falling edges within the window satisfies a second gradient threshold or whether the number of falling edges satisfies a second quantity threshold, and whether the difference between the number of rising edges and the number of falling edges at the same time satisfies a quantity difference threshold, thereby obtaining a first detection result.

[0203] The second determining module 300 can be specifically used for:

[0204] Detect whether the second interrupt parameter within the task cycle is abnormal, and obtain the second detection result;

[0205] If the second detection result indicates that the second interruption parameter within the task cycle is abnormal, the second weighting factor corresponding to the task cycle is set to zero.

[0206] If the second detection result indicates that the second interruption parameter within the task cycle is normal, the second weighting factor corresponding to the task cycle is determined according to the order of the task cycles; the later the order of the task cycles, the higher the corresponding second weighting factor.

[0207] The second determining module 300 detects the gradient and data range of the second interrupt parameter, determines whether the second interrupt parameter is abnormal within the task cycle, and obtains a second detection result, which may include:

[0208] The second interrupt parameters within the multiple task cycles are stored using a circular queue of a set length. It is determined whether the gradient of the second interrupt parameters in the circular queue meets the second gradient threshold and whether the second interrupt parameters in the circular queue meet the set time range, thereby obtaining the second detection result.

[0209] In this embodiment, the first interrupt parameter includes: the number of rising edges and the number of falling edges; the number of rising edges represents the number of times a low level rises to a high level; the number of falling edges represents the number of times a high level falls to a low level.

[0210] The third determining module 400 can be used specifically for:

[0211] Based on the first weighting factor corresponding to each task cycle in the plurality of task cycles of the wheel speed sensor, the number of rising edges and the number of falling edges in each task cycle are weighted to obtain the weighted interruption number.

[0212] The tooth pitch of the wheel speed sensor is determined based on the rolling radius of the target wheel and the number of teeth of the wheel speed sensor.

[0213] The distance the target wheel moves is determined based on the tooth pitch of the wheel speed sensor and the weighted number of interruptions.

[0214] Based on the distance traveled by the target wheel and the mission cycle, a first candidate wheel speed is determined.

[0215] The fourth module, 500, can be specifically used for:

[0216] Based on the second weighting factor corresponding to each task cycle in the multiple task cycles of the wheel speed sensor, it is determined that the second interrupt parameter in the multiple task cycles meets the abnormal conditions, and the set time upper limit threshold is used as the weighted interrupt time.

[0217] Based on the second weighting factor corresponding to each task cycle in the multiple task cycles of the wheel speed sensor, it is determined that the second interrupt parameter in the multiple task cycles does not meet the abnormal condition. Based on the second weighting factor corresponding to each task cycle, the second interrupt parameter pair corresponding to each task cycle is weighted to obtain the weighted interrupt time. The second interrupt parameter pair corresponding to the task cycle includes the second interrupt parameter in the task cycle and the second interrupt parameter in the previous task cycle adjacent to the task cycle.

[0218] The tooth pitch of the wheel speed sensor is determined based on the rolling radius of the target wheel and the number of teeth of the wheel speed sensor.

[0219] The second candidate wheel speed is determined based on the tooth pitch of the wheel speed sensor and the weighted interruption time.

[0220] The fifth determining module 600 can be specifically used for:

[0221] If the difference between the first candidate wheel speed and the second candidate wheel speed meets the set difference threshold or the second interruption parameter within the multiple task cycles is abnormal as determined by the second weighting factor corresponding to each task cycle, the first candidate wheel speed is selected from the first candidate wheel speed and the second candidate wheel speed and determined as the target wheel speed of the target wheel.

[0222] If the difference between the first candidate wheel speed and the second candidate wheel speed does not meet the set difference threshold, or if the second interruption parameter abnormality is determined to have not occurred within the multiple task cycles based on the second weighting factor corresponding to each task cycle, the second candidate wheel speed is selected from the first candidate wheel speed and the second candidate wheel speed and determined as the target wheel speed of the target wheel.

[0223] The vehicle wheel speed control device may further include:

[0224] The sixth determining module is used to determine the sensor abnormality flag bit of the target wheel based on the first weighting factor corresponding to each of the task cycles; the sensor abnormality flag bit is used to indicate whether the first interruption parameter has become abnormal within the multiple task cycles.

[0225] The output module is used to output the abnormal flag bit of the sensor.

[0226] It should also be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and 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 network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. In addition, in the device embodiment drawings provided in this application, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines.

[0227] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for this application, software program implementation is more often the preferred implementation method. Based on this understanding, the technical solution of this application, in essence, 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 readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk, or optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, training equipment, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0228] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0229] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, training device, or data center to another website, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

Claims

1. A method for processing vehicle wheel speed, characterized in that, include: Obtain the first and second interrupt parameters of the target wheel within multiple task cycles; The first interrupt parameter represents the number of high and low level changes collected by the wheel speed sensor corresponding to the target wheel during the movement of the target wheel; The second interrupt parameter characterizes the duration of the high and low level signals collected by the wheel speed sensor corresponding to the target wheel during the movement of the target wheel; Determine the first weighting factor and the second weighting factor corresponding to the task cycle; The first weighting factor characterizes the reliability of the first interruption parameter within the task cycle; The second weighting factor characterizes the reliability of the second interruption parameter within the task cycle; Based on the first interruption parameter of the wheel speed sensor in the multiple task cycles and the first weighting factor corresponding to the task cycle, the first candidate wheel speed is determined. Based on the second interruption parameter of the wheel speed sensor in the multiple task cycles and the second weighting factor corresponding to the task cycle, the second candidate wheel speed is determined. Select one of the first candidate wheel speed and the second candidate wheel speed to determine the target wheel speed of the target wheel.

2. The vehicle wheel speed processing method according to claim 1, characterized in that, Determining the first weighting factor corresponding to the task cycle includes: Detect whether the first interrupt parameter within the task cycle is abnormal, and obtain the first detection result; If the first detection result indicates that the first interruption parameter within the task cycle is abnormal, the first weighting factor corresponding to the task cycle is set to zero. If the first detection result indicates that the first interruption parameter within the task cycle is normal, the first weight factor corresponding to the task cycle is determined according to the order of the task cycles; the later the order of the task cycles, the higher the corresponding first weight factor.

3. The vehicle wheel speed processing method according to claim 2, characterized in that, The first interrupt parameter includes: the number of rising edges and the number of falling edges; the number of rising edges represents the number of times a low level rises to a high level; the number of falling edges represents the number of times a high level falls to a low level; The step of detecting whether the first interrupt parameter within the task cycle is abnormal, and obtaining the first detection result, includes: Using a window of a set size, sliding it across the multiple task cycles with a set step size, the system detects whether the gradient of the number of rising edges within the window satisfies a first gradient threshold or whether the number of rising edges satisfies a first quantity threshold, and whether the gradient of the number of falling edges within the window satisfies a second gradient threshold or whether the number of falling edges satisfies a second quantity threshold, and whether the difference between the number of rising edges and the number of falling edges at the same time satisfies a quantity difference threshold, thereby obtaining a first detection result.

4. The vehicle wheel speed processing method according to claim 1, characterized in that, The determination of the second weighting factor corresponding to the task cycle includes: Detect whether the second interrupt parameter within the task cycle is abnormal, and obtain the second detection result; If the second detection result indicates that the second interruption parameter within the task cycle is abnormal, the second weighting factor corresponding to the task cycle is set to zero. If the second detection result indicates that the second interruption parameter within the task cycle is normal, the second weighting factor corresponding to the task cycle is determined according to the order of the task cycles; the later the order of the task cycles, the higher the corresponding second weighting factor.

5. The vehicle wheel speed processing method according to claim 4, characterized in that, The process of detecting the gradient and data range of the second interrupt parameter to determine whether the second interrupt parameter is abnormal within the task cycle, and obtaining a second detection result, includes: The second interrupt parameters within the multiple task cycles are stored using a circular queue of a set length. It is determined whether the gradient of the second interrupt parameters in the circular queue meets the second gradient threshold and whether the second interrupt parameters in the circular queue meet the set time range, thereby obtaining the second detection result.

6. The vehicle wheel speed processing method according to claim 1, characterized in that, The first interrupt parameter includes: the number of rising edges and the number of falling edges; the number of rising edges represents the number of times a low level rises to a high level; the number of falling edges represents the number of times a high level falls to a low level; The step of determining the first candidate wheel speed based on the first interruption parameter of the wheel speed sensor in the multiple task cycles and the first weighting factor corresponding to the task cycle includes: Based on the first weighting factor corresponding to each task cycle in the plurality of task cycles of the wheel speed sensor, the number of rising edges and the number of falling edges in each task cycle are weighted to obtain the weighted interruption number. The tooth pitch of the wheel speed sensor is determined based on the rolling radius of the target wheel and the number of teeth of the wheel speed sensor. The distance the target wheel moves is determined based on the tooth pitch of the wheel speed sensor and the weighted number of interruptions. Based on the distance traveled by the target wheel and the mission cycle, a first candidate wheel speed is determined.

7. The vehicle wheel speed processing method according to claim 1, characterized in that, The step of determining the second candidate wheel speed based on the second interruption parameter of the wheel speed sensor in the multiple task cycles and the second weighting factor corresponding to the task cycle includes: Based on the second weighting factor corresponding to each task cycle in the multiple task cycles of the wheel speed sensor, it is determined that the second interrupt parameter in the multiple task cycles meets the abnormal conditions, and the set time upper limit threshold is used as the weighted interrupt time. Based on the second weighting factor corresponding to each task cycle in the multiple task cycles of the wheel speed sensor, it is determined that the second interrupt parameter in the multiple task cycles does not meet the abnormal condition. Based on the second weighting factor corresponding to each task cycle, the second interrupt parameter pair corresponding to each task cycle is weighted to obtain the weighted interrupt time. The second interrupt parameter pair corresponding to the task cycle includes the second interrupt parameter in the task cycle and the second interrupt parameter in the previous task cycle adjacent to the task cycle. The tooth pitch of the wheel speed sensor is determined based on the rolling radius of the target wheel and the number of teeth of the wheel speed sensor. The second candidate wheel speed is determined based on the tooth pitch of the wheel speed sensor and the weighted interruption time.

8. The vehicle wheel speed processing method according to claim 1, characterized in that, Selecting one from the first candidate wheel speed and the second candidate wheel speed to determine the target wheel speed of the target wheel includes: If the difference between the first candidate wheel speed and the second candidate wheel speed meets the set difference threshold or the second interruption parameter within the multiple task cycles is abnormal as determined by the second weighting factor corresponding to each task cycle, the first candidate wheel speed is selected from the first candidate wheel speed and the second candidate wheel speed and determined as the target wheel speed of the target wheel. If the difference between the first candidate wheel speed and the second candidate wheel speed does not meet the set difference threshold, or if the second interruption parameter abnormality is determined to have not occurred within the multiple task cycles based on the second weighting factor corresponding to each task cycle, the second candidate wheel speed is selected from the first candidate wheel speed and the second candidate wheel speed and determined as the target wheel speed of the target wheel.

9. The vehicle wheel speed processing method according to claim 2, characterized in that, The method further includes: Based on the first weighting factor corresponding to each of the task cycles, the sensor abnormality flag bit of the target wheel is determined; the sensor abnormality flag bit is used to indicate whether the first interruption parameter has become abnormal within the multiple task cycles. Output the sensor's abnormal flag bit.

10. A vehicle wheel speed processing device, characterized in that, include: The acquisition module is used to acquire the first and second interrupt parameters of the target wheel within multiple task cycles; The first interrupt parameter represents the number of high and low level changes collected by the wheel speed sensor corresponding to the target wheel during the movement of the target wheel; The second interrupt parameter characterizes the duration of the high and low level signals collected by the wheel speed sensor corresponding to the target wheel during the movement of the target wheel; The first determining module is used to determine the first weighting factor corresponding to the task cycle; The first weighting factor characterizes the reliability of the first interruption parameter within the task cycle; The second determining module is used to determine the second weighting factor corresponding to the task cycle; The second weighting factor characterizes the reliability of the second interruption parameter within the task cycle; The third determining module is used to determine the first candidate wheel speed based on the first interruption parameter of the wheel speed sensor in the multiple task cycles and the first weighting factor corresponding to the task cycle; The fourth determining module is used to determine the second candidate wheel speed based on the second interruption parameter of the wheel speed sensor in the multiple task cycles and the second weighting factor corresponding to the task cycle; The fifth determining module is used to select one from the first candidate wheel speed and the second candidate wheel speed and determine it as the target wheel speed of the target wheel.

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